From 94ad0bac28a32e83da71178632b4bcbf8ec3bcbd Mon Sep 17 00:00:00 2001 From: jinman Date: Mon, 10 Aug 2026 14:45:47 +0900 Subject: [PATCH 01/48] Split vision API into standalone package --- .github/workflows/publish.yml | 73 + .gitignore | 43 + .pre-commit-config.yaml | 8 + AGENTS.md | 73 +- CLAUDE.md | 4 +- README.md | 27 + benchmark/README.md | 172 ++ benchmark/benchmark_coco.py | 183 +++ benchmark/benchmark_imagenet.py | 137 ++ benchmark/organize_coco.py | 36 + benchmark/organize_imagenet.py | 33 + benchmark/organize_widerface.py | 36 + mblt_vision/README.md | 200 +++ mblt_vision/__init__.py | 86 + mblt_vision/_api.py | 53 + mblt_vision/_compat.py | 228 +++ mblt_vision/_model_paths.py | 139 ++ mblt_vision/_tasks.py | 32 + mblt_vision/datasets/__init__.py | 15 + mblt_vision/datasets/ade20k.yaml | 163 ++ mblt_vision/datasets/cityscapes.yaml | 54 + mblt_vision/datasets/coco.yaml | 174 ++ mblt_vision/datasets/dotav1.yaml | 28 + mblt_vision/datasets/imagenet.yaml | 1019 ++++++++++++ mblt_vision/datasets/nyu-depth.yaml | 7 + mblt_vision/datasets/registry.py | 158 ++ mblt_vision/datasets/widerface.yaml | 9 + mblt_vision/depth_estimation/__init__.py | 19 + mblt_vision/face_detection/__init__.py | 43 + mblt_vision/image_classification/__init__.py | 311 ++++ mblt_vision/instance_segmentation/__init__.py | 65 + mblt_vision/models/AlexNet.yaml | 23 + mblt_vision/models/CAFormer_B36.yaml | 22 + mblt_vision/models/CAFormer_M36.yaml | 22 + mblt_vision/models/CAFormer_S18.yaml | 22 + mblt_vision/models/CAFormer_S36.yaml | 22 + mblt_vision/models/CoAtNet_0_RW_224.yaml | 22 + mblt_vision/models/CoAtNet_1_RW_224.yaml | 22 + mblt_vision/models/CoAtNet_2_RW_224.yaml | 22 + mblt_vision/models/ConvFormer_B36.yaml | 22 + mblt_vision/models/ConvFormer_M36.yaml | 22 + mblt_vision/models/ConvFormer_S18.yaml | 22 + mblt_vision/models/ConvFormer_S36.yaml | 22 + mblt_vision/models/ConvNext_Base.yaml | 23 + mblt_vision/models/ConvNext_Large.yaml | 23 + 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create mode 100644 tests/TEST.md create mode 100644 tests/conftest.py create mode 100644 tests/rc/cr7.jpg create mode 100644 tests/rc/tabby.jpg create mode 100644 tests/rc/volcano.jpg create mode 100644 tests/test_resnet50.py create mode 100644 tests/test_vision_api.py create mode 100644 tests/test_yolo_det.py create mode 100644 tests/test_yolo_pose.py create mode 100644 tests/test_yolo_seg.py diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..dea7cbc --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,73 @@ +name: Publish to PyPI + +on: + release: + types: [published] + +permissions: + contents: read + +jobs: + build: + name: Build distribution ๐Ÿ› ๏ธ + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v5 + with: + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: '3.x' + + - name: Install build dependencies + run: | + python -m pip install --upgrade pip + python -m pip install build + + - name: Build package + run: python -m build + + - name: Upload built distributions + uses: actions/upload-artifact@v7 + with: + name: python-package-distributions + path: dist/ + + publish-to-testpypi: + name: Publish to TestPyPI ๐Ÿงช + needs: [build] + runs-on: ubuntu-latest + environment: testpypi + permissions: + id-token: write + steps: + - name: Download distribution artifacts + uses: actions/download-artifact@v7 + with: + name: python-package-distributions + path: dist/ + + - name: Publish to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ + + publish-to-pypi: + name: Publish to PyPI ๐Ÿš€ + needs: [build, publish-to-testpypi] + runs-on: ubuntu-latest + environment: pypi + permissions: + id-token: write + steps: + - name: Download distribution artifacts + uses: actions/download-artifact@v7 + with: + name: python-package-distributions + path: dist/ + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.gitignore b/.gitignore index e69de29..7bcf7eb 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1,43 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +.Python +.venv/ +venv/ +env/ +.env +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.coverage +coverage.xml +htmlcov/ +build/ +dist/ +*.egg-info/ + +# C/C++ build artifacts +build/ +cmake-build-*/ +CMakeFiles/ +CMakeCache.txt +cmake_install.cmake +compile_commands.json +*.o +*.obj +*.a +*.lib +*.so +*.dylib +*.dll +*.exe +*.out + +# IDE and OS files +.idea/ +.vscode/ +*.swp +*.swo +.DS_Store +Thumbs.db diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..b7350df --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,8 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.6.9 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format + diff --git a/AGENTS.md b/AGENTS.md index c9b161e..652bf0a 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -9,17 +9,17 @@ paths: ## Mission `mblt-vision-python` is the Python distribution and public compatibility layer for Mobilint -Vision. It binds to the supported native API in `mblt-vision`; it is not a second implementation -of the vision runtime. Its end-state is a drop-in replacement for the Vision API currently shipped -by `mblt-model-zoo`. +Vision. The immediate plan is a pure-Python implementation that can replace the Vision API +currently shipped by `mblt-model-zoo`. C++ bindings are deferred until `mblt-vision` has a stable, +supported native API. -The ownership boundary is deliberate: +The current ownership boundary is deliberate: -- `mblt-vision` owns inference, model loading, preprocessing, postprocessing, and native resource - management. -- This package owns Python ergonomics, Python object conversion, package metadata, wheels, API - documentation, and compatibility shims. -- Do not put GStreamer integration, C++ runtime logic, or duplicated numerical kernels in Python. +- This package owns the public Python API, model loading, preprocessing, postprocessing, runtime + integration, package metadata, wheels, documentation, and compatibility shims. +- Keep implementation code in Python; do not block Python API progress on C++ or GStreamer work. +- Design internal seams so a future optional `mblt-vision` backend can replace implementation + details without changing the documented Python API or result contracts. ## Before Editing @@ -29,8 +29,9 @@ The ownership boundary is deliberate: - For a Model Zoo replacement item, inspect the matching behavior in `../mblt-model-zoo/mblt_model_zoo/vision`, including its tests and model YAML configuration. Treat it as the compatibility reference until the new package explicitly supersedes it. -- Coordinate native API changes with `../mblt-vision`. Do not bind private, undocumented, or - build-tree-only native symbols. +- Do not make `../mblt-vision`, a compiled extension, or GStreamer a dependency of normal package + development, installation, import, or unit tests. Revisit integration only after its native API + is documented and versioned. ## Python API Contract @@ -46,32 +47,36 @@ The ownership boundary is deliberate: buffer for as long as native code can access it. - Never expose raw native pointers or require callers to manage native lifetime. Wrap resources in deterministic `close()`/context-manager behavior and safe finalization as appropriate. -- Maintain task vocabulary compatibility: canonical `obb`, with - `oriented_bounding_boxes` accepted only as an input compatibility alias. - -## Binding and Native Dependency Rules - -- The binding must use only the documented, versioned `mblt-vision` interface. Add a native - capability/version check when a Python feature needs a newer library. -- Keep ownership and the GIL explicit. Release the GIL only around blocking native work that does - not access Python objects; reacquire it before callbacks, exceptions, or Python buffer access. -- Do not catch broad native errors and return empty or plausible-looking results. Fail loudly with - an exception that identifies the invalid input, unsupported feature, or native-library problem. -- Keep native library discovery relocatable and diagnosable. Avoid hard-coded developer paths, - `LD_LIBRARY_PATH` requirements for normal wheels, or importing an optional native backend at - module import time if that prevents useful error reporting. +- Use `obb` as the sole oriented-bounding-box task name. + +## Python-First Architecture + +- Keep the public layer independent from a particular backend. Define small internal interfaces + for model execution and artifact resolution, but do not add speculative abstractions before a + second backend exists. +- Put preprocessing, postprocessing, model configuration, and compatibility behavior in tested + Python modules. Reuse Model Zoo semantics deliberately; do not copy code wholesale without + understanding its public contract and license context. +- Use established Python runtime dependencies only when they materially support the package goals. + Keep optional frameworks lazy-imported and raise a specific installation error when a requested + backend is unavailable. +- Do not catch broad runtime errors and return empty or plausible-looking results. Fail loudly with + an exception that identifies the invalid input, unsupported feature, or unavailable dependency. +- If/when a native backend is introduced, it must be optional, use a documented and versioned + `mblt-vision` interface, and preserve the Python public API, exceptions, result values, layouts, + and lifecycle behavior. Add native capability/version checks at that time. ## PyPI and Wheel Packaging - `pyproject.toml` is the source of truth for Python metadata, supported Python versions, dependencies, and build backend. Keep package versioning synchronized with the exposed API and native compatibility requirements. -- Distribute wheels that include or correctly depend on the matching native library according to - the chosen packaging strategy. Do not publish an sdist/wheel whose import or basic diagnostics - are broken without a locally installed development tree. +- Publish pure-Python wheels and sdists that install and import without a local C++ build, a native + library, or GStreamer. Do not publish artifacts whose import or basic diagnostics require a + developer environment. - Build and test each intended platform/architecture wheel in a clean environment. Verify wheel - contents, package metadata, install-from-wheel, import, and a minimal inference-free native - smoke test. Do not upload from a developer environment as the only validation. + contents, package metadata, install-from-wheel, import, and a minimal API smoke test. Do not + upload from a developer environment as the only validation. - Keep optional dependencies genuinely optional and avoid importing them from package top level. Do not add model weights, caches, test assets, or compiled build artifacts to source control. @@ -83,9 +88,11 @@ The ownership boundary is deliberate: - Use deterministic differential tests against Model Zoo for shared behavior. Cover edge cases, not only successful end-to-end examples: invalid layouts/dtypes, empty detections, threshold boundaries, image geometry, model aliases, task aliases, and resource cleanup. -- Numerical semantics belong to the native layer, but binding tests must verify that Python - conversion does not change values, layout, coordinates, ordering, dtype, or ownership. -- Avoid making hardware, downloaded models, or GStreamer a requirement for ordinary unit tests. +- Verify that Python preprocessing and postprocessing preserve expected values, layouts, + coordinates, ordering, dtype, and ownership. If a future backend is used, require the same + parity from its conversion boundary. +- Avoid making hardware, downloaded models, compiled extensions, or GStreamer a requirement for + ordinary unit tests. Mark and document integration prerequisites; run the narrowest relevant suite first. ## Code Quality and Documentation diff --git a/CLAUDE.md b/CLAUDE.md index 9d3f9ec..bae10da 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -2,5 +2,5 @@ @AGENTS.md -`AGENTS.md` is the canonical guide for this repository. Follow it for the thin native-binding, -PyPI-wheel, lifecycle, and mblt-model-zoo Vision compatibility requirements. +`AGENTS.md` is the canonical guide for this repository. Follow it for the Python-first +implementation, PyPI-wheel, lifecycle, and mblt-model-zoo Vision compatibility requirements. diff --git a/README.md b/README.md index e69de29..e35a5b0 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,27 @@ +# mblt-vision-python + +**Vision models** for Mobilint NPUs. + +Image classification, object detection, instance segmentation and pose estimation on +Mobilint NPUs. Each model is a class that carries its own pre- and post-processing: + +```python +from mblt_vision import ResNet50 + +model = ResNet50() +x = model.preprocess("image.jpg") +result = model.postprocess(model(x)) +``` + +Split out of `mblt-model-zoo`, which still re-exports it as `mblt_model_zoo.vision` +so existing code keeps working. + +## Installation + +```bash +pip install mblt-vision-python +``` + +## License + +BSD-3-Clause. diff --git a/benchmark/README.md b/benchmark/README.md new file mode 100644 index 0000000..884e327 --- /dev/null +++ b/benchmark/README.md @@ -0,0 +1,172 @@ +# Measuring the Performance of Vision Models + +You can run the benchmark as the following steps: + +1. Download the vision benchmark dataset and organize the dataset. +2. Prepare the model for benchmark. +3. Run the benchmark and evaluate the performance. + +> โš ๏ธ **Warning:** Mobilint does not provide the vision benchmark dataset or direct download link due to the terms of use of the dataset. +Mobilint model zoo provides utilities on dataset organization, model preparation, and benchmark evaluation, therefore you can easily reproduce the [published benchmark results](../mblt_vision/README.md). + +Furthermore, you can simply run the benchmark with the model you compiled with custom quantization recipe. + +## Benchmark with ImageNet Dataset + +### Download the ImageNet Dataset + +To download the ImageNet dataset, visit the [ImageNet website](https://image-net.org/) and download the dataset with the following steps: + +0. Login with your account. If you don't have an account, you can register a new account. (Using `.edu` email is highly recommended) +1. Click on the "Download" button on the menu bar. +2. Go to "ImageNet Large-scale Visual Recognition Challenge (ILSVRC)" section, and click 2012 button. +3. Download the following files: + - ILSVRC2012_img_val.tar: Displayes as "Validation images (all tasks)" with MD5 checksum `29b22e2961454d5413ddabcf34fc5622`. + - ILSVRC2012_bbox_val_v3.tgz: Displayes as "Validation bounding box annotations (all tasks)" with MD5 checksum `f4cd18b5ea29fe6bbea62ec9c20d80f0`. + +### Organize the ImageNet Dataset + +You can organize the ImageNet dataset with the following command: + +```bash +python organize_imagenet.py --image_dir {path_to_ILSVRC2012_img_val.tar} --xml_dir {path_to_ILSVRC2012_bbox_val_v3.tgz} --output_dir ~/.mblt_model_zoo/datasets/imagenet +``` + +This will organize the dataset into the following structure: + +```text +~/.mblt_model_zoo/datasets/imagenet/ +โ”œโ”€โ”€ n01440764/ +โ”‚ โ”œโ”€โ”€ ILSVRC2012_val_00000293.JPEG +โ”‚ โ”œโ”€โ”€ ILSVRC2012_val_00002138.JPEG +โ”‚ โ”œโ”€โ”€ ... +โ”œโ”€โ”€ n01443537/ +โ”œโ”€โ”€ n01484850/ +โ”œโ”€โ”€ ... +``` + +### Prepare the Model for ImageNet Benchmark + +You can prepare the model for ImageNet benchmark or simply run the benchmark with the model that is provided in [mblt-model-zoo](../mblt_vision/README.md). + +If you want to try with your own model, refer to the [tutorial guide](https://github.com/mobilint/mblt-sdk-tutorial/) to compile the model with custom quantization recipe. + +### Run the ImageNet Benchmark + +You can run the ImageNetbenchmark with the following command: + +```bash +python benchmark_imagenet.py --local_path {path to local mxq(optional)}\ +--model_type {model type(optional)}\ +--infer_mode {single, multi, global, global4, global8(optional). Default is global}\ +--product {aries, regulus(optional). Default is aries}\ +--batch_size {batch size(optional). Default is 1}\ +--data_path {path to the ImageNet data(optional). Default is ~/.mblt_model_zoo/datasets/imagenet} +``` + +Example: + +```bash +python benchmark_imagenet.py --local_path ./resnet50_IMAGENET1K_V1.mxq --model_type IMAGENET1K_V1 --infer_mode multi --product aries --batch_size 8 --data_path ~/.mblt_model_zoo/datasets/imagenet +``` + +## Benchmark with COCO Dataset + +### Download the COCO Dataset + +To download the COCO dataset, visit the [COCO website](https://cocodataset.org/) and download the dataset with the following steps: + +1. Click on the "Dataset" button on the menu bar, and click "Download" button. +2. On the "Images" column, click "2017 Val images" button to download `val2017.zip` file. +3. On the "Annotations" column, click "2017 Train/Val annotations" button to download `annotations_trainval2017.zip` file. + +### Organize the COCO Dataset + +You can organize the COCO dataset with the following command: + +```bash +python organize_coco.py --image_dir {path_to_val2017.zip} --annotation_dir {path_to_annotations_trainval2017.zip} --output_dir ~/.mblt_model_zoo/datasets/coco +``` + +This will organize the dataset into the following structure: + +```text +~/.mblt_model_zoo/datasets/coco/ +โ”œโ”€โ”€ val2017/ +โ”‚ โ”œโ”€โ”€ 000000000139.jpg +โ”‚ โ”œโ”€โ”€ 000000000285.jpg +โ”‚ โ”œโ”€โ”€ ... +โ”œโ”€โ”€ captions_val2017.json +โ”œโ”€โ”€ instances_val2017.json +โ”œโ”€โ”€ person_keypoints_val2017.json +``` + +### Prepare the Model for COCO Benchmark + +You can prepare the model for COCO benchmark or simply run the benchmark with the model that is provided in [mblt-model-zoo](../mblt_vision/README.md). + +If you want to try with your own model, refer to the [tutorial guide](https://github.com/mobilint/mblt-sdk-tutorial/) to compile the model with custom quantization recipe. + +### Run the COCO Benchmark + +You can run the COCO benchmark with the following command: + +```bash +python benchmark_coco.py --local_path {path to local mxq(optional)}\ +--model_type {model type(optional)}\ +--infer_mode {single, multi, global, global4, global8(optional). Default is global}\ +--product {aries, regulus(optional). Default is aries}\ +--batch_size {batch size(optional). Default is 1}\ +--data_path {path to the COCO data(optional). Default is ~/.mblt_model_zoo/datasets/coco} +--conf_thres {confidence threshold for object detection(optional). Default is 0.001}\ +--iou_thres {IOU threshold for object detection(optional). Default is 0.7} +``` + +Example: + +```bash +python benchmark_coco.py --infer_mode single --product aries --batch_size 8 --data_path ~/.mblt_model_zoo/datasets/coco --conf_thres 0.001 --iou_thres 0.7 +``` + +## Benchmark with WiderFace Dataset + +### Download the WiderFace Dataset + +To download the WiderFace dataset, visit the [WiderFace's Hugging Face page](https://huggingface.co/datasets/CUHK-CSE/wider_face) and download the dataset with the following steps: + +1. Go to the "Files and versions" section, and click "data" folder. +2. Download "WIDER_val.zip" file and "wider_face_split.zip" file. + +### Organize the WiderFace Dataset + +You can organize the WiderFace dataset with the following command: + +```bash +python organize_widerface.py --image_dir {path_to_WIDER_val.zip} --annotation_dir {path_to_wider_face_split.zip} --output_dir ~/.mblt_model_zoo/datasets/widerface +``` + +This will organize the dataset into the following structure: + +```text +~/.mblt_model_zoo/datasets/widerface/ +โ”œโ”€โ”€ images/ +โ”‚ โ”œโ”€โ”€ 0--Parade/ +| โ”œโ”€โ”€ 0_Parade_Parade_0_102.jpg +| โ”œโ”€โ”€ 0_Parade_Parade_0_120.jpg +| |... +| โ”œโ”€โ”€ 1--Handshaking/ +| |... +| โ”œโ”€โ”€ ... +โ”œโ”€โ”€ wider_face_val.mat +โ”œโ”€โ”€ wider_face_val_bbx_gt.txt +``` + +### Prepare the Model for WiderFace Benchmark + +You can prepare the model for WiderFace benchmark or simply run the benchmark with the model that is provided in [mblt-model-zoo](../mblt_vision/README.md). + +If you want to try with your own model, refer to the [tutorial guide](https://github.com/mobilint/mblt-sdk-tutorial/) to compile the model with custom quantization recipe. + +### Run the WiderFace Benchmark + +Pending \ No newline at end of file diff --git a/benchmark/benchmark_coco.py b/benchmark/benchmark_coco.py new file mode 100644 index 0000000..2135a4e --- /dev/null +++ b/benchmark/benchmark_coco.py @@ -0,0 +1,183 @@ +""" +Benchmark script for YOLOv5m on COCO dataset. + +This script runs the benchmark for the YOLOv5m model using the COCO dataset. +It initializes the model and evaluates its performance. +""" + +import argparse +import os + +import mblt_vision +from mblt_vision.utils.evaluation import eval_coco + +MODEL_LIST = ( + [ + "YOLO11lSeg", + "YOLO11mSeg", + "YOLO11nSeg", + "YOLO11sSeg", + "YOLO11xSeg", + "YOLO26lSeg", + "YOLO26mSeg", + "YOLO26nSeg", + "YOLO26sSeg", + "YOLO26xSeg", + "YOLOv5lSeg", + "YOLOv5mSeg", + "YOLOv5nSeg", + "YOLOv5sSeg", + "YOLOv5xSeg", + "YOLOv8lSeg", + "YOLOv8mSeg", + "YOLOv8nSeg", + "YOLOv8sSeg", + "YOLOv8xSeg", + "YOLOv9cSeg", + "YOLOv9eSeg", + ] + + [ + "GELANc", + "GELANe", + "GELANm", + "GELANs", + "YOLO11l", + "YOLO11m", + "YOLO11n", + "YOLO11s", + "YOLO11x", + "YOLO12l", + "YOLO12m", + "YOLO12n", + "YOLO12s", + "YOLO12x", + "YOLO26l", + "YOLO26m", + "YOLO26n", + "YOLO26s", + "YOLO26x", + "YOLOv10b", + "YOLOv10l", + "YOLOv10m", + "YOLOv10n", + "YOLOv10s", + "YOLOv10x", + "YOLOv3", + "YOLOv3_spp", + "YOLOv3_sppu", + "YOLOv3_tiny", + "YOLOv3_tinyu", + "YOLOv3u", + "YOLOv5l", + "YOLOv5l6", + "YOLOv5l6u", + "YOLOv5lu", + "YOLOv5m", + "YOLOv5m6", + "YOLOv5m6u", + "YOLOv5mu", + "YOLOv5n", + "YOLOv5n6", + "YOLOv5n6u", + "YOLOv5nu", + "YOLOv5s", + "YOLOv5s6", + "YOLOv5s6u", + "YOLOv5su", + "YOLOv5x", + "YOLOv5x6", + "YOLOv5x6u", + "YOLOv5xu", + "YOLOv7", + "YOLOv7d6", + "YOLOv7e6", + "YOLOv7e6e", + "YOLOv7w6", + "YOLOv7x", + "YOLOv8l", + "YOLOv8m", + "YOLOv8n", + "YOLOv8s", + "YOLOv8x", + "YOLOv9c", + "YOLOv9e", + "YOLOv9m", + "YOLOv9s", + "YOLOv9t", + ] + + [ + "YOLO11lPose", + "YOLO11mPose", + "YOLO11nPose", + "YOLO11sPose", + "YOLO11xPose", + "YOLO26lPose", + "YOLO26mPose", + "YOLO26nPose", + "YOLO26sPose", + "YOLO26xPose", + "YOLOv8lPose", + "YOLOv8mPose", + "YOLOv8nPose", + "YOLOv8sPose", + "YOLOv8xPose", + ] +) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run YOLOv5m Benchmark") + # --- Model Configuration --- + parser.add_argument( + "--model-name", + type=str, + default="YOLOv5m", + choices=MODEL_LIST, + help="Model name", + ) + parser.add_argument( + "--local-path", + type=str, + default=None, + help="Path to the YOLOv5m model file (.mxq)", + ) + parser.add_argument("--model-type", type=str, default="DEFAULT", help="Model type") + parser.add_argument( + "--infer-mode", + type=str, + default="global8", + choices=["single", "multi", "global4", "global8"], + help="Inference mode", + ) + parser.add_argument("--product", type=str, default="aries", help="Product") + # --- Benchmark Configuration --- + parser.add_argument("--batch-size", type=int, default=1, help="Batch size") + parser.add_argument( + "--data-path", + type=str, + default=os.path.expanduser("~/.mblt_model_zoo/datasets/coco"), + help="Path to the COCO data", + ) + parser.add_argument( + "--conf-thres", + type=float, + default=0.001, + help="Confidence threshold for object detection", + ) + parser.add_argument( + "--iou-thres", + type=float, + default=0.7, + help="IOU threshold for object detection", + ) + args = parser.parse_args() + + model_cls = getattr(mblt_vision, args.model_name) + # --- Model Initialization --- + model = model_cls(args.local_path, args.model_type, args.infer_mode, args.product) + + # --- Benchmark Execution --- + acc = eval_coco( + model, args.data_path, args.batch_size, args.conf_thres, args.iou_thres + ) + print(f"COCO evaluation completed. mAP 50:95: {acc:.5f}") diff --git a/benchmark/benchmark_imagenet.py b/benchmark/benchmark_imagenet.py new file mode 100644 index 0000000..dd7dab1 --- /dev/null +++ b/benchmark/benchmark_imagenet.py @@ -0,0 +1,137 @@ +""" +Benchmark script for ResNet50 on ImageNet dataset. + +This script runs the benchmark for the ResNet50 model using the ImageNet dataset. +It initializes the model and evaluates its performance. +""" + +import argparse +import os + +import mblt_vision +from mblt_vision.utils.evaluation import eval_imagenet + +MODEL_LIST = [ + "AlexNet", + "ConvNeXt_Base", + "ConvNeXt_Large", + "ConvNeXt_Small", + "ConvNeXt_Tiny", + "DeiT3_Base_Patch16_224", + "DeiT3_Base_Patch16_384", + "DeiT3_Large_Patch16_224", + "DeiT3_Large_Patch16_384", + "DeiT3_Medium_Patch16_224", + "DeiT3_Small_Patch16_224", + "DeiT3_Small_Patch16_384", + "DeiT_Base_Patch16_224", + "DeiT_Base_Patch16_384", + "DeiT_Small_Patch16_224", + "DeiT_Tiny_Patch16_224", + "DenseNet121", + "DenseNet169", + "DenseNet201", + "FlexiViT_Base", + "FlexiViT_Large", + "FlexiViT_Small", + "GoogLeNet", + "Inception_V3", + "MNasNet1_0", + "MNasNet1_3", + "MobileNet_V2", + "RegNet_X_16GF", + "RegNet_X_1_6GF", + "RegNet_X_32GF", + "RegNet_X_3_2GF", + "RegNet_X_400MF", + "RegNet_X_800MF", + "RegNet_X_8GF", + "RegNet_Y_16GF", + "RegNet_Y_1_6GF", + "RegNet_Y_32GF", + "RegNet_Y_3_2GF", + "RegNet_Y_400MF", + "RegNet_Y_800MF", + "RegNet_Y_8GF", + "ResNet101", + "ResNet152", + "ResNet18", + "ResNet34", + "ResNet50", + "ResNext101_32x8d", + "ResNext101_64x4d", + "ResNext50_32x4d", + "ShuffleNet_V2_X1_0", + "ShuffleNet_V2_X1_5", + "ShuffleNet_V2_X2_0", + "VGG11", + "VGG11_BN", + "VGG13", + "VGG13_BN", + "VGG16", + "VGG16_BN", + "VGG19", + "VGG19_BN", + "ViT_Base_Patch16_224", + "ViT_Base_Patch16_384", + "ViT_Base_Patch32_224", + "ViT_Base_Patch32_384", + "ViT_Base_Patch8_224", + "ViT_Large_Patch16_224", + "ViT_Large_Patch16_384", + "ViT_Large_Patch32_384", + "ViT_Small_Patch16_224", + "ViT_Small_Patch16_384", + "ViT_Small_Patch32_224", + "ViT_Small_Patch32_384", + "ViT_Tiny_Patch16_224", + "ViT_Tiny_Patch16_384", + "Wide_ResNet101_2", + "Wide_ResNet50_2", +] + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run ResNet50 Benchmark") + # --- Model Configuration --- + parser.add_argument( + "--model-name", + type=str, + default="ResNet50", + choices=MODEL_LIST, + help="Model name", + ) + parser.add_argument( + "--local-path", + type=str, + default=None, + help="Path to the ResNet50 model file (.mxq)", + ) + parser.add_argument("--model-type", type=str, default="DEFAULT", help="Model type") + parser.add_argument( + "--infer-mode", + type=str, + default="global8", + choices=["single", "multi", "global4", "global8"], + help="Inference mode", + ) + parser.add_argument("--product", type=str, default="aries", help="Product") + # --- Benchmark Configuration --- + parser.add_argument("--batch-size", type=int, default=1, help="Batch size") + parser.add_argument( + "--data-path", + type=str, + default=os.path.expanduser("~/.mblt_model_zoo/datasets/imagenet"), + help="Path to the ImageNet data", + ) + args = parser.parse_args() + model_cls = getattr(mblt_vision, args.model_name) + # --- Model Initialization --- + model = model_cls( + local_path=args.local_path, + model_type=args.model_type, + infer_mode=args.infer_mode, + product=args.product, + ) + + # --- Benchmark Execution --- + eval_imagenet(model, args.data_path, args.batch_size) diff --git a/benchmark/organize_coco.py b/benchmark/organize_coco.py new file mode 100644 index 0000000..28e52cf --- /dev/null +++ b/benchmark/organize_coco.py @@ -0,0 +1,36 @@ +""" +Script to organize the COCO dataset. + +This script takes the raw image and annotation zip files for the COCO dataset +and organizes them into a structure suitable for the model zoo. +""" + +import argparse +import os + +from mblt_vision.utils.datasets import organize_coco + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize COCO dataset") + parser.add_argument( + "--image-dir", type=str, required=True, help="Path to the image zip file" + ) + parser.add_argument( + "--annotation-dir", + type=str, + required=True, + help="Path to the annotation zip file", + ) + parser.add_argument( + "--output-dir", + type=str, + default=os.path.expanduser("~/.mblt_model_zoo/datasets/coco"), + help="Path to the directory to save the organized dataset", + ) + args = parser.parse_args() + + organize_coco( + image_dir=args.image_dir, + annotation_dir=args.annotation_dir, + output_dir=args.output_dir, + ) diff --git a/benchmark/organize_imagenet.py b/benchmark/organize_imagenet.py new file mode 100644 index 0000000..a946773 --- /dev/null +++ b/benchmark/organize_imagenet.py @@ -0,0 +1,33 @@ +""" +Script to organize the ImageNet dataset. + +This script takes the raw image tar file and xml tgz file for the ImageNet dataset +and organizes them into a structure suitable for the model zoo. +""" + +import argparse +import os + +from mblt_vision.utils.datasets import organize_imagenet + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize ImageNet dataset") + parser.add_argument( + "--image-dir", type=str, required=True, help="Path to the image tar file" + ) + parser.add_argument( + "--xml-dir", type=str, required=True, help="Path to the xml tgz file" + ) + parser.add_argument( + "--output-dir", + type=str, + default=os.path.expanduser("~/.mblt_model_zoo/datasets/imagenet"), + help="Path to the directory to save the organized dataset", + ) + args = parser.parse_args() + + organize_imagenet( + image_dir=args.image_dir, + xml_dir=args.xml_dir, + output_dir=args.output_dir, + ) diff --git a/benchmark/organize_widerface.py b/benchmark/organize_widerface.py new file mode 100644 index 0000000..617d5be --- /dev/null +++ b/benchmark/organize_widerface.py @@ -0,0 +1,36 @@ +""" +Script to organize the WiderFace dataset. + +This script takes the raw image and annotation zip files for the WiderFace dataset +and organizes them into a structure suitable for the model zoo. +""" + +import argparse +import os + +from mblt_vision.utils.datasets import organize_widerface + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize WiderFace dataset") + parser.add_argument( + "--image-dir", type=str, required=True, help="Path to the image zip file" + ) + parser.add_argument( + "--annotation-dir", + type=str, + required=True, + help="Path to the annotation zip file", + ) + parser.add_argument( + "--output-dir", + type=str, + default=os.path.expanduser("~/.mblt_model_zoo/datasets/widerface"), + help="Path to the directory to save the organized dataset", + ) + args = parser.parse_args() + + organize_widerface( + image_dir=args.image_dir, + annotation_dir=args.annotation_dir, + output_dir=args.output_dir, + ) diff --git a/mblt_vision/README.md b/mblt_vision/README.md new file mode 100644 index 0000000..413ff45 --- /dev/null +++ b/mblt_vision/README.md @@ -0,0 +1,200 @@ +# Pre-Trained Vision Models + +> Split out of `mblt-model-zoo`, which still re-exports this package as +> `mblt_model_zoo.vision`. + +Here, we give the full list of publicly pre-trained models supported by the Mobilint Model Zoo. + +Further usage examples can be found in the [tests](../tests) directory. + +## Image Classification + +| Model | Input Size
(H,W,C) | AccTop1
(NPU) | AccTop1
(GPU) | FLOPs (B) | params (M) | Source | Note | +|------------|------------|-----------|--------------------|--------|-------|------|------| +| AlexNet | (224,224,3)| 56.022 | 56.552 | 0.71 | 61.10 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.alexnet.html) | | +| ConvNeXt_Tiny | (224,224,3) | 82.302 | 82.460| 4.46 | 28.59 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_tiny.html) | | +| ConvNeXt_Small | (224,224,3) | 83.432 | 83.560 | 8.68 | 50.22 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_small.html) | | +| ConvNeXt_Base | (224,224,3) | 83.834 | 84.050 | 15.36 | 88.59 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_base.html) | | +| ConvNeXt_Large | (224,224,3) | 83.276 | 84.410 | 34.36 | 197.77 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_large.html) | | +| DeiT_Tiny_Patch16_224 | (224,224,3) | 71.864 | 72.030 | 2.53 | 5.72 | [Link](https://huggingface.co/timm/deit_tiny_patch16_224.fb_in1k) | fb_in1k | +| DeiT_Small_Patch16_224 | (224,224,3) | 79.770 | 79.790 | 9.25 | 22.05 | [Link](https://huggingface.co/timm/deit_small_patch16_224.fb_in1k) | fb_in1k | +| DeiT_Base_Patch16_224 | (224,224,3) | 81.886 | 81.980 | 35.23 | 86.57 | [Link](https://huggingface.co/timm/deit_base_patch16_224.fb_in1k) | fb_in1k | +| DeiT_Base_Patch16_384 | (384,384,3) | 83.046 | 83.100 | 111.34 | 86.86 | [Link](https://huggingface.co/timm/deit_base_patch16_384.fb_in1k) | fb_in1k | +| DeiT3_Small_Patch16_224 | (224,224,3) | 81.326 | 81.398 | 9.25 | 22.06 | [Link](https://huggingface.co/timm/deit3_small_patch16_224.fb_in1k) | fb_in1k | +| DeiT3_Small_Patch16_384 | (384,384,3) | 81.304 | 81.414 | 31.17 | 22.21 | [Link](https://huggingface.co/timm/deit3_small_patch16_384.fb_in1k) | fb_in1k | +| DeiT3_Medium_Patch16_224 | (224,224,3) | 82.930 | 83.044 | 16.05 | 38.85 | [Link](https://huggingface.co/timm/deit3_medium_patch16_224.fb_in1k) | fb_in1k | +| DeiT3_Base_Patch16_224 | (224,224,3) | 83.644 | 83.766 | 35.23 | 86.59 | [Link](https://huggingface.co/timm/deit3_base_patch16_224.fb_in1k) | fb_in1k | +| DeiT3_Base_Patch16_384 | (384,384,3) | 84.990 | 85.062 | 111.34 | 86.88 | [Link](https://huggingface.co/timm/deit3_base_patch16_384.fb_in1k) | fb_in1k | +| DeiT3_Large_Patch16_224 | (224,224,3) | 84.658 | 84.738 | 123.39 | 304.37 | [Link](https://huggingface.co/timm/deit3_large_patch16_224.fb_in1k) | fb_in1k | +| DeiT3_Large_Patch16_384 | (384,384,3) | 85.770 | 85.828 | 383.13 | 304.76 | [Link](https://huggingface.co/timm/deit3_large_patch16_384.fb_in1k) | fb_in1k | +| DenseNet121 | (224,224,3) | 74.194 | 74.414 | 2.83 | 7.98 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet121.html) | | +| DenseNet169 | (224,224,3) | 75.476 | 75.566 | 3.36 | 14.15 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet169.html) | | +| DenseNet201 | (224,224,3) | 76.650 | 76.880 | 4.29 | 20.01 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet201.html) | | +| FlexiViT_Small | (240,240,3) | 82.284 | 82.532 | 10.73 | 22.06 | [Link](https://huggingface.co/timm/flexivit_small.1200ep_in1k) | 1200ep_in1k | +| FlexiViT_Base | (240,240,3) | 84.670 | 84.670 | 40.66 | 86.59 | [Link](https://huggingface.co/timm/flexivit_base.1200ep_in1k) | 1200ep_in1k | +| FlexiViT_Large | (240,240,3) | 85.576 | 85.660 | 142.21 | 304.36 | [Link](https://huggingface.co/timm/flexivit_large.1200ep_in1k) | 1200ep_in1k | +| GoogLeNet | (224,224,3) | 69.566 | 69.780 | 1.50 | 6.62 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.googlenet.html) | | +| Inception_V3 | (299,299,3) | 77.120 | 77.278 | 5.71 | 27.16 | [Link](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.inception_v3.html)| | +| MNASNet1_0 | (224,224,3)| 72.696 | 73.422 | 0.31 | 4.38 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.mnasnet1_0.html) | | +| MNASNet1_3 | (224,224,3) | 75.720 | 76.466 | 0.53 | 6.28 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.mnasnet1_3.html) | | +| MobileNet_V2 | (224,224,3)| 71.624 | 72.138 | 0.30 | 3.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.mobilenet_v2.html) | IMAGENET1K_V2 | +| RegNet_X_400MF | (224,224,3) | 72.544 | 72.900 | 0.41 | 5.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_400mf.html) | IMAGENET1K_V1 | +| RegNet_X_400MF | (224,224,3) | 74.218 | 74.846 | 0.41 | 5.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_400mf.html) | IMAGENET1K_V2 | +| RegNet_X_800MF | (224,224,3) | 74.928 | 75.204 | 0.80 | 7.26 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_800mf.html) | IMAGENET1K_V1 | +| RegNet_X_800MF | (224,224,3) | 76.984 | 77.488 | 0.80 | 7.26 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_800mf.html) | IMAGENET1K_V2 | +| RegNet_X_1_6GF | (224,224,3) | 76.756 | 77.080 | 1.60 | 9.19 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_1_6gf.html) | IMAGENET1K_V1 | +| RegNet_X_1_6GF | (224,224,3) | 79.168 | 79.670 | 1.60 | 9.19 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_1_6gf.html) | IMAGENET1K_V2 | +| RegNet_X_3_2GF | (224,224,3) | 77.978 | 78.346 | 3.18 | 15.30 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_x_3_2gf.html) | IMAGENET1K_V1 | +| RegNet_X_3_2GF | (224,224,3) | 80.792 | 81.188 | 3.18 | 15.30 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_x_3_2gf.html) | IMAGENET1K_V2 | +| RegNet_X_8GF | (224,224,3) | 79.322 | 79.368 | 8.00 | 39.57 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_8gf.html) | IMAGENET1K_V1 | +| RegNet_X_8GF | (224,224,3) | 81.388 | 81.680 | 8.00 | 39.57 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_8gf.html) | IMAGENET1K_V2 | +| RegNet_X_16GF | (224,224,3) | 79.884 | 80.090 | 15.94 | 54.28 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_16gf.html) | IMAGENET1K_V1 | +| RegNet_X_16GF | (224,224,3) | 82.312 | 82.712 | 15.94 | 54.28 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_16gf.html) | IMAGENET1K_V2 | +| RegNet_X_32GF | (224,224,3) | 80.482 | 80.592 | 31.74 | 107.81 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_32gf.html) | IMAGENET1K_V1 | +| RegNet_X_32GF | (224,224,3) | 82.722 | 83.014 | 31.74 | 107.81 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_32gf.html) | IMAGENET1K_V2 | +| RegNet_Y_400MF | (224,224,3) | 73.594 | 73.998 | 0.40 | 4.34 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_400mf.html) | IMAGENET1K_V1 | +| RegNet_Y_400MF | (224,224,3) | 75.294 | 75.804 | 0.40 | 4.34 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_400mf.html) | IMAGENET1K_V2 | +| RegNet_Y_800MF | (224,224,3) | 76.132 | 76.406 | 0.83 | 6.43 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_800mf.html) | IMAGENET1K_V1| +| RegNet_Y_800MF | (224,224,3) | 78.280 | 78.904 | 0.83 | 6.43 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_800mf.html) | IMAGENET1K_V2| +| RegNet_Y_1_6GF | (224,224,3) | 77.260 | 77.934 | 1.61 | 11.20 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_1_6gf.html) | IMAGENET1K_V1 | +| RegNet_Y_1_6GF | (224,224,3) | 80.298 | 80.876 | 1.61 | 11.20 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_1_6gf.html) | IMAGENET1K_V2 | +| RegNet_Y_3_2GF | (224,224,3) | 78.504 | 78.962 | 3.18 | 19.44 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_y_3_2gf.html) | IMAGENET1K_V1 | +| RegNet_Y_3_2GF | (224,224,3) | 81.354 | 82.010 | 3.18 | 19.44 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_y_3_2gf.html) | IMAGENET1K_V2 | +| RegNet_Y_8GF | (224,224,3) | 79.768 | 80.052 | 8.47 | 39.38 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_8gf.html) | IMAGENET1K_V1 | +| RegNet_Y_8GF | (224,224,3) | 82.418 | 82.822 | 8.47 | 39.38 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_8gf.html) | IMAGENET1K_V2 | +| RegNet_Y_16GF | (224,224,3) | 80.240 | 80.428 | 15.91 | 83.59 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_16gf.html)| IMAGENET1K_V1 | +| RegNet_Y_16GF | (224,224,3) | 82.348 | 82.868 | 15.91 | 83.59 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_16gf.html)| IMAGENET1K_V2 | +| RegNet_Y_32GF | (224,224,3) | 80.510 | 80.840 | 32.28 | 145.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_32gf.html) | IMAGENET1K_V1 | +| RegNet_Y_32GF | (224,224,3) | 82.798 | 83.362 | 32.28 | 145.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_32gf.html) | IMAGENET1K_V2 | +| ResNet18 | (224,224,3) | 69.530 | 69.770 | 1.81 | 11.69 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet18.html) | | +| ResNet34 | (224,224,3) | 73.166 | 73.308 | 3.66 | 21.80 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet34.html) | | +| ResNet50 | (224,224,3) | 75.946 | 76.128 | 4.09 | 25.56 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet50.html) | IMAGENET1K_V1 | +| ResNet50 | (224,224,3) | 80.326 | 80.844 | 4.09 | 25.56 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet50.html) | IMAGENET1K_V2 | +| ResNet101 | (224,224,3) | 77.112 | 77.362 | 7.80 | 44.55 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet101.html) | IMAGENET1K_V1 | +| ResNet101 | (224,224,3) | 81.472 | 81.918 | 7.80 | 44.55 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet101.html) | IMAGENET1K_V2 | +| ResNet152 | (224,224,3) | 77.922 | 78.316 | 11.51 | 60.19 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet152.html) | IMAGENET1K_V1 | +| ResNet152 | (224,224,3) | 81.950 | 82.272 | 11.51 | 60.19 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet152.html) | IMAGENET1K_V2 | +| ResNeXt50_32X4D | (224,224,3) | 77.528 | 77.630 | 4.23 | 25.03 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext50_32x4d.html) | IMAGENET1K_V1 | +| ResNeXt50_32X4D | (224,224,3) | 80.728 | 81.220 | 4.23 | 25.03 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext50_32x4d.html) | IMAGENET1K_V2 | +| ResNeXt101_32X8D | (224,224,3) | 78.998 | 79.288 | 16.41 | 88.79 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_32x8d.html) | IMAGENET1K_V1 | +| ResNeXt101_32X8D | (224,224,3) | 82.510 | 82.780 | 16.41 | 88.79 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_32x8d.html) | IMAGENET1K_V2 | +| ResNeXt101_64X4D | (224,224,3) | 82.876 | 83.236 | 15.46 | 83.46 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_64x4d.html) | | +| ShuffleNet_V2_X1_0 | (224,224,3) | 68.616 | 69.312 | 0.14 | 2.28 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x1_0.html) | | +| ShuffleNet_V2_X1_5 | (224,224,3) | 72.240 | 72.966 | 0.30 | 3.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x1_5.html) | | +| ShuffleNet_V2_X2_0 | (224,224,3) | 75.540 | 76.222 | 0.58 | 7.39 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x2_0.html) | | +| VGG11 | (224,224,3) | 68.616 | 68.978 | 7.61 | 132.86 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg11.html) | | +| VGG11_BN | (224,224,3) | 70.010 | 70.328 | 7.61 | 132.87 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg11_bn.html) | | +| VGG13 | (224,224,3) | 69.588 | 69.886 | 11.31 | 133.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg13.html) | | +| VGG13_BN | (224,224,3) | 71.262 | 71.570 | 11.31 | 133.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg13_bn.html) | | +| VGG16 | (224,224,3) | 71.350 | 71.610 | 15.47 | 138.36 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg16.html) | | +| VGG16_BN | (224,224,3) | 73.178 | 73.408 | 15.47 | 138.37 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg16_bn.html) | | +| VGG19 | (224,224,3) | 72.140 | 72.384 | 19.63 | 143.67 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg19.html) | | +| VGG19_BN | (224,224,3) | 73.898 | 74.166 | 19.63 | 143.68 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg19_bn.html) | | +| ViT_Tiny_Patch16_224 | (224,224,3) | 75.308 | 75.458 | 2.53 | 5.72 | [Link](https://huggingface.co/timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Tiny_Patch16_384 | (384,384,3) | 78.213 | 78.466 | 9.46 | 5.79 | [Link](https://huggingface.co/timm/vit_tiny_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Small_Patch16_224 | (224,224,3) | 81.446 | 81.410 | 9.25 | 22.05 | [Link](https://huggingface.co/timm/vit_base_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Small_Patch16_384 | (384,384,3) | 83.724 | 83.788 | 31.17 | 22.20 | [Link](https://huggingface.co/timm/vit_base_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Small_Patch32_224 | (224,224,3) | 75.716 | 75.926 | 2.30 | 22.88 | [Link](https://huggingface.co/timm/vit_base_patch32_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Small_Patch32_384 | (384,384,3) | 80.412 | 80.460 | 6.92 | 22.92 | [Link](https://huggingface.co/timm/vit_base_patch32_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Base_Patch8_224 | (224,224,3) | 86.348 | 86.270 | 156.86 | 86.58 | [Link](https://huggingface.co/timm/vit_base_patch8_224.augreg2_in21k_ft_in1k) | augreg2_in21k_ft_in1k | +| ViT_Base_Patch16_224 | (224,224,3) | 85.040 | 85.114 | 35.23 | 86.57 | [Link](https://huggingface.co/timm/vit_base_patch16_224.augreg2_in21k_ft_in1k) | augreg2_in21k_ft_in1k | +| ViT_Base_Patch16_384 | (384,384,3) | 86.002 | 86.028 | 111.34 | 86.86 | [Link](https://huggingface.co/timm/vit_base_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Base_Patch32_224 | (224,224,3) | 80.598 | 80.694 | 8.84 | 88.22 | [Link](https://huggingface.co/timm/vit_base_patch32_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Base_Patch32_384 | (384,384,3) | 83.322 | 83.394 | 26.16 | 88.30 | [Link](https://huggingface.co/timm/vit_base_patch32_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Large_Patch16_224 | (224,224,3) | 85.886 | 85.872 | 123.39 | 304.33 | [Link](https://huggingface.co/timm/vit_large_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Large_Patch16_384 | (384,384,3) | 86.976 | 87.084 | 383.13 | 304.72 | [Link](https://huggingface.co/timm/vit_large_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Large_Patch32_384 | (384,384,3) | 81.494 | 81.510 | 90.75 | 306.63 | [Link](https://huggingface.co/timm/vit_large_patch32_384.orig_in21k_ft_in1k) | orig_in21k_ft_in1k | +| Wide_ResNet50_2 | (224,224,3) | 78.268 | 78.490 | 11.40 | 68.88 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet50_2.html) | IMAGENET1K_V1 | +| Wide_ResNet50_2 | (224,224,3) | 81.226 | 81.626 | 11.40 | 68.88 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet50_2.html) | IMAGENET1K_V2 | +| Wide_ResNet101_2 | (224,224,3) | 78.384 | 78.834 | 22.75 | 126.89 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet101_2.html) | IMAGENET1K_V1 | +| Wide_ResNet101_2 | (224,224,3) | 82.282 | 82.504 | 22.75 | 126.89 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet101_2.html) | IMAGENET1K_V2 | + +
+Image Classification (ImageNet) + +- AccTop1 values are model accuracies on the [ImageNet](https://www.image-net.org/index.php) dataset validation set. + +
+ +## Object Detection + +| Model | Input Size
(H,W,C) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{box}}}$
(NPU) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{box}}}$
(GPU) | FLOPs (B) | params (M) | Source | Note | +|-------|--------------------------|----------------|---------------|-----------|------------|--------|------| +| YOLOv3u | (640,640,3) | 51.053 | 51.577 | 282.68 | 103.73 | [Link](https://docs.ultralytics.com/models/yolov3/)| | +| YOLOv3-spp | (640,640,3) | 46.715 | 47.613 | 156.91 | 62.97 | [Link](https://github.com/ultralytics/yolov3/)| | +| YOLOv3-sppu | (640,640,3) | 51.382 | 51.763 | 283.57 | 104.78 | [Link](https://docs.ultralytics.com/models/yolov3/)| | +| YOLOv5su | (640,640,3) | 42.206 | 42.871 | 24.03 | 9.14 | [Link](https://docs.ultralytics.com/models/yolov5/)| | +| YOLOv5s6u | (1280,1280,3) | 48.008 | 48.632 | 97,75 | 15.29 | [Link](https://docs.ultralytics.com/models/yolov5/)| | +| YOLOv5mu | (640,640,3) | 48.217 | 48.906 | 64.27 | 25.09 | [Link](https://docs.ultralytics.com/models/yolov5/)| | +| YOLOv5m6u | (1280,1280,3) | 52.938 | 53.475 | 261.56 | 41.19 | [Link](https://docs.ultralytics.com/models/yolov5/)| | +| YOLOv5lu | (640,640,3) | 51.718 | 52.171 | 135.09 | 53.19 | [Link](https://docs.ultralytics.com/models/yolov5/)| | +| YOLOv5l6 | (1280,1280,3) | 52.402 | 53.368 | 445.31 | 72.73 | [Link](https://github.com/ultralytics/yolov5/)| | +| YOLOv5l6u | (1280,1280,3) | 54.974 | 55.465 | 549.86 | 80.05 | [Link](https://docs.ultralytics.com/models/yolov5/)| | +| YOLOv5xu | (640,640,3) | 52.600 | 53.090 | 246.55 | 97.23 | [Link](https://docs.ultralytics.com/models/yolov5/)| | +| YOLOv5x6 | (1280, 1280,3) | 53.706 | 54.706 | 838.81 | 140.73 | [Link](https://github.com/ultralytics/yolov5/)| | +| YOLOv5x6u | (1280,1280,3) | 56.141 | 56.489 | 1003.46 | 155.48 | [Link](https://docs.ultralytics.com/models/yolov5/)| | +| YOLOv7 | (640,640,3) | 50.402 | 50.941 | 104.67 | 36.91 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7d6 | (1280,1280,3) | 55.635 | 55.792 | 702.30 | 133.76 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7e6 | (1280,1280,3) | 55.304 | 55.567 | 514.89 | 97.20 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7e6e | (1280,1280,3) | 55.768 | 56.295 | 842.87 | 151.69 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7w6 | (1280,1280,3) | 53.783 | 54.131 | 359.99 | 70.39 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7x | (640,640,3) | 52.103 | 52.706 | 189.88 | 71.31 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv8s | (640,640,3) | 44.062 | 44.918 | 28.64 | 11.16 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8m | (640,640,3) | 49.657 | 50.240 | 79.00 | 25.89 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8l | (640,640,3) | 52.340 | 52.773 | 165.24 | 43.67 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8x | (640,640,3) | 53.365 | 53.802 | 257.92 | 68.20 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| GELANm | (640,640,3) | 50.108 | 50.923 | 76.44 | 19.98 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv9m | (640,640,3) | 50.351 | 51.191 | 76.43 | 19.98 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| GELANc | (640,640,3) | 51.938 | 52.274 | 102.34 | 25.29 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv9c | (640,640,3) | 52.391 | 52.918 | 102.34 | 25.29 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv10b | (640,640,3) | 51.125 | 52.096 | 92.23 | 19.07 | [Link](https://docs.ultralytics.com/models/yolov10/) | | +| YOLO11s | (640,640,3) | 45.733 | 46.617 | 21.69 | 9.44 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11m | (640,640,3) | 50.638 | 51.310 | 68.24 | 20.09 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11l | (640,640,3) | 52.468 | 53.165 | 87.37 | 25.34 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11x | (640,640,3) | 54.059 | 54.478 | 195.67 | 56.92 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO12s | (640,640,3) | 46.746 | 47.687 | 23.84 | 9.26 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12m | (640,640,3) | 51.568 | 52.297 | 71.91 | 20.17 | [Link](https://docs.ultralytics.com/models/yolo12/) | | + +
+Object Detection (COCO) + +- $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{box}}}$ values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset. + +
+ +## Instance Segmentation + +| Model | Input Size
(H,W,C) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{mask}}}$
(NPU) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{mask}}}$
(GPU) | FLOPs (B) | params (M) | Source | Note | +|-------|--------------------------|--------------------|-------------------|-----------|------------|------|------| +| YOLOv5l-seg | (640,640,3) | 39.041 | 39.784 | 147.83 | 47.89 | [Link](https://github.com/ultralytics/yolov5/) | | +| YOLOv5x-seg | (640,640,3) | 40.697 | 41.137 | 265.81 | 88.77 | [Link](https://github.com/ultralytics/yolov5/) | | +| YOLOv8s-seg | (640,640,3) | 36.072 | 36.582 | 42.64 | 11.81 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8m-seg | (640,640,3) | 39.978 | 40.444 | 110.26 | 27.27 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8l-seg | (640,640,3) | 41.993 | 42.316 | 220.55 | 45.97 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8x-seg | (640,640,3) | 42.913 | 43.020 | 344.20 | 71.78 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv9c-seg | (640,640,3) | 42.249 | 42.460 | 145.72 | 27.45 | [Link](https://docs.ultralytics.com/models/yolov9/) | | +| YOLO11s-seg | (640,640,3) | 36.874 | 37.602 | 35.69 | 10.10 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11m-seg | (640,640,3) | 41.031 | 41.546 | 123.56 | 22.40 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11l-seg | (640,640,3) | 42.509 | 42.831 | 142.68 | 27.65 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11x-seg | (640,640,3) | 43.614 | 43.746 | 319.78 | 62.09 | [Link](https://docs.ultralytics.com/models/yolo11/) | | + +
+ Instance Segmentation (COCO) + +- $\underset{50\text{โ€“}95}{\text{mAP}_{\text{val}}^{\text{mask}}}$ values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset. + +
+ +## Pose Estimation + +| Model | Input Size
(H,W,C) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{pose}}}$
(NPU) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{pose}}}$
(GPU) | FLOPs (B) | params (M) | Source | Note | +|-------|--------------------------|--------------------|-------------------|-----------|------------|------|------| +| YOLOv8x-pose | (640,640,3) | 66.416 | 68.367 | 263.33 | 69.46 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLO11x-pose | (640,640,3) | 67.220 | 68.599 | 203.54 | 58.75 | [Link](https://docs.ultralytics.com/models/yolo11/) | | + +
+Pose Estimation (COCO) + +- $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{pose}}}$ values are for single-model single-scale on the [COCO Keypoints val2017](https://cocodataset.org/) dataset. + +
diff --git a/mblt_vision/__init__.py b/mblt_vision/__init__.py index e69de29..3e24464 100644 --- a/mblt_vision/__init__.py +++ b/mblt_vision/__init__.py @@ -0,0 +1,86 @@ +"""MBLT vision task exports and discovery helpers. + +The vision package keeps task subpackages as the preferred import surface while +also supporting legacy top-level model imports such as +``from mblt_model_zoo.vision import ResNet50``. +""" + +from __future__ import annotations + +from . import depth_estimation as depth_estimation +from . import face_detection as face_detection +from . import image_classification as image_classification +from . import instance_segmentation as instance_segmentation +from . import obb as obb +from . import object_detection as object_detection +from . import pose_estimation as pose_estimation +from . import semantic_segmentation as semantic_segmentation +from ._api import list_models as list_models +from ._api import list_tasks as list_tasks +from .wrapper import MBLT_Engine as MBLT_Engine + +__version__ = "0.0.1" + +_TASK_MODULES = ( + face_detection, + depth_estimation, + image_classification, + instance_segmentation, + object_detection, + obb, + pose_estimation, + semantic_segmentation, +) + +_LEGACY_MODEL_EXPORTS: dict[str, object] = {} +for _task_module in _TASK_MODULES: + for _export_name in getattr(_task_module, "__all__", ()): + if _export_name in _LEGACY_MODEL_EXPORTS: + raise RuntimeError( + f"Duplicate vision export detected for '{_export_name}'." + ) + _LEGACY_MODEL_EXPORTS[_export_name] = _task_module + +_PUBLIC_EXPORTS = [ + "MBLT_Engine", + "list_models", + "list_tasks", + "face_detection", + "depth_estimation", + "image_classification", + "instance_segmentation", + "object_detection", + "obb", + "pose_estimation", + "semantic_segmentation", +] + sorted(_LEGACY_MODEL_EXPORTS) +# Keep legacy compatibility exports synchronized with their task packages. +__all__: list[str] = _PUBLIC_EXPORTS # pyright: ignore[reportUnsupportedDunderAll] + + +def __getattr__(name: str) -> object: + """Lazily resolve legacy top-level model exports. + + Args: + name: Attribute requested from the vision package. + + Returns: + The exported model wrapper class for the requested legacy name. + + Raises: + AttributeError: If the requested name is not exported by the package. + """ + + task_module = _LEGACY_MODEL_EXPORTS.get(name) + if task_module is None: + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + value = getattr(task_module, name) + globals()[name] = value + return value + + +def __dir__() -> list[str]: + """Return package attributes including lazy legacy exports.""" + + return sorted(set(globals()) | set(__all__)) diff --git a/mblt_vision/_api.py b/mblt_vision/_api.py new file mode 100644 index 0000000..42e6105 --- /dev/null +++ b/mblt_vision/_api.py @@ -0,0 +1,53 @@ +"""Public helpers for discovering available vision tasks and models.""" + +from __future__ import annotations + +import importlib +import inspect +from typing import Iterable + +from ._tasks import VISION_TASKS, normalize_vision_task +from .wrapper import MBLT_Engine + + +def list_tasks() -> list[str]: + """Lists the available vision tasks.""" + + return list(VISION_TASKS) + + +def list_models(tasks: str | Iterable[str] | None = None) -> dict[str, list[str]]: + """Lists available models for the selected vision tasks. + + Args: + tasks: Task name or names to inspect. When omitted, all tasks are used. + + Returns: + A mapping of task name to exported model class names. + + Raises: + ValueError: If an unknown task name is provided. + """ + + if tasks is None: + task_list = list(VISION_TASKS) + elif isinstance(tasks, str): + task_list = [tasks] + else: + task_list = list(tasks) + + available_models: dict[str, list[str]] = {} + for task in task_list: + module_name = normalize_vision_task(task) + module = importlib.import_module( + f".{module_name}", package=__name__.replace("._api", "") + ) + available_models[task] = sorted( + name + for name, obj in inspect.getmembers(module, inspect.isclass) + if issubclass(obj, MBLT_Engine) + and obj is not MBLT_Engine + and not getattr(obj, "_yaml_missing", False) + ) + + return available_models diff --git a/mblt_vision/_compat.py b/mblt_vision/_compat.py new file mode 100644 index 0000000..ca47c70 --- /dev/null +++ b/mblt_vision/_compat.py @@ -0,0 +1,228 @@ +"""Compatibility helpers for YAML-backed vision model exports. + +This module rebuilds the legacy task package exports that were removed during +the YAML migration. The generated classes keep the familiar import paths and +constructor shape while delegating model loading to ``MBLT_Engine``. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, Callable, Iterable, Sequence, TypeAlias, cast + +from ._model_paths import uses_shifted_compat_model_path_layout +from .wrapper import CoreMode, MBLT_Engine + +_MODEL_DIR = Path(__file__).parent / "models" + + +class CompatMBLTEngine(MBLT_Engine): + """Typed base for dynamically generated legacy compatibility wrappers.""" + + def __init__( + self, + local_path: str | None = None, + model_type: str = "DEFAULT", + infer_mode: CoreMode = "global8", + product: str = "aries", + dev_no: int = 0, + target_cores: Sequence[str] | None = None, + target_clusters: Sequence[int] | None = None, + mxq_path: str | None = None, + onnx_path: str | None = None, + framework: str | None = None, + model_path: str | None = None, + ) -> None: + """Type-only constructor matching the generated legacy wrappers.""" + del ( + local_path, + model_type, + infer_mode, + product, + dev_no, + target_cores, + target_clusters, + model_path, + mxq_path, + onnx_path, + framework, + ) + raise NotImplementedError + + +CompatMBLTEngineClass: TypeAlias = type[CompatMBLTEngine] + + +def _normalize_name(value: str) -> str: + """Returns an alphanumeric-only, case-insensitive identifier.""" + + return "".join(char for char in value.lower() if char.isalnum()) + + +def _resolve_yaml_name(class_name: str) -> str: + """Resolves the YAML config stem associated with a legacy class name. + + Args: + class_name: Exported legacy class name. + + Returns: + The YAML filename stem without the ``.yaml`` suffix. + + Raises: + ValueError: If no unique matching YAML file can be determined. + """ + + yaml_stems = [path.stem for path in _MODEL_DIR.glob("*.yaml")] + + exact_matches = [stem for stem in yaml_stems if stem.lower() == class_name.lower()] + if len(exact_matches) == 1: + return exact_matches[0] + + normalized_name = _normalize_name(class_name) + normalized_matches = [ + stem for stem in yaml_stems if _normalize_name(stem) == normalized_name + ] + if len(normalized_matches) == 1: + return normalized_matches[0] + + if not normalized_matches: + raise ValueError( + f"Could not find a YAML config for legacy class '{class_name}'." + ) + + raise ValueError( + f"Found multiple YAML configs for legacy class '{class_name}': {sorted(normalized_matches)}." + ) + + +def _build_init(yaml_name: str) -> Callable[..., None]: + """Builds a legacy-compatible ``__init__`` implementation.""" + + def __init__( + self, + local_path: str | None = None, + model_type: str = "DEFAULT", + infer_mode: CoreMode = "global8", + product: str = "aries", + dev_no: int = 0, + target_cores: Sequence[str] | None = None, + target_clusters: Sequence[int] | None = None, + mxq_path: str | None = None, + onnx_path: str | None = None, + framework: str | None = None, + model_path: str | None = None, + ) -> None: + """Initializes a YAML-backed compatibility wrapper. + + Args: + local_path: Deprecated legacy MXQ path alias. Prefer ``model_path`` + for generic MXQ or ONNX loading, or ``mxq_path`` for an + explicit MXQ-only override. + model_type: YAML config variant to load. + infer_mode: Execution mode forwarded to ``MBLT_Engine``. + product: Retained for backward compatibility but ignored by the + YAML-backed registry in `2.0.0`. + dev_no: Accelerator device number. + target_cores: Optional core selection for single-core mode. + target_clusters: Optional cluster selection for multi/global modes. + mxq_path: Optional explicit MXQ path alias. + onnx_path: Optional explicit ONNX path. + framework: Execution framework, either ``"mxq"`` or ``"onnx"``. When + omitted, ``model_path`` suffix is used first, then MXQ is the fallback. + model_path: Optional explicit local model path for MXQ or ONNX. + """ + + del product + if uses_shifted_compat_model_path_layout( + model_path, mxq_path, onnx_path, framework + ): + model_path, mxq_path, onnx_path, framework = ( + mxq_path, + onnx_path, + framework, + cast(str | None, model_path), + ) + MBLT_Engine.__init__( + self, + model_cls=yaml_name, + model_type=model_type, + model_path=model_path or "", + mxq_path=mxq_path or local_path or "", + onnx_path=onnx_path or "", + dev_no=dev_no, + core_mode=infer_mode, + target_cores=list(target_cores) if target_cores is not None else None, + target_clusters=list(target_clusters) + if target_clusters is not None + else None, + framework=framework, + ) + + return __init__ + + +def _build_missing_init(class_name: str, reason: str) -> Callable[..., None]: + """Builds an ``__init__`` that fails with a clear compatibility message.""" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + """Raises an informative error for removed YAML-backed models.""" + + del args, kwargs + raise ValueError( + f"Legacy vision model '{class_name}' is not available in the YAML model registry: {reason}" + ) + + return __init__ + + +def create_model_class(class_name: str, module_name: str) -> CompatMBLTEngineClass: + """Creates a legacy model class that delegates to ``MBLT_Engine``. + + Args: + class_name: Class name to expose from the task module. + module_name: Module path that should own the generated class. + + Returns: + A dynamically generated ``MBLT_Engine`` subclass. + """ + + class_doc = f"Compatibility wrapper for the legacy ``{class_name}`` vision model." + try: + yaml_name = _resolve_yaml_name(class_name) + except ValueError as exc: + return type( + class_name, + (CompatMBLTEngine,), + { + "__doc__": class_doc, + "__init__": _build_missing_init(class_name, str(exc)), + "__module__": module_name, + "_yaml_missing": True, + }, + ) + + return type( + class_name, + (CompatMBLTEngine,), + { + "__doc__": class_doc, + "__init__": _build_init(yaml_name), + "__module__": module_name, + "_yaml_name": yaml_name, + }, + ) + + +def export_model_classes( + namespace: dict[str, Any], class_names: Iterable[str], module_name: str +) -> None: + """Populates a module namespace with generated model classes. + + Args: + namespace: Target module globals. + class_names: Legacy class names to generate. + module_name: Import path for generated classes. + """ + + for class_name in class_names: + namespace[class_name] = create_model_class(class_name, module_name) diff --git a/mblt_vision/_model_paths.py b/mblt_vision/_model_paths.py new file mode 100644 index 0000000..dc51c47 --- /dev/null +++ b/mblt_vision/_model_paths.py @@ -0,0 +1,139 @@ +"""Framework and local-artifact path resolution for vision engines.""" + +from __future__ import annotations + +from pathlib import Path + +SUPPORTED_FRAMEWORKS = {"mxq", "onnx"} + + +def framework_from_model_path(model_path: str) -> str | None: + """Infer the runtime framework from a local model path suffix.""" + + suffix = Path(model_path).suffix.lower() + if suffix == ".mxq": + return "mxq" + if suffix == ".onnx": + return "onnx" + return None + + +def uses_shifted_engine_model_path_layout( + model_path: object, + mxq_path: object, + dev_no: object, + core_mode: object, + target_cores: object, + postprocess_kwargs: object, + framework: object, + onnx_providers: object, +) -> bool: + """Return whether engine arguments use the model-path-first layout. + + Public constructor layouts have used the third positional argument for + either ``mxq_path`` or ``model_path``. An ONNX suffix identifies the + model-path-first layout because it changes runtime routing. For MXQ, + remapping is needed only when later values have the types produced by a + one-slot positional shift; a path by itself behaves identically as the + ``mxq_path`` alias. + """ + + if not isinstance(mxq_path, str): + return False + inferred_framework = framework_from_model_path(mxq_path) + if inferred_framework not in SUPPORTED_FRAMEWORKS: + return False + if ( + isinstance(model_path, str) + and model_path + and model_path.lower() not in SUPPORTED_FRAMEWORKS + ): + return False + if inferred_framework == "onnx": + return True + return ( + isinstance(dev_no, str) + or isinstance(core_mode, int) + or isinstance(target_cores, str) + or (postprocess_kwargs is not None and not isinstance(postprocess_kwargs, dict)) + or isinstance(framework, dict) + or isinstance(onnx_providers, str) + or (model_path is not None and not isinstance(model_path, str)) + or (isinstance(model_path, str) and model_path.lower() in SUPPORTED_FRAMEWORKS) + ) + + +def uses_shifted_compat_model_path_layout( + model_path: object, + mxq_path: object, + onnx_path: object, + framework: object, +) -> bool: + """Return whether generated-wrapper arguments use a shifted model-path tail.""" + + if not isinstance(mxq_path, str): + return False + inferred_framework = framework_from_model_path(mxq_path) + if inferred_framework not in SUPPORTED_FRAMEWORKS: + return False + if ( + isinstance(model_path, str) + and model_path + and model_path.lower() not in SUPPORTED_FRAMEWORKS + ): + return False + if inferred_framework == "onnx": + return True + return ( + (isinstance(model_path, str) and model_path.lower() in SUPPORTED_FRAMEWORKS) + or ( + isinstance(onnx_path, str) and framework_from_model_path(onnx_path) == "mxq" + ) + or ( + isinstance(framework, str) + and framework_from_model_path(framework) is not None + ) + ) + + +def resolve_framework(framework: str | None, model_path: str = "") -> str: + """Resolve the execution framework from explicit input and model path.""" + + normalized_framework = framework.lower() if framework is not None else None + if ( + normalized_framework is not None + and normalized_framework not in SUPPORTED_FRAMEWORKS + ): + raise ValueError( + f"Unsupported framework: {framework}. Must be one of {sorted(SUPPORTED_FRAMEWORKS)}." + ) + inferred_framework = framework_from_model_path(model_path) if model_path else None + if ( + normalized_framework + and inferred_framework + and normalized_framework != inferred_framework + ): + raise ValueError( + f"Framework `{normalized_framework}` conflicts with model path `{model_path}`. " + f"Use framework `{inferred_framework}` or remove the explicit framework." + ) + return inferred_framework or normalized_framework or "mxq" + + +def split_model_paths( + *, framework: str, model_path: str = "", mxq_path: str = "", onnx_path: str = "" +) -> tuple[str, str]: + """Resolve generic and framework-specific local model path arguments.""" + + resolved_mxq_path = mxq_path + resolved_onnx_path = onnx_path + if not model_path: + return resolved_mxq_path, resolved_onnx_path + inferred_framework = framework_from_model_path(model_path) + if inferred_framework == "mxq": + resolved_mxq_path = resolved_mxq_path or model_path + elif inferred_framework == "onnx" or framework == "onnx": + resolved_onnx_path = resolved_onnx_path or model_path + else: + resolved_mxq_path = resolved_mxq_path or model_path + return resolved_mxq_path, resolved_onnx_path diff --git a/mblt_vision/_tasks.py b/mblt_vision/_tasks.py new file mode 100644 index 0000000..af5c442 --- /dev/null +++ b/mblt_vision/_tasks.py @@ -0,0 +1,32 @@ +"""Canonical task names used by the standalone Vision processing API.""" + +from __future__ import annotations + +from collections.abc import Iterable + +VISION_TASKS: tuple[str, ...] = ( + "image_classification", + "depth_estimation", + "object_detection", + "instance_segmentation", + "semantic_segmentation", + "obb", + "pose_estimation", + "face_detection", +) + +_INPUT_TASK_ALIASES = {"oriented_bounding_boxes": "obb"} + + +def normalize_vision_task(task: str, *, supported: Iterable[str] | None = None) -> str: + """Normalize a Vision task name and validate it against supported tasks.""" + + if not isinstance(task, str): + raise TypeError(f"Vision task must be a string, got {type(task).__name__}.") + normalized = _INPUT_TASK_ALIASES.get(task.lower(), task.lower()) + supported_tasks = tuple(VISION_TASKS if supported is None else supported) + if normalized not in supported_tasks: + raise ValueError( + f"Unsupported Vision task {task!r}; expected one of {sorted(supported_tasks)}." + ) + return normalized diff --git a/mblt_vision/datasets/__init__.py b/mblt_vision/datasets/__init__.py new file mode 100644 index 0000000..0d4dae4 --- /dev/null +++ b/mblt_vision/datasets/__init__.py @@ -0,0 +1,15 @@ +"""YAML-backed definitions for vision validation datasets.""" + +from .registry import ( + get_dataset_category_ids, + get_dataset_class_names, + get_dataset_config, + get_dataset_config_for_task, +) + +__all__ = [ + "get_dataset_category_ids", + "get_dataset_class_names", + "get_dataset_config", + "get_dataset_config_for_task", +] diff --git a/mblt_vision/datasets/ade20k.yaml b/mblt_vision/datasets/ade20k.yaml new file mode 100644 index 0000000..33fcb02 --- /dev/null +++ b/mblt_vision/datasets/ade20k.yaml @@ -0,0 +1,163 @@ +# ADE20K validation dataset. Organized paths are relative to `path`. +name: ade20k +path: ~/.mblt_model_zoo/datasets/ADEChallengeData2016 +val: images +tasks: + - semantic_segmentation +names: + 0: wall + 1: building + 2: sky + 3: floor + 4: tree + 5: ceiling + 6: road + 7: bed + 8: windowpane + 9: grass + 10: cabinet + 11: sidewalk + 12: person + 13: earth + 14: door + 15: table + 16: mountain + 17: plant + 18: curtain + 19: chair + 20: car + 21: water + 22: painting + 23: sofa + 24: shelf + 25: house + 26: sea + 27: mirror + 28: rug + 29: field + 30: armchair + 31: seat + 32: fence + 33: desk + 34: rock + 35: wardrobe + 36: lamp + 37: bathtub + 38: railing + 39: cushion + 40: base + 41: box + 42: column + 43: signboard + 44: chest of drawers + 45: counter + 46: sand + 47: sink + 48: skyscraper + 49: fireplace + 50: refrigerator + 51: grandstand + 52: path + 53: stairs + 54: runway + 55: case + 56: pool table + 57: pillow + 58: screen door + 59: stairway + 60: river + 61: bridge + 62: bookcase + 63: blind + 64: coffee table + 65: toilet + 66: flower + 67: book + 68: hill + 69: bench + 70: countertop + 71: stove + 72: palm + 73: kitchen island + 74: computer + 75: swivel chair + 76: boat + 77: bar + 78: arcade machine + 79: hovel + 80: bus + 81: towel + 82: light + 83: truck + 84: tower + 85: chandelier + 86: awning + 87: streetlight + 88: booth + 89: television receiver + 90: airplane + 91: dirt track + 92: apparel + 93: pole + 94: land + 95: bannister + 96: escalator + 97: ottoman + 98: bottle + 99: buffet + 100: poster + 101: stage + 102: van + 103: ship + 104: fountain + 105: conveyor belt + 106: canopy + 107: washer + 108: plaything + 109: swimming pool + 110: stool + 111: barrel + 112: basket + 113: waterfall + 114: tent + 115: bag + 116: minibike + 117: cradle + 118: oven + 119: ball + 120: food + 121: step + 122: tank + 123: trade name + 124: microwave + 125: pot + 126: animal + 127: bicycle + 128: lake + 129: dishwasher + 130: screen + 131: blanket + 132: sculpture + 133: hood + 134: sconce + 135: vase + 136: traffic light + 137: tray + 138: ashcan + 139: fan + 140: pier + 141: crt screen + 142: plate + 143: monitor + 144: bulletin board + 145: shower + 146: radiator + 147: glass + 148: clock + 149: flag +download: + url: http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip +nc: 150 +source_ignore_label: 0 +ignore_label: 255 +label_offset: 1 diff --git a/mblt_vision/datasets/cityscapes.yaml b/mblt_vision/datasets/cityscapes.yaml new file mode 100644 index 0000000..e33b106 --- /dev/null +++ b/mblt_vision/datasets/cityscapes.yaml @@ -0,0 +1,54 @@ +# Cityscapes validation taxonomy from the official train/validation/test archives. +# Organized paths are relative to `path`. +name: cityscapes +path: ~/.mblt_model_zoo/datasets/cityscapes +val: images +tasks: + - semantic_segmentation +nc: 19 +names: + 0: road + 1: sidewalk + 2: building + 3: wall + 4: fence + 5: pole + 6: traffic light + 7: traffic sign + 8: vegetation + 9: terrain + 10: sky + 11: person + 12: rider + 13: car + 14: truck + 15: bus + 16: train + 17: motorcycle + 18: bicycle +category_ids: [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33] +palette: + 0: [128, 64, 128] + 1: [244, 35, 232] + 2: [70, 70, 70] + 3: [102, 102, 156] + 4: [190, 153, 153] + 5: [153, 153, 153] + 6: [250, 170, 30] + 7: [220, 220, 0] + 8: [107, 142, 35] + 9: [152, 251, 152] + 10: [70, 130, 180] + 11: [220, 20, 60] + 12: [255, 0, 0] + 13: [0, 0, 142] + 14: [0, 0, 70] + 15: [0, 60, 100] + 16: [0, 80, 100] + 17: [0, 0, 230] + 18: [119, 11, 32] +download: + type: cityscapes + source: https://github.com/mcordts/cityscapesScripts + images_archive: leftImg8bit_trainvaltest.zip + annotations_archive: gtFine_trainvaltest.zip diff --git a/mblt_vision/datasets/coco.yaml b/mblt_vision/datasets/coco.yaml new file mode 100644 index 0000000..a3ade17 --- /dev/null +++ b/mblt_vision/datasets/coco.yaml @@ -0,0 +1,174 @@ +# COCO 2017 validation dataset. Paths are relative to `path` unless absolute. +name: coco +path: ~/.mblt_model_zoo/datasets/coco +tasks: + - object_detection + - instance_segmentation + - pose_estimation +val: val2017 +download: + images: http://images.cocodataset.org/zips/val2017.zip + annotations: http://images.cocodataset.org/annotations/annotations_trainval2017.zip + +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush +category_ids: +- 1 +- 2 +- 3 +- 4 +- 5 +- 6 +- 7 +- 8 +- 9 +- 10 +- 11 +- 13 +- 14 +- 15 +- 16 +- 17 +- 18 +- 19 +- 20 +- 21 +- 22 +- 23 +- 24 +- 25 +- 27 +- 28 +- 31 +- 32 +- 33 +- 34 +- 35 +- 36 +- 37 +- 38 +- 39 +- 40 +- 41 +- 42 +- 43 +- 44 +- 46 +- 47 +- 48 +- 49 +- 50 +- 51 +- 52 +- 53 +- 54 +- 55 +- 56 +- 57 +- 58 +- 59 +- 60 +- 61 +- 62 +- 63 +- 64 +- 65 +- 67 +- 70 +- 72 +- 73 +- 74 +- 75 +- 76 +- 77 +- 78 +- 79 +- 80 +- 81 +- 82 +- 84 +- 85 +- 86 +- 87 +- 88 +- 89 +- 90 diff --git a/mblt_vision/datasets/dotav1.yaml b/mblt_vision/datasets/dotav1.yaml new file mode 100644 index 0000000..827c28a --- /dev/null +++ b/mblt_vision/datasets/dotav1.yaml @@ -0,0 +1,28 @@ +# DOTAv1 validation dataset. Paths are relative to `path` unless absolute. +name: dotav1 +path: ~/.mblt_model_zoo/datasets/dotav1 +tasks: + - obb +val: images +labels: labels/val_original +names: + 0: plane + 1: ship + 2: storage-tank + 3: baseball-diamond + 4: tennis-court + 5: basketball-court + 6: ground-track-field + 7: harbor + 8: bridge + 9: large-vehicle + 10: small-vehicle + 11: helicopter + 12: roundabout + 13: soccer-ball-field + 14: swimming-pool +download: + type: google_drive_folder + url: https://drive.google.com/drive/folders/1n5w45suVOyaqY84hltJhIZdtVFD9B224 + images_archive: images/part1.zip + labels_archive: labelTxt-v1.0/labelTxt.zip diff --git a/mblt_vision/datasets/imagenet.yaml b/mblt_vision/datasets/imagenet.yaml new file mode 100644 index 0000000..973c153 --- /dev/null +++ b/mblt_vision/datasets/imagenet.yaml @@ -0,0 +1,1019 @@ +# ImageNet-1k validation dataset. Paths are relative to `path` unless absolute. +name: imagenet +path: ~/.mblt_model_zoo/datasets/imagenet +tasks: + - image_classification +val: . +download: + images: https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar + annotations: https://www.image-net.org/data/ILSVRC/2012/ILSVRC2012_bbox_val_v3.tgz + +names: + 0: tench, Tinca tinca + 1: goldfish, Carassius auratus + 2: great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias + 3: tiger shark, Galeocerdo cuvieri + 4: hammerhead, hammerhead shark + 5: electric ray, crampfish, numbfish, torpedo + 6: stingray + 7: cock + 8: hen + 9: ostrich, Struthio camelus + 10: brambling, Fringilla montifringilla + 11: goldfinch, Carduelis carduelis + 12: house finch, linnet, Carpodacus mexicanus + 13: junco, snowbird + 14: indigo bunting, indigo finch, indigo bird, Passerina cyanea + 15: robin, American robin, Turdus migratorius + 16: bulbul + 17: jay + 18: magpie + 19: chickadee + 20: water ouzel, dipper + 21: kite + 22: bald eagle, American eagle, Haliaeetus leucocephalus + 23: vulture + 24: great grey owl, great gray owl, Strix nebulosa + 25: European fire salamander, Salamandra salamandra + 26: common newt, Triturus vulgaris + 27: eft + 28: spotted salamander, Ambystoma maculatum + 29: axolotl, mud puppy, Ambystoma mexicanum + 30: bullfrog, Rana catesbeiana + 31: tree frog, tree-frog + 32: tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui + 33: loggerhead, loggerhead turtle, Caretta caretta + 34: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea + 35: mud turtle + 36: terrapin + 37: box turtle, box tortoise + 38: banded gecko + 39: common iguana, iguana, Iguana iguana + 40: American chameleon, anole, Anolis carolinensis + 41: whiptail, whiptail lizard + 42: agama + 43: frilled lizard, Chlamydosaurus kingi + 44: alligator lizard + 45: Gila monster, Heloderma suspectum + 46: green lizard, Lacerta viridis + 47: African chameleon, Chamaeleo chamaeleon + 48: Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis + 49: African crocodile, Nile crocodile, Crocodylus niloticus + 50: American alligator, Alligator mississipiensis + 51: triceratops + 52: thunder snake, worm snake, Carphophis amoenus + 53: ringneck snake, ring-necked snake, ring snake + 54: hognose snake, puff adder, sand viper + 55: green snake, grass snake + 56: king snake, kingsnake + 57: garter snake, grass snake + 58: water snake + 59: vine snake + 60: night snake, Hypsiglena torquata + 61: boa constrictor, Constrictor constrictor + 62: rock python, rock snake, Python sebae + 63: Indian cobra, Naja naja + 64: green mamba + 65: sea snake + 66: horned viper, cerastes, sand viper, horned asp, Cerastes cornutus + 67: diamondback, diamondback rattlesnake, Crotalus adamanteus + 68: sidewinder, horned rattlesnake, Crotalus cerastes + 69: trilobite + 70: harvestman, daddy longlegs, Phalangium opilio + 71: scorpion + 72: black and gold garden spider, Argiope aurantia + 73: barn spider, Araneus cavaticus + 74: garden spider, Aranea diademata + 75: black widow, Latrodectus mactans + 76: tarantula + 77: wolf spider, hunting spider + 78: tick + 79: centipede + 80: black grouse + 81: ptarmigan + 82: ruffed grouse, partridge, Bonasa umbellus + 83: prairie chicken, prairie grouse, prairie fowl + 84: peacock + 85: quail + 86: partridge + 87: African grey, African gray, Psittacus erithacus + 88: macaw + 89: sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita + 90: lorikeet + 91: coucal + 92: bee eater + 93: hornbill + 94: hummingbird + 95: jacamar + 96: toucan + 97: drake + 98: red-breasted merganser, Mergus serrator + 99: goose + 100: black swan, Cygnus atratus + 101: tusker + 102: echidna, spiny anteater, anteater + 103: platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus + anatinus + 104: wallaby, brush kangaroo + 105: koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus + 106: wombat + 107: jellyfish + 108: sea anemone, anemone + 109: brain coral + 110: flatworm, platyhelminth + 111: nematode, nematode worm, roundworm + 112: conch + 113: snail + 114: slug + 115: sea slug, nudibranch + 116: chiton, coat-of-mail shell, sea cradle, polyplacophore + 117: chambered nautilus, pearly nautilus, nautilus + 118: Dungeness crab, Cancer magister + 119: rock crab, Cancer irroratus + 120: fiddler crab + 121: king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica + 122: American lobster, Northern lobster, Maine lobster, Homarus americanus + 123: spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish + 124: crayfish, crawfish, crawdad, crawdaddy + 125: hermit crab + 126: isopod + 127: white stork, Ciconia ciconia + 128: black stork, Ciconia nigra + 129: spoonbill + 130: flamingo + 131: little blue heron, Egretta caerulea + 132: American egret, great white heron, Egretta albus + 133: bittern + 134: crane + 135: limpkin, Aramus pictus + 136: European gallinule, Porphyrio porphyrio + 137: American coot, marsh hen, mud hen, water hen, Fulica americana + 138: bustard + 139: ruddy turnstone, Arenaria interpres + 140: red-backed sandpiper, dunlin, Erolia alpina + 141: redshank, Tringa totanus + 142: dowitcher + 143: oystercatcher, oyster catcher + 144: pelican + 145: king penguin, Aptenodytes patagonica + 146: albatross, mollymawk + 147: grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus + 148: killer whale, killer, orca, grampus, sea wolf, Orcinus orca + 149: dugong, Dugong dugon + 150: sea lion + 151: Chihuahua + 152: Japanese spaniel + 153: Maltese dog, Maltese terrier, Maltese + 154: Pekinese, Pekingese, Peke + 155: Shih-Tzu + 156: Blenheim spaniel + 157: papillon + 158: toy terrier + 159: Rhodesian ridgeback + 160: Afghan hound, Afghan + 161: basset, basset hound + 162: beagle + 163: bloodhound, sleuthhound + 164: bluetick + 165: black-and-tan coonhound + 166: Walker hound, Walker foxhound + 167: English foxhound + 168: redbone + 169: borzoi, Russian wolfhound + 170: Irish wolfhound + 171: Italian greyhound + 172: whippet + 173: Ibizan hound, Ibizan Podenco + 174: Norwegian elkhound, elkhound + 175: otterhound, otter hound + 176: Saluki, gazelle hound + 177: Scottish deerhound, deerhound + 178: Weimaraner + 179: Staffordshire bullterrier, Staffordshire bull terrier + 180: American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, + pit bull terrier + 181: Bedlington terrier + 182: Border terrier + 183: Kerry blue terrier + 184: Irish terrier + 185: Norfolk terrier + 186: Norwich terrier + 187: Yorkshire terrier + 188: wire-haired fox terrier + 189: Lakeland terrier + 190: Sealyham terrier, Sealyham + 191: Airedale, Airedale terrier + 192: cairn, cairn terrier + 193: Australian terrier + 194: Dandie Dinmont, Dandie Dinmont terrier + 195: Boston bull, Boston terrier + 196: miniature schnauzer + 197: giant schnauzer + 198: standard schnauzer + 199: Scotch terrier, Scottish terrier, Scottie + 200: Tibetan terrier, chrysanthemum dog + 201: silky terrier, Sydney silky + 202: soft-coated wheaten terrier + 203: West Highland white terrier + 204: Lhasa, Lhasa apso + 205: flat-coated retriever + 206: curly-coated retriever + 207: golden retriever + 208: Labrador retriever + 209: Chesapeake Bay retriever + 210: German short-haired pointer + 211: vizsla, Hungarian pointer + 212: English setter + 213: Irish setter, red setter + 214: Gordon setter + 215: Brittany spaniel + 216: clumber, clumber spaniel + 217: English springer, English springer spaniel + 218: Welsh springer spaniel + 219: cocker spaniel, English cocker spaniel, cocker + 220: Sussex spaniel + 221: Irish water spaniel + 222: kuvasz + 223: schipperke + 224: groenendael + 225: malinois + 226: briard + 227: kelpie + 228: komondor + 229: Old English sheepdog, bobtail + 230: Shetland sheepdog, Shetland sheep dog, Shetland + 231: collie + 232: Border collie + 233: Bouvier des Flandres, Bouviers des Flandres + 234: Rottweiler + 235: German shepherd, German shepherd dog, German police dog, alsatian + 236: Doberman, Doberman pinscher + 237: miniature pinscher + 238: Greater Swiss Mountain dog + 239: Bernese mountain dog + 240: Appenzeller + 241: EntleBucher + 242: boxer + 243: bull mastiff + 244: Tibetan mastiff + 245: French bulldog + 246: Great Dane + 247: Saint Bernard, St Bernard + 248: Eskimo dog, husky + 249: malamute, malemute, Alaskan malamute + 250: Siberian husky + 251: dalmatian, coach dog, carriage dog + 252: affenpinscher, monkey pinscher, monkey dog + 253: basenji + 254: pug, pug-dog + 255: Leonberg + 256: Newfoundland, Newfoundland dog + 257: Great Pyrenees + 258: Samoyed, Samoyede + 259: Pomeranian + 260: chow, chow chow + 261: keeshond + 262: Brabancon griffon + 263: Pembroke, Pembroke Welsh corgi + 264: Cardigan, Cardigan Welsh corgi + 265: toy poodle + 266: miniature poodle + 267: standard poodle + 268: Mexican hairless + 269: timber wolf, grey wolf, gray wolf, Canis lupus + 270: white wolf, Arctic wolf, Canis lupus tundrarum + 271: red wolf, maned wolf, Canis rufus, Canis niger + 272: coyote, prairie wolf, brush wolf, Canis latrans + 273: dingo, warrigal, warragal, Canis dingo + 274: dhole, Cuon alpinus + 275: African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus + 276: hyena, hyaena + 277: red fox, Vulpes vulpes + 278: kit fox, Vulpes macrotis + 279: Arctic fox, white fox, Alopex lagopus + 280: grey fox, gray fox, Urocyon cinereoargenteus + 281: tabby, tabby cat + 282: tiger cat + 283: Persian cat + 284: Siamese cat, Siamese + 285: Egyptian cat + 286: cougar, puma, catamount, mountain lion, painter, panther, Felis concolor + 287: lynx, catamount + 288: leopard, Panthera pardus + 289: snow leopard, ounce, Panthera uncia + 290: jaguar, panther, Panthera onca, Felis onca + 291: lion, king of beasts, Panthera leo + 292: tiger, Panthera tigris + 293: cheetah, chetah, Acinonyx jubatus + 294: brown bear, bruin, Ursus arctos + 295: American black bear, black bear, Ursus americanus, Euarctos americanus + 296: ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus + 297: sloth bear, Melursus ursinus, Ursus ursinus + 298: mongoose + 299: meerkat, mierkat + 300: tiger beetle + 301: ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle + 302: ground beetle, carabid beetle + 303: long-horned beetle, longicorn, longicorn beetle + 304: leaf beetle, chrysomelid + 305: dung beetle + 306: rhinoceros beetle + 307: weevil + 308: fly + 309: bee + 310: ant, emmet, pismire + 311: grasshopper, hopper + 312: cricket + 313: walking stick, walkingstick, stick insect + 314: cockroach, roach + 315: mantis, mantid + 316: cicada, cicala + 317: leafhopper + 318: lacewing, lacewing fly + 319: dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, + snake doctor, mosquito hawk, skeeter hawk + 320: damselfly + 321: admiral + 322: ringlet, ringlet butterfly + 323: monarch, monarch butterfly, milkweed butterfly, Danaus plexippus + 324: cabbage butterfly + 325: sulphur butterfly, sulfur butterfly + 326: lycaenid, lycaenid butterfly + 327: starfish, sea star + 328: sea urchin + 329: sea cucumber, holothurian + 330: wood rabbit, cottontail, cottontail rabbit + 331: hare + 332: Angora, Angora rabbit + 333: hamster + 334: porcupine, hedgehog + 335: fox squirrel, eastern fox squirrel, Sciurus niger + 336: marmot + 337: beaver + 338: guinea pig, Cavia cobaya + 339: sorrel + 340: zebra + 341: hog, pig, grunter, squealer, Sus scrofa + 342: wild boar, boar, Sus scrofa + 343: warthog + 344: hippopotamus, hippo, river horse, Hippopotamus amphibius + 345: ox + 346: water buffalo, water ox, Asiatic buffalo, Bubalus bubalis + 347: bison + 348: ram, tup + 349: bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, + Ovis canadensis + 350: ibex, Capra ibex + 351: hartebeest + 352: impala, Aepyceros melampus + 353: gazelle + 354: Arabian camel, dromedary, Camelus dromedarius + 355: llama + 356: weasel + 357: mink + 358: polecat, fitch, foulmart, foumart, Mustela putorius + 359: black-footed ferret, ferret, Mustela nigripes + 360: otter + 361: skunk, polecat, wood pussy + 362: badger + 363: armadillo + 364: three-toed sloth, ai, Bradypus tridactylus + 365: orangutan, orang, orangutang, Pongo pygmaeus + 366: gorilla, Gorilla gorilla + 367: chimpanzee, chimp, Pan troglodytes + 368: gibbon, Hylobates lar + 369: siamang, Hylobates syndactylus, Symphalangus syndactylus + 370: guenon, guenon monkey + 371: patas, hussar monkey, Erythrocebus patas + 372: baboon + 373: macaque + 374: langur + 375: colobus, colobus monkey + 376: proboscis monkey, Nasalis larvatus + 377: marmoset + 378: capuchin, ringtail, Cebus capucinus + 379: howler monkey, howler + 380: titi, titi monkey + 381: spider monkey, Ateles geoffroyi + 382: squirrel monkey, Saimiri sciureus + 383: Madagascar cat, ring-tailed lemur, Lemur catta + 384: indri, indris, Indri indri, Indri brevicaudatus + 385: Indian elephant, Elephas maximus + 386: African elephant, Loxodonta africana + 387: lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens + 388: giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca + 389: barracouta, snoek + 390: eel + 391: coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch + 392: rock beauty, Holocanthus tricolor + 393: anemone fish + 394: sturgeon + 395: gar, garfish, garpike, billfish, Lepisosteus osseus + 396: lionfish + 397: puffer, pufferfish, blowfish, globefish + 398: abacus + 399: abaya + 400: academic gown, academic robe, judge's robe + 401: accordion, piano accordion, squeeze box + 402: acoustic guitar + 403: aircraft carrier, carrier, flattop, attack aircraft carrier + 404: airliner + 405: airship, dirigible + 406: altar + 407: ambulance + 408: amphibian, amphibious vehicle + 409: analog clock + 410: apiary, bee house + 411: apron + 412: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, + trash barrel, trash bin + 413: assault rifle, assault gun + 414: backpack, back pack, knapsack, packsack, rucksack, haversack + 415: bakery, bakeshop, bakehouse + 416: balance beam, beam + 417: balloon + 418: ballpoint, ballpoint pen, ballpen, Biro + 419: Band Aid + 420: banjo + 421: bannister, banister, balustrade, balusters, handrail + 422: barbell + 423: barber chair + 424: barbershop + 425: barn + 426: barometer + 427: barrel, cask + 428: barrow, garden cart, lawn cart, wheelbarrow + 429: baseball + 430: basketball + 431: bassinet + 432: bassoon + 433: bathing cap, swimming cap + 434: bath towel + 435: bathtub, bathing tub, bath, tub + 436: beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, + waggon + 437: beacon, lighthouse, beacon light, pharos + 438: beaker + 439: bearskin, busby, shako + 440: beer bottle + 441: beer glass + 442: bell cote, bell cot + 443: bib + 444: bicycle-built-for-two, tandem bicycle, tandem + 445: bikini, two-piece + 446: binder, ring-binder + 447: binoculars, field glasses, opera glasses + 448: birdhouse + 449: boathouse + 450: bobsled, bobsleigh, bob + 451: bolo tie, bolo, bola tie, bola + 452: bonnet, poke bonnet + 453: bookcase + 454: bookshop, bookstore, bookstall + 455: bottlecap + 456: bow + 457: bow tie, bow-tie, bowtie + 458: brass, memorial tablet, plaque + 459: brassiere, bra, bandeau + 460: breakwater, groin, groyne, mole, bulwark, seawall, jetty + 461: breastplate, aegis, egis + 462: broom + 463: bucket, pail + 464: buckle + 465: bulletproof vest + 466: bullet train, bullet + 467: butcher shop, meat market + 468: cab, hack, taxi, taxicab + 469: caldron, cauldron + 470: candle, taper, wax light + 471: cannon + 472: canoe + 473: can opener, tin opener + 474: cardigan + 475: car mirror + 476: carousel, carrousel, merry-go-round, roundabout, whirligig + 477: carpenter's kit, tool kit + 478: carton + 479: car wheel + 480: cash machine, cash dispenser, automated teller machine, automatic teller machine, + automated teller, automatic teller, ATM + 481: cassette + 482: cassette player + 483: castle + 484: catamaran + 485: CD player + 486: cello, violoncello + 487: cellular telephone, cellular phone, cellphone, cell, mobile phone + 488: chain + 489: chainlink fence + 490: chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour + 491: chain saw, chainsaw + 492: chest + 493: chiffonier, commode + 494: chime, bell, gong + 495: china cabinet, china closet + 496: Christmas stocking + 497: church, church building + 498: cinema, movie theater, movie theatre, movie house, picture palace + 499: cleaver, meat cleaver, chopper + 500: cliff dwelling + 501: cloak + 502: clog, geta, patten, sabot + 503: cocktail shaker + 504: coffee mug + 505: coffeepot + 506: coil, spiral, volute, whorl, helix + 507: combination lock + 508: computer keyboard, keypad + 509: confectionery, confectionary, candy store + 510: container ship, containership, container vessel + 511: convertible + 512: corkscrew, bottle screw + 513: cornet, horn, trumpet, trump + 514: cowboy boot + 515: cowboy hat, ten-gallon hat + 516: cradle + 517: crane + 518: crash helmet + 519: crate + 520: crib, cot + 521: Crock Pot + 522: croquet ball + 523: crutch + 524: cuirass + 525: dam, dike, dyke + 526: desk + 527: desktop computer + 528: dial telephone, dial phone + 529: diaper, nappy, napkin + 530: digital clock + 531: digital watch + 532: dining table, board + 533: dishrag, dishcloth + 534: dishwasher, dish washer, dishwashing machine + 535: disk brake, disc brake + 536: dock, dockage, docking facility + 537: dogsled, dog sled, dog sleigh + 538: dome + 539: doormat, welcome mat + 540: drilling platform, offshore rig + 541: drum, membranophone, tympan + 542: drumstick + 543: dumbbell + 544: Dutch oven + 545: electric fan, blower + 546: electric guitar + 547: electric locomotive + 548: entertainment center + 549: envelope + 550: espresso maker + 551: face powder + 552: feather boa, boa + 553: file, file cabinet, filing cabinet + 554: fireboat + 555: fire engine, fire truck + 556: fire screen, fireguard + 557: flagpole, flagstaff + 558: flute, transverse flute + 559: folding chair + 560: football helmet + 561: forklift + 562: fountain + 563: fountain pen + 564: four-poster + 565: freight car + 566: French horn, horn + 567: frying pan, frypan, skillet + 568: fur coat + 569: garbage truck, dustcart + 570: gasmask, respirator, gas helmet + 571: gas pump, gasoline pump, petrol pump, island dispenser + 572: goblet + 573: go-kart + 574: golf ball + 575: golfcart, golf cart + 576: gondola + 577: gong, tam-tam + 578: gown + 579: grand piano, grand + 580: greenhouse, nursery, glasshouse + 581: grille, radiator grille + 582: grocery store, grocery, food market, market + 583: guillotine + 584: hair slide + 585: hair spray + 586: half track + 587: hammer + 588: hamper + 589: hand blower, blow dryer, blow drier, hair dryer, hair drier + 590: hand-held computer, hand-held microcomputer + 591: handkerchief, hankie, hanky, hankey + 592: hard disc, hard disk, fixed disk + 593: harmonica, mouth organ, harp, mouth harp + 594: harp + 595: harvester, reaper + 596: hatchet + 597: holster + 598: home theater, home theatre + 599: honeycomb + 600: hook, claw + 601: hoopskirt, crinoline + 602: horizontal bar, high bar + 603: horse cart, horse-cart + 604: hourglass + 605: iPod + 606: iron, smoothing iron + 607: jack-o'-lantern + 608: jean, blue jean, denim + 609: jeep, landrover + 610: jersey, T-shirt, tee shirt + 611: jigsaw puzzle + 612: jinrikisha, ricksha, rickshaw + 613: joystick + 614: kimono + 615: knee pad + 616: knot + 617: lab coat, laboratory coat + 618: ladle + 619: lampshade, lamp shade + 620: laptop, laptop computer + 621: lawn mower, mower + 622: lens cap, lens cover + 623: letter opener, paper knife, paperknife + 624: library + 625: lifeboat + 626: lighter, light, igniter, ignitor + 627: limousine, limo + 628: liner, ocean liner + 629: lipstick, lip rouge + 630: Loafer + 631: lotion + 632: loudspeaker, speaker, speaker unit, loudspeaker system, speaker system + 633: loupe, jeweler's loupe + 634: lumbermill, sawmill + 635: magnetic compass + 636: mailbag, postbag + 637: mailbox, letter box + 638: maillot + 639: maillot, tank suit + 640: manhole cover + 641: maraca + 642: marimba, xylophone + 643: mask + 644: matchstick + 645: maypole + 646: maze, labyrinth + 647: measuring cup + 648: medicine chest, medicine cabinet + 649: megalith, megalithic structure + 650: microphone, mike + 651: microwave, microwave oven + 652: military uniform + 653: milk can + 654: minibus + 655: miniskirt, mini + 656: minivan + 657: missile + 658: mitten + 659: mixing bowl + 660: mobile home, manufactured home + 661: Model T + 662: modem + 663: monastery + 664: monitor + 665: moped + 666: mortar + 667: mortarboard + 668: mosque + 669: mosquito net + 670: motor scooter, scooter + 671: mountain bike, all-terrain bike, off-roader + 672: mountain tent + 673: mouse, computer mouse + 674: mousetrap + 675: moving van + 676: muzzle + 677: nail + 678: neck brace + 679: necklace + 680: nipple + 681: notebook, notebook computer + 682: obelisk + 683: oboe, hautboy, hautbois + 684: ocarina, sweet potato + 685: odometer, hodometer, mileometer, milometer + 686: oil filter + 687: organ, pipe organ + 688: oscilloscope, scope, cathode-ray oscilloscope, CRO + 689: overskirt + 690: oxcart + 691: oxygen mask + 692: packet + 693: paddle, boat paddle + 694: paddlewheel, paddle wheel + 695: padlock + 696: paintbrush + 697: pajama, pyjama, pj's, jammies + 698: palace + 699: panpipe, pandean pipe, syrinx + 700: paper towel + 701: parachute, chute + 702: parallel bars, bars + 703: park bench + 704: parking meter + 705: passenger car, coach, carriage + 706: patio, terrace + 707: pay-phone, pay-station + 708: pedestal, plinth, footstall + 709: pencil box, pencil case + 710: pencil sharpener + 711: perfume, essence + 712: Petri dish + 713: photocopier + 714: pick, plectrum, plectron + 715: pickelhaube + 716: picket fence, paling + 717: pickup, pickup truck + 718: pier + 719: piggy bank, penny bank + 720: pill bottle + 721: pillow + 722: ping-pong ball + 723: pinwheel + 724: pirate, pirate ship + 725: pitcher, ewer + 726: plane, carpenter's plane, woodworking plane + 727: planetarium + 728: plastic bag + 729: plate rack + 730: plow, plough + 731: plunger, plumber's helper + 732: Polaroid camera, Polaroid Land camera + 733: pole + 734: police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria + 735: poncho + 736: pool table, billiard table, snooker table + 737: pop bottle, soda bottle + 738: pot, flowerpot + 739: potter's wheel + 740: power drill + 741: prayer rug, prayer mat + 742: printer + 743: prison, prison house + 744: projectile, missile + 745: projector + 746: puck, hockey puck + 747: punching bag, punch bag, punching ball, punchball + 748: purse + 749: quill, quill pen + 750: quilt, comforter, comfort, puff + 751: racer, race car, racing car + 752: racket, racquet + 753: radiator + 754: radio, wireless + 755: radio telescope, radio reflector + 756: rain barrel + 757: recreational vehicle, RV, R.V. + 758: reel + 759: reflex camera + 760: refrigerator, icebox + 761: remote control, remote + 762: restaurant, eating house, eating place, eatery + 763: revolver, six-gun, six-shooter + 764: rifle + 765: rocking chair, rocker + 766: rotisserie + 767: rubber eraser, rubber, pencil eraser + 768: rugby ball + 769: rule, ruler + 770: running shoe + 771: safe + 772: safety pin + 773: saltshaker, salt shaker + 774: sandal + 775: sarong + 776: sax, saxophone + 777: scabbard + 778: scale, weighing machine + 779: school bus + 780: schooner + 781: scoreboard + 782: screen, CRT screen + 783: screw + 784: screwdriver + 785: seat belt, seatbelt + 786: sewing machine + 787: shield, buckler + 788: shoe shop, shoe-shop, shoe store + 789: shoji + 790: shopping basket + 791: shopping cart + 792: shovel + 793: shower cap + 794: shower curtain + 795: ski + 796: ski mask + 797: sleeping bag + 798: slide rule, slipstick + 799: sliding door + 800: slot, one-armed bandit + 801: snorkel + 802: snowmobile + 803: snowplow, snowplough + 804: soap dispenser + 805: soccer ball + 806: sock + 807: solar dish, solar collector, solar furnace + 808: sombrero + 809: soup bowl + 810: space bar + 811: space heater + 812: space shuttle + 813: spatula + 814: speedboat + 815: spider web, spider's web + 816: spindle + 817: sports car, sport car + 818: spotlight, spot + 819: stage + 820: steam locomotive + 821: steel arch bridge + 822: steel drum + 823: stethoscope + 824: stole + 825: stone wall + 826: stopwatch, stop watch + 827: stove + 828: strainer + 829: streetcar, tram, tramcar, trolley, trolley car + 830: stretcher + 831: studio couch, day bed + 832: stupa, tope + 833: submarine, pigboat, sub, U-boat + 834: suit, suit of clothes + 835: sundial + 836: sunglass + 837: sunglasses, dark glasses, shades + 838: sunscreen, sunblock, sun blocker + 839: suspension bridge + 840: swab, swob, mop + 841: sweatshirt + 842: swimming trunks, bathing trunks + 843: swing + 844: switch, electric switch, electrical switch + 845: syringe + 846: table lamp + 847: tank, army tank, armored combat vehicle, armoured combat vehicle + 848: tape player + 849: teapot + 850: teddy, teddy bear + 851: television, television system + 852: tennis ball + 853: thatch, thatched roof + 854: theater curtain, theatre curtain + 855: thimble + 856: thresher, thrasher, threshing machine + 857: throne + 858: tile roof + 859: toaster + 860: tobacco shop, tobacconist shop, tobacconist + 861: toilet seat + 862: torch + 863: totem pole + 864: tow truck, tow car, wrecker + 865: toyshop + 866: tractor + 867: trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi + 868: tray + 869: trench coat + 870: tricycle, trike, velocipede + 871: trimaran + 872: tripod + 873: triumphal arch + 874: trolleybus, trolley coach, trackless trolley + 875: trombone + 876: tub, vat + 877: turnstile + 878: typewriter keyboard + 879: umbrella + 880: unicycle, monocycle + 881: upright, upright piano + 882: vacuum, vacuum cleaner + 883: vase + 884: vault + 885: velvet + 886: vending machine + 887: vestment + 888: viaduct + 889: violin, fiddle + 890: volleyball + 891: waffle iron + 892: wall clock + 893: wallet, billfold, notecase, pocketbook + 894: wardrobe, closet, press + 895: warplane, military plane + 896: washbasin, handbasin, washbowl, lavabo, wash-hand basin + 897: washer, automatic washer, washing machine + 898: water bottle + 899: water jug + 900: water tower + 901: whiskey jug + 902: whistle + 903: wig + 904: window screen + 905: window shade + 906: Windsor tie + 907: wine bottle + 908: wing + 909: wok + 910: wooden spoon + 911: wool, woolen, woollen + 912: worm fence, snake fence, snake-rail fence, Virginia fence + 913: wreck + 914: yawl + 915: yurt + 916: web site, website, internet site, site + 917: comic book + 918: crossword puzzle, crossword + 919: street sign + 920: traffic light, traffic signal, stoplight + 921: book jacket, dust cover, dust jacket, dust wrapper + 922: menu + 923: plate + 924: guacamole + 925: consomme + 926: hot pot, hotpot + 927: trifle + 928: ice cream, icecream + 929: ice lolly, lolly, lollipop, popsicle + 930: French loaf + 931: bagel, beigel + 932: pretzel + 933: cheeseburger + 934: hotdog, hot dog, red hot + 935: mashed potato + 936: head cabbage + 937: broccoli + 938: cauliflower + 939: zucchini, courgette + 940: spaghetti squash + 941: acorn squash + 942: butternut squash + 943: cucumber, cuke + 944: artichoke, globe artichoke + 945: bell pepper + 946: cardoon + 947: mushroom + 948: Granny Smith + 949: strawberry + 950: orange + 951: lemon + 952: fig + 953: pineapple, ananas + 954: banana + 955: jackfruit, jak, jack + 956: custard apple + 957: pomegranate + 958: hay + 959: carbonara + 960: chocolate sauce, chocolate syrup + 961: dough + 962: meat loaf, meatloaf + 963: pizza, pizza pie + 964: potpie + 965: burrito + 966: red wine + 967: espresso + 968: cup + 969: eggnog + 970: alp + 971: bubble + 972: cliff, drop, drop-off + 973: coral reef + 974: geyser + 975: lakeside, lakeshore + 976: promontory, headland, head, foreland + 977: sandbar, sand bar + 978: seashore, coast, seacoast, sea-coast + 979: valley, vale + 980: volcano + 981: ballplayer, baseball player + 982: groom, bridegroom + 983: scuba diver + 984: rapeseed + 985: daisy + 986: yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium + parviflorum + 987: corn + 988: acorn + 989: hip, rose hip, rosehip + 990: buckeye, horse chestnut, conker + 991: coral fungus + 992: agaric + 993: gyromitra + 994: stinkhorn, carrion fungus + 995: earthstar + 996: hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa + 997: bolete + 998: ear, spike, capitulum + 999: toilet tissue, toilet paper, bathroom tissue diff --git a/mblt_vision/datasets/nyu-depth.yaml b/mblt_vision/datasets/nyu-depth.yaml new file mode 100644 index 0000000..43aded5 --- /dev/null +++ b/mblt_vision/datasets/nyu-depth.yaml @@ -0,0 +1,7 @@ +name: nyu-depth +path: ~/.mblt_model_zoo/datasets/nyu-depth +val: images +tasks: + - depth_estimation +download: + url: https://github.com/ultralytics/assets/releases/download/v0.0.0/nyu-depth.zip diff --git a/mblt_vision/datasets/registry.py b/mblt_vision/datasets/registry.py new file mode 100644 index 0000000..887f8b9 --- /dev/null +++ b/mblt_vision/datasets/registry.py @@ -0,0 +1,158 @@ +"""Load YAML-backed vision dataset definitions.""" + +from __future__ import annotations + +import copy +from functools import lru_cache +from pathlib import Path +from typing import Any + +import yaml + +from .._tasks import normalize_vision_task + +DATASET_CONFIG_DIR = Path(__file__).parent + + +@lru_cache(maxsize=None) +def _load_dataset_config(name: str) -> dict[str, Any]: + """Load and validate a dataset definition without mutating its cached value.""" + + config_path = DATASET_CONFIG_DIR / f"{name.lower()}.yaml" + if not config_path.is_file(): + raise FileNotFoundError(f"Vision dataset definition not found: {config_path}") + + with config_path.open(encoding="utf-8") as config_file: + config = yaml.safe_load(config_file) + if not isinstance(config, dict): + raise ValueError(f"Vision dataset definition must be a mapping: {config_path}") + if not isinstance(config.get("path"), str): + raise ValueError( + f"Vision dataset definition requires a string `path`: {config_path}" + ) + if not isinstance(config.get("name"), str): + raise ValueError( + f"Vision dataset definition requires a string `name`: {config_path}" + ) + if not isinstance(config.get("tasks"), list) or not all( + isinstance(task, str) for task in config["tasks"] + ): + raise ValueError( + f"Vision dataset definition requires a string-list `tasks`: {config_path}" + ) + config["path"] = str(Path(config["path"]).expanduser()) + return config + + +def get_dataset_config(name: str) -> dict[str, Any]: + """Load a named vision dataset definition. + + Args: + name: Dataset filename stem, such as ``dotav1``. + + Returns: + Parsed dataset configuration with its path expanded. + + Raises: + FileNotFoundError: If the dataset definition does not exist. + ValueError: If the definition is malformed. + """ + + return copy.deepcopy(_load_dataset_config(name)) + + +def get_dataset_class_names(name: str) -> tuple[str, ...]: + """Return ordered class names from a dataset definition. + + Args: + name: Dataset filename stem, such as ``coco``. + + Returns: + Class names ordered by contiguous zero-based index. + + Raises: + ValueError: If the dataset has no valid contiguous ``names`` mapping. + """ + + names = _load_dataset_config(name).get("names") + if not isinstance(names, dict) or not all( + isinstance(index, int) and isinstance(label, str) + for index, label in names.items() + ): + raise ValueError( + f"Vision dataset definition requires an integer-keyed `names` mapping: {name}" + ) + if set(names) != set(range(len(names))): + raise ValueError( + f"Vision dataset class IDs must be contiguous and zero-based: {name}" + ) + return tuple(names[index] for index in range(len(names))) + + +@lru_cache(maxsize=None) +def get_dataset_category_ids(name: str) -> tuple[int, ...]: + """Return immutable source category IDs from a dataset definition. + + Args: + name: Dataset filename stem, such as ``coco``. + + Returns: + Source category IDs ordered by contiguous model-output index. + + Raises: + ValueError: If the dataset has no integer ``category_ids`` list. + """ + + category_ids = _load_dataset_config(name).get("category_ids") + if not isinstance(category_ids, list) or not all( + isinstance(category_id, int) for category_id in category_ids + ): + raise ValueError( + f"Vision dataset definition requires an integer-list `category_ids`: {name}" + ) + return tuple(category_ids) + + +def get_dataset_config_for_task( + task: str, dataset: str | None = None +) -> dict[str, Any]: + """Return the validation dataset definition associated with a vision task. + + Args: + task: Vision task name from a model postprocess configuration. + dataset: Optional output-taxonomy name. When omitted, the first configured + task match is returned for backward compatibility. + + Returns: + Matching dataset configuration. + + Raises: + ValueError: If no configured dataset supports the task. + """ + + normalized_task = normalize_vision_task(task) + if dataset is not None: + try: + config = get_dataset_config(dataset) + except FileNotFoundError as exc: + raise ValueError( + f"No vision dataset definition exists for taxonomy `{dataset}`." + ) from exc + configured_tasks = { + normalize_vision_task(configured_task) + for configured_task in config["tasks"] + } + if normalized_task not in configured_tasks: + raise ValueError( + f"Vision dataset `{dataset}` does not support task `{task}`." + ) + return config + + for config_path in sorted(DATASET_CONFIG_DIR.glob("*.yaml")): + config = get_dataset_config(config_path.stem) + if normalized_task in { + normalize_vision_task(configured_task) + for configured_task in config["tasks"] + }: + return config + raise ValueError(f"No vision dataset definition supports task `{task}`.") diff --git a/mblt_vision/datasets/widerface.yaml b/mblt_vision/datasets/widerface.yaml new file mode 100644 index 0000000..2724a8b --- /dev/null +++ b/mblt_vision/datasets/widerface.yaml @@ -0,0 +1,9 @@ +# WiderFace validation dataset. Paths are relative to `path` unless absolute. +name: widerface +path: ~/.mblt_model_zoo/datasets/widerface +tasks: + - face_detection +val: images +download: + images: https://huggingface.co/datasets/CUHK-CSE/wider_face/resolve/main/data/WIDER_val.zip + annotations: https://huggingface.co/datasets/CUHK-CSE/wider_face/resolve/main/data/wider_face_split.zip diff --git a/mblt_vision/depth_estimation/__init__.py b/mblt_vision/depth_estimation/__init__.py new file mode 100644 index 0000000..6682234 --- /dev/null +++ b/mblt_vision/depth_estimation/__init__.py @@ -0,0 +1,19 @@ +"""Depth estimation model exports.""" + +from __future__ import annotations + +from .._compat import create_model_class + +__all__: list[str] = [ + "YOLO26lDepth", + "YOLO26mDepth", + "YOLO26nDepth", + "YOLO26sDepth", + "YOLO26xDepth", +] + +YOLO26lDepth = create_model_class("YOLO26lDepth", __name__) +YOLO26mDepth = create_model_class("YOLO26mDepth", __name__) +YOLO26nDepth = create_model_class("YOLO26nDepth", __name__) +YOLO26sDepth = create_model_class("YOLO26sDepth", __name__) +YOLO26xDepth = create_model_class("YOLO26xDepth", __name__) diff --git a/mblt_vision/face_detection/__init__.py b/mblt_vision/face_detection/__init__.py new file mode 100644 index 0000000..0e1ca62 --- /dev/null +++ b/mblt_vision/face_detection/__init__.py @@ -0,0 +1,43 @@ +"""Face detection exports for the vision package.""" + +from __future__ import annotations + +from .._compat import create_model_class + +__all__: list[str] = [ + "YOLO11l_face", + "YOLO11m_face", + "YOLO11n_face", + "YOLO11s_face", + "YOLO12l_face", + "YOLO12m_face", + "YOLO12n_face", + "YOLO12s_face", + "YOLOv10l_face", + "YOLOv10m_face", + "YOLOv10n_face", + "YOLOv10s_face", + "YOLOv6m_face", + "YOLOv6n_face", + "YOLOv8l_face", + "YOLOv8m_face", + "YOLOv8n_face", +] + +YOLO11l_face = create_model_class("YOLO11l_face", __name__) +YOLO11m_face = create_model_class("YOLO11m_face", __name__) +YOLO11n_face = create_model_class("YOLO11n_face", __name__) +YOLO11s_face = create_model_class("YOLO11s_face", __name__) +YOLO12l_face = create_model_class("YOLO12l_face", __name__) +YOLO12m_face = create_model_class("YOLO12m_face", __name__) +YOLO12n_face = create_model_class("YOLO12n_face", __name__) +YOLO12s_face = create_model_class("YOLO12s_face", __name__) +YOLOv10l_face = create_model_class("YOLOv10l_face", __name__) +YOLOv10m_face = create_model_class("YOLOv10m_face", __name__) +YOLOv10n_face = create_model_class("YOLOv10n_face", __name__) +YOLOv10s_face = create_model_class("YOLOv10s_face", __name__) +YOLOv6m_face = create_model_class("YOLOv6m_face", __name__) +YOLOv6n_face = create_model_class("YOLOv6n_face", __name__) +YOLOv8l_face = create_model_class("YOLOv8l_face", __name__) +YOLOv8m_face = create_model_class("YOLOv8m_face", __name__) +YOLOv8n_face = create_model_class("YOLOv8n_face", __name__) diff --git a/mblt_vision/image_classification/__init__.py b/mblt_vision/image_classification/__init__.py new file mode 100644 index 0000000..3223c59 --- /dev/null +++ b/mblt_vision/image_classification/__init__.py @@ -0,0 +1,311 @@ +"""Image classification model exports.""" + +from __future__ import annotations + +from .._compat import create_model_class + +__all__: list[str] = [ + "AlexNet", + "CAFormer_B36", + "CAFormer_M36", + "CAFormer_S18", + "CAFormer_S36", + "CoAtNet_0_RW_224", + "CoAtNet_1_RW_224", + "CoAtNet_2_RW_224", + "ConvFormer_B36", + "ConvFormer_M36", + "ConvFormer_S18", + "ConvFormer_S36", + "ConvNeXt_Base", + "ConvNeXt_Large", + "ConvNeXt_Small", + "ConvNeXt_Tiny", + "DeiT_Base_Patch16_224", + "DeiT_Base_Patch16_384", + "DeiT_Small_Patch16_224", + "DeiT_Tiny_Patch16_224", + "DeiT3_Base_Patch16_224", + "DeiT3_Base_Patch16_384", + "DeiT3_Large_Patch16_224", + "DeiT3_Large_Patch16_384", + "DeiT3_Medium_Patch16_224", + "DeiT3_Small_Patch16_224", + "DeiT3_Small_Patch16_384", + "DenseNet121", + "DenseNet161", + "DenseNet169", + "DenseNet201", + "EfficientFormer_L1", + "EfficientFormer_L3", + "EfficientFormer_L7", + "EfficientNet_B0", + "EfficientNet_B1", + "EfficientNet_B2", + "EfficientNet_B3", + "EfficientNet_B4", + "EfficientNet_B5", + "EfficientNet_B6", + "EfficientNet_B7", + "EfficientNet_V2_L", + "EfficientNet_V2_M", + "EfficientNet_V2_S", + "FlexiViT_Base", + "FlexiViT_Large", + "FlexiViT_Small", + "GoogLeNet", + "Inception_V3", + "LeViT_Conv_128", + "LeViT_Conv_128S", + "LeViT_Conv_192", + "LeViT_Conv_256", + "LeViT_Conv_384", + "MNASNet0_5", + "MNASNet0_75", + "MNASNet1_0", + "MNASNet1_3", + "MobileNet_V2", + "MobileNet_V3_Large", + "MobileNet_V3_Small", + "RegNet_X_1_6GF", + "RegNet_X_3_2GF", + "RegNet_X_8GF", + "RegNet_X_16GF", + "RegNet_X_32GF", + "RegNet_X_400MF", + "RegNet_X_800MF", + "RegNet_Y_1_6GF", + "RegNet_Y_3_2GF", + "RegNet_Y_8GF", + "RegNet_Y_16GF", + "RegNet_Y_32GF", + "RegNet_Y_400MF", + "RegNet_Y_800MF", + "RepViT_M0_9", + "RepViT_M1", + "RepViT_M1_0", + "RepViT_M1_1", + "RepViT_M1_5", + "RepViT_M2", + "RepViT_M2_3", + "RepViT_M3", + "ResNet18", + "ResNet34", + "ResNet50", + "ResNet101", + "ResNet152", + "ResNeXt50_32x4d", + "ResNeXt101_32x8d", + "ResNeXt101_64x4d", + "ShuffleNet_V2_X0_5", + "ShuffleNet_V2_X1_0", + "ShuffleNet_V2_X1_5", + "ShuffleNet_V2_X2_0", + "SqueezeNet1_0", + "SqueezeNet1_1", + "Swin_B", + "Swin_S", + "Swin_T", + "VGG11", + "VGG11_BN", + "VGG13", + "VGG13_BN", + "VGG16", + "VGG16_BN", + "VGG19", + "VGG19_BN", + "VisFormer_Small", + "VisFormer_Tiny", + "ViT_B_16", + "ViT_B_32", + "ViT_Base_Patch8_224", + "ViT_Base_Patch16_224", + "ViT_Base_Patch16_384", + "ViT_Base_Patch32_224", + "ViT_Base_Patch32_384", + "ViT_L_16", + "ViT_L_32", + "ViT_Large_Patch16_224", + "ViT_Large_Patch16_384", + "ViT_Large_Patch32_384", + "ViT_Small_Patch16_224", + "ViT_Small_Patch16_384", + "ViT_Small_Patch32_224", + "ViT_Small_Patch32_384", + "ViT_Tiny_Patch16_224", + "ViT_Tiny_Patch16_384", + "Wide_ResNet50_2", + "Wide_ResNet101_2", + "YOLO11lCls", + "YOLO11mCls", + "YOLO11nCls", + "YOLO11sCls", + "YOLO11xCls", + "YOLO26lCls", + "YOLO26mCls", + "YOLO26nCls", + "YOLO26sCls", + "YOLO26xCls", + "YOLOv5lCls", + "YOLOv5mCls", + "YOLOv5nCls", + "YOLOv5sCls", + "YOLOv5xCls", + "YOLOv8lCls", + "YOLOv8mCls", + "YOLOv8nCls", + "YOLOv8sCls", + "YOLOv8xCls", +] + +AlexNet = create_model_class("AlexNet", __name__) +CAFormer_B36 = create_model_class("CAFormer_B36", __name__) +CAFormer_M36 = create_model_class("CAFormer_M36", __name__) +CAFormer_S18 = create_model_class("CAFormer_S18", __name__) +CAFormer_S36 = create_model_class("CAFormer_S36", __name__) +CoAtNet_0_RW_224 = create_model_class("CoAtNet_0_RW_224", __name__) +CoAtNet_1_RW_224 = create_model_class("CoAtNet_1_RW_224", __name__) +CoAtNet_2_RW_224 = create_model_class("CoAtNet_2_RW_224", __name__) +ConvFormer_B36 = create_model_class("ConvFormer_B36", __name__) +ConvFormer_M36 = create_model_class("ConvFormer_M36", __name__) +ConvFormer_S18 = create_model_class("ConvFormer_S18", __name__) +ConvFormer_S36 = create_model_class("ConvFormer_S36", __name__) +ConvNeXt_Base = create_model_class("ConvNeXt_Base", __name__) +ConvNeXt_Large = create_model_class("ConvNeXt_Large", __name__) +ConvNeXt_Small = create_model_class("ConvNeXt_Small", __name__) +ConvNeXt_Tiny = create_model_class("ConvNeXt_Tiny", __name__) +DeiT_Base_Patch16_224 = create_model_class("DeiT_Base_Patch16_224", __name__) +DeiT_Base_Patch16_384 = create_model_class("DeiT_Base_Patch16_384", __name__) +DeiT_Small_Patch16_224 = create_model_class("DeiT_Small_Patch16_224", __name__) +DeiT_Tiny_Patch16_224 = create_model_class("DeiT_Tiny_Patch16_224", __name__) +DeiT3_Base_Patch16_224 = create_model_class("DeiT3_Base_Patch16_224", __name__) +DeiT3_Base_Patch16_384 = create_model_class("DeiT3_Base_Patch16_384", __name__) +DeiT3_Large_Patch16_224 = create_model_class("DeiT3_Large_Patch16_224", __name__) +DeiT3_Large_Patch16_384 = create_model_class("DeiT3_Large_Patch16_384", __name__) +DeiT3_Medium_Patch16_224 = create_model_class("DeiT3_Medium_Patch16_224", __name__) +DeiT3_Small_Patch16_224 = create_model_class("DeiT3_Small_Patch16_224", __name__) +DeiT3_Small_Patch16_384 = create_model_class("DeiT3_Small_Patch16_384", __name__) +DenseNet121 = create_model_class("DenseNet121", __name__) +DenseNet161 = create_model_class("DenseNet161", __name__) +DenseNet169 = create_model_class("DenseNet169", __name__) +DenseNet201 = create_model_class("DenseNet201", __name__) +EfficientFormer_L1 = create_model_class("EfficientFormer_L1", __name__) +EfficientFormer_L3 = create_model_class("EfficientFormer_L3", __name__) +EfficientFormer_L7 = create_model_class("EfficientFormer_L7", __name__) +EfficientNet_B0 = create_model_class("EfficientNet_B0", __name__) +EfficientNet_B1 = create_model_class("EfficientNet_B1", __name__) +EfficientNet_B2 = create_model_class("EfficientNet_B2", __name__) +EfficientNet_B3 = create_model_class("EfficientNet_B3", __name__) +EfficientNet_B4 = create_model_class("EfficientNet_B4", __name__) +EfficientNet_B5 = create_model_class("EfficientNet_B5", __name__) +EfficientNet_B6 = create_model_class("EfficientNet_B6", __name__) +EfficientNet_B7 = create_model_class("EfficientNet_B7", __name__) +EfficientNet_V2_L = create_model_class("EfficientNet_V2_L", __name__) +EfficientNet_V2_M = create_model_class("EfficientNet_V2_M", __name__) +EfficientNet_V2_S = create_model_class("EfficientNet_V2_S", __name__) +FlexiViT_Base = create_model_class("FlexiViT_Base", __name__) +FlexiViT_Large = create_model_class("FlexiViT_Large", __name__) +FlexiViT_Small = create_model_class("FlexiViT_Small", __name__) +GoogLeNet = create_model_class("GoogLeNet", __name__) +Inception_V3 = create_model_class("Inception_V3", __name__) +LeViT_Conv_128 = create_model_class("LeViT_Conv_128", __name__) +LeViT_Conv_128S = create_model_class("LeViT_Conv_128S", __name__) +LeViT_Conv_192 = create_model_class("LeViT_Conv_192", __name__) +LeViT_Conv_256 = create_model_class("LeViT_Conv_256", __name__) +LeViT_Conv_384 = create_model_class("LeViT_Conv_384", __name__) +MNASNet0_5 = create_model_class("MNASNet0_5", __name__) +MNASNet0_75 = create_model_class("MNASNet0_75", __name__) +MNASNet1_0 = create_model_class("MNASNet1_0", __name__) +MNASNet1_3 = create_model_class("MNASNet1_3", __name__) +MobileNet_V2 = create_model_class("MobileNet_V2", __name__) +MobileNet_V3_Large = create_model_class("MobileNet_V3_Large", __name__) +MobileNet_V3_Small = create_model_class("MobileNet_V3_Small", __name__) +RegNet_X_1_6GF = create_model_class("RegNet_X_1_6GF", __name__) +RegNet_X_3_2GF = create_model_class("RegNet_X_3_2GF", __name__) +RegNet_X_8GF = create_model_class("RegNet_X_8GF", __name__) +RegNet_X_16GF = create_model_class("RegNet_X_16GF", __name__) +RegNet_X_32GF = create_model_class("RegNet_X_32GF", __name__) +RegNet_X_400MF = create_model_class("RegNet_X_400MF", __name__) +RegNet_X_800MF = create_model_class("RegNet_X_800MF", __name__) +RegNet_Y_1_6GF = create_model_class("RegNet_Y_1_6GF", __name__) +RegNet_Y_3_2GF = create_model_class("RegNet_Y_3_2GF", __name__) +RegNet_Y_8GF = create_model_class("RegNet_Y_8GF", __name__) +RegNet_Y_16GF = create_model_class("RegNet_Y_16GF", __name__) +RegNet_Y_32GF = create_model_class("RegNet_Y_32GF", __name__) +RegNet_Y_400MF = create_model_class("RegNet_Y_400MF", __name__) +RegNet_Y_800MF = create_model_class("RegNet_Y_800MF", __name__) +RepViT_M0_9 = create_model_class("RepViT_M0_9", __name__) +RepViT_M1 = create_model_class("RepViT_M1", __name__) +RepViT_M1_0 = create_model_class("RepViT_M1_0", __name__) +RepViT_M1_1 = create_model_class("RepViT_M1_1", __name__) +RepViT_M1_5 = create_model_class("RepViT_M1_5", __name__) +RepViT_M2 = create_model_class("RepViT_M2", __name__) +RepViT_M2_3 = create_model_class("RepViT_M2_3", __name__) +RepViT_M3 = create_model_class("RepViT_M3", __name__) +ResNet18 = create_model_class("ResNet18", __name__) +ResNet34 = create_model_class("ResNet34", __name__) +ResNet50 = create_model_class("ResNet50", __name__) +ResNet101 = create_model_class("ResNet101", __name__) +ResNet152 = create_model_class("ResNet152", __name__) +ResNeXt50_32x4d = create_model_class("ResNeXt50_32x4d", __name__) +ResNeXt101_32x8d = create_model_class("ResNeXt101_32x8d", __name__) +ResNeXt101_64x4d = create_model_class("ResNeXt101_64x4d", __name__) +ShuffleNet_V2_X0_5 = create_model_class("ShuffleNet_V2_X0_5", __name__) +ShuffleNet_V2_X1_0 = create_model_class("ShuffleNet_V2_X1_0", __name__) +ShuffleNet_V2_X1_5 = create_model_class("ShuffleNet_V2_X1_5", __name__) +ShuffleNet_V2_X2_0 = create_model_class("ShuffleNet_V2_X2_0", __name__) +SqueezeNet1_0 = create_model_class("SqueezeNet1_0", __name__) +SqueezeNet1_1 = create_model_class("SqueezeNet1_1", __name__) +Swin_B = create_model_class("Swin_B", __name__) +Swin_S = create_model_class("Swin_S", __name__) +Swin_T = create_model_class("Swin_T", __name__) +VGG11 = create_model_class("VGG11", __name__) +VGG11_BN = create_model_class("VGG11_BN", __name__) +VGG13 = create_model_class("VGG13", __name__) +VGG13_BN = create_model_class("VGG13_BN", __name__) +VGG16 = create_model_class("VGG16", __name__) +VGG16_BN = create_model_class("VGG16_BN", __name__) +VGG19 = create_model_class("VGG19", __name__) +VGG19_BN = create_model_class("VGG19_BN", __name__) +VisFormer_Small = create_model_class("VisFormer_Small", __name__) +VisFormer_Tiny = create_model_class("VisFormer_Tiny", __name__) +ViT_B_16 = create_model_class("ViT_B_16", __name__) +ViT_B_32 = create_model_class("ViT_B_32", __name__) +ViT_Base_Patch8_224 = create_model_class("ViT_Base_Patch8_224", __name__) +ViT_Base_Patch16_224 = create_model_class("ViT_Base_Patch16_224", __name__) +ViT_Base_Patch16_384 = create_model_class("ViT_Base_Patch16_384", __name__) +ViT_Base_Patch32_224 = create_model_class("ViT_Base_Patch32_224", __name__) +ViT_Base_Patch32_384 = create_model_class("ViT_Base_Patch32_384", __name__) +ViT_L_16 = create_model_class("ViT_L_16", __name__) +ViT_L_32 = create_model_class("ViT_L_32", __name__) +ViT_Large_Patch16_224 = create_model_class("ViT_Large_Patch16_224", __name__) +ViT_Large_Patch16_384 = create_model_class("ViT_Large_Patch16_384", __name__) +ViT_Large_Patch32_384 = create_model_class("ViT_Large_Patch32_384", __name__) +ViT_Small_Patch16_224 = create_model_class("ViT_Small_Patch16_224", __name__) +ViT_Small_Patch16_384 = create_model_class("ViT_Small_Patch16_384", __name__) +ViT_Small_Patch32_224 = create_model_class("ViT_Small_Patch32_224", __name__) +ViT_Small_Patch32_384 = create_model_class("ViT_Small_Patch32_384", __name__) +ViT_Tiny_Patch16_224 = create_model_class("ViT_Tiny_Patch16_224", __name__) +ViT_Tiny_Patch16_384 = create_model_class("ViT_Tiny_Patch16_384", __name__) +Wide_ResNet50_2 = create_model_class("Wide_ResNet50_2", __name__) +Wide_ResNet101_2 = create_model_class("Wide_ResNet101_2", __name__) +YOLO11lCls = create_model_class("YOLO11lCls", __name__) +YOLO11mCls = create_model_class("YOLO11mCls", __name__) +YOLO11nCls = create_model_class("YOLO11nCls", __name__) +YOLO11sCls = create_model_class("YOLO11sCls", __name__) +YOLO11xCls = create_model_class("YOLO11xCls", __name__) +YOLO26lCls = create_model_class("YOLO26lCls", __name__) +YOLO26mCls = create_model_class("YOLO26mCls", __name__) +YOLO26nCls = create_model_class("YOLO26nCls", __name__) +YOLO26sCls = create_model_class("YOLO26sCls", __name__) +YOLO26xCls = create_model_class("YOLO26xCls", __name__) +YOLOv5lCls = create_model_class("YOLOv5lCls", __name__) +YOLOv5mCls = create_model_class("YOLOv5mCls", __name__) +YOLOv5nCls = create_model_class("YOLOv5nCls", __name__) +YOLOv5sCls = create_model_class("YOLOv5sCls", __name__) +YOLOv5xCls = create_model_class("YOLOv5xCls", __name__) +YOLOv8lCls = create_model_class("YOLOv8lCls", __name__) +YOLOv8mCls = create_model_class("YOLOv8mCls", __name__) +YOLOv8nCls = create_model_class("YOLOv8nCls", __name__) +YOLOv8sCls = create_model_class("YOLOv8sCls", __name__) +YOLOv8xCls = create_model_class("YOLOv8xCls", __name__) diff --git a/mblt_vision/instance_segmentation/__init__.py b/mblt_vision/instance_segmentation/__init__.py new file mode 100644 index 0000000..421e9ff --- /dev/null +++ b/mblt_vision/instance_segmentation/__init__.py @@ -0,0 +1,65 @@ +"""Instance segmentation model exports.""" + +from __future__ import annotations + +from .._compat import create_model_class + +__all__: list[str] = [ + "YOLO11lSeg", + "YOLO11mSeg", + "YOLO11nSeg", + "YOLO11sSeg", + "YOLO11xSeg", + "YOLO12lSeg", + "YOLO12mSeg", + "YOLO12nSeg", + "YOLO12sSeg", + "YOLO12xSeg", + "YOLO26lSeg", + "YOLO26mSeg", + "YOLO26nSeg", + "YOLO26sSeg", + "YOLO26xSeg", + "YOLOv5lSeg", + "YOLOv5mSeg", + "YOLOv5nSeg", + "YOLOv5sSeg", + "YOLOv5xSeg", + "YOLOv8lSeg", + "YOLOv8mSeg", + "YOLOv8nSeg", + "YOLOv8sSeg", + "YOLOv8xSeg", + "GELANcSeg", + "YOLOv9cSeg", + "YOLOv9eSeg", +] + +YOLO11lSeg = create_model_class("YOLO11lSeg", __name__) +YOLO11mSeg = create_model_class("YOLO11mSeg", __name__) +YOLO11nSeg = create_model_class("YOLO11nSeg", __name__) +YOLO11sSeg = create_model_class("YOLO11sSeg", __name__) +YOLO11xSeg = create_model_class("YOLO11xSeg", __name__) +YOLO12lSeg = create_model_class("YOLO12lSeg", __name__) +YOLO12mSeg = create_model_class("YOLO12mSeg", __name__) +YOLO12nSeg = create_model_class("YOLO12nSeg", __name__) +YOLO12sSeg = create_model_class("YOLO12sSeg", __name__) +YOLO12xSeg = create_model_class("YOLO12xSeg", __name__) +YOLO26lSeg = create_model_class("YOLO26lSeg", __name__) +YOLO26mSeg = create_model_class("YOLO26mSeg", __name__) +YOLO26nSeg = create_model_class("YOLO26nSeg", __name__) +YOLO26sSeg = create_model_class("YOLO26sSeg", __name__) +YOLO26xSeg = create_model_class("YOLO26xSeg", __name__) +YOLOv5lSeg = create_model_class("YOLOv5lSeg", __name__) +YOLOv5mSeg = create_model_class("YOLOv5mSeg", __name__) +YOLOv5nSeg = create_model_class("YOLOv5nSeg", __name__) +YOLOv5sSeg = create_model_class("YOLOv5sSeg", __name__) +YOLOv5xSeg = create_model_class("YOLOv5xSeg", __name__) +YOLOv8lSeg = create_model_class("YOLOv8lSeg", __name__) +YOLOv8mSeg = create_model_class("YOLOv8mSeg", __name__) +YOLOv8nSeg = create_model_class("YOLOv8nSeg", __name__) +YOLOv8sSeg = create_model_class("YOLOv8sSeg", __name__) +YOLOv8xSeg = create_model_class("YOLOv8xSeg", __name__) +GELANcSeg = create_model_class("GELANcSeg", __name__) +YOLOv9cSeg = create_model_class("YOLOv9cSeg", __name__) +YOLOv9eSeg = create_model_class("YOLOv9eSeg", __name__) diff --git a/mblt_vision/models/AlexNet.yaml b/mblt_vision/models/AlexNet.yaml new file mode 100644 index 0000000..b9dad06 --- /dev/null +++ b/mblt_vision/models/AlexNet.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/AlexNet + filename: alexnet_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/CAFormer_B36.yaml b/mblt_vision/models/CAFormer_B36.yaml new file mode 100644 index 0000000..691448e --- /dev/null +++ b/mblt_vision/models/CAFormer_B36.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/CAFormer_B36 + filename: caformer_b36.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/CAFormer_M36.yaml b/mblt_vision/models/CAFormer_M36.yaml new file mode 100644 index 0000000..0896e02 --- /dev/null +++ b/mblt_vision/models/CAFormer_M36.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/CAFormer_M36 + filename: caformer_m36.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/CAFormer_S18.yaml b/mblt_vision/models/CAFormer_S18.yaml new file mode 100644 index 0000000..0a381d6 --- /dev/null +++ b/mblt_vision/models/CAFormer_S18.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/CAFormer_S18 + filename: caformer_s18.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/CAFormer_S36.yaml b/mblt_vision/models/CAFormer_S36.yaml new file mode 100644 index 0000000..f0911b4 --- /dev/null +++ b/mblt_vision/models/CAFormer_S36.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/CAFormer_S36 + filename: caformer_s36.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/CoAtNet_0_RW_224.yaml b/mblt_vision/models/CoAtNet_0_RW_224.yaml new file mode 100644 index 0000000..d9b553f --- /dev/null +++ b/mblt_vision/models/CoAtNet_0_RW_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/CoAtNet_0_RW_224 + filename: coatnet_0_rw_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/CoAtNet_1_RW_224.yaml b/mblt_vision/models/CoAtNet_1_RW_224.yaml new file mode 100644 index 0000000..9d1f493 --- /dev/null +++ b/mblt_vision/models/CoAtNet_1_RW_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/CoAtNet_1_RW_224 + filename: coatnet_1_rw_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/CoAtNet_2_RW_224.yaml b/mblt_vision/models/CoAtNet_2_RW_224.yaml new file mode 100644 index 0000000..d1985c9 --- /dev/null +++ b/mblt_vision/models/CoAtNet_2_RW_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/CoAtNet_2_RW_224 + filename: coatnet_2_rw_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ConvFormer_B36.yaml b/mblt_vision/models/ConvFormer_B36.yaml new file mode 100644 index 0000000..144b6d2 --- /dev/null +++ b/mblt_vision/models/ConvFormer_B36.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ConvFormer_B36 + filename: convformer_b36.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ConvFormer_M36.yaml b/mblt_vision/models/ConvFormer_M36.yaml new file mode 100644 index 0000000..ba679c9 --- /dev/null +++ b/mblt_vision/models/ConvFormer_M36.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ConvFormer_M36 + filename: convformer_m36.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ConvFormer_S18.yaml b/mblt_vision/models/ConvFormer_S18.yaml new file mode 100644 index 0000000..2fbb09f --- /dev/null +++ b/mblt_vision/models/ConvFormer_S18.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ConvFormer_S18 + filename: convformer_s18.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ConvFormer_S36.yaml b/mblt_vision/models/ConvFormer_S36.yaml new file mode 100644 index 0000000..d39c46e --- /dev/null +++ b/mblt_vision/models/ConvFormer_S36.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ConvFormer_S36 + filename: convformer_s36.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ConvNext_Base.yaml b/mblt_vision/models/ConvNext_Base.yaml new file mode 100644 index 0000000..0de192a --- /dev/null +++ b/mblt_vision/models/ConvNext_Base.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ConvNext_Base + filename: convnext_base_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 232 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ConvNext_Large.yaml b/mblt_vision/models/ConvNext_Large.yaml new file mode 100644 index 0000000..3047676 --- /dev/null +++ b/mblt_vision/models/ConvNext_Large.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ConvNext_Large + filename: convnext_large_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 232 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ConvNext_Small.yaml b/mblt_vision/models/ConvNext_Small.yaml new file mode 100644 index 0000000..c986f90 --- /dev/null +++ b/mblt_vision/models/ConvNext_Small.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ConvNext_Small + filename: convnext_small_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 230 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ConvNext_Tiny.yaml b/mblt_vision/models/ConvNext_Tiny.yaml new file mode 100644 index 0000000..241cfe3 --- /dev/null +++ b/mblt_vision/models/ConvNext_Tiny.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ConvNext_Tiny + filename: convnext_tiny_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT3_Base_Patch16_224.yaml b/mblt_vision/models/DeiT3_Base_Patch16_224.yaml new file mode 100644 index 0000000..3e4c4da --- /dev/null +++ b/mblt_vision/models/DeiT3_Base_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT3_Base_Patch16_224 + filename: deit3_base_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT3_Base_Patch16_384.yaml b/mblt_vision/models/DeiT3_Base_Patch16_384.yaml new file mode 100644 index 0000000..5eab39b --- /dev/null +++ b/mblt_vision/models/DeiT3_Base_Patch16_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT3_Base_Patch16_384 + filename: deit3_base_patch16_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT3_Large_Patch16_224.yaml b/mblt_vision/models/DeiT3_Large_Patch16_224.yaml new file mode 100644 index 0000000..1db68c3 --- /dev/null +++ b/mblt_vision/models/DeiT3_Large_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT3_Large_Patch16_224 + filename: deit3_large_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT3_Large_Patch16_384.yaml b/mblt_vision/models/DeiT3_Large_Patch16_384.yaml new file mode 100644 index 0000000..92efd1e --- /dev/null +++ b/mblt_vision/models/DeiT3_Large_Patch16_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT3_Large_Patch16_384 + filename: deit3_large_patch16_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT3_Medium_Patch16_224.yaml b/mblt_vision/models/DeiT3_Medium_Patch16_224.yaml new file mode 100644 index 0000000..c4bb1da --- /dev/null +++ b/mblt_vision/models/DeiT3_Medium_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT3_Medium_Patch16_224 + filename: deit3_medium_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT3_Small_Patch16_224.yaml b/mblt_vision/models/DeiT3_Small_Patch16_224.yaml new file mode 100644 index 0000000..1aa66e9 --- /dev/null +++ b/mblt_vision/models/DeiT3_Small_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT3_Small_Patch16_224 + filename: deit3_small_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT3_Small_Patch16_384.yaml b/mblt_vision/models/DeiT3_Small_Patch16_384.yaml new file mode 100644 index 0000000..b576828 --- /dev/null +++ b/mblt_vision/models/DeiT3_Small_Patch16_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT3_Small_Patch16_384 + filename: deit3_small_patch16_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT_Base_Patch16_224.yaml b/mblt_vision/models/DeiT_Base_Patch16_224.yaml new file mode 100644 index 0000000..872adc0 --- /dev/null +++ b/mblt_vision/models/DeiT_Base_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT_Base_Patch16_224 + filename: deit_base_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT_Base_Patch16_384.yaml b/mblt_vision/models/DeiT_Base_Patch16_384.yaml new file mode 100644 index 0000000..5b23b09 --- /dev/null +++ b/mblt_vision/models/DeiT_Base_Patch16_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT_Base_Patch16_384 + filename: deit_base_patch16_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT_Small_Patch16_224.yaml b/mblt_vision/models/DeiT_Small_Patch16_224.yaml new file mode 100644 index 0000000..ab5372e --- /dev/null +++ b/mblt_vision/models/DeiT_Small_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT_Small_Patch16_224 + filename: deit_small_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DeiT_Tiny_Patch16_224.yaml b/mblt_vision/models/DeiT_Tiny_Patch16_224.yaml new file mode 100644 index 0000000..80f6f9d --- /dev/null +++ b/mblt_vision/models/DeiT_Tiny_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/DeiT_Tiny_Patch16_224 + filename: deit_tiny_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DenseNet121.yaml b/mblt_vision/models/DenseNet121.yaml new file mode 100644 index 0000000..17ac464 --- /dev/null +++ b/mblt_vision/models/DenseNet121.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/DenseNet121 + filename: densenet121_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DenseNet161.yaml b/mblt_vision/models/DenseNet161.yaml new file mode 100644 index 0000000..6ef129b --- /dev/null +++ b/mblt_vision/models/DenseNet161.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/DenseNet161 + filename: densenet161_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DenseNet169.yaml b/mblt_vision/models/DenseNet169.yaml new file mode 100644 index 0000000..02ef675 --- /dev/null +++ b/mblt_vision/models/DenseNet169.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/DenseNet169 + filename: densenet169_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/DenseNet201.yaml b/mblt_vision/models/DenseNet201.yaml new file mode 100644 index 0000000..661c37b --- /dev/null +++ b/mblt_vision/models/DenseNet201.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/DenseNet201 + filename: densenet201_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientFormer_L1.yaml b/mblt_vision/models/EfficientFormer_L1.yaml new file mode 100644 index 0000000..453b1b7 --- /dev/null +++ b/mblt_vision/models/EfficientFormer_L1.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/EfficientFormer_L1 + filename: efficientformer_l1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientFormer_L3.yaml b/mblt_vision/models/EfficientFormer_L3.yaml new file mode 100644 index 0000000..7ecf9c3 --- /dev/null +++ b/mblt_vision/models/EfficientFormer_L3.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/EfficientFormer_L3 + filename: efficientformer_l3.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientFormer_L7.yaml b/mblt_vision/models/EfficientFormer_L7.yaml new file mode 100644 index 0000000..403333f --- /dev/null +++ b/mblt_vision/models/EfficientFormer_L7.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/EfficientFormer_L7 + filename: efficientformer_l7.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_B0.yaml b/mblt_vision/models/EfficientNet_B0.yaml new file mode 100644 index 0000000..64b50b1 --- /dev/null +++ b/mblt_vision/models/EfficientNet_B0.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_B0 + filename: efficientnet_b0_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_B1.yaml b/mblt_vision/models/EfficientNet_B1.yaml new file mode 100644 index 0000000..53f63ac --- /dev/null +++ b/mblt_vision/models/EfficientNet_B1.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_B1.tv1_in1k + filename: efficientnet_b1_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bicubic + CenterCrop: + size: + - 240 + - 240 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/EfficientNet_B1.tv2_in1k + filename: efficientnet_b1_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 255 + interpolation: bilinear diff --git a/mblt_vision/models/EfficientNet_B2.yaml b/mblt_vision/models/EfficientNet_B2.yaml new file mode 100644 index 0000000..595086a --- /dev/null +++ b/mblt_vision/models/EfficientNet_B2.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_B2 + filename: efficientnet_b2_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 288 + interpolation: bicubic + CenterCrop: + size: + - 288 + - 288 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_B3.yaml b/mblt_vision/models/EfficientNet_B3.yaml new file mode 100644 index 0000000..686906c --- /dev/null +++ b/mblt_vision/models/EfficientNet_B3.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_B3 + filename: efficientnet_b3_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 320 + interpolation: bicubic + CenterCrop: + size: + - 300 + - 300 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_B4.yaml b/mblt_vision/models/EfficientNet_B4.yaml new file mode 100644 index 0000000..21f2694 --- /dev/null +++ b/mblt_vision/models/EfficientNet_B4.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_B4 + filename: efficientnet_b4_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 380 + - 380 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_B5.yaml b/mblt_vision/models/EfficientNet_B5.yaml new file mode 100644 index 0000000..94855c7 --- /dev/null +++ b/mblt_vision/models/EfficientNet_B5.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_B5 + filename: efficientnet_b5_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 456 + interpolation: bicubic + CenterCrop: + size: + - 456 + - 456 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_B6.yaml b/mblt_vision/models/EfficientNet_B6.yaml new file mode 100644 index 0000000..61723c4 --- /dev/null +++ b/mblt_vision/models/EfficientNet_B6.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_B6 + filename: efficientnet_b6_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 528 + interpolation: bicubic + CenterCrop: + size: + - 528 + - 528 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_B7.yaml b/mblt_vision/models/EfficientNet_B7.yaml new file mode 100644 index 0000000..00143ab --- /dev/null +++ b/mblt_vision/models/EfficientNet_B7.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_B7 + filename: efficientnet_b7_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 600 + interpolation: bicubic + CenterCrop: + size: + - 600 + - 600 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_V2_L.yaml b/mblt_vision/models/EfficientNet_V2_L.yaml new file mode 100644 index 0000000..03ec40f --- /dev/null +++ b/mblt_vision/models/EfficientNet_V2_L.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_V2_L + filename: efficientnet_v2_l_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 480 + interpolation: bicubic + CenterCrop: + size: + - 480 + - 480 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_V2_M.yaml b/mblt_vision/models/EfficientNet_V2_M.yaml new file mode 100644 index 0000000..f93ac0c --- /dev/null +++ b/mblt_vision/models/EfficientNet_V2_M.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_V2_M + filename: efficientnet_v2_m_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 480 + interpolation: bilinear + CenterCrop: + size: + - 480 + - 480 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/EfficientNet_V2_S.yaml b/mblt_vision/models/EfficientNet_V2_S.yaml new file mode 100644 index 0000000..1ced73c --- /dev/null +++ b/mblt_vision/models/EfficientNet_V2_S.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/EfficientNet_V2_S + filename: efficientnet_v2_s_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bilinear + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/FlexiVit_Base.yaml b/mblt_vision/models/FlexiVit_Base.yaml new file mode 100644 index 0000000..9999d9c --- /dev/null +++ b/mblt_vision/models/FlexiVit_Base.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/FlexiVit_Base + filename: flexivit_base.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 252 + interpolation: bicubic + CenterCrop: + size: + - 240 + - 240 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/FlexiVit_Large.yaml b/mblt_vision/models/FlexiVit_Large.yaml new file mode 100644 index 0000000..5d3e166 --- /dev/null +++ b/mblt_vision/models/FlexiVit_Large.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/FlexiVit_Large + filename: flexivit_large.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 252 + interpolation: bicubic + CenterCrop: + size: + - 240 + - 240 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/FlexiVit_Small.yaml b/mblt_vision/models/FlexiVit_Small.yaml new file mode 100644 index 0000000..81e87c9 --- /dev/null +++ b/mblt_vision/models/FlexiVit_Small.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/FlexiVit_Small + filename: flexivit_small.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 252 + interpolation: bicubic + CenterCrop: + size: + - 240 + - 240 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/GELANc-seg.yaml b/mblt_vision/models/GELANc-seg.yaml new file mode 100644 index 0000000..9ef40b4 --- /dev/null +++ b/mblt_vision/models/GELANc-seg.yaml @@ -0,0 +1,29 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/GELANc-seg + filename: gelanc-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nc: 80 + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/GELANc.yaml b/mblt_vision/models/GELANc.yaml new file mode 100644 index 0000000..02b40e8 --- /dev/null +++ b/mblt_vision/models/GELANc.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/GELANc + filename: gelanc.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nc: 80 + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/GELANe.yaml b/mblt_vision/models/GELANe.yaml new file mode 100644 index 0000000..e73e031 --- /dev/null +++ b/mblt_vision/models/GELANe.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/GELANe + filename: gelane.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nc: 80 + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/GELANm.yaml b/mblt_vision/models/GELANm.yaml new file mode 100644 index 0000000..71beb02 --- /dev/null +++ b/mblt_vision/models/GELANm.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/GELANm + filename: gelanm.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nc: 80 + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/GELANs.yaml b/mblt_vision/models/GELANs.yaml new file mode 100644 index 0000000..a3454e3 --- /dev/null +++ b/mblt_vision/models/GELANs.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/GELANs + filename: gelans.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nc: 80 + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/GoogLeNet.yaml b/mblt_vision/models/GoogLeNet.yaml new file mode 100644 index 0000000..e07dfd7 --- /dev/null +++ b/mblt_vision/models/GoogLeNet.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/GoogLeNet + filename: googlenet_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/Inception_V3.yaml b/mblt_vision/models/Inception_V3.yaml new file mode 100644 index 0000000..cec6a41 --- /dev/null +++ b/mblt_vision/models/Inception_V3.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/Inception_V3 + filename: inception_v3_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 342 + interpolation: bilinear + CenterCrop: + size: + - 299 + - 299 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/LeViT_Conv_128.yaml b/mblt_vision/models/LeViT_Conv_128.yaml new file mode 100644 index 0000000..9e20421 --- /dev/null +++ b/mblt_vision/models/LeViT_Conv_128.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/LeViT_Conv_128 + filename: levit_conv_128.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/LeViT_Conv_128S.yaml b/mblt_vision/models/LeViT_Conv_128S.yaml new file mode 100644 index 0000000..5a39599 --- /dev/null +++ b/mblt_vision/models/LeViT_Conv_128S.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/LeViT_Conv_128S + filename: levit_conv_128s.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/LeViT_Conv_192.yaml b/mblt_vision/models/LeViT_Conv_192.yaml new file mode 100644 index 0000000..86027f8 --- /dev/null +++ b/mblt_vision/models/LeViT_Conv_192.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/LeViT_Conv_192 + filename: levit_conv_192.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/LeViT_Conv_256.yaml b/mblt_vision/models/LeViT_Conv_256.yaml new file mode 100644 index 0000000..5d78350 --- /dev/null +++ b/mblt_vision/models/LeViT_Conv_256.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/LeViT_Conv_256 + filename: levit_conv_256.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/LeViT_Conv_384.yaml b/mblt_vision/models/LeViT_Conv_384.yaml new file mode 100644 index 0000000..9c5ebae --- /dev/null +++ b/mblt_vision/models/LeViT_Conv_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/LeViT_Conv_384 + filename: levit_conv_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/MNASNet0_5.yaml b/mblt_vision/models/MNASNet0_5.yaml new file mode 100644 index 0000000..12834ac --- /dev/null +++ b/mblt_vision/models/MNASNet0_5.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/MNASNet0_5 + filename: mnasnet0_5_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/MNASNet0_75.yaml b/mblt_vision/models/MNASNet0_75.yaml new file mode 100644 index 0000000..e47c643 --- /dev/null +++ b/mblt_vision/models/MNASNet0_75.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/MNASNet0_75 + filename: mnasnet0_75_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/MNASNet1_0.yaml b/mblt_vision/models/MNASNet1_0.yaml new file mode 100644 index 0000000..6c0f2ab --- /dev/null +++ b/mblt_vision/models/MNASNet1_0.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/MNASNet1_0 + filename: mnasnet1_0_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/MNASNet1_3.yaml b/mblt_vision/models/MNASNet1_3.yaml new file mode 100644 index 0000000..d20ccc1 --- /dev/null +++ b/mblt_vision/models/MNASNet1_3.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/MNASNet1_3 + filename: mnasnet1_3_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 232 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/MobileNet_V2.yaml b/mblt_vision/models/MobileNet_V2.yaml new file mode 100644 index 0000000..67349da --- /dev/null +++ b/mblt_vision/models/MobileNet_V2.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/MobileNet_V2.tv1_in1k + filename: mobilenet_v2_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/MobileNet_V2.tv2_in1k + filename: mobilenet_v2_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/MobileNet_V3_Large.yaml b/mblt_vision/models/MobileNet_V3_Large.yaml new file mode 100644 index 0000000..a94f87f --- /dev/null +++ b/mblt_vision/models/MobileNet_V3_Large.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/MobileNet_V3_Large.tv1_in1k + filename: mobilenet_v3_large_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/MobileNet_V3_Large.tv2_in1k + filename: mobilenet_v3_large_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/MobileNet_V3_Small.yaml b/mblt_vision/models/MobileNet_V3_Small.yaml new file mode 100644 index 0000000..c1e3fd6 --- /dev/null +++ b/mblt_vision/models/MobileNet_V3_Small.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/MobileNet_V3_Small + filename: mobilenet_v3_small_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RegNet_X_16GF.yaml b/mblt_vision/models/RegNet_X_16GF.yaml new file mode 100644 index 0000000..95dd2aa --- /dev/null +++ b/mblt_vision/models/RegNet_X_16GF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_X_16GF.tv1_in1k + filename: regnet_x_16gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_X_16GF.tv2_in1k + filename: regnet_x_16gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_X_1_6GF.yaml b/mblt_vision/models/RegNet_X_1_6GF.yaml new file mode 100644 index 0000000..f5c3d3c --- /dev/null +++ b/mblt_vision/models/RegNet_X_1_6GF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_X_1_6GF.tv1_in1k + filename: regnet_x_1_6gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_X_1_6GF.tv2_in1k + filename: regnet_x_1_6gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_X_32GF.yaml b/mblt_vision/models/RegNet_X_32GF.yaml new file mode 100644 index 0000000..e6250a2 --- /dev/null +++ b/mblt_vision/models/RegNet_X_32GF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_X_32GF.tv1_in1k + filename: regnet_x_32gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_X_32GF.tv2_in1k + filename: regnet_x_32gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_X_3_2GF.yaml b/mblt_vision/models/RegNet_X_3_2GF.yaml new file mode 100644 index 0000000..61f01ee --- /dev/null +++ b/mblt_vision/models/RegNet_X_3_2GF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_X_3_2GF.tv1_in1k + filename: regnet_x_3_2gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_X_3_2GF.tv2_in1k + filename: regnet_x_3_2gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_X_400MF.yaml b/mblt_vision/models/RegNet_X_400MF.yaml new file mode 100644 index 0000000..e6039d0 --- /dev/null +++ b/mblt_vision/models/RegNet_X_400MF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_X_400MF.tv1_in1k + filename: regnet_x_400mf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_X_400MF.tv2_in1k + filename: regnet_x_400mf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_X_800MF.yaml b/mblt_vision/models/RegNet_X_800MF.yaml new file mode 100644 index 0000000..7952fea --- /dev/null +++ b/mblt_vision/models/RegNet_X_800MF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_X_800MF.tv1_in1k + filename: regnet_x_800mf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_X_800MF.tv2_in1k + filename: regnet_x_800mf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_X_8GF.yaml b/mblt_vision/models/RegNet_X_8GF.yaml new file mode 100644 index 0000000..8033e6e --- /dev/null +++ b/mblt_vision/models/RegNet_X_8GF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_X_8GF.tv1_in1k + filename: regnet_x_8gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_X_8GF.tv2_in1k + filename: regnet_x_8gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_Y_16GF.yaml b/mblt_vision/models/RegNet_Y_16GF.yaml new file mode 100644 index 0000000..67a39e5 --- /dev/null +++ b/mblt_vision/models/RegNet_Y_16GF.yaml @@ -0,0 +1,76 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_16GF.tv1_in1k + filename: regnet_y_16gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_Y_16GF.tv2_in1k + filename: regnet_y_16gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear +IMAGENET1K_SWAG_E2E_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_16GF.swag_e2e_in1k + filename: regnet_y_16gf_IMAGENET1K_SWAG_E2E_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_SWAG_LINEAR_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_16GF.swag_linear_in1k + filename: regnet_y_16gf_IMAGENET1K_SWAG_LINEAR_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RegNet_Y_1_6GF.yaml b/mblt_vision/models/RegNet_Y_1_6GF.yaml new file mode 100644 index 0000000..216c58d --- /dev/null +++ b/mblt_vision/models/RegNet_Y_1_6GF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_1_6GF.tv1_in1k + filename: regnet_y_1_6gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_Y_1_6GF.tv2_in1k + filename: regnet_y_1_6gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_Y_32GF.yaml b/mblt_vision/models/RegNet_Y_32GF.yaml new file mode 100644 index 0000000..ddeab69 --- /dev/null +++ b/mblt_vision/models/RegNet_Y_32GF.yaml @@ -0,0 +1,76 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_32GF.tv1_in1k + filename: regnet_y_32gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_Y_32GF.tv2_in1k + filename: regnet_y_32gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear +IMAGENET1K_SWAG_E2E_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_32GF.swag_e2e_in1k + filename: regnet_y_32gf_IMAGENET1K_SWAG_E2E_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_SWAG_LINEAR_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_32GF.swag_linear_in1k + filename: regnet_y_32gf_IMAGENET1K_SWAG_LINEAR_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RegNet_Y_3_2GF.yaml b/mblt_vision/models/RegNet_Y_3_2GF.yaml new file mode 100644 index 0000000..ed10c4f --- /dev/null +++ b/mblt_vision/models/RegNet_Y_3_2GF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_3_2GF.tv1_in1k + filename: regnet_y_3_2gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_Y_3_2GF.tv2_in1k + filename: regnet_y_3_2gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_Y_400MF.yaml b/mblt_vision/models/RegNet_Y_400MF.yaml new file mode 100644 index 0000000..477037f --- /dev/null +++ b/mblt_vision/models/RegNet_Y_400MF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_400MF.tv1_in1k + filename: regnet_y_400mf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_Y_400MF.tv2_in1k + filename: regnet_y_400mf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_Y_800MF.yaml b/mblt_vision/models/RegNet_Y_800MF.yaml new file mode 100644 index 0000000..5df53df --- /dev/null +++ b/mblt_vision/models/RegNet_Y_800MF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_800MF.tv1_in1k + filename: regnet_y_800mf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_Y_800MF.tv2_in1k + filename: regnet_y_800mf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RegNet_Y_8GF.yaml b/mblt_vision/models/RegNet_Y_8GF.yaml new file mode 100644 index 0000000..20553d9 --- /dev/null +++ b/mblt_vision/models/RegNet_Y_8GF.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/RegNet_Y_8GF.tv1_in1k + filename: regnet_y_8gf_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/RegNet_Y_8GF.tv2_in1k + filename: regnet_y_8gf_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/RepViT_M0_9.yaml b/mblt_vision/models/RepViT_M0_9.yaml new file mode 100644 index 0000000..e2d2bf4 --- /dev/null +++ b/mblt_vision/models/RepViT_M0_9.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/RepViT_M0_9 + filename: repvit_m0_9.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RepViT_M1.yaml b/mblt_vision/models/RepViT_M1.yaml new file mode 100644 index 0000000..0ce92f7 --- /dev/null +++ b/mblt_vision/models/RepViT_M1.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/RepViT_M1 + filename: repvit_m1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RepViT_M1_0.yaml b/mblt_vision/models/RepViT_M1_0.yaml new file mode 100644 index 0000000..80f92b2 --- /dev/null +++ b/mblt_vision/models/RepViT_M1_0.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/RepViT_M1_0 + filename: repvit_m1_0.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RepViT_M1_1.yaml b/mblt_vision/models/RepViT_M1_1.yaml new file mode 100644 index 0000000..31b36e3 --- /dev/null +++ b/mblt_vision/models/RepViT_M1_1.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/RepViT_M1_1 + filename: repvit_m1_1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RepViT_M1_5.yaml b/mblt_vision/models/RepViT_M1_5.yaml new file mode 100644 index 0000000..8ce2309 --- /dev/null +++ b/mblt_vision/models/RepViT_M1_5.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/RepViT_M1_5 + filename: repvit_m1_5.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RepViT_M2.yaml b/mblt_vision/models/RepViT_M2.yaml new file mode 100644 index 0000000..1c3baef --- /dev/null +++ b/mblt_vision/models/RepViT_M2.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/RepViT_M2 + filename: repvit_m2.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RepViT_M2_3.yaml b/mblt_vision/models/RepViT_M2_3.yaml new file mode 100644 index 0000000..7ce1790 --- /dev/null +++ b/mblt_vision/models/RepViT_M2_3.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/RepViT_M2_3 + filename: repvit_m2_3.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/RepViT_M3.yaml b/mblt_vision/models/RepViT_M3.yaml new file mode 100644 index 0000000..ed8aaea --- /dev/null +++ b/mblt_vision/models/RepViT_M3.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/RepViT_M3 + filename: repvit_m3.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 236 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ResNeXt101_32X8D.yaml b/mblt_vision/models/ResNeXt101_32X8D.yaml new file mode 100644 index 0000000..a996ce1 --- /dev/null +++ b/mblt_vision/models/ResNeXt101_32X8D.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ResNeXt101_32X8D.tv1_in1k + filename: resnext101_32x8d_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/ResNeXt101_32X8D.tv2_in1k + filename: resnext101_32x8d_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/ResNeXt101_64X4D.yaml b/mblt_vision/models/ResNeXt101_64X4D.yaml new file mode 100644 index 0000000..bfe20ac --- /dev/null +++ b/mblt_vision/models/ResNeXt101_64X4D.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ResNeXt101_64X4D + filename: resnext101_64x4d_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 232 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ResNeXt50_32X4D.yaml b/mblt_vision/models/ResNeXt50_32X4D.yaml new file mode 100644 index 0000000..c3c8265 --- /dev/null +++ b/mblt_vision/models/ResNeXt50_32X4D.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ResNeXt50_32X4D.tv1_in1k + filename: resnext50_32x4d_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/ResNeXt50_32X4D.tv2_in1k + filename: resnext50_32x4d_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/ResNet101.yaml b/mblt_vision/models/ResNet101.yaml new file mode 100644 index 0000000..2367602 --- /dev/null +++ b/mblt_vision/models/ResNet101.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ResNet101.tv1_in1k + filename: resnet101_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/ResNet101.tv2_in1k + filename: resnet101_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/ResNet152.yaml b/mblt_vision/models/ResNet152.yaml new file mode 100644 index 0000000..af6e238 --- /dev/null +++ b/mblt_vision/models/ResNet152.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ResNet152.tv1_in1k + filename: resnet152_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/ResNet152.tv2_in1k + filename: resnet152_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/ResNet18.yaml b/mblt_vision/models/ResNet18.yaml new file mode 100644 index 0000000..4d64cf1 --- /dev/null +++ b/mblt_vision/models/ResNet18.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ResNet18 + filename: resnet18_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ResNet34.yaml b/mblt_vision/models/ResNet34.yaml new file mode 100644 index 0000000..b24ca76 --- /dev/null +++ b/mblt_vision/models/ResNet34.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ResNet34 + filename: resnet34_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ResNet50.yaml b/mblt_vision/models/ResNet50.yaml new file mode 100644 index 0000000..1459e8f --- /dev/null +++ b/mblt_vision/models/ResNet50.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ResNet50.tv1_in1k + filename: resnet50_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/ResNet50.tv2_in1k + filename: resnet50_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/ShuffleNet_V2_X0_5.yaml b/mblt_vision/models/ShuffleNet_V2_X0_5.yaml new file mode 100644 index 0000000..047e802 --- /dev/null +++ b/mblt_vision/models/ShuffleNet_V2_X0_5.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ShuffleNet_V2_X0_5 + filename: shufflenet_v2_x0_5_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ShuffleNet_V2_X1_0.yaml b/mblt_vision/models/ShuffleNet_V2_X1_0.yaml new file mode 100644 index 0000000..98b62ee --- /dev/null +++ b/mblt_vision/models/ShuffleNet_V2_X1_0.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ShuffleNet_V2_X1_0 + filename: shufflenet_v2_x1_0_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ShuffleNet_V2_X1_5.yaml b/mblt_vision/models/ShuffleNet_V2_X1_5.yaml new file mode 100644 index 0000000..e177b17 --- /dev/null +++ b/mblt_vision/models/ShuffleNet_V2_X1_5.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ShuffleNet_V2_X1_5 + filename: shufflenet_v2_x1_5_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 232 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ShuffleNet_V2_X2_0.yaml b/mblt_vision/models/ShuffleNet_V2_X2_0.yaml new file mode 100644 index 0000000..3656877 --- /dev/null +++ b/mblt_vision/models/ShuffleNet_V2_X2_0.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ShuffleNet_V2_X2_0 + filename: shufflenet_v2_x2_0_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 232 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/SqueezeNet1_0.yaml b/mblt_vision/models/SqueezeNet1_0.yaml new file mode 100644 index 0000000..38d77ef --- /dev/null +++ b/mblt_vision/models/SqueezeNet1_0.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/SqueezeNet1_0 + filename: squeezenet1_0_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/SqueezeNet1_1.yaml b/mblt_vision/models/SqueezeNet1_1.yaml new file mode 100644 index 0000000..43ee001 --- /dev/null +++ b/mblt_vision/models/SqueezeNet1_1.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/SqueezeNet1_1 + filename: squeezenet1_1_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/Swin_B.yaml b/mblt_vision/models/Swin_B.yaml new file mode 100644 index 0000000..a591788 --- /dev/null +++ b/mblt_vision/models/Swin_B.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/Swin_B + filename: swin_b_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 238 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/Swin_S.yaml b/mblt_vision/models/Swin_S.yaml new file mode 100644 index 0000000..ffa04ac --- /dev/null +++ b/mblt_vision/models/Swin_S.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/Swin_S + filename: swin_s_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 246 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/Swin_T.yaml b/mblt_vision/models/Swin_T.yaml new file mode 100644 index 0000000..fbe120e --- /dev/null +++ b/mblt_vision/models/Swin_T.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/Swin_T + filename: swin_t_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 232 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VGG11.yaml b/mblt_vision/models/VGG11.yaml new file mode 100644 index 0000000..89bdad5 --- /dev/null +++ b/mblt_vision/models/VGG11.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/VGG11 + filename: vgg11_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VGG11_BN.yaml b/mblt_vision/models/VGG11_BN.yaml new file mode 100644 index 0000000..983e185 --- /dev/null +++ b/mblt_vision/models/VGG11_BN.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/VGG11_BN + filename: vgg11_bn_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VGG13.yaml b/mblt_vision/models/VGG13.yaml new file mode 100644 index 0000000..517e451 --- /dev/null +++ b/mblt_vision/models/VGG13.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/VGG13 + filename: vgg13_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VGG13_BN.yaml b/mblt_vision/models/VGG13_BN.yaml new file mode 100644 index 0000000..1c0c89b --- /dev/null +++ b/mblt_vision/models/VGG13_BN.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/VGG13_BN + filename: vgg13_bn_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VGG16.yaml b/mblt_vision/models/VGG16.yaml new file mode 100644 index 0000000..4284f44 --- /dev/null +++ b/mblt_vision/models/VGG16.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/VGG16 + filename: vgg16_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VGG16_BN.yaml b/mblt_vision/models/VGG16_BN.yaml new file mode 100644 index 0000000..0decf5a --- /dev/null +++ b/mblt_vision/models/VGG16_BN.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/VGG16_BN + filename: vgg16_bn_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VGG19.yaml b/mblt_vision/models/VGG19.yaml new file mode 100644 index 0000000..782a541 --- /dev/null +++ b/mblt_vision/models/VGG19.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/VGG19 + filename: vgg19_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VGG19_BN.yaml b/mblt_vision/models/VGG19_BN.yaml new file mode 100644 index 0000000..716d500 --- /dev/null +++ b/mblt_vision/models/VGG19_BN.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/VGG19_BN + filename: vgg19_bn_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_B_16.yaml b/mblt_vision/models/ViT_B_16.yaml new file mode 100644 index 0000000..2111588 --- /dev/null +++ b/mblt_vision/models/ViT_B_16.yaml @@ -0,0 +1,67 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ViT_B_16.tv1_in1k + filename: vit_b_16_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_SWAG_E2E_V1: + file_cfg: + repo_id: mobilint/ViT_B_16.swag_e2e_in1k + filename: vit_b_16_IMAGENET1K_SWAG_E2E_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_SWAG_LINEAR_V1: + file_cfg: + repo_id: mobilint/ViT_B_16.swag_linear_in1k + filename: vit_b_16_IMAGENET1K_SWAG_LINEAR_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_B_32.yaml b/mblt_vision/models/ViT_B_32.yaml new file mode 100644 index 0000000..ef29cc0 --- /dev/null +++ b/mblt_vision/models/ViT_B_32.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ViT_B_32 + filename: vit_b_32_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Base_Patch16_224.yaml b/mblt_vision/models/ViT_Base_Patch16_224.yaml new file mode 100644 index 0000000..d4fb6c0 --- /dev/null +++ b/mblt_vision/models/ViT_Base_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Base_Patch16_224 + filename: vit_base_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Base_Patch16_384.yaml b/mblt_vision/models/ViT_Base_Patch16_384.yaml new file mode 100644 index 0000000..c4cc9c0 --- /dev/null +++ b/mblt_vision/models/ViT_Base_Patch16_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Base_Patch16_384 + filename: vit_base_patch16_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Base_Patch32_224.yaml b/mblt_vision/models/ViT_Base_Patch32_224.yaml new file mode 100644 index 0000000..d4c7f1c --- /dev/null +++ b/mblt_vision/models/ViT_Base_Patch32_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Base_Patch32_224 + filename: vit_base_patch32_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Base_Patch32_384.yaml b/mblt_vision/models/ViT_Base_Patch32_384.yaml new file mode 100644 index 0000000..f6bcd9c --- /dev/null +++ b/mblt_vision/models/ViT_Base_Patch32_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Base_Patch32_384 + filename: vit_base_patch32_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Base_Patch8_224.yaml b/mblt_vision/models/ViT_Base_Patch8_224.yaml new file mode 100644 index 0000000..af6ea08 --- /dev/null +++ b/mblt_vision/models/ViT_Base_Patch8_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Base_Patch8_224 + filename: vit_base_patch8_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_L_16.yaml b/mblt_vision/models/ViT_L_16.yaml new file mode 100644 index 0000000..02c0bb4 --- /dev/null +++ b/mblt_vision/models/ViT_L_16.yaml @@ -0,0 +1,67 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ViT_L_16.tv1_in1k + filename: vit_l_16_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 242 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_SWAG_E2E_V1: + file_cfg: + repo_id: mobilint/ViT_L_16.swag_e2e_in1k + filename: vit_l_16_IMAGENET1K_SWAG_E2E_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 512 + interpolation: bicubic + CenterCrop: + size: + - 512 + - 512 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_SWAG_LINEAR_V1: + file_cfg: + repo_id: mobilint/ViT_L_16.swag_linear_in1k + filename: vit_l_16_IMAGENET1K_SWAG_LINEAR_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_L_32.yaml b/mblt_vision/models/ViT_L_32.yaml new file mode 100644 index 0000000..b91f32b --- /dev/null +++ b/mblt_vision/models/ViT_L_32.yaml @@ -0,0 +1,23 @@ +DEFAULT: IMAGENET1K_V1 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/ViT_L_32 + filename: vit_l_32_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Large_Patch16_224.yaml b/mblt_vision/models/ViT_Large_Patch16_224.yaml new file mode 100644 index 0000000..75a871b --- /dev/null +++ b/mblt_vision/models/ViT_Large_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Large_Patch16_224 + filename: vit_large_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Large_Patch16_384.yaml b/mblt_vision/models/ViT_Large_Patch16_384.yaml new file mode 100644 index 0000000..f871425 --- /dev/null +++ b/mblt_vision/models/ViT_Large_Patch16_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Large_Patch16_384 + filename: vit_large_patch16_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Large_Patch32_384.yaml b/mblt_vision/models/ViT_Large_Patch32_384.yaml new file mode 100644 index 0000000..d4a7199 --- /dev/null +++ b/mblt_vision/models/ViT_Large_Patch32_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Large_Patch32_384 + filename: vit_large_patch32_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Small_Patch16_224.yaml b/mblt_vision/models/ViT_Small_Patch16_224.yaml new file mode 100644 index 0000000..a55c062 --- /dev/null +++ b/mblt_vision/models/ViT_Small_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Small_Patch16_224 + filename: vit_small_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Small_Patch16_384.yaml b/mblt_vision/models/ViT_Small_Patch16_384.yaml new file mode 100644 index 0000000..99dff13 --- /dev/null +++ b/mblt_vision/models/ViT_Small_Patch16_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Small_Patch16_384 + filename: vit_small_patch16_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Small_Patch32_224.yaml b/mblt_vision/models/ViT_Small_Patch32_224.yaml new file mode 100644 index 0000000..960ac46 --- /dev/null +++ b/mblt_vision/models/ViT_Small_Patch32_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Small_Patch32_224 + filename: vit_small_patch32_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Small_Patch32_384.yaml b/mblt_vision/models/ViT_Small_Patch32_384.yaml new file mode 100644 index 0000000..f634c45 --- /dev/null +++ b/mblt_vision/models/ViT_Small_Patch32_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Small_Patch32_384 + filename: vit_small_patch32_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Tiny_Patch16_224.yaml b/mblt_vision/models/ViT_Tiny_Patch16_224.yaml new file mode 100644 index 0000000..c5cb9af --- /dev/null +++ b/mblt_vision/models/ViT_Tiny_Patch16_224.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Tiny_Patch16_224 + filename: vit_tiny_patch16_224.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/ViT_Tiny_Patch16_384.yaml b/mblt_vision/models/ViT_Tiny_Patch16_384.yaml new file mode 100644 index 0000000..5ddd8ba --- /dev/null +++ b/mblt_vision/models/ViT_Tiny_Patch16_384.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/ViT_Tiny_Patch16_384 + filename: vit_tiny_patch16_384.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 384 + interpolation: bicubic + CenterCrop: + size: + - 384 + - 384 + SetOrder: + shape: HWC + Normalize: + style: tf + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VisFormer_Small.yaml b/mblt_vision/models/VisFormer_Small.yaml new file mode 100644 index 0000000..5a9e6a3 --- /dev/null +++ b/mblt_vision/models/VisFormer_Small.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/VisFormer_Small + filename: visformer_small.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/VisFormer_Tiny.yaml b/mblt_vision/models/VisFormer_Tiny.yaml new file mode 100644 index 0000000..ab8609e --- /dev/null +++ b/mblt_vision/models/VisFormer_Tiny.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/VisFormer_Tiny + filename: visformer_tiny.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 249 + interpolation: bicubic + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/Wide_ResNet101_2.yaml b/mblt_vision/models/Wide_ResNet101_2.yaml new file mode 100644 index 0000000..decd589 --- /dev/null +++ b/mblt_vision/models/Wide_ResNet101_2.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/Wide_ResNet101_2.tv1_in1k + filename: wide_resnet101_2_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/Wide_ResNet101_2.tv2_in1k + filename: wide_resnet101_2_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/Wide_ResNet50_2.yaml b/mblt_vision/models/Wide_ResNet50_2.yaml new file mode 100644 index 0000000..d1fd424 --- /dev/null +++ b/mblt_vision/models/Wide_ResNet50_2.yaml @@ -0,0 +1,32 @@ +DEFAULT: IMAGENET1K_V2 +IMAGENET1K_V1: + file_cfg: + repo_id: mobilint/Wide_ResNet50_2.tv1_in1k + filename: wide_resnet50_2_IMAGENET1K_V1.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 256 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet +IMAGENET1K_V2: + update: IMAGENET1K_V1 + file_cfg: + repo_id: mobilint/Wide_ResNet50_2.tv2_in1k + filename: wide_resnet50_2_IMAGENET1K_V2.mxq + pre_cfg: + Resize: + size: 232 + interpolation: bilinear diff --git a/mblt_vision/models/YOLO11l-cls.yaml b/mblt_vision/models/YOLO11l-cls.yaml new file mode 100644 index 0000000..f0d9845 --- /dev/null +++ b/mblt_vision/models/YOLO11l-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11l-cls + filename: yolo11l-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO11l-face.yaml b/mblt_vision/models/YOLO11l-face.yaml new file mode 100644 index 0000000..8c6982a --- /dev/null +++ b/mblt_vision/models/YOLO11l-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11l-face + filename: yolo11l-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11l-obb.yaml b/mblt_vision/models/YOLO11l-obb.yaml new file mode 100644 index 0000000..bb8d4af --- /dev/null +++ b/mblt_vision/models/YOLO11l-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11l-obb + filename: yolo11l-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11l-pose.yaml b/mblt_vision/models/YOLO11l-pose.yaml new file mode 100644 index 0000000..6bda47d --- /dev/null +++ b/mblt_vision/models/YOLO11l-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11l-pose + filename: yolo11l-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11l-seg.yaml b/mblt_vision/models/YOLO11l-seg.yaml new file mode 100644 index 0000000..55a7343 --- /dev/null +++ b/mblt_vision/models/YOLO11l-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11l-seg + filename: yolo11l-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11l.yaml b/mblt_vision/models/YOLO11l.yaml new file mode 100644 index 0000000..84faf43 --- /dev/null +++ b/mblt_vision/models/YOLO11l.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11l + filename: yolo11l.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11m-cls.yaml b/mblt_vision/models/YOLO11m-cls.yaml new file mode 100644 index 0000000..4dd25e6 --- /dev/null +++ b/mblt_vision/models/YOLO11m-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11m-cls + filename: yolo11m-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO11m-face.yaml b/mblt_vision/models/YOLO11m-face.yaml new file mode 100644 index 0000000..93d007e --- /dev/null +++ b/mblt_vision/models/YOLO11m-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11m-face + filename: yolo11m-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11m-obb.yaml b/mblt_vision/models/YOLO11m-obb.yaml new file mode 100644 index 0000000..91fccc5 --- /dev/null +++ b/mblt_vision/models/YOLO11m-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11m-obb + filename: yolo11m-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11m-pose.yaml b/mblt_vision/models/YOLO11m-pose.yaml new file mode 100644 index 0000000..bf7bc8d --- /dev/null +++ b/mblt_vision/models/YOLO11m-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11m-pose + filename: yolo11m-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11m-seg.yaml b/mblt_vision/models/YOLO11m-seg.yaml new file mode 100644 index 0000000..46f3e45 --- /dev/null +++ b/mblt_vision/models/YOLO11m-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11m-seg + filename: yolo11m-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11m.yaml b/mblt_vision/models/YOLO11m.yaml new file mode 100644 index 0000000..4831817 --- /dev/null +++ b/mblt_vision/models/YOLO11m.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11m + filename: yolo11m.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11n-cls.yaml b/mblt_vision/models/YOLO11n-cls.yaml new file mode 100644 index 0000000..e7658ac --- /dev/null +++ b/mblt_vision/models/YOLO11n-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11n-cls + filename: yolo11n-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO11n-face.yaml b/mblt_vision/models/YOLO11n-face.yaml new file mode 100644 index 0000000..0fd8395 --- /dev/null +++ b/mblt_vision/models/YOLO11n-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11n-face + filename: yolo11n-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11n-obb.yaml b/mblt_vision/models/YOLO11n-obb.yaml new file mode 100644 index 0000000..c2f00a9 --- /dev/null +++ b/mblt_vision/models/YOLO11n-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11n-obb + filename: yolo11n-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11n-pose.yaml b/mblt_vision/models/YOLO11n-pose.yaml new file mode 100644 index 0000000..fe7b8dc --- /dev/null +++ b/mblt_vision/models/YOLO11n-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11n-pose + filename: yolo11n-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11n-seg.yaml b/mblt_vision/models/YOLO11n-seg.yaml new file mode 100644 index 0000000..736e6bf --- /dev/null +++ b/mblt_vision/models/YOLO11n-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11n-seg + filename: yolo11n-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11n.yaml b/mblt_vision/models/YOLO11n.yaml new file mode 100644 index 0000000..809df97 --- /dev/null +++ b/mblt_vision/models/YOLO11n.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11n + filename: yolo11n.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11s-cls.yaml b/mblt_vision/models/YOLO11s-cls.yaml new file mode 100644 index 0000000..fd53c03 --- /dev/null +++ b/mblt_vision/models/YOLO11s-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11s-cls + filename: yolo11s-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO11s-face.yaml b/mblt_vision/models/YOLO11s-face.yaml new file mode 100644 index 0000000..ec966ef --- /dev/null +++ b/mblt_vision/models/YOLO11s-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11s-face + filename: yolo11s-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11s-obb.yaml b/mblt_vision/models/YOLO11s-obb.yaml new file mode 100644 index 0000000..0aae543 --- /dev/null +++ b/mblt_vision/models/YOLO11s-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11s-obb + filename: yolo11s-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11s-pose.yaml b/mblt_vision/models/YOLO11s-pose.yaml new file mode 100644 index 0000000..b0ed955 --- /dev/null +++ b/mblt_vision/models/YOLO11s-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11s-pose + filename: yolo11s-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11s-seg.yaml b/mblt_vision/models/YOLO11s-seg.yaml new file mode 100644 index 0000000..65a6554 --- /dev/null +++ b/mblt_vision/models/YOLO11s-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11s-seg + filename: yolo11s-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11s.yaml b/mblt_vision/models/YOLO11s.yaml new file mode 100644 index 0000000..4dc6a4f --- /dev/null +++ b/mblt_vision/models/YOLO11s.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11s + filename: yolo11s.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11x-cls.yaml b/mblt_vision/models/YOLO11x-cls.yaml new file mode 100644 index 0000000..c014ad6 --- /dev/null +++ b/mblt_vision/models/YOLO11x-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11x-cls + filename: yolo11x-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO11x-obb.yaml b/mblt_vision/models/YOLO11x-obb.yaml new file mode 100644 index 0000000..0d51b51 --- /dev/null +++ b/mblt_vision/models/YOLO11x-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11x-obb + filename: yolo11x-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11x-pose.yaml b/mblt_vision/models/YOLO11x-pose.yaml new file mode 100644 index 0000000..46e3132 --- /dev/null +++ b/mblt_vision/models/YOLO11x-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11x-pose + filename: yolo11x-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11x-seg.yaml b/mblt_vision/models/YOLO11x-seg.yaml new file mode 100644 index 0000000..fa7d992 --- /dev/null +++ b/mblt_vision/models/YOLO11x-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11x-seg + filename: yolo11x-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO11x.yaml b/mblt_vision/models/YOLO11x.yaml new file mode 100644 index 0000000..c3008c9 --- /dev/null +++ b/mblt_vision/models/YOLO11x.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO11x + filename: yolo11x.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12l-face.yaml b/mblt_vision/models/YOLO12l-face.yaml new file mode 100644 index 0000000..6a0ccbf --- /dev/null +++ b/mblt_vision/models/YOLO12l-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12l-face + filename: yolo12l-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12l-seg.yaml b/mblt_vision/models/YOLO12l-seg.yaml new file mode 100644 index 0000000..e67c1da --- /dev/null +++ b/mblt_vision/models/YOLO12l-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12l-seg + filename: yolo12l-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12l.yaml b/mblt_vision/models/YOLO12l.yaml new file mode 100644 index 0000000..63c3059 --- /dev/null +++ b/mblt_vision/models/YOLO12l.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12l + filename: yolo12l.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12m-face.yaml b/mblt_vision/models/YOLO12m-face.yaml new file mode 100644 index 0000000..5d8340d --- /dev/null +++ b/mblt_vision/models/YOLO12m-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12m-face + filename: yolo12m-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12m-seg.yaml b/mblt_vision/models/YOLO12m-seg.yaml new file mode 100644 index 0000000..2572df0 --- /dev/null +++ b/mblt_vision/models/YOLO12m-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12m-seg + filename: yolo12m-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12m.yaml b/mblt_vision/models/YOLO12m.yaml new file mode 100644 index 0000000..c6f4fa3 --- /dev/null +++ b/mblt_vision/models/YOLO12m.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12m + filename: yolo12m.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12n-face.yaml b/mblt_vision/models/YOLO12n-face.yaml new file mode 100644 index 0000000..686e61c --- /dev/null +++ b/mblt_vision/models/YOLO12n-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12n-face + filename: yolo12n-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12n-seg.yaml b/mblt_vision/models/YOLO12n-seg.yaml new file mode 100644 index 0000000..0672bd8 --- /dev/null +++ b/mblt_vision/models/YOLO12n-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12n-seg + filename: yolo12n-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12n.yaml b/mblt_vision/models/YOLO12n.yaml new file mode 100644 index 0000000..86897b6 --- /dev/null +++ b/mblt_vision/models/YOLO12n.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12n + filename: yolo12n.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12s-face.yaml b/mblt_vision/models/YOLO12s-face.yaml new file mode 100644 index 0000000..7573356 --- /dev/null +++ b/mblt_vision/models/YOLO12s-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12s-face + filename: yolo12s-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12s-seg.yaml b/mblt_vision/models/YOLO12s-seg.yaml new file mode 100644 index 0000000..8c9fb4e --- /dev/null +++ b/mblt_vision/models/YOLO12s-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12s-seg + filename: yolo12s-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12s.yaml b/mblt_vision/models/YOLO12s.yaml new file mode 100644 index 0000000..5e1c2d0 --- /dev/null +++ b/mblt_vision/models/YOLO12s.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12s + filename: yolo12s.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12x-seg.yaml b/mblt_vision/models/YOLO12x-seg.yaml new file mode 100644 index 0000000..1671116 --- /dev/null +++ b/mblt_vision/models/YOLO12x-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12x-seg + filename: yolo12x-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO12x.yaml b/mblt_vision/models/YOLO12x.yaml new file mode 100644 index 0000000..3484f8a --- /dev/null +++ b/mblt_vision/models/YOLO12x.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO12x + filename: yolo12x.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26l-cls.yaml b/mblt_vision/models/YOLO26l-cls.yaml new file mode 100644 index 0000000..d492a4c --- /dev/null +++ b/mblt_vision/models/YOLO26l-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l-cls + filename: yolo26l-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO26l-depth.yaml b/mblt_vision/models/YOLO26l-depth.yaml new file mode 100644 index 0000000..226a1d8 --- /dev/null +++ b/mblt_vision/models/YOLO26l-depth.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l-depth + filename: yolo26l-depth.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [768, 768] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: depth_estimation + dataset: nyu-depth diff --git a/mblt_vision/models/YOLO26l-distill.yaml b/mblt_vision/models/YOLO26l-distill.yaml new file mode 100644 index 0000000..52b0869 --- /dev/null +++ b/mblt_vision/models/YOLO26l-distill.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l-distill + filename: yolo26l-distill.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26l-obb.yaml b/mblt_vision/models/YOLO26l-obb.yaml new file mode 100644 index 0000000..df61c05 --- /dev/null +++ b/mblt_vision/models/YOLO26l-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l-obb + filename: yolo26l-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + dflfree: true + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26l-pose.yaml b/mblt_vision/models/YOLO26l-pose.yaml new file mode 100644 index 0000000..17fc25d --- /dev/null +++ b/mblt_vision/models/YOLO26l-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l-pose + filename: yolo26l-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26l-seg.yaml b/mblt_vision/models/YOLO26l-seg.yaml new file mode 100644 index 0000000..7b861fb --- /dev/null +++ b/mblt_vision/models/YOLO26l-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l-seg + filename: yolo26l-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26l-sem-ade20k.yaml b/mblt_vision/models/YOLO26l-sem-ade20k.yaml new file mode 100644 index 0000000..7fa0a5b --- /dev/null +++ b/mblt_vision/models/YOLO26l-sem-ade20k.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l-sem-ade20k + filename: yolo26l-sem-ade20k.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [640, 640] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: ade20k diff --git a/mblt_vision/models/YOLO26l-sem.yaml b/mblt_vision/models/YOLO26l-sem.yaml new file mode 100644 index 0000000..822123e --- /dev/null +++ b/mblt_vision/models/YOLO26l-sem.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l-sem + filename: yolo26l-sem.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [1024, 2048] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: cityscapes diff --git a/mblt_vision/models/YOLO26l.yaml b/mblt_vision/models/YOLO26l.yaml new file mode 100644 index 0000000..3df6898 --- /dev/null +++ b/mblt_vision/models/YOLO26l.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26l + filename: yolo26l.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26m-cls.yaml b/mblt_vision/models/YOLO26m-cls.yaml new file mode 100644 index 0000000..57abe6b --- /dev/null +++ b/mblt_vision/models/YOLO26m-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m-cls + filename: yolo26m-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO26m-depth.yaml b/mblt_vision/models/YOLO26m-depth.yaml new file mode 100644 index 0000000..ace9c16 --- /dev/null +++ b/mblt_vision/models/YOLO26m-depth.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m-depth + filename: yolo26m-depth.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [768, 768] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: depth_estimation + dataset: nyu-depth diff --git a/mblt_vision/models/YOLO26m-distill.yaml b/mblt_vision/models/YOLO26m-distill.yaml new file mode 100644 index 0000000..79e8550 --- /dev/null +++ b/mblt_vision/models/YOLO26m-distill.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m-distill + filename: yolo26m-distill.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26m-obb.yaml b/mblt_vision/models/YOLO26m-obb.yaml new file mode 100644 index 0000000..f6353fd --- /dev/null +++ b/mblt_vision/models/YOLO26m-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m-obb + filename: yolo26m-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + dflfree: true + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26m-pose.yaml b/mblt_vision/models/YOLO26m-pose.yaml new file mode 100644 index 0000000..eddd6fa --- /dev/null +++ b/mblt_vision/models/YOLO26m-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m-pose + filename: yolo26m-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26m-seg.yaml b/mblt_vision/models/YOLO26m-seg.yaml new file mode 100644 index 0000000..32be031 --- /dev/null +++ b/mblt_vision/models/YOLO26m-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m-seg + filename: yolo26m-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26m-sem-ade20k.yaml b/mblt_vision/models/YOLO26m-sem-ade20k.yaml new file mode 100644 index 0000000..1ca8020 --- /dev/null +++ b/mblt_vision/models/YOLO26m-sem-ade20k.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m-sem-ade20k + filename: yolo26m-sem-ade20k.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [640, 640] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: ade20k diff --git a/mblt_vision/models/YOLO26m-sem.yaml b/mblt_vision/models/YOLO26m-sem.yaml new file mode 100644 index 0000000..00ac704 --- /dev/null +++ b/mblt_vision/models/YOLO26m-sem.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m-sem + filename: yolo26m-sem.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [1024, 2048] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: cityscapes diff --git a/mblt_vision/models/YOLO26m.yaml b/mblt_vision/models/YOLO26m.yaml new file mode 100644 index 0000000..817db03 --- /dev/null +++ b/mblt_vision/models/YOLO26m.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26m + filename: yolo26m.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26n-cls.yaml b/mblt_vision/models/YOLO26n-cls.yaml new file mode 100644 index 0000000..df2663e --- /dev/null +++ b/mblt_vision/models/YOLO26n-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n-cls + filename: yolo26n-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO26n-depth.yaml b/mblt_vision/models/YOLO26n-depth.yaml new file mode 100644 index 0000000..fc31ab5 --- /dev/null +++ b/mblt_vision/models/YOLO26n-depth.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n-depth + filename: yolo26n-depth.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [768, 768] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: depth_estimation + dataset: nyu-depth diff --git a/mblt_vision/models/YOLO26n-distill.yaml b/mblt_vision/models/YOLO26n-distill.yaml new file mode 100644 index 0000000..b6e4d14 --- /dev/null +++ b/mblt_vision/models/YOLO26n-distill.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n-distill + filename: yolo26n-distill.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26n-obb.yaml b/mblt_vision/models/YOLO26n-obb.yaml new file mode 100644 index 0000000..aa92ffe --- /dev/null +++ b/mblt_vision/models/YOLO26n-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n-obb + filename: yolo26n-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + dflfree: true + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26n-pose.yaml b/mblt_vision/models/YOLO26n-pose.yaml new file mode 100644 index 0000000..110b715 --- /dev/null +++ b/mblt_vision/models/YOLO26n-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n-pose + filename: yolo26n-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26n-seg.yaml b/mblt_vision/models/YOLO26n-seg.yaml new file mode 100644 index 0000000..d67e642 --- /dev/null +++ b/mblt_vision/models/YOLO26n-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n-seg + filename: yolo26n-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26n-sem-ade20k.yaml b/mblt_vision/models/YOLO26n-sem-ade20k.yaml new file mode 100644 index 0000000..9223741 --- /dev/null +++ b/mblt_vision/models/YOLO26n-sem-ade20k.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n-sem-ade20k + filename: yolo26n-sem-ade20k.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [640, 640] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: ade20k diff --git a/mblt_vision/models/YOLO26n-sem.yaml b/mblt_vision/models/YOLO26n-sem.yaml new file mode 100644 index 0000000..c97909a --- /dev/null +++ b/mblt_vision/models/YOLO26n-sem.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n-sem + filename: yolo26n-sem.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [1024, 2048] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: cityscapes diff --git a/mblt_vision/models/YOLO26n.yaml b/mblt_vision/models/YOLO26n.yaml new file mode 100644 index 0000000..c1a3154 --- /dev/null +++ b/mblt_vision/models/YOLO26n.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26n + filename: yolo26n.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26s-cls.yaml b/mblt_vision/models/YOLO26s-cls.yaml new file mode 100644 index 0000000..9c8074a --- /dev/null +++ b/mblt_vision/models/YOLO26s-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s-cls + filename: yolo26s-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO26s-depth.yaml b/mblt_vision/models/YOLO26s-depth.yaml new file mode 100644 index 0000000..8e9a89a --- /dev/null +++ b/mblt_vision/models/YOLO26s-depth.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s-depth + filename: yolo26s-depth.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [768, 768] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: depth_estimation + dataset: nyu-depth diff --git a/mblt_vision/models/YOLO26s-distill.yaml b/mblt_vision/models/YOLO26s-distill.yaml new file mode 100644 index 0000000..a29d729 --- /dev/null +++ b/mblt_vision/models/YOLO26s-distill.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s-distill + filename: yolo26s-distill.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26s-obb.yaml b/mblt_vision/models/YOLO26s-obb.yaml new file mode 100644 index 0000000..8d4d302 --- /dev/null +++ b/mblt_vision/models/YOLO26s-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s-obb + filename: yolo26s-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + dflfree: true + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26s-pose.yaml b/mblt_vision/models/YOLO26s-pose.yaml new file mode 100644 index 0000000..6f1007d --- /dev/null +++ b/mblt_vision/models/YOLO26s-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s-pose + filename: yolo26s-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26s-seg.yaml b/mblt_vision/models/YOLO26s-seg.yaml new file mode 100644 index 0000000..ccd86f4 --- /dev/null +++ b/mblt_vision/models/YOLO26s-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s-seg + filename: yolo26s-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26s-sem-ade20k.yaml b/mblt_vision/models/YOLO26s-sem-ade20k.yaml new file mode 100644 index 0000000..cff9c9f --- /dev/null +++ b/mblt_vision/models/YOLO26s-sem-ade20k.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s-sem-ade20k + filename: yolo26s-sem-ade20k.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [640, 640] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: ade20k diff --git a/mblt_vision/models/YOLO26s-sem.yaml b/mblt_vision/models/YOLO26s-sem.yaml new file mode 100644 index 0000000..5040771 --- /dev/null +++ b/mblt_vision/models/YOLO26s-sem.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s-sem + filename: yolo26s-sem.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [1024, 2048] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: cityscapes diff --git a/mblt_vision/models/YOLO26s.yaml b/mblt_vision/models/YOLO26s.yaml new file mode 100644 index 0000000..2167b14 --- /dev/null +++ b/mblt_vision/models/YOLO26s.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26s + filename: yolo26s.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26x-cls.yaml b/mblt_vision/models/YOLO26x-cls.yaml new file mode 100644 index 0000000..03963e2 --- /dev/null +++ b/mblt_vision/models/YOLO26x-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x-cls + filename: yolo26x-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLO26x-depth.yaml b/mblt_vision/models/YOLO26x-depth.yaml new file mode 100644 index 0000000..60cc02b --- /dev/null +++ b/mblt_vision/models/YOLO26x-depth.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x-depth + filename: yolo26x-depth.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [768, 768] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: depth_estimation + dataset: nyu-depth diff --git a/mblt_vision/models/YOLO26x-distill.yaml b/mblt_vision/models/YOLO26x-distill.yaml new file mode 100644 index 0000000..4a5ba55 --- /dev/null +++ b/mblt_vision/models/YOLO26x-distill.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x-distill + filename: yolo26x-distill.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26x-obb.yaml b/mblt_vision/models/YOLO26x-obb.yaml new file mode 100644 index 0000000..9e178d0 --- /dev/null +++ b/mblt_vision/models/YOLO26x-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x-obb + filename: yolo26x-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + dflfree: true + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26x-pose.yaml b/mblt_vision/models/YOLO26x-pose.yaml new file mode 100644 index 0000000..2f3a9cf --- /dev/null +++ b/mblt_vision/models/YOLO26x-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x-pose + filename: yolo26x-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26x-seg.yaml b/mblt_vision/models/YOLO26x-seg.yaml new file mode 100644 index 0000000..0469a92 --- /dev/null +++ b/mblt_vision/models/YOLO26x-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x-seg + filename: yolo26x-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLO26x-sem-ade20k.yaml b/mblt_vision/models/YOLO26x-sem-ade20k.yaml new file mode 100644 index 0000000..38f3546 --- /dev/null +++ b/mblt_vision/models/YOLO26x-sem-ade20k.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x-sem-ade20k + filename: yolo26x-sem-ade20k.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [640, 640] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: ade20k diff --git a/mblt_vision/models/YOLO26x-sem.yaml b/mblt_vision/models/YOLO26x-sem.yaml new file mode 100644 index 0000000..3826871 --- /dev/null +++ b/mblt_vision/models/YOLO26x-sem.yaml @@ -0,0 +1,17 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x-sem + filename: yolo26x-sem.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: [1024, 2048] + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: semantic_segmentation + dataset: cityscapes diff --git a/mblt_vision/models/YOLO26x.yaml b/mblt_vision/models/YOLO26x.yaml new file mode 100644 index 0000000..777fc7f --- /dev/null +++ b/mblt_vision/models/YOLO26x.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLO26x + filename: yolo26x.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + dflfree: true + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10b.yaml b/mblt_vision/models/YOLOv10b.yaml new file mode 100644 index 0000000..013c1c4 --- /dev/null +++ b/mblt_vision/models/YOLOv10b.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10b + filename: yolov10b.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10l-face.yaml b/mblt_vision/models/YOLOv10l-face.yaml new file mode 100644 index 0000000..e9d9fde --- /dev/null +++ b/mblt_vision/models/YOLOv10l-face.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10l-face + filename: yolov10l-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10l.yaml b/mblt_vision/models/YOLOv10l.yaml new file mode 100644 index 0000000..a1ce2c8 --- /dev/null +++ b/mblt_vision/models/YOLOv10l.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10l + filename: yolov10l.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10m-face.yaml b/mblt_vision/models/YOLOv10m-face.yaml new file mode 100644 index 0000000..61b9df3 --- /dev/null +++ b/mblt_vision/models/YOLOv10m-face.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10m-face + filename: yolov10m-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10m.yaml b/mblt_vision/models/YOLOv10m.yaml new file mode 100644 index 0000000..cef0e70 --- /dev/null +++ b/mblt_vision/models/YOLOv10m.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10m + filename: yolov10m.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10n-face.yaml b/mblt_vision/models/YOLOv10n-face.yaml new file mode 100644 index 0000000..0b3c266 --- /dev/null +++ b/mblt_vision/models/YOLOv10n-face.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10n-face + filename: yolov10n-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10n.yaml b/mblt_vision/models/YOLOv10n.yaml new file mode 100644 index 0000000..bd5585d --- /dev/null +++ b/mblt_vision/models/YOLOv10n.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10n + filename: yolov10n.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10s-face.yaml b/mblt_vision/models/YOLOv10s-face.yaml new file mode 100644 index 0000000..a66ebcb --- /dev/null +++ b/mblt_vision/models/YOLOv10s-face.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10s-face + filename: yolov10s-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10s.yaml b/mblt_vision/models/YOLOv10s.yaml new file mode 100644 index 0000000..374cdae --- /dev/null +++ b/mblt_vision/models/YOLOv10s.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10s + filename: yolov10s.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv10x.yaml b/mblt_vision/models/YOLOv10x.yaml new file mode 100644 index 0000000..ce5a4c3 --- /dev/null +++ b/mblt_vision/models/YOLOv10x.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv10x + filename: yolov10x.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + nmsfree: true + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv3-spp.yaml b/mblt_vision/models/YOLOv3-spp.yaml new file mode 100644 index 0000000..48e8c42 --- /dev/null +++ b/mblt_vision/models/YOLOv3-spp.yaml @@ -0,0 +1,44 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv3-spp + filename: yolov3-spp.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv3-sppu.yaml b/mblt_vision/models/YOLOv3-sppu.yaml new file mode 100644 index 0000000..cd2c845 --- /dev/null +++ b/mblt_vision/models/YOLOv3-sppu.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv3-sppu + filename: yolov3-sppu.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv3-tiny.yaml b/mblt_vision/models/YOLOv3-tiny.yaml new file mode 100644 index 0000000..047451b --- /dev/null +++ b/mblt_vision/models/YOLOv3-tiny.yaml @@ -0,0 +1,38 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv3-tiny + filename: yolov3-tiny.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 10 + - 14 + - 23 + - 27 + - 37 + - 58 + - - 81 + - 82 + - 135 + - 169 + - 344 + - 319 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv3-tinyu.yaml b/mblt_vision/models/YOLOv3-tinyu.yaml new file mode 100644 index 0000000..a263aca --- /dev/null +++ b/mblt_vision/models/YOLOv3-tinyu.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv3-tinyu + filename: yolov3-tinyu.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 2 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv3.yaml b/mblt_vision/models/YOLOv3.yaml new file mode 100644 index 0000000..29be313 --- /dev/null +++ b/mblt_vision/models/YOLOv3.yaml @@ -0,0 +1,44 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv3 + filename: yolov3.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv3u.yaml b/mblt_vision/models/YOLOv3u.yaml new file mode 100644 index 0000000..bc1cf32 --- /dev/null +++ b/mblt_vision/models/YOLOv3u.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv3u + filename: yolov3u.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5l-cls.yaml b/mblt_vision/models/YOLOv5l-cls.yaml new file mode 100644 index 0000000..2f0a17c --- /dev/null +++ b/mblt_vision/models/YOLOv5l-cls.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5l-cls + filename: yolov5l-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/YOLOv5l-seg.yaml b/mblt_vision/models/YOLOv5l-seg.yaml new file mode 100644 index 0000000..ba386b8 --- /dev/null +++ b/mblt_vision/models/YOLOv5l-seg.yaml @@ -0,0 +1,45 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5l-seg + filename: yolov5l-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + n_extra: 32 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5l.yaml b/mblt_vision/models/YOLOv5l.yaml new file mode 100644 index 0000000..181aae6 --- /dev/null +++ b/mblt_vision/models/YOLOv5l.yaml @@ -0,0 +1,44 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5l + filename: yolov5l.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5l6.yaml b/mblt_vision/models/YOLOv5l6.yaml new file mode 100644 index 0000000..ad68e5c --- /dev/null +++ b/mblt_vision/models/YOLOv5l6.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5l6 + filename: yolov5l6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5l6u.yaml b/mblt_vision/models/YOLOv5l6u.yaml new file mode 100644 index 0000000..380b80a --- /dev/null +++ b/mblt_vision/models/YOLOv5l6u.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5l6u + filename: yolov5l6u.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 4 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5lu.yaml b/mblt_vision/models/YOLOv5lu.yaml new file mode 100644 index 0000000..b1dc1e4 --- /dev/null +++ b/mblt_vision/models/YOLOv5lu.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5lu + filename: yolov5lu.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5m-cls.yaml b/mblt_vision/models/YOLOv5m-cls.yaml new file mode 100644 index 0000000..8fdb619 --- /dev/null +++ b/mblt_vision/models/YOLOv5m-cls.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5m-cls + filename: yolov5m-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/YOLOv5m-seg.yaml b/mblt_vision/models/YOLOv5m-seg.yaml new file mode 100644 index 0000000..6696be4 --- /dev/null +++ b/mblt_vision/models/YOLOv5m-seg.yaml @@ -0,0 +1,45 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5m-seg + filename: yolov5m-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + n_extra: 32 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5m.yaml b/mblt_vision/models/YOLOv5m.yaml new file mode 100644 index 0000000..505d44f --- /dev/null +++ b/mblt_vision/models/YOLOv5m.yaml @@ -0,0 +1,45 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5m + filename: yolov5m.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5m6.yaml b/mblt_vision/models/YOLOv5m6.yaml new file mode 100644 index 0000000..4a21683 --- /dev/null +++ b/mblt_vision/models/YOLOv5m6.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5m6 + filename: yolov5m6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5m6u.yaml b/mblt_vision/models/YOLOv5m6u.yaml new file mode 100644 index 0000000..fb12e16 --- /dev/null +++ b/mblt_vision/models/YOLOv5m6u.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5m6u + filename: yolov5m6u.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 4 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5mu.yaml b/mblt_vision/models/YOLOv5mu.yaml new file mode 100644 index 0000000..6fa0880 --- /dev/null +++ b/mblt_vision/models/YOLOv5mu.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5mu + filename: yolov5mu.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5n-cls.yaml b/mblt_vision/models/YOLOv5n-cls.yaml new file mode 100644 index 0000000..1447532 --- /dev/null +++ b/mblt_vision/models/YOLOv5n-cls.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5n-cls + filename: yolov5n-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/YOLOv5n-seg.yaml b/mblt_vision/models/YOLOv5n-seg.yaml new file mode 100644 index 0000000..1437343 --- /dev/null +++ b/mblt_vision/models/YOLOv5n-seg.yaml @@ -0,0 +1,45 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5n-seg + filename: yolov5n-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + n_extra: 32 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5n.yaml b/mblt_vision/models/YOLOv5n.yaml new file mode 100644 index 0000000..f135ba1 --- /dev/null +++ b/mblt_vision/models/YOLOv5n.yaml @@ -0,0 +1,44 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5n + filename: yolov5n.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5n6.yaml b/mblt_vision/models/YOLOv5n6.yaml new file mode 100644 index 0000000..b0c86b8 --- /dev/null +++ b/mblt_vision/models/YOLOv5n6.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5n6 + filename: yolov5n6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5n6u.yaml b/mblt_vision/models/YOLOv5n6u.yaml new file mode 100644 index 0000000..207f82a --- /dev/null +++ b/mblt_vision/models/YOLOv5n6u.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5n6u + filename: yolov5n6u.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 4 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5nu.yaml b/mblt_vision/models/YOLOv5nu.yaml new file mode 100644 index 0000000..29872aa --- /dev/null +++ b/mblt_vision/models/YOLOv5nu.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5nu + filename: yolov5nu.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5s-cls.yaml b/mblt_vision/models/YOLOv5s-cls.yaml new file mode 100644 index 0000000..fc928f8 --- /dev/null +++ b/mblt_vision/models/YOLOv5s-cls.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5s-cls + filename: yolov5s-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/YOLOv5s-seg.yaml b/mblt_vision/models/YOLOv5s-seg.yaml new file mode 100644 index 0000000..e551e73 --- /dev/null +++ b/mblt_vision/models/YOLOv5s-seg.yaml @@ -0,0 +1,45 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5s-seg + filename: yolov5s-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + n_extra: 32 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5s.yaml b/mblt_vision/models/YOLOv5s.yaml new file mode 100644 index 0000000..b633770 --- /dev/null +++ b/mblt_vision/models/YOLOv5s.yaml @@ -0,0 +1,44 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5s + filename: yolov5s.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5s6.yaml b/mblt_vision/models/YOLOv5s6.yaml new file mode 100644 index 0000000..d31e09a --- /dev/null +++ b/mblt_vision/models/YOLOv5s6.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5s6 + filename: yolov5s6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5s6u.yaml b/mblt_vision/models/YOLOv5s6u.yaml new file mode 100644 index 0000000..cb4dbd2 --- /dev/null +++ b/mblt_vision/models/YOLOv5s6u.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5s6u + filename: yolov5s6u.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 4 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5su.yaml b/mblt_vision/models/YOLOv5su.yaml new file mode 100644 index 0000000..a7fe72d --- /dev/null +++ b/mblt_vision/models/YOLOv5su.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5su + filename: yolov5su.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5x-cls.yaml b/mblt_vision/models/YOLOv5x-cls.yaml new file mode 100644 index 0000000..93f8073 --- /dev/null +++ b/mblt_vision/models/YOLOv5x-cls.yaml @@ -0,0 +1,22 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5x-cls + filename: yolov5x-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: torch + post_cfg: + task: image_classification + dataset: imagenet diff --git a/mblt_vision/models/YOLOv5x-seg.yaml b/mblt_vision/models/YOLOv5x-seg.yaml new file mode 100644 index 0000000..efd2e09 --- /dev/null +++ b/mblt_vision/models/YOLOv5x-seg.yaml @@ -0,0 +1,45 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5x-seg + filename: yolov5x-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + n_extra: 32 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5x.yaml b/mblt_vision/models/YOLOv5x.yaml new file mode 100644 index 0000000..d20e4f1 --- /dev/null +++ b/mblt_vision/models/YOLOv5x.yaml @@ -0,0 +1,44 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5x + filename: yolov5x.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 10 + - 13 + - 16 + - 30 + - 33 + - 23 + - - 30 + - 61 + - 62 + - 45 + - 59 + - 119 + - - 116 + - 90 + - 156 + - 198 + - 373 + - 326 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5x6.yaml b/mblt_vision/models/YOLOv5x6.yaml new file mode 100644 index 0000000..c9928f2 --- /dev/null +++ b/mblt_vision/models/YOLOv5x6.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5x6 + filename: yolov5x6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.65 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5x6u.yaml b/mblt_vision/models/YOLOv5x6u.yaml new file mode 100644 index 0000000..5ceae88 --- /dev/null +++ b/mblt_vision/models/YOLOv5x6u.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5x6u + filename: yolov5x6u.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 4 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv5xu.yaml b/mblt_vision/models/YOLOv5xu.yaml new file mode 100644 index 0000000..2d155bf --- /dev/null +++ b/mblt_vision/models/YOLOv5xu.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv5xu + filename: yolov5xu.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv6m-face.yaml b/mblt_vision/models/YOLOv6m-face.yaml new file mode 100644 index 0000000..5c56065 --- /dev/null +++ b/mblt_vision/models/YOLOv6m-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv6m-face + filename: yolov6m-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv6n-face.yaml b/mblt_vision/models/YOLOv6n-face.yaml new file mode 100644 index 0000000..f1e7541 --- /dev/null +++ b/mblt_vision/models/YOLOv6n-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv6n-face + filename: yolov6n-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv7.yaml b/mblt_vision/models/YOLOv7.yaml new file mode 100644 index 0000000..65f88c7 --- /dev/null +++ b/mblt_vision/models/YOLOv7.yaml @@ -0,0 +1,44 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv7 + filename: yolov7.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 12 + - 16 + - 19 + - 36 + - 40 + - 28 + - - 36 + - 75 + - 76 + - 55 + - 72 + - 146 + - - 142 + - 110 + - 192 + - 243 + - 459 + - 401 + conf_thres: 0.001 + iou_thres: 0.6 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv7d6.yaml b/mblt_vision/models/YOLOv7d6.yaml new file mode 100644 index 0000000..7c777d7 --- /dev/null +++ b/mblt_vision/models/YOLOv7d6.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv7d6 + filename: yolov7d6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.6 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv7e6.yaml b/mblt_vision/models/YOLOv7e6.yaml new file mode 100644 index 0000000..9c2dd47 --- /dev/null +++ b/mblt_vision/models/YOLOv7e6.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv7e6 + filename: yolov7e6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.6 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv7e6e.yaml b/mblt_vision/models/YOLOv7e6e.yaml new file mode 100644 index 0000000..cc13bcb --- /dev/null +++ b/mblt_vision/models/YOLOv7e6e.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv7e6e + filename: yolov7e6e.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.6 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv7w6.yaml b/mblt_vision/models/YOLOv7w6.yaml new file mode 100644 index 0000000..776dc21 --- /dev/null +++ b/mblt_vision/models/YOLOv7w6.yaml @@ -0,0 +1,50 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv7w6 + filename: yolov7w6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 19 + - 27 + - 44 + - 40 + - 38 + - 94 + - - 96 + - 68 + - 86 + - 152 + - 180 + - 137 + - - 140 + - 301 + - 303 + - 264 + - 238 + - 542 + - - 436 + - 615 + - 739 + - 380 + - 925 + - 792 + conf_thres: 0.001 + iou_thres: 0.6 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv7x.yaml b/mblt_vision/models/YOLOv7x.yaml new file mode 100644 index 0000000..ae4e3b2 --- /dev/null +++ b/mblt_vision/models/YOLOv7x.yaml @@ -0,0 +1,44 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv7x + filename: yolov7x.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + anchors: + - - 12 + - 16 + - 19 + - 36 + - 40 + - 28 + - - 36 + - 75 + - 76 + - 55 + - 72 + - 146 + - - 142 + - 110 + - 192 + - 243 + - 459 + - 401 + conf_thres: 0.001 + iou_thres: 0.6 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8l-cls.yaml b/mblt_vision/models/YOLOv8l-cls.yaml new file mode 100644 index 0000000..08c800e --- /dev/null +++ b/mblt_vision/models/YOLOv8l-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8l-cls + filename: yolov8l-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLOv8l-face.yaml b/mblt_vision/models/YOLOv8l-face.yaml new file mode 100644 index 0000000..12f7093 --- /dev/null +++ b/mblt_vision/models/YOLOv8l-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8l-face + filename: yolov8l-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8l-obb.yaml b/mblt_vision/models/YOLOv8l-obb.yaml new file mode 100644 index 0000000..280cb4b --- /dev/null +++ b/mblt_vision/models/YOLOv8l-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8l-obb + filename: yolov8l-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8l-pose.yaml b/mblt_vision/models/YOLOv8l-pose.yaml new file mode 100644 index 0000000..ea366b9 --- /dev/null +++ b/mblt_vision/models/YOLOv8l-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8l-pose + filename: yolov8l-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8l-seg.yaml b/mblt_vision/models/YOLOv8l-seg.yaml new file mode 100644 index 0000000..c34ac0f --- /dev/null +++ b/mblt_vision/models/YOLOv8l-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8l-seg + filename: yolov8l-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8l.yaml b/mblt_vision/models/YOLOv8l.yaml new file mode 100644 index 0000000..62609bf --- /dev/null +++ b/mblt_vision/models/YOLOv8l.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8l + filename: yolov8l.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8m-cls.yaml b/mblt_vision/models/YOLOv8m-cls.yaml new file mode 100644 index 0000000..37c82f5 --- /dev/null +++ b/mblt_vision/models/YOLOv8m-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8m-cls + filename: yolov8m-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLOv8m-face.yaml b/mblt_vision/models/YOLOv8m-face.yaml new file mode 100644 index 0000000..135340c --- /dev/null +++ b/mblt_vision/models/YOLOv8m-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8m-face + filename: yolov8m-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8m-obb.yaml b/mblt_vision/models/YOLOv8m-obb.yaml new file mode 100644 index 0000000..fcb5307 --- /dev/null +++ b/mblt_vision/models/YOLOv8m-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8m-obb + filename: yolov8m-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8m-pose.yaml b/mblt_vision/models/YOLOv8m-pose.yaml new file mode 100644 index 0000000..098fc23 --- /dev/null +++ b/mblt_vision/models/YOLOv8m-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8m-pose + filename: yolov8m-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8m-seg.yaml b/mblt_vision/models/YOLOv8m-seg.yaml new file mode 100644 index 0000000..10f5b81 --- /dev/null +++ b/mblt_vision/models/YOLOv8m-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8m-seg + filename: yolov8m-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8m.yaml b/mblt_vision/models/YOLOv8m.yaml new file mode 100644 index 0000000..06fa61e --- /dev/null +++ b/mblt_vision/models/YOLOv8m.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8m + filename: yolov8m.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8n-cls.yaml b/mblt_vision/models/YOLOv8n-cls.yaml new file mode 100644 index 0000000..6a8f357 --- /dev/null +++ b/mblt_vision/models/YOLOv8n-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8n-cls + filename: yolov8n-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLOv8n-face.yaml b/mblt_vision/models/YOLOv8n-face.yaml new file mode 100644 index 0000000..5c7ee1f --- /dev/null +++ b/mblt_vision/models/YOLOv8n-face.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8n-face + filename: yolov8n-face.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: face_detection + dataset: widerface + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8n-obb.yaml b/mblt_vision/models/YOLOv8n-obb.yaml new file mode 100644 index 0000000..2a827b8 --- /dev/null +++ b/mblt_vision/models/YOLOv8n-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8n-obb + filename: yolov8n-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8n-pose.yaml b/mblt_vision/models/YOLOv8n-pose.yaml new file mode 100644 index 0000000..34f5d6f --- /dev/null +++ b/mblt_vision/models/YOLOv8n-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8n-pose + filename: yolov8n-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8n-seg.yaml b/mblt_vision/models/YOLOv8n-seg.yaml new file mode 100644 index 0000000..94920f6 --- /dev/null +++ b/mblt_vision/models/YOLOv8n-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8n-seg + filename: yolov8n-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8n.yaml b/mblt_vision/models/YOLOv8n.yaml new file mode 100644 index 0000000..1ea9850 --- /dev/null +++ b/mblt_vision/models/YOLOv8n.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8n + filename: yolov8n.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8s-cls.yaml b/mblt_vision/models/YOLOv8s-cls.yaml new file mode 100644 index 0000000..a70abb9 --- /dev/null +++ b/mblt_vision/models/YOLOv8s-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8s-cls + filename: yolov8s-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLOv8s-obb.yaml b/mblt_vision/models/YOLOv8s-obb.yaml new file mode 100644 index 0000000..755b321 --- /dev/null +++ b/mblt_vision/models/YOLOv8s-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8s-obb + filename: yolov8s-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8s-pose.yaml b/mblt_vision/models/YOLOv8s-pose.yaml new file mode 100644 index 0000000..be4d4f5 --- /dev/null +++ b/mblt_vision/models/YOLOv8s-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8s-pose + filename: yolov8s-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8s-seg.yaml b/mblt_vision/models/YOLOv8s-seg.yaml new file mode 100644 index 0000000..26fc679 --- /dev/null +++ b/mblt_vision/models/YOLOv8s-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8s-seg + filename: yolov8s-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8s.yaml b/mblt_vision/models/YOLOv8s.yaml new file mode 100644 index 0000000..f705f91 --- /dev/null +++ b/mblt_vision/models/YOLOv8s.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8s + filename: yolov8s.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8x-cls.yaml b/mblt_vision/models/YOLOv8x-cls.yaml new file mode 100644 index 0000000..bb7242e --- /dev/null +++ b/mblt_vision/models/YOLOv8x-cls.yaml @@ -0,0 +1,23 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8x-cls + filename: yolov8x-cls.mxq + revision: main + pre_cfg: + Reader: + style: pil + Resize: + size: 224 + interpolation: bilinear + CenterCrop: + size: + - 224 + - 224 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: image_classification + dataset: imagenet + softmax: true diff --git a/mblt_vision/models/YOLOv8x-obb.yaml b/mblt_vision/models/YOLOv8x-obb.yaml new file mode 100644 index 0000000..0eccaf9 --- /dev/null +++ b/mblt_vision/models/YOLOv8x-obb.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8x-obb + filename: yolov8x-obb.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1024 + - 1024 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: obb + dataset: dotav1 + nl: 3 + n_extra: 1 + reg_max: 16 + conf_thres: 0.01 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8x-pose-p6.yaml b/mblt_vision/models/YOLOv8x-pose-p6.yaml new file mode 100644 index 0000000..e42b723 --- /dev/null +++ b/mblt_vision/models/YOLOv8x-pose-p6.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8x-pose-p6 + filename: yolov8x-pose-p6.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 1280 + - 1280 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 4 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8x-pose.yaml b/mblt_vision/models/YOLOv8x-pose.yaml new file mode 100644 index 0000000..694d47e --- /dev/null +++ b/mblt_vision/models/YOLOv8x-pose.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8x-pose + filename: yolov8x-pose.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: pose_estimation + dataset: coco + nl: 3 + n_extra: 51 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8x-seg.yaml b/mblt_vision/models/YOLOv8x-seg.yaml new file mode 100644 index 0000000..ddbc1dd --- /dev/null +++ b/mblt_vision/models/YOLOv8x-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8x-seg + filename: yolov8x-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv8x.yaml b/mblt_vision/models/YOLOv8x.yaml new file mode 100644 index 0000000..808e7f9 --- /dev/null +++ b/mblt_vision/models/YOLOv8x.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv8x + filename: yolov8x.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv9c-seg.yaml b/mblt_vision/models/YOLOv9c-seg.yaml new file mode 100644 index 0000000..4eb0730 --- /dev/null +++ b/mblt_vision/models/YOLOv9c-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv9c-seg + filename: yolov9c-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv9c.yaml b/mblt_vision/models/YOLOv9c.yaml new file mode 100644 index 0000000..3c73d9a --- /dev/null +++ b/mblt_vision/models/YOLOv9c.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv9c + filename: yolov9c.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv9e-seg.yaml b/mblt_vision/models/YOLOv9e-seg.yaml new file mode 100644 index 0000000..a645374 --- /dev/null +++ b/mblt_vision/models/YOLOv9e-seg.yaml @@ -0,0 +1,28 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv9e-seg + filename: yolov9e-seg.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: instance_segmentation + dataset: coco + nl: 3 + n_extra: 32 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv9e.yaml b/mblt_vision/models/YOLOv9e.yaml new file mode 100644 index 0000000..e92b7b0 --- /dev/null +++ b/mblt_vision/models/YOLOv9e.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv9e + filename: yolov9e.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv9m.yaml b/mblt_vision/models/YOLOv9m.yaml new file mode 100644 index 0000000..b83d2a9 --- /dev/null +++ b/mblt_vision/models/YOLOv9m.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv9m + filename: yolov9m.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv9s.yaml b/mblt_vision/models/YOLOv9s.yaml new file mode 100644 index 0000000..cd4c9af --- /dev/null +++ b/mblt_vision/models/YOLOv9s.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv9s + filename: yolov9s.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/models/YOLOv9t.yaml b/mblt_vision/models/YOLOv9t.yaml new file mode 100644 index 0000000..cf0166b --- /dev/null +++ b/mblt_vision/models/YOLOv9t.yaml @@ -0,0 +1,27 @@ +DEFAULT: + file_cfg: + repo_id: mobilint/YOLOv9t + filename: yolov9t.mxq + revision: main + pre_cfg: + Reader: + style: numpy + LetterBox: + img_size: + - 640 + - 640 + SetOrder: + shape: HWC + Normalize: + style: cv + post_cfg: + task: object_detection + dataset: coco + nl: 3 + reg_max: 16 + conf_thres: 0.001 + iou_thres: 0.7 +TURBO: + update: DEFAULT + file_cfg: + revision: TURBO diff --git a/mblt_vision/obb/__init__.py b/mblt_vision/obb/__init__.py new file mode 100644 index 0000000..fc20fd7 --- /dev/null +++ b/mblt_vision/obb/__init__.py @@ -0,0 +1,39 @@ +"""Oriented-bounding-box Vision model exports.""" + +from __future__ import annotations + +from .._compat import create_model_class + +__all__: list[str] = [ + "YOLO11lOBB", + "YOLO11mOBB", + "YOLO11nOBB", + "YOLO11sOBB", + "YOLO11xOBB", + "YOLO26lOBB", + "YOLO26mOBB", + "YOLO26nOBB", + "YOLO26sOBB", + "YOLO26xOBB", + "YOLOv8lOBB", + "YOLOv8mOBB", + "YOLOv8nOBB", + "YOLOv8sOBB", + "YOLOv8xOBB", +] + +YOLO11lOBB = create_model_class("YOLO11lOBB", __name__) +YOLO11mOBB = create_model_class("YOLO11mOBB", __name__) +YOLO11nOBB = create_model_class("YOLO11nOBB", __name__) +YOLO11sOBB = create_model_class("YOLO11sOBB", __name__) +YOLO11xOBB = create_model_class("YOLO11xOBB", __name__) +YOLO26lOBB = create_model_class("YOLO26lOBB", __name__) +YOLO26mOBB = create_model_class("YOLO26mOBB", __name__) +YOLO26nOBB = create_model_class("YOLO26nOBB", __name__) +YOLO26sOBB = create_model_class("YOLO26sOBB", __name__) +YOLO26xOBB = create_model_class("YOLO26xOBB", __name__) +YOLOv8lOBB = create_model_class("YOLOv8lOBB", __name__) +YOLOv8mOBB = create_model_class("YOLOv8mOBB", __name__) +YOLOv8nOBB = create_model_class("YOLOv8nOBB", __name__) +YOLOv8sOBB = create_model_class("YOLOv8sOBB", __name__) +YOLOv8xOBB = create_model_class("YOLOv8xOBB", __name__) diff --git a/mblt_vision/object_detection/__init__.py b/mblt_vision/object_detection/__init__.py new file mode 100644 index 0000000..3e2cd2f --- /dev/null +++ b/mblt_vision/object_detection/__init__.py @@ -0,0 +1,153 @@ +"""Object detection model exports.""" + +from __future__ import annotations + +from .._compat import create_model_class + +__all__: list[str] = [ + "YOLO11l", + "YOLO11m", + "YOLO11n", + "YOLO11s", + "YOLO11x", + "YOLO12l", + "YOLO12m", + "YOLO12n", + "YOLO12s", + "YOLO12x", + "YOLO26l", + "YOLO26lDistill", + "YOLO26m", + "YOLO26mDistill", + "YOLO26n", + "YOLO26nDistill", + "YOLO26s", + "YOLO26sDistill", + "YOLO26x", + "YOLO26xDistill", + "YOLOv3", + "YOLOv3_spp", + "YOLOv3_sppu", + "YOLOv3_tiny", + "YOLOv3_tinyu", + "YOLOv3u", + "YOLOv5l", + "YOLOv5l6", + "YOLOv5l6u", + "YOLOv5lu", + "YOLOv5m", + "YOLOv5m6", + "YOLOv5m6u", + "YOLOv5mu", + "YOLOv5n", + "YOLOv5n6", + "YOLOv5n6u", + "YOLOv5nu", + "YOLOv5s", + "YOLOv5s6", + "YOLOv5s6u", + "YOLOv5su", + "YOLOv5x", + "YOLOv5x6", + "YOLOv5x6u", + "YOLOv5xu", + "YOLOv7", + "YOLOv7d6", + "YOLOv7e6", + "YOLOv7e6e", + "YOLOv7w6", + "YOLOv7x", + "YOLOv8l", + "YOLOv8m", + "YOLOv8n", + "YOLOv8s", + "YOLOv8x", + "GELANc", + "GELANe", + "GELANm", + "GELANs", + "YOLOv9c", + "YOLOv9e", + "YOLOv9m", + "YOLOv9s", + "YOLOv9t", + "YOLOv10b", + "YOLOv10l", + "YOLOv10m", + "YOLOv10n", + "YOLOv10s", + "YOLOv10x", +] + +YOLO11l = create_model_class("YOLO11l", __name__) +YOLO11m = create_model_class("YOLO11m", __name__) +YOLO11n = create_model_class("YOLO11n", __name__) +YOLO11s = create_model_class("YOLO11s", __name__) +YOLO11x = create_model_class("YOLO11x", __name__) +YOLO12l = create_model_class("YOLO12l", __name__) +YOLO12m = create_model_class("YOLO12m", __name__) +YOLO12n = create_model_class("YOLO12n", __name__) +YOLO12s = create_model_class("YOLO12s", __name__) +YOLO12x = create_model_class("YOLO12x", __name__) +YOLO26l = create_model_class("YOLO26l", __name__) +YOLO26lDistill = create_model_class("YOLO26lDistill", __name__) +YOLO26m = create_model_class("YOLO26m", __name__) +YOLO26mDistill = create_model_class("YOLO26mDistill", __name__) +YOLO26n = create_model_class("YOLO26n", __name__) +YOLO26nDistill = create_model_class("YOLO26nDistill", __name__) +YOLO26s = create_model_class("YOLO26s", __name__) +YOLO26sDistill = create_model_class("YOLO26sDistill", __name__) +YOLO26x = create_model_class("YOLO26x", __name__) +YOLO26xDistill = create_model_class("YOLO26xDistill", __name__) +YOLOv3 = create_model_class("YOLOv3", __name__) +YOLOv3_spp = create_model_class("YOLOv3_spp", __name__) +YOLOv3_sppu = create_model_class("YOLOv3_sppu", __name__) +YOLOv3_tiny = create_model_class("YOLOv3_tiny", __name__) +YOLOv3_tinyu = create_model_class("YOLOv3_tinyu", __name__) +YOLOv3u = create_model_class("YOLOv3u", __name__) +YOLOv5l = create_model_class("YOLOv5l", __name__) +YOLOv5l6 = create_model_class("YOLOv5l6", __name__) +YOLOv5l6u = create_model_class("YOLOv5l6u", __name__) +YOLOv5lu = create_model_class("YOLOv5lu", __name__) +YOLOv5m = create_model_class("YOLOv5m", __name__) +YOLOv5m6 = create_model_class("YOLOv5m6", __name__) +YOLOv5m6u = create_model_class("YOLOv5m6u", __name__) +YOLOv5mu = create_model_class("YOLOv5mu", __name__) +YOLOv5n = create_model_class("YOLOv5n", __name__) +YOLOv5n6 = create_model_class("YOLOv5n6", __name__) +YOLOv5n6u = create_model_class("YOLOv5n6u", __name__) +YOLOv5nu = create_model_class("YOLOv5nu", __name__) +YOLOv5s = create_model_class("YOLOv5s", __name__) +YOLOv5s6 = create_model_class("YOLOv5s6", __name__) +YOLOv5s6u = create_model_class("YOLOv5s6u", __name__) +YOLOv5su = create_model_class("YOLOv5su", __name__) +YOLOv5x = create_model_class("YOLOv5x", __name__) +YOLOv5x6 = create_model_class("YOLOv5x6", __name__) +YOLOv5x6u = create_model_class("YOLOv5x6u", __name__) +YOLOv5xu = create_model_class("YOLOv5xu", __name__) +YOLOv7 = create_model_class("YOLOv7", __name__) +YOLOv7d6 = create_model_class("YOLOv7d6", __name__) +YOLOv7e6 = create_model_class("YOLOv7e6", __name__) +YOLOv7e6e = create_model_class("YOLOv7e6e", __name__) +YOLOv7w6 = create_model_class("YOLOv7w6", __name__) +YOLOv7x = create_model_class("YOLOv7x", __name__) +YOLOv8l = create_model_class("YOLOv8l", __name__) +YOLOv8m = create_model_class("YOLOv8m", __name__) +YOLOv8n = create_model_class("YOLOv8n", __name__) +YOLOv8s = create_model_class("YOLOv8s", __name__) +YOLOv8x = create_model_class("YOLOv8x", __name__) +GELANc = create_model_class("GELANc", __name__) +GELANe = create_model_class("GELANe", __name__) +GELANm = create_model_class("GELANm", __name__) +GELANs = create_model_class("GELANs", __name__) +YOLOv9c = create_model_class("YOLOv9c", __name__) +YOLOv9e = create_model_class("YOLOv9e", __name__) +YOLOv9m = create_model_class("YOLOv9m", __name__) +YOLOv9s = create_model_class("YOLOv9s", __name__) +YOLOv9t = create_model_class("YOLOv9t", __name__) +YOLOv10b = create_model_class("YOLOv10b", __name__) +YOLOv10l = create_model_class("YOLOv10l", __name__) +YOLOv10m = create_model_class("YOLOv10m", __name__) +YOLOv10n = create_model_class("YOLOv10n", __name__) +YOLOv10s = create_model_class("YOLOv10s", __name__) +YOLOv10x = create_model_class("YOLOv10x", __name__) diff --git a/mblt_vision/pose_estimation/__init__.py b/mblt_vision/pose_estimation/__init__.py new file mode 100644 index 0000000..ab136d7 --- /dev/null +++ b/mblt_vision/pose_estimation/__init__.py @@ -0,0 +1,41 @@ +"""Pose estimation model exports.""" + +from __future__ import annotations + +from .._compat import create_model_class + +__all__: list[str] = [ + "YOLO11lPose", + "YOLO11mPose", + "YOLO11nPose", + "YOLO11sPose", + "YOLO11xPose", + "YOLO26lPose", + "YOLO26mPose", + "YOLO26nPose", + "YOLO26sPose", + "YOLO26xPose", + "YOLOv8lPose", + "YOLOv8mPose", + "YOLOv8nPose", + "YOLOv8sPose", + "YOLOv8xPose", + "YOLOv8xPoseP6", +] + +YOLO11lPose = create_model_class("YOLO11lPose", __name__) +YOLO11mPose = create_model_class("YOLO11mPose", __name__) +YOLO11nPose = create_model_class("YOLO11nPose", __name__) +YOLO11sPose = create_model_class("YOLO11sPose", __name__) +YOLO11xPose = create_model_class("YOLO11xPose", __name__) +YOLO26lPose = create_model_class("YOLO26lPose", __name__) +YOLO26mPose = create_model_class("YOLO26mPose", __name__) +YOLO26nPose = create_model_class("YOLO26nPose", __name__) +YOLO26sPose = create_model_class("YOLO26sPose", __name__) +YOLO26xPose = create_model_class("YOLO26xPose", __name__) +YOLOv8lPose = create_model_class("YOLOv8lPose", __name__) +YOLOv8mPose = create_model_class("YOLOv8mPose", __name__) +YOLOv8nPose = create_model_class("YOLOv8nPose", __name__) +YOLOv8sPose = create_model_class("YOLOv8sPose", __name__) +YOLOv8xPose = create_model_class("YOLOv8xPose", __name__) +YOLOv8xPoseP6 = create_model_class("YOLOv8xPoseP6", __name__) diff --git a/mblt_vision/semantic_segmentation/__init__.py b/mblt_vision/semantic_segmentation/__init__.py new file mode 100644 index 0000000..6fd6ff5 --- /dev/null +++ b/mblt_vision/semantic_segmentation/__init__.py @@ -0,0 +1,29 @@ +"""Semantic segmentation model exports.""" + +from __future__ import annotations + +from .._compat import create_model_class + +__all__: list[str] = [ + "YOLO26lSem", + "YOLO26lSemADE20K", + "YOLO26mSem", + "YOLO26mSemADE20K", + "YOLO26nSem", + "YOLO26nSemADE20K", + "YOLO26sSem", + "YOLO26sSemADE20K", + "YOLO26xSem", + "YOLO26xSemADE20K", +] + +YOLO26lSem = create_model_class("YOLO26lSem", __name__) +YOLO26lSemADE20K = create_model_class("YOLO26lSemADE20K", __name__) +YOLO26mSem = create_model_class("YOLO26mSem", __name__) +YOLO26mSemADE20K = create_model_class("YOLO26mSemADE20K", __name__) +YOLO26nSem = create_model_class("YOLO26nSem", __name__) +YOLO26nSemADE20K = create_model_class("YOLO26nSemADE20K", __name__) +YOLO26sSem = create_model_class("YOLO26sSem", __name__) +YOLO26sSemADE20K = create_model_class("YOLO26sSemADE20K", __name__) +YOLO26xSem = create_model_class("YOLO26xSem", __name__) +YOLO26xSemADE20K = create_model_class("YOLO26xSemADE20K", __name__) diff --git a/mblt_vision/utils/__init__.py b/mblt_vision/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mblt_vision/utils/datasets/__init__.py b/mblt_vision/utils/datasets/__init__.py new file mode 100644 index 0000000..dec390f --- /dev/null +++ b/mblt_vision/utils/datasets/__init__.py @@ -0,0 +1,28 @@ +"""Dataset metadata helpers used by Vision result rendering.""" + +from .ade20k import get_ade20k_palette +from .cityscapes import get_cityscapes_palette +from .coco import ( + get_coco_class_num, + get_coco_det_palette, + get_coco_keypoint_palette, + get_coco_label, + get_coco_limb_palette, + get_coco_pose_skeleton, +) +from .dotav1 import get_dotav1_label, get_dotav1_palette +from .imagenet import get_imagenet_label + +__all__ = [ + "get_ade20k_palette", + "get_cityscapes_palette", + "get_coco_class_num", + "get_coco_det_palette", + "get_coco_keypoint_palette", + "get_coco_label", + "get_coco_limb_palette", + "get_coco_pose_skeleton", + "get_dotav1_label", + "get_dotav1_palette", + "get_imagenet_label", +] diff --git a/mblt_vision/utils/datasets/ade20k.py b/mblt_vision/utils/datasets/ade20k.py new file mode 100644 index 0000000..8075db9 --- /dev/null +++ b/mblt_vision/utils/datasets/ade20k.py @@ -0,0 +1,41 @@ +""" +ADE20K dataset constants and utilities. +""" + +from __future__ import annotations + +ADE20K_PALETTE_BGR = [ + (255, 42, 4), + (235, 219, 11), + (243, 243, 243), + (183, 223, 0), + (104, 31, 17), + (221, 111, 255), + (79, 68, 255), + (0, 237, 204), + (68, 243, 0), + (255, 0, 189), + (255, 180, 0), + (186, 0, 221), + (255, 255, 0), + (0, 192, 38), + (179, 255, 1), + (255, 36, 125), + (104, 0, 123), + (108, 27, 255), + (47, 109, 252), + (11, 255, 162), +] + + +def get_ade20k_palette(idx: int) -> tuple[int, int, int]: + """Get an ADE20K visualization color in OpenCV BGR order. + + Args: + idx: ADE20K class index. + + Returns: + BGR color tuple. Colors repeat when the index exceeds the palette length. + """ + + return ADE20K_PALETTE_BGR[idx % len(ADE20K_PALETTE_BGR)] diff --git a/mblt_vision/utils/datasets/cityscapes.py b/mblt_vision/utils/datasets/cityscapes.py new file mode 100644 index 0000000..161a666 --- /dev/null +++ b/mblt_vision/utils/datasets/cityscapes.py @@ -0,0 +1,66 @@ +"""Cityscapes dataset visualization utilities.""" + +from __future__ import annotations + +import numpy as np + +# OpenCV images use BGR channel order. These are the official Cityscapes train-ID +# colors converted from RGB so familiar classes retain their standard appearance. +CITYSCAPES_PALETTE_BGR: tuple[tuple[int, int, int], ...] = ( + (128, 64, 128), # road + (232, 35, 244), # sidewalk + (70, 70, 70), # building + (156, 102, 102), # wall + (153, 153, 190), # fence + (153, 153, 153), # pole + (30, 170, 250), # traffic light + (0, 220, 220), # traffic sign + (35, 142, 107), # vegetation + (152, 251, 152), # terrain + (180, 130, 70), # sky + (60, 20, 220), # person + (0, 0, 255), # rider + (142, 0, 0), # car + (70, 0, 0), # truck + (100, 60, 0), # bus + (100, 80, 0), # train + (230, 0, 0), # motorcycle + (32, 11, 119), # bicycle +) + +CITYSCAPES_SOURCE_IDS: tuple[int, ...] = ( + 7, + 8, + 11, + 12, + 13, + 17, + *range(19, 29), + 31, + 32, + 33, +) +CITYSCAPES_SOURCE_TO_TRAIN_ID = np.full(256, 255, dtype=np.uint8) +CITYSCAPES_SOURCE_TO_TRAIN_ID[list(CITYSCAPES_SOURCE_IDS)] = np.arange( + 19, dtype=np.uint8 +) + + +def get_cityscapes_palette(class_id: int) -> tuple[int, int, int]: + """Return the BGR visualization color for a Cityscapes train ID. + + Args: + class_id: Cityscapes train ID in the range 0 through 18. + + Returns: + The corresponding color in OpenCV BGR order. + + Raises: + ValueError: If ``class_id`` is outside the Cityscapes train-ID range. + """ + + if not 0 <= class_id < len(CITYSCAPES_PALETTE_BGR): + raise ValueError( + f"Cityscapes class ID must be in [0, {len(CITYSCAPES_PALETTE_BGR) - 1}]." + ) + return CITYSCAPES_PALETTE_BGR[class_id] diff --git a/mblt_vision/utils/datasets/coco.py b/mblt_vision/utils/datasets/coco.py new file mode 100644 index 0000000..b2f06e4 --- /dev/null +++ b/mblt_vision/utils/datasets/coco.py @@ -0,0 +1,243 @@ +""" +COCO dataset constants and utilities. +""" + +from __future__ import annotations + +from ...datasets import get_dataset_category_ids, get_dataset_class_names + +DET_PALETTE = [ + (220, 20, 60), + (119, 11, 32), + (0, 0, 142), + (0, 0, 230), + (106, 0, 228), + (0, 60, 100), + (0, 80, 100), + (0, 0, 70), + (0, 0, 192), + (250, 170, 30), + (100, 170, 30), + (220, 220, 0), + (175, 116, 175), + (250, 0, 30), + (165, 42, 42), + (255, 77, 255), + (0, 226, 252), + (182, 182, 255), + (0, 82, 0), + (120, 166, 157), + (110, 76, 0), + (174, 57, 255), + (199, 100, 0), + (72, 0, 118), + (255, 179, 240), + (0, 125, 92), + (209, 0, 151), + (188, 208, 182), + (0, 220, 176), + (255, 99, 164), + (92, 0, 73), + (133, 129, 255), + (78, 180, 255), + (0, 228, 0), + (174, 255, 243), + (45, 89, 255), + (134, 134, 103), + (145, 148, 174), + (255, 208, 186), + (197, 226, 255), + (171, 134, 1), + (109, 63, 54), + (207, 138, 255), + (151, 0, 95), + (9, 80, 61), + (84, 105, 51), + (74, 65, 105), + (166, 196, 102), + (208, 195, 210), + (255, 109, 65), + (0, 143, 149), + (179, 0, 194), + (209, 99, 106), + (5, 121, 0), + (227, 255, 205), + (147, 186, 208), + (153, 69, 1), + (3, 95, 161), + (163, 255, 0), + (119, 0, 170), + (0, 182, 199), + (0, 165, 120), + (183, 130, 88), + (95, 32, 0), + (130, 114, 135), + (110, 129, 133), + (166, 74, 118), + (219, 142, 185), + (79, 210, 114), + (178, 90, 62), + (65, 70, 15), + (127, 167, 115), + (59, 105, 106), + (142, 108, 45), + (196, 172, 0), + (95, 54, 80), + (128, 76, 255), + (201, 57, 1), + (246, 0, 122), + (191, 162, 208), +] + +POSE_PALETTE = [ + [255, 128, 0], + [255, 153, 51], + [255, 178, 102], + [230, 230, 0], + [255, 153, 255], + [153, 204, 255], + [255, 102, 255], + [255, 51, 255], + [102, 178, 255], + [51, 153, 255], + [255, 153, 153], + [255, 102, 102], + [255, 51, 51], + [153, 255, 153], + [102, 255, 102], + [51, 255, 51], + [0, 255, 0], + [0, 0, 255], + [255, 0, 0], + [255, 255, 255], +] + +POSE_SKELETON = [ + [16, 14], + [14, 12], + [17, 15], + [15, 13], + [12, 13], + [6, 12], + [7, 13], + [6, 7], + [6, 8], + [7, 9], + [8, 10], + [9, 11], + [2, 3], + [1, 2], + [1, 3], + [2, 4], + [3, 5], + [4, 6], + [5, 7], +] + +LIMB_PALETTE = [ + POSE_PALETTE[i] + for i in [9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16] +] +KEYPOINT_PALETTE = [ + POSE_PALETTE[i] for i in [16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9] +] + + +def get_coco_class_num() -> int: + """Get the number of COCO classes. + + Returns: + int: The number of COCO classes. + """ + return len(get_dataset_class_names("coco")) + + +def get_coco_label(idx: int) -> str: + """ + Get the COCO class label for a given index. + + Args: + idx (int): Zero-based class index. + + Returns: + str: Descriptive label for the class (e.g., "person"). + + Raises: + ValueError: If index is out of range. + """ + if not 0 <= idx < get_coco_class_num(): + raise ValueError( + f"COCO class index must be in [0, {get_coco_class_num() - 1}], got {idx}." + ) + + return get_dataset_class_names("coco")[idx] + + +def get_coco_inv(idx: int) -> int: + """Get the original COCO category ID for a given model output index. + + Args: + idx (int): Model output class index. + + Returns: + int: Original COCO category ID. + """ + return get_dataset_category_ids("coco")[idx] + + +def get_coco_det_palette(idx: int) -> tuple[int, int, int]: + """ + Get a distinct color for a COCO detection class. + + Args: + idx (int): Class index. + + Returns: + tuple[int, int, int]: (R, G, B) color tuple. + """ + return DET_PALETTE[idx] + + +def get_coco_pose_palette(idx: int) -> list[int]: + """Get the COCO pose palette by index. + + Args: + idx (int): The index of the COCO pose palette. + + Returns: + list[int]: The COCO pose palette as an [R, G, B] list. + """ + return POSE_PALETTE[idx] + + +def get_coco_pose_skeleton() -> list[list[int]]: + """Get the COCO pose skeleton. + + Returns: + list[list[int]]: The COCO pose skeleton. + """ + return POSE_SKELETON + + +def get_coco_limb_palette(idx: int) -> list[int]: + """Get the COCO limb palette by index. + + Args: + idx (int): The index of the COCO limb palette. + + Returns: + list[int]: The COCO limb palette as an [R, G, B] list. + """ + return LIMB_PALETTE[idx] + + +def get_coco_keypoint_palette(idx: int) -> list[int]: + """Get the COCO keypoint palette by index. + + Args: + idx (int): The index of the COCO keypoint palette. + + Returns: + list[int]: The COCO keypoint palette as an [R, G, B] list. + """ + return KEYPOINT_PALETTE[idx] diff --git a/mblt_vision/utils/datasets/dotav1.py b/mblt_vision/utils/datasets/dotav1.py new file mode 100644 index 0000000..6e5464f --- /dev/null +++ b/mblt_vision/utils/datasets/dotav1.py @@ -0,0 +1,59 @@ +""" +DOTAv1 dataset constants and utilities. +""" + +from __future__ import annotations + +from ...datasets import get_dataset_class_names + +DOTAV1_PALETTE_BGR = [ + (220, 20, 60), + (119, 11, 32), + (0, 0, 142), + (0, 0, 230), + (106, 0, 228), + (0, 60, 100), + (0, 80, 100), + (0, 0, 70), + (0, 0, 192), + (250, 170, 30), + (100, 170, 30), + (220, 220, 0), + (175, 116, 175), + (250, 0, 30), + (165, 42, 42), +] + + +def get_dotav1_class_num() -> int: + """Returns the number of DOTAv1 classes. + + Returns: + Number of DOTAv1 classes. + """ + return len(get_dataset_class_names("dotav1")) + + +def get_dotav1_palette(index: int) -> tuple[int, int, int]: + """Returns the DOTAv1 visualization color for a class index. + + Args: + index: Class index. + + Returns: + OpenCV BGR color tuple. + """ + + return DOTAV1_PALETTE_BGR[index % len(DOTAV1_PALETTE_BGR)] + + +def get_dotav1_label(index: int) -> str: + """Returns the DOTAv1 class name for a class index. + + Args: + index: Class index. + + Returns: + DOTAv1 class name. + """ + return get_dataset_class_names("dotav1")[index] diff --git a/mblt_vision/utils/datasets/imagenet.py b/mblt_vision/utils/datasets/imagenet.py new file mode 100644 index 0000000..b5a7596 --- /dev/null +++ b/mblt_vision/utils/datasets/imagenet.py @@ -0,0 +1,29 @@ +""" +ImageNet dataset constants and utilities. +""" + +from __future__ import annotations + +from ...datasets import get_dataset_class_names + + +def get_imagenet_label(idx: int) -> str: + """Get the descriptive label for an ImageNet class index. + + Args: + idx (int): ImageNet class index (0-999). + + Returns: + str: Primary class label string. + """ + label_str = get_dataset_class_names("imagenet")[idx] + return label_str.split(",")[0] + + +def get_imagenet_class_num() -> int: + """Get the total number of classes in the ImageNet dataset. + + Returns: + int: Total number of ImageNet classes. + """ + return len(get_dataset_class_names("imagenet")) diff --git a/mblt_vision/utils/labels.py b/mblt_vision/utils/labels.py new file mode 100644 index 0000000..4119905 --- /dev/null +++ b/mblt_vision/utils/labels.py @@ -0,0 +1,112 @@ +"""Small dataset metadata tables needed by output conversion.""" + +from __future__ import annotations + +COCO_CATEGORY_IDS = ( + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 27, + 28, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 67, + 70, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 84, + 85, + 86, + 87, + 88, + 89, + 90, +) + +DOTAV1_LABELS = ( + "plane", + "ship", + "storage-tank", + "baseball-diamond", + "tennis-court", + "basketball-court", + "ground-track-field", + "harbor", + "bridge", + "large-vehicle", + "small-vehicle", + "helicopter", + "roundabout", + "soccer-ball-field", + "swimming-pool", +) + + +def get_coco_inv(index: int) -> int: + return COCO_CATEGORY_IDS[index] + + +def get_dotav1_label(index: int) -> str: + return DOTAV1_LABELS[index] diff --git a/mblt_vision/utils/letterbox.py b/mblt_vision/utils/letterbox.py new file mode 100644 index 0000000..b2ac02c --- /dev/null +++ b/mblt_vision/utils/letterbox.py @@ -0,0 +1,118 @@ +"""Shared forward and inverse geometry for aspect-preserving letterboxing.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import TypeAlias + +RatioPad: TypeAlias = tuple[tuple[float, float], tuple[float, float]] + + +@dataclass(frozen=True) +class LetterBoxGeometry: + """Geometry shared by letterbox preprocessing and output restoration.""" + + input_shape: tuple[int, int] + original_shape: tuple[int, int] + ratio: float + resized_shape: tuple[int, int] + pad: tuple[int, int] + + @classmethod + def from_shapes( + cls, + input_shape: tuple[int, int], + original_shape: tuple[int, int], + ) -> LetterBoxGeometry: + """Calculate YOLO-style centered letterbox geometry. + + Args: + input_shape: Target shape as ``(height, width)``. + original_shape: Source shape as ``(height, width)``. + + Returns: + Calculated resize ratio, resized shape, and top-left padding. + """ + + input_height, input_width = input_shape + original_height, original_width = original_shape + ratio = min(input_height / original_height, input_width / original_width) + resized_height = int(round(original_height * ratio)) + resized_width = int(round(original_width * ratio)) + left = int(round((input_width - resized_width) / 2 - 0.1)) + top = int(round((input_height - resized_height) / 2 - 0.1)) + return cls( + input_shape=input_shape, + original_shape=original_shape, + ratio=ratio, + resized_shape=(resized_height, resized_width), + pad=(left, top), + ) + + @property + def ratio_pad(self) -> RatioPad: + """Return metadata consumed by inverse letterbox operations.""" + + return ((self.ratio, self.ratio), self.pad) + + @property + def borders(self) -> tuple[int, int, int, int]: + """Return OpenCV border widths as ``(top, bottom, left, right)``.""" + + input_height, input_width = self.input_shape + resized_height, resized_width = self.resized_shape + left, top = self.pad + return ( + top, + input_height - resized_height - top, + left, + input_width - resized_width - left, + ) + + def crop_bounds( + self, + output_shape: tuple[int, int], + pad: tuple[float, float] | None = None, + ) -> tuple[int, int, int, int]: + """Scale inverse-letterbox crop bounds to a dense output shape. + + Args: + output_shape: Dense output shape as ``(height, width)``. + pad: Optional exact top-left padding metadata as ``(x, y)``. + + Returns: + Crop bounds as ``(top, bottom, left, right)``. + """ + + output_height, output_width = output_shape + input_height, input_width = self.input_shape + scale_x = output_width / input_width + scale_y = output_height / input_height + pad_x, pad_y = self.pad if pad is None else pad + left = int(round(pad_x * scale_x)) + top = int(round(pad_y * scale_y)) + resized_height, resized_width = self.resized_shape + right = left + int(round(resized_width * scale_x)) + bottom = top + int(round(resized_height * scale_y)) + return top, bottom, left, right + + +def resolve_ratio_pad( + input_shape: tuple[int, int], + original_shape: tuple[int, int], + ratio_pad: RatioPad | None = None, +) -> RatioPad: + """Return supplied letterbox metadata or derive it from image shapes. + + Args: + input_shape: Letterboxed shape as ``(height, width)``. + original_shape: Source shape as ``(height, width)``. + ratio_pad: Optional metadata recorded during preprocessing. + + Returns: + Resize ratios and top-left padding as ``((ratio_x, ratio_y), (pad_x, pad_y))``. + """ + + if ratio_pad is not None: + return ratio_pad + return LetterBoxGeometry.from_shapes(input_shape, original_shape).ratio_pad diff --git a/mblt_vision/utils/postprocess/__init__.py b/mblt_vision/utils/postprocess/__init__.py new file mode 100644 index 0000000..e5090d0 --- /dev/null +++ b/mblt_vision/utils/postprocess/__init__.py @@ -0,0 +1,9 @@ +""" +Postprocessing utilities for vision models. +""" + +from .build_post import build_postprocess +from .depth_post import DepthPost +from .semantic_seg_post import SemanticSegPost + +__all__ = ["DepthPost", "SemanticSegPost", "build_postprocess"] diff --git a/mblt_vision/utils/postprocess/_letterbox.py b/mblt_vision/utils/postprocess/_letterbox.py new file mode 100644 index 0000000..eaaddaf --- /dev/null +++ b/mblt_vision/utils/postprocess/_letterbox.py @@ -0,0 +1,102 @@ +"""Private helpers shared by dense prediction postprocessors.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +import torch + +from ..letterbox import LetterBoxGeometry, RatioPad, resolve_ratio_pad +from .common import normalize_ratio_pads + + +def get_letterbox_input_shape( + pre_cfg: dict[str, Any], + requirement_name: str, + size_name: str | None = None, +) -> tuple[int, int]: + """Validate and return a dense task's configured letterbox input shape. + + Args: + pre_cfg: Model preprocessing configuration. + requirement_name: Task name used when LetterBox is absent. + size_name: Optional shorter name used for invalid-size errors. + + Returns: + Configured input height and width. + + Raises: + ValueError: If LetterBox or its two-item image size is missing or invalid. + """ + + letterbox_cfg = pre_cfg.get("LetterBox") + if not isinstance(letterbox_cfg, dict) or "img_size" not in letterbox_cfg: + raise ValueError( + f"{requirement_name} requires a LetterBox configuration in pre_cfg." + ) + image_size = letterbox_cfg["img_size"] + if not isinstance(image_size, list) or len(image_size) != 2: + raise ValueError( + f"{size_name or requirement_name} LetterBox img_size must be a two-item [height, width] list." + ) + return int(image_size[0]), int(image_size[1]) + + +def resolve_ratio_pads( + ratio_pad: RatioPad | Sequence[RatioPad | None] | None, + batch_size: int, + shapes: Sequence[tuple[int, int]], + input_shape: tuple[int, int], +) -> list[RatioPad]: + """Normalize letterbox metadata and derive values missing from a dense task batch. + + Args: + ratio_pad: Shared or per-image letterbox metadata. + batch_size: Number of images in the output batch. + shapes: Original image shapes. + input_shape: Configured model input shape. + + Returns: + One resolved ratio/padding pair per batch item. + + Raises: + ValueError: If ratio/padding metadata is invalid for the batch. + """ + + pads = normalize_ratio_pads(ratio_pad, batch_size) + return [ + resolve_ratio_pad(input_shape, shape, pad) for pad, shape in zip(pads, shapes) + ] + + +def crop_letterbox( + output: torch.Tensor, + shape: tuple[int, int], + ratio_pad: RatioPad, + input_shape: tuple[int, int], + task_name: str, +) -> torch.Tensor: + """Crop letterbox padding from a dense two-dimensional output. + + Args: + output: Dense two-dimensional model output. + shape: Original image height and width. + ratio_pad: Resize ratio and padding applied during preprocessing. + input_shape: Configured model input height and width. + task_name: Task label used in validation errors. + + Returns: + Output with letterbox padding removed. + + Raises: + ValueError: If inverse letterboxing produces an empty crop. + """ + + geometry = LetterBoxGeometry.from_shapes(input_shape, shape) + output_shape = (int(output.shape[0]), int(output.shape[1])) + top, bottom, left, right = geometry.crop_bounds(output_shape, pad=ratio_pad[1]) + cropped = output[top:bottom, left:right] + if cropped.numel() == 0: + raise ValueError(f"{task_name} letterbox restoration produced an empty crop.") + return cropped diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py new file mode 100644 index 0000000..d141939 --- /dev/null +++ b/mblt_vision/utils/postprocess/base.py @@ -0,0 +1,639 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import Any, cast + +import numpy as np +import torch + +from ..._tasks import normalize_vision_task +from ..preprocess._validation import normalize_image_size +from ..types import ListTensorLike, TensorLike +from .common import RatioPad, nmsout2eval, process_mask_upsample + + +class PostBase(ABC): + """Abstract base class for postprocessing.""" + + def __init__(self) -> None: + """Initialize PostBase.""" + super().__init__() + self.device = torch.device("cpu") + + @abstractmethod + def __call__( + self, x: TensorLike | ListTensorLike, *args: Any, **kwargs: Any + ) -> Any: + """Executes postprocessing on the model output. + + Args: + x (TensorLike | ListTensorLike): Input tensor or list of tensors from the model. + *args (Any): Additional positional arguments depending on the specific task. + **kwargs (Any): Additional keyword arguments depending on the specific task. + + Returns: + Any: Postprocessed results, format depends on the specific task. + """ + pass + + def to(self, device: str | torch.device) -> None: + """Move the operations to the specified device. + Args: + device (str | torch.device): Device to move the operations to. + """ + if isinstance(device, str): + self.device = torch.device(device) + elif isinstance(device, torch.device): + self.device = device + else: + raise TypeError(f"Got unexpected type for device={type(device)}.") + for name, value in self.__dict__.items(): + if isinstance(value, torch.Tensor): + setattr(self, name, value.to(self.device)) + + +class YOLODetectionPostBase(PostBase): + """Base class for YOLO postprocessing.""" + + NC_BY_DATASET_TASK: dict[tuple[str, str], int] = { + ("coco", "object_detection"): 80, + ("coco", "instance_segmentation"): 80, + ("coco", "pose_estimation"): 1, + ("dotav1", "obb"): 15, + ("widerface", "face_detection"): 1, + } + DEFAULT_NC_BY_TASK: dict[str, int] = { + "object_detection": 80, + "instance_segmentation": 80, + "pose_estimation": 1, + "obb": 15, + "face_detection": 1, + } + + def __init__( + self, pre_cfg: dict[str, Any], post_cfg: dict[str, Any], **kwargs + ) -> None: + """Initialize the common YOLO detection postprocessor. + + Args: + pre_cfg (dict): Preprocessing configuration. + post_cfg (dict): Postprocessing configuration. + **kwargs: Optional runtime overrides for postprocess behavior. + + Raises: + TypeError: If unsupported keyword overrides are provided. + """ + super().__init__() + letterbox_cfg = pre_cfg.get("LetterBox") + if letterbox_cfg is None: + raise ValueError("LetterBox configuration should be provided in pre_cfg") + img_size = letterbox_cfg["img_size"] + self.imh: int + self.imw: int + self.imh, self.imw = normalize_image_size( + img_size, name="pre_cfg.LetterBox.img_size" + ) + task = post_cfg.get("task") + if task is None: + raise ValueError("task should be provided in post_cfg") + self.task = normalize_vision_task(task) + task_key = self.task + dataset = post_cfg.get("dataset") + self.dataset = dataset.lower() if isinstance(dataset, str) else None + dataset_nc = ( + self.NC_BY_DATASET_TASK.get((self.dataset, task_key)) + if self.dataset is not None + else None + ) + configured_nc = kwargs.pop("nc", post_cfg.get("nc")) + if ( + configured_nc is not None + and dataset_nc is not None + and int(configured_nc) != dataset_nc + ): + raise ValueError( + f"nc={configured_nc} conflicts with dataset '{self.dataset}' and task '{self.task}', " + f"which require nc={dataset_nc}." + ) + default_nc = ( + dataset_nc + if dataset_nc is not None + else self.DEFAULT_NC_BY_TASK.get(task_key) + ) + nc = configured_nc if configured_nc is not None else default_nc + if nc is None: + raise ValueError( + f"nc should be provided in post_cfg or kwargs for task '{self.task}'." + ) + self.nc: int = int(nc) + self.anchors: list[Any] | torch.Tensor | None = post_cfg.get( + "anchors", None + ) # anchor coordinates + self.stride: list[int] | torch.Tensor + self.nl: int + self.na: int + self.conf_thres: float + self.iou_thres: float + self.inv_conf_thres: float + + self.e2e = bool(kwargs.pop("e2e", post_cfg.get("e2e", True))) + if kwargs: + unexpected = ", ".join(sorted(kwargs)) + raise TypeError(f"Unexpected YOLO postprocess kwargs: {unexpected}") + + if self.anchors is None: + nl = post_cfg.get("nl") + if nl is None: + raise ValueError("nl should be provided in post_cfg") + self.nl = nl + if self.nl == 2: + self.stride = [2 ** (4 + i) for i in range(self.nl)] + else: + self.stride = [2 ** (3 + i) for i in range(self.nl)] + self.make_anchors() + else: + if not isinstance(self.anchors, list): + raise TypeError( + f"anchors must be a list, got {type(self.anchors).__name__}." + ) + self.nl = len(self.anchors) + self.na = len(self.anchors[0]) // 2 + self.n_extra: int = post_cfg.get("n_extra", 0) + self.conf_thres = float(post_cfg.get("conf_thres", 0.25)) + self.iou_thres = float(post_cfg.get("iou_thres", 0.7)) + self.set_threshold() + + def anchors_as_list(self) -> list[Any]: + """Return anchors as the configured anchor list.""" + if not isinstance(self.anchors, list): + raise TypeError( + "anchors should be a list for anchor-based YOLO postprocessing." + ) + return self.anchors + + def anchors_as_tensor(self) -> torch.Tensor: + """Return anchors as the generated anchor-point tensor.""" + if not isinstance(self.anchors, torch.Tensor): + raise TypeError( + "anchors should be a tensor for anchor-free YOLO postprocessing." + ) + return cast(torch.Tensor, self.anchors) + + def stride_as_tensor(self) -> torch.Tensor: + """Return strides as the generated stride tensor.""" + if not isinstance(self.stride, torch.Tensor): + raise TypeError( + "stride should be a tensor for anchor-free YOLO postprocessing." + ) + return cast(torch.Tensor, self.stride) + + def __call__( + self, + x: TensorLike | ListTensorLike, + conf_thres: float | None = None, + iou_thres: float | None = None, + multi_label: bool = False, + ) -> list[Any]: + """Executes YOLO postprocessing. + + Includes rearranging, decoding, and NMS. + + Args: + x (TensorLike | ListTensorLike): Raw model outputs. + conf_thres (float | None): Confidence threshold for detection. + iou_thres (float | None): IoU threshold for NMS. + multi_label: Whether to emit one candidate for every class above the + confidence threshold. Validation uses this to match Ultralytics. + + Returns: + list: List of detections per image. + """ + self.set_threshold(conf_thres, iou_thres) + final_detections, proto_outs = self.extract_final_outputs(x) + if final_detections is not None: + if proto_outs is not None: + return self.masking(final_detections, proto_outs) + return final_detections + checked_input = self.check_input(x) + + if not self.e2e: + return self.non_e2e(checked_input) + + predictions, proto_outs = self._pre_process(checked_input) + + nms_output = ( + self.nms_multilabel(predictions) if multi_label else self.nms(predictions) + ) + + if proto_outs is not None: + return self.masking(nms_output, proto_outs) + return nms_output + + def non_e2e(self, x: list[torch.Tensor]) -> Any: + """Return the export-style postprocess output when end-to-end mode is disabled. + + Args: + x: Checked raw model outputs. + + Returns: + Export-style tensors whose batch dimensions remain intact. + """ + if len(x) == 1: + return self.conversion(x) + return self.rearrange(x) + + def _pre_process( + self, + x: list[torch.Tensor], + ) -> tuple[Any, torch.Tensor | list[torch.Tensor] | None]: + """Protected method to preprocess inputs into (predictions, prototypes). + + Args: + x: List of input tensors. + + Returns: + Tuple of (predictions, prototypes). Prototypes may be None. + """ + if len(x) == 1: + converted = self.conversion(x) + if not isinstance(converted, torch.Tensor): + raise TypeError( + "conversion should return a tensor for single-output YOLO postprocessing." + ) + return self.filter_conversion(converted), None + rearranged = self.rearrange(x) + return self.decode(rearranged), None + + def nmsout2eval( + self, + nms_out: Any, + img1_shape: tuple[int, int], + img0_shape: tuple[int, int] | list[tuple[int, int]], + ratio_pad: RatioPad | list[RatioPad | None] | None = None, + ) -> tuple[Any, ...]: + """Converts NMS output to evaluation format (labels, boxes, scores). + + Args: + nms_out: NMS output (tensor or list of tensors). + img1_shape: Resized image shape (height, width). + img0_shape: Original image shape(s). + + Returns: + Tuple: task-specific results. + - Detection: (labels_list, boxes_list, scores_list) + - Segmentation/Pose: (labels_list, boxes_list, scores_list, extra_list) + """ + + return nmsout2eval(nms_out, img1_shape, img0_shape, ratio_pads=ratio_pad) + + def extract_final_outputs( + self, + x: TensorLike | ListTensorLike, + ) -> tuple[list[torch.Tensor] | None, torch.Tensor | None]: + """Extract already-decoded ONNX-style detections when present. + + Args: + x: Raw postprocess input. + + Returns: + A tuple of ``(detections, prototypes)`` when the input already contains + final detections, otherwise ``(None, None)``. + """ + + final_det_dim = 6 + self.n_extra + + if isinstance(x, Sequence): + if not x: + return None, None + + normalized_detections: np.ndarray | torch.Tensor | None = None + normalized_proto: torch.Tensor | None = None + for output in x: + if not isinstance(output, (np.ndarray, torch.Tensor)): + continue + if normalized_detections is None: + normalized_detections = self._normalize_final_detection_tensor( + output, final_det_dim + ) + if normalized_detections is not None: + continue + if normalized_proto is None: + try: + normalized_proto = self._normalize_proto_batch(output) + except ValueError: + continue + + if normalized_detections is not None: + return self._final_detection_batches( + normalized_detections + ), normalized_proto + return None, None + + normalized_x = self._normalize_final_detection_tensor(x, final_det_dim) + if normalized_x is not None: + return self._final_detection_batches(normalized_x), None + + return None, None + + def _normalize_final_detection_tensor( + self, + x: TensorLike, + final_det_dim: int, + ) -> np.ndarray | torch.Tensor | None: + """Return a batched final-detection tensor when ``x`` already contains decoded rows.""" + + while x.ndim == 4 and 1 in (x.shape[0], x.shape[1]): + if x.shape[1] == 1: + x = x[:, 0] + elif x.shape[0] == 1: + x = x[0] + if x.ndim == 2 and x.shape[-1] == final_det_dim: + x = x[None] + if x.ndim == 3 and x.shape[-1] == final_det_dim: + return x + if x.ndim == 3 and x.shape[1] == final_det_dim: + if isinstance(x, np.ndarray): + return np.swapaxes(x, 1, 2) + return x.transpose(1, 2) + + return None + + def _final_detection_batches( + self, x: np.ndarray | torch.Tensor + ) -> list[torch.Tensor]: + """Convert batched final detections to the internal per-image tensor list.""" + + if isinstance(x, np.ndarray): + tensor = torch.from_numpy(x).to(self.device) + else: + tensor = x.to(self.device) + return [batch[batch[:, 4] > self.conf_thres] for batch in tensor] + + def _normalize_proto_batch( + self, proto_outs: np.ndarray | torch.Tensor + ) -> torch.Tensor: + """Normalize prototype masks to ``(B, H, W, C)`` layout.""" + + if isinstance(proto_outs, np.ndarray): + proto = torch.from_numpy(proto_outs).to(self.device) + else: + proto = proto_outs.to(self.device) + + if proto.ndim != 4: + raise ValueError( + f"Expected 4D prototype tensor, got shape {tuple(proto.shape)}." + ) + if proto.shape[-1] == self.n_extra: + return proto + if proto.shape[1] == self.n_extra: + return proto.permute(0, 2, 3, 1) + raise ValueError(f"Unsupported prototype tensor shape {tuple(proto.shape)}.") + + def make_anchors(self, offset: float = 0.5) -> None: + """ + Generate anchor points and stride tensors based on image size and strides. + Args: + offset (float, optional): Offset for anchor points. Defaults to 0.5. + """ + anchor_points, stride_tensor = [], [] + strides = [2 ** (3 + i) for i in range(self.nl)] + if self.nl == 2: + strides = [strd * 2 for strd in strides] + for strd in strides: + ny, nx = self.imh // strd, self.imw // strd + sy = torch.arange(ny, dtype=torch.float32, device=self.device) + offset + sx = torch.arange(nx, dtype=torch.float32, device=self.device) + offset + yv, xv = torch.meshgrid(sy, sx, indexing="ij") + anchor_points.append(torch.stack((xv, yv), -1).reshape(-1, 2)) + stride_tensor.append( + torch.full((ny * nx, 1), strd, dtype=torch.float32, device=self.device) + ) + self.anchors = torch.cat(anchor_points, dim=0).permute(1, 0) + self.stride = torch.cat(stride_tensor, dim=0).permute(1, 0) + + def set_threshold( + self, conf_thres: float | None = None, iou_thres: float | None = None + ) -> None: + """Set confidence and IoU thresholds. + Args: + conf_thres (float, optional): Confidence threshold. + iou_thres (float, optional): IoU threshold. + """ + conf_thres = self.conf_thres if conf_thres is None else conf_thres + iou_thres = self.iou_thres if iou_thres is None else iou_thres + if isinstance(conf_thres, bool) or not isinstance(conf_thres, (int, float)): + raise TypeError( + f"conf_thres must be numeric, got {type(conf_thres).__name__}." + ) + if isinstance(iou_thres, bool) or not isinstance(iou_thres, (int, float)): + raise TypeError( + f"iou_thres must be numeric, got {type(iou_thres).__name__}." + ) + if not 0 < conf_thres < 1: + raise ValueError(f"conf_thres must be in (0, 1), got {conf_thres}.") + if not 0 < iou_thres < 1: + raise ValueError(f"iou_thres must be in (0, 1), got {iou_thres}.") + self.conf_thres = float(conf_thres) + self.iou_thres = float(iou_thres) + self.inv_conf_thres = -np.log(1 / conf_thres - 1) + + def check_input(self, x: TensorLike | ListTensorLike) -> list[torch.Tensor]: + """Check and prepare input tensors. + Args: + x (TensorLike | ListTensorLike): Input tensor or list of tensors. + Returns: + list[torch.Tensor]: List of tensors on the correct device. + """ + if isinstance(x, np.ndarray): + tensors = [torch.from_numpy(x).to(self.device)] + elif isinstance(x, torch.Tensor): + tensor_input = cast(torch.Tensor, x) + tensors = [tensor_input.to(self.device)] + else: + if not isinstance(x, Sequence): + raise TypeError(f"Got unexpected type for x={type(x)}.") + if all(isinstance(xi, np.ndarray) for xi in x): + tensors = [torch.from_numpy(xi).to(self.device) for xi in x] + elif all(isinstance(xi, torch.Tensor) for xi in x): + torch_inputs = cast(Sequence[torch.Tensor], x) + tensors = [xi.to(self.device) for xi in torch_inputs] + else: + raise TypeError(f"Got unexpected element type for x[0]={type(x[0])}.") + return self.check_dim(tensors) + + def check_dim(self, x: list[torch.Tensor]) -> list[torch.Tensor]: + """Check tensor dimensions. + Args: + x (list[torch.Tensor]): List of tensors. + Returns: + list[torch.Tensor]: List of tensors with corrected dimensions. + """ + y = [] + for xi in x: + if xi.ndim == 3: + xi = xi.unsqueeze(0) + elif xi.ndim in (4, 5): + pass + else: + raise ValueError(f"Got unexpected dim for xi={xi.ndim}.") + y.append(xi) + return y + + def normalize_split_head( + self, x: torch.Tensor, expected_channels: set[int] + ) -> torch.Tensor: + """Normalize a split detection head to ``(B, C, H, W)`` layout. + + This accepts the channel-last tensors produced by ONNX export flows as + well as the channel-first tensors commonly returned by MXQ/NPU inference. + + Args: + x: Raw split-head tensor. + expected_channels: Valid channel sizes for the current head group. + + Returns: + The normalized tensor in ``(B, C, H, W)`` format. + + Raises: + ValueError: If the tensor shape cannot be interpreted. + """ + while x.ndim > 4: + singleton_dims = [idx for idx, size in enumerate(x.shape) if size == 1] + if not singleton_dims: + raise ValueError( + f"Expected up to 4D split-head tensor, got shape {tuple(x.shape)}." + ) + x = x.squeeze(singleton_dims[0]) + if x.ndim == 3: + x = x.unsqueeze(0) + if x.ndim != 4: + raise ValueError( + f"Expected 3D or 4D split-head tensor, got shape {tuple(x.shape)}." + ) + + if x.shape[1] in expected_channels and x.shape[-1] not in expected_channels: + return x + if x.shape[-1] in expected_channels and x.shape[1] not in expected_channels: + return x.permute(0, 3, 1, 2) + if x.shape[1] in expected_channels and x.shape[-1] in expected_channels: + return x + + raise ValueError( + f"Could not infer split-head layout for shape {tuple(x.shape)} with expected channels {expected_channels}." + ) + + @abstractmethod + def rearrange(self, x: list[torch.Tensor]) -> Any: + """Rearranges raw model outputs into a task-specific intermediate form. + + Args: + x: Raw output tensors from the model. + + Returns: + A task-specific intermediate representation used by ``decode``. + """ + + @abstractmethod + def decode(self, x: Any) -> Any: + """Decodes rearranged outputs into a family-specific batched representation. + + Args: + x: Rearranged output tensors. + + Returns: + Decoded detections in the canonical representation for that YOLO family. + """ + + def conversion( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Converts raw outputs into a task-specific intermediate form. + + Args: + x: Input tensors. + + Returns: + A converted detection tensor, or a ``(detections, prototypes)`` tuple + for segmentation-style subclasses. + """ + if len(x) != 1: + raise ValueError( + f"Expected exactly one converted model output, got {len(x)}." + ) + return x[0] + + @abstractmethod + def filter_conversion(self, x: torch.Tensor) -> list[torch.Tensor]: + """Filters converted outputs into per-image detections before NMS. + + Args: + x: Converted output tensor. + + Returns: + Filtered detections for each image in the batch. + """ + + @abstractmethod + def nms(self, x: Any) -> list[torch.Tensor]: + """Performs non-maximum suppression on decoded detections. + + Args: + x: Decoded detections for each image. + + Returns: + Detections after NMS for each image in the batch. + """ + + def nms_multilabel(self, x: Any) -> list[torch.Tensor]: + """Perform validation NMS with all above-threshold class candidates. + + Args: + x: Decoded detections for each image. + + Returns: + Detections after NMS for each image in the batch. + """ + return self.nms(x) + + def masking( + self, x: list[torch.Tensor], proto_outs: torch.Tensor | list[torch.Tensor] + ) -> list[list[torch.Tensor]]: + """Apply prototype masks to detection results. + + Args: + x: Detection results. + proto_outs: Prototype outputs for masks. + + Returns: + list: Detection results with masks. + """ + masks = [] + for pred, proto in zip(x, proto_outs): + if proto.ndim != 3: + raise ValueError( + f"Expected 3D prototype tensor, got shape {tuple(proto.shape)}." + ) + if proto.shape[-1] == self.n_extra: + proto = proto.permute(2, 0, 1) + elif proto.shape[0] != self.n_extra: + raise ValueError( + f"Unsupported prototype tensor shape {tuple(proto.shape)}." + ) + if len(pred) == 0: + masks.append( + torch.zeros( + (0, self.imh, self.imw), dtype=torch.float32, device=self.device + ) + ) + continue + masks.append( + process_mask_upsample( + proto, pred[:, 6:], pred[:, :4], [self.imh, self.imw] + ) + ) + return [[xi, mask] for xi, mask in zip(x, masks)] + + +# Name retained from the first standalone draft. +YOLOPostBase = YOLODetectionPostBase diff --git a/mblt_vision/utils/postprocess/build_post.py b/mblt_vision/utils/postprocess/build_post.py new file mode 100644 index 0000000..305ac44 --- /dev/null +++ b/mblt_vision/utils/postprocess/build_post.py @@ -0,0 +1,120 @@ +""" +Postprocessing builder. +""" + +from __future__ import annotations + +from ..._tasks import normalize_vision_task +from .base import PostBase +from .cls_post import ClsPost +from .depth_post import DepthPost +from .semantic_seg_post import SemanticSegPost +from .yolo_anchor_post import YOLOAnchorDetectionPost, YOLOAnchorSegPost +from .yolo_anchorless_post import ( + YOLOAnchorlessDetectionPost, + YOLOAnchorlessOBBPost, + YOLOAnchorlessPosePost, + YOLOAnchorlessSegPost, +) +from .yolo_dflfree_post import ( + YOLODFLFreeDetectionPost, + YOLODFLFreeOBBPost, + YOLODFLFreePosePost, + YOLODFLFreeSegPost, +) +from .yolo_nmsfree_post import YOLONMSFreeDetectionPost + + +def build_postprocess( + pre_cfg: dict, + post_cfg: dict, + **kwargs: object, +) -> PostBase: + """Builds a postprocessing object based on the model configuration. + + Args: + pre_cfg (dict): Preprocessing configuration from the model info. + post_cfg (dict): Postprocessing configuration from the model info. + Must contain "task" and relevant flags for the specific task. + **kwargs: Optional runtime overrides passed to the postprocessor. + + Returns: + PostBase: An instance of a postprocessing class tailored for the task. + + Raises: + NotImplementedError: If the specified task is not supported. + """ + task = normalize_vision_task(post_cfg["task"]) + if task == "image_classification": + return ClsPost(pre_cfg, post_cfg) + if task == "depth_estimation": + return DepthPost(pre_cfg, post_cfg) + if task == "semantic_segmentation": + return SemanticSegPost(pre_cfg, post_cfg) + if task in {"object_detection", "face_detection"}: + if post_cfg.get("anchors", False): + return YOLOAnchorDetectionPost( + pre_cfg, + post_cfg, + **kwargs, + ) + if post_cfg.get("dflfree", False): # nms free is only available for detection + return YOLODFLFreeDetectionPost( + pre_cfg, + post_cfg, + **kwargs, + ) + if post_cfg.get("nmsfree", False): + return YOLONMSFreeDetectionPost( + pre_cfg, + post_cfg, + **kwargs, + ) + return YOLOAnchorlessDetectionPost( + pre_cfg, + post_cfg, + **kwargs, + ) + if task == "instance_segmentation": + if post_cfg.get("anchors", False): + return YOLOAnchorSegPost( + pre_cfg, + post_cfg, + **kwargs, + ) + if post_cfg.get("dflfree", False): + return YOLODFLFreeSegPost( + pre_cfg, + post_cfg, + **kwargs, + ) + return YOLOAnchorlessSegPost( + pre_cfg, + post_cfg, + **kwargs, + ) + if task == "pose_estimation": + if post_cfg.get("dflfree", False): + return YOLODFLFreePosePost( + pre_cfg, + post_cfg, + **kwargs, + ) + return YOLOAnchorlessPosePost( + pre_cfg, + post_cfg, + **kwargs, + ) + if task == "obb": + if post_cfg.get("dflfree", False): + return YOLODFLFreeOBBPost( + pre_cfg, + post_cfg, + **kwargs, + ) + return YOLOAnchorlessOBBPost( + pre_cfg, + post_cfg, + **kwargs, + ) + raise NotImplementedError(f"Task {post_cfg['task']} is not implemented yet") diff --git a/mblt_vision/utils/postprocess/cls_post.py b/mblt_vision/utils/postprocess/cls_post.py new file mode 100644 index 0000000..2ad4730 --- /dev/null +++ b/mblt_vision/utils/postprocess/cls_post.py @@ -0,0 +1,69 @@ +""" +Classification postprocessing. +""" + +from __future__ import annotations + +from collections.abc import Sequence + +import numpy as np +import torch + +from ..types import ListTensorLike, TensorLike +from .base import PostBase + + +class ClsPost(PostBase): + """Post-processing for image classification models. + + Typically applies softmax to logits and ensures correct output shape. + """ + + def __init__(self, pre_cfg: dict, post_cfg: dict) -> None: + """Initializes the classification post-processing. + + Args: + pre_cfg (dict): Preprocessing configuration. + post_cfg (dict): Postprocessing configuration. + """ + super().__init__() + self.softmax = post_cfg.get("softmax", False) + + def __call__(self, x: TensorLike | ListTensorLike) -> torch.Tensor: + """Executes classification post-processing. + + Typically applies softmax to convert logits to probabilities. + + Args: + x (TensorLike | ListTensorLike): Raw model outputs. + Expected to be pre-softmax logits. + + Returns: + torch.Tensor: Softmax probabilities of shape (N, C). + """ + if isinstance(x, Sequence): + if len(x) != 1: + raise ValueError( + f"Classification postprocessing expects one output tensor, got {len(x)}." + ) + x = x[0] + if isinstance(x, np.ndarray): + x = torch.from_numpy(x).to(self.device) + elif isinstance(x, torch.Tensor): + x = x.to(self.device) + else: + raise TypeError(f"Got unexpected type for x={type(x)}.") + if x.ndim == 2: + x = x.unsqueeze(-1).unsqueeze(-1) + elif x.ndim == 3: + x = x.unsqueeze(0) + if x.ndim != 4: + raise ValueError( + f"Classification output must be convertible to NCHW, got shape {tuple(x.shape)}." + ) + x = x.flatten( + 1 + ) # Classification heads may retain singleton spatial dimensions. + if self.softmax: + return x + return x.softmax(dim=-1) diff --git a/mblt_vision/utils/postprocess/common.py b/mblt_vision/utils/postprocess/common.py new file mode 100644 index 0000000..0579aa8 --- /dev/null +++ b/mblt_vision/utils/postprocess/common.py @@ -0,0 +1,1446 @@ +"""Common postprocessing utility functions.""" + +from __future__ import annotations + +import math +from collections.abc import Callable, Sequence +from typing import Any, TypeGuard, overload + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + +from ..labels import get_coco_inv, get_dotav1_label +from ..letterbox import RatioPad, resolve_ratio_pad + + +def _is_ratio_pad(value: object) -> TypeGuard[RatioPad]: + """Return whether a value has the nested numeric shape of one RatioPad.""" + + return ( + isinstance(value, tuple) + and len(value) == 2 + and all( + isinstance(pair, tuple) + and len(pair) == 2 + and all(isinstance(component, (int, float)) for component in pair) + for pair in value + ) + ) + + +def normalize_image_shapes( + image_shapes: tuple[int, int] | Sequence[tuple[int, int]], + batch_size: int | None = None, +) -> list[tuple[int, int]]: + """Normalize one or many image shapes to a list, optionally validating its batch size.""" + + if len(image_shapes) == 2 and isinstance(image_shapes[0], int): + shapes = [(int(image_shapes[0]), int(image_shapes[1]))] # type: ignore[index] + if batch_size is not None: + shapes *= batch_size + else: + shapes = [(int(shape[0]), int(shape[1])) for shape in image_shapes] # type: ignore[union-attr] + if batch_size is not None and len(shapes) != batch_size: + raise ValueError(f"Expected {batch_size} image shapes, got {len(shapes)}.") + return shapes + + +def normalize_ratio_pads( + ratio_pads: RatioPad | Sequence[RatioPad | None] | None, + batch_size: int, +) -> list[RatioPad | None]: + """Normalize optional letterbox metadata to a batch-sized list.""" + + if ratio_pads is None: + return [None] * batch_size + if _is_ratio_pad(ratio_pads): + return [ratio_pads] * batch_size + pads: list[RatioPad | None] = [] + for ratio_pad in ratio_pads: + if ratio_pad is None: + pads.append(None) + elif _is_ratio_pad(ratio_pad): + pads.append(ratio_pad) + else: + raise TypeError( + "Each ratio_pad must be a ((ratio_x, ratio_y), (pad_x, pad_y)) tuple or None." + ) + if len(pads) != batch_size: + raise ValueError(f"Expected {batch_size} ratio_pad values, got {len(pads)}.") + return pads + + +# --- Box Conversion Utilities --- +@overload +def xywh2xyxy(x: np.ndarray) -> np.ndarray: + """Converts numpy boxes from ``xywh`` to ``xyxy`` format.""" + + +@overload +def xywh2xyxy(x: torch.Tensor) -> torch.Tensor: + """Converts torch boxes from ``xywh`` to ``xyxy`` format.""" + + +def xywh2xyxy(x: np.ndarray | torch.Tensor) -> np.ndarray | torch.Tensor: + """Converts bounding box coordinates from (cx, cy, w, h) to (x1, y1, x2, y2). + + (x1, y1) is the top-left corner and (x2, y2) is the bottom-right corner. + + Args: + x: Input bounding boxes in (cx, cy, w, h) format. + + Returns: + Bounding boxes in (x1, y1, x2, y2) format. + """ + if isinstance(x, np.ndarray): + y = np.copy(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 + y[..., 1] = x[..., 1] - x[..., 3] / 2 + y[..., 2] = x[..., 0] + x[..., 2] / 2 + y[..., 3] = x[..., 1] + x[..., 3] / 2 + return y + + if isinstance(x, torch.Tensor): + y = torch.clone(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 + y[..., 1] = x[..., 1] - x[..., 3] / 2 + y[..., 2] = x[..., 0] + x[..., 2] / 2 + y[..., 3] = x[..., 1] + x[..., 3] / 2 + return y + + raise ValueError("x should be np.ndarray or torch.Tensor") + + +@overload +def xyxy2xywh(x: np.ndarray) -> np.ndarray: + """Converts numpy boxes from ``xyxy`` to ``xywh`` format.""" + + +@overload +def xyxy2xywh(x: torch.Tensor) -> torch.Tensor: + """Converts torch boxes from ``xyxy`` to ``xywh`` format.""" + + +def xyxy2xywh(x: np.ndarray | torch.Tensor) -> np.ndarray | torch.Tensor: + """Converts bounding box coordinates from (x1, y1, x2, y2) to (cx, cy, w, h). + + (x1, y1) is the top-left corner and (x2, y2) is the bottom-right corner. + (cx, cy) is the center of the bounding box. + + Args: + x: Input bounding boxes in (x1, y1, x2, y2) format. + + Returns: + Bounding boxes in (cx, cy, w, h) format. + """ + if isinstance(x, np.ndarray): + y = np.copy(x) + y[..., 0] = (x[..., 0] + x[..., 2]) / 2 + y[..., 1] = (x[..., 1] + x[..., 3]) / 2 + y[..., 2] = x[..., 2] - x[..., 0] + y[..., 3] = x[..., 3] - x[..., 1] + return y + + if isinstance(x, torch.Tensor): + y = torch.clone(x) + y[..., 0] = (x[..., 0] + x[..., 2]) / 2 + y[..., 1] = (x[..., 1] + x[..., 3]) / 2 + y[..., 2] = x[..., 2] - x[..., 0] + y[..., 3] = x[..., 3] - x[..., 1] + return y + + raise ValueError("x should be np.ndarray or torch.Tensor") + + +def dist2bbox( + distance: torch.Tensor, + anchor_points: torch.Tensor, + xywh: bool = True, + dim: int = -1, +) -> torch.Tensor: + """ + Transform distance (ltrb) to bounding box (xywh or xyxy). + Args: + distance (torch.Tensor): Distance from anchor points to box boundaries + (left, top, right, bottom). + anchor_points (torch.Tensor): Anchor points (center points). + xywh (bool, optional): If True, return boxes in (cx, cy, w, h) format. + If False, return in (x1, y1, x2, y2) format. Defaults to True. + dim (int, optional): Dimension along which to chunk the distance tensor. Defaults to -1. + Returns: + torch.Tensor: Transformed bounding boxes. + """ + lt, rb = distance.chunk(2, dim) + x1y1 = anchor_points - lt + x2y2 = anchor_points + rb + if xywh: + return torch.cat(((x1y1 + x2y2) / 2, x2y2 - x1y1), dim) # xywh bbox + else: + return torch.cat((x1y1, x2y2), dim) # xyxy bbox + + +def dist2rbox( + distance: torch.Tensor, + angle: torch.Tensor, + anchor_points: torch.Tensor, + dim: int = -1, +) -> torch.Tensor: + """Decode rotated boxes from anchor-relative distances and angles. + + Args: + distance: Distance tensor in ``ltrb`` format. + angle: Rotation angle tensor in radians. + anchor_points: Anchor center points. + dim: Dimension along which box channels are split. + + Returns: + Rotated boxes in ``cx, cy, w, h`` format. + """ + lt, rb = distance.split(2, dim=dim) + cos_value = torch.cos(angle) + sin_value = torch.sin(angle) + xf, yf = ((rb - lt) / 2).split(1, dim=dim) + x = xf * cos_value - yf * sin_value + y = xf * sin_value + yf * cos_value + xy = torch.cat([x, y], dim=dim) + anchor_points + return torch.cat([xy, lt + rb], dim=dim) + + +@overload +def xywhr2xyxyxyxy(x: np.ndarray) -> np.ndarray: + """Converts numpy OBBs from ``xywhr`` to polygon corners.""" + + +@overload +def xywhr2xyxyxyxy(x: torch.Tensor) -> torch.Tensor: + """Converts torch OBBs from ``xywhr`` to polygon corners.""" + + +def xywhr2xyxyxyxy(x: np.ndarray | torch.Tensor) -> np.ndarray | torch.Tensor: + """Converts oriented boxes from ``cx, cy, w, h, angle`` to four corner points. + + Args: + x: Oriented boxes with shape ``(..., 5)`` and angle in radians. + + Returns: + Corner points with shape ``(..., 4, 2)``. + """ + if isinstance(x, torch.Tensor): + ctr = x[..., :2] + w, h, angle = (x[..., i : i + 1] for i in range(2, 5)) + cos_value = torch.cos(angle) + sin_value = torch.sin(angle) + vec1 = torch.cat([w / 2 * cos_value, w / 2 * sin_value], dim=-1) + vec2 = torch.cat([-h / 2 * sin_value, h / 2 * cos_value], dim=-1) + return torch.stack( + [ + ctr + vec1 + vec2, + ctr + vec1 - vec2, + ctr - vec1 - vec2, + ctr - vec1 + vec2, + ], + dim=-2, + ) + + if isinstance(x, np.ndarray): + ctr = x[..., :2] + w, h, angle = (x[..., i : i + 1] for i in range(2, 5)) + cos_value = np.cos(angle) + sin_value = np.sin(angle) + vec1 = np.concatenate([w / 2 * cos_value, w / 2 * sin_value], axis=-1) + vec2 = np.concatenate([-h / 2 * sin_value, h / 2 * cos_value], axis=-1) + return np.stack( + [ + ctr + vec1 + vec2, + ctr + vec1 - vec2, + ctr - vec1 - vec2, + ctr - vec1 + vec2, + ], + axis=-2, + ) + + raise ValueError("x should be np.ndarray or torch.Tensor") + + +def xyxyxyxy2xywhr(points: np.ndarray | torch.Tensor) -> np.ndarray | torch.Tensor: + """Converts OBB corner points to regularized ``xywhr`` boxes. + + Args: + points: Corner points with shape ``(..., 4, 2)``. + + Returns: + Rotated boxes in ``cx, cy, w, h, angle`` format. + """ + is_torch = isinstance(points, torch.Tensor) + points_np = points.detach().cpu().numpy() if is_torch else np.asarray(points) + flat_points = points_np.reshape(-1, 4, 2).astype(np.float32) + rboxes = [] + for pts in flat_points: + (cx, cy), (w, h), angle = cv2.minAreaRect(pts) + theta = angle / 180 * np.pi + if w < h: + w, h = h, w + theta += np.pi / 2 + while theta >= 3 * np.pi / 4: + theta -= np.pi + while theta < -np.pi / 4: + theta += np.pi + rboxes.append([cx, cy, w, h, theta]) + result_np = np.asarray(rboxes, dtype=points_np.dtype).reshape( + *points_np.shape[:-2], 5 + ) + if is_torch: + return torch.tensor(result_np, device=points.device, dtype=points.dtype) + return result_np + + +def regularize_rboxes(rboxes: torch.Tensor) -> torch.Tensor: + """Regularize rotated boxes to the angle range ``[0, pi / 2)``. + + Args: + rboxes: Rotated boxes in ``xywhr`` format. + + Returns: + Regularized rotated boxes. + """ + x, y, w, h, angle = rboxes.unbind(dim=-1) + swap = angle % math.pi >= math.pi / 2 + regularized_w = torch.where(swap, h, w) + regularized_h = torch.where(swap, w, h) + regularized_angle = angle % (math.pi / 2) + return torch.stack([x, y, regularized_w, regularized_h, regularized_angle], dim=-1) + + +def _get_covariance_matrix( + boxes: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Return Gaussian covariance components for probabilistic OBB IoU.""" + gbbs = torch.cat((boxes[:, 2:4].pow(2) / 12, boxes[:, 4:]), dim=-1) + a, b, c = gbbs.split(1, dim=-1) + cos_value = c.cos() + sin_value = c.sin() + cos2 = cos_value.pow(2) + sin2 = sin_value.pow(2) + return a * cos2 + b * sin2, a * sin2 + b * cos2, (a - b) * cos_value * sin_value + + +def batch_probiou( + obb1: torch.Tensor | np.ndarray, obb2: torch.Tensor | np.ndarray, eps: float = 1e-7 +) -> torch.Tensor: + """Calculate pairwise probabilistic IoU for oriented boxes. + + Args: + obb1: First set of OBBs in ``xywhr`` format with shape ``(N, 5)``. + obb2: Second set of OBBs in ``xywhr`` format with shape ``(M, 5)``. + eps: Small value used for numerical stability. + + Returns: + Pairwise OBB similarities with shape ``(N, M)``. + """ + obb1 = torch.from_numpy(obb1) if isinstance(obb1, np.ndarray) else obb1 + obb2 = torch.from_numpy(obb2) if isinstance(obb2, np.ndarray) else obb2 + obb2 = obb2.to(device=obb1.device, dtype=obb1.dtype) + + x1, y1 = obb1[..., :2].split(1, dim=-1) + x2, y2 = (x.squeeze(-1)[None] for x in obb2[..., :2].split(1, dim=-1)) + a1, b1, c1 = _get_covariance_matrix(obb1) + a2, b2, c2 = (x.squeeze(-1)[None] for x in _get_covariance_matrix(obb2)) + + denominator = (a1 + a2) * (b1 + b2) - (c1 + c2).pow(2) + eps + t1 = ( + ((a1 + a2) * (y1 - y2).pow(2) + (b1 + b2) * (x1 - x2).pow(2)) / denominator + ) * 0.25 + t2 = (((c1 + c2) * (x2 - x1) * (y1 - y2)) / denominator) * 0.5 + t3 = ( + ((a1 + a2) * (b1 + b2) - (c1 + c2).pow(2)) + / ( + 4 + * ((a1 * b1 - c1.pow(2)).clamp_(0) * (a2 * b2 - c2.pow(2)).clamp_(0)).sqrt() + + eps + ) + + eps + ).log() * 0.5 + bd = (t1 + t2 + t3).clamp(eps, 100.0) + hd = (1.0 - (-bd).exp() + eps).sqrt() + return 1 - hd + + +def rotated_nms( + boxes: torch.Tensor, + scores: torch.Tensor, + iou_threshold: float, + iou_func: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = batch_probiou, +) -> torch.Tensor: + """Apply fast rotated NMS using an upper-triangular pairwise IoU matrix. + + Args: + boxes: OBBs in ``xywhr`` format. + scores: Confidence scores. + iou_threshold: IoU threshold for suppression. + iou_func: Pairwise IoU function. + + Returns: + Kept indices into the original inputs. + """ + if boxes.numel() == 0: + return torch.empty((0,), dtype=torch.int64, device=boxes.device) + sorted_idx = torch.argsort(scores, descending=True) + sorted_boxes = boxes[sorted_idx] + ious = iou_func(sorted_boxes, sorted_boxes).triu_(diagonal=1) + keep = torch.nonzero((ious >= iou_threshold).sum(0) <= 0).squeeze_(-1) + return sorted_idx[keep] + + +# --- Detection Utilities --- +def non_max_suppression( + boxes: torch.Tensor, scores: torch.Tensor, iou_threshold: float, max_output: int +) -> list[int]: + """ + Modified non-maximum suppression (NMS) implemented with PyTorch. + Args: + boxes (torch.Tensor): Bounding boxes in (x1, y1, x2, y2) format. + scores (torch.Tensor): Confidence scores for each box (assumed to be sorted in + descending order). + iou_threshold (float): IoU threshold for suppression. + max_output (int): Maximum number of boxes to keep. + Returns: + list[int]: Indices of the boxes that have been kept after NMS. + """ + if boxes.numel() == 0: + return [] + # Coordinates of bounding boxes + start_x = boxes[:, 0] + start_y = boxes[:, 1] + end_x = boxes[:, 2] + end_y = boxes[:, 3] + picked_indices: list[int] = [] + # Compute areas of bounding boxes + areas = (end_x - start_x) * (end_y - start_y) + # Create an index order (assumed scores are already sorted in descending order) + order = torch.arange(scores.size(0)).to(boxes.device) + while order.numel() > 0 and len(picked_indices) < max_output: + # The index with the highest score + index = int(order[0].item()) + picked_indices.append(index) + order = order[1:] # Remove the index from the order + if order.numel() == 0 or len(picked_indices) >= max_output: + break + # Compute the coordinates of the intersection boxes + x1 = torch.maximum(start_x[index], start_x[order]) + y1 = torch.maximum(start_y[index], start_y[order]) + x2 = torch.minimum(end_x[index], end_x[order]) + y2 = torch.minimum(end_y[index], end_y[order]) + # Compute width and height of the intersection boxes + w = torch.clamp(x2 - x1, min=0.0) + h = torch.clamp(y2 - y1, min=0.0) + intersection = w * h + # Compute the IoU ratio + union = areas[index] + areas[order] - intersection + ratio = intersection / union + # Keep boxes with IoU less than or equal to the threshold + keep = (ratio <= iou_threshold).to(order.device) + order = order[keep] + return picked_indices + + +def dual_topk( + pre_topk: torch.Tensor, + nc: int, + n_extra: int, + max_det: int = 300, + conf_thres: float = 0.25, + score_is_logits: bool = False, +) -> torch.Tensor: + """ + Perform dual-stage topk selection for NMS-free models. + Args: + pre_topk (torch.Tensor): Input tensor of shape (*, 4 + nc + n_extra). + nc (int): Number of classes. + n_extra (int): Number of extra elements (e.g., masks, keypoints). + max_det (int): Maximum detections to keep. Defaults to 300. + conf_thres (float): Confidence threshold. Defaults to 0.25. + score_is_logits (bool): Whether class scores are logits. When true, apply + the confidence cutoff and both rankings before sigmoid, then convert + only selected scores to probabilities. Defaults to false. + Returns: + torch.Tensor: Filtered detections of shape (*, 6 + n_extra). + """ + score_start = 4 + score_end = 4 + nc + score_view = pre_topk[:, score_start:score_end] + threshold = ( + math.log(conf_thres / (1.0 - conf_thres)) if score_is_logits else conf_thres + ) + ic = score_view.amax(dim=-1) > threshold + pre_topk = pre_topk[ic] + + if pre_topk.shape[0] == 0: + return torch.zeros( + (0, 6 + n_extra), dtype=torch.float32, device=pre_topk.device + ) + max_det = min(pre_topk.shape[0], max_det) + + row_index = torch.topk( + pre_topk[:, score_start:score_end].amax(dim=-1), max_det, dim=0 + ).indices + selected = pre_topk[row_index] + top_scores, flat_index = torch.topk( + selected[:, score_start:score_end].reshape(-1), max_det + ) + keep = top_scores > threshold + if not torch.any(keep): + return torch.zeros( + (0, 6 + n_extra), dtype=torch.float32, device=pre_topk.device + ) + + top_scores = top_scores[keep] + flat_index = flat_index[keep] + box_index = flat_index // nc + labels = (flat_index % nc).to(selected.dtype).unsqueeze(-1) + + output = torch.empty( + (top_scores.shape[0], 6 + n_extra), dtype=selected.dtype, device=selected.device + ) + output[:, :4] = selected[box_index, :4] + output[:, 4] = top_scores.sigmoid() if score_is_logits else top_scores + output[:, 5:6] = labels + if n_extra > 0: + output[:, 6:] = selected[box_index, score_end:] + return output + + +def yolo_multilabel_candidates( + detections: torch.Tensor, + nc: int, + n_extra: int, + conf_thres: float, +) -> torch.Tensor: + """Expand YOLO rows into one detection per class score above threshold. + + Args: + detections: Row-major detections with columns ``box, class scores, extra``. + nc: Number of classes. + n_extra: Number of extra channels after class scores. + conf_thres: Confidence threshold. + + Returns: + Canonical detection rows with columns ``box, score, class, extra``. + """ + if detections.numel() == 0: + return torch.zeros( + (0, 6 + n_extra), dtype=torch.float32, device=detections.device + ) + + boxes = detections[:, :4] + scores = detections[:, 4 : 4 + nc] + extra = detections[:, 4 + nc :] + box_index, class_index = torch.where(scores > conf_thres) + if box_index.numel() == 0: + return torch.zeros( + (0, 6 + n_extra), dtype=torch.float32, device=detections.device + ) + + output = torch.empty( + (box_index.numel(), 6 + n_extra), + dtype=detections.dtype, + device=detections.device, + ) + output[:, :4] = boxes[box_index] + output[:, 4] = scores[box_index, class_index] + output[:, 5] = class_index.to(detections.dtype) + if n_extra > 0: + output[:, 6:] = extra[box_index] + return output + + +def normalize_converted_obb_part(x: torch.Tensor, channel_count: int) -> torch.Tensor: + """Normalize a converted OBB output part to ``(batch, anchors, channels)``. + + Args: + x: Converted output part from a model runtime. + channel_count: Expected feature-channel count for this part. + + Returns: + The normalized row-major tensor. + """ + while x.ndim > 3: + singleton_dims = [ + idx for idx, size in enumerate(x.shape) if idx != 0 and size == 1 + ] + if not singleton_dims: + raise ValueError( + f"Expected converted OBB part with up to 3 non-batch dimensions, got {tuple(x.shape)}." + ) + x = x.squeeze(singleton_dims[0]) + if x.ndim == 2: + x = x.unsqueeze(0) + if x.ndim != 3: + raise ValueError( + f"Expected 2D or 3D converted OBB part, got shape {tuple(x.shape)}." + ) + if x.shape[-1] == channel_count: + return x + if x.shape[1] == channel_count: + return x.transpose(1, 2) + raise ValueError( + f"Could not find channel count {channel_count} in converted OBB part with shape {tuple(x.shape)}." + ) + + +def concat_converted_obb_outputs( + x: list[torch.Tensor], nc: int, n_extra: int +) -> torch.Tensor: + """Concatenate converted OBB box, class, and angle outputs in canonical order. + + Args: + x: Converted OBB runtime outputs. + nc: Number of OBB classes. + n_extra: Number of extra OBB channels. + + Returns: + Detections in ``cx, cy, w, h, class scores..., angle`` format. + """ + if len(x) == 1: + return x[0] + if len(x) != 3: + raise ValueError(f"Expected 1 or 3 converted OBB outputs, got {len(x)}.") + + expected_parts = {"box": 4, "scores": nc, "angle": n_extra} + parts: dict[str, torch.Tensor] = {} + for xi in x: + matches: list[tuple[str, torch.Tensor]] = [] + for name, channel_count in expected_parts.items(): + try: + matches.append((name, normalize_converted_obb_part(xi, channel_count))) + except ValueError: + continue + if len(matches) != 1: + match_names = ", ".join(name for name, _ in matches) or "none" + raise ValueError( + f"Could not uniquely classify converted OBB output {tuple(xi.shape)}; matches: {match_names}." + ) + name, normalized = matches[0] + if name in parts: + raise ValueError(f"Duplicate converted OBB {name} output.") + parts[name] = normalized + + missing = [name for name in expected_parts if name not in parts] + if missing: + raise ValueError(f"Missing converted OBB outputs: {', '.join(missing)}.") + return torch.cat([parts["box"], parts["scores"], parts["angle"]], dim=-1) + + +def decode_split_converted_obb_outputs( + x: list[torch.Tensor], + nc: int, + n_extra: int, + anchors: torch.Tensor, + stride: torch.Tensor, +) -> torch.Tensor: + """Decode MXQ decode-true OBB outputs split into score, angle, and coordinate tensors. + + Args: + x: Five converted runtime outputs: class scores, rotation angle, and + coordinate tensors containing decoded ``wh`` and pre-rotated center offsets. + nc: Number of OBB classes. + n_extra: Number of extra OBB channels. + anchors: Anchor points in ``(2, anchors)`` format. + stride: Stride tensor in ``(1, anchors)`` format. + + Returns: + Detections in ``cx, cy, w, h, class scores..., angle`` format. + """ + if n_extra != 1: + raise ValueError(f"Expected one OBB angle channel, got n_extra={n_extra}.") + if len(x) != 5: + raise ValueError(f"Expected five split converted OBB outputs, got {len(x)}.") + + try: + scores = normalize_converted_obb_part(x[0], nc) + angle = normalize_converted_obb_part(x[1], n_extra) + except ValueError: + scores = normalize_converted_obb_part(x[1], nc) + angle = normalize_converted_obb_part(x[0], n_extra) + wh = normalize_converted_obb_part(x[2], 2) + x_offset = normalize_converted_obb_part(x[3], 1) + y_offset = normalize_converted_obb_part(x[4], 1) + cos_value = torch.cos(angle) + sin_value = torch.sin(angle) + center_offset = torch.cat( + [ + x_offset * cos_value - y_offset * sin_value, + x_offset * sin_value + y_offset * cos_value, + ], + dim=-1, + ) + anchors_t = ( + anchors.transpose(0, 1).unsqueeze(0).to(device=wh.device, dtype=wh.dtype) + ) + stride_t = stride.transpose(0, 1).unsqueeze(0).to(device=wh.device, dtype=wh.dtype) + if anchors_t.shape[1] < wh.shape[1]: + raise ValueError( + f"Got {wh.shape[1]} OBB coordinate rows but only {anchors_t.shape[1]} anchors." + ) + anchors_t = anchors_t[:, : wh.shape[1]] + stride_t = stride_t[:, : wh.shape[1]] + box = torch.cat([anchors_t + center_offset, wh], dim=-1) * stride_t + return torch.cat([box, scores, angle], dim=-1) + + +# --- Scaling & Clipping Utilities --- +@overload +def scale_boxes( + img1_shape: tuple[int, int], + boxes: np.ndarray, + img0_shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, + padding: bool = True, +) -> np.ndarray: ... + + +@overload +def scale_boxes( + img1_shape: tuple[int, int], + boxes: torch.Tensor, + img0_shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, + padding: bool = True, +) -> torch.Tensor: ... + + +def scale_boxes( + img1_shape: tuple[int, int], + boxes: np.ndarray | torch.Tensor, + img0_shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, + padding: bool = True, +) -> np.ndarray | torch.Tensor: + """ + Original Source: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py#L92 + Rescales bounding boxes (in the format of xyxy) from the shape of the image they + were originally specified in (img1_shape) to the shape of a different image (img0_shape). + Args: + img1_shape (tuple): The shape of the image that the bounding boxes are for, + in the format of (height, width). + boxes (np.ndarray | torch.Tensor): the bounding boxes of the objects in the image, + in the format of (x1, y1, x2, y2) + img0_shape (tuple): the shape of the target image, in the format of (height, width). + ratio_pad (tuple): a tuple of (ratio, pad) for scaling the boxes. + If not provided, the ratio and pad will be calculated based on the size + difference between the two images. + padding (bool): If True, assuming the boxes is based on image augmented by + yolo style. If False then do regular rescaling. + Returns: + np.ndarray | torch.Tensor: The scaled bounding boxes, in the format of (x1, y1, x2, y2) + """ + ratio, pad = resolve_ratio_pad(img1_shape, img0_shape, ratio_pad) + gain = ratio[0] + if isinstance(boxes, np.ndarray): + if padding: + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + return clip_boxes(boxes, img0_shape) + if padding: + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + return clip_boxes(boxes, img0_shape) + + +@overload +def scale_coords( + img1_shape: tuple[int, int], + coords: np.ndarray, + img0_shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, + padding: bool = True, +) -> np.ndarray: ... + + +@overload +def scale_coords( + img1_shape: tuple[int, int], + coords: torch.Tensor, + img0_shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, + padding: bool = True, +) -> torch.Tensor: ... + + +def scale_coords( + img1_shape: tuple[int, int], + coords: np.ndarray | torch.Tensor, + img0_shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, + padding: bool = True, +) -> np.ndarray | torch.Tensor: + """ + Original Source: + https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py#L756 + Args: + img1_shape (tuple): The shape of the image that the bounding boxes are for, in the format of (height, width). + coords (np.ndarray | torch.Tensor): The coordinates of the objects in the image, in the format of (x, y). + img0_shape (tuple): The shape of the target image, in the format of (height, width). + ratio_pad (tuple): a tuple of (ratio, pad) for scaling the boxes. If not provided, the ratio and pad will be + calculated based on the size difference between the two images. + padding (bool): If True, assuming the boxes is based on image augmented by yolo style. If False then do regular + rescaling. + Returns: + np.ndarray | torch.Tensor: The scaled coordinates, in the format of (x, y) + """ + ratio, pad = resolve_ratio_pad(img1_shape, img0_shape, ratio_pad) + gain = ratio[0] + if isinstance(coords, np.ndarray): + if padding: + coords[..., 0] -= pad[0] # x padding + coords[..., 1] -= pad[1] # y padding + coords[..., :2] /= gain + return clip_coords(coords, img0_shape) + if padding: + coords[..., 0] -= pad[0] # x padding + coords[..., 1] -= pad[1] # y padding + coords[..., :2] /= gain + return clip_coords(coords, img0_shape) + + +def scale_rboxes( + img1_shape: tuple[int, int], + rboxes: torch.Tensor, + img0_shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, + padding: bool = True, +) -> torch.Tensor: + """Rescale rotated boxes from model input size to an original image size. + + Args: + img1_shape: Processed image shape. + rboxes: Rotated boxes in ``xywhr`` format. + img0_shape: Original image shape. + ratio_pad: Optional precomputed resize ratio and padding. + padding: Whether YOLO-style letterbox padding was applied. + + Returns: + Rescaled rotated boxes in ``xywhr`` format. + """ + ratio, pad = resolve_ratio_pad(img1_shape, img0_shape, ratio_pad) + gain = ratio[0] + scaled = rboxes.clone() + if padding: + scaled[..., 0] -= pad[0] + scaled[..., 1] -= pad[1] + scaled[..., :4] /= gain + return scaled + + +def compute_ratio_pad( + img1_shape: tuple[int, int], + img0_shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, +) -> tuple[float, tuple[float, float]]: + """Return letterbox gain and padding for compatibility with existing callers. + + Args: + img1_shape (tuple): The target shape (height, width). + img0_shape (tuple): The original shape (height, width). + ratio_pad (tuple, optional): Pre-calculated (ratio, pad) tuple. + If None, it will be calculated from the shapes. Defaults to None. + + Returns: + tuple: (gain, pad) where gain is the scaling factor and pad is the (x, y) padding. + """ + ratio, pad = resolve_ratio_pad(img1_shape, img0_shape, ratio_pad) + return ratio[0], pad + + +@overload +def clip_boxes(boxes: np.ndarray, shape: tuple[int, int]) -> np.ndarray: ... + + +@overload +def clip_boxes(boxes: torch.Tensor, shape: tuple[int, int]) -> torch.Tensor: ... + + +def clip_boxes( + boxes: np.ndarray | torch.Tensor, shape: tuple[int, int] +) -> np.ndarray | torch.Tensor: + """ + Clip bounding boxes to image shape. + Args: + boxes (np.ndarray | torch.Tensor): Bounding boxes. + shape (tuple): Image shape (height, width). + Returns: + np.ndarray | torch.Tensor: Clipped bounding boxes. + """ + if isinstance(boxes, torch.Tensor): + boxes[..., 0] = boxes[..., 0].clamp(0, shape[1]) + boxes[..., 1] = boxes[..., 1].clamp(0, shape[0]) + boxes[..., 2] = boxes[..., 2].clamp(0, shape[1]) + boxes[..., 3] = boxes[..., 3].clamp(0, shape[0]) + else: + boxes[..., 0] = np.clip(boxes[..., 0], 0, shape[1]) + boxes[..., 1] = np.clip(boxes[..., 1], 0, shape[0]) + boxes[..., 2] = np.clip(boxes[..., 2], 0, shape[1]) + boxes[..., 3] = np.clip(boxes[..., 3], 0, shape[0]) + return boxes + + +@overload +def clip_coords(coords: np.ndarray, shape: tuple[int, int]) -> np.ndarray: ... + + +@overload +def clip_coords(coords: torch.Tensor, shape: tuple[int, int]) -> torch.Tensor: ... + + +def clip_coords( + coords: np.ndarray | torch.Tensor, shape: tuple[int, int] +) -> np.ndarray | torch.Tensor: + """Clips coordinates to the image shape. + + Args: + coords (np.ndarray | torch.Tensor): Coordinates to clip. + shape (tuple): Image shape (height, width). + + Returns: + np.ndarray | torch.Tensor: Clipped coordinates. + """ + if isinstance(coords, torch.Tensor): + coords[..., 0] = coords[..., 0].clamp(0, shape[1]) + coords[..., 1] = coords[..., 1].clamp(0, shape[0]) + else: + coords[..., 0] = np.clip(coords[..., 0], 0, shape[1]) + coords[..., 1] = np.clip(coords[..., 1], 0, shape[0]) + return coords + + +# --- Segmentation Utilities --- +def process_mask( + protos: torch.Tensor, + masks_in: torch.Tensor, + bboxes: torch.Tensor, + shape: tuple[int, int], + upsample: bool = False, +) -> torch.Tensor: + """Processes masks by applying coefficients to prototypes and cropping. + + Ref: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py#L680 + + Args: + protos (torch.Tensor): Prototype masks of shape [mask_dim, mask_h, mask_w]. + masks_in (torch.Tensor): Mask coefficients of shape [n, mask_dim]. + bboxes (torch.Tensor): Bounding boxes of shape [n, 4]. + shape (tuple): Input image size (h, w). + upsample (bool, optional): Whether to upsample the masks to the original image size. + Defaults to False. + + Returns: + torch.Tensor: Processed binary masks. + """ + c, mh, mw = protos.shape # CHW + ih, iw = shape + masks = (masks_in @ protos.float().view(c, -1)).view(-1, mh, mw) # n, CHW + downsampled_bboxes = bboxes.clone() + downsampled_bboxes[:, 0] *= mw / iw + downsampled_bboxes[:, 2] *= mw / iw + downsampled_bboxes[:, 3] *= mh / ih + downsampled_bboxes[:, 1] *= mh / ih + masks = crop_mask(masks, downsampled_bboxes) # CHW + if upsample: + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[ + 0 + ] # CHW + return masks.gt_(0.0) + + +def process_mask_upsample( + protos: torch.Tensor, + masks_in: torch.Tensor, + bboxes: torch.Tensor, + shape: tuple[int, int] | list[int], +) -> torch.Tensor: + """Applies masks to bounding boxes with upsampling for higher quality. + + Ref: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py#L713 + This produces higher quality masks than `process_mask` but is slower. + + Args: + protos (torch.Tensor): Prototype masks of shape [mask_dim, mask_h, mask_w]. + masks_in (torch.Tensor): Mask coefficients of shape [n, mask_dim]. + bboxes (torch.Tensor): Bounding boxes of shape [n, 4]. + shape (tuple): Target image size (h, w). + + Returns: + torch.Tensor: Upsampled and thresholded binary masks. + """ + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).view(-1, mh, mw) # n, CHW + masks = scale_masks(masks, (shape[0], shape[1])) # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.0) + + +def crop_mask(masks: torch.Tensor, boxes: torch.Tensor) -> torch.Tensor: + """Crops masks to bounding boxes. + + Args: + masks (torch.Tensor): Masks of shape [n, h, w]. + boxes (torch.Tensor): Bounding boxes of shape [n, 4] in (x1, y1, x2, y2) format. + + Returns: + torch.Tensor: Cropped masks. + """ + if boxes.device != masks.device: + boxes = boxes.to(masks.device) + _, h, w = masks.shape + x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) + rows = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] + cols = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] + return masks * ((rows >= x1) * (rows < x2) * (cols >= y1) * (cols < y2)) + + +def scale_masks( + masks: torch.Tensor, + shape: tuple[int, int], + ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None, + padding: bool = True, +) -> torch.Tensor: + """Rescales segment masks to the target shape. + + Args: + masks (torch.Tensor): Input masks of shape (C, H, W). + shape (tuple): Target shape (height, width). + ratio_pad (tuple, optional): Pre-calculated (ratio, pad) tuple. + If None, it will be calculated from the shapes. Defaults to None. + padding (bool, optional): If True, assumes the masks were generated from + an image with YOLO-style padding. Defaults to True. + + Returns: + torch.Tensor: Rescaled masks of shape (C, target_h, target_w). + """ + im1_h, im1_w = masks.shape[1:] + im0_h, im0_w = shape[:2] + if masks.numel() == 0: + return torch.zeros((0, im0_h, im0_w), dtype=masks.dtype, device=masks.device) + if im1_h == im0_h and im1_w == im0_w: + return masks + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_h / im0_h, im1_w / im0_w) # gain = old / new + pad_w, pad_h = ( + (im1_w - round(im0_w * gain)), + (im1_h - round(im0_h * gain)), + ) # wh padding + if padding: + pad_w /= 2 + pad_h /= 2 + else: + pad_w, pad_h = ratio_pad[1] + top, left = (round(pad_h - 0.1), round(pad_w - 0.1)) if padding else (0, 0) + bottom, right = im1_h - round(pad_h + 0.1), im1_w - round(pad_w + 0.1) + masks = masks[..., top:bottom, left:right] + if isinstance(masks, np.ndarray): + masks = torch.from_numpy(masks) + masks = F.interpolate( + masks[None], shape, mode="bilinear", align_corners=False + ) # 1NHW + return masks[0] + + +def to_string(counts: list[int]) -> str: + """Converts the RLE object into a compact string representation. + + Each count is delta-encoded and variable-length encoded as a string. + + Args: + counts (list[int]): List of RLE counts. + + Returns: + str: Compact string representation of the RLE object. + """ + result = [] + + for i, x in enumerate(counts): + x = int(x) + + # Apply delta encoding for all counts after the second entry + if i > 2: + x -= int(counts[i - 2]) + + # Variable-length encode the value + while True: + c = x & 0x1F # Take 5 bits + x >>= 5 + + # If the sign bit (0x10) is set, continue if x != -1; + # otherwise, continue if x != 0 + more = (x != -1) if (c & 0x10) else (x != 0) + if more: + c |= 0x20 # Set continuation bit + c += 48 # Shift to ASCII + result.append(chr(c)) + if not more: + break + + return "".join(result) + + +def multi_encode(pixels: torch.Tensor) -> list[list[int]]: + """Convert multiple binary masks using Run-Length Encoding (RLE). + + Args: + pixels (torch.Tensor): A 2D tensor where each row represents a flattened binary mask + with shape [N, H*W]. + + Returns: + list[list[int]]: A list of RLE counts for each mask. + """ + pixel_rows = pixels.detach().cpu().numpy().astype(np.uint8, copy=False) + width = pixel_rows.shape[1] + counts = [] + for i in range(pixel_rows.shape[0]): + pixel_row = pixel_rows[i] + positions = np.flatnonzero(pixel_row[1:] != pixel_row[:-1]) + 1 + if positions.size: + count = np.diff(positions).tolist() + count.insert(0, int(positions[0])) + count.append(int(width - positions[-1])) + else: + count = [width] + if pixel_row[0] == 1: + count = [0, *count] + counts.append(count) + + return counts + + +def nmsout2eval( + nms_outs: list[torch.Tensor] | torch.Tensor, + img1_shape: tuple[int, int], + img0_shapes: tuple[int, int] | Sequence[tuple[int, int]], + ratio_pads: RatioPad | Sequence[RatioPad | None] | None = None, +) -> tuple[list[list[int]], list[list[list[float]]], list[list[float]]]: + """Converts NMS output to COCO evaluation format. + + Args: + nms_outs (list[torch.Tensor] | torch.Tensor): The output of the NMS + operation of shape (n, 6), where n is the number of objects. + img1_shape (tuple): Processed image shape (H, W). + img0_shapes (list[tuple]): Original image shapes [(H, W), ...]. + + Returns: + tuple: A tuple containing: + - labels (list[list]): The labels of the objects for each image. + - boxes (list[list]): The bounding boxes (xywh) for each image. + - scores (list[list]): The confidence scores for each image. + """ + + if not isinstance(nms_outs, list): + nms_outs = [nms_outs] + actual_img0_shapes = normalize_image_shapes(img0_shapes, len(nms_outs)) + actual_ratio_pads = normalize_ratio_pads(ratio_pads, len(nms_outs)) + labels_list: list[list[int]] = [] + boxes_list: list[list[list[float]]] = [] + scores_list: list[list[float]] = [] + for nms_out, img0_shape, ratio_pad in zip( + nms_outs, actual_img0_shapes, actual_ratio_pads + ): + boxes = nms_out[:, :4].clone() + scores = nms_out[:, 4] + labels = nms_out[:, 5] + boxes = scale_boxes( + img1_shape, boxes, img0_shape, ratio_pad=ratio_pad + ) # scale boxes to original image size + boxes[:, 2:] = boxes[:, 2:] - boxes[:, :2] # xyxy to xywh with corner xy + + boxes_tolist = [ + [round(float(value), 3) for value in box] for box in boxes.tolist() + ] + scores_tolist = [round(float(score), 5) for score in scores.tolist()] + labels_tolist = labels.tolist() + labels_res = [get_coco_inv(int(label)) for label in labels_tolist] + + labels_list.append(labels_res) + boxes_list.append(boxes_tolist) + scores_list.append(scores_tolist) + + return labels_list, boxes_list, scores_list + + +def nmsout2eval_seg( + nms_outs: Any, + img1_shape: tuple[int, int], + img0_shapes: tuple[int, int] | list[tuple[int, int]], + ratio_pads: RatioPad | list[RatioPad | None] | None = None, +) -> tuple[ + list[list[int]], + list[list[list[float]]], + list[list[float]], + list[list[dict[str, Any]]], +]: + """Converts segmentation NMS output to COCO evaluation format. + + Args: + nms_outs (Union[list, tuple]): Segmentation postprocess output in one of two forms: + `(det_result, seg_result)` for a single image or a list of those pairs for a batch. + img1_shape (tuple): Processed image shape (H, W). + img0_shapes (tuple | list[tuple]): Original image shape for a single image or + a list of original shapes for a batch. + + Returns: + tuple: A tuple containing: + - labels (list[list]): The labels of the objects for each image. + - boxes (list[list]): The bounding boxes (xywh) for each image. + - scores (list[list]): The confidence scores for each image. + - extra (list[list]): The encoded segmentation masks for each image. + """ + actual_img0_shapes = normalize_image_shapes(img0_shapes) + actual_ratio_pads = normalize_ratio_pads(ratio_pads, len(actual_img0_shapes)) + + if not isinstance(nms_outs[0], (list, tuple)): + actual_nms_outs = [nms_outs] + else: + actual_nms_outs = nms_outs + + det_results = [] + seg_results = [] + for nms_out in actual_nms_outs: + det_results.append(nms_out[0]) + seg_results.append(nms_out[1]) + + labels_list, boxes_list, scores_list = nmsout2eval( + det_results, + img1_shape, + actual_img0_shapes, + ratio_pads=actual_ratio_pads, + ) + + scaled_seg_results = [ + scale_masks( + seg_result.to(torch.float32), + (img0_shape[0], img0_shape[1]), + ratio_pad=ratio_pad, + ) + for seg_result, img0_shape, ratio_pad in zip( + seg_results, actual_img0_shapes, actual_ratio_pads + ) + ] + + def mask_encode(seg_result: torch.Tensor) -> list[dict[str, Any]]: + extra = [] + h, w = seg_result.shape[1:3] + seg_result = ( + seg_result.permute(0, 2, 1) + .contiguous() + .view(seg_result.shape[0], h * w) + .byte() + ) + counts = multi_encode(seg_result) + if len(counts) != seg_result.shape[0]: + raise RuntimeError( + f"Encoded {len(counts)} masks for a mask tensor batch of {seg_result.shape[0]}." + ) + for c in counts: + extra.append({"size": [h, w], "counts": to_string(c)}) + return extra + + extra_list = [mask_encode(seg_result) for seg_result in scaled_seg_results] + for labels, boxes, scores, extra in zip( + labels_list, boxes_list, scores_list, extra_list + ): + if not len(labels) == len(boxes) == len(scores) == len(extra): + raise RuntimeError( + "Segmentation evaluation produced mismatched label, box, score, and mask counts." + ) + return labels_list, boxes_list, scores_list, extra_list + + +def nmsout2eval_pose( + nms_outs: list[torch.Tensor] | torch.Tensor, + img1_shape: tuple[int, int], + img0_shapes: tuple[int, int] | list[tuple[int, int]], + ratio_pads: RatioPad | list[RatioPad | None] | None = None, +) -> tuple[ + list[list[int]], list[list[list[float]]], list[list[float]], list[list[list[float]]] +]: + """Converts pose estimation NMS output to COCO evaluation format. + + Args: + nms_outs (list): The output of the NMS operation. + img1_shape (tuple): Processed image shape (H, W). + img0_shapes (list[tuple]): Original image shapes [(H, W), ...]. + + Returns: + tuple: A tuple containing: + - labels (list[list]): The labels of the objects for each image. + - boxes (list[list]): The bounding boxes (xywh) for each image. + - scores (list[list]): The confidence scores for each image. + - keypoints (list[list]): The scaled keypoints for each image. + """ + actual_img0_shapes = normalize_image_shapes(img0_shapes) + actual_ratio_pads = normalize_ratio_pads(ratio_pads, len(actual_img0_shapes)) + if not isinstance(nms_outs, list): + actual_nms_outs = [nms_outs] + else: + actual_nms_outs = nms_outs + labels_list, boxes_list, scores_list = nmsout2eval( + actual_nms_outs, + img1_shape, + actual_img0_shapes, + ratio_pads=actual_ratio_pads, + ) + extra = [ + scale_coords( + img1_shape, + nms_out[:, 6:].reshape(-1, 17, 3), + img0_shape, + ratio_pad=ratio_pad, + ).reshape(-1, 51) + for nms_out, img0_shape, ratio_pad in zip( + actual_nms_outs, actual_img0_shapes, actual_ratio_pads + ) + ] + return labels_list, boxes_list, scores_list, [x.tolist() for x in extra] + + +def nmsout2eval_obb( + nms_outs: list[torch.Tensor] | torch.Tensor, + img1_shape: tuple[int, int], + img0_shapes: tuple[int, int] | list[tuple[int, int]], + ratio_pads: RatioPad | list[RatioPad | None] | None = None, + include_xywhr: bool = False, +) -> tuple[Any, ...]: + """Converts OBB NMS output to DOTAv1 evaluation format. + + Args: + nms_outs: Detections with rows ``cx, cy, w, h, score, cls, angle``. + img1_shape: Processed image shape. + img0_shapes: Original image shape or shapes. + ratio_pads: Optional letterbox metadata. + include_xywhr: Whether to include scaled ``xywhr`` boxes in the return value. + + Returns: + DOTAv1 labels, polygons, scores, and optionally scaled ``xywhr`` boxes. + """ + actual_img0_shapes = normalize_image_shapes(img0_shapes) + actual_ratio_pads = normalize_ratio_pads(ratio_pads, len(actual_img0_shapes)) + actual_nms_outs = [nms_outs] if not isinstance(nms_outs, list) else nms_outs + + labels_list: list[list[str]] = [] + polygons_list: list[list[list[float]]] = [] + scores_list: list[list[float]] = [] + xywhr_list: list[list[list[float]]] = [] + for nms_out, img0_shape, ratio_pad in zip( + actual_nms_outs, actual_img0_shapes, actual_ratio_pads + ): + if nms_out.numel() == 0: + labels_list.append([]) + polygons_list.append([]) + scores_list.append([]) + xywhr_list.append([]) + continue + + rboxes = torch.cat([nms_out[:, :4], nms_out[:, 6:7]], dim=-1) + rboxes = scale_rboxes(img1_shape, rboxes, img0_shape, ratio_pad=ratio_pad) + polygons = xywhr2xyxyxyxy(rboxes).reshape(-1, 8) + polygons = scale_coords( + img0_shape, polygons.reshape(-1, 4, 2), img0_shape + ).reshape(-1, 8) + + labels = [get_dotav1_label(int(label)) for label in nms_out[:, 5].tolist()] + scores = [round(float(score), 5) for score in nms_out[:, 4].tolist()] + polygons_tolist = [ + [round(float(value), 3) for value in polygon] + for polygon in polygons.tolist() + ] + xywhr_tolist = [ + [round(float(value), 3) for value in rbox] for rbox in rboxes.tolist() + ] + + labels_list.append(labels) + polygons_list.append(polygons_tolist) + scores_list.append(scores) + xywhr_list.append(xywhr_tolist) + + if include_xywhr: + return labels_list, polygons_list, scores_list, xywhr_list + return labels_list, polygons_list, scores_list + + +class YOLOSegPostMixin: + """Mixin class for YOLO segmentation postprocessing.""" + + def nmsout2eval( + self, + nms_out: Any, + img1_shape: tuple[int, int], + img0_shape: tuple[int, int] | list[tuple[int, int]], + ratio_pad: RatioPad | list[RatioPad | None] | None = None, + ) -> tuple[Any, ...]: + """Converts NMS output to evaluation format for segmentation. + + Args: + nms_out: NMS output (detections and prototypes). + img1_shape: Resized image shape. + img0_shape: List of original image shapes. + + Returns: + Tuple: (labels_list, boxes_list, scores_list, extra_list). + """ + return nmsout2eval_seg(nms_out, img1_shape, img0_shape, ratio_pads=ratio_pad) + + +class YOLOPosePostMixin: + """Mixin class for YOLO pose estimation postprocessing.""" + + def nmsout2eval( + self, + nms_out: Any, + img1_shape: tuple[int, int], + img0_shape: tuple[int, int] | list[tuple[int, int]], + ratio_pad: RatioPad | list[RatioPad | None] | None = None, + ) -> tuple[Any, ...]: + """Converts NMS output to evaluation format for pose estimation. + + Args: + nms_out: NMS output (detections with keypoints). + img1_shape: Resized image shape. + img0_shape: List of original image shapes. + + Returns: + Tuple: (labels_list, boxes_list, scores_list, extra_list). + """ + return nmsout2eval_pose(nms_out, img1_shape, img0_shape, ratio_pads=ratio_pad) + + +class YOLOOBBPostMixin: + """Mixin class for YOLO oriented-bounding-box postprocessing.""" + + def nmsout2eval( + self, + nms_out: Any, + img1_shape: tuple[int, int], + img0_shape: tuple[int, int] | list[tuple[int, int]], + ratio_pad: RatioPad | list[RatioPad | None] | None = None, + include_xywhr: bool = False, + ) -> tuple[Any, ...]: + """Converts OBB detections to DOTAv1 labels, polygons, and scores. + + Args: + nms_out: NMS output with rows ``cx, cy, w, h, score, cls, angle``. + img1_shape: Resized image shape. + img0_shape: Original image shape or shapes. + ratio_pad: Optional letterbox metadata. + include_xywhr: Whether to include scaled rotated boxes. + + Returns: + DOTAv1 labels, polygons, scores, and optionally scaled ``xywhr`` boxes. + """ + return nmsout2eval_obb( + nms_out, + img1_shape, + img0_shape, + ratio_pads=ratio_pad, + include_xywhr=include_xywhr, + ) diff --git a/mblt_vision/utils/postprocess/depth_post.py b/mblt_vision/utils/postprocess/depth_post.py new file mode 100644 index 0000000..1c9514b --- /dev/null +++ b/mblt_vision/utils/postprocess/depth_post.py @@ -0,0 +1,107 @@ +"""Postprocessing for monocular depth-estimation models.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +import torch +import torch.nn.functional as functional + +from ..types import ListTensorLike, TensorLike +from ._letterbox import crop_letterbox, get_letterbox_input_shape, resolve_ratio_pads +from .base import PostBase +from .common import RatioPad, normalize_image_shapes + + +class DepthPost(PostBase): + """Normalize depth outputs and undo letterbox padding when metadata is available.""" + + def __init__(self, pre_cfg: dict[str, Any], post_cfg: dict[str, Any]) -> None: + """Initialize depth restoration from the model letterbox configuration.""" + + super().__init__() + del post_cfg + self.input_shape = get_letterbox_input_shape( + pre_cfg, "Depth estimation", "Depth" + ) + + def __call__( + self, + x: TensorLike | ListTensorLike, + img0_shape: tuple[int, int] | Sequence[tuple[int, int]] | None = None, + ratio_pad: RatioPad | Sequence[RatioPad | None] | None = None, + **kwargs: Any, + ) -> torch.Tensor | list[torch.Tensor]: + """Return normalized depth maps, optionally restored to original image sizes.""" + + if kwargs: + raise TypeError( + f"Unexpected depth postprocess kwargs: {', '.join(sorted(kwargs))}" + ) + depth = self._normalize_output(x) + if img0_shape is None: + return depth + + shapes = normalize_image_shapes(img0_shape, depth.shape[0]) + pads = resolve_ratio_pads(ratio_pad, depth.shape[0], shapes, self.input_shape) + restored = [ + self._restore(depth[index], shapes[index], pads[index]) + for index in range(depth.shape[0]) + ] + return restored[0] if len(restored) == 1 else restored + + def _normalize_output(self, x: TensorLike | ListTensorLike) -> torch.Tensor: + """Validate dense output and normalize it to input-sized ``[B, H, W]`` tensors.""" + + if isinstance(x, (list, tuple)): + if len(x) != 1: + raise ValueError( + f"Depth estimation expects one output tensor, received {len(x)}." + ) + x = x[0] + depth = torch.as_tensor(x) + if depth.ndim == 4: + if depth.shape[1] == 1: + depth = depth[:, 0] + elif depth.shape[-1] == 1: + depth = depth[..., 0] + else: + raise ValueError( + f"Depth estimation expects [B, 1, H, W] or [B, H, W, 1], got {tuple(depth.shape)}." + ) + elif ( + depth.ndim == 3 + and depth.shape[-1] == 1 + and tuple(depth.shape[:2]) == self.input_shape + ): + depth = depth[..., 0].unsqueeze(0) + elif depth.ndim != 3: + raise ValueError( + "Depth estimation expects [B, H, W], [B, 1, H, W], [H, W, 1], or [B, H, W, 1], " + f"got {tuple(depth.shape)}." + ) + depth = depth.to(device=self.device, dtype=torch.float32) + if tuple(depth.shape[-2:]) == self.input_shape: + return depth + + quarter_shape = tuple(dimension // 4 for dimension in self.input_shape) + if tuple(depth.shape[-2:]) == quarter_shape: + return functional.interpolate( + depth[:, None], scale_factor=4.0, mode="bilinear", align_corners=False + )[:, 0] + + raise ValueError( + f"Depth estimation output spatial shape must be {self.input_shape} or quarter-resolution {quarter_shape}, " + f"got {tuple(depth.shape[-2:])}." + ) + + def _restore( + self, depth: torch.Tensor, shape: tuple[int, int], ratio_pad: RatioPad + ) -> torch.Tensor: + """Crop padded depth pixels and bilinearly resize to an original image shape.""" + + cropped = crop_letterbox(depth, shape, ratio_pad, self.input_shape, "Depth") + return functional.interpolate( + cropped[None, None], size=shape, mode="bilinear", align_corners=False + )[0, 0] diff --git a/mblt_vision/utils/postprocess/semantic_seg_post.py b/mblt_vision/utils/postprocess/semantic_seg_post.py new file mode 100644 index 0000000..25d5668 --- /dev/null +++ b/mblt_vision/utils/postprocess/semantic_seg_post.py @@ -0,0 +1,184 @@ +"""Postprocessing for semantic-segmentation models.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +import torch +import torch.nn.functional as functional + +from ..types import ListTensorLike, TensorLike +from ._letterbox import crop_letterbox, get_letterbox_input_shape, resolve_ratio_pads +from .base import PostBase +from .common import RatioPad, normalize_image_shapes + + +class SemanticSegPost(PostBase): + """Convert semantic logits to class maps and undo letterbox padding.""" + + NC_BY_DATASET: dict[str, int] = { + "ade20k": 150, + "cityscapes": 19, + } + + def __init__(self, pre_cfg: dict[str, Any], post_cfg: dict[str, Any]) -> None: + """Initialize semantic output handling for a configured dataset taxonomy.""" + + super().__init__() + self.input_shape = get_letterbox_input_shape( + pre_cfg, "Semantic segmentation", "Semantic" + ) + dataset = post_cfg.get("dataset") + if not isinstance(dataset, str): + raise ValueError( + "Semantic segmentation requires a string dataset in post_cfg." + ) + self.dataset = dataset.lower() + dataset_nc = self.NC_BY_DATASET.get(self.dataset) + configured_nc = post_cfg.get("nc") + if configured_nc is None: + if dataset_nc is None: + raise ValueError( + f"Semantic segmentation requires nc for unknown dataset '{self.dataset}'." + ) + self.nc = dataset_nc + else: + self.nc = int(configured_nc) + if dataset_nc is not None and self.nc != dataset_nc: + raise ValueError( + f"nc={configured_nc} conflicts with semantic dataset '{self.dataset}', which requires nc={dataset_nc}." + ) + + def __call__( + self, + x: TensorLike | ListTensorLike, + img0_shape: tuple[int, int] | Sequence[tuple[int, int]] | None = None, + ratio_pad: RatioPad | Sequence[RatioPad | None] | None = None, + **kwargs: Any, + ) -> torch.Tensor | list[torch.Tensor]: + """Return class maps, optionally restored to original image sizes.""" + + if kwargs: + raise TypeError( + f"Unexpected semantic postprocess kwargs: {', '.join(sorted(kwargs))}" + ) + output, is_logits = self._normalize_output(x) + if img0_shape is None: + return self._to_input_space(output, is_logits) + + shapes = normalize_image_shapes(img0_shape, output.shape[0]) + pads = resolve_ratio_pads(ratio_pad, output.shape[0], shapes, self.input_shape) + restored = [ + self._restore(output[index], is_logits, shapes[index], pads[index]) + for index in range(output.shape[0]) + ] + return restored[0] if len(restored) == 1 else restored + + def _normalize_output( + self, x: TensorLike | ListTensorLike + ) -> tuple[torch.Tensor, bool]: + """Validate logits in NCHW/NHWC layout or a baked class-map tensor.""" + + if isinstance(x, (list, tuple)): + if len(x) != 1: + raise ValueError( + f"Semantic segmentation expects one output tensor, received {len(x)}." + ) + x = x[0] + output = torch.as_tensor(x, device=self.device) + if output.ndim == 4: + if output.shape[1] == self.nc: + return output.to(dtype=torch.float32), True + if output.shape[-1] == self.nc: + return output.permute(0, 3, 1, 2).to(dtype=torch.float32), True + raise ValueError( + f"Semantic segmentation for '{self.dataset}' expects [B, {self.nc}, H, W] or " + f"[B, H, W, {self.nc}] logits, got {tuple(output.shape)}." + ) + if output.ndim == 3: + if tuple(output.shape[:2]) == self.input_shape: + if output.shape[-1] != self.nc: + raise ValueError( + f"Semantic segmentation for '{self.dataset}' expects [H, W, {self.nc}] MXQ logits, " + f"got {tuple(output.shape)}." + ) + return output.permute(2, 0, 1).unsqueeze(0).to( + dtype=torch.float32 + ), True + if output.is_complex(): + raise ValueError("Semantic class-map values must be finite integers.") + if output.is_floating_point(): + if not bool(torch.isfinite(output).all()): + raise ValueError("Semantic class-map values must be finite.") + if not bool(torch.eq(output, output.trunc()).all()): + raise ValueError( + "Semantic class-map values must be integer-valued." + ) + if output.numel() and ( + int(output.min()) < 0 or int(output.max()) >= self.nc + ): + raise ValueError( + f"Semantic class-map values must be in [0, {self.nc - 1}]." + ) + return output.to(dtype=torch.int64), False + raise ValueError( + f"Semantic segmentation expects [B, C, H, W] or [B, H, W, C] logits, or [B, H, W] class maps, " + f"got {tuple(output.shape)}." + ) + + def _to_input_space(self, output: torch.Tensor, is_logits: bool) -> torch.Tensor: + """Restore model output resolution to configured input space.""" + + if is_logits: + if tuple(output.shape[-2:]) != self.input_shape: + output = functional.interpolate( + output, size=self.input_shape, mode="bilinear", align_corners=False + ) + return output.argmax(dim=1).to(dtype=torch.int64) + if tuple(output.shape[-2:]) != self.input_shape: + output = functional.interpolate( + output[:, None].float(), size=self.input_shape, mode="nearest" + )[:, 0] + return output.to(dtype=torch.int64) + + def _restore( + self, + output: torch.Tensor, + is_logits: bool, + shape: tuple[int, int], + ratio_pad: RatioPad, + ) -> torch.Tensor: + """Undo letterboxing, preserving logits until after bilinear restoration.""" + + if is_logits: + if tuple(output.shape[-2:]) != self.input_shape: + output = functional.interpolate( + output[None], + size=self.input_shape, + mode="bilinear", + align_corners=False, + )[0] + channels = [ + crop_letterbox(channel, shape, ratio_pad, self.input_shape, "Semantic") + for channel in output + ] + cropped_logits = torch.stack(channels) + restored_logits = functional.interpolate( + cropped_logits[None], + size=shape, + mode="bilinear", + align_corners=False, + )[0] + return restored_logits.argmax(dim=0).to(dtype=torch.int64) + + if tuple(output.shape[-2:]) != self.input_shape: + output = functional.interpolate( + output[None, None].float(), + size=self.input_shape, + mode="nearest", + )[0, 0].to(dtype=torch.int64) + cropped = crop_letterbox(output, shape, ratio_pad, self.input_shape, "Semantic") + return functional.interpolate( + cropped[None, None].float(), size=shape, mode="nearest" + )[0, 0].to(torch.int64) diff --git a/mblt_vision/utils/postprocess/yolo_anchor_post.py b/mblt_vision/utils/postprocess/yolo_anchor_post.py new file mode 100644 index 0000000..19d58da --- /dev/null +++ b/mblt_vision/utils/postprocess/yolo_anchor_post.py @@ -0,0 +1,443 @@ +""" +YOLO anchor-based postprocessing. +""" + +from __future__ import annotations + +from typing import Any, cast + +import torch + +from .base import YOLODetectionPostBase +from .common import YOLOSegPostMixin, non_max_suppression + + +class YOLOAnchorDetectionPost(YOLODetectionPostBase): + """Postprocessing for YOLO models with anchors.""" + + def __init__( + self, pre_cfg: dict[str, Any], post_cfg: dict[str, Any], **kwargs: Any + ) -> None: + """Initialize anchor-based YOLO detection postprocessing. + + Args: + pre_cfg (dict): Preprocessing configuration. + post_cfg (dict): Postprocessing configuration. + **kwargs: Optional runtime overrides for postprocess behavior. + """ + super().__init__(pre_cfg, post_cfg, **kwargs) + self.no = self.nc + 5 + self.n_extra + self.grid: torch.Tensor + self.anchor_grid: torch.Tensor + self.make_anchor_grid() + + def non_e2e(self, x: list[torch.Tensor]) -> torch.Tensor | list[torch.Tensor]: + """Return the export-style output tensor for anchor-based YOLO models.""" + if len(x) == 1: + converted = self.conversion(x) + if isinstance(converted, torch.Tensor): + return self._converted_to_batch_output(converted) + det_out, proto_out = converted + return [self._converted_to_batch_output(det_out), proto_out] + + rearranged = self.rearrange(x) + if isinstance(rearranged, tuple): + det_out, proto_out = rearranged + return [self.decode_batch(det_out), proto_out.permute(0, 3, 1, 2)] + return self.decode_batch(rearranged) + + def _converted_to_batch_output(self, x: torch.Tensor) -> torch.Tensor: + """Normalize converted outputs to the export-style batched layout.""" + while x.ndim == 4 and 1 in (x.shape[0], x.shape[1]): + if x.shape[0] == 1: + x = x.squeeze(0) + elif x.shape[1] == 1: + x = x.squeeze(1) + if x.ndim != 3: + raise ValueError( + f"Expected 3D converted tensor, got shape {tuple(x.shape)}." + ) + if x.shape[-1] == self.no: + return x + if x.shape[1] == self.no: + return x.transpose(1, 2) + raise ValueError( + f"Unsupported converted tensor shape {tuple(x.shape)} for non-e2e output." + ) + + def decode_batch(self, x: torch.Tensor) -> torch.Tensor: + """Decode every anchor without filtering and preserve batch shape.""" + batch_size = x.shape[0] + grid = self.grid.unsqueeze(0).expand(batch_size, -1, -1) + anchor_grid = self.anchor_grid.unsqueeze(0).expand(batch_size, -1, -1) + stride = self.stride_as_tensor().unsqueeze(0).expand(batch_size, -1, -1) + + decoded = x.clone() + decoded[..., :2] = ( + decoded[..., :2].sigmoid().mul(2.0).add(grid).add(-0.5).mul(stride) + ) + decoded[..., 2:4] = ( + decoded[..., 2:4].sigmoid().mul(2.0).pow(2.0).mul(anchor_grid) + ) + conf = decoded[..., 4:5].sigmoid() + decoded[..., 4:5] = conf + decoded[..., 5 : 5 + self.nc] = decoded[..., 5 : 5 + self.nc].sigmoid() + if self.task == "instance_segmentation" and self.n_extra > 0: + decoded[..., 5 + self.nc :] = decoded[..., 5 + self.nc :] * conf + return decoded + + def rearrange( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Rearranges raw model output tensors into a concatenated decode input. + + Args: + x (list[torch.Tensor]): Raw output tensors from the model detection heads. + + Returns: + torch.Tensor | tuple[torch.Tensor, torch.Tensor]: Concatenated tensor in + ``(batch, anchors, no)`` format, optionally paired with prototype masks in + segmentation subclasses. + """ + if len(x) != self.nl: + raise ValueError(f"Expected {self.nl} detection heads, got {len(x)}.") + y = [] + for i in range(self.nl): + tmp = x[i] + if tmp.shape[3] == self.no * self.na: + y.append( + tmp.permute(0, 3, 1, 2) + ) # (b, 80, 80, 255) -> (b, 255, 80, 80) + else: + raise NotImplementedError( + f"Got unsupported shape for input: {tmp.shape}." + ) + # sort by image size descending + y = sorted(y, key=lambda x: x.numel(), reverse=True) + return torch.cat( + [ + xi.reshape(xi.shape[0], self.na, self.no, xi.shape[-2], xi.shape[-1]) + .permute(0, 1, 3, 4, 2) + .reshape(xi.shape[0], -1, self.no) + for xi in y + ], + dim=1, + ) + + def decode(self, x: torch.Tensor) -> list[torch.Tensor]: + """Decodes model outputs into box coordinates and class scores. + + Applies sigmoid to predictions and transforms boxes from anchor-relative + to image-relative coordinates. + + Args: + x (torch.Tensor): Concatenated output tensor from `rearrange`. + + Returns: + list[torch.Tensor]: Per-image decoded detections after confidence filtering. + """ + return [self.process_box_cls(box_cls) for box_cls in x] + + def process_box_cls(self, x: torch.Tensor) -> torch.Tensor: + """Processes a single image's detection tensor. + + Args: + x: Raw detections for one image. + + Returns: + Decoded boxes, confidence, and scores. + """ + ic = x[:, 4] > self.inv_conf_thres # candidates + box_cls = x[ic] # (n, 85) + if box_cls.numel() == 0: + return torch.zeros( + (0, 5 + self.nc + self.n_extra), dtype=torch.float32, device=x.device + ) + + grid = self.grid[ic, :] # (n, 2) + anchor_grid = self.anchor_grid[ic, :] # (n, 2) + stride = self.stride_as_tensor()[ic, :] # (n, 2) + + # Advanced indexing above materializes ``box_cls``, so in-place decode avoids a second output allocation. + box_cls[:, :2] = ( + box_cls[:, :2].sigmoid_().mul_(2.0).add_(grid).add_(-0.5).mul_(stride) + ) + box_cls[:, 2:4] = ( + box_cls[:, 2:4].sigmoid_().mul_(2.0).pow_(2.0).mul_(anchor_grid) + ) + conf = box_cls[:, 4:5].sigmoid_() + box_cls[:, 5 : 5 + self.nc].sigmoid_() + if self.task == "instance_segmentation" and self.n_extra > 0: + box_cls[:, 5 + self.nc :] *= conf + return box_cls + + def filter_conversion(self, x: torch.Tensor) -> list[torch.Tensor]: + """Filters out low-confidence detections from a single concatenated output tensor. + + Args: + x (torch.Tensor): Concatenated output tensor from the model. + + Returns: + list[torch.Tensor]: Filtered detections for each image in the batch. + """ + x_list = torch.split( + self._converted_to_batch_output(x), 1, dim=0 + ) # [(1, 25200, 85), ...] + + def process_conversion(x: torch.Tensor) -> torch.Tensor: + x = x.squeeze(0) # (25200, 85) + ic = x[:, 4] > self.conf_thres # candidates + x = x[ic] # (n, 85) + if len(x) == 0: + return torch.zeros((0, self.no), dtype=torch.float32) + return x + + return [process_conversion(xi) for xi in x_list] + + def _nms_single( + self, xi: torch.Tensor, max_det: int, max_nms: int, max_wh: int + ) -> torch.Tensor: + """Apply anchor-based NMS to a single decoded image tensor.""" + mi = 5 + self.nc # mask index + if xi.numel() == 0: + return torch.zeros( + (0, 6 + self.n_extra), dtype=torch.float32, device=self.device + ) + + scores = xi[:, 5:mi] * xi[:, 4:5] + match_index = (scores > self.conf_thres).nonzero(as_tuple=False) + if match_index.numel() == 0: + return torch.zeros( + (0, 6 + self.n_extra), dtype=torch.float32, device=xi.device + ) + + i = match_index[:, 0] + j = match_index[:, 1] + rows = xi[i] + boxes_xywh = rows[:, :4] + out = torch.empty( + (rows.shape[0], 6 + self.n_extra), dtype=rows.dtype, device=rows.device + ) + out[:, 0] = boxes_xywh[:, 0] - boxes_xywh[:, 2] / 2 + out[:, 1] = boxes_xywh[:, 1] - boxes_xywh[:, 3] / 2 + out[:, 2] = boxes_xywh[:, 0] + boxes_xywh[:, 2] / 2 + out[:, 3] = boxes_xywh[:, 1] + boxes_xywh[:, 3] / 2 + out[:, 4] = scores[i, j] + out[:, 5] = j.to(rows.dtype) + if self.n_extra > 0: + out[:, 6:] = rows[:, mi:] + out = out[out[:, 4].argsort(descending=True)[:max_nms]] + c = out[:, 5:6] * max_wh + boxes, score = out[:, :4] + c, out[:, 4] + i_idx = non_max_suppression(boxes, score, self.iou_thres, max_det) + return out[i_idx] + + def nms( + self, + x: torch.Tensor | list[torch.Tensor], + max_det: int = 300, + max_nms: int = 30000, + max_wh: int = 7680, + ) -> list[torch.Tensor]: + """ + Perform Non-Maximum Suppression (NMS) on the decoded detections. + Args: + x (list[torch.Tensor]): Decoded detections for each image. + max_det (int, optional): Maximum number of detections to keep. Defaults to 300. + max_nms (int, optional): Maximum number of candidates to consider for NMS. + Defaults to 30000. + max_wh (int, optional): Maximum box width/height for offset calculation. + Defaults to 7680. + Returns: + list[torch.Tensor]: Post-NMS detections for each image. + """ + if isinstance(x, list): + return [ + self._nms_single(xi, max_det=max_det, max_nms=max_nms, max_wh=max_wh) + for xi in x + ] + return [ + self._nms_single(xi, max_det=max_det, max_nms=max_nms, max_wh=max_wh) + for xi in x + ] + + def make_anchor_grid(self) -> None: + """ + Pre-calculate the anchor grid for decoding. + """ + grid_parts: list[torch.Tensor] = [] + anchor_grid_parts: list[torch.Tensor] = [] + stride_parts: list[torch.Tensor] = [] + strides = [2 ** (3 + i) for i in range(self.nl)] + if self.nl == 2: + strides = [strd * 2 for strd in strides] + out_sizes = [ + [self.imh // strd, self.imw // strd] for strd in strides + ] # (80, 80), (40, 40), (20, 20) + for anchr, (ny, nx), strd in zip(self.anchors_as_list(), out_sizes, strides): + yv, xv = torch.meshgrid( + torch.arange(ny, dtype=torch.float32, device=self.device), + torch.arange(nx, dtype=torch.float32, device=self.device), + indexing="ij", + ) + grid = torch.stack((xv, yv), 2).expand(self.na, ny, nx, 2) + grid_parts.append(grid) + anchr_tensor = torch.broadcast_to( + torch.tensor(anchr).reshape(self.na, 1, 1, 2), + (self.na, ny, nx, 2), + ) + anchor_grid_parts.append(anchr_tensor) + stride_parts.append(strd * torch.ones(self.na, ny, nx, 2)) + self.grid = torch.cat([grd.reshape(-1, 2) for grd in grid_parts], dim=0) + self.anchor_grid = torch.cat( + [anc.reshape(-1, 2) for anc in anchor_grid_parts], dim=0 + ) + self.stride = torch.cat([strd.reshape(-1, 2) for strd in stride_parts], dim=0) + + def chop(self, npu_out: torch.Tensor, idx: int = 0) -> tuple[torch.Tensor, ...]: + """Splits the detection tensor into individual components (xy, wh, conf, scores, extra). + + Args: + npu_out (torch.Tensor): Raw detection tensor from one detection head. + idx (int, optional): Detection head index. Defaults to 0. + + Returns: + tuple: (xy, wh, conf, scores, extra). + """ + xy, wh, conf, scores, extra = torch.split( + npu_out, [2, 2, 1, self.nc, self.n_extra], dim=-1 + ) + return xy, wh, conf, scores, extra + + +class YOLOAnchorSegPost(YOLOSegPostMixin, YOLOAnchorDetectionPost): + """Postprocessing for YOLO segmentation models with anchors.""" + + def non_e2e(self, x: list[torch.Tensor]) -> torch.Tensor | list[torch.Tensor]: + """Return the export-style output tensor for anchor-based YOLO segmentation models. + + Args: + x: Checked raw model outputs. + + Returns: + A detection tensor, or detections paired with prototype masks. + """ + if any(xi.ndim <= 4 and self.no in xi.shape[1:] for xi in x): + converted, proto_outs = self.conversion(x) + return [ + self._converted_to_batch_output(converted), + self._proto_to_nchw(proto_outs), + ] + return super().non_e2e(x) + + def _proto_to_nchw(self, proto: torch.Tensor) -> torch.Tensor: + """Convert prototype tensors to ``(B, C, H, W)`` if needed. + + Args: + proto: Prototype tensor from a model runtime. + + Returns: + Prototype tensor in channel-first batch layout. + """ + if proto.ndim == 4 and proto.shape[1] == self.n_extra: + return proto + if proto.ndim == 4 and proto.shape[-1] == self.n_extra: + return proto.permute(0, 3, 1, 2) + raise ValueError( + f"Unsupported proto tensor shape {tuple(proto.shape)} for non-e2e output." + ) + + def _pre_process(self, x: list[torch.Tensor]) -> tuple[Any, torch.Tensor | None]: + """Preprocesses intermediate inputs into (boxes, proto) format. + + Args: + x (list[torch.Tensor]): Raw model output tensors. + + Returns: + tuple: (decoded_detections, prototype_masks). + """ + if any(xi.ndim <= 4 and self.no in xi.shape[1:] for xi in x): + converted, proto_outs = cast( + tuple[torch.Tensor, torch.Tensor], self.conversion(x) + ) + return self.filter_conversion(converted), proto_outs + rearranged, proto_outs = self.rearrange(x) + return self.decode(rearranged), proto_outs + + def conversion(self, x: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]: + """Converts raw model output tensors into detections and prototypes. + + Args: + x (list[torch.Tensor]): List of raw output tensors. + + Returns: + tuple: (detections, prototypes) + """ + det_out: torch.Tensor | None = None + proto_out: torch.Tensor | None = None + for xi in x: + if xi.ndim <= 4 and self.no in xi.shape[1:]: + det_out = xi + elif xi.ndim == 4 and self.n_extra in xi.shape[1:]: + proto_out = xi + if det_out is None or proto_out is None: + shapes = ", ".join(str(tuple(xi.shape)) for xi in x) + raise NotImplementedError( + f"Input shapes not supported for anchor segmentation: {shapes}." + ) + return det_out, proto_out + + def rearrange(self, x: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]: + """Rearranges model output tensors for segmentation tasks. + + Args: + x (list[torch.Tensor]): Raw output tensors from detection and prototype heads. + + Returns: + tuple[torch.Tensor, torch.Tensor]: Concatenated detections and prototype masks. + """ + proto: torch.Tensor | None = None + for i, xi in enumerate(x): + if self.n_extra == xi.shape[-1]: + proto = x.pop(i) + break + if proto is None: + raise ValueError("Proto output is missing.") + y = [] + for xi in x: + if xi.shape[-1] == self.no * self.nl: + y.append(xi.permute(0, 3, 1, 2)) + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + # sort by image size descending + y = sorted(y, key=lambda x: x.numel(), reverse=True) + return ( + torch.cat( + [ + xi.reshape( + xi.shape[0], self.na, self.no, xi.shape[-2], xi.shape[-1] + ) + .permute(0, 1, 3, 4, 2) + .reshape(xi.shape[0], -1, self.no) + for xi in y + ], + dim=1, + ), + proto, + ) + + def chop(self, npu_out: torch.Tensor, idx: int = 0) -> tuple[torch.Tensor, ...]: + """Splits the detection tensor for segmentation tasks. + + Args: + npu_out (torch.Tensor): Raw detection tensor. + idx (int, optional): Detection head index. Defaults to 0. + + Returns: + tuple: (xy, wh, conf, scores, masks). + """ + xy, wh, conf, scores, masks = torch.split( + npu_out, [2, 2, 1, self.nc, self.n_extra], dim=-1 + ) + masks = masks * conf.sigmoid() + return xy, wh, conf, scores, masks diff --git a/mblt_vision/utils/postprocess/yolo_anchorless_post.py b/mblt_vision/utils/postprocess/yolo_anchorless_post.py new file mode 100644 index 0000000..30c0260 --- /dev/null +++ b/mblt_vision/utils/postprocess/yolo_anchorless_post.py @@ -0,0 +1,948 @@ +""" +YOLO anchorless postprocessing. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal + +import torch + +from .base import YOLODetectionPostBase +from .common import ( + YOLOOBBPostMixin, + YOLOPosePostMixin, + YOLOSegPostMixin, + concat_converted_obb_outputs, + decode_split_converted_obb_outputs, + dist2bbox, + dist2rbox, + non_max_suppression, + rotated_nms, + xywh2xyxy, + yolo_multilabel_candidates, +) + +AnchorlessOutputLayout = Literal["channels_first", "candidates_first"] + + +@dataclass(frozen=True) +class _AnchorlessNMSInput: + """Decoded anchorless detections together with their source layout.""" + + detections: torch.Tensor | list[torch.Tensor] + layout: AnchorlessOutputLayout + + +class YOLOAnchorlessDetectionPost(YOLODetectionPostBase): + """Postprocessing for YOLO models without anchors.""" + + def __init__(self, pre_cfg: dict, post_cfg: dict, **kwargs: object) -> None: + """Initialize the anchorless YOLO postprocessor. + + Args: + pre_cfg: Preprocessing configuration. + post_cfg: Postprocessing configuration. + **kwargs: Optional runtime overrides for postprocess behavior. + """ + super().__init__(pre_cfg, post_cfg, **kwargs) + self.reg_max = post_cfg.get("reg_max", 0) # DFL channels + self.no = self.nc + self.reg_max * 4 # number of outputs per anchor (144) + self.dfl_weight = torch.arange( + self.reg_max, dtype=torch.float32, device=self.device + ).reshape(1, -1, 1, 1) + + def non_e2e(self, x: list[torch.Tensor]) -> torch.Tensor | list[torch.Tensor]: + """Return the export-style output tensor for anchorless YOLO models.""" + if len(x) == 1: + converted = self.conversion(x) + if isinstance(converted, torch.Tensor): + return self._converted_to_batch_output(converted) + det_out, proto_out = converted + return [self._converted_to_batch_output(det_out), proto_out] + + rearranged = self.rearrange(x) + if isinstance(rearranged, tuple): + det_out, proto_out = rearranged + return [self.decode_batch(det_out), proto_out.permute(0, 3, 1, 2)] + return self.decode_batch(rearranged) + + def _converted_to_batch_output(self, x: torch.Tensor) -> torch.Tensor: + """Normalize converted outputs to the export-style batched layout.""" + if x.ndim == 4 and x.shape[1] == 1: + x = x.squeeze(1) + if x.ndim != 3: + raise ValueError( + f"Expected 3D converted tensor, got shape {tuple(x.shape)}." + ) + if x.shape[1] == 4 + self.nc + self.n_extra: + return x + if x.shape[-1] == 4 + self.nc + self.n_extra: + return x.transpose(1, 2) + raise ValueError( + f"Unsupported converted tensor shape {tuple(x.shape)} for non-e2e output." + ) + + def decode_batch(self, x: torch.Tensor) -> torch.Tensor: + """Decode every anchor without confidence filtering for export-style output.""" + box, scores, extra = torch.split( + x, [self.reg_max * 4, self.nc, self.n_extra], dim=1 + ) + anchors = self.anchors_as_tensor().unsqueeze(0) + stride = self.stride_as_tensor().unsqueeze(0) + dbox = dist2bbox(self.dfl(box), anchors, xywh=False, dim=1) * stride + return torch.cat([dbox, scores.sigmoid(), extra], dim=1) + + def rearrange( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Rearranges raw model output tensors into a concatenated decode input. + + Args: + x (list[torch.Tensor]): List of raw output tensors from the model detection heads. + + Returns: + torch.Tensor | tuple[torch.Tensor, torch.Tensor]: Concatenated tensor in + ``(batch, channels, anchors)`` format, optionally paired with prototype masks. + """ + y_det = [] + y_cls = [] + for xi in x: # list of bchw outputs + if xi.ndim == 3: + xi = xi[None] + elif xi.ndim == 4: + pass + else: + raise NotImplementedError(f"Got unsupported ndim for input: {xi.ndim}.") + if xi.shape[-1] == self.reg_max * 4: + y_det.append( + xi.permute(0, 3, 1, 2) + ) # (b, 64, 80, 80), (b, 64 ,40, 40), ... + elif xi.shape[-1] == self.nc: + y_cls.append( + xi.permute(0, 3, 1, 2) + ) # (b, 80, 80, 80), (b, 80, 40, 40), ... + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + # sort as box, scores + y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) + y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + return torch.cat( + [ + torch.cat((yi_det, yi_cls), dim=1).flatten(2) + for yi_det, yi_cls in zip(y_det, y_cls) + ], + dim=-1, + ) + + def decode(self, x: torch.Tensor) -> list[torch.Tensor]: + """Decodes model outputs into box coordinates and class scores. + + Args: + x (torch.Tensor): Concatenated output tensor from `rearrange`. + + Returns: + list[torch.Tensor]: Per-image decoded detections in ``(channels, anchors)`` format. + """ + return [self.process_box_cls(box_cls) for box_cls in x] + + def _pre_process( + self, + x: list[torch.Tensor], + ) -> tuple[_AnchorlessNMSInput, torch.Tensor | None]: + """Decode detections while retaining whether the source was raw or converted. + + Args: + x: Checked model output tensors. + + Returns: + Layout-aware detections and an optional prototype output. + """ + if len(x) == 1: + converted = self.conversion(x) + if not isinstance(converted, torch.Tensor): + raise TypeError( + "conversion should return a tensor for single-output YOLO postprocessing." + ) + detections = self.filter_conversion(converted) + return _AnchorlessNMSInput(detections, "candidates_first"), None + rearranged = self.rearrange(x) + if not isinstance(rearranged, torch.Tensor): + raise TypeError( + "rearrange should return a tensor for non-segmentation YOLO postprocessing." + ) + detections = self.decode(rearranged) + return _AnchorlessNMSInput(detections, "channels_first"), None + + def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: + """Processes detection results for a single image. + + Args: + box_cls: Raw detections for one image. + + Returns: + Decoded boxes, scores, and extra data. + """ + if self.n_extra == 0: + ic = torch.amax(box_cls[-self.nc :, :], dim=0) > self.inv_conf_thres + else: + ic = ( + torch.amax(box_cls[-self.nc - self.n_extra : -self.n_extra, :], dim=0) + > self.inv_conf_thres + ) + box_cls = box_cls[:, ic] # (144, *) + if box_cls.numel() == 0: + return torch.zeros( + (4 + self.nc + self.n_extra, 0), dtype=torch.float32 + ) # (84, 0) + anchors = self.anchors_as_tensor() + stride = self.stride_as_tensor() + box, scores, extra = torch.split( + box_cls[None], [self.reg_max * 4, self.nc, self.n_extra], dim=1 + ) # (1, 64, *), (1, 80, *), (1, 32, *) + dbox = ( + dist2bbox( + self.dfl(box), + anchors[:, ic], + xywh=False, + dim=1, + ) + * stride[:, ic] + ) + return torch.cat([dbox, scores.sigmoid(), extra], dim=1).squeeze(0) + + def filter_conversion(self, x: torch.Tensor) -> list[torch.Tensor]: + """Filters out low-confidence detections from a single concatenated output tensor. + + Args: + x (torch.Tensor): Concatenated output tensor from the model. + + Returns: + list[torch.Tensor]: Filtered detections for each image in the batch. + """ + while x.ndim == 4 and 1 in (x.shape[0], x.shape[1]): + if x.shape[0] == 1: + x = x.squeeze(0) + elif x.shape[1] == 1: + x = x.squeeze(1) + if x.ndim != 3: + raise ValueError( + f"Expected 3D converted tensor, got shape {tuple(x.shape)}." + ) + expected_dim = 4 + self.nc + self.n_extra + if x.shape[-1] == expected_dim: + normalized = x + elif x.shape[1] == expected_dim: + normalized = x.transpose(1, 2) + else: + raise ValueError(f"Unsupported converted tensor shape {tuple(x.shape)}.") + x_list = torch.split( + normalized, 1, dim=0 + ) # [(1, 8400, 84), (1, 8400, 84), ...] + + def process_conversion(x: torch.Tensor) -> torch.Tensor: + x = x.squeeze(0) # (8400, 84) + if self.n_extra == 0: + ic = torch.amax(x[:, -self.nc :], dim=1) > self.conf_thres + else: + ic = ( + torch.amax(x[:, -self.nc - self.n_extra : -self.n_extra], dim=1) + > self.conf_thres + ) + x = x[ic] + if x.numel() == 0: + return torch.zeros((0, 4 + self.nc + self.n_extra), dtype=torch.float32) + x = xywh2xyxy(x) + return x + + return [process_conversion(xi) for xi in x_list] + + def _nms_single( + self, + xi: torch.Tensor, + max_det: int, + max_nms: int, + max_wh: int, + multi_label: bool, + ) -> torch.Tensor: + """Apply anchorless NMS to a single decoded image tensor.""" + return self._nms_single_legacy_rows( + self._normalize_nms_input(xi, "channels_first"), + max_det=max_det, + max_nms=max_nms, + max_wh=max_wh, + multi_label=multi_label, + ) + + def _normalize_nms_input( + self, + xi: torch.Tensor, + layout: AnchorlessOutputLayout | None = None, + ) -> torch.Tensor: + """Normalize one decoded tensor to ``(candidates, channels)``. + + Source provenance resolves square tensors. Shape inference remains available + for callers that pass decoded tensors directly, with raw channel-first + layout taking precedence when both dimensions match. + + Args: + xi: One image's decoded detections. + layout: Known source layout, if available. + + Returns: + Detections in canonical ``(candidates, channels)`` layout. + + Raises: + ValueError: If the tensor shape conflicts with the expected channel count + or with the supplied source layout. + """ + if xi.ndim != 2: + raise ValueError( + f"Expected 2D decoded tensor, got shape {tuple(xi.shape)}." + ) + + expected_dim = 4 + self.nc + self.n_extra + if layout == "channels_first": + if xi.shape[0] != expected_dim: + raise ValueError( + f"Expected channel-first decoded tensor with {expected_dim} channels, got shape {tuple(xi.shape)}." + ) + return xi.transpose(0, 1) + if layout == "candidates_first": + if xi.shape[1] != expected_dim: + raise ValueError( + "Expected candidates-first decoded tensor " + f"with {expected_dim} channels, got shape {tuple(xi.shape)}." + ) + return xi + + if xi.shape[0] == expected_dim: + return xi.transpose(0, 1) + if xi.shape[1] == expected_dim: + return xi + raise ValueError(f"Unsupported decoded tensor shape {tuple(xi.shape)}.") + + def _nms_single_legacy_rows( + self, + xi: torch.Tensor, + max_det: int, + max_nms: int, + max_wh: int, + multi_label: bool, + ) -> torch.Tensor: + """Apply anchorless NMS to a single decoded image in row-major ``(anchors, channels)`` form.""" + if xi.numel() == 0: + return torch.zeros( + (0, 6 + self.n_extra), dtype=torch.float32, device=self.device + ) + if multi_label: + xi_out = yolo_multilabel_candidates( + xi, self.nc, self.n_extra, self.conf_thres + ) + else: + box, score, extra = xi[:, :4], xi[:, 4 : 4 + self.nc], xi[:, 4 + self.nc :] + conf, cls_idx = score.max(dim=1) + filt = conf > self.conf_thres + if not torch.any(filt): + return torch.zeros( + (0, 6 + self.n_extra), dtype=torch.float32, device=self.device + ) + box = box[filt] + conf = conf[filt] + cls_idx = cls_idx[filt] + extra = extra[filt] + xi_out = torch.empty( + (box.shape[0], 6 + self.n_extra), dtype=xi.dtype, device=xi.device + ) + xi_out[:, :4] = box + xi_out[:, 4] = conf + xi_out[:, 5] = cls_idx.to(xi.dtype) + if self.n_extra > 0: + xi_out[:, 6:] = extra + xi_out = xi_out[torch.argsort(xi_out[:, 4], descending=True)[:max_nms]] + c = xi_out[:, 5:6] * max_wh + boxes, scores = xi_out[:, :4] + c, xi_out[:, 4] + i_idx = non_max_suppression(boxes, scores, self.iou_thres, max_det) + return xi_out[i_idx] + + def nms( + self, + x: _AnchorlessNMSInput | torch.Tensor | list[torch.Tensor], + max_det: int = 300, + max_nms: int = 30000, + max_wh: int = 7680, + multi_label: bool = False, + ) -> list[torch.Tensor]: + """Performs Non-Maximum Suppression (NMS) on the decoded detections. + + Args: + x: Decoded detections for each image, optionally with source-layout + provenance. + max_det (int, optional): Maximum number of detections to keep. Defaults to 300. + max_nms (int, optional): Maximum number of candidates to consider for NMS. + Defaults to 30000. + max_wh (int, optional): Maximum box width/height for offset calculation. + Defaults to 7680. + multi_label: Whether to retain every class candidate above threshold. + + Returns: + list[torch.Tensor]: Post-NMS detections for each image. + """ + layout: AnchorlessOutputLayout | None = None + if isinstance(x, _AnchorlessNMSInput): + layout = x.layout + x = x.detections + + tensors = x if isinstance(x, list) else list(x) + return [ + self._nms_single_legacy_rows( + self._normalize_nms_input(xi, layout), + max_det=max_det, + max_nms=max_nms, + max_wh=max_wh, + multi_label=multi_label, + ) + for xi in tensors + ] + + def nms_multilabel( + self, + x: _AnchorlessNMSInput | torch.Tensor | list[torch.Tensor], + ) -> list[torch.Tensor]: + """Perform Ultralytics-compatible multi-label NMS for validation.""" + return self.nms(x, multi_label=True) + + def dfl(self, x: torch.Tensor) -> torch.Tensor: + """Applies Distribution Focal Loss projection. + + Args: + x: Tensor with shape ``(B, 4 * reg_max, A)``. + + Returns: + Tensor with shape ``(B, 4, A)`` containing projected distances. + """ + if self.reg_max == 0: # skip dfl for yolov6 s, n models + return x + if x.ndim != 3: + raise ValueError( + f"DFL input must have shape (B, 4 * reg_max, A), got {tuple(x.shape)}." + ) + B, _, A = x.shape + x = x.view(B, 4, self.reg_max, A).softmax(dim=2) + return (x * self.dfl_weight.view(1, 1, self.reg_max, 1)).sum(dim=2) + + +class YOLOAnchorlessSegPost(YOLOSegPostMixin, YOLOAnchorlessDetectionPost): + """Postprocessing for YOLO segmentation models without anchors.""" + + def non_e2e(self, x: list[torch.Tensor]) -> torch.Tensor | list[torch.Tensor]: + """Return the export-style output tensor for anchorless YOLO segmentation models. + + Args: + x: Checked raw model outputs. + + Returns: + A detection tensor, or detections paired with prototype masks. + """ + if len(x) == 2: + converted, proto_outs = self.conversion(x) + return [ + self._converted_to_batch_output(converted), + self._proto_to_nchw(proto_outs), + ] + return super().non_e2e(x) + + def _proto_to_nchw(self, proto: torch.Tensor) -> torch.Tensor: + """Convert prototype tensors to ``(B, C, H, W)`` if needed. + + Args: + proto: Prototype tensor from a model runtime. + + Returns: + Prototype tensor in channel-first batch layout. + """ + if proto.ndim == 4 and proto.shape[1] == self.n_extra: + return proto + if proto.ndim == 4 and proto.shape[-1] == self.n_extra: + return proto.permute(0, 3, 1, 2) + raise ValueError( + f"Unsupported proto tensor shape {tuple(proto.shape)} for non-e2e output." + ) + + def _pre_process( + self, x: list[torch.Tensor] + ) -> tuple[_AnchorlessNMSInput, torch.Tensor]: + """Preprocesses intermediate inputs into (boxes, proto) format. + + Args: + x (list[torch.Tensor]): Raw model output tensors. + + Returns: + tuple: (decoded_detections, prototype_masks). + """ + if len(x) == 2: + converted, proto_outs = self.conversion(x) + detections = self.filter_conversion(converted) + return _AnchorlessNMSInput(detections, "candidates_first"), proto_outs + rearranged, proto_outs = self.rearrange(x) + return _AnchorlessNMSInput( + self.decode(rearranged), "channels_first" + ), proto_outs + + def conversion(self, x: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]: + """Converts raw model output tensors into detections and prototypes. + + Args: + x (list[torch.Tensor]): List of raw output tensors. + + Returns: + tuple: (detections, prototypes) + """ + if (self.nc + self.n_extra + 4) in x[0].shape[1:] and self.n_extra in x[ + 1 + ].shape[1:]: + return ( + x[0], + x[1], + ) + if (self.nc + self.n_extra + 4) in x[1].shape[1:] and self.n_extra in x[ + 0 + ].shape[1:]: + return ( + x[1], + x[0], + ) + raise ValueError(f"Wrong shape of input: {x[0].shape}, {x[1].shape}") + + def rearrange(self, x: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]: + """ + Rearrange model output tensors for segmentation tasks. + Args: + x (list[torch.Tensor]): Raw output tensors. + Returns: + tuple: (concatenated_detections, prototype_masks) + """ + y_det: list[torch.Tensor] = [] + y_cls: list[torch.Tensor] = [] + y_ext: list[torch.Tensor] = [] + for xi in x: + if xi.shape[-1] == self.n_extra: + y_ext.append( + xi.permute(0, 3, 1, 2) + ) # (b, 32, 160, 160), (b, 32, 80, 80), ... + elif xi.shape[-1] == self.reg_max * 4: + y_det.append( + xi.permute(0, 3, 1, 2) + ) # (b, 64, 80, 80), (b, 64 ,40, 40), ... + elif xi.shape[-1] == self.nc: + y_cls.append( + xi.permute(0, 3, 1, 2) + ) # (b, 80, 80, 80), (b, 80, 40, 40), ... + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + # sort as box, scores + y_ext = sorted(y_ext, key=lambda x: x.numel(), reverse=True) + proto = y_ext.pop(0).permute(0, 2, 3, 1) + y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) + y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + if not len(y_cls) == len(y_det) == len(y_ext): + raise ValueError( + "Classification, detection, and extra output counts must match." + ) + y = torch.cat( + [ + torch.cat((yi_det, yi_cls, yi_ext), dim=1).flatten(2) + for yi_det, yi_cls, yi_ext in zip(y_det, y_cls, y_ext) + ], + dim=-1, + ) + return y, proto + + +class YOLOAnchorlessPosePost(YOLOPosePostMixin, YOLOAnchorlessDetectionPost): + """Postprocessing for YOLO pose estimation models without anchors.""" + + def rearrange(self, x: list[torch.Tensor]) -> torch.Tensor: + """Rearranges model output tensors for pose estimation tasks. + + Args: + x (list[torch.Tensor]): Raw output tensors. + + Returns: + torch.Tensor: Concatenated tensor for decode. + """ + y_det = [] + y_cls = [] + y_kpt = [] + for xi in x: # list of bchw outputs + if xi.ndim == 3: + xi = xi[None] + elif xi.ndim == 4: + pass + else: + raise NotImplementedError(f"Got unsupported ndim for input: {xi.ndim}.") + if xi.shape[-1] == self.reg_max * 4: + y_det.append( + xi.permute(0, 3, 1, 2) + ) # (b, 64, 80, 80), (b, 64 ,40, 40), ... + elif xi.shape[-1] == self.nc: + y_cls.append( + xi.permute(0, 3, 1, 2) + ) # (b, 1, 80, 80), (b, 1, 40, 40), ... + elif xi.shape[-1] == self.n_extra: + y_kpt.append( + xi.permute(0, 3, 1, 2).flatten(2) + ) # (b, 51, 80, 80), (b, 1, 40, 40), ... + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + # sort as box, scores + y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) + y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + y_kpt = sorted( + y_kpt, key=lambda x: x.numel(), reverse=True + ) # (b, 51, 6400), (b, 51, 1600), (b, 51, 400) + y_tmp = [ + torch.cat((yi_det, yi_cls), dim=1).flatten(2) + for (yi_det, yi_cls) in zip( + y_det, y_cls + ) # (b, 65, 6400), (b, 65, 1600), (b, 65, 400) + ] + return torch.cat( + [ + torch.cat((yi_tmp, yi_kpt), dim=1) + for yi_tmp, yi_kpt in zip(y_tmp, y_kpt) + ], + dim=-1, + ) + + def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: + """Processes pose estimation results for a single image. + + Args: + box_cls: Raw detections for one image. + + Returns: + Decoded boxes, scores, and keypoints. + """ + ic = ( + torch.amax(box_cls[-self.nc - self.n_extra : -self.n_extra, :], dim=0) + > self.inv_conf_thres + ) + box_cls = box_cls[:, ic] # (116, *) + if box_cls.numel() == 0: + return torch.zeros( + (4 + self.nc + self.n_extra, 0), dtype=torch.float32 + ) # (56, 0) + anchors = self.anchors_as_tensor() + stride = self.stride_as_tensor() + box, scores, keypoints = torch.split( + box_cls[None], [self.reg_max * 4, self.nc, self.n_extra], dim=1 + ) # (1, 64, *), (1, 1, *), (1, 51, *) + dbox = ( + dist2bbox( + self.dfl(box), + anchors[:, ic], + xywh=False, + dim=1, + ) + * stride[:, ic] + ) + keypoints = keypoints.view(1, 17, 3, -1) + key_coord, key_conf = torch.split( + keypoints, [2, 1], dim=2 + ) # (1, 17, 2, 8400), (1, 17, 1, 8400) + key_coord = (key_coord * 2 + anchors[:, ic] - 0.5) * stride[ + :, ic + ] # (1, 17, 2, *) + keypoints = torch.cat([key_coord, key_conf.sigmoid()], dim=2).view( + 1, self.n_extra, -1 + ) # (1, 51, *) + return torch.cat([dbox, scores.sigmoid(), keypoints], dim=1).squeeze( + 0 + ) # (56, *) + + def decode_batch(self, x: torch.Tensor) -> torch.Tensor: + """Decode every anchor without confidence filtering for export-style pose output.""" + box, scores, keypoints = torch.split( + x, [self.reg_max * 4, self.nc, self.n_extra], dim=1 + ) + anchors = self.anchors_as_tensor().unsqueeze(0) + stride = self.stride_as_tensor().unsqueeze(0) + dbox = dist2bbox(self.dfl(box), anchors, xywh=False, dim=1) * stride + keypoints = keypoints.view(x.shape[0], 17, 3, -1) + key_coord, key_conf = torch.split(keypoints, [2, 1], dim=2) + key_coord = (key_coord * 2 + anchors.unsqueeze(1) - 0.5) * stride.unsqueeze(1) + keypoints = torch.cat([key_coord, key_conf.sigmoid()], dim=2).view( + x.shape[0], self.n_extra, -1 + ) + return torch.cat([dbox, scores.sigmoid(), keypoints], dim=1) + + +class YOLOAnchorlessOBBPost(YOLOOBBPostMixin, YOLOAnchorlessDetectionPost): + """Postprocessing for anchorless YOLO OBB models.""" + + def _angle_from_raw(self, angle: torch.Tensor) -> torch.Tensor: + """Decode YOLOv8/YOLO11 raw angle logits to radians.""" + return (angle.sigmoid() - 0.25) * torch.pi + + def _pre_process( + self, x: list[torch.Tensor] + ) -> tuple[_AnchorlessNMSInput, torch.Tensor | None]: + """Preprocess OBB inputs into row-major detections. + + Args: + x: Raw model outputs. + + Returns: + A tuple of detections and no prototype output. + """ + if len(x) in {1, 3, 5}: + detections = self.filter_conversion(self.conversion(x)) + return _AnchorlessNMSInput(detections, "candidates_first"), None + return _AnchorlessNMSInput( + self.decode(self.rearrange(x)), "channels_first" + ), None + + def conversion(self, x: list[torch.Tensor]) -> torch.Tensor: + """Convert exported OBB outputs to one canonical tensor. + + Args: + x: Converted OBB runtime outputs. + + Returns: + Detections in ``cx, cy, w, h, class scores..., angle`` format. + """ + if len(x) == 5: + return decode_split_converted_obb_outputs( + x, + self.nc, + self.n_extra, + self.anchors_as_tensor(), + self.stride_as_tensor(), + ) + return concat_converted_obb_outputs(x, self.nc, self.n_extra) + + def rearrange(self, x: list[torch.Tensor]) -> torch.Tensor: + """Rearrange raw OBB heads into ``(batch, channels, anchors)`` format. + + Args: + x: Raw model output tensors. + + Returns: + Concatenated OBB detection tensor. + """ + target_count = len(x) // 3 + y_det: list[torch.Tensor] = [] + y_cls: list[torch.Tensor] = [] + y_angle: list[torch.Tensor] = [] + ambiguous: list[tuple[torch.Tensor, list[int]]] = [] + for xi in x: + if xi.ndim == 3: + xi = xi.unsqueeze(0) + elif xi.ndim > 4: + while xi.ndim > 4 and 1 in xi.shape: + xi = xi.squeeze( + next(idx for idx, size in enumerate(xi.shape) if size == 1) + ) + if xi.ndim == 3: + xi = xi.unsqueeze(0) + if xi.ndim != 4: + raise ValueError( + f"Expected 3D or 4D OBB head, got shape {tuple(xi.shape)}." + ) + + candidates: list[tuple[int, torch.Tensor]] = [] + if xi.shape[1] in {self.reg_max * 4, self.nc, self.n_extra}: + candidates.append((int(xi.shape[1]), xi)) + if xi.shape[-1] in {self.reg_max * 4, self.nc, self.n_extra}: + candidates.append((int(xi.shape[-1]), xi.permute(0, 3, 1, 2))) + + deduped: list[tuple[int, torch.Tensor]] = [] + seen_channels: set[int] = set() + for channel_count, candidate in candidates: + if channel_count not in seen_channels: + seen_channels.add(channel_count) + deduped.append((channel_count, candidate)) + + if len(candidates) == 2 and len(deduped) == 1: + channel_count, _ = deduped[0] + normalized = xi.permute(0, 3, 1, 2) + if channel_count == self.reg_max * 4: + y_det.append(normalized) + elif channel_count == self.nc: + y_cls.append(normalized) + elif channel_count == self.n_extra: + y_angle.append(normalized) + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + elif len(deduped) == 1: + channel_count, normalized = deduped[0] + if channel_count == self.reg_max * 4: + y_det.append(normalized) + elif channel_count == self.nc: + y_cls.append(normalized) + elif channel_count == self.n_extra: + y_angle.append(normalized) + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + elif len(deduped) > 1: + ambiguous.append((xi, [channel_count for channel_count, _ in deduped])) + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + + for xi, channel_options in ambiguous: + if self.reg_max * 4 in channel_options and len(y_det) < target_count: + y_det.append( + xi if xi.shape[1] == self.reg_max * 4 else xi.permute(0, 3, 1, 2) + ) + continue + if self.nc in channel_options and len(y_cls) < target_count: + y_cls.append(xi if xi.shape[1] == self.nc else xi.permute(0, 3, 1, 2)) + continue + if self.n_extra in channel_options and len(y_angle) < target_count: + y_angle.append( + xi if xi.shape[1] == self.n_extra else xi.permute(0, 3, 1, 2) + ) + continue + raise ValueError(f"Wrong shape of input: {xi.shape}") + + y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) + y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + y_angle = sorted(y_angle, key=lambda x: x.numel(), reverse=True) + if not (len(y_det) == len(y_cls) == len(y_angle)): + raise ValueError("OBB output arguments are not in a proper form.") + return torch.cat( + [ + torch.cat((yi_det, yi_cls, yi_angle), dim=1).flatten(2) + for yi_det, yi_cls, yi_angle in zip(y_det, y_cls, y_angle) + ], + dim=-1, + ) + + def decode_batch(self, x: torch.Tensor) -> torch.Tensor: + """Decode every OBB anchor without confidence filtering.""" + box, scores, angle = torch.split( + x, [self.reg_max * 4, self.nc, self.n_extra], dim=1 + ) + anchors = self.anchors_as_tensor().unsqueeze(0) + stride = self.stride_as_tensor().unsqueeze(0) + angle = self._angle_from_raw(angle) + rbox = dist2rbox(self.dfl(box), angle, anchors, dim=1) * stride + return torch.cat([rbox, scores.sigmoid(), angle], dim=1) + + def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: + """Processes OBB results for a single image. + + Args: + box_cls: Raw detections for one image. + + Returns: + Decoded rotated boxes, scores, and angle. + """ + ic = ( + torch.amax(box_cls[-self.nc - self.n_extra : -self.n_extra, :], dim=0) + > self.inv_conf_thres + ) + box_cls = box_cls[:, ic] + if box_cls.numel() == 0: + return torch.zeros( + (4 + self.nc + self.n_extra, 0), dtype=torch.float32, device=self.device + ) + anchors = self.anchors_as_tensor() + stride = self.stride_as_tensor() + box, scores, angle = torch.split( + box_cls[None], [self.reg_max * 4, self.nc, self.n_extra], dim=1 + ) + angle = self._angle_from_raw(angle) + rbox = dist2rbox(self.dfl(box), angle, anchors[:, ic], dim=1) * stride[:, ic] + return torch.cat([rbox, scores.sigmoid(), angle], dim=1).squeeze(0) + + def filter_conversion(self, x: torch.Tensor) -> list[torch.Tensor]: + """Filters converted OBB outputs from a single ONNX tensor. + + Args: + x: Converted output tensor. + + Returns: + Per-image row-major tensors in ``cx, cy, w, h, scores..., angle`` format. + """ + while x.ndim == 4 and 1 in (x.shape[0], x.shape[1]): + if x.shape[0] == 1: + x = x.squeeze(0) + elif x.shape[1] == 1: + x = x.squeeze(1) + if x.ndim != 3: + raise ValueError( + f"Expected 3D converted tensor, got shape {tuple(x.shape)}." + ) + expected_dim = 4 + self.nc + self.n_extra + if x.shape[-1] == expected_dim: + normalized = x + elif x.shape[1] == expected_dim: + normalized = x.transpose(1, 2) + else: + raise ValueError(f"Unsupported converted tensor shape {tuple(x.shape)}.") + + outputs = [] + for xi in torch.split(normalized, 1, dim=0): + xi = xi.squeeze(0) + ic = torch.amax(xi[:, 4 : 4 + self.nc], dim=1) > self.conf_thres + if torch.any(ic): + outputs.append(xi[ic]) + else: + outputs.append( + torch.zeros((0, expected_dim), dtype=xi.dtype, device=xi.device) + ) + return outputs + + def _nms_single( + self, + xi: torch.Tensor, + max_det: int, + max_nms: int, + max_wh: int, + multi_label: bool, + ) -> torch.Tensor: + """Apply rotated NMS to a single decoded OBB image tensor.""" + if xi.numel() == 0: + return torch.zeros((0, 7), dtype=torch.float32, device=self.device) + xi_t = xi.transpose(0, 1) + return self._nms_single_legacy_rows( + xi_t, + max_det=max_det, + max_nms=max_nms, + max_wh=max_wh, + multi_label=multi_label, + ) + + def _nms_single_legacy_rows( + self, + xi: torch.Tensor, + max_det: int, + max_nms: int, + max_wh: int, + multi_label: bool, + ) -> torch.Tensor: + """Apply rotated NMS to row-major OBB detections.""" + del multi_label + if xi.numel() == 0: + return torch.zeros((0, 7), dtype=torch.float32, device=self.device) + xi_out = yolo_multilabel_candidates(xi, self.nc, self.n_extra, self.conf_thres) + if xi_out.numel() == 0: + return torch.zeros((0, 7), dtype=torch.float32, device=self.device) + xi_out = xi_out[torch.argsort(xi_out[:, 4], descending=True)[:max_nms]] + c = xi_out[:, 5:6] * max_wh + boxes = torch.cat([xi_out[:, :2] + c, xi_out[:, 2:4], xi_out[:, 6:7]], dim=-1) + keep = rotated_nms(boxes, xi_out[:, 4], self.iou_thres)[:max_det] + return xi_out[keep] + + def nms_multilabel( + self, + x: _AnchorlessNMSInput | torch.Tensor | list[torch.Tensor], + ) -> list[torch.Tensor]: + """Preserve existing OBB NMS behavior outside the COCO validation scope.""" + return self.nms(x) + + +YOLOAnchorlessPost = YOLOAnchorlessDetectionPost diff --git a/mblt_vision/utils/postprocess/yolo_dflfree_post.py b/mblt_vision/utils/postprocess/yolo_dflfree_post.py new file mode 100644 index 0000000..7cf47e5 --- /dev/null +++ b/mblt_vision/utils/postprocess/yolo_dflfree_post.py @@ -0,0 +1,991 @@ +from __future__ import annotations + +from typing import Any, cast + +import torch + +from .base import YOLODetectionPostBase +from .common import ( + YOLOOBBPostMixin, + YOLOPosePostMixin, + YOLOSegPostMixin, + concat_converted_obb_outputs, + decode_split_converted_obb_outputs, + dist2bbox, + dist2rbox, + dual_topk, + rotated_nms, + yolo_multilabel_candidates, +) + + +class YOLODFLFreeDetectionPost(YOLODetectionPostBase): + """Postprocessing for YOLO DFL-free models.""" + + max_det = 300 + reducemax_rtol = 1e-3 + reducemax_atol = 5e-2 + + def __init__(self, pre_cfg: dict, post_cfg: dict, **kwargs: object) -> None: + """Initialize the DFL-free YOLO postprocessor. + + Args: + pre_cfg: Preprocessing configuration. + post_cfg: Postprocessing configuration. + **kwargs: Optional runtime overrides for postprocess behavior. + """ + super().__init__(pre_cfg, post_cfg, **kwargs) + + def _normalize_converted_part( + self, x: torch.Tensor, channel_count: int + ) -> torch.Tensor | None: + """Normalize a split decode-true part to ``(B, anchors, channels)``.""" + + while x.ndim > 3 and 1 in x.shape: + x = x.squeeze(next(idx for idx, size in enumerate(x.shape) if size == 1)) + + if x.ndim == 2: + if x.shape[-1] == channel_count: + return x.unsqueeze(0) + if x.shape[0] == channel_count: + return x.transpose(0, 1).unsqueeze(0) + return None + + if x.ndim == 3: + if x.shape[-1] == channel_count: + return x + if x.shape[1] == channel_count: + return x.transpose(1, 2) + + return None + + def _collect_converted_parts( + self, + x: list[torch.Tensor], + *, + require_extra: bool, + ) -> tuple[torch.Tensor, set[int]] | None: + """Collect decode-true box/class/extra parts while ignoring reducemax.""" + + part_by_role: dict[str, torch.Tensor] = {} + used_indices: set[int] = set() + required_parts: list[tuple[str, int]] = [("boxes", 4), ("scores", self.nc)] + if require_extra: + required_parts.append(("extra", self.n_extra)) + + score_candidates = [ + (idx, cast(torch.Tensor, normalized)) + for idx, xi in enumerate(x) + if (normalized := self._normalize_converted_part(xi, self.nc)) is not None + ] + reducemax_candidates = [ + (idx, cast(torch.Tensor, normalized)) + for idx, xi in enumerate(x) + if (normalized := self._normalize_converted_part(xi, 1)) is not None + ] + + def _matches_reducemax(candidate_idx: int, candidate: torch.Tensor) -> bool: + if candidate.shape[-1] != self.nc: + return False + reduced = candidate.max(dim=-1, keepdim=True).values + return any( + reduced.shape == reducemax.shape + and torch.allclose( + reduced, + reducemax, + rtol=self.reducemax_rtol, + atol=self.reducemax_atol, + ) + for reducemax_idx, reducemax in reducemax_candidates + if reducemax_idx != candidate_idx + ) + + preferred_single_class_score_idx: int | None = None + if self.nc == 1 and len(score_candidates) > 1: + matched_score_candidates = [ + (idx, candidate) + for idx, candidate in score_candidates + if _matches_reducemax(idx, candidate) + ] + if matched_score_candidates: + preferred_single_class_score_idx, _ = max( + matched_score_candidates, + key=lambda item: float(item[1].sum()), + ) + + for idx, xi in enumerate(x): + for role, channel_count in required_parts: + if role in part_by_role: + continue + normalized = self._normalize_converted_part(xi, channel_count) + if normalized is None: + continue + + if role == "scores": + if ( + preferred_single_class_score_idx is not None + and idx != preferred_single_class_score_idx + ): + continue + if not _matches_reducemax(idx, normalized): + continue + elif ( + channel_count == self.nc + and self.nc == 4 + and _matches_reducemax(idx, normalized) + ): + continue + + part_by_role[role] = normalized + used_indices.add(idx) + break + + if any(role not in part_by_role for role, _ in required_parts): + return None + + batch_size = part_by_role["boxes"].shape[0] + anchor_count = part_by_role["boxes"].shape[1] + for role, _channel_count in required_parts[1:]: + part = part_by_role[role] + if part.shape[0] != batch_size or part.shape[1] != anchor_count: + return None + + ordered_parts = [part_by_role["boxes"], part_by_role["scores"]] + if require_extra: + ordered_parts.append(part_by_role["extra"]) + return torch.cat(ordered_parts, dim=-1), used_indices + + def non_e2e(self, x: list[torch.Tensor]) -> torch.Tensor | list[torch.Tensor]: + """Return the export-style output tensor for DFL-free YOLO models.""" + if len(x) == 2: + converted = cast(torch.Tensor, self.conversion(x)) + return self._stack_topk_outputs(self.filter_conversion(converted)) + if len(x) == 4: + converted, proto_outs = cast( + tuple[torch.Tensor, torch.Tensor], self.conversion(x) + ) + return [ + self._stack_topk_outputs(self.filter_conversion(converted)), + self._proto_to_nchw(proto_outs), + ] + if len(x) == 3: + converted = cast(torch.Tensor, self.conversion(x)) + return self._stack_topk_outputs(self.filter_conversion(converted)) + + rearranged = self.rearrange(x) + if isinstance(rearranged, tuple): + det_out, proto_outs = rearranged + return [self.decode_batch(det_out), self._proto_to_nchw(proto_outs)] + return self.decode_batch(rearranged) + + def _proto_to_nchw(self, proto: torch.Tensor) -> torch.Tensor: + """Convert prototype tensors to ``(B, C, H, W)`` if needed.""" + if proto.ndim == 4 and proto.shape[1] == self.n_extra: + return proto + if proto.ndim == 4 and proto.shape[-1] == self.n_extra: + return proto.permute(0, 3, 1, 2) + raise ValueError( + f"Unsupported proto tensor shape {tuple(proto.shape)} for non-e2e output." + ) + + def _stack_topk_outputs(self, outputs: list[torch.Tensor]) -> torch.Tensor: + """Pad or trim per-image detections to a fixed batch tensor.""" + if not outputs: + raise ValueError("At least one output tensor is required.") + + output_dim = int(outputs[0].shape[1]) + padded_outputs = [] + for output in outputs: + if output.ndim != 2: + raise ValueError( + f"Expected 2D detection rows, got shape {tuple(output.shape)}." + ) + if output.shape[1] != output_dim: + raise ValueError( + f"Inconsistent detection row width {output.shape[1]}; expected {output_dim}." + ) + output = output[: self.max_det] + if output.shape[0] < self.max_det: + pad = torch.zeros( + (self.max_det - output.shape[0], output_dim), + dtype=output.dtype, + device=output.device, + ) + output = torch.cat([output, pad], dim=0) + padded_outputs.append(output) + return torch.stack(padded_outputs, dim=0) + + def decode_batch(self, x: torch.Tensor) -> torch.Tensor: + """Decode every anchor, then apply batched top-k selection for export-style output.""" + box, scores, extra = torch.split(x, [4, self.nc, self.n_extra], dim=1) + anchors = self.anchors_as_tensor().unsqueeze(0) + stride = self.stride_as_tensor().unsqueeze(0) + dbox = dist2bbox(box, anchors, xywh=False, dim=1) * stride + decoded = torch.cat([dbox, scores, extra], dim=1).transpose(1, 2) + return self._stack_topk_outputs( + [ + dual_topk( + image, + self.nc, + self.n_extra, + max_det=self.max_det, + conf_thres=self.conf_thres, + score_is_logits=True, + ) + for image in decoded + ] + ) + + def _pre_process(self, x: list[torch.Tensor]) -> tuple[Any, torch.Tensor | None]: + """Preprocesses inputs for DFL-free models. + + Args: + x (list[torch.Tensor]): Raw model outputs. + + Returns: + tuple: (processed detections, None). + """ + if len(x) in {2, 3}: + converted = cast(torch.Tensor, self.conversion(x)) + return self.filter_conversion(converted), None + rearranged = self.rearrange(x) + if not isinstance(rearranged, torch.Tensor): + raise TypeError( + "rearrange should return a tensor for DFL-free detection postprocessing." + ) + return self.decode(rearranged), None + + def conversion( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Converts raw model output tensors into a single concatenated tensor. + + Args: + x (list[torch.Tensor]): List of raw output tensors. + + Returns: + torch.Tensor: + Concatenated tensor of shape ``(batch, num_anchors, 4 + nc + n_extra)``. + """ + converted_parts = self._collect_converted_parts( + x, require_extra=self.n_extra > 0 + ) + if converted_parts is not None: + converted, _ = converted_parts + return converted + + # sort by element number + x = sorted(x, key=lambda x: x.size(), reverse=self.nc < 4) + return torch.cat(x, dim=-1).squeeze(1) # [b, 8400, 84] + + def rearrange( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Rearranges raw outputs into a task-specific intermediate representation. + + Args: + x: Raw model output tensors. + + Returns: + A concatenated intermediate representation used by ``decode``. + """ + y_det = [] + y_cls = [] + for xi in x: # list of bchw outputs + if xi.ndim == 3: + xi = xi[None] + elif xi.ndim == 4: + pass + else: + raise NotImplementedError(f"Got unsupported ndim for input: {xi.ndim}.") + if xi.shape[-1] == 4: + y_det.append( + xi.permute(0, 3, 1, 2) + ) # (b, 4, 80, 80), (b, 4, 40, 40), ... + elif xi.shape[-1] == self.nc: + y_cls.append( + xi.permute(0, 3, 1, 2) + ) # (b, 80, 80, 80), (b, 80, 40, 40), ... + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + # sort as box, scores + y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) + y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + return torch.cat( + [ + torch.cat((yi_det, yi_cls), dim=1).flatten(2) + for yi_det, yi_cls in zip(y_det, y_cls) + ], + dim=-1, + ) + + def decode(self, x: torch.Tensor) -> list[torch.Tensor]: + """Decodes model outputs into box coordinates and class scores. + + Args: + x (torch.Tensor): Concatenated output tensor from `rearrange`. + + Returns: + list[torch.Tensor]: Per-image decoded detections after filtering and top-k selection. + """ + return [self.process_box_cls(box_cls) for box_cls in x] + + def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: + """Processes detection results for a single image. + + Args: + box_cls: Raw detections for one image. + + Returns: + Decoded boxes, scores, and extra data. + """ + if self.n_extra == 0: + ic = torch.amax(box_cls[-self.nc :, :], dim=0) > self.inv_conf_thres + else: + ic = ( + torch.amax(box_cls[-self.nc - self.n_extra : -self.n_extra, :], dim=0) + > self.inv_conf_thres + ) + box_cls = box_cls[:, ic] # (84, *) + if box_cls.numel() == 0: + return torch.zeros( + (0, 4 + self.nc + self.n_extra), dtype=torch.float32 + ) # (0, 84) + anchors = self.anchors_as_tensor() + stride = self.stride_as_tensor() + box, scores, extra = torch.split( + box_cls[None], [4, self.nc, self.n_extra], dim=1 + ) # (*, 4), (*, 80), (*, 32) + dbox = ( + dist2bbox( + box, + anchors[:, ic], + xywh=False, + dim=1, + ) + * stride[:, ic] + ) + pre_topk = ( + torch.cat([dbox, scores, extra], dim=1).squeeze(0).transpose(0, 1) + ) # (*, 84) + return dual_topk( + pre_topk, + self.nc, + self.n_extra, + conf_thres=self.conf_thres, + score_is_logits=True, + ) + + def filter_conversion(self, x: torch.Tensor) -> list[torch.Tensor]: + """Filters out low-confidence detections from a single concatenated output tensor. + + Args: + x (torch.Tensor): Output tensor from the model. + + Returns: + list[torch.Tensor]: Filtered detections for each image in the batch. + """ + x_list = torch.split(x, 1, dim=0) # [(1, 8400, 84), (1, 8400, 84), ...] + + return [ + dual_topk(xi.squeeze(0), self.nc, self.n_extra, conf_thres=self.conf_thres) + for xi in x_list + ] + + def nms( + self, + x: torch.Tensor | list[torch.Tensor], + _max_det: int = 300, + _max_nms: int = 30000, + _max_wh: int = 7680, + ) -> list[torch.Tensor]: + """Performs Non-Maximum Suppression (no-op for NMS-free models). + + Args: + x (list[torch.Tensor]): Decoded detections. + _max_det (int, optional): Maximum number of detections to keep. Defaults to 300. + _max_nms (int, optional): Maximum candidates for NMS. Defaults to 30000. + _max_wh (int, optional): Maximum box width/height. Defaults to 7680. + + Returns: + list[torch.Tensor]: Per-image detections with padded zero rows removed. + """ + if isinstance(x, list): + return x + return [xi[xi[:, 4] > 0] for xi in x] + + +class YOLODFLFreeSegPost(YOLOSegPostMixin, YOLODFLFreeDetectionPost): + """Postprocessing for YOLO NMS-free segmentation models.""" + + def non_e2e(self, x: list[torch.Tensor]) -> torch.Tensor | list[torch.Tensor]: + """Return export-style segmentation outputs for converted or raw split heads.""" + + if len(x) in {4, 5}: + converted, proto_outs = cast( + tuple[torch.Tensor, torch.Tensor], self.conversion(x) + ) + return [ + self._stack_topk_outputs(self.filter_conversion(converted)), + self._proto_to_nchw(proto_outs), + ] + + rearranged, proto_outs = self.rearrange(x) + return [self.decode_batch(rearranged), self._proto_to_nchw(proto_outs)] + + def _pre_process( + self, x: list[torch.Tensor] + ) -> tuple[list[torch.Tensor], torch.Tensor]: + """Preprocesses intermediate inputs into (boxes, proto) format. + + Args: + x (list[torch.Tensor]): Raw model output tensors. + + Returns: + tuple: (decoded_detections, prototype_masks). + """ + if len(x) in {4, 5}: + converted, proto_outs = cast( + tuple[torch.Tensor, torch.Tensor], self.conversion(x) + ) + return self.filter_conversion(converted), proto_outs + rearranged, proto_outs = self.rearrange(x) + return self.decode(rearranged), proto_outs + + def conversion( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Converts raw outputs into detections and prototype masks. + + Args: + x: Input tensors. + + Returns: + A tuple of processed detections and prototype masks. + """ + + converted_parts = self._collect_converted_parts(x, require_extra=True) + if converted_parts is not None: + converted, used_indices = converted_parts + batch_size, anchor_count = converted.shape[:2] + reducemax_candidate_indices = { + idx + for idx, xi in enumerate(x) + if (normalized := self._normalize_converted_part(xi, 1)) is not None + and normalized.shape[0] == batch_size + and normalized.shape[1] == anchor_count + } + proto_candidates = [] + for idx, xi in enumerate(x): + if idx in used_indices or idx in reducemax_candidate_indices: + continue + proto = xi + if proto.ndim == 3: + proto = proto.unsqueeze(0) + if proto.ndim == 4 and ( + proto.shape[-1] == self.n_extra or proto.shape[1] == self.n_extra + ): + proto_candidates.append(proto) + if len(proto_candidates) == 1: + return converted, proto_candidates[0] + + x = sorted(x, key=lambda x: x.size(), reverse=self.nc < 4) + outputs: list[torch.Tensor] = [] + protos: list[torch.Tensor] = [] + for xi in x: + if xi.shape[-1] == self.n_extra: + protos.append(xi) + else: + outputs.append(xi) + proto = protos.pop(0 if self.nc < 4 else -1) + converted = torch.cat(outputs + protos, dim=-1).squeeze(1) + return converted, proto + + def rearrange(self, x: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]: + """Rearranges segmentation outputs into detections and prototype masks. + + Args: + x: Raw model output tensors. + + Returns: + A tuple of concatenated detections and prototype masks. + """ + y_det = [] + y_cls = [] + y_ext = [] + for xi in x: # list of bchw outputs + if xi.ndim == 3: + xi = xi[None] + elif xi.ndim == 4: + pass + else: + raise NotImplementedError(f"Got unsupported ndim for input: {xi.ndim}.") + if xi.shape[-1] == self.n_extra: + y_ext.append( + xi.permute(0, 3, 1, 2) + ) # (b, 32, 160, 160), (b, 32, 80, 80), ... + elif xi.shape[-1] == 4: + y_det.append( + xi.permute(0, 3, 1, 2) + ) # (b, 4, 80, 80), (b, 4 ,40, 40), ... + elif xi.shape[-1] == self.nc: + y_cls.append( + xi.permute(0, 3, 1, 2) + ) # (b, 80, 80, 80), (b, 80, 40, 40), ... + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + # sort as box, scores + y_ext = sorted(y_ext, key=lambda x: x.numel(), reverse=True) + proto = y_ext.pop(0).permute(0, 2, 3, 1) + y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) + y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + if not len(y_cls) == len(y_det) == len(y_ext): + raise ValueError( + "Classification, detection, and extra output counts must match." + ) + y = torch.cat( + [ + torch.cat((yi_det, yi_cls, yi_ext), dim=1).flatten(2) + for yi_det, yi_cls, yi_ext in zip(y_det, y_cls, y_ext) + ], + dim=-1, + ) + return y, proto + + +class YOLODFLFreePosePost(YOLOPosePostMixin, YOLODFLFreeDetectionPost): + """Postprocessing for YOLO NMS-free pose estimation models.""" + + def non_e2e(self, x: list[torch.Tensor]) -> torch.Tensor | list[torch.Tensor]: + """Return export-style pose outputs for both converted and raw split heads.""" + + if len(x) in {3, 4}: + converted = cast(torch.Tensor, self.conversion(x)) + return self._stack_topk_outputs(self.filter_conversion(converted)) + + rearranged = self.rearrange(x) + return self.decode_batch(rearranged) + + def _pre_process( + self, x: list[torch.Tensor] + ) -> tuple[list[torch.Tensor], torch.Tensor | None]: + """Preprocesses inputs for pose estimation. + + Args: + x (list[torch.Tensor]): Raw model outputs. + + Returns: + tuple: (processed_detections, None). + """ + if len(x) in {3, 4}: + converted = cast(torch.Tensor, self.conversion(x)) + return self.filter_conversion(converted), None + rearranged = self.rearrange(x) + return self.decode(rearranged), None + + def conversion( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Convert input tensors. + Args: + x (list[torch.Tensor]): Input tensors. + Returns: + torch.Tensor: Converted tensor. + """ + converted_parts = self._collect_converted_parts(x, require_extra=True) + if converted_parts is not None: + converted, _ = converted_parts + return converted + + # sort by element number + x = sorted(x, key=lambda x: x.size(), reverse=True) + kpt: torch.Tensor = x.pop(0) + kpt = kpt.permute(0, 3, 1, 2).flatten(-2) + return torch.cat( + [torch.cat(x, dim=-1).squeeze(1), kpt], dim=-1 + ) # [b, 8400, 56] + + def rearrange(self, x: list[torch.Tensor]) -> torch.Tensor: + y_det = [] + y_cls = [] + y_kpt = [] + for xi in x: # list of bchw outputs + if xi.ndim == 3: + xi = xi[None] + elif xi.ndim == 4: + pass + else: + raise NotImplementedError(f"Got unsupported ndim for input: {xi.ndim}.") + if xi.shape[-1] == 4: + y_det.append( + xi.permute(0, 3, 1, 2) + ) # (b, 4, 80, 80), (b, 4 ,40, 40), ... + elif xi.shape[-1] == self.nc: + y_cls.append( + xi.permute(0, 3, 1, 2) + ) # (b, 1, 80, 80), (b, 1, 40, 40), ... + elif xi.shape[-1] == self.n_extra: + y_kpt.append( + xi.permute(0, 3, 1, 2).flatten(2) + ) # (b, 51, 80, 80), (b, 1, 40, 40), ... + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + # sort as box, scores + y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) + y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + y_kpt = sorted( + y_kpt, key=lambda x: x.numel(), reverse=True + ) # (b, 51, 6400), (b, 51, 1600), (b, 51, 400) + y_tmp = [ + torch.cat((yi_det, yi_cls), dim=1).flatten(2) + for (yi_det, yi_cls) in zip( + y_det, y_cls + ) # (b, 65, 6400), (b, 65, 1600), (b, 65, 400) + ] + return torch.cat( + [ + torch.cat((yi_tmp, yi_kpt), dim=1) + for yi_tmp, yi_kpt in zip(y_tmp, y_kpt) + ], + dim=-1, + ) + + def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: + """Processes pose estimation results for a single image. + + Args: + box_cls: Raw detections for one image. + + Returns: + Decoded boxes, scores, and keypoints. + """ + ic = ( + torch.amax(box_cls[-self.nc - self.n_extra : -self.n_extra, :], dim=0) + > self.inv_conf_thres + ) + box_cls = box_cls[:, ic] # (116, *) + if box_cls.numel() == 0: + return torch.zeros( + (0, 4 + self.nc + self.n_extra), dtype=torch.float32 + ) # (0, 56) + anchors = self.anchors_as_tensor() + stride = self.stride_as_tensor() + box, scores, keypoints = torch.split( + box_cls[None], [4, self.nc, self.n_extra], dim=1 + ) # (1, 4, *), (1, 1, *), (1, 51, *) + dbox = ( + dist2bbox( + box, + anchors[:, ic], + xywh=False, + dim=1, + ) + * stride[:, ic] + ) + keypoints = keypoints.view(1, 17, 3, -1) + key_coord, key_conf = torch.split( + keypoints, [2, 1], dim=2 + ) # (1, 17, 2, 8400), (1, 17, 1, 8400) + key_coord = (key_coord + anchors[:, ic]) * stride[:, ic] # (1, 17, 2, *) + keypoints = torch.cat([key_coord, key_conf.sigmoid()], dim=2).view( + 1, self.n_extra, -1 + ) # (1, 51, *) + pre_topk = ( + torch.cat([dbox, scores, keypoints], dim=1).squeeze(0).transpose(0, 1) + ) # (*, 56) + return dual_topk( + pre_topk, + self.nc, + self.n_extra, + conf_thres=self.conf_thres, + score_is_logits=True, + ) + + def decode_batch(self, x: torch.Tensor) -> torch.Tensor: + """Decode every anchor, then apply batched top-k selection for export-style pose output.""" + box, scores, keypoints = torch.split(x, [4, self.nc, self.n_extra], dim=1) + anchors = self.anchors_as_tensor().unsqueeze(0) + stride = self.stride_as_tensor().unsqueeze(0) + dbox = dist2bbox(box, anchors, xywh=False, dim=1) * stride + keypoints = keypoints.view(x.shape[0], 17, 3, -1) + key_coord, key_conf = torch.split(keypoints, [2, 1], dim=2) + key_coord = (key_coord + anchors.unsqueeze(1)) * stride.unsqueeze(1) + keypoints = torch.cat([key_coord, key_conf.sigmoid()], dim=2).view( + x.shape[0], self.n_extra, -1 + ) + decoded = torch.cat([dbox, scores, keypoints], dim=1).transpose(1, 2) + return self._stack_topk_outputs( + [ + dual_topk( + image, + self.nc, + self.n_extra, + max_det=self.max_det, + conf_thres=self.conf_thres, + score_is_logits=True, + ) + for image in decoded + ] + ) + + +class YOLODFLFreeOBBPost(YOLOOBBPostMixin, YOLODFLFreeDetectionPost): + """Postprocessing for DFL-free YOLO OBB models.""" + + def _pre_process( + self, x: list[torch.Tensor] + ) -> tuple[list[torch.Tensor], torch.Tensor | None]: + """Preprocess OBB inputs into row-major detections. + + Args: + x: Raw model outputs. + + Returns: + A tuple of detections and no prototype output. + """ + if len(x) in {1, 3, 5}: + converted = cast(torch.Tensor, self.conversion(x)) + return self.filter_conversion(converted), None + rearranged = self.rearrange(x) + if not isinstance(rearranged, torch.Tensor): + raise TypeError( + "rearrange should return a tensor for DFL-free OBB postprocessing." + ) + return self.decode(rearranged), None + + def conversion( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Convert DFL-free OBB outputs to a single tensor. + + Args: + x: Input tensors. + + Returns: + Converted tensor with last dimension ``4 + nc + 1``. + """ + if len(x) == 5: + return decode_split_converted_obb_outputs( + x, + self.nc, + self.n_extra, + self.anchors_as_tensor(), + self.stride_as_tensor(), + ) + return concat_converted_obb_outputs(x, self.nc, self.n_extra) + + def rearrange(self, x: list[torch.Tensor]) -> torch.Tensor: + """Rearrange split raw DFL-free OBB heads. + + Args: + x: Raw model output tensors. + + Returns: + Concatenated tensor in ``(batch, channels, anchors)`` format. + """ + target_count = len(x) // 3 + y_det: list[torch.Tensor] = [] + y_cls: list[torch.Tensor] = [] + y_angle: list[torch.Tensor] = [] + ambiguous: list[tuple[torch.Tensor, list[int]]] = [] + for xi in x: + if xi.ndim == 3: + xi = xi.unsqueeze(0) + elif xi.ndim > 4: + while xi.ndim > 4 and 1 in xi.shape: + xi = xi.squeeze( + next(idx for idx, size in enumerate(xi.shape) if size == 1) + ) + if xi.ndim == 3: + xi = xi.unsqueeze(0) + if xi.ndim != 4: + raise ValueError( + f"Expected 3D or 4D OBB head, got shape {tuple(xi.shape)}." + ) + + candidates: list[tuple[int, torch.Tensor]] = [] + if xi.shape[1] in {4, self.nc, self.n_extra}: + candidates.append((int(xi.shape[1]), xi)) + if xi.shape[-1] in {4, self.nc, self.n_extra}: + candidates.append((int(xi.shape[-1]), xi.permute(0, 3, 1, 2))) + + deduped: list[tuple[int, torch.Tensor]] = [] + seen_channels: set[int] = set() + for channel_count, candidate in candidates: + if channel_count not in seen_channels: + seen_channels.add(channel_count) + deduped.append((channel_count, candidate)) + + if len(candidates) == 2 and len(deduped) == 1: + channel_count, _ = deduped[0] + normalized = xi.permute(0, 3, 1, 2) + if channel_count == 4: + y_det.append(normalized) + elif channel_count == self.nc: + y_cls.append(normalized) + elif channel_count == self.n_extra: + y_angle.append(normalized) + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + elif len(deduped) == 1: + channel_count, normalized = deduped[0] + if channel_count == 4: + y_det.append(normalized) + elif channel_count == self.nc: + y_cls.append(normalized) + elif channel_count == self.n_extra: + y_angle.append(normalized) + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + elif len(deduped) > 1: + ambiguous.append((xi, [channel_count for channel_count, _ in deduped])) + else: + raise ValueError(f"Wrong shape of input: {xi.shape}") + + for xi, channel_options in ambiguous: + if 4 in channel_options and len(y_det) < target_count: + y_det.append(xi if xi.shape[1] == 4 else xi.permute(0, 3, 1, 2)) + continue + if self.nc in channel_options and len(y_cls) < target_count: + y_cls.append(xi if xi.shape[1] == self.nc else xi.permute(0, 3, 1, 2)) + continue + if self.n_extra in channel_options and len(y_angle) < target_count: + y_angle.append( + xi if xi.shape[1] == self.n_extra else xi.permute(0, 3, 1, 2) + ) + continue + raise ValueError(f"Wrong shape of input: {xi.shape}") + + y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) + y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + y_angle = sorted(y_angle, key=lambda x: x.numel(), reverse=True) + if not (len(y_det) == len(y_cls) == len(y_angle)): + raise ValueError("OBB output arguments are not in a proper form.") + return torch.cat( + [ + torch.cat((yi_det, yi_cls, yi_angle), dim=1).flatten(2) + for yi_det, yi_cls, yi_angle in zip(y_det, y_cls, y_angle) + ], + dim=-1, + ) + + def decode_batch(self, x: torch.Tensor) -> torch.Tensor: + """Decode every OBB anchor for export-style output.""" + box, scores, angle = torch.split(x, [4, self.nc, self.n_extra], dim=1) + anchors = self.anchors_as_tensor().unsqueeze(0) + stride = self.stride_as_tensor().unsqueeze(0) + rbox = dist2rbox(box, angle, anchors, dim=1) * stride + return torch.cat([rbox, scores.sigmoid(), angle], dim=1).transpose(1, 2) + + def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: + """Processes raw DFL-free OBB results for one image. + + Args: + box_cls: Raw detections for one image. + + Returns: + Raw OBB rows ``cx, cy, w, h, class scores, angle`` before NMS. + """ + ic = ( + torch.amax(box_cls[-self.nc - self.n_extra : -self.n_extra, :], dim=0) + > self.inv_conf_thres + ) + box_cls = box_cls[:, ic] + if box_cls.numel() == 0: + return torch.zeros( + (0, 4 + self.nc + self.n_extra), dtype=torch.float32, device=self.device + ) + anchors = self.anchors_as_tensor() + stride = self.stride_as_tensor() + box, scores, angle = torch.split( + box_cls[None], [4, self.nc, self.n_extra], dim=1 + ) + rbox = dist2rbox(box, angle, anchors[:, ic], dim=1) * stride[:, ic] + return ( + torch.cat([rbox, scores.sigmoid(), angle], dim=1).squeeze(0).transpose(0, 1) + ) + + def filter_conversion(self, x: torch.Tensor) -> list[torch.Tensor]: + """Filters converted DFL-free OBB outputs. + + Args: + x: Converted output tensor. + + Returns: + Per-image canonical OBB detection rows before rotated NMS. + """ + while x.ndim == 4 and 1 in (x.shape[0], x.shape[1]): + if x.shape[0] == 1: + x = x.squeeze(0) + elif x.shape[1] == 1: + x = x.squeeze(1) + if x.ndim != 3: + raise ValueError( + f"Expected 3D converted tensor, got shape {tuple(x.shape)}." + ) + expected_dim = 4 + self.nc + self.n_extra + if x.shape[-1] == expected_dim: + normalized = x + elif x.shape[1] == expected_dim: + normalized = x.transpose(1, 2) + else: + raise ValueError(f"Unsupported converted tensor shape {tuple(x.shape)}.") + outputs = [] + for xi in normalized: + keep = xi[:, 4 : 4 + self.nc].amax(dim=1) > self.conf_thres + if torch.any(keep): + outputs.append(xi[keep]) + else: + outputs.append( + torch.zeros((0, expected_dim), dtype=xi.dtype, device=xi.device) + ) + return outputs + + def nms( + self, + x: torch.Tensor | list[torch.Tensor], + max_det: int = 300, + max_nms: int = 30000, + max_wh: int = 7680, + ) -> list[torch.Tensor]: + """Apply rotated NMS to DFL-free OBB detections. + + Args: + x: Decoded detections. + max_det: Maximum detections to keep. + max_nms: Maximum candidates to consider. + max_wh: Class offset size. + + Returns: + Per-image OBB detections after rotated NMS. + """ + detections = x if isinstance(x, list) else list(x) + output = [] + for xi in detections: + if xi.numel() == 0: + output.append( + torch.zeros((0, 7), dtype=torch.float32, device=self.device) + ) + continue + if xi.shape[1] == 4 + self.nc + self.n_extra: + xi = yolo_multilabel_candidates( + xi, self.nc, self.n_extra, self.conf_thres + ) + elif xi.shape[1] == 6 + self.n_extra: + xi = xi[xi[:, 4] > self.conf_thres] + else: + raise ValueError(f"Unsupported OBB detection shape {tuple(xi.shape)}.") + if xi.numel() == 0: + output.append( + torch.zeros((0, 7), dtype=torch.float32, device=self.device) + ) + continue + xi = xi[torch.argsort(xi[:, 4], descending=True)[:max_nms]] + c = xi[:, 5:6] * max_wh + boxes = torch.cat([xi[:, :2] + c, xi[:, 2:4], xi[:, 6:7]], dim=-1) + keep = rotated_nms(boxes, xi[:, 4], self.iou_thres)[:max_det] + output.append(xi[keep]) + return output + + +YOLODFLFreePost = YOLODFLFreeDetectionPost diff --git a/mblt_vision/utils/postprocess/yolo_nmsfree_post.py b/mblt_vision/utils/postprocess/yolo_nmsfree_post.py new file mode 100644 index 0000000..e2cdfac --- /dev/null +++ b/mblt_vision/utils/postprocess/yolo_nmsfree_post.py @@ -0,0 +1,176 @@ +""" +YOLO NMS-free postprocessing. +""" + +from __future__ import annotations + +from typing import Any, cast + +import torch + +from .common import dist2bbox, dual_topk +from .yolo_anchorless_post import YOLOAnchorlessDetectionPost, _AnchorlessNMSInput + + +class YOLONMSFreeDetectionPost(YOLOAnchorlessDetectionPost): + """Postprocessing for YOLO NMS-free models.""" + + max_det = 300 + + def non_e2e(self, x: list[torch.Tensor]) -> torch.Tensor: + """Return the export-style output tensor for NMS-free YOLO models.""" + if len(x) == 2: + converted = cast(torch.Tensor, self.conversion(x)) + return self._stack_topk_outputs(self.filter_conversion(converted)) + + rearranged = cast(torch.Tensor, self.rearrange(x)) + return self.decode_batch(rearranged) + + def _stack_topk_outputs(self, outputs: list[torch.Tensor]) -> torch.Tensor: + """Pad or trim per-image detections to a fixed batch tensor.""" + padded_outputs = [] + for output in outputs: + output = output[: self.max_det] + if output.shape[0] < self.max_det: + pad = torch.zeros( + (self.max_det - output.shape[0], 6), + dtype=output.dtype, + device=output.device, + ) + output = torch.cat([output, pad], dim=0) + padded_outputs.append(output) + return torch.stack(padded_outputs, dim=0) + + def decode_batch(self, x: torch.Tensor) -> torch.Tensor: + """Decode every anchor, then apply batched top-k selection for export-style output.""" + box, scores = torch.split(x, [self.reg_max * 4, self.nc], dim=1) + anchors = self.anchors_as_tensor().unsqueeze(0) + stride = self.stride_as_tensor().unsqueeze(0) + dbox = dist2bbox(self.dfl(box), anchors, xywh=False, dim=1) * stride + decoded = torch.cat([dbox, scores], dim=1).transpose(1, 2) + return self._stack_topk_outputs( + [ + dual_topk( + image, + self.nc, + self.n_extra, + max_det=self.max_det, + conf_thres=self.conf_thres, + score_is_logits=True, + ) + for image in decoded + ] + ) + + def _pre_process(self, x: list[torch.Tensor]) -> tuple[Any, torch.Tensor | None]: + """Preprocesses inputs for NMS-free models. + + Args: + x (list[torch.Tensor]): Raw model outputs. + + Returns: + tuple: (processed_detections, None). + """ + if len(x) == 2: + converted = cast(torch.Tensor, self.conversion(x)) + return self.filter_conversion(converted), None + rearranged = cast(torch.Tensor, self.rearrange(x)) + return self.decode(rearranged), None + + def conversion( + self, x: list[torch.Tensor] + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Convert input tensors. + Args: + x (list[torch.Tensor]): Input tensors. + Returns: + torch.Tensor: Converted tensor. + """ + # sort by element number + x = sorted(x, key=lambda x: x.size(), reverse=self.nc < 4) + return torch.cat(x, dim=-1).squeeze(1) # [b, 8400, 84] + + def filter_conversion(self, x: torch.Tensor) -> list[torch.Tensor]: + """Filters out low-confidence detections from a single output tensor. + + Args: + x (torch.Tensor): Model output tensor. + + Returns: + list[torch.Tensor]: Decoded and filtered outputs for each image. + """ + x_list = torch.split(x, 1, dim=0) # [(1, 8400, 84), (1, 8400, 84), ...] + + return [ + dual_topk(xi.squeeze(0), self.nc, self.n_extra, conf_thres=self.conf_thres) + for xi in x_list + ] + + def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: + """Processes detection results for a single image. + + Args: + box_cls: Raw detections for one image. + + Returns: + Decoded and top-k filtered detections. + """ + ic = torch.amax(box_cls[-self.nc :, :], dim=0) > self.inv_conf_thres + box_cls = box_cls[:, ic] # (144, *) + if box_cls.numel() == 0: + return torch.zeros((0, 6), dtype=torch.float32) # (0, 6) + anchors = self.anchors_as_tensor() + stride = self.stride_as_tensor() + box, scores = torch.split( + box_cls[None], [self.reg_max * 4, self.nc], dim=1 + ) # (1, 64, *), (1, 80, *) + dbox = ( + dist2bbox( + self.dfl(box), + anchors[:, ic], + xywh=False, + dim=1, + ) + * stride[:, ic] + ) + pre_topk = ( + torch.cat([dbox, scores], dim=1).squeeze(0).transpose(0, 1) + ) # (*, 84) + return dual_topk( + pre_topk, + self.nc, + self.n_extra, + conf_thres=self.conf_thres, + score_is_logits=True, + ) + + def nms( + self, + x: _AnchorlessNMSInput | torch.Tensor | list[torch.Tensor], + max_det: int = 300, + max_nms: int = 30000, + max_wh: int = 7680, + multi_label: bool = False, + ) -> list[torch.Tensor]: + """Perform Non-Maximum Suppression (no-op for NMS-free models). + + Args: + x: Decoded detections, optionally with source-layout provenance. + max_det (int, optional): Maximum number of detections to keep. Defaults to 300. + max_nms (int, optional): Maximum candidates for NMS. Defaults to 30000. + max_wh (int, optional): Maximum box width/height. Defaults to 7680. + multi_label: Ignored because NMS-free outputs already select one + class per candidate. + + Returns: + list[torch.Tensor]: Per-image detections with padded zero rows removed. + """ + del max_det, max_nms, max_wh, multi_label + if isinstance(x, _AnchorlessNMSInput): + x = x.detections + if isinstance(x, list): + return x + return [xi[xi[:, 4] > 0] for xi in x] + + +YOLONMSFreePost = YOLONMSFreeDetectionPost diff --git a/mblt_vision/utils/preprocess/__init__.py b/mblt_vision/utils/preprocess/__init__.py new file mode 100644 index 0000000..497dcea --- /dev/null +++ b/mblt_vision/utils/preprocess/__init__.py @@ -0,0 +1,27 @@ +""" +Preprocessing utilities for vision models. +""" + +from .base import PreBase, PreOps +from .build_pre import build_preprocess +from .center_crop import CenterCrop +from .letterbox import LetterBox, letterbox_semantic_mask +from .normalize import Normalize +from .order import SetOrder +from .reader import Reader +from .resize import Resize +from .yolo_pre import YoloPre + +__all__ = [ + "CenterCrop", + "LetterBox", + "Normalize", + "PreBase", + "PreOps", + "Reader", + "Resize", + "SetOrder", + "YoloPre", + "build_preprocess", + "letterbox_semantic_mask", +] diff --git a/mblt_vision/utils/preprocess/_validation.py b/mblt_vision/utils/preprocess/_validation.py new file mode 100644 index 0000000..fff82ae --- /dev/null +++ b/mblt_vision/utils/preprocess/_validation.py @@ -0,0 +1,32 @@ +"""Validation helpers shared by image preprocessing operations.""" + +from __future__ import annotations + +from collections.abc import Sequence + + +def normalize_image_size(size: int | Sequence[int], *, name: str = "size") -> list[int]: + """Normalize a positive scalar or two-dimensional image size to ``[height, width]``.""" + + if isinstance(size, bool): + raise TypeError( + f"{name} must be a positive integer or a two-item integer sequence, got bool." + ) + if isinstance(size, int): + if size <= 0: + raise ValueError(f"{name} must be positive, got {size}.") + return [size, size] + if isinstance(size, Sequence) and not isinstance(size, (str, bytes)): + if len(size) != 2: + raise ValueError(f"{name} must contain exactly two items, got {size!r}.") + if not all( + isinstance(value, int) and not isinstance(value, bool) for value in size + ): + raise TypeError(f"{name} items must be integers, got {size!r}.") + normalized = [int(size[0]), int(size[1])] + if any(value <= 0 for value in normalized): + raise ValueError(f"{name} items must be positive, got {size!r}.") + return normalized + raise TypeError( + f"{name} must be a positive integer or a two-item integer sequence, got {type(size).__name__}." + ) diff --git a/mblt_vision/utils/preprocess/base.py b/mblt_vision/utils/preprocess/base.py new file mode 100644 index 0000000..520c23e --- /dev/null +++ b/mblt_vision/utils/preprocess/base.py @@ -0,0 +1,157 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any + +import torch + + +def _spatial_shape(value: Any) -> tuple[int, int] | None: + """Return the height and width of image-like preprocessing input.""" + + shape = getattr(value, "shape", None) + if shape is None or len(shape) < 2: + return None + if len(shape) == 2: + return int(shape[0]), int(shape[1]) + if len(shape) == 3 and int(shape[-1]) in {1, 3, 4}: + return int(shape[0]), int(shape[1]) + return int(shape[-2]), int(shape[-1]) + + +class PreOps(ABC): + """Abstract base class for individual preprocessing operations. + + Attributes: + device: The torch device where tensors should be placed. + """ + + def __init__(self) -> None: + """Initializes the preprocessing operation.""" + super().__init__() + self.device = torch.device("cpu") + + @abstractmethod + def __call__( + self, + x: Any, + ) -> Any: + """Executes the preprocess operation. + + Args: + x: Input data to be processed. + + Returns: + Processed data. + """ + + def to( + self, + device: str | torch.device, + ) -> None: + """Move the operation to the specified device. + + Args: + device: Device to move the operation to. + """ + if isinstance(device, str): + self.device = torch.device(device) + elif isinstance(device, torch.device): + self.device = device + else: + raise TypeError(f"Got unexpected type for device={type(device)}.") + for name, value in self.__dict__.items(): + if isinstance(value, torch.Tensor): + setattr(self, name, value.to(self.device)) + + +class PreBase: + """Base class for orchestrating a series of preprocessing operations. + + Attributes: + Ops: List of ordered PreOps instances to be applied. + device: The torch device being used. + """ + + def __init__( + self, + Ops: list[PreOps], + ) -> None: + """Initializes the PreBase class with a list of operations. + + Args: + Ops: List of ordered PreOps instances to be applied. + """ + self.Ops = Ops + self._check_ops() + self.device = torch.device("cpu") + + def _check_ops(self) -> None: + """Check if the operations are valid.""" + for op in self.Ops: + if not isinstance(op, PreOps): + raise TypeError(f"Got unsupported type={type(op)}.") + + def __call__( + self, + x: Any, + ) -> Any: + """Applies the sequence of preprocessing operations to the input. + + Args: + x: Initial input data. + + Returns: + Fully processed data. + """ + for op in self.Ops: + x = op(x) + return x + + def with_metadata( + self, + x: Any, + ) -> tuple[Any, dict[str, Any]]: + """Apply preprocessing and return metadata produced by preprocessing operations. + + Args: + x: Initial input data. + + Returns: + A tuple of the processed data and collected metadata. + """ + metadata: dict[str, Any] = {} + img0_shape = _spatial_shape(x) + if img0_shape is not None: + metadata["img0_shape"] = img0_shape + for op in self.Ops: + x = op(x) + if "img0_shape" not in metadata: + img0_shape = _spatial_shape(x) + if img0_shape is not None: + metadata["img0_shape"] = img0_shape + ratio_pad = getattr(op, "ratio_pad", None) + if ratio_pad is not None: + metadata["ratio_pad"] = ratio_pad + return x, metadata + + def to( + self, + device: str | torch.device, + ) -> None: + """Move the operations to the specified device. + + Args: + device: Device to move the operations to. + """ + if isinstance(device, str): + self.device = torch.device(device) + elif isinstance(device, torch.device): + self.device = device + else: + raise TypeError(f"Got unexpected type for device={type(device)}.") + for name, value in self.__dict__.items(): + if isinstance(value, torch.Tensor): + setattr(self, name, value.to(self.device)) + for op in self.Ops: + op.to(self.device) diff --git a/mblt_vision/utils/preprocess/build_pre.py b/mblt_vision/utils/preprocess/build_pre.py new file mode 100644 index 0000000..3dcc432 --- /dev/null +++ b/mblt_vision/utils/preprocess/build_pre.py @@ -0,0 +1,48 @@ +""" +Preprocessing builder. +""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import Any + +from .base import PreBase +from .center_crop import CenterCrop +from .letterbox import LetterBox +from .normalize import Normalize +from .order import SetOrder +from .reader import Reader +from .resize import Resize + + +def build_preprocess( + pre_cfg: Mapping[str, Mapping[str, Any]], +) -> PreBase: + """Builds a preprocessing pipeline based on the configuration. + + Args: + pre_cfg: Preprocessing configuration mapping operations to attributes. + + Returns: + An orchestrator for the sequence of preprocessing steps. + """ + res = [] + for pre_type, pre_attr in pre_cfg.items(): + pre_type_lower = pre_type.lower() + if pre_type_lower == Reader.__name__.lower(): + res.append(Reader(**pre_attr)) + elif pre_type_lower == Resize.__name__.lower(): + res.append(Resize(**pre_attr)) + elif pre_type_lower == CenterCrop.__name__.lower(): + res.append(CenterCrop(**pre_attr)) + elif pre_type_lower == SetOrder.__name__.lower(): + res.append(SetOrder(**pre_attr)) + elif pre_type_lower == LetterBox.__name__.lower(): + res.append(LetterBox(**pre_attr)) + elif pre_type_lower == Normalize.__name__.lower(): + res.append(Normalize(**pre_attr)) + else: + raise ValueError(f"Got unsupported pre_type={pre_type}.") + + return PreBase(res) diff --git a/mblt_vision/utils/preprocess/center_crop.py b/mblt_vision/utils/preprocess/center_crop.py new file mode 100644 index 0000000..5ea1cfa --- /dev/null +++ b/mblt_vision/utils/preprocess/center_crop.py @@ -0,0 +1,75 @@ +""" +Center crop preprocessing. +""" + +from __future__ import annotations + +import cv2 +import numpy as np +import torch +from PIL import Image + +from ..types import TensorLike +from ._validation import normalize_image_size +from .base import PreOps + + +class CenterCrop(PreOps): + """ + Center crop the image to a specified size. + """ + + def __init__(self, size: int | list[int] | tuple[int, int]) -> None: + """Initializes the CenterCrop operation. + + Args: + size (int | list[int]): Target size [h, w]. If int, size is [size, size]. + """ + super().__init__() + self.size = normalize_image_size(size) + + def __call__(self, x: TensorLike | Image.Image) -> np.ndarray: + """Applies center crop to the image. + + Args: + x (np.ndarray | torch.Tensor | Image.Image): Input image. + + Returns: + np.ndarray: Center-cropped image in HWC format. + """ + if isinstance(x, torch.Tensor): + image = x.cpu().numpy() + elif isinstance(x, Image.Image): + image = np.array(x) + elif isinstance(x, np.ndarray): + image = x + else: + raise TypeError( + f"CenterCrop expects a NumPy array, tensor, or PIL image, got {type(x).__name__}." + ) + if image.ndim != 3: + raise ValueError( + f"CenterCrop expects a three-dimensional image, got shape {image.shape}." + ) + H, W = image.shape[:2] + if (self.size[0] == H) and (self.size[1] == W): + return image + elif (self.size[1] > W) or (self.size[0] > H): + image = cv2.copyMakeBorder( + image, + (self.size[0] - H) // 2 if self.size[0] > H else 0, + (self.size[0] - H + 1) // 2 if self.size[0] > H else 0, + (self.size[1] - W) // 2 if self.size[1] > W else 0, + (self.size[1] - W + 1) // 2 if self.size[1] > W else 0, + cv2.BORDER_CONSTANT, + value=(0.0,), + ) + H, W = image.shape[:2] + crop_top = round((H - self.size[0]) / 2.0) + crop_left = round((W - self.size[1]) / 2.0) + image = image[ + crop_top : crop_top + self.size[0], + crop_left : crop_left + self.size[1], + :, + ] + return image.astype(np.uint8) diff --git a/mblt_vision/utils/preprocess/letterbox.py b/mblt_vision/utils/preprocess/letterbox.py new file mode 100644 index 0000000..e6d11b6 --- /dev/null +++ b/mblt_vision/utils/preprocess/letterbox.py @@ -0,0 +1,120 @@ +from __future__ import annotations + +import cv2 +import numpy as np +import torch + +from ..letterbox import LetterBoxGeometry, RatioPad +from ..types import TensorLike +from ._validation import normalize_image_size +from .base import PreOps + + +def _apply_letterbox( + image: np.ndarray, + img_size: list[int], + interpolation: int, + padding_value: int | tuple[int, int, int], +) -> tuple[np.ndarray, RatioPad]: + """Resize and pad an array while preserving its aspect ratio. + + Args: + image: Image or two-dimensional semantic mask. + img_size: Target size as ``[height, width]``. + interpolation: OpenCV interpolation mode. + padding_value: Constant border value. + + Returns: + The letterboxed array and its resize/padding metadata. + """ + + input_shape = (int(img_size[0]), int(img_size[1])) + original_shape = (int(image.shape[0]), int(image.shape[1])) + geometry = LetterBoxGeometry.from_shapes(input_shape, original_shape) + resized_height, resized_width = geometry.resized_shape + if image.shape[:2] != geometry.resized_shape: + image = cv2.resize( + image, (resized_width, resized_height), interpolation=interpolation + ) + top, bottom, left, right = geometry.borders + image = cv2.copyMakeBorder( + image, + top, + bottom, + left, + right, + cv2.BORDER_CONSTANT, + value=padding_value, + ) + return image, geometry.ratio_pad + + +def letterbox_semantic_mask( + mask: np.ndarray, + img_size: list[int], + ignore_label: int = 255, +) -> tuple[np.ndarray, RatioPad]: + """Letterbox a semantic mask without interpolating class IDs. + + Args: + mask: Two-dimensional semantic class map. + img_size: Target size as ``[height, width]``. + ignore_label: Class value used for padded pixels. + + Returns: + The letterboxed mask and its resize/padding metadata. + + Raises: + ValueError: If the mask is not two-dimensional. + """ + + if mask.ndim != 2: + raise ValueError( + f"Semantic masks must be two-dimensional, got shape {mask.shape}." + ) + return _apply_letterbox(mask, img_size, cv2.INTER_NEAREST, ignore_label) + + +class LetterBox(PreOps): + """Preprocessing for YOLO models, implementing letterbox resizing. + + Resizes the image while maintaining aspect ratio, adding padding to meet + target dimensions. Based on Ultralytics implementation. + + Ref: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py#L1535 + """ + + def __init__(self, img_size: list[int]) -> None: + """Initializes LetterBox with target image size. + + Args: + img_size (list[int]): Target image size [h, w]. + """ + super().__init__() + self.img_size = normalize_image_size(img_size, name="img_size") + self.ratio_pad: tuple[tuple[float, float], tuple[float, float]] | None = None + + def __call__(self, x: TensorLike) -> torch.Tensor: + """Executes YOLO preprocessing (letterbox resizing). + + Args: + x (TensorLike): Input image. + + Returns: + torch.Tensor: Preprocessed image in HWC format on the selected device. + """ + if isinstance(x, torch.Tensor): + x = x.cpu().numpy() + elif not isinstance(x, np.ndarray): + raise TypeError( + f"LetterBox expects a NumPy array or tensor, got {type(x).__name__}." + ) + if x.ndim != 3: + raise ValueError(f"LetterBox expects an HWC image, got shape {x.shape}.") + img, self.ratio_pad = _apply_letterbox( + x, + self.img_size, + cv2.INTER_LINEAR, + (114, 114, 114), + ) + return torch.from_numpy(img).to(self.device).byte() diff --git a/mblt_vision/utils/preprocess/normalize.py b/mblt_vision/utils/preprocess/normalize.py new file mode 100644 index 0000000..8d7316b --- /dev/null +++ b/mblt_vision/utils/preprocess/normalize.py @@ -0,0 +1,78 @@ +"""Normalization operation for image preprocessing.""" + +from __future__ import annotations + +import numpy as np +import torch +from PIL import Image + +from ..types import TensorLike +from .base import PreOps + +STYLE_PARAMS = { + "torch": ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + "tf": ([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), + "openai": ( + [0.48145466, 0.4578275, 0.40821073], + [0.26862954, 0.26130258, 0.27577711], + ), + "cv": ([0.0, 0.0, 0.0], [1.0, 1.0, 1.0]), +} +STYLE_LIST = list(STYLE_PARAMS.keys()) + + +class Normalize(PreOps): + """Normalization layer to scale and shift image data. + + Attributes: + style: Data source style (e.g., 'torch', 'tf', 'openai', 'cv'). + mean: Array of mean values for normalization. + std: Array of standard deviation values for normalization. + """ + + def __init__(self, style: str) -> None: + """Initializes the Normalize layer with a specific style. + + Args: + style: The preprocessing style to use. Must be one of STYLE_LIST. + """ + super().__init__() + + if not isinstance(style, str): + raise TypeError( + f"Normalize style must be a string, got {type(style).__name__}." + ) + if style.lower() not in STYLE_LIST: + raise ValueError( + f"Unsupported Normalize style {style!r}; expected one of {STYLE_LIST}." + ) + + self.style = style.lower() + mean, std = STYLE_PARAMS[self.style] + self.mean = np.array(mean) + self.std = np.array(std) + + def __call__(self, x: TensorLike | Image.Image) -> np.ndarray: + """Applies normalization to the input image or tensor. + + Args: + x (TensorLike | Image.Image): Input data as a torch.Tensor, PIL Image, or numpy-like array. + + Returns: + np.ndarray: The normalized image as a float32 numpy array. + """ + if isinstance(x, torch.Tensor): + x = x.cpu().numpy() + elif isinstance(x, Image.Image): + x = np.array(x) + elif not isinstance(x, np.ndarray): + raise TypeError( + f"Normalize expects a NumPy array, tensor, or PIL image, got {type(x).__name__}." + ) + if x.ndim != 3: + raise ValueError( + f"Normalize expects a three-dimensional image, got shape {x.shape}." + ) + x = x.astype(np.float32) / 255.0 + x = (x - self.mean) / self.std + return x.astype(np.float32) diff --git a/mblt_vision/utils/preprocess/order.py b/mblt_vision/utils/preprocess/order.py new file mode 100644 index 0000000..d4931bd --- /dev/null +++ b/mblt_vision/utils/preprocess/order.py @@ -0,0 +1,74 @@ +""" +Channel order preprocessing. +""" + +from __future__ import annotations + +import numpy as np +import torch + +from ..types import TensorLike +from .base import PreOps + + +class SetOrder(PreOps): + """Sets the channel order of the image to either HWC or CHW format.""" + + def __init__(self, shape: str = "HWC") -> None: + """Initializes the SetOrder operation. + + Args: + shape (str, optional): Target channel order, either "HWC" or "CHW". + Defaults to "HWC". + """ + super().__init__() + if not isinstance(shape, str): + raise TypeError( + f"SetOrder shape must be a string, got {type(shape).__name__}." + ) + if shape.lower() not in {"hwc", "chw"}: + raise ValueError( + f"Unsupported channel order {shape!r}; expected 'HWC' or 'CHW'." + ) + self.shape = shape.lower() + + def __call__(self, x: TensorLike) -> TensorLike: + """Reorders the dimensions of the input image. + + Args: + x (TensorLike): Input image of shape (3, H, W) or (H, W, 3). + + Returns: + TensorLike: Image with the specified channel order. + """ + if not isinstance(x, (np.ndarray, torch.Tensor)): + raise TypeError( + f"SetOrder expects a NumPy array or tensor, got {type(x).__name__}." + ) + if x.ndim != 3: + raise ValueError( + f"SetOrder expects a three-dimensional color image, got shape {x.shape}." + ) + channels_first = x.shape[0] == 3 + channels_last = x.shape[-1] == 3 + if channels_first and channels_last: + raise ValueError( + f"SetOrder cannot infer channel order from ambiguous shape {x.shape}." + ) + if channels_first: + cdim = 0 + elif channels_last: + cdim = 2 + else: + raise ValueError( + f"SetOrder expects HWC or CHW data with three channels, got shape {x.shape}." + ) + if cdim == 0 and self.shape == "hwc": + if isinstance(x, torch.Tensor): + return torch.permute(x, (1, 2, 0)) + return np.transpose(x, (1, 2, 0)) + elif cdim == 2 and self.shape == "chw": + if isinstance(x, torch.Tensor): + return torch.permute(x, (2, 0, 1)) + return np.transpose(x, (2, 0, 1)) + return x diff --git a/mblt_vision/utils/preprocess/reader.py b/mblt_vision/utils/preprocess/reader.py new file mode 100644 index 0000000..69a83e7 --- /dev/null +++ b/mblt_vision/utils/preprocess/reader.py @@ -0,0 +1,85 @@ +""" +Image reader preprocessing. +""" + +from __future__ import annotations + +from pathlib import Path + +import cv2 +import numpy as np +import torch +from PIL import Image + +from ..types import TensorLike +from .base import PreOps + + +class Reader(PreOps): + """ + Reader for loading images from file paths or converting existing objects. + Supports "pil" and "numpy" reading styles. + """ + + def __init__(self, style: str) -> None: + """Initializes the Reader operation. + + Args: + style (str): Reading style, either "pil" or "numpy". + """ + super().__init__() + if not isinstance(style, str): + raise TypeError( + f"Reader style must be a string, got {type(style).__name__}." + ) + if style.lower() not in {"pil", "numpy"}: + raise ValueError( + f"Unsupported Reader style {style!r}; expected 'pil' or 'numpy'." + ) + self.style = style.lower() + + def __call__( + self, x: str | Path | TensorLike | Image.Image + ) -> np.ndarray | Image.Image: + """Reads/converts the input into an image object. + + Args: + x (str | Path | TensorLike | Image.Image): Input image path or image object. + + Returns: + np.ndarray | Image.Image: Read image in the specified style. + """ + if self.style == "numpy": + if isinstance(x, np.ndarray): + return x + elif isinstance(x, torch.Tensor): + return x.cpu().numpy() + elif isinstance(x, (str, Path)): + image = cv2.imread(str(x)) + if image is None: + raise FileNotFoundError(f"Image not found: {x}") + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + elif isinstance(x, Image.Image): + return np.array(x) + else: + raise TypeError( + f"Reader(style='numpy') does not support input type {type(x).__name__}." + ) + elif self.style == "pil": + if isinstance(x, np.ndarray): + return Image.fromarray(x.astype(np.uint8)) + elif isinstance(x, torch.Tensor): + x = x.cpu().numpy() + return Image.fromarray(x.astype(np.uint8)) + elif isinstance(x, (str, Path)): + return Image.open(x).convert("RGB") + elif isinstance(x, Image.Image): + return x + else: + raise TypeError( + f"Reader(style='pil') does not support input type {type(x).__name__}." + ) + else: + raise RuntimeError( + f"Reader has an invalid validated style: {self.style!r}." + ) diff --git a/mblt_vision/utils/preprocess/resize.py b/mblt_vision/utils/preprocess/resize.py new file mode 100644 index 0000000..5115095 --- /dev/null +++ b/mblt_vision/utils/preprocess/resize.py @@ -0,0 +1,190 @@ +""" +Image resizing preprocessing. +""" + +from __future__ import annotations + +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image + +from ..types import TensorLike +from ._validation import normalize_image_size +from .base import PreOps + +PIL_INTERP_CODES = { + "nearest": Image.Resampling.NEAREST, + "bilinear": Image.Resampling.BILINEAR, + "bicubic": Image.Resampling.BICUBIC, + "box": Image.Resampling.BOX, + "hamming": Image.Resampling.HAMMING, + "lanczos": Image.Resampling.LANCZOS, +} + + +class Resize(PreOps): + """Resizes the image to a specified size using various interpolation modes. + + Supports PyTorch tensors in CHW or BCHW format, HWC NumPy arrays, and PIL images. + """ + + def __init__( + self, + size: int | list[int], + interpolation: str, + ) -> None: + """ + Initialize the Resize operation. + Args: + size (int | list[int]): Target size. If int, the shorter edge is resized to this size + maintaining aspect ratio. If [h, w], it is resized to exactly this size. + interpolation (str): Interpolation mode (e.g., "bilinear", "bicubic", "nearest"). + """ + # Note that this behaves different for npy image and PIL image + super().__init__() + self.size = ( + size + if isinstance(size, int) and not isinstance(size, bool) + else normalize_image_size(size) + ) + if isinstance(self.size, int) and self.size <= 0: + raise ValueError(f"size must be positive, got {self.size}.") + if interpolation not in PIL_INTERP_CODES: + raise ValueError( + f"Unsupported resize interpolation {interpolation!r}; expected one of {sorted(PIL_INTERP_CODES)}." + ) + self.interpolation = interpolation + + def __call__( + self, x: TensorLike | Image.Image + ) -> np.ndarray | torch.Tensor | Image.Image: + """Resizes the input image. + + Args: + x (TensorLike | Image.Image): Image to be resized. + + Returns: + np.ndarray | torch.Tensor | Image.Image: Resized image in the same format as input. + + Raises: + TypeError: If input type is not supported. + ValueError: If a NumPy input is not HWC or a tensor input is not CHW or BCHW. + """ + if isinstance(x, np.ndarray): + if x.ndim != 3: + raise ValueError( + f"Expected an HWC NumPy array, but got x.shape={x.shape}." + ) + img_h, img_w = x.shape[:2] + new_h, new_w = self._compute_resized_output_size(img_h, img_w) + if [img_h, img_w] == [new_h, new_w]: + return x + + tensor_x = torch.from_numpy(x).to(self.device) + tensor_x = tensor_x.permute(2, 0, 1) + tensor_x, need_cast, need_squeeze, out_dtype = self._cast_squeeze_in( + tensor_x, [torch.float32, torch.float64] + ) + tensor_x = F.interpolate( + tensor_x, + size=(new_h, new_w), + mode=self.interpolation, + align_corners=( + False if self.interpolation in ["bilinear", "bicubic"] else None + ), + antialias=self.interpolation in ["bilinear", "bicubic"], + ) + tensor_x = self._cast_squeeze_out( + tensor_x, need_cast, need_squeeze, out_dtype + ) + return tensor_x.permute(1, 2, 0).cpu().numpy() + elif isinstance(x, torch.Tensor): + tensor_x = x.to(self.device) + elif isinstance(x, Image.Image): + img_w, img_h = x.size + new_h, new_w = self._compute_resized_output_size(img_h, img_w) + if [img_h, img_w] == [new_h, new_w]: + return x + return x.resize( + size=(new_w, new_h), + resample=PIL_INTERP_CODES[self.interpolation], + ) + else: + raise TypeError(f"Got unexpected type for x={type(x)}.") + + if tensor_x.ndim not in (3, 4): + raise ValueError( + f"Expected a CHW or BCHW tensor, but got x.shape={tensor_x.shape}." + ) + img_h, img_w = tensor_x.shape[-2:] + new_h, new_w = self._compute_resized_output_size(img_h, img_w) + if [img_h, img_w] == [new_h, new_w]: + return tensor_x + tensor_x, need_cast, need_squeeze, out_dtype = self._cast_squeeze_in( + tensor_x, [torch.float32, torch.float64] + ) + tensor_x = F.interpolate( + tensor_x, + size=(new_h, new_w), + mode=self.interpolation, + align_corners=( + False if self.interpolation in ["bilinear", "bicubic"] else None + ), + antialias=self.interpolation in ["bilinear", "bicubic"], + ) + tensor_x = self._cast_squeeze_out(tensor_x, need_cast, need_squeeze, out_dtype) + return tensor_x.to(self.device) + + def _compute_resized_output_size(self, img_h: int, img_w: int) -> list[int]: + if isinstance(self.size, int): + # to match the shortest side to self.size with the same ratio + if img_w <= img_h: + new_w = self.size + new_h = int(self.size * img_h / img_w) + else: + new_h = self.size + new_w = int(self.size * img_w / img_h) + elif isinstance(self.size, list): + new_h, new_w = self.size + else: + raise RuntimeError(f"Resize has an invalid validated size: {self.size!r}.") + return [new_h, new_w] + + def _cast_squeeze_in( + self, img: torch.Tensor, req_dtypes: list[torch.dtype] + ) -> tuple[torch.Tensor, bool, bool, torch.dtype]: + need_squeeze = False + # make image NCHW + if img.ndim < 4: + img = img.unsqueeze(dim=0) + need_squeeze = True + out_dtype = img.dtype + need_cast = False + if out_dtype not in req_dtypes: + need_cast = True + req_dtype = req_dtypes[0] + img = img.to(req_dtype) + return img, need_cast, need_squeeze, out_dtype + + def _cast_squeeze_out( + self, + img: torch.Tensor, + need_cast: bool, + need_squeeze: bool, + out_dtype: torch.dtype, + ) -> torch.Tensor: + if need_squeeze: + img = img.squeeze(dim=0) + if need_cast: + if out_dtype in ( + torch.uint8, + torch.int8, + torch.int16, + torch.int32, + torch.int64, + ): + # it is better to round before cast + img = torch.round(img) + img = img.to(out_dtype) + return img diff --git a/mblt_vision/utils/preprocess/yolo_pre.py b/mblt_vision/utils/preprocess/yolo_pre.py new file mode 100644 index 0000000..f2fc151 --- /dev/null +++ b/mblt_vision/utils/preprocess/yolo_pre.py @@ -0,0 +1,7 @@ +"""Compatibility name for the extracted letterbox operation.""" + +from .letterbox import LetterBox + +YoloPre = LetterBox + +__all__ = ["YoloPre"] diff --git a/mblt_vision/utils/results.py b/mblt_vision/utils/results.py new file mode 100644 index 0000000..36d769f --- /dev/null +++ b/mblt_vision/utils/results.py @@ -0,0 +1,722 @@ +""" +Results processing and plotting. +""" + +from __future__ import annotations + +from collections.abc import Sequence +from pathlib import Path +from typing import cast + +import cv2 +import numpy as np +import torch +from PIL import Image + +from .._tasks import normalize_vision_task +from .datasets import ( + get_ade20k_palette, + get_cityscapes_palette, + get_coco_det_palette, + get_coco_keypoint_palette, + get_coco_label, + get_coco_limb_palette, + get_coco_pose_skeleton, + get_dotav1_label, + get_dotav1_palette, + get_imagenet_label, +) +from .letterbox import LetterBoxGeometry +from mblt_vision.utils.postprocess.common import ( + crop_mask, + scale_boxes, + scale_coords, + scale_masks, + scale_rboxes, + xywhr2xyxyxyxy, +) +from .types import ListTensorLike, NestedListTensorLike, TensorLike + +LW = 2 # line width +RADIUS = 5 # circle radius +ALPHA = 0.3 # alpha for overlay +DENSE_OVERLAY_ALPHA = 0.6 + + +class Results: + """Handle, process, and plot model inference results.""" + + def __init__( + self, + pre_cfg: dict, + post_cfg: dict, + output: TensorLike | ListTensorLike | NestedListTensorLike, + **kwargs, + ) -> None: + """ + Initializes the Results object. + Args: + pre_cfg (dict): Preprocessing configuration. + post_cfg (dict): Postprocessing configuration. + output (TensorLike | ListTensorLike | NestedListTensorLike): Raw model output. + **kwargs: Additional arguments. + """ + self.pre_cfg = pre_cfg + self.post_cfg = post_cfg + self.task = normalize_vision_task(post_cfg["task"]) + self.conf_thres = kwargs.get("conf_thres", 0.25) + self.acc: torch.Tensor | np.ndarray | None = None + self.box_cls: torch.Tensor | np.ndarray | None = None + self.mask: torch.Tensor | np.ndarray | None = None + self.depth: torch.Tensor | np.ndarray | list[TensorLike] | None = None + self.semantic_mask: torch.Tensor | np.ndarray | list[TensorLike] | None = None + self.output: TensorLike | ListTensorLike | NestedListTensorLike | None = None + self.labels: torch.Tensor | None = None + self.scores: torch.Tensor | None = None + self.boxes: torch.Tensor | None = None + self.rboxes: torch.Tensor | None = None + self.kpts: torch.Tensor | None = None + self.set_output(output) + + def _read_image( + self, source_path: str | Path | np.ndarray | Image.Image + ) -> np.ndarray: + """ + Internal method to read an image from various input types and convert to BGR format. + Args: + source_path (str | np.ndarray | Image.Image): Path to image or image object. + Returns: + np.ndarray: Image in BGR format (cv2 style). + """ + source_img = None + if isinstance(source_path, Image.Image): # PIL image open + source_img = source_path.convert("RGB") + source_img = np.array(source_img) + source_img = cv2.cvtColor(source_img, cv2.COLOR_RGB2BGR) + elif isinstance(source_path, np.ndarray): + source_img = np.array(source_path) + if source_img.ndim != 3 or source_img.shape[2] != 3: + raise ValueError( + f"Image arrays must have HWC shape with three channels, got {source_img.shape}." + ) + elif isinstance(source_path, (str, Path)): + image_path = Path(source_path) + if not image_path.is_file(): + raise FileNotFoundError(f"Image file not found: {image_path}") + source_img = cv2.imread(str(image_path), cv2.IMREAD_COLOR) + else: + raise TypeError( + f"Unsupported image source type: {type(source_path).__name__}." + ) + if source_img is None: + raise ValueError(f"Failed to decode image from {source_path!r}.") + return source_img + + @staticmethod + def _save_image(save_path: str | Path, image: np.ndarray) -> None: + """Save an image and report encoder or filesystem failures.""" + + path = Path(save_path) + if path.parent != Path("."): + path.parent.mkdir(parents=True, exist_ok=True) + if not cv2.imwrite(str(path), image): + raise OSError(f"Failed to write result image: {path}") + + def set_output( + self, output: TensorLike | ListTensorLike | NestedListTensorLike + ) -> None: + """ + Sets variables from the raw model output based on the task. + Args: + output (TensorLike | ListTensorLike | NestedListTensorLike): Raw model output. + Raises: + NotImplementedError: If the task is not supported. + """ + self.acc = None + self.box_cls = None + self.mask = None + self.depth = None + self.semantic_mask = None + if self.task == "image_classification": + if not isinstance(output, (np.ndarray, torch.Tensor)): + raise TypeError( + f"Expected tensor output for task {self.task}, got {type(output).__name__}." + ) + self.acc = cast(TensorLike, output) + elif self.task in { + "object_detection", + "face_detection", + "pose_estimation", + "obb", + }: + if not isinstance(output, Sequence): + raise TypeError( + f"Expected list output for task {self.task}, got {type(output).__name__}." + ) + if len(output) == 0: + raise ValueError( + f"Expected a non-empty output list for task {self.task}." + ) + if not isinstance(output[0], (np.ndarray, torch.Tensor)): + raise TypeError( + f"Expected a tensor as the first output for task {self.task}, got {type(output[0]).__name__}." + ) + self.box_cls = cast(TensorLike, output[0]) + elif self.task == "instance_segmentation": + if not isinstance(output, Sequence): + raise TypeError( + f"Expected nested list output for task {self.task}, got {type(output).__name__}." + ) + if len(output) == 0: + raise ValueError( + f"Expected a non-empty output list for task {self.task}." + ) + if not isinstance(output[0], Sequence): + raise TypeError( + f"Expected a nested output sequence for task {self.task}, got {type(output[0]).__name__}." + ) + if len(output[0]) < 2: + raise ValueError( + "Instance segmentation output must contain detections and masks." + ) + seg_output = cast(ListTensorLike, output[0]) + if not all( + isinstance(item, (np.ndarray, torch.Tensor)) for item in seg_output[:2] + ): + raise TypeError( + "Instance segmentation detections and masks must be tensors." + ) + self.box_cls = cast(TensorLike, seg_output[0]) + self.mask = cast(TensorLike, seg_output[1]) + elif self.task == "depth_estimation": + if isinstance(output, Sequence) and not isinstance( + output, (np.ndarray, torch.Tensor) + ): + if len(output) == 0: + raise ValueError("Expected at least one depth-map tensor.") + if not all( + isinstance(item, (np.ndarray, torch.Tensor)) for item in output + ): + raise TypeError( + f"Expected depth-map tensors for task {self.task}, got {type(output).__name__}." + ) + self.depth = [cast(TensorLike, item) for item in output] + elif isinstance(output, (np.ndarray, torch.Tensor)): + self.depth = output + else: + raise TypeError( + f"Expected tensor depth output for task {self.task}, got {type(output)}." + ) + elif self.task == "semantic_segmentation": + if isinstance(output, Sequence) and not isinstance( + output, (np.ndarray, torch.Tensor) + ): + if len(output) == 0: + raise ValueError("Expected at least one semantic-map tensor.") + if not all( + isinstance(item, (np.ndarray, torch.Tensor)) for item in output + ): + raise TypeError( + f"Expected semantic-map tensors for task {self.task}, got {type(output).__name__}." + ) + self.semantic_mask = [cast(TensorLike, item) for item in output] + elif isinstance(output, (np.ndarray, torch.Tensor)): + self.semantic_mask = output + else: + raise TypeError( + f"Expected tensor semantic output for task {self.task}, got {type(output)}." + ) + else: + raise NotImplementedError( + f"Task {self.task} is not supported for plotting results." + ) + self.output = output # store raw output + + def plot( + self, + source_path: str | Path | np.ndarray | Image.Image, + save_path: str | Path | None = None, + **kwargs, + ) -> np.ndarray | None: + """Plot inference results on the source image. + + Args: + source_path: Image path or object to plot on. + save_path: Optional output image path. + **kwargs: Additional task-specific plotting options (e.g., topk for classification). + + Returns: + Image with results visualized in BGR format, or ``None`` for classification without an output path. + + Raises: + NotImplementedError: If the task is not supported for plotting. + """ + if self.task == "image_classification": + return self._plot_image_classification(source_path, save_path, **kwargs) + elif self.task in {"object_detection", "face_detection"}: + return self._plot_object_detection(source_path, save_path, **kwargs) + elif self.task == "instance_segmentation": + return self._plot_instance_segmentation(source_path, save_path, **kwargs) + elif self.task == "depth_estimation": + return self._plot_depth_estimation(source_path, save_path, **kwargs) + elif self.task == "semantic_segmentation": + return self._plot_semantic_segmentation(source_path, save_path, **kwargs) + elif self.task == "pose_estimation": + return self._plot_pose_estimation(source_path, save_path, **kwargs) + elif self.task == "obb": + return self._plot_obb(source_path, save_path, **kwargs) + else: + raise NotImplementedError( + f"Task {self.task} is not supported for plotting results." + ) + + def _plot_depth_estimation( + self, + source_path: str | Path | np.ndarray | Image.Image, + save_path: str | Path | None = None, + **kwargs, + ) -> np.ndarray: + """Colorize the first depth map with near objects in red and blend it over the original image.""" + + del kwargs + if self.depth is None: + raise ValueError("No depth output found.") + depth_value = self.depth[0] if isinstance(self.depth, list) else self.depth + depth = ( + depth_value.detach().cpu().numpy() + if isinstance(depth_value, torch.Tensor) + else depth_value + ) + if depth.ndim == 3: + depth = depth[0] + if depth.ndim != 2: + raise ValueError( + f"Expected a 2D depth map or [B, H, W], got {depth.shape}." + ) + image = self._read_image(source_path) + image_shape = (int(image.shape[0]), int(image.shape[1])) + if tuple(depth.shape) != image_shape: + depth = self._restore_depth_map(depth, image_shape) + valid = np.isfinite(depth) & (depth > 0) + if not valid.any(): + raise ValueError("Depth output contains no positive finite values.") + disparity = np.zeros(depth.shape, dtype=np.float32) + disparity[valid] = 1.0 / depth[valid] + lower, upper = np.percentile(disparity[valid], (2, 98)) + if upper <= lower: + upper = lower + 1e-6 + normalized = np.zeros(depth.shape, dtype=np.uint8) + normalized[valid] = np.clip( + (disparity[valid] - lower) * 255 / (upper - lower), 0, 255 + ).astype(np.uint8) + overlay = cv2.applyColorMap(normalized, cv2.COLORMAP_JET) + overlay[~valid] = 0 + result = cv2.addWeighted( + image, 1.0 - DENSE_OVERLAY_ALPHA, overlay, DENSE_OVERLAY_ALPHA, 0 + ) + if save_path is not None: + self._save_image(save_path, result) + return result + + def _plot_semantic_segmentation( + self, + source_path: str | Path | np.ndarray | Image.Image, + save_path: str | Path | None = None, + **kwargs, + ) -> np.ndarray: + """Colorize a semantic class map and blend it over the original image.""" + + del kwargs + if self.semantic_mask is None: + raise ValueError("No semantic output found.") + semantic_value = ( + self.semantic_mask[0] + if isinstance(self.semantic_mask, list) + else self.semantic_mask + ) + class_map = ( + semantic_value.detach().cpu().numpy() + if isinstance(semantic_value, torch.Tensor) + else semantic_value + ) + if class_map.ndim == 3: + class_map = class_map[0] + if class_map.ndim != 2: + raise ValueError( + f"Expected a 2D semantic map or [B, H, W], got {class_map.shape}." + ) + image = self._read_image(source_path) + image_shape = (int(image.shape[0]), int(image.shape[1])) + if tuple(class_map.shape) != image_shape: + class_map = self._restore_semantic_map(class_map, image_shape) + dataset_value = self.post_cfg.get("dataset") + dataset = ( + dataset_value.lower() if isinstance(dataset_value, str) else dataset_value + ) + if dataset == "ade20k": + default_nc = 150 + palette_getter = get_ade20k_palette + elif dataset == "cityscapes": + default_nc = 19 + palette_getter = get_cityscapes_palette + else: + raise ValueError( + f"Unsupported semantic segmentation dataset palette: {dataset!r}." + ) + nc = int(self.post_cfg.get("nc", default_nc)) + valid = class_map != 255 + if valid.any() and ( + int(class_map[valid].min()) < 0 or int(class_map[valid].max()) >= nc + ): + raise ValueError( + f"Semantic class-map values must be in [0, {nc - 1}] or 255." + ) + palette = np.array( + [palette_getter(index) for index in range(nc)], dtype=np.uint8 + ) + overlay = np.zeros_like(image) + overlay[valid] = palette[class_map[valid].astype(np.int64)] + blended = cv2.addWeighted( + image, 1.0 - DENSE_OVERLAY_ALPHA, overlay, DENSE_OVERLAY_ALPHA, 0 + ) + result = image.copy() + result[valid] = blended[valid] + if save_path is not None: + self._save_image(save_path, result) + return result + + def _restore_semantic_map( + self, class_map: np.ndarray, image_shape: tuple[int, int] + ) -> np.ndarray: + """Undo the configured letterbox transform using nearest-neighbor interpolation.""" + + return self._restore_dense_map( + class_map, image_shape, cv2.INTER_NEAREST, "Semantic" + ) + + def _restore_depth_map( + self, depth: np.ndarray, image_shape: tuple[int, int] + ) -> np.ndarray: + """Undo the configured letterbox transform and resize a depth map to an image.""" + + return self._restore_dense_map(depth, image_shape, cv2.INTER_LINEAR, "Depth") + + def _restore_dense_map( + self, + output: np.ndarray, + image_shape: tuple[int, int], + interpolation: int, + task_name: str, + ) -> np.ndarray: + """Undo configured letterboxing for a dense two-dimensional output.""" + + letterbox_cfg = self.pre_cfg.get("LetterBox", {}) + input_shape = letterbox_cfg.get("img_size") + if not isinstance(input_shape, list) or len(input_shape) != 2: + return cv2.resize( + output, (image_shape[1], image_shape[0]), interpolation=interpolation + ) + geometry = LetterBoxGeometry.from_shapes( + (int(input_shape[0]), int(input_shape[1])), image_shape + ) + output_shape = (int(output.shape[0]), int(output.shape[1])) + top, bottom, left, right = geometry.crop_bounds(output_shape) + cropped = output[top:bottom, left:right] + if cropped.size == 0: + raise ValueError( + f"{task_name} letterbox restoration produced an empty crop." + ) + return cv2.resize( + cropped, (image_shape[1], image_shape[0]), interpolation=interpolation + ) + + def _plot_image_classification( + self, + source_path: str | Path | np.ndarray | Image.Image | None = None, + save_path: str | Path | None = None, + topk: int = 5, + **kwargs, + ) -> np.ndarray | None: + if self.acc is None: + raise ValueError("No accuracy output found.") + if isinstance(topk, bool) or not isinstance(topk, int): + raise TypeError(f"topk must be an integer, got {type(topk).__name__}.") + if topk <= 0: + raise ValueError(f"topk must be positive, got {topk}.") + if isinstance(self.acc, np.ndarray): + self.acc = torch.tensor(self.acc) + scores = self.acc.squeeze() + if scores.ndim != 1: + raise ValueError( + f"Classification plotting expects one class-score vector, got shape {tuple(self.acc.shape)}." + ) + topk = min(topk, int(scores.numel())) + topk_probs, topk_indices = torch.topk(scores, topk) + topk_probs = np.atleast_1d(topk_probs.squeeze().detach().cpu().numpy()) + topk_indices = np.atleast_1d(topk_indices.squeeze().detach().cpu().numpy()) + # load labels + labels = [get_imagenet_label(i) for i in topk_indices] + comments = [] + for i in range(topk): + comments.append(f"{labels[i]}: {topk_probs[i] * 100:.2f}%") + print(f"Label: {labels[i]}, Probability: {topk_probs[i] * 100:.2f}%") + if source_path is not None and save_path is not None: + comments_str = "\n".join(comments) + img = self._read_image(source_path) + avg_color = img.mean(axis=(0, 1)) + txt_color = ( + int(255 - avg_color[0]), + int(255 - avg_color[1]), + int(255 - avg_color[2]), + ) + for i, line in enumerate(comments_str.splitlines()): + (_, h), _ = cv2.getTextSize( + text=line, + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + fontScale=0.5, + thickness=1, + ) + img = cv2.putText( + img, + line, + (15, 15 + int(1.5 * i * h)), # line spacing + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + fontScale=0.5, + color=txt_color, + thickness=1, + lineType=cv2.LINE_AA, + ) + self._save_image(save_path, img) + return img + else: + return None + + def _plot_object_detection( + self, + source_path: str | Path | np.ndarray | Image.Image, + save_path: str | Path | None = None, + **kwargs, + ) -> np.ndarray: + box_cls = self._box_cls_tensor() + expected_columns = 6 + self.post_cfg.get("n_extra", 0) + if box_cls.ndim != 2 or box_cls.shape[1] != expected_columns: + raise ValueError( + f"Object detection output must have shape [N, {expected_columns}], got {tuple(box_cls.shape)}." + ) + img = self._read_image(source_path) + img1_shape = cast(tuple[int, int], self.pre_cfg["LetterBox"]["img_size"]) + img0_shape: tuple[int, int] = (img.shape[0], img.shape[1]) + self.labels = box_cls[:, 5].to(torch.int64) + self.scores = box_cls[:, 4] + self.boxes = scale_boxes( + img1_shape, + box_cls[:, :4], + img0_shape, + ) + boxes = self.boxes + scores = self.scores + labels = self.labels + contours: dict[int, list[np.ndarray]] = {} + for box, score, label in zip(boxes, scores, labels): + label_idx = int(label.item()) + palette = self._get_detection_palette(label_idx) + img = cv2.putText( + img, + f"{self._get_detection_label(label_idx)} {int(100 * score)}%", + (int(box[0]), int(box[1]) - 10), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + palette, + 1, + cv2.LINE_AA, + ) + contours.setdefault(label_idx, []).append( + np.array( + [ + [int(box[0]), int(box[1])], + [int(box[2]), int(box[1])], + [int(box[2]), int(box[3])], + [int(box[0]), int(box[3])], + ] + ) + ) + for label, contour in contours.items(): + if len(contour) > 0: + cv2.drawContours( + img, + contour, + -1, + self._get_detection_palette(label), + LW, + ) + if save_path is not None: + self._save_image(save_path, img) + return img + + def _plot_instance_segmentation( + self, + source_path: str | Path | np.ndarray | Image.Image, + save_path: str | Path | None = None, + **kwargs, + ) -> np.ndarray: + img = self._plot_object_detection(source_path, None, **kwargs) + if self.mask is None: + raise RuntimeError("Instance segmentation output has no mask tensor.") + if self.boxes is None: + raise RuntimeError("Instance segmentation boxes were not initialized.") + if self.labels is None: + raise RuntimeError("Instance segmentation labels were not initialized.") + mask = self._mask_tensor() + img0_shape: tuple[int, int] = (img.shape[0], img.shape[1]) + masks = ( + crop_mask(scale_masks(mask, img0_shape), self.boxes) + .gt_(0.0) + .permute(1, 2, 0) + .to(torch.float32) + .cpu() + .numpy() + ) + overlay = np.zeros((masks.shape[0], masks.shape[1], 3)) + for i, label in enumerate(self.labels): + label_idx = int(label.item()) + overlay = np.maximum( + overlay, + masks[:, :, i][:, :, np.newaxis] + * np.array(get_coco_det_palette(label_idx)).reshape(1, 1, 3), + ) + total_mask = overlay.max(axis=2, keepdims=True) + inv_mask = 1 - ALPHA * total_mask / 255 + img = (img * inv_mask + overlay * ALPHA).astype(np.uint8) + if save_path is not None: + self._save_image(save_path, img) + return img + + def _plot_pose_estimation( + self, + source_path: str | Path | np.ndarray | Image.Image, + save_path: str | Path | None = None, + **kwargs, + ) -> np.ndarray: + img = self._plot_object_detection(source_path, None, **kwargs) + box_cls = self._box_cls_tensor() + img0_shape: tuple[int, int] = (img.shape[0], img.shape[1]) + self.kpts = scale_coords( + self.pre_cfg["LetterBox"]["img_size"], + box_cls[:, 6:].reshape(-1, 17, 3), + img0_shape, + ) + kpts = self.kpts + if kpts is None: + raise ValueError("No keypoints output found.") + for kpt in kpts: + for i, (x, y, v) in enumerate(kpt): + color_k = get_coco_keypoint_palette(i) + # if v < self.conf_thres: + # continue + cv2.circle( + img, + (int(x), int(y)), + RADIUS, + color_k, + -1, + lineType=cv2.LINE_AA, + ) + for j, sk in enumerate(get_coco_pose_skeleton()): + pos1 = (int(kpt[sk[0] - 1, 0]), int(kpt[sk[0] - 1, 1])) + pos2 = (int(kpt[sk[1] - 1, 0]), int(kpt[sk[1] - 1, 1])) + # conf1 = kpt[sk[0] - 1, 2] + # conf2 = kpt[sk[1] - 1, 2] + # if conf1 < self.conf_thres or conf2 < self.conf_thres: + # continue + cv2.line( + img, + pos1, + pos2, + get_coco_limb_palette(j), + thickness=int(np.ceil(LW / 2)), + lineType=cv2.LINE_AA, + ) + if save_path is not None: + self._save_image(save_path, img) + return img + + def _plot_obb( + self, + source_path: str | Path | np.ndarray | Image.Image, + save_path: str | Path | None = None, + **kwargs, + ) -> np.ndarray: + """Plots oriented bounding boxes on an image. + + Args: + source_path: Path or image object. + save_path: Optional path to save the plotted image. + **kwargs: Additional plotting arguments. + + Returns: + The plotted BGR image. + """ + del kwargs + box_cls = self._box_cls_tensor() + if box_cls.ndim != 2 or box_cls.shape[1] != 7: + raise ValueError( + f"OBB output must have shape [N, 7], got {tuple(box_cls.shape)}." + ) + img = self._read_image(source_path) + img0_shape: tuple[int, int] = (img.shape[0], img.shape[1]) + self.labels = box_cls[:, 5].to(torch.int64) + self.scores = box_cls[:, 4] + self.rboxes = scale_rboxes( + self.pre_cfg["LetterBox"]["img_size"], + torch.cat([box_cls[:, :4], box_cls[:, 6:7]], dim=-1), + img0_shape, + ) + polygons = xywhr2xyxyxyxy(self.rboxes).to(torch.int32).cpu().numpy() + for polygon, score, label in zip(polygons, self.scores, self.labels): + label_idx = int(label.item()) + color = get_dotav1_palette(label_idx) + text_anchor = polygon.min(axis=0) + img = cv2.putText( + img, + f"{get_dotav1_label(label_idx)} {int(100 * score)}%", + (int(text_anchor[0]), int(text_anchor[1]) - 10), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color, + 1, + cv2.LINE_AA, + ) + cv2.drawContours(img, [polygon.reshape(-1, 1, 2)], -1, color, LW) + if save_path is not None: + self._save_image(save_path, img) + return img + + def _box_cls_tensor(self) -> torch.Tensor: + """Returns detection output as a torch tensor.""" + if self.box_cls is None: + raise ValueError("No box_cls output found.") + if isinstance(self.box_cls, np.ndarray): + return torch.from_numpy(self.box_cls) + return self.box_cls + + def _get_detection_label(self, label_idx: int) -> str: + """Return the display label for detection-style tasks.""" + if self.task == "face_detection": + if label_idx != 0: + raise ValueError(f"Unexpected face_detection class index: {label_idx}.") + return "face" + return get_coco_label(label_idx) + + def _get_detection_palette(self, label_idx: int) -> tuple[int, int, int]: + """Return the display color for detection-style tasks.""" + if self.task == "face_detection": + return get_coco_det_palette(0) + return get_coco_det_palette(label_idx) + + def _mask_tensor(self) -> torch.Tensor: + """Returns segmentation mask output as a torch tensor.""" + if self.mask is None: + raise ValueError("No mask output found.") + if isinstance(self.mask, np.ndarray): + return torch.from_numpy(self.mask) + return self.mask diff --git a/mblt_vision/utils/types.py b/mblt_vision/utils/types.py new file mode 100644 index 0000000..5963c94 --- /dev/null +++ b/mblt_vision/utils/types.py @@ -0,0 +1,15 @@ +""" +Type definitions for MBLT vision models. +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import TypeAlias + +import numpy as np +import torch + +TensorLike: TypeAlias = torch.Tensor | np.ndarray +ListTensorLike: TypeAlias = Sequence[TensorLike] +NestedListTensorLike: TypeAlias = Sequence[TensorLike | ListTensorLike] diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py new file mode 100644 index 0000000..75a4c0f --- /dev/null +++ b/mblt_vision/wrapper.py @@ -0,0 +1,924 @@ +""" +Wrapper classes for MBLT model execution. +""" + +from __future__ import annotations + +import copy +import importlib +import os +import sys +import tempfile +from pathlib import Path +from typing import Any, Sequence, cast + +import numpy as np +import torch +import yaml +from huggingface_hub import hf_hub_download +from huggingface_hub.errors import EntryNotFoundError +from qbruntime import Cluster, CoreId + +from mblt_npu import MobilintNPUBackend +from ._model_paths import resolve_framework as _resolve_framework +from ._model_paths import split_model_paths as _split_model_paths +from ._model_paths import ( + uses_shifted_engine_model_path_layout as _uses_shifted_engine_model_path_layout, +) +from .utils.postprocess import build_postprocess +from .utils.preprocess import build_preprocess +from .utils.results import Results +from .utils.types import TensorLike + +MODEL_CONFIG_DIR = Path(__file__).parent / "models" + +ONNXRUNTIME_INSTALL_GUIDE = ( + "onnxruntime is not installed. To use ONNX inference, install one of the optional extras:\n" + "pip install mblt-vision-python[onnxruntime]\n" + + ( + "or\npip install mblt-vision-python[onnxruntime-gpu]" + if sys.platform != "darwin" + else "" + ) +) +CoreMode = str + + +def normalize_core_mode(core_mode: str) -> CoreMode: + """Validate a Vision engine core-mode value.""" + + valid_modes = {"single", "multi", "global4", "global8"} + if core_mode not in valid_modes: + raise ValueError( + f"Invalid core mode '{core_mode}'. Expected one of {sorted(valid_modes)}." + ) + return core_mode + + +__all__ = ["CoreMode", "normalize_core_mode", "resolve_model_config", "MBLT_Engine"] + + +def _derive_onnx_filename(file_cfg: dict[str, Any]) -> str | None: + """Return the configured or MXQ-derived ONNX artifact filename. + + ``onnx_filename`` is only required when the Hub ONNX artifact does not share + the MXQ artifact stem. Otherwise every model configuration follows the same + ``.mxq`` to ``.onnx`` convention. + """ + + onnx_filename = file_cfg.get("onnx_filename") + if isinstance(onnx_filename, str) and onnx_filename: + return onnx_filename + + filename = file_cfg.get("filename") + if not isinstance(filename, str) or not filename: + return None + + return f"{Path(filename).stem}.onnx" + + +def _normalize_model_artifacts(model_config: dict[str, Any]) -> dict[str, Any]: + """Populate the derived ONNX artifact name in a resolved model configuration.""" + + normalized = copy.deepcopy(model_config) + file_cfg = normalized.get("file_cfg") + if not isinstance(file_cfg, dict): + return normalized + + onnx_filename = _derive_onnx_filename(file_cfg) + if onnx_filename is not None: + file_cfg["onnx_filename"] = onnx_filename + return normalized + + +def _default_cache_dir() -> str: + """Returns a writable cache directory for downloaded vision artifacts.""" + + preferred = Path(os.path.expanduser("~/.mblt_model_zoo")) + try: + preferred.mkdir(parents=True, exist_ok=True) + test_file = preferred / ".write_test" + test_file.touch(exist_ok=True) + test_file.unlink(missing_ok=True) + return str(preferred) + except OSError: + return tempfile.mkdtemp(prefix="mblt_model_zoo-", dir=tempfile.gettempdir()) + + +MOBILINT_CACHE_DIR = _default_cache_dir() + + +def _load_onnxruntime() -> Any: + """Loads ``onnxruntime`` only when ONNX inference is requested. + + Returns: + The imported ``onnxruntime`` module. + + Raises: + ImportError: If ``onnxruntime`` is unavailable in the current environment. + """ + + try: + module = importlib.import_module("onnxruntime") + except ImportError as exc: + raise ImportError(ONNXRUNTIME_INSTALL_GUIDE) from exc + + if not hasattr(module, "InferenceSession"): + module_path = getattr(module, "__file__", None) or "" + msg = ( + "onnxruntime is installed, but the package is incomplete or broken and does not expose " + f"`InferenceSession` (resolved from {module_path}). " + f"{ONNXRUNTIME_INSTALL_GUIDE.replace('is not installed. To use ONNX inference, install', 'Reinstall')}" + ) + raise ImportError(msg) + + return module + + +def _resolve_onnx_providers( + ort_module: Any, requested_providers: Sequence[str] | None = None +) -> list[str]: + """Selects ONNX Runtime execution providers. + + Args: + ort_module: Imported ``onnxruntime`` module or compatible test double. + requested_providers: Optional provider order requested by the caller. + + Returns: + The provider list passed to ``InferenceSession``. + """ + + return ( + list(requested_providers) + if requested_providers is not None + else ["CPUExecutionProvider"] + ) + + +def _model_name_aliasing(model_name: str) -> str: + """Find the YAML filename matching a model name. + + Args: + model_name: Model identifier provided by the caller. + + Returns: + Exact YAML filename stored in ``MODEL_CONFIG_DIR``. + + Raises: + ValueError: If no YAML file matches or the normalized name is ambiguous. + """ + + def _stem(name: str) -> str: + return name[: -len(".yaml")] if name.lower().endswith(".yaml") else name + + def _normalize_separators(name: str) -> str: + return "_".join( + part + for part in _stem(name) + .replace("-", "_") + .replace(" ", "_") + .lower() + .split("_") + if part + ) + + requested = _normalize_separators(model_name) + config_names = sorted(path.name for path in MODEL_CONFIG_DIR.glob("*.yaml")) + separator_matches = [ + name for name in config_names if _normalize_separators(name) == requested + ] + if len(separator_matches) == 1: + return separator_matches[0] + if len(separator_matches) > 1: + raise ValueError( + f"Ambiguous model name '{model_name}'. Matches: {separator_matches}." + ) + + compact_requested = requested.replace("_", "") + compact_matches = [ + name + for name in config_names + if _normalize_separators(name).replace("_", "") == compact_requested + ] + if len(compact_matches) == 1: + return compact_matches[0] + if len(compact_matches) > 1: + raise ValueError( + f"Ambiguous model name '{model_name}'. Matches: {compact_matches}." + ) + raise ValueError(f"Model name '{model_name}' is not supported.") + + +def resolve_model_config( + model_cls: str | dict[str, Any], model_type: str = "DEFAULT" +) -> dict[str, Any]: + """Resolve a vision model configuration without constructing a runtime. + + Args: + model_cls: Model name, YAML path, or direct model configuration. + model_type: Variant key within a YAML model definition. + + Returns: + A deep copy of the resolved ``file_cfg``, ``pre_cfg``, and ``post_cfg`` mapping. + ``file_cfg`` always includes an ONNX filename when an MXQ filename is available. + + Raises: + TypeError: If the YAML or resolved configuration is not a mapping. + ValueError: If a requested variant, alias, or update base is unavailable. + """ + + if isinstance(model_cls, dict): + return _normalize_model_artifacts(model_cls) + + config_path = Path(model_cls) + if not config_path.is_file(): + config_path = MODEL_CONFIG_DIR / _model_name_aliasing(model_cls) + + with config_path.open(encoding="utf-8") as config_file: + full_config = yaml.safe_load(config_file) + if not isinstance(full_config, dict): + raise TypeError( + f"Model configuration '{config_path}' should define a dictionary." + ) + + resolving: set[str] = set() + + def _resolve_variant(variant: str) -> dict[str, Any]: + if variant in resolving: + raise ValueError( + f"Circular model configuration reference detected for '{variant}'." + ) + resolving.add(variant) + try: + model_config_part = full_config.get(variant) + if model_config_part is None: + raise ValueError(f"Model type '{variant}' not found in configuration.") + if isinstance(model_config_part, str): + if model_config_part not in full_config: + raise ValueError( + f"Model alias '{model_config_part}' not found in configuration." + ) + return _resolve_variant(model_config_part) + if not isinstance(model_config_part, dict): + raise TypeError( + f"Resolved model configuration for '{variant}' is not a dictionary." + ) + + resolved = copy.deepcopy(model_config_part) + base_config_key = resolved.pop("update", None) + if base_config_key is None: + return resolved + if ( + not isinstance(base_config_key, str) + or base_config_key not in full_config + ): + raise ValueError( + f"Base configuration '{base_config_key}' not found for update." + ) + + merged_config = _resolve_variant(base_config_key) + for key, value in resolved.items(): + if ( + key in merged_config + and isinstance(merged_config[key], dict) + and isinstance(value, dict) + ): + merged_config[key].update(value) + else: + merged_config[key] = value + return merged_config + finally: + resolving.remove(variant) + + return _normalize_model_artifacts(_resolve_variant(model_type)) + + +class MBLT_Engine: + """Main engine class for running vision models from the MBLT zoo. + + Handles the full pipeline: Preprocessing -> Inference -> Postprocessing. + + Attributes: + file_cfg: Model configuration. + pre_cfg: Preprocessing configuration. + post_cfg: Postprocessing configuration. + model: The underlying MXQ_Model. + device: The torch device being used. + """ + + def __init__( + self, + model_cls: str | dict[str, Any], + model_type: str = "DEFAULT", + mxq_path: str = "", + onnx_path: str = "", + dev_no: int | None = None, + core_mode: CoreMode | None = None, + target_cores: Sequence[str | CoreId] | None = None, + target_clusters: Sequence[int | Cluster] | None = None, + postprocess_kwargs: dict[str, Any] | None = None, + framework: str | None = None, + onnx_providers: Sequence[str] | None = None, + model_path: str = "", + ) -> None: + """Initializes the MBLT_Engine. + + Args: + model_cls(if dict): + file_cfg: Model configuration. + mxq_path: path to mxq file + onnx_path: path to onnx file + model_path: generic path to local model file + dev_no: Accelerator No. + core_mode: single, multi, global4, global8 + target_cores: single mode + target_clusters: multi, global modes + pre_cfg: Preprocessing configuration. + post_cfg: Postprocessing configuration. + model_cls(not dict): model name or yaml path + postprocess_kwargs: Optional runtime overrides passed to the postprocessor builder. + framework: Execution framework, either "mxq" or "onnx". When omitted, + ``model_path`` suffix is used first, then MXQ is the fallback. + onnx_providers: Optional ONNX Runtime execution provider order. + """ + + if _uses_shifted_engine_model_path_layout( + model_path, + mxq_path, + dev_no, + core_mode, + target_cores, + postprocess_kwargs, + framework, + onnx_providers, + ): + ( + model_path, + mxq_path, + onnx_path, + dev_no, + core_mode, + target_cores, + target_clusters, + postprocess_kwargs, + framework, + onnx_providers, + ) = ( + mxq_path, + onnx_path, + cast(str, dev_no or ""), + cast(int | None, core_mode), + cast(CoreMode | None, target_cores), + cast(Sequence[str | CoreId] | None, target_clusters), + cast(Sequence[int | Cluster] | None, postprocess_kwargs), + cast(dict[str, Any] | None, framework), + cast(str | None, onnx_providers), + cast(Sequence[str] | None, model_path or None), + ) + model_config_part = resolve_model_config(model_cls, model_type) + + file_cfg_model_path = str(model_config_part["file_cfg"].get("model_path", "")) + framework_model_path = model_path or file_cfg_model_path + self.framework = _resolve_framework(framework, framework_model_path) + mxq_path, onnx_path = _split_model_paths( + framework=self.framework, + model_path=model_path, + mxq_path=mxq_path, + onnx_path=onnx_path, + ) + + _mxq_path_passed = bool(mxq_path) + _onnx_path_passed = bool(onnx_path) + _dev_no_passed = dev_no is not None + _core_mode_passed = core_mode is not None + _target_cores_passed = target_cores is not None + _target_clusters_passed = target_clusters is not None + + if dev_no is None: + dev_no = 0 + if core_mode is None: + core_mode = "single" + if target_cores is None: + target_cores = ["0:0", "0:1", "0:2", "0:3", "1:0", "1:1", "1:2", "1:3"] + if target_clusters is None: + target_clusters = [0, 1] + + self.file_cfg = copy.deepcopy(model_config_part["file_cfg"]) + file_cfg_model_path = self.file_cfg.pop("model_path", "") + if file_cfg_model_path: + yaml_mxq_path, yaml_onnx_path = _split_model_paths( + framework=self.framework, + model_path=file_cfg_model_path, + mxq_path=str(self.file_cfg.get("mxq_path", "")), + onnx_path=str(self.file_cfg.get("onnx_path", "")), + ) + self.file_cfg["mxq_path"] = yaml_mxq_path + self.file_cfg["onnx_path"] = yaml_onnx_path + if _mxq_path_passed or "mxq_path" not in self.file_cfg: + self.file_cfg["mxq_path"] = mxq_path + if _onnx_path_passed or "onnx_path" not in self.file_cfg: + self.file_cfg["onnx_path"] = onnx_path + if _core_mode_passed or "core_mode" not in self.file_cfg: + self.file_cfg["core_mode"] = core_mode + if _target_cores_passed or "target_cores" not in self.file_cfg: + self.file_cfg["target_cores"] = target_cores + if _target_clusters_passed or "target_clusters" not in self.file_cfg: + self.file_cfg["target_clusters"] = target_clusters + if _dev_no_passed or "dev_no" not in self.file_cfg: + self.file_cfg["dev_no"] = dev_no + + self.pre_cfg = copy.deepcopy(model_config_part["pre_cfg"]) + self.post_cfg = copy.deepcopy(model_config_part["post_cfg"]) + self.postprocess_kwargs = ( + {} if postprocess_kwargs is None else dict(postprocess_kwargs) + ) + self.file_config_cleansing() + + self.model: Any + self._mxq_model: MobilintNPUBackend | None = None + self._onnx_session: Any = None + + if self.framework == "onnx": + ort = _load_onnxruntime() + resolved_onnx_path = self.file_cfg.get("onnx_path") + if not resolved_onnx_path: + raise RuntimeError( + f"ONNX path not resolved for model {model_cls}. Make sure the model repository has an ONNX file." + ) + if not os.path.isfile(resolved_onnx_path): + raise FileNotFoundError(f"ONNX file not found at: {resolved_onnx_path}") + + providers = _resolve_onnx_providers(ort, onnx_providers) + self._onnx_session = ort.InferenceSession( + resolved_onnx_path, providers=providers + ) + self.model = self._onnx_session + self.input_name = self._onnx_session.get_inputs()[0].name + self.output_names = [o.name for o in self._onnx_session.get_outputs()] + else: + self._mxq_model = MobilintNPUBackend(**self._mxq_backend_kwargs()) + self.model = self._mxq_model + self._mxq_model.create() + self._mxq_model.launch() + + if self._mxq_model.get_dtype() == "DataType.Uint8": + self.pre_cfg.pop("Normalize", None) + + self.preprocessor = build_preprocess(self.pre_cfg) + self.postprocessor = build_postprocess( + self.pre_cfg, self.post_cfg, **self.postprocess_kwargs + ) + self.device = torch.device("cpu") + + def _mxq_backend_kwargs(self) -> dict[str, Any]: + """Builds the MXQ backend kwargs from the resolved file config.""" + + excluded_keys = { + "repo_id", + "filename", + "revision", + "onnx_filename", + "onnx_path", + } + return { + key: value + for key, value in self.file_cfg.items() + if key not in excluded_keys + } + + def _derive_onnx_filename(self) -> str | None: + """Returns the ONNX filename associated with the configured MXQ artifact.""" + + onnx_filename = _derive_onnx_filename(self.file_cfg) + if onnx_filename is not None: + self.file_cfg["onnx_filename"] = onnx_filename + return onnx_filename + + def _resolve_local_onnx_path(self, mxq_path: str) -> str | None: + """Tries to resolve a sibling ONNX file next to a local MXQ artifact.""" + + onnx_path = self.file_cfg.get("onnx_path", "") + if onnx_path and os.path.isfile(onnx_path): + return onnx_path + + onnx_filename = self._derive_onnx_filename() + if onnx_filename: + sibling_path = Path(mxq_path).with_name(onnx_filename) + if sibling_path.is_file(): + return str(sibling_path) + + if mxq_path.endswith(".mxq"): + suffix_swapped = f"{mxq_path[:-4]}.onnx" + if os.path.isfile(suffix_swapped): + return suffix_swapped + + return None + + def _download_hub_artifact( + self, + *, + repo_id: str, + filename: str, + revision: str, + subfolders: Sequence[str] | None = None, + ) -> str: + """Downloads a model artifact from Hugging Face Hub and returns its cache path.""" + + last_error: Exception | None = None + normalized_subfolders = [""] if subfolders is None else list(subfolders) + for subfolder in normalized_subfolders: + kwargs: dict[str, Any] = { + "repo_id": repo_id, + "filename": filename, + "revision": revision, + "local_dir": MOBILINT_CACHE_DIR, + } + if subfolder: + kwargs["subfolder"] = subfolder + try: + return hf_hub_download(**kwargs) + except EntryNotFoundError as exc: + last_error = exc + + attempted_paths = ", ".join( + f"{subfolder}/{filename}" if subfolder else filename + for subfolder in normalized_subfolders + ) + raise RuntimeError( + f"Failed to download model from Hugging Face. Tried repo '{repo_id}' at: {attempted_paths}." + ) from last_error + + def file_config_cleansing(self) -> None: + """Validates and resolves the MXQ and ONNX model file paths in ``self.file_cfg``.""" + framework = getattr(self, "framework", "mxq") + model_path = self.file_cfg.pop("model_path", "") + if model_path: + mxq_path, onnx_path = _split_model_paths( + framework=framework, + model_path=model_path, + mxq_path=str(self.file_cfg.get("mxq_path", "")), + onnx_path=str(self.file_cfg.get("onnx_path", "")), + ) + self.file_cfg["mxq_path"] = mxq_path + self.file_cfg["onnx_path"] = onnx_path + mxq_path = self.file_cfg.get("mxq_path", "") + onnx_path = self.file_cfg.get("onnx_path", "") + onnx_filename = self._derive_onnx_filename() + + if onnx_path and os.path.isfile(onnx_path): + self.file_cfg["onnx_path"] = onnx_path + if framework == "onnx": + return + + if mxq_path and os.path.isfile(mxq_path): + resolved_local_onnx = self._resolve_local_onnx_path(mxq_path) + if resolved_local_onnx is not None: + self.file_cfg["onnx_path"] = resolved_local_onnx + self.file_cfg.pop("repo_id", None) + self.file_cfg.pop("filename", None) + self.file_cfg.pop("revision", None) + elif framework == "mxq": + self.file_cfg.pop("repo_id", None) + self.file_cfg.pop("filename", None) + self.file_cfg.pop("revision", None) + if framework == "mxq": + return + + repo_id = self.file_cfg.pop("repo_id", None) + filename = self.file_cfg.pop("filename", None) + revision = self.file_cfg.pop("revision", None) + if not repo_id or not revision: + return + + if filename and framework == "mxq": + core_mode = self.file_cfg.get("core_mode") + subfolders = ["aries", f"aries/{core_mode}"] if core_mode else ["aries"] + self.file_cfg["mxq_path"] = self._download_hub_artifact( + repo_id=repo_id, + filename=filename, + revision=revision, + subfolders=subfolders, + ) + + if onnx_filename and framework == "onnx" and not self.file_cfg.get("onnx_path"): + self.file_cfg["onnx_path"] = self._download_hub_artifact( + repo_id=repo_id, + filename=onnx_filename, + revision=revision, + ) + + def _prepare_onnx_inputs(self, x: TensorLike) -> dict[str, np.ndarray]: + """Normalizes runtime inputs to match the ONNX session contract.""" + + if isinstance(x, torch.Tensor): + x_np = x.detach().cpu().numpy() + elif isinstance(x, np.ndarray): + x_np = x + else: + raise TypeError(f"Got unexpected type for ONNX input x={type(x)}.") + + if x_np.dtype == np.float64: + x_np = x_np.astype(np.float32) + + expected_shape = self._require_onnx_session().get_inputs()[0].shape + if len(expected_shape) == 4: + if x_np.ndim == 3: + first_channel_count = x_np.shape[0] + last_channel_count = x_np.shape[-1] + elif x_np.ndim == 4: + first_channel_count = x_np.shape[1] + last_channel_count = x_np.shape[-1] + else: + first_channel_count = None + last_channel_count = None + + expected_second_dim = expected_shape[1] + expected_last_dim = expected_shape[-1] + second_matches_channels = isinstance(expected_second_dim, int) and ( + expected_second_dim == first_channel_count + or expected_second_dim == last_channel_count + ) + last_matches_channels = isinstance(expected_last_dim, int) and ( + expected_last_dim == first_channel_count + or expected_last_dim == last_channel_count + ) + + expected_layout = None + expected_channels = None + if second_matches_channels and not last_matches_channels: + expected_layout = "nchw" + expected_channels = expected_second_dim + elif last_matches_channels and not second_matches_channels: + expected_layout = "nhwc" + expected_channels = expected_last_dim + elif isinstance(expected_second_dim, int) and expected_second_dim in { + 1, + 2, + 3, + 4, + }: + expected_layout = "nchw" + expected_channels = expected_second_dim + elif isinstance(expected_last_dim, int) and expected_last_dim in { + 1, + 2, + 3, + 4, + }: + expected_layout = "nhwc" + expected_channels = expected_last_dim + + if x_np.ndim == 3: + if ( + expected_layout == "nchw" + and expected_channels is not None + and x_np.shape[0] == expected_channels + ): + x_np = np.expand_dims(x_np, axis=0) + elif ( + expected_layout == "nchw" + and expected_channels is not None + and x_np.shape[-1] == expected_channels + ): + x_np = np.transpose(x_np, (2, 0, 1)) + x_np = np.expand_dims(x_np, axis=0) + elif ( + expected_layout == "nhwc" + and expected_channels is not None + and x_np.shape[-1] == expected_channels + ): + x_np = np.expand_dims(x_np, axis=0) + elif ( + expected_layout == "nhwc" + and expected_channels is not None + and x_np.shape[0] == expected_channels + ): + x_np = np.transpose(x_np, (1, 2, 0)) + x_np = np.expand_dims(x_np, axis=0) + elif x_np.ndim == 4: + if expected_layout == "nchw" and expected_channels is not None: + if x_np.shape[1] == expected_channels: + pass + elif x_np.shape[-1] == expected_channels: + x_np = np.transpose(x_np, (0, 3, 1, 2)) + elif expected_layout == "nhwc" and expected_channels is not None: + if x_np.shape[-1] == expected_channels: + pass + elif x_np.shape[1] == expected_channels: + x_np = np.transpose(x_np, (0, 2, 3, 1)) + + return {self.input_name: x_np} + + def _require_onnx_session(self) -> Any: + """Return the active ONNX session.""" + + session = getattr(self, "_onnx_session", None) + if session is None: + fallback = getattr(self, "model", None) + if ( + fallback is not None + and hasattr(fallback, "get_inputs") + and hasattr(fallback, "run") + ): + return fallback + raise RuntimeError("ONNX session is not initialized.") + return session + + def _require_mxq_model(self) -> MobilintNPUBackend: + """Return the active MXQ backend.""" + + model = getattr(self, "_mxq_model", None) + if model is None: + fallback = getattr(self, "model", None) + if ( + fallback is not None + and hasattr(fallback, "create") + and hasattr(fallback, "launch") + ): + return cast(MobilintNPUBackend, fallback) + raise RuntimeError("MXQ backend is not initialized.") + return model + + def __call__( + self, + x: TensorLike, + ) -> Any: + """Runs raw model inference on the input. + + Note: + This does NOT include preprocessing or postprocessing. + + Args: + x: Input tensor for the model. + + Returns: + Raw model output. + """ + if self.framework == "onnx": + outputs = self._require_onnx_session().run( + self.output_names, self._prepare_onnx_inputs(x) + ) + if len(outputs) == 1: + return outputs[0] + return outputs + return cast(Any, self._require_mxq_model())(x) + + def preprocess( + self, + x: Any, + **kwargs: Any, + ) -> Any: + """Runs preprocessing on the input. + + Args: + x: Input data. + **kwargs: Additional arguments for preprocessing. + + Returns: + Preprocessed data. + """ + return self.preprocessor(x, **kwargs) + + def preprocess_with_metadata( + self, + x: Any, + ) -> tuple[Any, dict[str, Any]]: + """Runs preprocessing and returns metadata needed for exact postprocess scaling. + + Args: + x: Input data. + + Returns: + A tuple of preprocessed data and metadata such as ``ratio_pad``. + """ + return self.preprocessor.with_metadata(x) + + def postprocess( + self, + x: Any, + **kwargs: Any, + ) -> Results: + """Runs postprocessing on the input. + + Args: + x: Input data. + **kwargs: Additional arguments for postprocessing. + + Returns: + Postprocessed results. + """ + pre_result = self.postprocessor(x, **kwargs) + result_kwargs = dict(kwargs) + conf_thres = getattr(self.postprocessor, "conf_thres", None) + iou_thres = getattr(self.postprocessor, "iou_thres", None) + if "conf_thres" not in result_kwargs and conf_thres is not None: + result_kwargs["conf_thres"] = conf_thres + if "iou_thres" not in result_kwargs and iou_thres is not None: + result_kwargs["iou_thres"] = iou_thres + return Results(self.pre_cfg, self.post_cfg, pre_result, **result_kwargs) + + def set_postprocess_thresholds( + self, conf_thres: float | None = None, iou_thres: float | None = None + ) -> None: + """Updates configurable postprocess thresholds for the current model. + + Args: + conf_thres: Optional confidence threshold override. + iou_thres: Optional IoU threshold override. + + Raises: + NotImplementedError: If the current postprocessor does not support thresholds. + """ + set_threshold = getattr(self.postprocessor, "set_threshold", None) + if set_threshold is None: + raise NotImplementedError( + f"Threshold overrides are not supported for task `{self.post_cfg.get('task', 'unknown')}`." + ) + set_threshold(conf_thres=conf_thres, iou_thres=iou_thres) + + def to( + self, + device: str | torch.device, + ) -> None: + """Moves the engine and its components to the specified device. + + Args: + device: Target device. + + Raises: + TypeError: If device type is unexpected. + """ + self.preprocessor.to(device) + self.postprocessor.to(device) + + if isinstance(device, str): + self.device = torch.device(device) + elif isinstance(device, torch.device): + self.device = device + else: + raise TypeError(f"Got unexpected type for device={type(device)}.") + + def cpu(self) -> None: + """Moves the engine to CPU.""" + self.to(device="cpu") + + def gpu(self) -> None: + """Moves the engine to GPU (CUDA).""" + self.to(device="cuda") + + def cuda( + self, + device: str | int = 0, + ) -> None: + """Moves the engine to CUDA device. + + Args: + device: CUDA device identifier. Defaults to 0. + + Raises: + ValueError: If device string is invalid. + RuntimeError: If CUDA is not available. + """ + if isinstance(device, int): + device = f"cuda:{device}" + elif isinstance(device, str): + if not device.startswith("cuda:"): + raise ValueError("Invalid device string. It should start with 'cuda:'.") + + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is not available. Please check your environment.") + self.to(device=device) + + def launch(self) -> None: + """Launches the underlying model.""" + if self.framework == "mxq": + self._require_mxq_model().launch() + + def dispose(self) -> None: + """Disposes the underlying model.""" + if self.framework == "mxq": + self._require_mxq_model().dispose() + + def model_name_aliasing(self, model_name: str) -> str: + """Finds the YAML config filename that matches the given model name. + + Matching is case-insensitive and first preserves separator boundaries + so names such as ``regnet_x_16gf`` do not collide with + ``regnet_x_1_6gf``. A separator-stripped fallback is used only when it + resolves to a single unique configuration, so inputs like ``resnet50``, + ``ResNet50``, ``Resnet_50``, and ``resnet-50`` all resolve to + ``ResNet50.yaml``. + + Args: + model_name: The model identifier provided by the caller. + + Returns: + The exact YAML filename (basename only) stored in + ``MODEL_CONFIG_DIR`` that corresponds to ``model_name``. + + Raises: + ValueError: If no YAML file matches, or if the name is ambiguous + (i.e., multiple files match after normalization). + """ + + return _model_name_aliasing(model_name) diff --git a/pyproject.toml b/pyproject.toml index c94d3ed..f6edd6f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,6 +2,68 @@ requires = ["setuptools>=70.0.0", "wheel"] build-backend = "setuptools.build_meta" +[project] +name = "mblt-vision-python" +dynamic = ["version"] +description = "Mobilint NPU vision models: classification, detection, segmentation, pose" +readme = "README.md" +dependencies = [ + "mblt-npu-python>=0.0.1", + "mobilint-qb-runtime>=1.0.0", + "numpy>=1.26.0", + "torch>=2.4.1", + "opencv-python>=4.11.0.86", + "pillow>=11.1.0", + "faster-coco-eval", + "huggingface-hub", + "scipy", + "tqdm", + "PyYAML", +] +requires-python = ">=3.10,<3.13" +license = { text = "BSD-3-Clause" } +authors = [{ name = "Mobilint" }] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: BSD License", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] + [project.urls] Home = "https://www.mobilint.com/" -Repository = "https://github.com/mobilint/mblt-vision-python" \ No newline at end of file +Repository = "https://github.com/mobilint/mblt-vision-python" + +[project.optional-dependencies] +onnxruntime = [ + "onnx", + "onnxruntime", +] +onnxruntime-gpu = [ + "onnx; sys_platform != 'darwin'", + "onnxruntime-gpu; sys_platform != 'darwin'", +] + +[tool.setuptools] +packages = { find = { where = ["."], include = ["mblt_vision", "mblt_vision.*"] } } + +[tool.setuptools.dynamic] +version = { attr = "mblt_vision.__version__" } + +[tool.setuptools.package-data] +"mblt_vision" = [ + "models/*.yaml", + "datasets/*.yaml", +] + +[dependency-groups] +dev = [ + "pre-commit", + "pytest", + "pytest-timeout", +] diff --git a/tests/TEST.md b/tests/TEST.md new file mode 100644 index 0000000..3d45b51 --- /dev/null +++ b/tests/TEST.md @@ -0,0 +1,28 @@ +# Test `vision` + +You can validate Mobilint's Vision API with [`pytest`](https://docs.pytest.org/en/stable/). The snippets below assume your virtual environment is already activated. + +## Install Packages + +Install the runtime extras plus the developer tooling required by the test suite: + +```bash +pip install -e . +pip install pytest +``` + +## Run All Tests + +Execute the entire Vision test matrix (this may take a while because it loads every supported model): + +```bash +pytest tests/vision +``` + +## Run a Single Test File + +Target a specific test file to focus on one model family: + +```bash +pytest tests/vision/test_resnet50.py +``` diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..c9e9b9f --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,5 @@ +# The shared options and fixtures come from mblt_npu. 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zA+8l#HwiZ~kQfFiZSekx=Y_QF3C{<&dR3y@Q;tx8tVfx7P;y0S<&3~?GSLJ$6?WFj zB$OA3kty)HZbYaF>v`WcC{1Y!HNeLT`S|%Qa^r-YfksT&%wm+Pwc>gi@!fZ?Xq}Pf zTI(}e5h6di-=A>a<9eOZYNaGb0ljwkz)TBjc@6fRQ+LZ$JkfevP7Jk|x; zTI?xd*>tP@#Bx*bHfmK7;VscmFJ6}JxLDKXHVlO z!D$WiG-2DKq$!$HXS=K$3rTR>5rShAC9;iD^ZDOiFEifncZ}nNah$ki-dPx>lsF^- z1I>;obSv1uQYd#)0D=SxdG8QHfCh;%<20bFBh!FeB1 zJKqZpaKc?V)iqXrE{Mbcz&y{yAa+oyr>X7jtz#S~JeL)>%av2o3Zce843ogJto->s z5s}-zVF)xEYbeTEWnvrmZ-rYC-Z&IDm3-Zse3m;U2Ga=Q2>nS?VYAE0>@;7|S`q#J zfN7ktEQ@T~D)v2{-0;?jc)s7CP|%n!^SRl`A{jG=Q)zB`6v8lw`IM4gCuUkoR7i9> z(5c6?_>k~r`E$Mtw0;|@Ra&b;iXudV>~!%pfEaBbzHKKCrO_x+4;k8 zN>+|0F{gYM+S+JtibUG7uDfg$*f1Ea0S)wi7<23C?+Kz4S4v|V$CKccb4G0im+J-V zx{0*sXy6+Tobw?+iz#6khx6ZYE1}UleV9y%r6R&f+GrPgkP$ Date: Mon, 10 Aug 2026 15:31:48 +0900 Subject: [PATCH 02/48] transfer vision datasets benchmarks and compilation --- benchmark/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 benchmark/__init__.py diff --git a/benchmark/__init__.py b/benchmark/__init__.py new file mode 100644 index 0000000..4e91524 --- /dev/null +++ b/benchmark/__init__.py @@ -0,0 +1 @@ +"""Dataset and performance benchmark tools for Mobilint Vision.""" From fe9fc498abc5a0862b3abb2369b51465370a1099 Mon Sep 17 00:00:00 2001 From: jinman Date: Mon, 10 Aug 2026 15:32:00 +0900 Subject: [PATCH 03/48] transfer vision datasets benchmarks and compilation --- .agents/skills/mblt-vision-readme/SKILL.md | 39 + .agents/skills/mblt-vision/SKILL.md | 62 + .claude/skills/mblt-vision-readme/SKILL.md | 9 + .claude/skills/mblt-vision/SKILL.md | 9 + AGENTS.md | 36 +- CLAUDE.md | 3 + README.md | 74 +- benchmark/common/__init__.py | 1 + benchmark/common/argparse_utils.py | 106 + benchmark/common/chart_utils.py | 146 + benchmark/common/io_utils.py | 64 + benchmark/common/summary_utils.py | 770 ++++++ benchmark/vision/README.md | 62 + benchmark/vision/__init__.py | 1 + benchmark/vision/benchmark_vision_models.py | 452 +++ benchmark/vision/compare_benchmark_results.py | 179 ++ benchmark/vision/organize_ade20k.py | 22 + benchmark/vision/organize_cityscapes.py | 40 + benchmark/vision/organize_coco.py | 44 + benchmark/vision/organize_dotav1.py | 35 + benchmark/vision/organize_imagenet.py | 45 + benchmark/vision/organize_nyu_depth.py | 29 + benchmark/vision/organize_widerface.py | 42 + compile/vision/README.md | 119 + compile/vision/make_ade20k_subset.py | 47 + compile/vision/make_cityscapes_subset.py | 49 + compile/vision/make_coco_subset.py | 47 + compile/vision/make_dotav1_subset.py | 45 + compile/vision/make_imagenet_subset.py | 52 + compile/vision/make_nyu_depth_subset.py | 49 + compile/vision/make_widerface_subset.py | 52 + compile/vision/vision_model_compile.py | 83 + mblt_vision/README.md | 480 ++-- mblt_vision/compile/__init__.py | 21 + mblt_vision/compile/vision.py | 981 +++++++ mblt_vision/utils/datasets/__init__.py | 63 +- mblt_vision/utils/datasets/dataloader.py | 928 +++++++ mblt_vision/utils/datasets/organizer.py | 1567 +++++++++++ mblt_vision/utils/datasets/readiness.py | 368 +++ mblt_vision/utils/evaluation/__init__.py | 51 + mblt_vision/utils/evaluation/_result.py | 22 + mblt_vision/utils/evaluation/eval_ade20k.py | 290 ++ .../utils/evaluation/eval_cityscapes.py | 32 + mblt_vision/utils/evaluation/eval_coco.py | 276 ++ mblt_vision/utils/evaluation/eval_dota.py | 621 +++++ mblt_vision/utils/evaluation/eval_imagenet.py | 117 + .../utils/evaluation/eval_nyu_depth.py | 160 ++ .../utils/evaluation/eval_widerface.py | 382 +++ pyproject.toml | 13 + tests/TEST.md | 24 +- tests/test_api.py | 56 + tests/test_benchmark_vision.py | 321 +++ tests/test_compile_vision.py | 873 ++++++ tests/test_dataset_organizer.py | 1002 +++++++ tests/test_dataset_readiness.py | 296 ++ tests/test_depth_estimation.py | 94 + tests/test_face_detection.py | 123 + tests/test_model_aliasing.py | 40 + tests/test_model_metadata.py | 42 + tests/test_mxq_inference.py | 25 + tests/test_onnx_classification.py | 40 + tests/test_onnx_yolo.py | 54 + tests/test_postprocess_hierarchy.py | 123 + tests/test_preprocess_validation.py | 86 + tests/test_resnet50.py | 109 - tests/test_results.py | 179 ++ tests/test_semantic_segmentation.py | 144 + tests/test_vision_api.py | 12 - tests/test_wrapper.py | 2446 +++++++++++++++++ tests/test_yolo_det.py | 124 - tests/test_yolo_pose.py | 124 - tests/test_yolo_seg.py | 124 - 72 files changed, 14975 insertions(+), 671 deletions(-) create mode 100644 .agents/skills/mblt-vision-readme/SKILL.md create mode 100644 .agents/skills/mblt-vision/SKILL.md create mode 100644 .claude/skills/mblt-vision-readme/SKILL.md create mode 100644 .claude/skills/mblt-vision/SKILL.md create mode 100644 benchmark/common/__init__.py create mode 100644 benchmark/common/argparse_utils.py create mode 100644 benchmark/common/chart_utils.py create mode 100644 benchmark/common/io_utils.py create mode 100644 benchmark/common/summary_utils.py create mode 100644 benchmark/vision/README.md create mode 100644 benchmark/vision/__init__.py create mode 100644 benchmark/vision/benchmark_vision_models.py create mode 100644 benchmark/vision/compare_benchmark_results.py create mode 100644 benchmark/vision/organize_ade20k.py create mode 100644 benchmark/vision/organize_cityscapes.py create mode 100644 benchmark/vision/organize_coco.py create mode 100644 benchmark/vision/organize_dotav1.py create mode 100644 benchmark/vision/organize_imagenet.py create mode 100644 benchmark/vision/organize_nyu_depth.py create mode 100644 benchmark/vision/organize_widerface.py create mode 100644 compile/vision/README.md create mode 100644 compile/vision/make_ade20k_subset.py create mode 100644 compile/vision/make_cityscapes_subset.py create mode 100644 compile/vision/make_coco_subset.py create mode 100644 compile/vision/make_dotav1_subset.py create mode 100644 compile/vision/make_imagenet_subset.py create mode 100644 compile/vision/make_nyu_depth_subset.py create mode 100644 compile/vision/make_widerface_subset.py create mode 100644 compile/vision/vision_model_compile.py create mode 100644 mblt_vision/compile/__init__.py create mode 100644 mblt_vision/compile/vision.py create mode 100644 mblt_vision/utils/datasets/dataloader.py create mode 100644 mblt_vision/utils/datasets/organizer.py create mode 100644 mblt_vision/utils/datasets/readiness.py create mode 100644 mblt_vision/utils/evaluation/__init__.py create mode 100644 mblt_vision/utils/evaluation/_result.py create mode 100644 mblt_vision/utils/evaluation/eval_ade20k.py create mode 100644 mblt_vision/utils/evaluation/eval_cityscapes.py create mode 100644 mblt_vision/utils/evaluation/eval_coco.py create mode 100644 mblt_vision/utils/evaluation/eval_dota.py create mode 100644 mblt_vision/utils/evaluation/eval_imagenet.py create mode 100644 mblt_vision/utils/evaluation/eval_nyu_depth.py create mode 100644 mblt_vision/utils/evaluation/eval_widerface.py create mode 100644 tests/test_api.py create mode 100644 tests/test_benchmark_vision.py create mode 100644 tests/test_compile_vision.py create mode 100644 tests/test_dataset_organizer.py create mode 100644 tests/test_dataset_readiness.py create mode 100644 tests/test_depth_estimation.py create mode 100644 tests/test_face_detection.py create mode 100644 tests/test_model_aliasing.py create mode 100644 tests/test_model_metadata.py create mode 100644 tests/test_mxq_inference.py create mode 100644 tests/test_onnx_classification.py create mode 100644 tests/test_onnx_yolo.py create mode 100644 tests/test_postprocess_hierarchy.py create mode 100644 tests/test_preprocess_validation.py delete mode 100644 tests/test_resnet50.py create mode 100644 tests/test_results.py create mode 100644 tests/test_semantic_segmentation.py delete mode 100644 tests/test_vision_api.py create mode 100644 tests/test_wrapper.py delete mode 100644 tests/test_yolo_det.py delete mode 100644 tests/test_yolo_pose.py delete mode 100644 tests/test_yolo_seg.py diff --git a/.agents/skills/mblt-vision-readme/SKILL.md b/.agents/skills/mblt-vision-readme/SKILL.md new file mode 100644 index 0000000..5445395 --- /dev/null +++ b/.agents/skills/mblt-vision-readme/SKILL.md @@ -0,0 +1,39 @@ +--- +name: mblt-vision-readme +description: >- + Write and maintain mblt-vision-python README documentation, API examples, model references, + and migration notes. +--- + +# Mobilint Vision README Writing + +## Documentation Ownership + +- Keep the root README concise: package purpose, installation, a minimal example, and a link to + mblt_vision/README.md. +- Keep the detailed Vision API reference in mblt_vision/README.md. It owns Python construction, + framework selection, model discovery, model-family tables, output taxonomy, and migration notes. +- Document Model Zoo compatibility as migration context only. Do not present Model Zoo CLI, + validation, dataset organization, or compilation commands as features of this package. + +## Accuracy Rules + +- Use the public mblt_vision namespace in every executable example. +- Use model_path for new local-artifact examples. Mention mxq_path and onnx_path only as + compatibility aliases. +- State that .mxq and .onnx paths select their framework automatically when framework is omitted. + Document the explicit-framework conflict error. +- Describe file_cfg.filename as the MXQ source artifact and same-stem ONNX derivation. Mention + onnx_filename only for a genuinely different published artifact. +- Keep post_cfg.dataset terminology precise: it identifies output taxonomy, not just a task. +- Use obb as the only oriented-bounding-box name in standalone documentation. + +## Style and Validation + +- Use ATX headings, one blank line between blocks, hyphen lists, concise paragraphs, and + language-tagged code fences. +- Prefer generated discovery examples such as list_tasks() and list_models() over manually + maintained exhaustive name lists. +- When changing models, package metadata, dependencies, public APIs, or runtime behavior, update + the relevant README and this guidance if the workflow changes. +- For documentation-only updates, run git diff --check and verify relative links and headings. diff --git a/.agents/skills/mblt-vision/SKILL.md b/.agents/skills/mblt-vision/SKILL.md new file mode 100644 index 0000000..509d53a --- /dev/null +++ b/.agents/skills/mblt-vision/SKILL.md @@ -0,0 +1,62 @@ +--- +name: mblt-vision +description: >- + Work on the standalone Mobilint Vision Python API, model registry, preprocessing, + postprocessing, results, runtime integration, and package compatibility contracts. +--- + +# Mobilint Vision Python + +## Start Here + +1. Read AGENTS.md. +2. Run git status --short before changing files. +3. Read pyproject.toml, the affected package exports, matching model YAML, and relevant tests. +4. For a compatibility migration, compare against + ../mblt-model-zoo/mblt_model_zoo/vision deliberately; do not make it a runtime dependency. + +## Public API and Model Registry + +- Use mblt_vision.MBLT_Engine and task subpackages as the public surface. +- Keep mblt_vision as the sole intended import namespace. Use obb as the sole + oriented-bounding-box task name. +- Update a task package, top-level lazy exports, and list_models() discovery together. +- Preserve constructor arguments including model_path, mxq_path, onnx_path, + model_type, and core-selection options unless intentionally changing the API. +- Keep .mxq/.onnx suffix routing and explicit-framework conflict errors intact. +- Every model YAML must define stable file_cfg, pre_cfg, and post_cfg mappings. + Use file_cfg.filename for MXQ and derive the same-stem ONNX artifact unless + onnx_filename is required. +- Every post_cfg declares dataset; resolve output taxonomy from the dataset/task pair. + +## Processing and Results + +- Reuse the shared letterbox geometry for both preprocessing and inverse coordinate restoration. +- Detection requires pre_cfg.LetterBox. Keep semantic metadata (img0_shape and + ratio_pad) through postprocessing so logits restore to the original geometry before + argmax. +- Preserve decoded-output layout provenance through NMS. For ambiguous tensors without + provenance, prioritize channels-first raw-output normalization. +- Normalize dense depth and semantic outputs before inverse letterboxing. Validate baked semantic + maps are finite, integral, and in-range before converting them to integer class IDs. +- Keep result shapes, ordering, coordinates, dtype, and empty-result behavior compatible with + the Model Zoo reference. + +## Runtime and Packaging + +- Route NPU runtime access through mblt-npu-python; do not copy backend classes into Vision. +- Keep ONNX Runtime optional and lazy-imported. Raise a specific installation error when it is + requested but unavailable. +- Include model and dataset YAML files as package data. Build a wheel and inspect it after + changing metadata or assets. +- Do not require native bindings, GStreamer, hardware, downloaded models, or caches for normal + imports and unit tests. + +## Validate Proportionately + +- Begin with the smallest relevant test file or -k selection. +- Add deterministic differential tests for Model Zoo compatibility, including invalid inputs, + empty detections, threshold boundaries, task discovery, and image geometry. +- Run pre-commit run --files when available. For docs, run + git diff --check. +- Report unavailable hardware, downloads, or optional dependencies rather than weakening tests. diff --git a/.claude/skills/mblt-vision-readme/SKILL.md b/.claude/skills/mblt-vision-readme/SKILL.md new file mode 100644 index 0000000..ea6a148 --- /dev/null +++ b/.claude/skills/mblt-vision-readme/SKILL.md @@ -0,0 +1,9 @@ +--- +name: mblt-vision-readme +description: Write and maintain documentation for the standalone Mobilint Vision Python package. +--- + +# Mobilint Vision README Writing + +Read and follow the canonical skill at +../../../.agents/skills/mblt-vision-readme/SKILL.md. diff --git a/.claude/skills/mblt-vision/SKILL.md b/.claude/skills/mblt-vision/SKILL.md new file mode 100644 index 0000000..6120c85 --- /dev/null +++ b/.claude/skills/mblt-vision/SKILL.md @@ -0,0 +1,9 @@ +--- +name: mblt-vision +description: Work effectively on the standalone Mobilint Vision Python API and model registry. +--- + +# Mobilint Vision Python + +Read and follow the canonical skill at +../../../.agents/skills/mblt-vision/SKILL.md. diff --git a/AGENTS.md b/AGENTS.md index 652bf0a..30e45a3 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -16,7 +16,8 @@ supported native API. The current ownership boundary is deliberate: - This package owns the public Python API, model loading, preprocessing, postprocessing, runtime - integration, package metadata, wheels, documentation, and compatibility shims. + integration, dataset organization, benchmark evaluation, model compilation, package metadata, + wheels, documentation, and compatibility shims. - Keep implementation code in Python; do not block Python API progress on C++ or GStreamer work. - Design internal seams so a future optional `mblt-vision` backend can replace implementation details without changing the documented Python API or result contracts. @@ -49,6 +50,30 @@ The current ownership boundary is deliberate: deterministic `close()`/context-manager behavior and safe finalization as appropriate. - Use `obb` as the sole oriented-bounding-box task name. +## Vision Models and Processing Contracts + +- Use mblt_vision.MBLT_Engine for loading. Prefer model_path; retain mxq_path and + onnx_path as compatibility aliases. +- Update task-package exports and lazy top-level exports together. Confirm that + list_models() discovers every public model class. +- Keep each model YAML's file_cfg, pre_cfg, and post_cfg shape stable. + file_cfg.filename is the canonical MXQ Hub artifact; derive the same-stem ONNX filename + unless the Hub artifact requires an explicit onnx_filename. +- Require post_cfg.dataset in every model YAML and resolve output class counts using the + dataset/task pair. Do not assume one output taxonomy for every model in a task. +- Preserve automatic .mxq/.onnx framework detection and the fail-fast error when a local + suffix conflicts with an explicitly selected framework. +- Preserve anchorless decoded-output layout provenance through NMS. When a tensor is ambiguous + and provenance is unavailable, normalize it as raw channels-first before candidates-first. +- Use the shared letterbox helpers for forward geometry and inverse output restoration. Detection + postprocessors require pre_cfg.LetterBox; metadata-aware semantic preprocessing returns the + original image shape and ratio_pad so logits can be restored before argmax. +- Normalize dense outputs before inverse letterboxing: upsample quarter-resolution depth maps by + four, preserve baked-resize depth maps, convert Cityscapes NHWC logits to NCHW, and reject + non-finite, fractional, or out-of-range baked semantic IDs before casting. +- Keep hardware-specific runtime access behind mblt-npu-python. Optional ONNX Runtime imports + must remain lazy and report the appropriate package extra when unavailable. + ## Python-First Architecture - Keep the public layer independent from a particular backend. Define small internal interfaces @@ -94,6 +119,8 @@ The current ownership boundary is deliberate: - Avoid making hardware, downloaded models, compiled extensions, or GStreamer a requirement for ordinary unit tests. Mark and document integration prerequisites; run the narrowest relevant suite first. +- Use a deterministic default seed of 0 for any public API that samples or otherwise uses + randomness. ## Code Quality and Documentation @@ -102,6 +129,13 @@ The current ownership boundary is deliberate: - Keep imports ordered as standard library, third-party, then local. Catch specific exceptions. - Update the README and API examples whenever installation, native-library discovery, supported platforms, imports, or migration compatibility changes. +- Keep the root README focused on installation and navigation. Maintain the complete Vision API, + model-family, runtime, and taxonomy reference in mblt_vision/README.md. +- Write documentation with ATX headings, one blank line between blocks, hyphen lists, + language-tagged code fences, and concise paragraphs. Keep examples executable against the + public mblt_vision namespace and do not document Model Zoo CLI commands as standalone features. +- When a durable public fact changes, update this guide, the matching agent skill, CLAUDE.md, and + the relevant README in the same change. - For documentation-only changes, run `git diff --check` and verify headings and links. Report skipped platform, hardware, or native-runtime checks clearly. diff --git a/CLAUDE.md b/CLAUDE.md index bae10da..ddefdfd 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -4,3 +4,6 @@ `AGENTS.md` is the canonical guide for this repository. Follow it for the Python-first implementation, PyPI-wheel, lifecycle, and mblt-model-zoo Vision compatibility requirements. + +For focused model, preprocessing, postprocessing, and model-registry work, also read +.claude/skills/mblt-vision/SKILL.md. diff --git a/README.md b/README.md index e35a5b0..a7d8199 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,32 @@ -# mblt-vision-python +# Mobilint Vision Python -**Vision models** for Mobilint NPUs. +Run pre-trained Mobilint Vision models from Python. `mblt-vision-python` provides +model configuration, artifact loading, preprocessing, inference integration, and +typed postprocessing results for image classification, depth estimation, face and +object detection, oriented bounding boxes (OBB), instance and semantic segmentation, +and pose estimation. -Image classification, object detection, instance segmentation and pose estimation on -Mobilint NPUs. Each model is a class that carries its own pre- and post-processing: +Version `0.0.1` is the initial standalone release. + +## Installation + +```bash +pip install mblt-vision-python +``` + +MXQ inference requires a supported Mobilint NPU environment. Model artifacts are +downloaded from the Mobilint Hugging Face organization when no local `model_path` +is supplied. For ONNX execution, install one of the optional extras: + +```bash +pip install "mblt-vision-python[onnxruntime]" +# Or, on supported systems: +pip install "mblt-vision-python[onnxruntime-gpu]" +``` + +## Quick start + +Each model includes its matching preprocess and postprocess behavior: ```python from mblt_vision import ResNet50 @@ -13,15 +36,46 @@ x = model.preprocess("image.jpg") result = model.postprocess(model(x)) ``` -Split out of `mblt-model-zoo`, which still re-exports it as `mblt_model_zoo.vision` -so existing code keeps working. +For configurable model selection and local MXQ or ONNX artifacts, use +`MBLT_Engine`: -## Installation +```python +from mblt_vision import MBLT_Engine -```bash -pip install mblt-vision-python +model = MBLT_Engine(model_cls="resnet50", model_type="DEFAULT") +try: + result = model.postprocess(model(model.preprocess("image.jpg"))) +finally: + model.dispose() ``` +Discover supported tasks and models with `list_tasks()` and `list_models()`. New +code should use the task subpackages (for example, +`mblt_vision.object_detection`) or `MBLT_Engine`. Top-level model imports such as +`from mblt_vision import ResNet50` remain supported for convenience. + +`obb` is the canonical oriented-bounding-box task name. + +## Model Zoo migration + +Vision is now maintained in this package. `mblt-model-zoo` retains +`mblt_model_zoo.vision` as a compatibility facade for existing applications; new +projects should import from `mblt_vision` directly. + +## Documentation and tests + +See [the Vision API guide](mblt_vision/README.md) for supported model families, +model details, artifact selection, and output taxonomy behavior. See the +[compilation guide](compile/vision/README.md) for calibration-data preparation +and MXQ compilation. The [test guide](tests/TEST.md) explains offline, Hugging +Face, and NPU test runs. + +## Support and issues + +For installation, model, or runtime support, visit the +[Mobilint forum](https://discuss.mobilint.com/). Report reproducible package issues in the +[mblt-vision-python issue tracker](https://github.com/mobilint/mblt-vision-python/issues). + ## License -BSD-3-Clause. +Distributed under the [BSD 3-Clause License](LICENSE). diff --git a/benchmark/common/__init__.py b/benchmark/common/__init__.py new file mode 100644 index 0000000..40712df --- /dev/null +++ b/benchmark/common/__init__.py @@ -0,0 +1 @@ +"""Shared helpers for Vision benchmark scripts.""" diff --git a/benchmark/common/argparse_utils.py b/benchmark/common/argparse_utils.py new file mode 100644 index 0000000..bfa9bf6 --- /dev/null +++ b/benchmark/common/argparse_utils.py @@ -0,0 +1,106 @@ +"""Shared argparse validators for benchmark scripts.""" + +from __future__ import annotations + +import argparse + + +def parse_positive_int(raw: str) -> int: + """Parses a positive integer for argparse. + + Args: + raw: Raw command-line value. + + Returns: + Parsed positive integer. + + Raises: + argparse.ArgumentTypeError: If the value is not a positive integer. + """ + try: + value = int(raw) + except (TypeError, ValueError) as e: + raise argparse.ArgumentTypeError("expected a positive integer") from e + if value <= 0: + raise argparse.ArgumentTypeError("expected a positive integer") + return value + + +def parse_positive_int_optional(raw: str | None) -> int | None: + """Parses an optional positive integer for argparse. + + Args: + raw: Raw command-line value or ``None``. + + Returns: + Parsed positive integer, or ``None`` for empty input. + + Raises: + argparse.ArgumentTypeError: If the value is not empty and not a positive integer. + """ + if raw is None or raw == "": + return None + return parse_positive_int(raw) + + +def parse_range_arg(raw: str) -> tuple[int, int, int]: + """Parses ``start:end:step`` or ``start,end,step`` positive integer ranges. + + Args: + raw: Raw command-line value. + + Returns: + Parsed ``(start, end, step)`` tuple. + + Raises: + argparse.ArgumentTypeError: If the value is malformed. + """ + sep = ":" if ":" in raw else ("," if "," in raw else None) + if sep is None: + raise argparse.ArgumentTypeError( + "expected format 'start:end:step' or 'start,end,step'" + ) + parts = [part.strip() for part in raw.split(sep)] + if len(parts) != 3: + raise argparse.ArgumentTypeError( + "expected exactly 3 integers: 'start:end:step' or 'start,end,step'" + ) + try: + start, end, step = (int(part) for part in parts) + except ValueError as e: + raise argparse.ArgumentTypeError("range values must be integers") from e + if start <= 0 or end <= 0 or step <= 0: + raise argparse.ArgumentTypeError("range values must be positive integers") + if start > end: + raise argparse.ArgumentTypeError("range start must be <= end") + return start, end, step + + +def parse_int_csv( + raw: str, *, unique_sorted: bool = True, allow_empty: bool = False +) -> list[int]: + """Parses comma-separated positive integers. + + Args: + raw: Raw command-line value. + unique_sorted: Whether to sort and de-duplicate parsed values. + allow_empty: Whether an empty input should return an empty list. + + Returns: + Parsed positive integers. + + Raises: + argparse.ArgumentTypeError: If the value is malformed. + """ + parts = [item.strip() for item in str(raw).split(",") if item.strip()] + if not parts: + if allow_empty: + return [] + raise argparse.ArgumentTypeError("expected at least one integer") + try: + values = [int(item) for item in parts] + except ValueError as e: + raise argparse.ArgumentTypeError("all values must be integers") from e + if any(value <= 0 for value in values): + raise argparse.ArgumentTypeError("all values must be positive integers") + return sorted(set(values)) if unique_sorted else values diff --git a/benchmark/common/chart_utils.py b/benchmark/common/chart_utils.py new file mode 100644 index 0000000..24a70ce --- /dev/null +++ b/benchmark/common/chart_utils.py @@ -0,0 +1,146 @@ +import os +import re +from pathlib import Path +from typing import Optional, Sequence + +import matplotlib + +if "MPLBACKEND" not in os.environ: + matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + + +def sanitize_text(text: str) -> str: + cleaned = re.sub(r"[^A-Za-z0-9._-]+", "-", text).strip("-") + return cleaned or "unnamed" + + +def source_prefix(sources: list[Path], *, use_stem: bool) -> str: + parts: list[str] = [] + for source in sources: + raw = (source.stem if use_stem else source.name) or str(source) + parts.append(sanitize_text(raw)) + return "_".join(parts) + + +def default_charts_dir( + script_dir: Path, + sources: list[Path], + *, + use_stem: bool, +) -> Path: + return script_dir / "results" / "charts" / source_prefix(sources, use_stem=use_stem) + + +def source_labels(sources: list[Path], *, use_stem: bool) -> list[str]: + labels = [ + ((source.stem if use_stem else source.name) or str(source)) + for source in sources + ] + counts: dict[str, int] = {} + for label in labels: + counts[label] = counts.get(label, 0) + 1 + + seen: dict[str, int] = {} + out: list[str] = [] + for idx, label in enumerate(labels): + if counts[label] == 1: + out.append(label) + continue + seen[label] = seen.get(label, 0) + 1 + out.append(f"{label} [{seen[label]}/{counts[label]}]") + + if len(set(out)) != len(out): + out = [f"{label}#{idx + 1}" for idx, label in enumerate(labels)] + return out + + +def plot_grouped_scalar_barh( + *, + models: list[str], + group_labels: list[str], + grouped_values: list[dict[str, Optional[float]]], + x_label: str, + y_label: str, + title: str, + output_path: Path, + fig_width: float = 14.0, +) -> None: + if not models: + return + + y = np.arange(len(models), dtype=float) + group_height = 0.82 + bar_h = group_height / max(len(grouped_values), 1) + start = -group_height / 2 + bar_h / 2 + fig_h = max(5.0, 0.45 * len(models) + 2.0) + fig, ax = plt.subplots(figsize=(fig_width, fig_h)) + cmap = plt.get_cmap("tab10") + + for idx, (label, source_values) in enumerate(zip(group_labels, grouped_values)): + x_vals = [] + y_vals = [] + for i, model in enumerate(models): + value = source_values.get(model) + if value is None: + continue + x_vals.append(float(value)) + y_vals.append(y[i] + start + idx * bar_h) + if x_vals: + ax.barh( + y_vals, + x_vals, + height=bar_h * 0.95, + label=label, + color=cmap(idx % 10), + ) + + ax.set_yticks(y) + ax.set_yticklabels(models) + ax.invert_yaxis() + ax.set_xlabel(x_label) + ax.set_ylabel(y_label) + ax.set_title(title) + ax.grid(axis="x", linestyle="--", alpha=0.3) + handles, labels = ax.get_legend_handles_labels() + if handles: + ax.legend(loc="best") + plt.tight_layout() + fig.savefig(output_path, dpi=220) + plt.close(fig) + + +def plot_simple_barh( + *, + labels: Sequence[str], + values: Sequence[float], + x_label: str, + title: str, + output_path: Path, + fig_width: float = 12.0, +) -> None: + """Plots a single horizontal bar chart. + + Args: + labels: Y-axis labels. + values: Numeric values aligned with labels. + x_label: X-axis label. + title: Chart title. + output_path: Destination PNG path. + fig_width: Figure width. + """ + if not labels: + return + fig, ax = plt.subplots(figsize=(fig_width, max(4.0, 0.45 * len(labels) + 2.0))) + y = list(range(len(labels))) + ax.barh(y, [float(value) for value in values]) + ax.set_yticks(y) + ax.set_yticklabels(list(labels)) + ax.invert_yaxis() + ax.set_xlabel(x_label) + ax.set_title(title) + ax.grid(axis="x", linestyle="--", alpha=0.3) + plt.tight_layout() + fig.savefig(output_path, dpi=220) + plt.close(fig) diff --git a/benchmark/common/io_utils.py b/benchmark/common/io_utils.py new file mode 100644 index 0000000..8d59f83 --- /dev/null +++ b/benchmark/common/io_utils.py @@ -0,0 +1,64 @@ +"""Shared file I/O helpers for benchmark scripts.""" + +from __future__ import annotations + +import csv +import json +from pathlib import Path +from typing import Any, Mapping, Sequence + +JsonObject = dict[str, Any] +CsvRow = dict[str, Any] + + +def safe_filename(text: str, *, replace_slash_only: bool = False) -> str: + """Returns a filename-safe representation of text. + + Args: + text: Source text to convert. + replace_slash_only: Whether to preserve the legacy behavior of replacing only forward slashes. + + Returns: + Sanitized text suitable for benchmark output filenames. + """ + if replace_slash_only: + return text.replace("/", "__") + + cleaned = ( + text.replace("/", "__").replace("\\", "__").replace(":", "_").replace(" ", "_") + ) + return cleaned or "unnamed" + + +def write_json(path: Path, payload: Any) -> None: + """Writes an indented UTF-8 JSON file after creating its parent directory. + + Args: + path: Destination path. + payload: JSON-serializable payload. + """ + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as f: + json.dump(payload, f, ensure_ascii=False, indent=2) + + +def write_csv(path: Path, rows: Sequence[Mapping[str, Any]]) -> None: + """Writes dictionaries to CSV using the union of row keys as the field order. + + Args: + path: Destination path. + rows: Row mappings to write. Empty rows are ignored to preserve existing script behavior. + """ + if not rows: + return + path.parent.mkdir(parents=True, exist_ok=True) + fieldnames = list(rows[0].keys()) + for row in rows[1:]: + for key in row: + if key not in fieldnames: + fieldnames.append(key) + with path.open("w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + for row in rows: + writer.writerow({key: row.get(key, "") for key in fieldnames}) diff --git a/benchmark/common/summary_utils.py b/benchmark/common/summary_utils.py new file mode 100644 index 0000000..443ba35 --- /dev/null +++ b/benchmark/common/summary_utils.py @@ -0,0 +1,770 @@ +"""Markdown summary helpers for benchmark outputs.""" + +from __future__ import annotations + +import json +import re +import subprocess +from collections.abc import Mapping, Sequence +from datetime import datetime +from pathlib import Path +from typing import Any + +HOST_PC_INFO_FILENAME = "host_pc_info.json" + +_HOST_INFO_SECTION_ORDER = ("CPU", "Motherboard", "DRAM", "NPU") + +_HOST_INFO_SECTION_ALIASES = { + "cpu": "CPU", + "processor": "CPU", + "motherboard": "Motherboard", + "mainboard": "Motherboard", + "baseboard": "Motherboard", + "board": "Motherboard", + "dram": "DRAM", + "memory": "DRAM", + "ram": "DRAM", + "dimm": "DRAM", + "npu": "NPU", + "npus": "NPU", + "neural": "NPU", + "accelerator": "NPU", +} + +_NPU_ARRAY_KEY_RE = re.compile(r"(?:^|\.)npus\[(\d+)\]\.(.+)$") + + +_PLOT_TITLES_BY_NAME = { + "rtf.png": "Real-Time Factor", + "inverse_rtf.png": "Inverse Real-Time Factor", + "sec_per_j.png": "Seconds Per Joule", + "j_per_sec.png": "Joules Per Audio Second", + "wer.png": "Word Error Rate", + "cer.png": "Character Error Rate", + "p95_latency_s.png": "P95 Latency", + "throughput_samples_per_s.png": "Throughput", + "decode_tokens_per_s.png": "Decode Tokens Per Second", + "prefill_tps.png": "Prefill Tokens Per Second", + "measure_prefill_tps.png": "Prefill Tokens Per Second", + "llm_prefill_tps.png": "Prefill Tokens Per Second", + "measure_llm_prefill_tps.png": "Prefill Tokens Per Second", + "prefill_tps_per_w.png": "Prefill TPS/W", + "measure_prefill_tps_per_w.png": "Prefill TPS/W", + "llm_prefill_tps_per_w.png": "Prefill TPS/W", + "measure_llm_prefill_tps_per_w.png": "Prefill TPS/W", + "decode_tps.png": "Decode Tokens Per Second", + "measure_decode_tps.png": "Decode Tokens Per Second", + "llm_decode_tps.png": "Decode Tokens Per Second", + "measure_llm_decode_tps.png": "Decode Tokens Per Second", + "decode_tps_per_w.png": "Decode TPS/W", + "measure_decode_tps_per_w.png": "Decode TPS/W", + "llm_decode_tps_per_w.png": "Decode TPS/W", + "measure_llm_decode_tps_per_w.png": "Decode TPS/W", + "avg_power_w.png": "Power", + "measure_avg_power_w.png": "Power", + "avg_temperature_c.png": "Temperature", + "measure_avg_temperature_c.png": "Temperature", + "avg_utilization_pct.png": "Utilization", + "measure_avg_utilization_pct.png": "Utilization", + "avg_memory_used_mb.png": "Memory Used Megabytes", + "measure_avg_memory_used_mb.png": "Memory Used Megabytes", + "total_energy_j.png": "Total Energy", + "measure_total_energy_j.png": "Total Energy", + "vision_fps.png": "Vision FPS", + "vision_encode_ms.png": "Vision Encode ms", + "vision_img_per_j.png": "Vision Images Per Joule", +} + +_PLOT_NAME_ORDER = {name: idx for idx, name in enumerate(_PLOT_TITLES_BY_NAME)} + + +def collect_host_pc_info( + results_dir: Path | str, *, filename: str = HOST_PC_INFO_FILENAME +) -> Path: + """Run ``mblt-tracker collect`` and save its JSON output. + + The benchmark should not fail just because host information collection is unavailable. Failures are therefore + converted into a JSON payload that can still be rendered in the summary. + + Args: + results_dir: Benchmark output directory. + filename: JSON filename to write under ``results_dir``. + + Returns: + Path to the written JSON file. + """ + out_dir = Path(results_dir) + out_dir.mkdir(parents=True, exist_ok=True) + output_path = out_dir / filename + payload: Any + try: + proc = subprocess.run( + ["mblt-tracker", "collect"], + check=False, + capture_output=True, + encoding="utf-8", + timeout=30, + ) + if proc.returncode == 0: + try: + payload = json.loads(proc.stdout) + except json.JSONDecodeError: + payload = { + "status": "error", + "message": "mblt-tracker collect did not return valid JSON.", + "stdout": proc.stdout, + "stderr": proc.stderr, + "returncode": proc.returncode, + } + else: + payload = { + "status": "error", + "message": "mblt-tracker collect failed.", + "stdout": proc.stdout, + "stderr": proc.stderr, + "returncode": proc.returncode, + } + except FileNotFoundError: + payload = {"status": "error", "message": "mblt-tracker CLI was not found."} + except subprocess.TimeoutExpired as e: + payload = { + "status": "error", + "message": "mblt-tracker collect timed out.", + "stdout": e.stdout, + "stderr": e.stderr, + "timeout_s": e.timeout, + } + + payload = _with_collection_metadata(payload) + with output_path.open("w", encoding="utf-8") as f: + json.dump(payload, f, ensure_ascii=False, indent=2) + print(f"Saved Host PC Info: {output_path.name}") + return output_path + + +def write_summary_markdown( + path: Path | str, + *, + title: str, + host_info_path: Path | str | None, + table_markdown_path: Path | str | None, + plot_paths: Sequence[Path | str], + plot_tables: Mapping[str, str] | None = None, + host_info_paths: Mapping[str, Path | str] | None = None, +) -> None: + """Write a benchmark summary Markdown file with host info, plots, and table. + + Args: + path: Summary Markdown destination. + title: Document title. + host_info_path: JSON file written by :func:`collect_host_pc_info`. + table_markdown_path: Existing Markdown table to include. + plot_paths: PNG files to embed in the summary. + plot_tables: Optional Markdown tables keyed by plot PNG filename. When provided, matching tables are rendered + directly below each plot and the bottom combined table is omitted. + host_info_paths: Optional source-labeled host info JSON files. When provided, host info is rendered per source. + """ + summary_path = Path(path) + summary_path.parent.mkdir(parents=True, exist_ok=True) + host_info_lines = ( + _host_infos_markdown( + {label: Path(src_path) for label, src_path in host_info_paths.items()} + ) + if host_info_paths + else _host_info_markdown(Path(host_info_path) if host_info_path else None) + ) + lines = [f"# {title}\n\n"] + lines.extend(_device_energy_note_markdown()) + lines.extend( + _plots_markdown( + summary_path.parent, [Path(p) for p in plot_paths], plot_tables=plot_tables + ) + ) + if not plot_tables: + lines.extend( + _table_markdown(Path(table_markdown_path) if table_markdown_path else None) + ) + lines.extend(host_info_lines) + summary_path.write_text("".join(lines), encoding="utf-8") + + +def _device_energy_note_markdown() -> list[str]: + """Return a reusable note describing trace-integrated energy limitations.""" + + return [ + "## Device energy note\n\n", + "Energy and energy-efficiency metrics are computed from mblt-tracker power traces using " + "trapezoidal integration. At least two valid power samples are required, so measurements shorter " + "than the tracker sampling interval may leave energy fields empty.\n\n", + ] + + +def read_csv_rows(path: Path | str) -> list[dict[str, str]]: + """Read CSV rows if the file exists. + + Args: + path: CSV path to read. + + Returns: + CSV rows as dictionaries, or an empty list when the file does not exist. + """ + csv_path = Path(path) + if not csv_path.is_file(): + return [] + import csv + + with csv_path.open("r", encoding="utf-8", newline="") as f: + return list(csv.DictReader(f)) + + +def markdown_table(headers: Sequence[str], rows: Sequence[Sequence[Any]]) -> str: + """Build a compact Markdown table with right-aligned metric columns. + + Args: + headers: Table headers. + rows: Table row values. + + Returns: + Markdown table text, or an empty string for empty rows. + """ + if not rows: + return "" + lines = [ + "| " + " | ".join(_escape_markdown(header) for header in headers) + " |\n", + "| " + " | ".join(["---"] + ["---:" for _ in headers[1:]]) + " |\n", + ] + for row in rows: + lines.append( + "| " + " | ".join(_format_summary_cell(value) for value in row) + " |\n" + ) + return "".join(lines) + + +def scalar_plot_table( + rows: Sequence[Mapping[str, Any]], *, value_key: str, unit_header: str +) -> str: + """Build a model/value table for one scalar plot. + + Args: + rows: Rows containing a ``model`` key and the requested scalar key. + value_key: Key containing the scalar value. + unit_header: Header for the value column. + + Returns: + Markdown table text. + """ + return markdown_table( + ["Model", unit_header], [[row.get("model"), row.get(value_key)] for row in rows] + ) + + +def token_sweep_plot_table( + models: Sequence[str], + metrics_by_model: Mapping[str, Any], + *, + value_key: str, +) -> str: + """Build a model/token table for one token-sweep plot. + + Args: + models: Model names to include. + metrics_by_model: Mapping from model name to metric object with token dictionaries. + value_key: Attribute name containing a ``dict[int, float]`` token metric. + + Returns: + Markdown table text. + """ + token_set: set[int] = set() + for model in models: + token_set.update(getattr(metrics_by_model[model], value_key).keys()) + tokens = sorted(token_set) + if not tokens: + return "" + table_rows = [] + for model in models: + values = getattr(metrics_by_model[model], value_key) + table_rows.append([model, *(values.get(token) for token in tokens)]) + return markdown_table( + ["Model", *(f"{token} tokens" for token in tokens)], table_rows + ) + + +def write_token_combined_markdown( + path: Path | str, + tps_rows: Sequence[Mapping[str, Any]], + device_rows: Sequence[Mapping[str, Any]], +) -> None: + """Write a wide-form token sweep Markdown table shared by transformer benchmarks. + + Args: + path: Markdown output path. + tps_rows: Long-form TPS rows from ``BenchmarkResult.iter_rows``. + device_rows: Per-model device metric rows. + """ + if not tps_rows: + return + models = sorted({str(r["model"]) for r in tps_rows}) + prefill_tokens = sorted( + { + int(r["tokens"]) + for r in tps_rows + if str(r.get("phase")) == "prefill" and _is_int_like(r.get("tokens")) + } + ) + decode_tokens = sorted( + { + int(r["tokens"]) + for r in tps_rows + if str(r.get("phase")) == "decode" and _is_int_like(r.get("tokens")) + } + ) + tps_map: dict[tuple[str, str, int], float] = {} + time_map: dict[tuple[str, str, int], float] = {} + npu_pct_map: dict[tuple[str, str, int], float] = {} + for row in tps_rows: + model = str(row["model"]) + phase = str(row["phase"]) + token = int(row["tokens"]) + tps_val = row.get("tps") + time_ms_val = row.get("time_ms") + npu_pct_val = row.get("avg_npu_token_latency_pct") + if isinstance(tps_val, (int, float)): + tps_map[(model, phase, token)] = float(tps_val) + if isinstance(time_ms_val, (int, float)): + time_map[(model, phase, token)] = float(time_ms_val) + if isinstance(npu_pct_val, (int, float)): + npu_pct_map[(model, phase, token)] = float(npu_pct_val) + + device_map = { + str(r["model"]): r for r in device_rows if isinstance(r.get("model"), str) + } + device_cols = [ + "avg_power_w", + "p99_power_w", + "avg_utilization_pct", + "p99_utilization_pct", + "avg_temperature_c", + "p99_temperature_c", + "avg_memory_used_mb", + "p99_memory_used_mb", + "total_memory_mb", + "avg_memory_used_pct", + "p99_memory_used_pct", + "total_energy_j", + "prefill_tps_last", + "decode_tps_last", + "prefill_tps_per_w_last", + "decode_tps_per_w_last", + "prefill_j_per_tok_last", + "decode_j_per_tok_last", + ] + + headers = ["model"] + headers.extend([f"prefill_tps_{t}" for t in prefill_tokens]) + headers.extend([f"decode_tps_{t}" for t in decode_tokens]) + headers.extend([f"prefill_latency_ms_{t}" for t in prefill_tokens]) + headers.extend([f"decode_duration_ms_{t}" for t in decode_tokens]) + headers.extend([f"prefill_npu_latency_pct_{t}" for t in prefill_tokens]) + headers.extend([f"decode_npu_latency_pct_{t}" for t in decode_tokens]) + headers.extend(device_cols) + + rows: list[list[str]] = [] + for model in models: + values: list[str] = [model] + for token in prefill_tokens: + values.append( + _format_optional_float(tps_map.get((model, "prefill", token))) + ) + for token in decode_tokens: + values.append(_format_optional_float(tps_map.get((model, "decode", token)))) + for token in prefill_tokens: + values.append( + _format_optional_float(time_map.get((model, "prefill", token))) + ) + for token in decode_tokens: + values.append( + _format_optional_float(time_map.get((model, "decode", token))) + ) + for token in prefill_tokens: + values.append( + _format_optional_float(npu_pct_map.get((model, "prefill", token))) + ) + for token in decode_tokens: + values.append( + _format_optional_float(npu_pct_map.get((model, "decode", token))) + ) + + drow = device_map.get(model, {}) + for col in device_cols: + v = drow.get(col) if isinstance(drow, Mapping) else None + values.append(_format_optional_float(v)) + rows.append(values) + + Path(path).write_text(markdown_table(headers, rows), encoding="utf-8") + + +def existing_png_paths( + results_dir: Path | str, *, prefixes: Sequence[str] | None = None +) -> list[Path]: + """Return sorted PNG paths under a benchmark results directory. + + Args: + results_dir: Directory to scan. + prefixes: Optional filename prefixes to include. + + Returns: + Sorted PNG files matching the optional prefixes. + """ + out_dir = Path(results_dir) + paths = sorted( + out_dir.glob("*.png"), + key=lambda path: (_plot_sort_key(path), path.name), + ) + if prefixes is None: + return [path for path in paths if _plot_title(path) is not None] + prefix_tuple = tuple(prefixes) + return [path for path in paths if path.name.startswith(prefix_tuple)] + + +def _with_collection_metadata(payload: Any) -> dict[str, Any]: + collected_at = datetime.now().astimezone().isoformat(timespec="seconds") + if isinstance(payload, dict): + out = dict(payload) + out.setdefault("status", "ok") + out.setdefault("collected_at", collected_at) + return out + return {"status": "ok", "collected_at": collected_at, "data": payload} + + +def _host_info_markdown(path: Path | None) -> list[str]: + lines = ["## Host PC Info\n\n"] + if path is None or not path.is_file(): + lines.append("Host PC info is not available.\n\n") + return lines + try: + with path.open("r", encoding="utf-8") as f: + payload = json.load(f) + except (OSError, json.JSONDecodeError) as e: + lines.append(f"Failed to read `{path.name}`: {e}\n\n") + return lines + + sections = _host_info_sections(payload) + if not sections: + lines.append("Host PC info is empty.\n\n") + return lines + lines.append(f"Source: `{path.name}`\n\n") + for title, rows in sections: + if title == "NPU": + _append_npu_info_markdown(lines, rows) + else: + _append_host_info_table(lines, title, rows) + return lines + + +def _host_infos_markdown(paths_by_label: Mapping[str, Path]) -> list[str]: + """Render source-labeled host info JSON files as merged comparison tables.""" + + lines = ["## Host PC Info\n\n"] + if not paths_by_label: + lines.append("Host PC info is not available.\n\n") + return lines + + metadata_rows: list[tuple[str, dict[str, str]]] = [("Source", {}), ("Status", {})] + sections_by_label: dict[str, dict[str, list[tuple[str, str]]]] = {} + section_order: list[str] = [] + for label, path in paths_by_label.items(): + metadata_rows[0][1][label] = path.as_posix() + if not path.is_file(): + metadata_rows[1][1][label] = ( + "missing: host_pc_info.json was not found in the input folder" + ) + sections_by_label[label] = {} + continue + try: + with path.open("r", encoding="utf-8") as f: + payload = json.load(f) + except (OSError, json.JSONDecodeError) as e: + metadata_rows[1][1][label] = f"error: {e}" + sections_by_label[label] = {} + continue + + sections = _host_info_sections(payload) + if not sections: + metadata_rows[1][1][label] = "empty" + sections_by_label[label] = {} + continue + metadata_rows[1][1][label] = "ok" + label_sections: dict[str, list[tuple[str, str]]] = {} + for title, rows in sections: + label_sections[title] = rows + if title not in section_order: + section_order.append(title) + sections_by_label[label] = label_sections + + labels = list(paths_by_label) + _append_merged_host_info_table(lines, "Sources", labels, metadata_rows) + for section in section_order: + field_values: dict[str, dict[str, str]] = {} + for label in labels: + for field, value in sections_by_label.get(label, {}).get(section, []): + field_values.setdefault(field, {})[label] = value + rows = [(field, values) for field, values in field_values.items()] + _append_merged_host_info_table(lines, section, labels, rows) + return lines + + +def _append_merged_host_info_table( + lines: list[str], + title: str, + labels: Sequence[str], + rows: Sequence[tuple[str, Mapping[str, str]]], +) -> None: + """Append a source-merged host info table.""" + + lines.append(f"### {title}\n\n") + lines.append( + "| Field | " + " | ".join(_escape_markdown(label) for label in labels) + " |\n" + ) + lines.append("| --- | " + " | ".join("---" for _ in labels) + " |\n") + for field, values_by_label in rows: + values = [_escape_markdown(values_by_label.get(label, "")) for label in labels] + lines.append(f"| `{_escape_markdown(field)}` | " + " | ".join(values) + " |\n") + lines.append("\n") + + +def _plots_markdown( + base_dir: Path, + plot_paths: Sequence[Path], + *, + plot_tables: Mapping[str, str] | None = None, +) -> list[str]: + lines = ["## Plots\n\n"] + existing = [path for path in plot_paths if path.is_file()] + if not existing: + lines.append("No plot PNG files were generated.\n\n") + return lines + tables = plot_tables or {} + for path in existing: + rel = path.relative_to(base_dir) if path.is_relative_to(base_dir) else path + title = _plot_title(path) or path.stem.replace("_", " ").title() + lines.append(f"### {title}\n\n") + lines.append(f"![{title}]({rel.as_posix()})\n\n") + table = tables.get(path.name) + if table: + lines.append(table) + if not lines[-1].endswith("\n"): + lines.append("\n") + lines.append("\n") + return lines + + +def _plot_title(path: Path) -> str | None: + title = _PLOT_TITLES_BY_NAME.get(path.name) + if title is not None: + return title + + stem = path.stem + if stem.startswith("rtf_beams"): + return "Real-Time Factor" + if stem.startswith("wer_beams"): + return "Word Error Rate" + if stem.startswith("cer_beams"): + return "Character Error Rate" + return None + + +def _plot_sort_key(path: Path) -> int: + if path.name in _PLOT_NAME_ORDER: + return _PLOT_NAME_ORDER[path.name] + stem = path.stem + if stem.startswith("rtf_beams"): + return len(_PLOT_NAME_ORDER) + if stem.startswith("wer_beams"): + return len(_PLOT_NAME_ORDER) + 1 + if stem.startswith("cer_beams"): + return len(_PLOT_NAME_ORDER) + 2 + return len(_PLOT_NAME_ORDER) + 100 + + +def _host_info_sections(payload: Any) -> list[tuple[str, list[tuple[str, str]]]]: + rows = _flatten_json(payload) + if not rows: + return [] + + grouped: dict[str, list[tuple[str, str]]] = { + section: [] for section in _HOST_INFO_SECTION_ORDER + } + general_rows: list[tuple[str, str]] = [] + for key, value in rows: + section = _host_info_section_for_key(key) + if section is None: + general_rows.append((key, value)) + else: + grouped[section].append((key, value)) + + sections: list[tuple[str, list[tuple[str, str]]]] = [] + if general_rows: + sections.append(("General", general_rows)) + sections.extend( + (section, grouped[section]) + for section in _HOST_INFO_SECTION_ORDER + if grouped[section] + ) + return sections + + +def _append_host_info_table( + lines: list[str], + title: str, + rows: Sequence[tuple[str, str]], + *, + heading_level: int = 3, +) -> None: + """Append one host info section as a Markdown table.""" + lines.append(f"{'#' * heading_level} {title}\n\n") + _append_field_value_table(lines, rows) + + +def _append_npu_info_markdown( + lines: list[str], rows: Sequence[tuple[str, str]], *, heading_level: int = 3 +) -> None: + """Append NPU host info with ``npus`` array entries grouped by index.""" + common_rows: list[tuple[str, str]] = [] + indexed_rows: dict[int, list[tuple[str, str]]] = {} + for key, value in rows: + npu_key = _split_npu_array_key(key) + if npu_key is None: + common_rows.append((key, value)) + continue + index, field = npu_key + indexed_rows.setdefault(index, []).append((field, value)) + + if not indexed_rows: + _append_host_info_table(lines, "NPU", rows, heading_level=heading_level) + return + + lines.append(f"{'#' * heading_level} NPU\n\n") + if common_rows: + lines.append(f"{'#' * (heading_level + 1)} General\n\n") + _append_field_value_table(lines, common_rows) + for index in sorted(indexed_rows): + lines.append(f"{'#' * (heading_level + 1)} NPU {index}\n\n") + _append_field_value_table(lines, indexed_rows[index]) + + +def _append_field_value_table( + lines: list[str], rows: Sequence[tuple[str, str]] +) -> None: + """Append field/value rows as a Markdown table.""" + lines.append("| Field | Value |\n") + lines.append("| --- | --- |\n") + for key, value in rows: + lines.append(f"| `{_escape_markdown(key)}` | {_escape_markdown(value)} |\n") + lines.append("\n") + + +def _host_info_section_for_key(key: str) -> str | None: + if _split_npu_array_key(key) is not None: + return "NPU" + normalized = key.replace("_", ".").replace("-", ".").replace(" ", ".").lower() + parts = [ + part + for part in normalized.replace("[", ".").replace("]", ".").split(".") + if part + ] + for part in parts: + section = _HOST_INFO_SECTION_ALIASES.get(part) + if section is not None: + return section + return None + + +def _split_npu_array_key(key: str) -> tuple[int, str] | None: + """Return the NPU array index and field for flattened ``npus`` keys.""" + match = _NPU_ARRAY_KEY_RE.search(key) + if match is None: + return None + return int(match.group(1)), match.group(2) + + +def _host_info_section_title(value: str) -> str: + upper_names = {"cpu": "CPU", "dram": "DRAM", "gpu": "GPU", "npu": "NPU", "os": "OS"} + normalized = value.replace("_", " ").replace("-", " ").strip() + lowered = normalized.lower() + if lowered in upper_names: + return upper_names[lowered] + return " ".join( + upper_names.get(part.lower(), part.capitalize()) for part in normalized.split() + ) + + +def _table_markdown(path: Path | None) -> list[str]: + lines = ["## Results Table\n\n"] + if path is None or not path.is_file(): + lines.append("Results table is not available.\n") + return lines + lines.append(path.read_text(encoding="utf-8")) + if not lines[-1].endswith("\n"): + lines.append("\n") + return lines + + +def _flatten_json(value: Any, *, prefix: str = "") -> list[tuple[str, str]]: + if isinstance(value, Mapping): + rows: list[tuple[str, str]] = [] + for key, child in value.items(): + child_key = f"{prefix}.{key}" if prefix else str(key) + rows.extend(_flatten_json(child, prefix=child_key)) + return rows + if isinstance(value, list): + if all(not isinstance(item, (Mapping, list)) for item in value): + return [(prefix, ", ".join(_scalar_to_text(item) for item in value))] + rows = [] + for idx, child in enumerate(value): + rows.extend(_flatten_json(child, prefix=f"{prefix}[{idx}]")) + return rows + return [(prefix, _scalar_to_text(value))] if prefix else [] + + +def _scalar_to_text(value: Any) -> str: + if value is None: + return "" + if isinstance(value, (str, int, float, bool)): + return str(value) + return json.dumps(value, ensure_ascii=False) + + +def _format_summary_cell(value: Any) -> str: + """Format one benchmark summary table value.""" + if value is None or value == "": + return "" + if isinstance(value, (int, float)): + return f"{float(value):.6f}" + if isinstance(value, str): + try: + return f"{float(value):.6f}" + except ValueError: + return _escape_markdown(value) + return _escape_markdown(str(value)) + + +def _format_optional_float(value: Any) -> str: + """Format a numeric value for compact benchmark tables.""" + return f"{float(value):.6f}" if isinstance(value, (int, float)) else "" + + +def _is_int_like(value: Any) -> bool: + """Return whether a value can be losslessly parsed as an integer token count.""" + if isinstance(value, int): + return True + if isinstance(value, str): + try: + int(value) + except ValueError: + return False + return True + return False + + +def _escape_markdown(value: str) -> str: + return value.replace("|", "\\|").replace("\n", "
    ") diff --git a/benchmark/vision/README.md b/benchmark/vision/README.md new file mode 100644 index 0000000..3d7ef4d --- /dev/null +++ b/benchmark/vision/README.md @@ -0,0 +1,62 @@ +# Vision datasets and benchmarks + +`mblt-vision-python` owns the utilities that organize validation datasets and +evaluate Vision models. The standardized benchmark runner writes JSON, CSV, +Markdown, and optional chart artifacts for one or more models. + +Run scripts from the repository root, or invoke them as modules after installing +the package from source. + +## Dataset organization + +Most organizers can download their public source archives when paths are omitted; +pass local paths to reuse existing downloads. Organized datasets default to +`~/.mblt_vision/datasets`. + +```bash +python benchmark/vision/organize_coco.py +python benchmark/vision/organize_ade20k.py +python benchmark/vision/organize_nyu_depth.py +``` + +Cityscapes is not downloaded automatically. Supply the official archives: + +```bash +python benchmark/vision/organize_cityscapes.py \ + --image-dir benchmark/leftImg8bit_trainvaltest.zip \ + --annotation-dir benchmark/gtFine_trainvaltest.zip +``` + +The Cityscapes organizer selects only the 500 validation RGB images and their +`gtFine_labelIds` masks, installing them as `images/` and `annotations/` under the +chosen output directory. + +ImageNet and DOTA require their source archives or directories because their terms +of use may restrict automatic retrieval. See each organizer's `--help` output for +required arguments. + +## Running a benchmark + +```bash +python benchmark/vision/benchmark_vision_models.py \ + --models ResNet50 \ + --task image_classification \ + --data-path ~/.mblt_vision/datasets/imagenet +``` + +Use `--framework onnx` (or an `.onnx` `--model-path`) for ONNX Runtime. Use +`--core-mode all` to compare NPU core modes. The runner keeps evaluating remaining +models after an individual failure unless `--fail-fast` is set. + +Compare two or more benchmark result directories with: + +```bash +python benchmark/vision/compare_benchmark_results.py run-a run-b +``` + +## Verification + +The focused tests cover archive download recovery, safe staging and replacement, +dataset readiness, evaluator dispatch, standardized benchmark artifacts, and +comparison validation. Hardware-backed runs require a configured Mobilint NPU; +ONNX benchmarks require the relevant optional ONNX Runtime extra. diff --git a/benchmark/vision/__init__.py b/benchmark/vision/__init__.py new file mode 100644 index 0000000..1adafd3 --- /dev/null +++ b/benchmark/vision/__init__.py @@ -0,0 +1 @@ +"""Vision dataset organization and benchmark command-line tools.""" diff --git a/benchmark/vision/benchmark_vision_models.py b/benchmark/vision/benchmark_vision_models.py new file mode 100644 index 0000000..e5eed1b --- /dev/null +++ b/benchmark/vision/benchmark_vision_models.py @@ -0,0 +1,452 @@ +"""Run reproducible multi-model vision accuracy benchmarks. + +This entry point follows the artifact contract used by the Transformers benchmark: +one results directory contains machine-readable JSON and CSV output, an optional +summary, and an accuracy chart. +""" + +from __future__ import annotations + +import argparse +import sys +import time +from collections.abc import Sequence +from pathlib import Path +from typing import Any, Literal, cast + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[2] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) + +from benchmark.common.argparse_utils import parse_positive_int +from benchmark.common.io_utils import safe_filename, write_csv, write_json +from benchmark.common.summary_utils import ( + collect_host_pc_info, + markdown_table, + write_summary_markdown, +) +from mblt_vision._model_paths import resolve_framework +from mblt_vision._tasks import VISION_TASKS, normalize_vision_task + +CoreMode = Literal["single", "multi", "global4", "global8"] +CORE_MODES: tuple[CoreMode, ...] = ("single", "multi", "global4", "global8") +TASK_CHOICES = VISION_TASKS + + +def parse_unit_interval(value: str) -> float: + """Parse a floating-point value strictly between zero and one.""" + + try: + parsed = float(value) + except ValueError as exc: + raise argparse.ArgumentTypeError( + "expected a number in the open interval (0, 1)" + ) from exc + if not 0 < parsed < 1: + raise argparse.ArgumentTypeError( + f"expected a number in the open interval (0, 1), got {value}" + ) + return parsed + + +def normalize_core_mode(core_mode: str) -> CoreMode: + """Validate and narrow a benchmark NPU core mode.""" + + if core_mode not in CORE_MODES: + raise ValueError( + f"Invalid core mode {core_mode!r}; expected one of {list(CORE_MODES)}." + ) + return cast(CoreMode, core_mode) + + +def _parse_task(value: str) -> str: + """Normalize a benchmark task for argparse.""" + + try: + return normalize_vision_task(value) + except (TypeError, ValueError) as exc: + raise argparse.ArgumentTypeError(str(exc)) from exc + + +def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + """Parse command-line arguments for the standardized vision benchmark. + + Args: + argv: Optional argument sequence. ``None`` reads process arguments. + + Returns: + Parsed benchmark options. + """ + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--models", nargs="+", required=True, help="Vision model classes to benchmark." + ) + parser.add_argument( + "--task", + type=_parse_task, + choices=TASK_CHOICES, + required=True, + help="Task shared by all requested models.", + ) + parser.add_argument( + "--model-type", + default="DEFAULT", + help="Model variant from the YAML configuration.", + ) + parser.add_argument( + "--model-path", + default="", + help="Optional local MXQ or ONNX model path for one target.", + ) + parser.add_argument( + "--mxq-path", default="", help="Compatibility alias for a local MXQ path." + ) + parser.add_argument( + "--onnx-path", default="", help="Optional local ONNX path for one target." + ) + parser.add_argument( + "--framework", choices=["mxq", "onnx"], help="Explicit inference framework." + ) + parser.add_argument( + "--core-mode", + default="global8", + choices=[*CORE_MODES, "all"], + help="NPU core mode, or `all` to run every supported mode.", + ) + parser.add_argument("--dev-no", type=int, default=0, help="NPU device number.") + parser.add_argument( + "--batch-size", + type=parse_positive_int, + default=1, + help="Validation batch size.", + ) + parser.add_argument( + "--data-path", required=True, help="Path to an organized validation dataset." + ) + parser.add_argument( + "--conf-thres", + type=parse_unit_interval, + default=None, + help="Optional confidence threshold override.", + ) + parser.add_argument( + "--iou-thres", + type=parse_unit_interval, + default=None, + help="Optional IoU threshold override.", + ) + parser.add_argument( + "--results-dir", + type=Path, + default=Path("benchmark/vision/results"), + help="Output directory.", + ) + parser.add_argument( + "--no-plot", action="store_true", help="Do not write an accuracy chart." + ) + parser.add_argument( + "--collect-host-info", + action="store_true", + help="Collect host metadata with mblt-tracker.", + ) + parser.add_argument( + "--fail-fast", + action="store_true", + help="Stop after the first failed model run.", + ) + args = parser.parse_args(argv) + local_paths = [args.model_path, args.mxq_path, args.onnx_path] + if len(args.models) != 1 and any(local_paths): + parser.error( + "--model-path, --mxq-path, and --onnx-path require exactly one --models target." + ) + return args + + +def _core_modes(core_mode: str, framework: str | None = None) -> tuple[str, ...]: + """Expand the multi-run core-mode shorthand. + + Args: + core_mode: Requested core mode. + framework: Resolved inference framework. + + Returns: + One or more concrete core modes. + """ + if framework == "onnx": + return ("onnx",) + return CORE_MODES if core_mode == "all" else (normalize_core_mode(core_mode),) + + +def _evaluate( + model: Any, args: argparse.Namespace, run_dir: Path +) -> tuple[float, str, dict[str, float]]: + """Evaluate one model and normalize task metrics for benchmark artifacts. + + Args: + model: Initialized vision engine. + args: Parsed benchmark options. + run_dir: Per-run output directory. + + Returns: + Primary score, its name, and all normalized metrics. + + Raises: + ValueError: If the model task differs from the requested benchmark task. + """ + from mblt_vision.utils.evaluation import ( + eval_ade20k, + eval_cityscapes, + eval_coco_metrics, + eval_dota, + eval_imagenet_metrics, + eval_nyu_depth, + eval_widerface, + ) + + model_task = normalize_vision_task(model.post_cfg.get("task", "")) + if model_task != args.task: + raise ValueError( + f"Model task '{model_task}' does not match requested task '{args.task}'." + ) + if args.task == "image_classification": + result = eval_imagenet_metrics(model, args.data_path, args.batch_size) + return ( + float(result.primary_score), + "top1_accuracy", + { + "top1_accuracy": float(result.top1), + "top5_accuracy": float(result.top5), + }, + ) + if args.task in {"object_detection", "instance_segmentation", "pose_estimation"}: + result = eval_coco_metrics( + model, args.data_path, args.batch_size, args.conf_thres, args.iou_thres + ) + return ( + float(result.primary_score), + "map50_95", + {"map50_95": float(result.map5095), "map50": float(result.map50)}, + ) + if args.task == "depth_estimation": + result = eval_nyu_depth(model, args.data_path, args.batch_size) + return ( + float(result.primary_score), + "delta1", + { + "delta1": float(result.delta1), + "abs_rel": float(result.abs_rel), + "rmse": float(result.rmse), + }, + ) + if args.task == "semantic_segmentation": + dataset = str(model.post_cfg.get("dataset", "")).lower() + if dataset == "ade20k": + result = eval_ade20k(model, args.data_path, args.batch_size) + elif dataset == "cityscapes": + result = eval_cityscapes(model, args.data_path, args.batch_size) + else: + raise ValueError( + f"Unsupported semantic segmentation benchmark taxonomy {dataset!r}; expected 'ade20k' or 'cityscapes'." + ) + return ( + float(result.primary_score), + "miou", + { + "miou": float(result.miou), + "pixel_accuracy": float(result.pixel_accuracy), + }, + ) + if args.task == "obb": + result = eval_dota( + model, + args.data_path, + args.batch_size, + args.conf_thres, + args.iou_thres, + str(run_dir / "dota_task1"), + ) + return ( + float(result.primary_score), + "map50_95", + { + "map50_95": float(result.map5095), + "map50": float(result.map50), + }, + ) + if args.task == "face_detection": + result = eval_widerface( + model, args.data_path, args.batch_size, args.conf_thres, args.iou_thres + ) + return ( + float(result.primary_score), + "mean_ap", + { + "easy_ap": float(result.easy_ap), + "medium_ap": float(result.medium_ap), + "hard_ap": float(result.hard_ap), + "mean_ap": float(result.mean_ap), + }, + ) + raise ValueError(f"Unsupported vision benchmark task: {args.task}") + + +def _run_target( + model_name: str, core_mode: str, args: argparse.Namespace, results_dir: Path +) -> dict[str, Any]: + """Run and record one model/core-mode benchmark target. + + Args: + model_name: Vision model class name. + core_mode: Concrete NPU core mode or the neutral ONNX runtime label. + args: Parsed benchmark options. + results_dir: Root directory for benchmark artifacts. + + Returns: + A normalized benchmark result row. + """ + from mblt_vision import MBLT_Engine + + label = f"{model_name}@{core_mode}" + run_dir = results_dir / "runs" / safe_filename(label) + run_dir.mkdir(parents=True, exist_ok=True) + row: dict[str, Any] = { + "model": model_name, + "core_mode": core_mode, + "task": args.task, + "batch_size": args.batch_size, + "status": "error", + } + model = None + started = time.perf_counter() + try: + engine_kwargs: dict[str, Any] = { + "model_cls": model_name, + "model_type": args.model_type, + "model_path": args.model_path, + "mxq_path": args.mxq_path, + "onnx_path": args.onnx_path, + "framework": args.framework, + "dev_no": args.dev_no, + } + if core_mode != "onnx": + engine_kwargs["core_mode"] = core_mode + model = MBLT_Engine( + **engine_kwargs, + ) + score, score_name, metrics = _evaluate(model, args, run_dir) + row.update( + {"status": "ok", "score": score, "score_name": score_name, **metrics} + ) + except ( + ImportError, + OSError, + RuntimeError, + TypeError, + ValueError, + NotImplementedError, + ) as exc: + row["error"] = f"{type(exc).__name__}: {exc}" + finally: + row["elapsed_s"] = round(time.perf_counter() - started, 6) + if model is not None: + model.dispose() + return row + + +def _write_outputs( + rows: list[dict[str, Any]], args: argparse.Namespace, results_dir: Path +) -> None: + """Write the shared JSON, CSV, chart, and Markdown benchmark artifacts. + + Args: + rows: Normalized benchmark rows. + args: Parsed benchmark options. + results_dir: Destination directory. + """ + results_dir.mkdir(parents=True, exist_ok=True) + payload = { + "schema_version": 1, + "benchmark": "vision", + "task": args.task, + "results": rows, + } + write_json(results_dir / "results.json", payload) + write_csv(results_dir / "results.csv", rows) + + successful = [row for row in rows if row["status"] == "ok"] + plot_paths: list[Path] = [] + if successful and not args.no_plot: + from benchmark.common.chart_utils import plot_simple_barh + + chart_path = results_dir / "accuracy.png" + plot_simple_barh( + labels=[f"{row['model']} ({row['core_mode']})" for row in successful], + values=[float(row["score"]) for row in successful], + x_label="Accuracy score", + title=f"{args.task} benchmark accuracy", + output_path=chart_path, + ) + plot_paths.append(chart_path) + + host_info_path = ( + collect_host_pc_info(results_dir) if args.collect_host_info else None + ) + table = markdown_table( + ["Model", "Core mode", "Metric", "Score", "Elapsed (s)", "Status"], + [ + [ + row["model"], + row["core_mode"], + row.get("score_name", "-"), + f"{float(row['score']):.5f}" if row.get("score") is not None else "-", + row["elapsed_s"], + row["status"], + ] + for row in rows + ], + ) + table_path = results_dir / "results.md" + table_path.write_text(table, encoding="utf-8") + write_summary_markdown( + results_dir / "summary.md", + title=f"Vision benchmark: {args.task}", + host_info_path=host_info_path, + table_markdown_path=table_path, + plot_paths=plot_paths, + ) + + +def main(argv: Sequence[str] | None = None) -> int: + """Run the standardized vision benchmark. + + Args: + argv: Optional command-line arguments. + + Returns: + Zero when every target succeeds, otherwise one. + """ + args = _parse_args(argv) + results_dir = args.results_dir.expanduser().resolve() + framework = resolve_framework(args.framework, args.model_path) + rows: list[dict[str, Any]] = [] + for model_name in args.models: + for core_mode in _core_modes(args.core_mode, framework): + print(f"Benchmarking {model_name} with core mode {core_mode}...") + row = _run_target(model_name, core_mode, args, results_dir) + rows.append(row) + if row["status"] == "ok": + print(f" {row['score_name']}: {row['score']:.5f}") + else: + print(f" failed: {row['error']}") + if args.fail_fast: + _write_outputs(rows, args, results_dir) + return 1 + _write_outputs(rows, args, results_dir) + print(f"Saved benchmark artifacts to: {results_dir}") + return 0 if all(row["status"] == "ok" for row in rows) else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmark/vision/compare_benchmark_results.py b/benchmark/vision/compare_benchmark_results.py new file mode 100644 index 0000000..5f5a253 --- /dev/null +++ b/benchmark/vision/compare_benchmark_results.py @@ -0,0 +1,179 @@ +"""Compare standardized vision benchmark result directories.""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path +from typing import NamedTuple, Sequence + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[2] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) + +from benchmark.common.summary_utils import read_csv_rows + + +class BenchmarkScore(NamedTuple): + """One successful standardized vision benchmark score.""" + + task: str + metric: str + value: float + + +def _resolve_results_csv(raw: str) -> Path: + """Resolve a standard results CSV file from a path argument. + + Args: + raw: A results directory or explicit CSV path. + + Returns: + Resolved CSV path. + + Raises: + ValueError: If the path is not a standard benchmark results file. + """ + path = Path(raw).expanduser().resolve() + if path.is_dir(): + path = path / "results.csv" + if not path.is_file() or path.name != "results.csv": + raise ValueError( + f"Expected a vision benchmark results.csv file or containing directory: {path}" + ) + return path + + +def _collect_scores(path: Path) -> dict[str, BenchmarkScore]: + """Read successful model/core-mode scores from one results CSV. + + Args: + path: Standardized benchmark ``results.csv`` path. + + Returns: + Scores keyed by model and core mode. + + Raises: + ValueError: If a successful result has invalid standardized fields. + """ + scores: dict[str, BenchmarkScore] = {} + for row in read_csv_rows(path): + if row.get("status") != "ok": + continue + model = (row.get("model") or "").strip() + core_mode = (row.get("core_mode") or "").strip() + task = (row.get("task") or "").strip() + metric = (row.get("score_name") or "").strip() + if not model or not core_mode or not task or not metric: + raise ValueError( + f"Missing model, core_mode, task, or score_name in {path}." + ) + try: + score = float(row["score"]) + except (KeyError, TypeError, ValueError) as exc: + raise ValueError( + f"Invalid score for {model}@{core_mode} in {path}." + ) from exc + scores.setdefault( + f"{model}@{core_mode}", + BenchmarkScore(task=task, metric=metric, value=score), + ) + return scores + + +def _common_targets(scores_by_source: Sequence[dict[str, BenchmarkScore]]) -> list[str]: + """Return model/core-mode targets shared by all comparison sources. + + Args: + scores_by_source: Scores parsed from each result source. + + Returns: + Sorted shared target labels. + """ + if not scores_by_source: + return [] + return sorted(set.intersection(*(set(scores) for scores in scores_by_source))) + + +def main(argv: Sequence[str] | None = None) -> int: + """Generate a grouped score chart from standardized vision result files. + + Args: + argv: Optional command-line arguments. + + Returns: + Zero on success. + """ + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "inputs", + nargs="+", + help="At least two result directories or results.csv files.", + ) + parser.add_argument( + "--output-dir", type=Path, help="Directory for the grouped comparison chart." + ) + args = parser.parse_args(argv) + if len(args.inputs) < 2: + parser.error("Provide at least two benchmark result sources.") + try: + sources = [_resolve_results_csv(raw) for raw in args.inputs] + scores_by_source = [_collect_scores(source) for source in sources] + except ValueError as exc: + parser.error(str(exc)) + + task_names = { + score.task for scores in scores_by_source for score in scores.values() + } + if len(task_names) != 1: + raise SystemExit( + f"Inputs contain incompatible benchmark tasks: {', '.join(sorted(task_names))}." + ) + targets = _common_targets(scores_by_source) + if not targets: + raise SystemExit( + "No successful model/core-mode target is shared by all input sources." + ) + metric_names = { + score.metric for scores in scores_by_source for score in scores.values() + } + if len(metric_names) != 1: + raise SystemExit( + f"Inputs contain incompatible benchmark metrics: {', '.join(sorted(metric_names))}." + ) + metric_name = next(iter(metric_names)) + from benchmark.common.chart_utils import ( + default_charts_dir, + plot_grouped_scalar_barh, + source_labels, + ) + + source_dirs = [source.parent for source in sources] + output_dir = ( + args.output_dir.expanduser().resolve() + if args.output_dir + else default_charts_dir( + Path(__file__).resolve().parent, source_dirs, use_stem=False + ) + ) + output_dir.mkdir(parents=True, exist_ok=True) + plot_grouped_scalar_barh( + models=targets, + group_labels=source_labels(source_dirs, use_stem=False), + grouped_values=[ + {target: scores[target].value for target in targets} + for scores in scores_by_source + ], + x_label=metric_name, + y_label="model@core_mode", + title=f"Vision benchmark comparison: {metric_name}", + output_path=output_dir / "score.png", + ) + print(f"Compared {len(targets)} shared targets using {metric_name}.") + print(f"Saved chart to: {output_dir / 'score.png'}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmark/vision/organize_ade20k.py b/benchmark/vision/organize_ade20k.py new file mode 100644 index 0000000..55cada6 --- /dev/null +++ b/benchmark/vision/organize_ade20k.py @@ -0,0 +1,22 @@ +"""Organize the ADE20K validation dataset for local use.""" + +import argparse +import os + +from mblt_vision.utils.datasets import organize_ade20k +from mblt_vision.utils.datasets.organizer import ADE20K_URL + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize ADE20K validation dataset") + parser.add_argument( + "--dataset-path", + default=ADE20K_URL, + help="Local path or download URL for ADE20K", + ) + parser.add_argument( + "--output-dir", + default=os.path.expanduser("~/.mblt_vision/datasets/ADEChallengeData2016"), + help="Path to the organized validation dataset", + ) + args = parser.parse_args() + organize_ade20k(dataset_path=args.dataset_path, output_dir=args.output_dir) diff --git a/benchmark/vision/organize_cityscapes.py b/benchmark/vision/organize_cityscapes.py new file mode 100644 index 0000000..74a9840 --- /dev/null +++ b/benchmark/vision/organize_cityscapes.py @@ -0,0 +1,40 @@ +"""Organize the Cityscapes validation dataset for local use.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.utils.datasets import organize_cityscapes + + +def main() -> None: + """Parse organizer options and materialize Cityscapes validation data.""" + + parser = argparse.ArgumentParser( + description="Organize Cityscapes validation data from official ZIP archives" + ) + parser.add_argument( + "--image-dir", + required=True, + help="Path to leftImg8bit_trainvaltest.zip", + ) + parser.add_argument( + "--annotation-dir", + required=True, + help="Path to gtFine_trainvaltest.zip", + ) + parser.add_argument( + "--output-dir", + default="~/.mblt_vision/datasets/cityscapes", + help="Destination for the flat images/ and annotations/ directories", + ) + args = parser.parse_args() + organize_cityscapes( + image_dir=args.image_dir, + annotation_dir=args.annotation_dir, + output_dir=args.output_dir, + ) + + +if __name__ == "__main__": + main() diff --git a/benchmark/vision/organize_coco.py b/benchmark/vision/organize_coco.py new file mode 100644 index 0000000..abe1ab7 --- /dev/null +++ b/benchmark/vision/organize_coco.py @@ -0,0 +1,44 @@ +""" +Script to organize the COCO dataset. + +This script takes local archives or downloadable sources for the COCO dataset +and organizes them into a structure suitable for the model zoo. +""" + +import argparse +import os + +from mblt_vision.utils.datasets import organize_coco + +DEFAULT_COCO_IMAGE_SOURCE = "http://images.cocodataset.org/zips/val2017.zip" +DEFAULT_COCO_ANNOTATION_SOURCE = ( + "http://images.cocodataset.org/annotations/annotations_trainval2017.zip" +) + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize COCO dataset") + parser.add_argument( + "--image-dir", + type=str, + default=DEFAULT_COCO_IMAGE_SOURCE, + help="Local path or download URL for the image zip file", + ) + parser.add_argument( + "--ann-dir", + type=str, + default=DEFAULT_COCO_ANNOTATION_SOURCE, + help="Local path or download URL for the annotation zip file", + ) + parser.add_argument( + "--output-dir", + type=str, + default=os.path.expanduser("~/.mblt_vision/datasets/coco"), + help="Path to the directory to save the organized dataset", + ) + args = parser.parse_args() + + organize_coco( + image_dir=args.image_dir, + annotation_dir=args.ann_dir, + output_dir=args.output_dir, + ) diff --git a/benchmark/vision/organize_dotav1.py b/benchmark/vision/organize_dotav1.py new file mode 100644 index 0000000..16f0c56 --- /dev/null +++ b/benchmark/vision/organize_dotav1.py @@ -0,0 +1,35 @@ +""" +Script to organize the DOTAv1 validation dataset. + +This script takes a local archive, extracted directory, or downloadable source +for DOTAv1 and organizes only the validation split into a structure suitable for +the model zoo. +""" + +import argparse +import os + +from mblt_vision.datasets import get_dataset_config +from mblt_vision.utils.datasets import organize_dotav1 + +DEFAULT_DOTAV1_SOURCE = get_dataset_config("dotav1")["download"]["url"] +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize DOTAv1 validation dataset") + parser.add_argument( + "--dataset-path", + type=str, + default=DEFAULT_DOTAV1_SOURCE, + help="Local path, archive URL, or Google Drive folder URL for DOTAv1", + ) + parser.add_argument( + "--output-dir", + type=str, + default=os.path.expanduser("~/.mblt_vision/datasets/dotav1"), + help="Path to the directory to save the organized dataset", + ) + args = parser.parse_args() + + organize_dotav1( + dataset_path=args.dataset_path, + output_dir=args.output_dir, + ) diff --git a/benchmark/vision/organize_imagenet.py b/benchmark/vision/organize_imagenet.py new file mode 100644 index 0000000..8e7c001 --- /dev/null +++ b/benchmark/vision/organize_imagenet.py @@ -0,0 +1,45 @@ +""" +Script to organize the ImageNet dataset. + +This script takes local archives or downloadable sources for the ImageNet +dataset and organizes them into a structure suitable for the model zoo. +""" + +import argparse +import os + +from mblt_vision.utils.datasets import organize_imagenet + +DEFAULT_IMAGENET_IMAGE_SOURCE = ( + "https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar" +) +DEFAULT_IMAGENET_XML_SOURCE = ( + "https://www.image-net.org/data/ILSVRC/2012/ILSVRC2012_bbox_val_v3.tgz" +) +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize ImageNet dataset") + parser.add_argument( + "--image-dir", + type=str, + default=DEFAULT_IMAGENET_IMAGE_SOURCE, + help="Local path or download URL for the image tar file", + ) + parser.add_argument( + "--xml-dir", + type=str, + default=DEFAULT_IMAGENET_XML_SOURCE, + help="Local path or download URL for the XML tgz file", + ) + parser.add_argument( + "--output-dir", + type=str, + default=os.path.expanduser("~/.mblt_vision/datasets/imagenet"), + help="Path to the directory to save the organized dataset", + ) + args = parser.parse_args() + + organize_imagenet( + image_dir=args.image_dir, + xml_dir=args.xml_dir, + output_dir=args.output_dir, + ) diff --git a/benchmark/vision/organize_nyu_depth.py b/benchmark/vision/organize_nyu_depth.py new file mode 100644 index 0000000..2073004 --- /dev/null +++ b/benchmark/vision/organize_nyu_depth.py @@ -0,0 +1,29 @@ +"""Organize the NYU Depth dataset for local use. + +The script downloads the published NYU Depth archive by default, or accepts a +local zip file or extracted dataset directory. +""" + +import argparse +import os + +from mblt_vision.utils.datasets import organize_nyu_depth +from mblt_vision.utils.datasets.organizer import NYU_DEPTH_URL + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize NYU Depth dataset") + parser.add_argument( + "--dataset-path", + type=str, + default=NYU_DEPTH_URL, + help="Local path or download URL for the NYU Depth zip file or extracted dataset", + ) + parser.add_argument( + "--output-dir", + type=str, + default=os.path.expanduser("~/.mblt_vision/datasets/nyu-depth"), + help="Path to the directory to save the organized dataset", + ) + args = parser.parse_args() + + organize_nyu_depth(dataset_path=args.dataset_path, output_dir=args.output_dir) diff --git a/benchmark/vision/organize_widerface.py b/benchmark/vision/organize_widerface.py new file mode 100644 index 0000000..b44e369 --- /dev/null +++ b/benchmark/vision/organize_widerface.py @@ -0,0 +1,42 @@ +""" +Script to organize the WiderFace dataset. + +This script takes local archives or downloadable sources for the WiderFace +dataset and organizes them into a structure suitable for the model zoo. +""" + +import argparse +import os + +from mblt_vision.utils.datasets import organize_widerface + +DEFAULT_WIDERFACE_IMAGE_SOURCE = "https://huggingface.co/datasets/CUHK-CSE/wider_face/resolve/main/data/WIDER_val.zip" +DEFAULT_WIDERFACE_ANNOTATION_SOURCE = "https://huggingface.co/datasets/CUHK-CSE/wider_face/resolve/main/data/wider_face_split.zip" + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Organize WiderFace dataset") + parser.add_argument( + "--image-dir", + type=str, + default=DEFAULT_WIDERFACE_IMAGE_SOURCE, + help="Local path or download URL for the image zip file", + ) + parser.add_argument( + "--annotation-dir", + type=str, + default=DEFAULT_WIDERFACE_ANNOTATION_SOURCE, + help="Local path or download URL for the annotation zip file", + ) + parser.add_argument( + "--output-dir", + type=str, + default=os.path.expanduser("~/.mblt_vision/datasets/widerface"), + help="Path to the directory to save the organized dataset", + ) + args = parser.parse_args() + + organize_widerface( + image_dir=args.image_dir, + annotation_dir=args.annotation_dir, + output_dir=args.output_dir, + ) diff --git a/compile/vision/README.md b/compile/vision/README.md new file mode 100644 index 0000000..b211414 --- /dev/null +++ b/compile/vision/README.md @@ -0,0 +1,119 @@ +# Vision Model Compilation + +The installable vision compiler uses each model's packaged YAML configuration and +`MBLT_Engine.preprocess` implementation to compile its ONNX artifact for ARIES. + +## Installation + +Install the package with the compilation extra: + +```bash +pip install -e ".[qbcompiler]" +``` + +`qbcompiler` is currently distributed through Mobilint's external package channel and may not be +available from the public Python Package Index. Obtain access to the compiler package before using +this extra. + +qbcompiler is loaded only when `compile_vision_model()` or `mblt-vision-python compile` begins an +actual compilation request. Importing the base package, vision APIs, this compilation module, or +the main CLI does not import qbcompiler. If qbcompiler is unavailable, only the compilation request +fails with an installation message; non-compile APIs and CLI commands remain usable. + +## Python API + +```python +from mblt_vision.compile.vision import compile_vision_model + +output_path = compile_vision_model( + "alexnet", + data_path="~/.mblt_vision/datasets/imagenet", + save_path="./alexnet.mxq", +) +print(output_path) +``` + +## CLI + +```bash +mblt-vision-python compile \ + --model-cls alexnet \ + --data-path ~/.mblt_vision/datasets/imagenet \ + --save-path ./alexnet.mxq +``` + +Start from an existing sampled image subset or a ready calibration tensor directory when those +stages have already been completed: + +```bash +mblt-vision-python compile --model-cls alexnet --subset-path ./sampled-images +mblt-vision-python compile --model-cls alexnet --calib-data-path ./preprocessed-npy +``` + +Use `--model-type` for a non-default YAML variant and `--model-path` or `--onnx-path` to prefer a +local ONNX file. If that path is omitted or does not exist, the configured Hugging Face repository +supplies the ONNX artifact. Downloaded ONNX files and compiled outputs use +`~/.mblt_vision`; without `--save-path`, the compiler writes +`~/.mblt_vision/.mxq`. + +The ONNX preprocessing engine uses ONNX Runtime's CPU provider by default, so compilation does not +probe TensorRT, CUDA, or other accelerators. Callers that construct `MBLT_Engine` directly can opt +into another provider order with `onnx_providers`. + +## Calibration Data + +The compilation data pipeline has three levels: + +1. `data_path` / `--data-path`: Original organized dataset containing all images. The pipeline + prepares the dataset when needed, samples a task-specific subset, and preprocesses it. +2. `subset_path` / `--subset-path`: Already-sampled images. The pipeline skips original dataset + preparation and subset generation, then preprocesses every image in the supplied folder. +3. `calib_data_path` / `--calib-data-path`: Ready HWC, three-channel, contiguous `float32` `.npy` + tensors. The directory is validated and passed directly to qbcompiler without image processing. + +Supply at most one of these paths. When none is supplied, the task's registry-backed original +dataset path is used. `--calib-data-dir` remains an alias for `--calib-data-path` in the CLI and +standalone compatibility wrapper. + +Compilation maps model tasks to the packaged dataset registry: + +- Image classification uses ImageNet. +- Depth estimation uses NYU Depth V2. +- Object detection, instance segmentation, and pose estimation use COCO. +- Semantic segmentation uses the dataset declared by the model's `post_cfg.dataset`: ADE20K or + Cityscapes. +- Face detection uses WiderFace. +- Oriented bounding boxes use DOTAv1. + +`--data-path` is an organized dataset root. An existing ready layout is reused; otherwise the +registry-backed organizer downloads and prepares the dataset. When no path is supplied, the +registry default under `~/.mblt_vision/datasets` is used. + +NYU Depth and ADE20K use their registry download URLs. Cityscapes requires the manually downloaded +official `leftImg8bit_trainvaltest.zip` and `gtFine_trainvaltest.zip` archives; place both inside the +organized dataset path or its parent before compiling a Cityscapes model. + +Selection is deterministic with seed `0`. ImageNet and WiderFace select one image from every +category subfolder by default; COCO, DOTAv1, NYU Depth, ADE20K, and Cityscapes select 100 images +total. Override these values with `--subset-size` and `--seed`. Selected images and preprocessed +NumPy arrays live only in temporary directories and are removed after compilation, including when +compilation fails. + +The compiler uses explicit `--percentile` and `--topk-ratio` values first. Missing values are read +independently from `aries/best_result.json`; if optional hosted values are unavailable, defaults of +`0.9999` and `0.01` are used with a warning. + +## Compatibility Scripts + +The standalone scripts remain available: + +```bash +python compile/vision/vision_model_compile.py --model-cls alexnet +python compile/vision/make_imagenet_subset.py --output-dir ./imagenet-calibration +python compile/vision/make_coco_subset.py --output-dir ./coco-calibration +python compile/vision/make_dotav1_subset.py --output-dir ./dotav1-calibration +python compile/vision/make_widerface_subset.py --output-dir ./widerface-calibration +python compile/vision/make_nyu_depth_subset.py --output-dir ./nyu-depth-calibration +python compile/vision/make_ade20k_subset.py --output-dir ./ade20k-calibration +python compile/vision/make_cityscapes_subset.py --output-dir ./cityscapes-calibration +``` diff --git a/compile/vision/make_ade20k_subset.py b/compile/vision/make_ade20k_subset.py new file mode 100644 index 0000000..8823590 --- /dev/null +++ b/compile/vision/make_ade20k_subset.py @@ -0,0 +1,47 @@ +"""Compatibility entry point for ADE20K calibration subsets.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.compile.vision import make_calibration_subset + +DEFAULT_DATA_DIR = "~/.mblt_model_zoo/datasets/ADEChallengeData2016" + + +def make_ade20k_subset( + data_dir: str, output_dir: str, subset_size: int, seed: int = 0 +) -> None: + """Create a deterministic ADE20K calibration subset. + + Args: + data_dir: Organized ADE20K root. + output_dir: Flat subset destination. + subset_size: Total image count. + seed: Random selection seed. + """ + + copied = make_calibration_subset( + "semantic_segmentation", data_dir, output_dir, subset_size, seed + ) + print(f"Created ADE20K subset with {len(copied)} images at {output_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Create an ADE20K calibration subset") + parser.add_argument( + "--data-dir", + default=DEFAULT_DATA_DIR, + help="Path to the organized ADE20K dataset", + ) + parser.add_argument( + "--output-dir", required=True, help="Path to save the selected images" + ) + parser.add_argument( + "--subset-size", type=int, default=100, help="Number of images to select" + ) + parser.add_argument( + "--seed", type=int, default=0, help="Random seed used to select images" + ) + args = parser.parse_args() + make_ade20k_subset(args.data_dir, args.output_dir, args.subset_size, args.seed) diff --git a/compile/vision/make_cityscapes_subset.py b/compile/vision/make_cityscapes_subset.py new file mode 100644 index 0000000..4efd6c9 --- /dev/null +++ b/compile/vision/make_cityscapes_subset.py @@ -0,0 +1,49 @@ +"""Compatibility entry point for Cityscapes calibration subsets.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.compile.vision import make_calibration_subset + +DEFAULT_DATA_DIR = "~/.mblt_model_zoo/datasets/cityscapes" + + +def make_cityscapes_subset( + data_dir: str, output_dir: str, subset_size: int, seed: int = 0 +) -> None: + """Create a deterministic Cityscapes calibration subset. + + Args: + data_dir: Organized Cityscapes root. + output_dir: Flat subset destination. + subset_size: Total image count. + seed: Random selection seed. + """ + + copied = make_calibration_subset( + "semantic_segmentation", data_dir, output_dir, subset_size, seed + ) + print(f"Created Cityscapes subset with {len(copied)} images at {output_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Create a Cityscapes calibration subset" + ) + parser.add_argument( + "--data-dir", + default=DEFAULT_DATA_DIR, + help="Path to the organized Cityscapes dataset", + ) + parser.add_argument( + "--output-dir", required=True, help="Path to save the selected images" + ) + parser.add_argument( + "--subset-size", type=int, default=100, help="Number of images to select" + ) + parser.add_argument( + "--seed", type=int, default=0, help="Random seed used to select images" + ) + args = parser.parse_args() + make_cityscapes_subset(args.data_dir, args.output_dir, args.subset_size, args.seed) diff --git a/compile/vision/make_coco_subset.py b/compile/vision/make_coco_subset.py new file mode 100644 index 0000000..b936f58 --- /dev/null +++ b/compile/vision/make_coco_subset.py @@ -0,0 +1,47 @@ +"""Compatibility entry point for COCO calibration subsets.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.compile.vision import make_calibration_subset + +DEFAULT_DATA_DIR = "~/.mblt_model_zoo/datasets/coco" + + +def make_coco_subset( + data_dir: str, output_dir: str, subset_size: int, seed: int = 0 +) -> None: + """Create a deterministic COCO calibration subset. + + Args: + data_dir: Organized COCO root. + output_dir: Flat subset destination. + subset_size: Total image count. + seed: Random selection seed. + """ + + copied = make_calibration_subset( + "object_detection", data_dir, output_dir, subset_size, seed + ) + print(f"Created COCO subset with {len(copied)} images at {output_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Create a COCO calibration subset") + parser.add_argument( + "--data-dir", + default=DEFAULT_DATA_DIR, + help="Path to the organized COCO dataset", + ) + parser.add_argument( + "--output-dir", required=True, help="Path to save the selected images" + ) + parser.add_argument( + "--subset-size", type=int, default=100, help="Number of images to select" + ) + parser.add_argument( + "--seed", type=int, default=0, help="Random seed used to select images" + ) + args = parser.parse_args() + make_coco_subset(args.data_dir, args.output_dir, args.subset_size, args.seed) diff --git a/compile/vision/make_dotav1_subset.py b/compile/vision/make_dotav1_subset.py new file mode 100644 index 0000000..730496c --- /dev/null +++ b/compile/vision/make_dotav1_subset.py @@ -0,0 +1,45 @@ +"""Compatibility entry point for DOTAv1 calibration subsets.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.compile.vision import make_calibration_subset + +DEFAULT_DATA_DIR = "~/.mblt_model_zoo/datasets/dotav1" + + +def make_dotav1_subset( + data_dir: str, output_dir: str, subset_size: int, seed: int = 0 +) -> None: + """Create a deterministic DOTAv1 calibration subset. + + Args: + data_dir: Organized DOTAv1 root. + output_dir: Flat subset destination. + subset_size: Total image count. + seed: Random selection seed. + """ + + copied = make_calibration_subset("obb", data_dir, output_dir, subset_size, seed) + print(f"Created DOTAv1 subset with {len(copied)} images at {output_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Create a DOTAv1 calibration subset") + parser.add_argument( + "--data-dir", + default=DEFAULT_DATA_DIR, + help="Path to the organized DOTAv1 dataset", + ) + parser.add_argument( + "--output-dir", required=True, help="Path to save the selected images" + ) + parser.add_argument( + "--subset-size", type=int, default=100, help="Number of images to select" + ) + parser.add_argument( + "--seed", type=int, default=0, help="Random seed used to select images" + ) + args = parser.parse_args() + make_dotav1_subset(args.data_dir, args.output_dir, args.subset_size, args.seed) diff --git a/compile/vision/make_imagenet_subset.py b/compile/vision/make_imagenet_subset.py new file mode 100644 index 0000000..f70ffd6 --- /dev/null +++ b/compile/vision/make_imagenet_subset.py @@ -0,0 +1,52 @@ +"""Compatibility entry point for ImageNet calibration subsets.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.compile.vision import make_calibration_subset + +DEFAULT_DATA_DIR = "~/.mblt_model_zoo/datasets/imagenet" + + +def make_imagenet_subset( + data_dir: str, output_dir: str, subset_size: int, seed: int = 0 +) -> None: + """Create a deterministic ImageNet calibration subset. + + Args: + data_dir: Organized ImageNet root. + output_dir: Flat subset destination. + subset_size: Images selected per class. + seed: Random selection seed. + """ + + copied = make_calibration_subset( + "image_classification", data_dir, output_dir, subset_size, seed + ) + print(f"Created ImageNet subset with {len(copied)} images at {output_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Create an ImageNet calibration subset" + ) + parser.add_argument( + "--data-dir", + default=DEFAULT_DATA_DIR, + help="Path to the organized ImageNet dataset", + ) + parser.add_argument( + "--output-dir", required=True, help="Path to save the selected images" + ) + parser.add_argument( + "--subset-size", + type=int, + default=1, + help="Number of images to select per category", + ) + parser.add_argument( + "--seed", type=int, default=0, help="Random seed used to select images" + ) + args = parser.parse_args() + make_imagenet_subset(args.data_dir, args.output_dir, args.subset_size, args.seed) diff --git a/compile/vision/make_nyu_depth_subset.py b/compile/vision/make_nyu_depth_subset.py new file mode 100644 index 0000000..6ae128b --- /dev/null +++ b/compile/vision/make_nyu_depth_subset.py @@ -0,0 +1,49 @@ +"""Compatibility entry point for NYU Depth calibration subsets.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.compile.vision import make_calibration_subset + +DEFAULT_DATA_DIR = "~/.mblt_model_zoo/datasets/nyu-depth" + + +def make_nyu_depth_subset( + data_dir: str, output_dir: str, subset_size: int, seed: int = 0 +) -> None: + """Create a deterministic NYU Depth calibration subset. + + Args: + data_dir: Organized NYU Depth root. + output_dir: Flat subset destination. + subset_size: Total image count. + seed: Random selection seed. + """ + + copied = make_calibration_subset( + "depth_estimation", data_dir, output_dir, subset_size, seed + ) + print(f"Created NYU Depth subset with {len(copied)} images at {output_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Create an NYU Depth calibration subset" + ) + parser.add_argument( + "--data-dir", + default=DEFAULT_DATA_DIR, + help="Path to the organized NYU Depth dataset", + ) + parser.add_argument( + "--output-dir", required=True, help="Path to save the selected images" + ) + parser.add_argument( + "--subset-size", type=int, default=100, help="Number of images to select" + ) + parser.add_argument( + "--seed", type=int, default=0, help="Random seed used to select images" + ) + args = parser.parse_args() + make_nyu_depth_subset(args.data_dir, args.output_dir, args.subset_size, args.seed) diff --git a/compile/vision/make_widerface_subset.py b/compile/vision/make_widerface_subset.py new file mode 100644 index 0000000..977a244 --- /dev/null +++ b/compile/vision/make_widerface_subset.py @@ -0,0 +1,52 @@ +"""Compatibility entry point for WiderFace calibration subsets.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.compile.vision import make_calibration_subset + +DEFAULT_DATA_DIR = "~/.mblt_model_zoo/datasets/widerface" + + +def make_widerface_subset( + data_dir: str, output_dir: str, subset_size: int, seed: int = 0 +) -> None: + """Create a deterministic WiderFace calibration subset. + + Args: + data_dir: Organized WiderFace root. + output_dir: Flat subset destination. + subset_size: Images selected per category. + seed: Random selection seed. + """ + + copied = make_calibration_subset( + "face_detection", data_dir, output_dir, subset_size, seed + ) + print(f"Created WiderFace subset with {len(copied)} images at {output_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Create a WiderFace calibration subset" + ) + parser.add_argument( + "--data-dir", + default=DEFAULT_DATA_DIR, + help="Path to the organized WiderFace dataset", + ) + parser.add_argument( + "--output-dir", required=True, help="Path to save the selected images" + ) + parser.add_argument( + "--subset-size", + type=int, + default=1, + help="Number of images to select per category", + ) + parser.add_argument( + "--seed", type=int, default=0, help="Random seed used to select images" + ) + args = parser.parse_args() + make_widerface_subset(args.data_dir, args.output_dir, args.subset_size, args.seed) diff --git a/compile/vision/vision_model_compile.py b/compile/vision/vision_model_compile.py new file mode 100644 index 0000000..cfea216 --- /dev/null +++ b/compile/vision/vision_model_compile.py @@ -0,0 +1,83 @@ +"""Compatibility entry point for packaged vision compilation.""" + +from __future__ import annotations + +import argparse + +from mblt_vision.compile.vision import compile_vision_model + + +def build_parser() -> argparse.ArgumentParser: + """Build the standalone compatibility parser. + + Returns: + Configured argument parser. + """ + + parser = argparse.ArgumentParser( + description="Compile a configured vision ONNX model to MXQ" + ) + parser.add_argument("--model-cls", required=True, help="Vision model name.") + parser.add_argument( + "--model-type", + default="DEFAULT", + help="Model variant from the YAML configuration.", + ) + parser.add_argument( + "--model-path", + "--onnx-path", + dest="model_path", + help="Preferred local ONNX file.", + ) + data_group = parser.add_mutually_exclusive_group() + data_group.add_argument("--data-path", help="Original organized dataset root.") + data_group.add_argument("--subset-path", help="Already-sampled image subset root.") + data_group.add_argument( + "--calib-data-path", + "--calib-data-dir", + dest="calib_data_path", + help="Ready directory of preprocessed .npy tensors.", + ) + parser.add_argument( + "--save-path", + help="Output MXQ path. Defaults to the ONNX stem under ~/.mblt_model_zoo.", + ) + parser.add_argument( + "--subset-size", + type=int, + help="Per-category ImageNet/WiderFace count or total count for other datasets.", + ) + parser.add_argument( + "--seed", type=int, default=0, help="Calibration selection seed." + ) + parser.add_argument( + "--percentile", type=float, help="Quantization percentile override." + ) + parser.add_argument( + "--topk-ratio", type=float, help="Quantization top-k ratio override." + ) + return parser + + +def main() -> None: + """Run standalone vision compilation.""" + + args = build_parser().parse_args() + output_path = compile_vision_model( + model_cls=args.model_cls, + model_type=args.model_type, + model_path=args.model_path, + data_path=args.data_path, + subset_path=args.subset_path, + calib_data_path=args.calib_data_path, + save_path=args.save_path, + subset_size=args.subset_size, + seed=args.seed, + percentile=args.percentile, + topk_ratio=args.topk_ratio, + ) + print(f"Compiled MXQ model to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/mblt_vision/README.md b/mblt_vision/README.md index 413ff45..0e81206 100644 --- a/mblt_vision/README.md +++ b/mblt_vision/README.md @@ -1,159 +1,284 @@ -# Pre-Trained Vision Models +# Mobilint Vision API -> Split out of `mblt-model-zoo`, which still re-exports this package as -> `mblt_model_zoo.vision`. +`mblt-vision-python` provides Python access to pre-trained Mobilint Vision models: +image classification, depth estimation, face and object detection, oriented bounding +boxes (OBB), instance and semantic segmentation, and pose estimation. Each model +configuration includes the artifact, preprocessing, output taxonomy, and +postprocessing contract needed to produce task-specific results. -Here, we give the full list of publicly pre-trained models supported by the Mobilint Model Zoo. +## Loading models -Further usage examples can be found in the [tests](../tests) directory. +Use a task subpackage for a known model, or `MBLT_Engine` to choose a model and +artifact at runtime. These imports are both supported: -## Image Classification +```python +from mblt_vision import ResNet50 +from mblt_vision import YOLO11m +``` + +```python +from mblt_vision import MBLT_Engine + +model = MBLT_Engine(model_cls="resnet50", model_type="DEFAULT", model_path="", core_mode="global8") +``` + +```python +from mblt_vision import MBLT_Engine + +# Install the optional `onnxruntime` or `onnxruntime-gpu` extra first. +model = MBLT_Engine(model_cls="alexnet", framework="onnx") +``` + +When `framework="onnx"` is selected, the engine uses ONNX Runtime's `CPUExecutionProvider` by +default. Pass `onnx_providers` to select another provider order explicitly when needed. If +`model_path` or `file_cfg.model_path` ends +with `.mxq` or `.onnx`, the engine auto-detects the framework from that suffix when `framework` is +omitted. + +For Hub-backed models, `file_cfg.filename` is the canonical MXQ artifact name. The engine derives +the corresponding ONNX artifact by replacing its `.mxq` suffix with `.onnx`, so every model uses +the same ONNX loading path. Set `file_cfg.onnx_filename` only when a Hub repository uses a +non-matching ONNX filename. + +Every model configuration also declares `post_cfg.dataset`. This identifies the output taxonomy, +not merely the broad task: COCO detection uses 80 classes, Cityscapes semantic segmentation uses +19, and ADE20K semantic segmentation uses 150. YOLO postprocessing resolves shape-sensitive class +counts from the dataset and task together. + +```python +from mblt_vision.image_classification import ResNet50 +from mblt_vision.object_detection import YOLO11m +``` + +The task subpackages are the clearest import surface; `MBLT_Engine`, `list_tasks()`, +and `list_models()` are the preferred discovery and loading APIs for new code. +Top-level model imports remain available for compatibility. `obb` is the sole +oriented-bounding-box task name. + +## Pre-Trained Vision Models + +This section lists the publicly pre-trained models supported by the vision framework. + +### Image Classification | Model | Input Size
    (H,W,C) | AccTop1
    (NPU) | AccTop1
    (GPU) | FLOPs (B) | params (M) | Source | Note | -|------------|------------|-----------|--------------------|--------|-------|------|------| -| AlexNet | (224,224,3)| 56.022 | 56.552 | 0.71 | 61.10 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.alexnet.html) | | -| ConvNeXt_Tiny | (224,224,3) | 82.302 | 82.460| 4.46 | 28.59 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_tiny.html) | | -| ConvNeXt_Small | (224,224,3) | 83.432 | 83.560 | 8.68 | 50.22 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_small.html) | | -| ConvNeXt_Base | (224,224,3) | 83.834 | 84.050 | 15.36 | 88.59 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_base.html) | | -| ConvNeXt_Large | (224,224,3) | 83.276 | 84.410 | 34.36 | 197.77 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_large.html) | | -| DeiT_Tiny_Patch16_224 | (224,224,3) | 71.864 | 72.030 | 2.53 | 5.72 | [Link](https://huggingface.co/timm/deit_tiny_patch16_224.fb_in1k) | fb_in1k | -| DeiT_Small_Patch16_224 | (224,224,3) | 79.770 | 79.790 | 9.25 | 22.05 | [Link](https://huggingface.co/timm/deit_small_patch16_224.fb_in1k) | fb_in1k | -| DeiT_Base_Patch16_224 | (224,224,3) | 81.886 | 81.980 | 35.23 | 86.57 | [Link](https://huggingface.co/timm/deit_base_patch16_224.fb_in1k) | fb_in1k | -| DeiT_Base_Patch16_384 | (384,384,3) | 83.046 | 83.100 | 111.34 | 86.86 | [Link](https://huggingface.co/timm/deit_base_patch16_384.fb_in1k) | fb_in1k | -| DeiT3_Small_Patch16_224 | (224,224,3) | 81.326 | 81.398 | 9.25 | 22.06 | [Link](https://huggingface.co/timm/deit3_small_patch16_224.fb_in1k) | fb_in1k | -| DeiT3_Small_Patch16_384 | (384,384,3) | 81.304 | 81.414 | 31.17 | 22.21 | [Link](https://huggingface.co/timm/deit3_small_patch16_384.fb_in1k) | fb_in1k | -| DeiT3_Medium_Patch16_224 | (224,224,3) | 82.930 | 83.044 | 16.05 | 38.85 | [Link](https://huggingface.co/timm/deit3_medium_patch16_224.fb_in1k) | fb_in1k | -| DeiT3_Base_Patch16_224 | (224,224,3) | 83.644 | 83.766 | 35.23 | 86.59 | [Link](https://huggingface.co/timm/deit3_base_patch16_224.fb_in1k) | fb_in1k | -| DeiT3_Base_Patch16_384 | (384,384,3) | 84.990 | 85.062 | 111.34 | 86.88 | [Link](https://huggingface.co/timm/deit3_base_patch16_384.fb_in1k) | fb_in1k | -| DeiT3_Large_Patch16_224 | (224,224,3) | 84.658 | 84.738 | 123.39 | 304.37 | [Link](https://huggingface.co/timm/deit3_large_patch16_224.fb_in1k) | fb_in1k | -| DeiT3_Large_Patch16_384 | (384,384,3) | 85.770 | 85.828 | 383.13 | 304.76 | [Link](https://huggingface.co/timm/deit3_large_patch16_384.fb_in1k) | fb_in1k | -| DenseNet121 | (224,224,3) | 74.194 | 74.414 | 2.83 | 7.98 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet121.html) | | -| DenseNet169 | (224,224,3) | 75.476 | 75.566 | 3.36 | 14.15 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet169.html) | | -| DenseNet201 | (224,224,3) | 76.650 | 76.880 | 4.29 | 20.01 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet201.html) | | -| FlexiViT_Small | (240,240,3) | 82.284 | 82.532 | 10.73 | 22.06 | [Link](https://huggingface.co/timm/flexivit_small.1200ep_in1k) | 1200ep_in1k | -| FlexiViT_Base | (240,240,3) | 84.670 | 84.670 | 40.66 | 86.59 | [Link](https://huggingface.co/timm/flexivit_base.1200ep_in1k) | 1200ep_in1k | -| FlexiViT_Large | (240,240,3) | 85.576 | 85.660 | 142.21 | 304.36 | [Link](https://huggingface.co/timm/flexivit_large.1200ep_in1k) | 1200ep_in1k | -| GoogLeNet | (224,224,3) | 69.566 | 69.780 | 1.50 | 6.62 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.googlenet.html) | | -| Inception_V3 | (299,299,3) | 77.120 | 77.278 | 5.71 | 27.16 | [Link](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.inception_v3.html)| | -| MNASNet1_0 | (224,224,3)| 72.696 | 73.422 | 0.31 | 4.38 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.mnasnet1_0.html) | | -| MNASNet1_3 | (224,224,3) | 75.720 | 76.466 | 0.53 | 6.28 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.mnasnet1_3.html) | | -| MobileNet_V2 | (224,224,3)| 71.624 | 72.138 | 0.30 | 3.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.mobilenet_v2.html) | IMAGENET1K_V2 | -| RegNet_X_400MF | (224,224,3) | 72.544 | 72.900 | 0.41 | 5.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_400mf.html) | IMAGENET1K_V1 | -| RegNet_X_400MF | (224,224,3) | 74.218 | 74.846 | 0.41 | 5.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_400mf.html) | IMAGENET1K_V2 | -| RegNet_X_800MF | (224,224,3) | 74.928 | 75.204 | 0.80 | 7.26 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_800mf.html) | IMAGENET1K_V1 | -| RegNet_X_800MF | (224,224,3) | 76.984 | 77.488 | 0.80 | 7.26 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_800mf.html) | IMAGENET1K_V2 | -| RegNet_X_1_6GF | (224,224,3) | 76.756 | 77.080 | 1.60 | 9.19 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_1_6gf.html) | IMAGENET1K_V1 | -| RegNet_X_1_6GF | (224,224,3) | 79.168 | 79.670 | 1.60 | 9.19 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_1_6gf.html) | IMAGENET1K_V2 | -| RegNet_X_3_2GF | (224,224,3) | 77.978 | 78.346 | 3.18 | 15.30 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_x_3_2gf.html) | IMAGENET1K_V1 | -| RegNet_X_3_2GF | (224,224,3) | 80.792 | 81.188 | 3.18 | 15.30 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_x_3_2gf.html) | IMAGENET1K_V2 | -| RegNet_X_8GF | (224,224,3) | 79.322 | 79.368 | 8.00 | 39.57 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_8gf.html) | IMAGENET1K_V1 | -| RegNet_X_8GF | (224,224,3) | 81.388 | 81.680 | 8.00 | 39.57 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_8gf.html) | IMAGENET1K_V2 | -| RegNet_X_16GF | (224,224,3) | 79.884 | 80.090 | 15.94 | 54.28 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_16gf.html) | IMAGENET1K_V1 | -| RegNet_X_16GF | (224,224,3) | 82.312 | 82.712 | 15.94 | 54.28 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_16gf.html) | IMAGENET1K_V2 | -| RegNet_X_32GF | (224,224,3) | 80.482 | 80.592 | 31.74 | 107.81 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_32gf.html) | IMAGENET1K_V1 | -| RegNet_X_32GF | (224,224,3) | 82.722 | 83.014 | 31.74 | 107.81 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_32gf.html) | IMAGENET1K_V2 | -| RegNet_Y_400MF | (224,224,3) | 73.594 | 73.998 | 0.40 | 4.34 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_400mf.html) | IMAGENET1K_V1 | -| RegNet_Y_400MF | (224,224,3) | 75.294 | 75.804 | 0.40 | 4.34 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_400mf.html) | IMAGENET1K_V2 | -| RegNet_Y_800MF | (224,224,3) | 76.132 | 76.406 | 0.83 | 6.43 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_800mf.html) | IMAGENET1K_V1| -| RegNet_Y_800MF | (224,224,3) | 78.280 | 78.904 | 0.83 | 6.43 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_800mf.html) | IMAGENET1K_V2| -| RegNet_Y_1_6GF | (224,224,3) | 77.260 | 77.934 | 1.61 | 11.20 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_1_6gf.html) | IMAGENET1K_V1 | -| RegNet_Y_1_6GF | (224,224,3) | 80.298 | 80.876 | 1.61 | 11.20 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_1_6gf.html) | IMAGENET1K_V2 | -| RegNet_Y_3_2GF | (224,224,3) | 78.504 | 78.962 | 3.18 | 19.44 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_y_3_2gf.html) | IMAGENET1K_V1 | -| RegNet_Y_3_2GF | (224,224,3) | 81.354 | 82.010 | 3.18 | 19.44 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_y_3_2gf.html) | IMAGENET1K_V2 | -| RegNet_Y_8GF | (224,224,3) | 79.768 | 80.052 | 8.47 | 39.38 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_8gf.html) | IMAGENET1K_V1 | -| RegNet_Y_8GF | (224,224,3) | 82.418 | 82.822 | 8.47 | 39.38 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_8gf.html) | IMAGENET1K_V2 | -| RegNet_Y_16GF | (224,224,3) | 80.240 | 80.428 | 15.91 | 83.59 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_16gf.html)| IMAGENET1K_V1 | -| RegNet_Y_16GF | (224,224,3) | 82.348 | 82.868 | 15.91 | 83.59 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_16gf.html)| IMAGENET1K_V2 | -| RegNet_Y_32GF | (224,224,3) | 80.510 | 80.840 | 32.28 | 145.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_32gf.html) | IMAGENET1K_V1 | -| RegNet_Y_32GF | (224,224,3) | 82.798 | 83.362 | 32.28 | 145.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_32gf.html) | IMAGENET1K_V2 | -| ResNet18 | (224,224,3) | 69.530 | 69.770 | 1.81 | 11.69 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet18.html) | | -| ResNet34 | (224,224,3) | 73.166 | 73.308 | 3.66 | 21.80 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet34.html) | | -| ResNet50 | (224,224,3) | 75.946 | 76.128 | 4.09 | 25.56 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet50.html) | IMAGENET1K_V1 | -| ResNet50 | (224,224,3) | 80.326 | 80.844 | 4.09 | 25.56 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet50.html) | IMAGENET1K_V2 | -| ResNet101 | (224,224,3) | 77.112 | 77.362 | 7.80 | 44.55 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet101.html) | IMAGENET1K_V1 | -| ResNet101 | (224,224,3) | 81.472 | 81.918 | 7.80 | 44.55 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet101.html) | IMAGENET1K_V2 | -| ResNet152 | (224,224,3) | 77.922 | 78.316 | 11.51 | 60.19 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet152.html) | IMAGENET1K_V1 | -| ResNet152 | (224,224,3) | 81.950 | 82.272 | 11.51 | 60.19 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet152.html) | IMAGENET1K_V2 | -| ResNeXt50_32X4D | (224,224,3) | 77.528 | 77.630 | 4.23 | 25.03 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext50_32x4d.html) | IMAGENET1K_V1 | -| ResNeXt50_32X4D | (224,224,3) | 80.728 | 81.220 | 4.23 | 25.03 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext50_32x4d.html) | IMAGENET1K_V2 | -| ResNeXt101_32X8D | (224,224,3) | 78.998 | 79.288 | 16.41 | 88.79 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_32x8d.html) | IMAGENET1K_V1 | -| ResNeXt101_32X8D | (224,224,3) | 82.510 | 82.780 | 16.41 | 88.79 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_32x8d.html) | IMAGENET1K_V2 | -| ResNeXt101_64X4D | (224,224,3) | 82.876 | 83.236 | 15.46 | 83.46 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_64x4d.html) | | -| ShuffleNet_V2_X1_0 | (224,224,3) | 68.616 | 69.312 | 0.14 | 2.28 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x1_0.html) | | -| ShuffleNet_V2_X1_5 | (224,224,3) | 72.240 | 72.966 | 0.30 | 3.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x1_5.html) | | -| ShuffleNet_V2_X2_0 | (224,224,3) | 75.540 | 76.222 | 0.58 | 7.39 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x2_0.html) | | -| VGG11 | (224,224,3) | 68.616 | 68.978 | 7.61 | 132.86 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg11.html) | | -| VGG11_BN | (224,224,3) | 70.010 | 70.328 | 7.61 | 132.87 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg11_bn.html) | | -| VGG13 | (224,224,3) | 69.588 | 69.886 | 11.31 | 133.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg13.html) | | -| VGG13_BN | (224,224,3) | 71.262 | 71.570 | 11.31 | 133.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg13_bn.html) | | -| VGG16 | (224,224,3) | 71.350 | 71.610 | 15.47 | 138.36 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg16.html) | | -| VGG16_BN | (224,224,3) | 73.178 | 73.408 | 15.47 | 138.37 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg16_bn.html) | | -| VGG19 | (224,224,3) | 72.140 | 72.384 | 19.63 | 143.67 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg19.html) | | -| VGG19_BN | (224,224,3) | 73.898 | 74.166 | 19.63 | 143.68 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg19_bn.html) | | -| ViT_Tiny_Patch16_224 | (224,224,3) | 75.308 | 75.458 | 2.53 | 5.72 | [Link](https://huggingface.co/timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Tiny_Patch16_384 | (384,384,3) | 78.213 | 78.466 | 9.46 | 5.79 | [Link](https://huggingface.co/timm/vit_tiny_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Small_Patch16_224 | (224,224,3) | 81.446 | 81.410 | 9.25 | 22.05 | [Link](https://huggingface.co/timm/vit_base_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Small_Patch16_384 | (384,384,3) | 83.724 | 83.788 | 31.17 | 22.20 | [Link](https://huggingface.co/timm/vit_base_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Small_Patch32_224 | (224,224,3) | 75.716 | 75.926 | 2.30 | 22.88 | [Link](https://huggingface.co/timm/vit_base_patch32_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Small_Patch32_384 | (384,384,3) | 80.412 | 80.460 | 6.92 | 22.92 | [Link](https://huggingface.co/timm/vit_base_patch32_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Base_Patch8_224 | (224,224,3) | 86.348 | 86.270 | 156.86 | 86.58 | [Link](https://huggingface.co/timm/vit_base_patch8_224.augreg2_in21k_ft_in1k) | augreg2_in21k_ft_in1k | -| ViT_Base_Patch16_224 | (224,224,3) | 85.040 | 85.114 | 35.23 | 86.57 | [Link](https://huggingface.co/timm/vit_base_patch16_224.augreg2_in21k_ft_in1k) | augreg2_in21k_ft_in1k | -| ViT_Base_Patch16_384 | (384,384,3) | 86.002 | 86.028 | 111.34 | 86.86 | [Link](https://huggingface.co/timm/vit_base_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Base_Patch32_224 | (224,224,3) | 80.598 | 80.694 | 8.84 | 88.22 | [Link](https://huggingface.co/timm/vit_base_patch32_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Base_Patch32_384 | (384,384,3) | 83.322 | 83.394 | 26.16 | 88.30 | [Link](https://huggingface.co/timm/vit_base_patch32_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Large_Patch16_224 | (224,224,3) | 85.886 | 85.872 | 123.39 | 304.33 | [Link](https://huggingface.co/timm/vit_large_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Large_Patch16_384 | (384,384,3) | 86.976 | 87.084 | 383.13 | 304.72 | [Link](https://huggingface.co/timm/vit_large_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | -| ViT_Large_Patch32_384 | (384,384,3) | 81.494 | 81.510 | 90.75 | 306.63 | [Link](https://huggingface.co/timm/vit_large_patch32_384.orig_in21k_ft_in1k) | orig_in21k_ft_in1k | -| Wide_ResNet50_2 | (224,224,3) | 78.268 | 78.490 | 11.40 | 68.88 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet50_2.html) | IMAGENET1K_V1 | -| Wide_ResNet50_2 | (224,224,3) | 81.226 | 81.626 | 11.40 | 68.88 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet50_2.html) | IMAGENET1K_V2 | -| Wide_ResNet101_2 | (224,224,3) | 78.384 | 78.834 | 22.75 | 126.89 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet101_2.html) | IMAGENET1K_V1 | -| Wide_ResNet101_2 | (224,224,3) | 82.282 | 82.504 | 22.75 | 126.89 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet101_2.html) | IMAGENET1K_V2 | +| --- | --- | --- | --- | --- | --- | --- | --- | +| AlexNet | (224,224,3) | 56.084 | 56.556 | 1.43 | 61.10 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.alexnet.html) | | +| CAFormer_S18 | (224,224,3) | 82.592 | 83.626 | 9.10 | 26.34 | [Link](https://huggingface.co/timm/caformer_s18.sail_in1k) | sail_in1k | +| CAFormer_B36 | (224,224,3) | 83.938 | 85.482 | 49.41 | 98.75 | [Link](https://huggingface.co/timm/caformer_b36.sail_in1k) | sail_in1k | +| CoAtNet_0_RW_224 | (224,224,3) | 81.842 | 82.418 | 10.06 | 30.46 | [Link](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) | sw_in1k | +| CoAtNet_1_RW_224 | (224,224,3) | 83.506 | 83.600 | 18.17 | 47.91 | [Link](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) | sw_in1k | +| CoAtNet_2_RW_224 | (224,224,3) | 86.084 | 86.534 | 32.80 | 82.11 | [Link](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) | sw_in12k_ft_in1k | +| ConvFormer S36 | (224,224,3) | 83.360 | 84.016 | 16.66 | 40.01 | [Link](https://huggingface.co/timm/convformer_s36.sail_in1k) | sail_in1k | +| ConvFormer M36 | (224,224,3) | 84.014 | 84.448 | 27.58 | 57.05 | [Link](https://huggingface.co/timm/convformer_m36.sail_in1k) | sail_in1k | +| ConvFormer B36 | (224,224,3) | 84.244 | 84.830 | 47.79 | 99.88 | [Link](https://huggingface.co/timm/convformer_b36.sail_in1k) | sail_in1k | +| ConvNeXt_Tiny | (224,224,3) | 82.354 | 82.458 | 9.11 | 28.59 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_tiny.html) | | +| ConvNeXt_Small | (224,224,3) | 83.434 | 83.560 | 17.68 | 50.22 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_small.html) | | +| ConvNeXt_Base | (224,224,3) | 83.940 | 84.048 | 31.13 | 88.59 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_base.html) | | +| ConvNeXt_Large | (224,224,3) | 84.316 | 84.410 | 69.35 | 197.77 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.convnext_large.html) | | +| DeiT_Tiny_Patch16_224 | (224,224,3) | 71.944 | 72.030 | 2.66 | 5.72 | [Link](https://huggingface.co/timm/deit_tiny_patch16_224.fb_in1k) | fb_in1k | +| DeiT_Small_Patch16_224 | (224,224,3) | 79.722 | 79.790 | 9.50 | 22.05 | [Link](https://huggingface.co/timm/deit_small_patch16_224.fb_in1k) | fb_in1k | +| DeiT_Base_Patch16_224 | (224,224,3) | 81.932 | 81.980 | 35.73 | 86.57 | [Link](https://huggingface.co/timm/deit_base_patch16_224.fb_in1k) | fb_in1k | +| DeiT_Base_Patch16_384 | (384,384,3) | 83.046 | 83.100 | 115.00 | 86.86 | [Link](https://huggingface.co/timm/deit_base_patch16_384.fb_in1k) | fb_in1k | +| DeiT3_Small_Patch16_224 | (224,224,3) | 81.368 | 81.390 | 9.50 | 22.06 | [Link](https://huggingface.co/timm/deit3_small_patch16_224.fb_in1k) | fb_in1k | +| DeiT3_Small_Patch16_384 | (384,384,3) | 83.350 | 83.420 | 33.01 | 22.21 | [Link](https://huggingface.co/timm/deit3_small_patch16_384.fb_in1k) | fb_in1k | +| DeiT3_Medium_Patch16_224 | (224,224,3) | 83.020 | 83.044 | 16.39 | 38.85 | [Link](https://huggingface.co/timm/deit3_medium_patch16_224.fb_in1k) | fb_in1k | +| DeiT3_Base_Patch16_224 | (224,224,3) | 83.708 | 83.766 | 35.74 | 86.59 | [Link](https://huggingface.co/timm/deit3_base_patch16_224.fb_in1k) | fb_in1k | +| DeiT3_Base_Patch16_384 | (384,384,3) | 84.986 | 85.064 | 115.02 | 86.88 | [Link](https://huggingface.co/timm/deit3_base_patch16_384.fb_in1k) | fb_in1k | +| DeiT3_Large_Patch16_224 | (224,224,3) | 84.734 | 84.736 | 124.73 | 304.37 | [Link](https://huggingface.co/timm/deit3_large_patch16_224.fb_in1k) | fb_in1k | +| DeiT3_Large_Patch16_384 | (384,384,3) | 85.806 | 85.826 | 392.94 | 304.76 | [Link](https://huggingface.co/timm/deit3_large_patch16_384.fb_in1k) | fb_in1k | +| ConvFormer S18 | (224,224,3) | 81.862 | 82.866 | 8.59 | 26.77 | [Link](https://huggingface.co/timm/convformer_s18.sail_in1k) | sail_in1k | +| EfficientFormer_L7 | (224,224,3) | 82.526 | 83.352 | 20.67 | 82.14 | [Link](https://huggingface.co/timm/efficientformer_l7.snap_dist_in1k) | snap_dist_in1k | +| DenseNet121 | (224,224,3) | 74.320 | 74.422 | 6.37 | 8.04 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet121.html) | | +| DenseNet161 | (224,224,3) | 77.200 | 77.142 | 16.85 | 28.86 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet161.html) | | +| DenseNet169 | (224,224,3) | 75.554 | 75.568 | 7.62 | 14.28 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet169.html) | | +| DenseNet201 | (224,224,3) | 76.742 | 76.882 | 9.82 | 20.21 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.densenet201.html) | | +| FlexiViT_Small | (240,240,3) | 82.150 | 82.536 | 11.05 | 22.06 | [Link](https://huggingface.co/timm/flexivit_small.1200ep_in1k) | 1200ep_in1k | +| FlexiViT_Base | (240,240,3) | 84.484 | 84.664 | 41.30 | 86.59 | [Link](https://huggingface.co/timm/flexivit_base.1200ep_in1k) | 1200ep_in1k | +| FlexiViT_Large | (240,240,3) | 85.540 | 85.658 | 143.89 | 304.36 | [Link](https://huggingface.co/timm/flexivit_large.1200ep_in1k) | 1200ep_in1k | +| Inception_V3 | (299,299,3) | 77.246 | 77.278 | 11.51 | 23.82 | [Link](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.inception_v3.html) | | +| LeViT_Conv_128 | (224,224,3) | 77.282 | 78.488 | 0.88 | 9.19 | [Link](https://huggingface.co/timm/levit_conv_128.fb_dist_in1k) | fb_dist_in1k | +| LeViT_Conv_128S | (224,224,3) | 75.204 | 76.574 | 0.65 | 7.76 | [Link](https://huggingface.co/timm/levit_conv_128s.fb_dist_in1k) | fb_dist_in1k | +| LeViT_Conv_192 | (224,224,3) | 79.138 | 79.876 | 1.38 | 10.92 | [Link](https://huggingface.co/timm/levit_conv_192.fb_dist_in1k) | fb_dist_in1k | +| LeViT_Conv_256 | (224,224,3) | 80.960 | 81.538 | 2.34 | 18.86 | [Link](https://huggingface.co/timm/levit_conv_256.fb_dist_in1k) | fb_dist_in1k | +| LeViT_Conv_384 | (224,224,3) | 82.170 | 82.582 | 4.83 | 39.08 | [Link](https://huggingface.co/timm/levit_conv_384.fb_dist_in1k) | fb_dist_in1k | +| MNASNet1_0 | (224,224,3) | 72.806 | 73.416 | 0.65 | 4.36 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.mnasnet1_0.html) | | +| MobileNet_V2 | (224,224,3) | 71.728 | 72.142 | 0.64 | 3.49 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.mobilenet_v2.html) | IMAGENET1K_V2 | +| RegNet_X_400MF | (224,224,3) | 72.530 | 72.908 | 0.84 | 5.48 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_400mf.html) | IMAGENET1K_V1 | +| RegNet_X_400MF | (224,224,3) | 74.182 | 74.860 | 0.84 | 5.48 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_400mf.html) | IMAGENET1K_V2 | +| RegNet_X_800MF | (224,224,3) | 74.972 | 75.210 | 1.62 | 7.24 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_800mf.html) | IMAGENET1K_V1 | +| RegNet_X_800MF | (224,224,3) | 77.056 | 77.496 | 1.62 | 7.24 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_800mf.html) | IMAGENET1K_V2 | +| RegNet_X_1_6GF | (224,224,3) | 76.900 | 77.084 | 3.24 | 9.17 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_1_6gf.html) | IMAGENET1K_V1 | +| RegNet_X_1_6GF | (224,224,3) | 79.254 | 79.676 | 3.24 | 9.17 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_1_6gf.html) | IMAGENET1K_V2 | +| RegNet_X_3_2GF | (224,224,3) | 78.142 | 78.342 | 6.40 | 15.27 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_x_3_2gf.html) | IMAGENET1K_V1 | +| RegNet_X_3_2GF | (224,224,3) | 80.888 | 81.194 | 6.40 | 15.27 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_x_3_2gf.html) | IMAGENET1K_V2 | +| RegNet_X_8GF | (224,224,3) | 79.338 | 79.372 | 16.05 | 39.53 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_8gf.html) | IMAGENET1K_V1 | +| RegNet_X_8GF | (224,224,3) | 81.386 | 81.692 | 16.05 | 39.53 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_8gf.html) | IMAGENET1K_V2 | +| RegNet_X_16GF | (224,224,3) | 79.932 | 80.092 | 31.99 | 54.22 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_16gf.html) | IMAGENET1K_V1 | +| RegNet_X_16GF | (224,224,3) | 82.434 | 82.712 | 31.99 | 54.22 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_16gf.html) | IMAGENET1K_V2 | +| RegNet_X_32GF | (224,224,3) | 80.550 | 80.592 | 63.63 | 107.73 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_32gf.html) | IMAGENET1K_V1 | +| RegNet_X_32GF | (224,224,3) | 82.856 | 83.022 | 63.63 | 107.73 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_x_32gf.html) | IMAGENET1K_V2 | +| RegNet_Y_400MF | (224,224,3) | 73.690 | 74.004 | 0.82 | 4.33 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_400mf.html) | IMAGENET1K_V1 | +| RegNet_Y_400MF | (224,224,3) | 75.312 | 75.802 | 0.82 | 4.33 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_400mf.html) | IMAGENET1K_V2 | +| RegNet_Y_800MF | (224,224,3) | 76.100 | 76.396 | 1.70 | 6.42 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_800mf.html) | IMAGENET1K_V1 | +| RegNet_Y_800MF | (224,224,3) | 78.448 | 78.890 | 1.70 | 6.42 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.regnet_y_800mf.html) | IMAGENET1K_V2 | +| RegNet_Y_1_6GF | (224,224,3) | 77.370 | 77.926 | 3.27 | 11.18 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_1_6gf.html) | IMAGENET1K_V1 | +| RegNet_Y_1_6GF | (224,224,3) | 80.490 | 80.882 | 3.27 | 11.18 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_1_6gf.html) | IMAGENET1K_V2 | +| RegNet_Y_3_2GF | (224,224,3) | 78.744 | 78.962 | 6.41 | 19.40 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_y_3_2gf.html) | IMAGENET1K_V1 | +| RegNet_Y_3_2GF | (224,224,3) | 81.454 | 82.018 | 6.41 | 19.40 | [Link](https://docs.pytorch.org//vision/2.0/models/generated/torchvision.models.regnet_y_3_2gf.html) | IMAGENET1K_V2 | +| RegNet_Y_8GF | (224,224,3) | 79.874 | 80.052 | 17.05 | 39.34 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_8gf.html) | IMAGENET1K_V1 | +| RegNet_Y_8GF | (224,224,3) | 82.546 | 82.824 | 17.05 | 39.34 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_8gf.html) | IMAGENET1K_V2 | +| RegNet_Y_16GF | (224,224,3) | 80.326 | 80.424 | 31.95 | 83.53 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_16gf.html) | IMAGENET1K_V1 | +| RegNet_Y_16GF | (224,224,3) | 82.470 | 82.862 | 31.95 | 83.53 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_16gf.html) | IMAGENET1K_V2 | +| RegNet_Y_32GF | (224,224,3) | 80.700 | 80.834 | 64.72 | 144.97 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_32gf.html) | IMAGENET1K_V1 | +| RegNet_Y_32GF | (224,224,3) | 82.890 | 83.362 | 64.72 | 144.97 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.regnet_y_32gf.html) | IMAGENET1K_V2 | +| ResNet18 | (224,224,3) | 69.558 | 69.778 | 3.64 | 11.68 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet18.html) | | +| ResNet34 | (224,224,3) | 73.166 | 73.304 | 7.35 | 21.79 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet34.html) | | +| ResNet50 | (224,224,3) | 75.980 | 76.116 | 8.23 | 25.53 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet50.html) | IMAGENET1K_V1 | +| ResNet50 | (224,224,3) | 80.574 | 80.852 | 8.23 | 25.53 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet50.html) | IMAGENET1K_V2 | +| ResNet101 | (224,224,3) | 77.076 | 77.350 | 15.69 | 44.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet101.html) | IMAGENET1K_V1 | +| ResNet101 | (224,224,3) | 81.534 | 81.914 | 15.69 | 44.50 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet101.html) | IMAGENET1K_V2 | +| ResNet152 | (224,224,3) | 78.044 | 78.306 | 23.14 | 60.12 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet152.html) | IMAGENET1K_V1 | +| ResNet152 | (224,224,3) | 81.952 | 82.272 | 23.14 | 60.12 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnet152.html) | IMAGENET1K_V2 | +| ResNeXt50_32X4D | (224,224,3) | 77.568 | 77.634 | 8.53 | 24.99 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext50_32x4d.html) | IMAGENET1K_V1 | +| ResNeXt50_32X4D | (224,224,3) | 80.896 | 81.212 | 8.53 | 24.99 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext50_32x4d.html) | IMAGENET1K_V2 | +| ResNeXt101_32X8D | (224,224,3) | 79.234 | 79.290 | 32.97 | 88.69 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_32x8d.html) | IMAGENET1K_V1 | +| ResNeXt101_32X8D | (224,224,3) | 82.598 | 82.784 | 32.97 | 88.69 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_32x8d.html) | IMAGENET1K_V2 | +| ResNeXt101_64X4D | (224,224,3) | 82.980 | 83.234 | 31.06 | 83.35 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.resnext101_64x4d.html) | | +| ShuffleNet_V2_X1_0 | (224,224,3) | 68.734 | 69.312 | 0.30 | 2.27 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x1_0.html) | | +| ShuffleNet_V2_X1_5 | (224,224,3) | 72.458 | 72.966 | 0.60 | 3.49 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x1_5.html) | | +| ShuffleNet_V2_X2_0 | (224,224,3) | 75.614 | 76.224 | 1.18 | 7.38 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.shufflenet_v2_x2_0.html) | | +| Swin_B | (224,224,3) | 83.254 | 83.562 | 31.58 | 88.61 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.swin_b.html) | | +| Swin_S | (224,224,3) | 82.848 | 83.180 | 18.02 | 50.24 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.swin_s.html) | | +| Swin_T | (224,224,3) | 81.076 | 81.444 | 9.32 | 28.60 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.swin_t.html) | | +| VGG11 | (224,224,3) | 68.706 | 68.974 | 15.26 | 132.86 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg11.html) | | +| VGG11_BN | (224,224,3) | 70.074 | 70.328 | 15.26 | 132.86 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg11_bn.html) | | +| VGG13 | (224,224,3) | 69.742 | 69.888 | 22.68 | 133.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg13.html) | | +| VGG13_BN | (224,224,3) | 71.370 | 71.564 | 22.68 | 133.05 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg13_bn.html) | | +| VGG16 | (224,224,3) | 71.526 | 71.616 | 31.01 | 138.36 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg16.html) | | +| VGG16_BN | (224,224,3) | 73.276 | 73.406 | 31.01 | 138.36 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg16_bn.html) | | +| VGG19 | (224,224,3) | 72.284 | 72.386 | 39.34 | 143.67 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg19.html) | | +| VGG19_BN | (224,224,3) | 74.022 | 74.170 | 39.34 | 143.67 | [Link](https://docs.pytorch.org//vision/stable/models/generated/torchvision.models.vgg19_bn.html) | | +| ViT_B_16 | (224,224,3) | 81.002 | 81.040 | 35.73 | 86.57 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html) | IMAGENET1K_V1 | +| ViT_B_16 | (384,384,3) | 85.118 | 85.276 | 115.01 | 86.86 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html) | IMAGENET1K_SWAG_E2E_V1 | +| ViT_B_16 | (224,224,3) | 81.516 | 81.926 | 35.73 | 86.57 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html) | IMAGENET1K_SWAG_LINEAR_V1 | +| ViT_B_32 | (224,224,3) | 75.680 | 75.908 | 8.90 | 88.22 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_32.html) | | +| RepViT_M1 | (224,224,3) | 77.668 | 78.510 | 1.70 | 5.46 | [Link](https://huggingface.co/timm/repvit_m1.dist_in1k) | dist_in1k | +| RepViT_M1_0 | (224,224,3) | 79.210 | 80.198 | 2.39 | 7.29 | [Link](https://huggingface.co/timm/repvit_m1_0.dist_300e_in1k) | dist_300e_in1k | +| RepViT_M1_1 | (224,224,3) | 79.838 | 80.828 | 2.87 | 8.79 | [Link](https://huggingface.co/timm/repvit_m1_1.dist_300e_in1k) | dist_300e_in1k | +| RepViT_M1_5 | (224,224,3) | 81.532 | 82.374 | 4.88 | 14.62 | [Link](https://huggingface.co/timm/repvit_m1_5.dist_300e_in1k) | dist_300e_in1k | +| RepViT_M2 | (224,224,3) | 79.882 | 80.526 | 2.77 | 8.77 | [Link](https://huggingface.co/timm/repvit_m2.dist_in1k) | dist_in1k | +| RepViT_M2_3 | (224,224,3) | 82.960 | 83.466 | 9.59 | 23.66 | [Link](https://huggingface.co/timm/repvit_m2_3.dist_300e_in1k) | dist_300e_in1k | +| RepViT_M3 | (224,224,3) | 80.896 | 81.478 | 3.85 | 10.65 | [Link](https://huggingface.co/timm/repvit_m3.dist_in1k) | dist_in1k | +| VisFormer_Small | (224,224,3) | 81.586 | 82.106 | 10.05 | 40.24 | [Link](https://huggingface.co/timm/visformer_small.in1k) | in1k | +| VisFormer_Tiny | (224,224,3) | 77.564 | 78.262 | 2.68 | 10.33 | [Link](https://huggingface.co/timm/visformer_tiny.in1k) | in1k | +| ViT_Tiny_Patch16_224 | (224,224,3) | 74.568 | 75.456 | 2.66 | 5.72 | [Link](https://huggingface.co/timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Tiny_Patch16_384 | (384,384,3) | 77.312 | 78.470 | 10.37 | 5.79 | [Link](https://huggingface.co/timm/vit_tiny_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Small_Patch16_224 | (224,224,3) | 81.404 | 81.412 | 9.50 | 22.05 | [Link](https://huggingface.co/timm/vit_small_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Small_Patch16_384 | (384,384,3) | 83.560 | 83.782 | 33.00 | 22.20 | [Link](https://huggingface.co/timm/vit_small_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Small_Patch32_224 | (224,224,3) | 75.792 | 75.922 | 2.32 | 22.88 | [Link](https://huggingface.co/timm/vit_small_patch32_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Small_Patch32_384 | (384,384,3) | 80.092 | 80.452 | 7.07 | 22.92 | [Link](https://huggingface.co/timm/vit_small_patch32_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Base_Patch8_224 | (224,224,3) | 86.302 | 86.268 | 163.48 | 86.58 | [Link](https://huggingface.co/timm/vit_base_patch8_224.augreg2_in21k_ft_in1k) | augreg2_in21k_ft_in1k | +| ViT_Base_Patch16_224 | (224,224,3) | 85.080 | 85.112 | 35.73 | 86.57 | [Link](https://huggingface.co/timm/vit_base_patch16_224.augreg2_in21k_ft_in1k) | augreg2_in21k_ft_in1k | +| ViT_Base_Patch16_384 | (384,384,3) | 85.860 | 86.018 | 115.00 | 86.86 | [Link](https://huggingface.co/timm/vit_base_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Base_Patch32_224 | (224,224,3) | 80.612 | 80.698 | 8.89 | 88.22 | [Link](https://huggingface.co/timm/vit_base_patch32_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Base_Patch32_384 | (384,384,3) | 83.138 | 83.398 | 26.45 | 88.30 | [Link](https://huggingface.co/timm/vit_base_patch32_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Large_Patch16_224 | (224,224,3) | 85.880 | 85.870 | 124.71 | 304.33 | [Link](https://huggingface.co/timm/vit_large_patch16_224.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Large_Patch16_384 | (384,384,3) | 86.980 | 87.086 | 392.88 | 304.72 | [Link](https://huggingface.co/timm/vit_large_patch16_384.augreg_in21k_ft_in1k) | augreg_in21k_ft_in1k | +| ViT_Large_Patch32_384 | (384,384,3) | 81.122 | 81.512 | 91.52 | 306.63 | [Link](https://huggingface.co/timm/vit_large_patch32_384.orig_in21k_ft_in1k) | orig_in21k_ft_in1k | +| Wide_ResNet50_2 | (224,224,3) | 78.402 | 78.488 | 22.87 | 68.85 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet50_2.html) | IMAGENET1K_V1 | +| Wide_ResNet50_2 | (224,224,3) | 81.268 | 81.630 | 22.87 | 68.85 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet50_2.html) | IMAGENET1K_V2 | +| Wide_ResNet101_2 | (224,224,3) | 78.500 | 78.830 | 45.61 | 126.82 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet101_2.html) | IMAGENET1K_V1 | +| Wide_ResNet101_2 | (224,224,3) | 82.358 | 82.510 | 45.61 | 126.82 | [Link](https://docs.pytorch.org/vision/stable/models/generated/torchvision.models.wide_resnet101_2.html) | IMAGENET1K_V2 | +| YOLOv5n-cls | (224,224,3) | 63.538 | 63.982 | 0.43 | 2.49 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5s-cls | (224,224,3) | 70.698 | 70.854 | 1.42 | 5.45 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5m-cls | (224,224,3) | 75.348 | 75.418 | 4.03 | 12.95 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5l-cls | (224,224,3) | 77.536 | 77.528 | 8.82 | 26.54 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5x-cls | (224,224,3) | 78.338 | 78.312 | 16.46 | 48.07 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv8s-cls | (224,224,3) | 72.630 | 73.774 | 1.67 | 6.36 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8m-cls | (224,224,3) | 76.032 | 76.824 | 5.37 | 17.04 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8l-cls | (224,224,3) | 77.708 | 78.276 | 12.53 | 37.47 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8x-cls | (224,224,3) | 78.440 | 78.936 | 19.38 | 57.40 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLO11s-cls | (224,224,3) | 74.532 | 75.244 | 1.63 | 6.72 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11m-cls | (224,224,3) | 76.484 | 77.388 | 5.17 | 11.62 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11l-cls | (224,224,3) | 77.658 | 78.284 | 6.51 | 14.10 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11x-cls | (224,224,3) | 78.666 | 79.426 | 14.20 | 29.61 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO26s-cls | (224,224,3) | 75.300 | 75.910 | 1.63 | 6.72 | [Link](https://docs.ultralytics.com/models/yolo26/) | | +| YOLO26m-cls | (224,224,3) | 77.608 | 78.078 | 5.17 | 11.62 | [Link](https://docs.ultralytics.com/models/yolo26/) | | +| YOLO26l-cls | (224,224,3) | 78.614 | 79.060 | 6.51 | 14.10 | [Link](https://docs.ultralytics.com/models/yolo26/) | | +| YOLO26x-cls | (224,224,3) | 79.478 | 79.866 | 14.20 | 29.61 | [Link](https://docs.ultralytics.com/models/yolo26/) | |

    Image Classification (ImageNet) -- AccTop1 values are model accuracies on the [ImageNet](https://www.image-net.org/index.php) dataset validation set. +- AccTop1 values are the primary model accuracies on the + [ImageNet](https://www.image-net.org/index.php) validation set. Validation also reports + AccTop5 as the secondary metric.
    -## Object Detection +### Object Detection | Model | Input Size
    (H,W,C) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{box}}}$
    (NPU) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{box}}}$
    (GPU) | FLOPs (B) | params (M) | Source | Note | -|-------|--------------------------|----------------|---------------|-----------|------------|--------|------| -| YOLOv3u | (640,640,3) | 51.053 | 51.577 | 282.68 | 103.73 | [Link](https://docs.ultralytics.com/models/yolov3/)| | -| YOLOv3-spp | (640,640,3) | 46.715 | 47.613 | 156.91 | 62.97 | [Link](https://github.com/ultralytics/yolov3/)| | -| YOLOv3-sppu | (640,640,3) | 51.382 | 51.763 | 283.57 | 104.78 | [Link](https://docs.ultralytics.com/models/yolov3/)| | -| YOLOv5su | (640,640,3) | 42.206 | 42.871 | 24.03 | 9.14 | [Link](https://docs.ultralytics.com/models/yolov5/)| | -| YOLOv5s6u | (1280,1280,3) | 48.008 | 48.632 | 97,75 | 15.29 | [Link](https://docs.ultralytics.com/models/yolov5/)| | -| YOLOv5mu | (640,640,3) | 48.217 | 48.906 | 64.27 | 25.09 | [Link](https://docs.ultralytics.com/models/yolov5/)| | -| YOLOv5m6u | (1280,1280,3) | 52.938 | 53.475 | 261.56 | 41.19 | [Link](https://docs.ultralytics.com/models/yolov5/)| | -| YOLOv5lu | (640,640,3) | 51.718 | 52.171 | 135.09 | 53.19 | [Link](https://docs.ultralytics.com/models/yolov5/)| | -| YOLOv5l6 | (1280,1280,3) | 52.402 | 53.368 | 445.31 | 72.73 | [Link](https://github.com/ultralytics/yolov5/)| | -| YOLOv5l6u | (1280,1280,3) | 54.974 | 55.465 | 549.86 | 80.05 | [Link](https://docs.ultralytics.com/models/yolov5/)| | -| YOLOv5xu | (640,640,3) | 52.600 | 53.090 | 246.55 | 97.23 | [Link](https://docs.ultralytics.com/models/yolov5/)| | -| YOLOv5x6 | (1280, 1280,3) | 53.706 | 54.706 | 838.81 | 140.73 | [Link](https://github.com/ultralytics/yolov5/)| | -| YOLOv5x6u | (1280,1280,3) | 56.141 | 56.489 | 1003.46 | 155.48 | [Link](https://docs.ultralytics.com/models/yolov5/)| | -| YOLOv7 | (640,640,3) | 50.402 | 50.941 | 104.67 | 36.91 | [Link](https://github.com/WongKinYiu/yolov7/) | | -| YOLOv7d6 | (1280,1280,3) | 55.635 | 55.792 | 702.30 | 133.76 | [Link](https://github.com/WongKinYiu/yolov7/) | | -| YOLOv7e6 | (1280,1280,3) | 55.304 | 55.567 | 514.89 | 97.20 | [Link](https://github.com/WongKinYiu/yolov7/) | | -| YOLOv7e6e | (1280,1280,3) | 55.768 | 56.295 | 842.87 | 151.69 | [Link](https://github.com/WongKinYiu/yolov7/) | | -| YOLOv7w6 | (1280,1280,3) | 53.783 | 54.131 | 359.99 | 70.39 | [Link](https://github.com/WongKinYiu/yolov7/) | | -| YOLOv7x | (640,640,3) | 52.103 | 52.706 | 189.88 | 71.31 | [Link](https://github.com/WongKinYiu/yolov7/) | | -| YOLOv8s | (640,640,3) | 44.062 | 44.918 | 28.64 | 11.16 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| YOLOv8m | (640,640,3) | 49.657 | 50.240 | 79.00 | 25.89 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| YOLOv8l | (640,640,3) | 52.340 | 52.773 | 165.24 | 43.67 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| YOLOv8x | (640,640,3) | 53.365 | 53.802 | 257.92 | 68.20 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| GELANm | (640,640,3) | 50.108 | 50.923 | 76.44 | 19.98 | [Link](https://github.com/WongKinYiu/yolov9/) | | -| YOLOv9m | (640,640,3) | 50.351 | 51.191 | 76.43 | 19.98 | [Link](https://github.com/WongKinYiu/yolov9/) | | -| GELANc | (640,640,3) | 51.938 | 52.274 | 102.34 | 25.29 | [Link](https://github.com/WongKinYiu/yolov9/) | | -| YOLOv9c | (640,640,3) | 52.391 | 52.918 | 102.34 | 25.29 | [Link](https://github.com/WongKinYiu/yolov9/) | | -| YOLOv10b | (640,640,3) | 51.125 | 52.096 | 92.23 | 19.07 | [Link](https://docs.ultralytics.com/models/yolov10/) | | -| YOLO11s | (640,640,3) | 45.733 | 46.617 | 21.69 | 9.44 | [Link](https://docs.ultralytics.com/models/yolo11/) | | -| YOLO11m | (640,640,3) | 50.638 | 51.310 | 68.24 | 20.09 | [Link](https://docs.ultralytics.com/models/yolo11/) | | -| YOLO11l | (640,640,3) | 52.468 | 53.165 | 87.37 | 25.34 | [Link](https://docs.ultralytics.com/models/yolo11/) | | -| YOLO11x | (640,640,3) | 54.059 | 54.478 | 195.67 | 56.92 | [Link](https://docs.ultralytics.com/models/yolo11/) | | -| YOLO12s | (640,640,3) | 46.746 | 47.687 | 23.84 | 9.26 | [Link](https://docs.ultralytics.com/models/yolo12/) | | -| YOLO12m | (640,640,3) | 51.568 | 52.297 | 71.91 | 20.17 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| --- | --- | --- | --- | --- | --- | --- | --- | +| YOLOv3 | (640,640,3) | 46.354 | 46.839 | 162.27 | 61.92 | [Link](https://docs.ultralytics.com/models/yolov3/) | | +| YOLOv3u | (640,640,3) | 51.214 | 51.582 | 289.23 | 103.73 | [Link](https://docs.ultralytics.com/models/yolov3/) | | +| YOLOv3-spp | (640,640,3) | 47.106 | 47.616 | 163.23 | 62.97 | [Link](https://docs.ultralytics.com/models/yolov3/) | | +| YOLOv3-sppu | (640,640,3) | 51.710 | 51.754 | 290.20 | 104.78 | [Link](https://docs.ultralytics.com/models/yolov3/) | | +| YOLOv5nu | (640,640,3) | 33.549 | 34.286 | 8.79 | 2.65 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5n6 | (1280,1280,3) | 35.021 | 35.892 | 22.10 | 3.24 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5n6u | (1280,1280,3) | 41.637 | 42.111 | 35.63 | 4.33 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5s | (640,640,3) | 36.780 | 37.540 | 18.21 | 7.23 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5su | (640,640,3) | 42.414 | 42.877 | 25.93 | 9.14 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5s6 | (1280,1280,3) | 43.878 | 44.512 | 74.33 | 12.61 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5s6u | (1280,1280,3) | 48.363 | 48.636 | 105.38 | 15.29 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5m | (640,640,3) | 44.446 | 45.238 | 52.14 | 21.17 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5mu | (640,640,3) | 48.388 | 48.910 | 67.70 | 25.09 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5m6 | (1280,1280,3) | 50.637 | 51.078 | 212.83 | 35.70 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5m6u | (1280,1280,3) | 53.259 | 53.476 | 275.41 | 41.19 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5l | (640,640,3) | 48.128 | 48.914 | 114.20 | 46.53 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5lu | (640,640,3) | 51.824 | 52.172 | 140.50 | 53.19 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5l6 | (1280,1280,3) | 52.892 | 53.376 | 466.00 | 76.73 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5l6u | (1280,1280,3) | 55.344 | 55.466 | 571.74 | 86.05 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5x | (640,640,3) | 49.986 | 50.554 | 213.04 | 86.71 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5xu | (640,640,3) | 52.792 | 53.090 | 254.38 | 97.23 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5x6 | (1280,1280,3) | 54.197 | 54.706 | 869.05 | 140.73 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5x6u | (1280,1280,3) | 56.516 | 56.488 | 1035.24 | 155.48 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv7 | (640,640,3) | 50.442 | 50.942 | 110.55 | 36.91 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7x | (640,640,3) | 52.389 | 52.706 | 197.67 | 71.31 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7w6 | (1280,1280,3) | 53.886 | 54.128 | 374.79 | 70.39 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7d6 | (1280,1280,3) | 55.510 | 55.799 | 730.75 | 133.76 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7e6 | (1280,1280,3) | 55.379 | 55.567 | 538.42 | 97.20 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv7e6e | (1280,1280,3) | 55.932 | 56.298 | 878.43 | 151.69 | [Link](https://github.com/WongKinYiu/yolov7/) | | +| YOLOv8n | (640,640,3) | 36.650 | 37.328 | 9.78 | 3.15 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8s | (640,640,3) | 44.243 | 44.918 | 30.49 | 11.16 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8m | (640,640,3) | 49.915 | 50.239 | 82.26 | 25.89 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8l | (640,640,3) | 52.485 | 52.772 | 170.26 | 43.67 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8x | (640,640,3) | 53.497 | 53.802 | 264.17 | 68.20 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| GELANs | (640,640,3) | 45.679 | 46.486 | 29.01 | 7.11 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv9s | (640,640,3) | 45.637 | 46.777 | 29.01 | 7.11 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| GELANm | (640,640,3) | 50.564 | 50.928 | 80.81 | 19.98 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv9m | (640,640,3) | 50.902 | 51.191 | 80.81 | 19.98 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| GELANc | (640,640,3) | 52.086 | 52.273 | 107.83 | 25.29 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv9c | (640,640,3) | 52.598 | 52.917 | 107.83 | 25.29 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| GELANe | (640,640,3) | 54.568 | 54.927 | 199.86 | 57.35 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv9e | (640,640,3) | 55.466 | 55.528 | 199.86 | 57.35 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv10n | (640,640,3) | 37.432 | 38.382 | 8.06 | 2.30 | [Link](https://docs.ultralytics.com/models/yolov10/) | | +| YOLOv10s | (640,640,3) | 45.386 | 46.015 | 24.10 | 7.25 | [Link](https://docs.ultralytics.com/models/yolov10/) | | +| YOLOv10m | (640,640,3) | 50.075 | 50.838 | 63.51 | 15.36 | [Link](https://docs.ultralytics.com/models/yolov10/) | | +| YOLOv10b | (640,640,3) | 51.525 | 52.096 | 97.77 | 19.07 | [Link](https://docs.ultralytics.com/models/yolov10/) | | +| YOLOv10l | (640,640,3) | 52.246 | 52.816 | 127.32 | 24.37 | [Link](https://docs.ultralytics.com/models/yolov10/) | | +| YOLOv10x | (640,640,3) | 53.427 | 53.993 | 170.13 | 29.47 | [Link](https://docs.ultralytics.com/models/yolov10/) | | +| YOLO11n | (640,640,3) | 38.567 | 39.298 | 7.76 | 2.62 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11s | (640,640,3) | 46.179 | 46.617 | 23.80 | 9.44 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11m | (640,640,3) | 50.887 | 51.310 | 72.81 | 20.09 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11l | (640,640,3) | 52.770 | 53.165 | 93.21 | 25.34 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11x | (640,640,3) | 54.136 | 54.478 | 204.31 | 56.92 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO12n | (640,640,3) | 40.172 | 40.740 | 9.27 | 2.59 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12s | (640,640,3) | 47.124 | 47.687 | 26.73 | 9.26 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12m | (640,640,3) | 51.899 | 52.297 | 77.22 | 20.17 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12l | (640,640,3) | 53.200 | 53.508 | 105.07 | 26.40 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12x | (640,640,3) | 54.758 | 55.061 | 223.27 | 59.14 | [Link](https://docs.ultralytics.com/models/yolo12/) | |
    Object Detection (COCO) @@ -162,35 +287,57 @@ Further usage examples can be found in the [tests](../tests) directory.
    -## Instance Segmentation +### Instance Segmentation | Model | Input Size
    (H,W,C) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{mask}}}$
    (NPU) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{mask}}}$
    (GPU) | FLOPs (B) | params (M) | Source | Note | -|-------|--------------------------|--------------------|-------------------|-----------|------------|------|------| -| YOLOv5l-seg | (640,640,3) | 39.041 | 39.784 | 147.83 | 47.89 | [Link](https://github.com/ultralytics/yolov5/) | | -| YOLOv5x-seg | (640,640,3) | 40.697 | 41.137 | 265.81 | 88.77 | [Link](https://github.com/ultralytics/yolov5/) | | -| YOLOv8s-seg | (640,640,3) | 36.072 | 36.582 | 42.64 | 11.81 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| YOLOv8m-seg | (640,640,3) | 39.978 | 40.444 | 110.26 | 27.27 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| YOLOv8l-seg | (640,640,3) | 41.993 | 42.316 | 220.55 | 45.97 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| YOLOv8x-seg | (640,640,3) | 42.913 | 43.020 | 344.20 | 71.78 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| YOLOv9c-seg | (640,640,3) | 42.249 | 42.460 | 145.72 | 27.45 | [Link](https://docs.ultralytics.com/models/yolov9/) | | -| YOLO11s-seg | (640,640,3) | 36.874 | 37.602 | 35.69 | 10.10 | [Link](https://docs.ultralytics.com/models/yolo11/) | | -| YOLO11m-seg | (640,640,3) | 41.031 | 41.546 | 123.56 | 22.40 | [Link](https://docs.ultralytics.com/models/yolo11/) | | -| YOLO11l-seg | (640,640,3) | 42.509 | 42.831 | 142.68 | 27.65 | [Link](https://docs.ultralytics.com/models/yolo11/) | | -| YOLO11x-seg | (640,640,3) | 43.614 | 43.746 | 319.78 | 62.09 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| --- | --- | --- | --- | --- | --- | --- | --- | +| YOLOv5n-seg | (640,640,3) | 22.671 | 23.334 | 8.23 | 1.99 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5s-seg | (640,640,3) | 31.164 | 31.592 | 28.47 | 7.61 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5m-seg | (640,640,3) | 36.764 | 37.148 | 74.57 | 21.97 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5l-seg | (640,640,3) | 39.685 | 39.942 | 153.53 | 47.89 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv5x-seg | (640,640,3) | 41.185 | 41.318 | 273.99 | 88.77 | [Link](https://docs.ultralytics.com/models/yolov5/) | | +| YOLOv8n-seg | (640,640,3) | 29.951 | 30.464 | 13.91 | 3.40 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8s-seg | (640,640,3) | 36.534 | 36.691 | 44.86 | 11.81 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8m-seg | (640,640,3) | 40.362 | 40.596 | 114.06 | 27.27 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8l-seg | (640,640,3) | 42.316 | 42.462 | 226.27 | 45.97 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8x-seg | (640,640,3) | 43.048 | 43.155 | 351.31 | 71.80 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| GELANc-seg | (640,640,3) | 42.150 | 42.204 | 150.93 | 27.42 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv9c-seg | (640,640,3) | 42.574 | 42.655 | 152.44 | 27.45 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLOv9e-seg | (640,640,3) | 44.410 | 44.394 | 256.38 | 59.74 | [Link](https://github.com/WongKinYiu/yolov9/) | | +| YOLO11n-seg | (640,640,3) | 31.509 | 32.129 | 11.88 | 2.87 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11s-seg | (640,640,3) | 37.379 | 37.726 | 38.18 | 10.10 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11m-seg | (640,640,3) | 41.552 | 41.683 | 128.82 | 22.40 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11l-seg | (640,640,3) | 42.948 | 42.974 | 149.22 | 27.65 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11x-seg | (640,640,3) | 43.895 | 43.910 | 329.44 | 62.09 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO12n-seg | (640,640,3) | 32.516 | 32.941 | 12.94 | 2.80 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12s-seg | (640,640,3) | 38.470 | 38.787 | 39.26 | 9.76 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12m-seg | (640,640,3) | 42.270 | 42.433 | 125.74 | 21.94 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12l-seg | (640,640,3) | 43.429 | 43.456 | 154.99 | 28.76 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO12x-seg | (640,640,3) | 44.187 | 44.418 | 334.55 | 64.51 | [Link](https://docs.ultralytics.com/models/yolo12/) | | +| YOLO26m-seg | (640,640,3) | 42.961 | 43.732 | 137.37 | 23.57 | [Link](https://docs.ultralytics.com/models/yolo26/) | | +| YOLO26l-seg | (640,640,3) | 44.246 | 45.242 | 157.06 | 27.97 | [Link](https://docs.ultralytics.com/models/yolo26/) | | +| YOLO26x-seg | (640,640,3) | 46.033 | 46.717 | 346.96 | 62.82 | [Link](https://docs.ultralytics.com/models/yolo26/) | |
    - Instance Segmentation (COCO) +Instance Segmentation (COCO) - $\underset{50\text{โ€“}95}{\text{mAP}_{\text{val}}^{\text{mask}}}$ values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset.
    -## Pose Estimation +### Pose Estimation | Model | Input Size
    (H,W,C) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{pose}}}$
    (NPU) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{pose}}}$
    (GPU) | FLOPs (B) | params (M) | Source | Note | -|-------|--------------------------|--------------------|-------------------|-----------|------------|------|------| -| YOLOv8x-pose | (640,640,3) | 66.416 | 68.367 | 263.33 | 69.46 | [Link](https://docs.ultralytics.com/models/yolov8/) | | -| YOLO11x-pose | (640,640,3) | 67.220 | 68.599 | 203.54 | 58.75 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| --- | --- | --- | --- | --- | --- | --- | --- | +| YOLOv8s-pose | (640,640,3) | 57.007 | 59.366 | 32.10 | 11.62 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8m-pose | (640,640,3) | 62.153 | 64.451 | 84.37 | 26.45 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8l-pose | (640,640,3) | 65.173 | 66.822 | 173.70 | 44.47 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8x-pose | (640,640,3) | 67.063 | 68.357 | 269.63 | 69.46 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLOv8x-pose-p6 | (1280,1280,3) | 69.221 | 70.758 | 1092.51 | 99.14 | [Link](https://docs.ultralytics.com/models/yolov8/) | | +| YOLO11s-pose | (640,640,3) | 55.731 | 57.846 | 25.41 | 9.90 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11m-pose | (640,640,3) | 62.136 | 64.280 | 76.25 | 20.89 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11l-pose | (640,640,3) | 63.174 | 65.414 | 96.65 | 26.14 | [Link](https://docs.ultralytics.com/models/yolo11/) | | +| YOLO11x-pose | (640,640,3) | 67.218 | 68.600 | 212.26 | 58.75 | [Link](https://docs.ultralytics.com/models/yolo11/) | |
    Pose Estimation (COCO) @@ -198,3 +345,18 @@ Further usage examples can be found in the [tests](../tests) directory. - $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{pose}}}$ values are for single-model single-scale on the [COCO Keypoints val2017](https://cocodataset.org/) dataset.
    + +### OBB + +| Model | Input Size
    (H,W,C) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{obb}}}$
    (NPU) | $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{obb}}}$
    (GPU) | FLOPs (B) | params (M) | Source | Note | +| --- | --- | --- | --- | --- | --- | --- | --- | + +
    +Oriented Bounding Boxes (DOTA v1.0) + +- $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{obb}}}$ is the primary metric + for single-model single-scale validation on the + [DOTA v1.0](https://docs.ultralytics.com/datasets/obb/dota-v2#) dataset. Validation also reports + rotated mAP50 as the secondary metric. + +
    diff --git a/mblt_vision/compile/__init__.py b/mblt_vision/compile/__init__.py new file mode 100644 index 0000000..9c09c9d --- /dev/null +++ b/mblt_vision/compile/__init__.py @@ -0,0 +1,21 @@ +"""Vision model compilation and calibration-data preparation.""" + +from .vision import ( + compile_vision_model, + copy_calibration_subset, + ensure_calibration_dataset, + make_calibration_subset, + prepare_calibration_arrays, + resolve_quantization_values, + select_calibration_images, +) + +__all__ = [ + "compile_vision_model", + "copy_calibration_subset", + "ensure_calibration_dataset", + "make_calibration_subset", + "prepare_calibration_arrays", + "resolve_quantization_values", + "select_calibration_images", +] diff --git a/mblt_vision/compile/vision.py b/mblt_vision/compile/vision.py new file mode 100644 index 0000000..9858dd9 --- /dev/null +++ b/mblt_vision/compile/vision.py @@ -0,0 +1,981 @@ +"""Vision model compilation and calibration-data preparation.""" + +from __future__ import annotations + +import hashlib +import importlib +import json +import shutil +import warnings +from collections.abc import Callable, Sequence +from pathlib import Path +from tempfile import TemporaryDirectory, mkdtemp +from typing import Any + +import numpy as np +import torch +from huggingface_hub import hf_hub_download +from huggingface_hub.errors import HfHubHTTPError +from mblt_vision.utils.datasets.readiness import dataset_ready + +from mblt_vision._tasks import normalize_vision_task +from mblt_vision.datasets import get_dataset_config_for_task +from mblt_vision.wrapper import MOBILINT_CACHE_DIR, MBLT_Engine, resolve_model_config + +DEFAULT_PERCENTILE = 0.9999 +DEFAULT_TOPK_RATIO = 0.01 +DEFAULT_SEED = 0 +DEFAULT_MODEL_DIR = Path(MOBILINT_CACHE_DIR) +DEFAULT_SUBSET_SIZES = { + "image_classification": 1, + "depth_estimation": 100, + "object_detection": 100, + "instance_segmentation": 100, + "semantic_segmentation": 100, + "pose_estimation": 100, + "face_detection": 1, + "obb": 100, +} +IMAGE_SUFFIXES = {".bmp", ".jpeg", ".jpg", ".png", ".tif", ".tiff", ".webp"} + + +def _normalize_task(task: str) -> str: + """Validate and normalize a supported task name. + + Args: + task: Task value from a model postprocess configuration. + + Returns: + Canonical task name used by the dataset registry. + + Raises: + ValueError: If the task is not supported by vision compilation. + """ + + return normalize_vision_task(task, supported=DEFAULT_SUBSET_SIZES) + + +def _dataset_ready(task: str, data_path: Path, dataset: str | None = None) -> bool: + """Return whether an organized dataset contains calibration images. + + Args: + task: Canonical vision task name. + data_path: Organized dataset root. + dataset: Optional dense dataset taxonomy. + + Returns: + Whether the expected dataset identity, metadata, and full validation split are present. + """ + + return dataset_ready(data_path, task, dataset) + + +def _find_dataset_source(data_path: Path, filename: str) -> Path | None: + """Find a manually supplied dataset source at or beside its organized root. + + Args: + data_path: Organized dataset destination. + filename: Archive filename to locate. + + Returns: + Existing archive path, or ``None`` when it is unavailable. + """ + + for directory in (data_path, data_path.parent): + candidate = directory / filename + if candidate.is_file(): + return candidate + return None + + +def _organize_dataset(task: str, data_path: Path, dataset: str | None = None) -> None: + """Organize the registry-backed dataset required by a task. + + Args: + task: Canonical vision task name. + data_path: Destination dataset root. + dataset: Optional dataset taxonomy used to disambiguate semantic datasets. + """ + + from mblt_vision.utils.datasets import ( + organize_ade20k, + organize_cityscapes, + organize_coco, + organize_dotav1, + organize_imagenet, + organize_nyu_depth, + organize_widerface, + ) + + config = get_dataset_config_for_task(task, dataset) + download = config.get("download") + if not isinstance(download, dict): + raise ValueError( + f"Dataset `{config.get('name', task)}` does not define download metadata." + ) + + data_path.parent.mkdir(parents=True, exist_ok=True) + if task == "image_classification": + organize_imagenet( + image_dir=str(download["images"]), + xml_dir=str(download["annotations"]), + output_dir=str(data_path), + ) + elif task in {"object_detection", "instance_segmentation", "pose_estimation"}: + organize_coco( + image_dir=str(download["images"]), + annotation_dir=str(download["annotations"]), + output_dir=str(data_path), + ) + elif task == "face_detection": + organize_widerface( + image_dir=str(download["images"]), + annotation_dir=str(download["annotations"]), + output_dir=str(data_path), + ) + elif task == "obb": + organize_dotav1(dataset_path=str(download["url"]), output_dir=str(data_path)) + elif task == "depth_estimation": + organize_nyu_depth(dataset_path=str(download["url"]), output_dir=str(data_path)) + elif config["name"] == "ade20k": + organize_ade20k(dataset_path=str(download["url"]), output_dir=str(data_path)) + elif config["name"] == "cityscapes": + image_archive = _find_dataset_source(data_path, str(download["images_archive"])) + annotation_archive = _find_dataset_source( + data_path, str(download["annotations_archive"]) + ) + if image_archive is None or annotation_archive is None: + raise ValueError( + "Cityscapes compilation requires leftImg8bit_trainvaltest.zip and gtFine_trainvaltest.zip " + f"at {data_path} or {data_path.parent}." + ) + organize_cityscapes( + image_dir=str(image_archive), + annotation_dir=str(annotation_archive), + output_dir=str(data_path), + ) + else: + raise ValueError( + f"Unsupported calibration dataset `{config['name']}` for task `{task}`." + ) + + +def ensure_calibration_dataset( + task: str, + data_path: str | Path | None = None, + dataset: str | None = None, +) -> Path: + """Resolve and, when needed, organize a calibration dataset. + + Args: + task: Vision task name. + data_path: Optional organized dataset root. + dataset: Optional dataset taxonomy from the model postprocess configuration. + + Returns: + Expanded organized dataset root. + """ + + normalized_task = _normalize_task(task) + config = get_dataset_config_for_task(normalized_task, dataset) + dataset_name = str(config["name"]) + resolved_path = Path( + data_path if data_path is not None else config["path"] + ).expanduser() + if not _dataset_ready(normalized_task, resolved_path, dataset_name): + _organize_dataset(normalized_task, resolved_path, dataset) + if not _dataset_ready(normalized_task, resolved_path, dataset_name): + raise ValueError( + f"Organized calibration dataset at {resolved_path} is incomplete or does not match " + f"the expected {dataset_name} dataset." + ) + return resolved_path + + +def _validate_subset_size(subset_size: int) -> None: + """Validate a requested calibration subset size. + + Args: + subset_size: Requested per-class or total sample count. + + Raises: + ValueError: If the size is not positive. + """ + + if subset_size <= 0: + raise ValueError("subset_size must be greater than zero.") + + +def _validate_calibration_image(image_path: Path, dataset_root: Path) -> Path: + """Resolve and validate a calibration image within its dataset root. + + Args: + image_path: Candidate calibration image path. + dataset_root: Resolved organized dataset root. + + Returns: + Resolved regular image path. + + Raises: + ValueError: If the candidate is a symlink, not a regular image, or escapes the dataset root. + """ + + if image_path.is_symlink(): + raise ValueError(f"Calibration image must not be a symlink: {image_path}.") + try: + source = image_path.resolve(strict=True) + except OSError as exc: + raise ValueError( + f"Unable to resolve calibration image {image_path}: {exc}." + ) from exc + if not source.is_file() or source.suffix.lower() not in IMAGE_SUFFIXES: + raise ValueError( + f"Calibration image must be a supported regular image file: {image_path}." + ) + if not source.is_relative_to(dataset_root): + raise ValueError( + f"Calibration image must remain within dataset root: {image_path}." + ) + return source + + +def _selectable_calibration_images( + paths: Sequence[Path], dataset_root: Path +) -> list[Path]: + """Validate supported image candidates before they enter subset sampling. + + Args: + paths: Candidate filesystem paths. + dataset_root: Resolved organized dataset root. + + Returns: + Validated candidate paths, retaining their original names for stable sampling. + """ + + images: list[Path] = [] + for path in paths: + if path.suffix.lower() in IMAGE_SUFFIXES and ( + path.is_symlink() or path.is_file() + ): + _validate_calibration_image(path, dataset_root) + images.append(path) + return images + + +def select_calibration_images( + task: str, + data_path: str | Path, + subset_size: int | None = None, + seed: int = DEFAULT_SEED, +) -> list[Path]: + """Select deterministic calibration images from an organized dataset. + + For ImageNet and WiderFace, ``subset_size`` is the number selected from + every category. For all other datasets it is the total number of selected + validation images. + + Args: + task: Vision task name. + data_path: Organized dataset root. + subset_size: Optional sample count, using task-specific defaults when omitted. + seed: Random selection seed. + + Returns: + Selected image paths in deterministic order. + + Raises: + ValueError: If the dataset layout or requested size is invalid. + """ + + import random + + normalized_task = _normalize_task(task) + root = Path(data_path).expanduser() + resolved_root = root.resolve() + requested_size = ( + DEFAULT_SUBSET_SIZES[normalized_task] if subset_size is None else subset_size + ) + _validate_subset_size(requested_size) + random_generator = random.Random(seed) + + if normalized_task in {"image_classification", "face_detection"}: + category_root = ( + root if normalized_task == "image_classification" else root / "images" + ) + dataset_name = ( + "ImageNet" if normalized_task == "image_classification" else "WiderFace" + ) + category_dirs = ( + sorted(path for path in category_root.iterdir() if path.is_dir()) + if category_root.is_dir() + else [] + ) + if not category_dirs: + raise ValueError( + f"No {dataset_name} category directories found in {category_root}." + ) + selected: list[Path] = [] + for category_dir in category_dirs: + images = _selectable_calibration_images( + sorted(category_dir.iterdir()), resolved_root + ) + if requested_size > len(images): + raise ValueError( + f"subset_size ({requested_size}) exceeds the {len(images)} available images in {category_dir.name}." + ) + selected.extend(random_generator.sample(images, requested_size)) + return selected + + image_dir = { + "depth_estimation": root / "images", + "object_detection": root / "val2017", + "instance_segmentation": root / "val2017", + "semantic_segmentation": root / "images", + "pose_estimation": root / "val2017", + "obb": root / "images", + }[normalized_task] + images = ( + _selectable_calibration_images(sorted(image_dir.rglob("*")), resolved_root) + if image_dir.is_dir() + else [] + ) + if not images: + raise ValueError(f"No calibration images found in {image_dir}.") + if requested_size > len(images): + raise ValueError( + f"subset_size ({requested_size}) exceeds the {len(images)} available images in {image_dir}." + ) + return random_generator.sample(images, requested_size) + + +def copy_calibration_subset( + images: Sequence[Path], data_path: str | Path, output_dir: str | Path +) -> list[Path]: + """Copy selected images into a flat directory using collision-safe names. + + Args: + images: Selected source image paths. + data_path: Dataset root used to derive stable relative names. + output_dir: Destination directory. + + Returns: + Copied image paths in input order. + """ + + root = Path(data_path).expanduser().resolve() + destination = Path(output_dir).expanduser() + destination.mkdir(parents=True, exist_ok=True) + copied: list[Path] = [] + for image_path in images: + source = _validate_calibration_image(image_path, root) + relative_name = source.relative_to(root).as_posix() + digest = hashlib.sha256(relative_name.encode()).hexdigest()[:12] + target = destination / f"{digest}_{source.name}" + if target.exists(): + raise ValueError(f"Calibration subset filename collision for {source}.") + shutil.copy2(source, target) + copied.append(target) + return copied + + +def make_calibration_subset( + task: str, + data_path: str | Path, + output_dir: str | Path, + subset_size: int | None = None, + seed: int = DEFAULT_SEED, +) -> list[Path]: + """Select and copy a deterministic flat calibration subset. + + Args: + task: Vision task name. + data_path: Organized dataset root. + output_dir: Destination for copied images. + subset_size: Optional task-specific selection count. + seed: Random selection seed. + + Returns: + Copied image paths. + """ + + source_root = Path(data_path).expanduser().resolve() + destination = Path(output_dir).expanduser().resolve() + if source_root.is_relative_to(destination) or destination.is_relative_to( + source_root + ): + raise ValueError("output_dir must not overlap data_path.") + images = select_calibration_images(task, data_path, subset_size, seed) + destination.parent.mkdir(parents=True, exist_ok=True) + temporary_destination = Path( + mkdtemp(prefix=f".{destination.name}-", dir=destination.parent) + ) + try: + copied = copy_calibration_subset(images, data_path, temporary_destination) + backup_destination: Path | None = None + if destination.exists(): + backup_destination = Path( + mkdtemp(prefix=f".{destination.name}-backup-", dir=destination.parent) + ) + backup_destination.rmdir() + destination.replace(backup_destination) + try: + temporary_destination.replace(destination) + except BaseException: + if backup_destination is not None and not destination.exists(): + backup_destination.replace(destination) + raise + if backup_destination is not None: + shutil.rmtree(backup_destination) + return [destination / path.name for path in copied] + finally: + if temporary_destination.exists(): + shutil.rmtree(temporary_destination) + + +def _as_hwc_float32(value: Any, image_path: Path) -> np.ndarray: + """Validate and convert engine preprocessing output for qbcompiler. + + Args: + value: Engine preprocessing result. + image_path: Source image used for error context. + + Returns: + Contiguous HWC float32 array. + + Raises: + TypeError: If preprocessing did not return a tensor or array. + ValueError: If preprocessing did not return HWC three-channel data. + """ + + if isinstance(value, torch.Tensor): + array = value.detach().cpu().numpy() + elif isinstance(value, np.ndarray): + array = value + else: + raise TypeError( + f"Preprocessing {image_path} returned unsupported type {type(value).__name__}." + ) + if array.ndim != 3 or array.shape[-1] != 3: + raise ValueError( + f"Preprocessing {image_path} must produce an HWC three-channel array; got shape {array.shape}." + ) + return np.ascontiguousarray(array, dtype=np.float32) + + +def prepare_calibration_arrays( + engine: MBLT_Engine, images: Sequence[Path], output_dir: str | Path +) -> list[Path]: + """Preprocess calibration images and save one NumPy array per sample. + + Args: + engine: ONNX vision engine providing authoritative preprocessing. + images: Flat calibration image paths. + output_dir: Destination directory for ``.npy`` arrays. + + Returns: + Saved NumPy paths. + """ + + destination = Path(output_dir) + destination.mkdir(parents=True, exist_ok=True) + saved: list[Path] = [] + for index, image_path in enumerate(images): + array = _as_hwc_float32(engine.preprocess(str(image_path)), image_path) + array_path = destination / f"{index:06d}.npy" + np.save(array_path, array) + saved.append(array_path) + return saved + + +def get_subset_images(subset_path: str | Path) -> list[Path]: + """Load all images from an already-sampled subset. + + Args: + subset_path: Root containing sampled images, either flat or nested. + + Returns: + Image paths in deterministic order. + + Raises: + ValueError: If the subset contains no supported image files. + """ + + root = Path(subset_path).expanduser() + images = ( + sorted( + path + for path in root.rglob("*") + if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES + ) + if root.is_dir() + else [] + ) + if not images: + raise ValueError(f"No calibration subset images found in {root}.") + return images + + +def validate_calibration_dataset(calib_data_path: str | Path) -> Path: + """Validate a ready directory of preprocessed calibration arrays. + + Args: + calib_data_path: Directory containing HWC float32 ``.npy`` tensors. + + Returns: + Expanded calibration directory path. + + Raises: + ValueError: If the directory is empty or contains an invalid calibration tensor. + """ + + root = Path(calib_data_path).expanduser() + array_paths = sorted(root.glob("*.npy")) if root.is_dir() else [] + if not array_paths: + raise ValueError(f"No calibration .npy files found in {root}.") + for array_path in array_paths: + try: + array = np.load(array_path, mmap_mode="r", allow_pickle=False) + except (OSError, ValueError) as exc: + raise ValueError( + f"Unable to load calibration tensor {array_path}: {exc}." + ) from exc + if array.ndim != 3 or array.shape[-1] != 3: + raise ValueError( + f"Calibration tensor {array_path} must be HWC with three channels; got {array.shape}." + ) + if array.dtype != np.float32: + raise ValueError( + f"Calibration tensor {array_path} must use float32; got {array.dtype}." + ) + if not array.flags.c_contiguous: + raise ValueError(f"Calibration tensor {array_path} must be C-contiguous.") + return root + + +def _validate_data_level_paths( + data_path: str | Path | None, + subset_path: str | Path | None, + calib_data_path: str | Path | None, +) -> None: + """Ensure at most one calibration pipeline entry level is supplied. + + Args: + data_path: Original organized dataset root. + subset_path: Already-sampled image subset root. + calib_data_path: Ready preprocessed NumPy dataset root. + + Raises: + ValueError: If more than one data level is supplied. + """ + + supplied = [ + name + for name, value in ( + ("data_path", data_path), + ("subset_path", subset_path), + ("calib_data_path", calib_data_path), + ) + if value is not None + ] + if len(supplied) > 1: + raise ValueError( + "Provide only one calibration pipeline input: `data_path`, `subset_path`, or `calib_data_path`; " + f"got {', '.join(supplied)}." + ) + + +def _load_qbcompiler() -> tuple[Callable[..., Any], type[Any]]: + """Load qbcompiler only when compilation is requested. + + Returns: + The ``mxq_compile`` function and ``CalibrationConfig`` class. + + Raises: + ImportError: If qbcompiler is not installed. + """ + + try: + module = importlib.import_module("qbcompiler") + except ImportError as exc: + raise ImportError( + "Vision compilation requires qbcompiler>=1.2.0. Install the compiler package supplied by Mobilint." + ) from exc + return module.mxq_compile, module.CalibrationConfig + + +def _validate_ratio(name: str, value: Any) -> float: + """Validate a quantization ratio. + + Args: + name: Field name used in an error message. + value: Candidate numeric ratio. + + Returns: + Validated float value. + + Raises: + ValueError: If the value is non-numeric or outside zero to one. + """ + + if isinstance(value, bool) or not isinstance(value, (int, float)): + raise ValueError( + f"Quantization metadata `{name}` must be numeric, got {value!r}." + ) + resolved = float(value) + if not 0.0 <= resolved <= 1.0: + raise ValueError( + f"Quantization metadata `{name}` must be between 0 and 1, got {resolved}." + ) + return resolved + + +def _fetch_quantization_config(repo_id: str, revision: str) -> dict[str, Any] | None: + """Fetch optional hosted Aries quantization metadata. + + Args: + repo_id: Hugging Face model repository ID. + revision: Repository revision. + + Returns: + Hosted ``config`` mapping, or ``None`` when the optional file is unavailable. + + Raises: + ValueError: If the hosted JSON or its ``config`` field is malformed. + """ + + try: + metadata_path = hf_hub_download( + repo_id=repo_id, + filename="best_result.json", + subfolder="aries", + revision=revision, + ) + except (HfHubHTTPError, OSError) as exc: + warnings.warn( + f"Unable to load optional quantization metadata for {repo_id}: {exc}. Using fallback values.", + stacklevel=2, + ) + return None + + try: + with Path(metadata_path).open(encoding="utf-8") as metadata_file: + metadata = json.load(metadata_file) + except json.JSONDecodeError as exc: + raise ValueError( + f"Malformed quantization metadata JSON at {metadata_path}: {exc.msg}." + ) from exc + if not isinstance(metadata, dict) or not isinstance(metadata.get("config"), dict): + raise ValueError( + f"Quantization metadata at {metadata_path} must contain a `config` object." + ) + return metadata["config"] + + +def resolve_quantization_values( + file_cfg: dict[str, Any], + percentile: float | None, + topk_ratio: float | None, +) -> tuple[float, float]: + """Resolve explicit, hosted, and fallback quantization values independently. + + Args: + file_cfg: Resolved model file configuration. + percentile: Explicit compiler percentile override. + topk_ratio: Explicit compiler top-k ratio override. + + Returns: + Resolved percentile and top-k ratio. + + Raises: + ValueError: If explicit or hosted values are malformed. + """ + + resolved_percentile = ( + _validate_ratio("percentile", percentile) if percentile is not None else None + ) + resolved_topk = ( + _validate_ratio("topk_ratio", topk_ratio) if topk_ratio is not None else None + ) + hosted_config: dict[str, Any] | None = None + if resolved_percentile is None or resolved_topk is None: + repo_id = file_cfg.get("repo_id") + revision = file_cfg.get("revision", "main") + if isinstance(repo_id, str) and repo_id: + hosted_config = _fetch_quantization_config(repo_id, str(revision)) + else: + warnings.warn( + "Model configuration has no repository ID; using fallback quantization values.", + stacklevel=2, + ) + + if ( + resolved_percentile is None + and hosted_config is not None + and "percentile" in hosted_config + ): + hosted_percentile = _validate_ratio( + "config.percentile", hosted_config["percentile"] + ) + resolved_percentile = 1.0 - hosted_percentile + if resolved_topk is None and hosted_config is not None: + hosted_topk = hosted_config.get("topk_ratio", hosted_config.get("topk")) + if hosted_topk is not None: + resolved_topk = _validate_ratio("config.topk_ratio", hosted_topk) + + if resolved_percentile is None: + warnings.warn( + f"Quantization percentile is unavailable; using {DEFAULT_PERCENTILE}.", + stacklevel=2, + ) + resolved_percentile = DEFAULT_PERCENTILE + if resolved_topk is None: + warnings.warn( + f"Quantization top-k ratio is unavailable; using {DEFAULT_TOPK_RATIO}.", + stacklevel=2, + ) + resolved_topk = DEFAULT_TOPK_RATIO + return resolved_percentile, resolved_topk + + +def _configured_onnx_filename(file_cfg: dict[str, Any]) -> str | None: + """Derive the configured ONNX filename. + + Args: + file_cfg: Resolved model file configuration. + + Returns: + ONNX filename when it can be determined. + """ + + onnx_filename = file_cfg.get("onnx_filename") + if isinstance(onnx_filename, str) and onnx_filename: + return onnx_filename + filename = file_cfg.get("filename") + if isinstance(filename, str) and filename: + return f"{Path(filename).stem}.onnx" + return None + + +def _resolve_compile_onnx_path( + file_cfg: dict[str, Any], model_path: str | Path | None +) -> Path: + """Resolve an ONNX artifact for compilation without constructing an inference runtime. + + Args: + file_cfg: Resolved model file configuration. + model_path: Optional caller-provided ONNX path. + + Returns: + Existing local or downloaded ONNX artifact path. + + Raises: + FileNotFoundError: If no ONNX artifact can be resolved. + """ + + if model_path is not None: + local_path = Path(model_path).expanduser() + if local_path.is_file(): + return local_path.resolve() + configured_path = Path(str(file_cfg.get("onnx_path", ""))).expanduser() + if configured_path.is_file(): + return configured_path.resolve() + repo_id = file_cfg.get("repo_id") + revision = file_cfg.get("revision") + filename = _configured_onnx_filename(file_cfg) + if isinstance(repo_id, str) and isinstance(revision, str) and filename: + downloaded_path = Path( + hf_hub_download( + repo_id=repo_id, + filename=filename, + revision=revision, + local_dir=MOBILINT_CACHE_DIR, + ) + ) + if downloaded_path.is_file(): + return downloaded_path.resolve() + configured_name = filename or "the configured ONNX artifact" + raise FileNotFoundError(f"Unable to resolve {configured_name} for compilation.") + + +def compile_vision_model( + model_cls: str, + *, + model_type: str = "DEFAULT", + model_path: str | Path | None = None, + onnx_path: str | Path | None = None, + data_path: str | Path | None = None, + subset_path: str | Path | None = None, + calib_data_path: str | Path | None = None, + save_path: str | Path | None = None, + subset_size: int | None = None, + seed: int = DEFAULT_SEED, + percentile: float | None = None, + topk_ratio: float | None = None, +) -> Path: + """Compile a configured vision ONNX model into an Aries MXQ artifact. + + Args: + model_cls: Vision model name or YAML path. + model_type: Model variant from the YAML configuration. + model_path: Preferred local ONNX path compatibility option. + onnx_path: Local ONNX path alias. + data_path: Original organized dataset root. The pipeline organizes it when needed, samples + an image subset, and preprocesses that subset. + subset_path: Already-sampled image subset. The pipeline preprocesses every image directly + without organizing or sampling an original dataset. + calib_data_path: Ready directory of HWC float32 ``.npy`` tensors. The pipeline passes it + directly to qbcompiler without dataset preparation, sampling, or preprocessing. + save_path: Output MXQ path. Defaults to the ONNX stem under ``~/.mblt_model_zoo``. + subset_size: ImageNet/WiderFace per-category count or total count for other datasets. + seed: Deterministic calibration selection seed. + percentile: Explicit quantization percentile. + topk_ratio: Explicit quantization top-k ratio. + + Returns: + Output MXQ path. + + Raises: + ImportError: If qbcompiler is unavailable. + ValueError: If model metadata, calibration data, or quantization values are invalid. + """ + + _validate_data_level_paths(data_path, subset_path, calib_data_path) + mxq_compile, calibration_config_class = _load_qbcompiler() + model_config = resolve_model_config(model_cls, model_type) + file_cfg = model_config.get("file_cfg") + post_cfg = model_config.get("post_cfg") + if not isinstance(file_cfg, dict) or not isinstance(post_cfg, dict): + raise ValueError( + "Resolved vision model configuration requires `file_cfg` and `post_cfg` objects." + ) + + task = _normalize_task(str(post_cfg.get("task", ""))) + dataset = post_cfg.get("dataset") + if not isinstance(dataset, str) or not dataset: + raise ValueError( + "Resolved vision model configuration requires a non-empty `post_cfg.dataset` value." + ) + selected_local_path = model_path if model_path is not None else onnx_path + if calib_data_path is not None: + resolved_onnx = _resolve_compile_onnx_path(file_cfg, selected_local_path) + output_path = ( + Path(save_path).expanduser() + if save_path is not None + else DEFAULT_MODEL_DIR / f"{resolved_onnx.stem}.mxq" + ).resolve() + output_path.parent.mkdir(parents=True, exist_ok=True) + resolved_percentile, resolved_topk = resolve_quantization_values( + file_cfg, percentile, topk_ratio + ) + calibration_config = calibration_config_class( + method=1, + output=0 if task == "image_classification" else 1, + mode=1, + max_percentile={ + "percentile": resolved_percentile, + "topk_ratio": resolved_topk, + }, + ) + mxq_compile( + model=str(resolved_onnx), + calib_data_path=str(validate_calibration_dataset(calib_data_path)), + save_path=str(output_path), + image_channels=3, + backend="onnx", + device="gpu", + target_device="aries-rb", + inference_scheme="all", + calibration_config=calibration_config, + ) + return output_path + engine_kwargs: dict[str, Any] = { + "model_cls": model_cls, + "model_type": model_type, + "framework": "onnx", + "onnx_providers": ["CPUExecutionProvider"], + } + if selected_local_path is not None: + expanded_local_path = Path(selected_local_path).expanduser() + if expanded_local_path.is_file(): + engine_kwargs["model_path"] = str(expanded_local_path) + + engine: MBLT_Engine | None = None + try: + engine = MBLT_Engine(**engine_kwargs) + resolved_onnx = Path(str(engine.file_cfg.get("onnx_path", ""))).expanduser() + if not resolved_onnx.is_file(): + configured_name = ( + _configured_onnx_filename(file_cfg) or "the configured ONNX artifact" + ) + raise FileNotFoundError( + f"Unable to resolve {configured_name} for model `{model_cls}`." + ) + + output_path = ( + Path(save_path).expanduser() + if save_path is not None + else DEFAULT_MODEL_DIR / f"{resolved_onnx.stem}.mxq" + ) + output_path = output_path.resolve() + output_path.parent.mkdir(parents=True, exist_ok=True) + resolved_percentile, resolved_topk = resolve_quantization_values( + file_cfg, percentile, topk_ratio + ) + calibration_config = calibration_config_class( + method=1, + output=0 if task == "image_classification" else 1, + mode=1, + max_percentile={ + "percentile": resolved_percentile, + "topk_ratio": resolved_topk, + }, + ) + + def _compile(calibration_path: Path) -> None: + """Run qbcompiler with a resolved calibration directory.""" + + mxq_compile( + model=str(resolved_onnx), + calib_data_path=str(calibration_path), + save_path=str(output_path), + image_channels=3, + backend="onnx", + device="gpu", + target_device="aries-rb", + inference_scheme="all", + calibration_config=calibration_config, + ) + + with TemporaryDirectory(prefix="mblt-vision-calibration-") as temporary_root: + temporary_path = Path(temporary_root) + if subset_path is not None: + subset_images = get_subset_images(subset_path) + else: + dataset_path = ensure_calibration_dataset(task, data_path, dataset) + subset_images = make_calibration_subset( + task, + dataset_path, + temporary_path / "images", + subset_size=subset_size, + seed=seed, + ) + array_dir = temporary_path / "arrays" + prepare_calibration_arrays(engine, subset_images, array_dir) + _compile(array_dir) + return output_path + finally: + if engine is not None: + engine.dispose() + + +__all__ = [ + "compile_vision_model", + "copy_calibration_subset", + "ensure_calibration_dataset", + "get_subset_images", + "make_calibration_subset", + "prepare_calibration_arrays", + "resolve_quantization_values", + "select_calibration_images", + "validate_calibration_dataset", +] diff --git a/mblt_vision/utils/datasets/__init__.py b/mblt_vision/utils/datasets/__init__.py index dec390f..b0751a8 100644 --- a/mblt_vision/utils/datasets/__init__.py +++ b/mblt_vision/utils/datasets/__init__.py @@ -1,28 +1,85 @@ -"""Dataset metadata helpers used by Vision result rendering.""" +""" +Datasets utilities and loaders. +""" + +from __future__ import annotations from .ade20k import get_ade20k_palette from .cityscapes import get_cityscapes_palette from .coco import ( get_coco_class_num, get_coco_det_palette, + get_coco_inv, get_coco_keypoint_palette, get_coco_label, get_coco_limb_palette, get_coco_pose_skeleton, ) -from .dotav1 import get_dotav1_label, get_dotav1_palette +from .dataloader import ( + CustomADE20K, + CustomCityscapes, + CustomCocodata, + CustomCOCODataset, + CustomDOTAv1, + CustomImageFolder, + CustomNYUDepth, + CustomWiderface, + CustomWiderFaceDataset, + get_ade20k_loader, + get_cityscapes_loader, + get_coco_loader, + get_dota_loader, + get_imagenet_loader, + get_nyu_depth_loader, + get_widerface_loader, +) +from .dotav1 import get_dotav1_class_num, get_dotav1_label, get_dotav1_palette from .imagenet import get_imagenet_label +from .organizer import ( + organize_ade20k, + organize_cityscapes, + organize_coco, + organize_dotav1, + organize_imagenet, + organize_nyu_depth, + organize_widerface, +) -__all__ = [ +__all__: list[str] = [ "get_ade20k_palette", "get_cityscapes_palette", "get_coco_class_num", "get_coco_det_palette", + "get_coco_inv", "get_coco_keypoint_palette", "get_coco_label", "get_coco_limb_palette", "get_coco_pose_skeleton", + "get_dotav1_class_num", "get_dotav1_label", "get_dotav1_palette", + "CustomADE20K", + "CustomCityscapes", + "CustomCOCODataset", + "CustomCocodata", + "CustomDOTAv1", + "CustomImageFolder", + "CustomNYUDepth", + "CustomWiderFaceDataset", + "CustomWiderface", + "get_ade20k_loader", + "get_cityscapes_loader", + "get_coco_loader", + "get_dota_loader", + "get_imagenet_loader", + "get_nyu_depth_loader", + "get_widerface_loader", "get_imagenet_label", + "organize_coco", + "organize_ade20k", + "organize_cityscapes", + "organize_dotav1", + "organize_imagenet", + "organize_nyu_depth", + "organize_widerface", ] diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py new file mode 100644 index 0000000..5713b91 --- /dev/null +++ b/mblt_vision/utils/datasets/dataloader.py @@ -0,0 +1,928 @@ +""" +Custom dataloaders for vision datasets. +""" + +from __future__ import annotations + +import os +from typing import Any, Callable + +import cv2 +import numpy as np +import torch +from faster_coco_eval import COCO +from mblt_vision.utils.preprocess.letterbox import letterbox_semantic_mask +from PIL import Image + +from .cityscapes import CITYSCAPES_SOURCE_TO_TRAIN_ID + + +class CustomCOCODataset(torch.utils.data.Dataset[tuple[np.ndarray, int, int, int]]): + """Custom COCO dataset class for loading images and metadata. + + This class provides a simple interface for accessing COCO formatted data + without requiring external library dependencies like torchvision. + + Attributes: + root (str): Root directory path containing the images. + coco (COCO): COCO helper object from faster_coco_eval. + ids (list[int]): Sorted list of image IDs in the dataset. + """ + + def __init__( + self, root: str, annFile: str, min_keypoints: int | None = None + ) -> None: + """Initialize the custom COCO dataset. + + Args: + root (str): Path to the directory containing images. + annFile (str): Path to the COCO annotation JSON file. + min_keypoints: If set, keep only images with at least one + annotation whose ``num_keypoints`` is greater than this value. + """ + self.root = root + self.coco = COCO(annFile) + if min_keypoints is None: + self.ids = list(sorted(self.coco.imgs.keys())) + else: + self.ids = list( + sorted( + { + ann["image_id"] + for ann in self.coco.anns.values() + if ann.get("num_keypoints", 0) > min_keypoints + } + ) + ) + + def _load_image(self, image_id: int) -> np.ndarray: + """Load image by ID""" + image_path = os.path.join( + self.root, self.coco.loadImgs(image_id)[0]["file_name"] + ) + image = cv2.imread(image_path) # Load image (BGR format) + + if image is None: + raise FileNotFoundError(f"Image not found: {image_path}") + + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert to RGB + + def __getitem__(self, index: int) -> tuple[np.ndarray, int, int, int]: + """Get the image and target by index""" + image_id = self.ids[index] + image = self._load_image(image_id) + height = self.coco.imgs[image_id]["height"] + width = self.coco.imgs[image_id]["width"] + return image, index, height, width + + def __len__(self) -> int: + """Return the total number of images""" + return len(self.ids) + + +CustomCocodata = CustomCOCODataset + + +def get_coco_loader( + dataset: CustomCOCODataset, + batch_size: int, + preprocess_fn: Callable, +) -> torch.utils.data.DataLoader: + """Creates a DataLoader for the COCO dataset. + + Args: + dataset (CustomCOCODataset): The dataset instance to load from. + batch_size (int): Number of samples per batch. + preprocess_fn (Callable): Function used to preprocess images. + + Returns: + torch.utils.data.DataLoader: A configured DataLoader for the COCO dataset. + """ + + def loader( + batch: list[Any], + ) -> tuple[np.ndarray, np.ndarray, list[Any], tuple[int, ...]]: + """Collate function for COCO DataLoader.""" + batch = list(filter(lambda x: x is not None, batch)) + images, idx, height, width = zip(*batch) + + processed_images = [] + ratio_pads = [] + for img in images: + processed = preprocess_fn(img) + if ( + isinstance(processed, tuple) + and len(processed) == 2 + and isinstance(processed[1], dict) + ): + processed_img, metadata = processed + ratio_pads.append(metadata.get("ratio_pad")) + else: + processed_img = processed + ratio_pads.append(None) + processed_images.append(processed_img) + + height_arr = np.array(height) + width_arr = np.array(width) + + return ( + np.stack(processed_images, axis=0), + np.stack((height_arr, width_arr), axis=1), + ratio_pads, + idx, + ) + + return torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=0, + collate_fn=loader, + ) + + +class CustomNYUDepth(torch.utils.data.Dataset[tuple[np.ndarray, np.ndarray, str]]): + """NYU Depth V2 validation dataset with paired RGB images and ``.npy`` depth maps.""" + + IMG_EXTENSIONS = (".jpg", ".jpeg", ".png", ".bmp") + + def __init__(self, root: str) -> None: + """Validate the organizer's ``images/`` and ``depth/`` validation-only layout.""" + + self.root = root + image_root, depth_root = ( + os.path.join(root, "images"), + os.path.join(root, "depth"), + ) + if not os.path.isdir(image_root) or not os.path.isdir(depth_root): + raise FileNotFoundError( + f"NYU Depth requires images/ and depth/ directories under: {root}" + ) + images = { + os.path.splitext(name)[0]: os.path.join(image_root, name) + for name in os.listdir(image_root) + if name.lower().endswith(self.IMG_EXTENSIONS) + } + depths = { + os.path.splitext(name)[0]: os.path.join(depth_root, name) + for name in os.listdir(depth_root) + if name.lower().endswith(".npy") + } + missing_depths, missing_images = ( + sorted(set(images) - set(depths)), + sorted(set(depths) - set(images)), + ) + if missing_depths or missing_images: + details = [] + if missing_depths: + details.append( + f"images without depth maps: {', '.join(missing_depths[:5])}" + ) + if missing_images: + details.append( + f"depth maps without images: {', '.join(missing_images[:5])}" + ) + raise ValueError(f"NYU Depth image/depth mismatch ({'; '.join(details)}).") + if not images: + raise ValueError(f"NYU Depth contains no image/depth pairs: {root}") + self.samples = [(images[stem], depths[stem], stem) for stem in sorted(images)] + + def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: + """Load an RGB image and finite-safe depth target.""" + + image_path, depth_path, stem = self.samples[index] + image = cv2.imread(image_path) + if image is None: + raise FileNotFoundError(f"NYU Depth image not found: {image_path}") + depth = np.asarray(np.load(depth_path), dtype=np.float32) + if depth.ndim != 2: + raise ValueError( + f"NYU Depth target must be two-dimensional, got {depth.shape}: {depth_path}" + ) + return ( + cv2.cvtColor(image, cv2.COLOR_BGR2RGB), + np.nan_to_num(depth, nan=0.0, posinf=0.0, neginf=0.0), + stem, + ) + + def __len__(self) -> int: + """Return the number of paired validation samples.""" + + return len(self.samples) + + +def get_nyu_depth_loader( + dataset: CustomNYUDepth, + batch_size: int, + preprocess_fn: Callable, + image_size: tuple[int, int] | None = None, +) -> torch.utils.data.DataLoader: + """Create a NYU Depth loader with optional stretch-to-size validation preprocessing. + + Args: + dataset: Paired NYU Depth validation dataset. + batch_size: Number of samples per batch. + preprocess_fn: Preprocessing applied after an optional validation resize. + image_size: Optional ``(height, width)`` used to stretch RGB inputs with bilinear + interpolation and depth targets with nearest-neighbor interpolation. This + matches the Ultralytics depth validation pipeline. + + Returns: + Configured NYU Depth validation loader. + """ + + def loader( + batch: list[Any], + ) -> tuple[ + np.ndarray, list[np.ndarray], list[tuple[int, int]], list[Any], tuple[str, ...] + ]: + images, targets, stems = zip(*batch) + processed_images, shapes, ratio_pads = [], [], [] + processed_targets = [] + for image, target in zip(images, targets): + if image_size is not None: + height, width = image_size + image = cv2.resize( + image, (width, height), interpolation=cv2.INTER_LINEAR + ) + target = cv2.resize( + target, (width, height), interpolation=cv2.INTER_NEAREST + ) + shapes.append(tuple(image.shape[:2])) + processed = preprocess_fn(image) + if ( + isinstance(processed, tuple) + and len(processed) == 2 + and isinstance(processed[1], dict) + ): + processed_image, metadata = processed + ratio_pads.append(metadata.get("ratio_pad")) + else: + processed_image = processed + ratio_pads.append(None) + processed_images.append(processed_image) + processed_targets.append(target) + return np.stack(processed_images), processed_targets, shapes, ratio_pads, stems + + return torch.utils.data.DataLoader( + dataset, batch_size=batch_size, shuffle=False, num_workers=0, collate_fn=loader + ) + + +class CustomADE20K(torch.utils.data.Dataset[tuple[np.ndarray, np.ndarray, str]]): + """ADE20K validation dataset with paired RGB images and semantic PNG masks.""" + + IMG_EXTENSIONS = (".jpg", ".jpeg", ".png", ".bmp") + + def __init__(self, root: str) -> None: + """Validate the organizer's flat ``images/`` and ``annotations/`` layout.""" + + self.root = root + image_root = os.path.join(root, "images") + annotation_root = os.path.join(root, "annotations") + if not os.path.isdir(image_root) or not os.path.isdir(annotation_root): + raise FileNotFoundError( + f"ADE20K requires images/ and annotations/ directories under: {root}" + ) + images = { + os.path.splitext(name)[0]: os.path.join(image_root, name) + for name in os.listdir(image_root) + if name.lower().endswith(self.IMG_EXTENSIONS) + } + annotations = { + os.path.splitext(name)[0]: os.path.join(annotation_root, name) + for name in os.listdir(annotation_root) + if name.lower().endswith(".png") + } + missing_annotations = sorted(set(images) - set(annotations)) + missing_images = sorted(set(annotations) - set(images)) + if missing_annotations or missing_images: + details = [] + if missing_annotations: + details.append( + f"images without annotations: {', '.join(missing_annotations[:5])}" + ) + if missing_images: + details.append( + f"annotations without images: {', '.join(missing_images[:5])}" + ) + raise ValueError( + f"ADE20K image/annotation mismatch ({'; '.join(details)})." + ) + if not images: + raise ValueError(f"ADE20K contains no image/annotation pairs: {root}") + self.samples = [ + (images[stem], annotations[stem], stem) for stem in sorted(images) + ] + + def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: + """Load one RGB image and map its source labels to model class IDs.""" + + image_path, annotation_path, stem = self.samples[index] + image = cv2.imread(image_path, cv2.IMREAD_COLOR) + if image is None: + raise FileNotFoundError(f"ADE20K image not found: {image_path}") + annotation = cv2.imread(annotation_path, cv2.IMREAD_GRAYSCALE) + if annotation is None: + raise FileNotFoundError(f"ADE20K annotation not found: {annotation_path}") + if image.shape[:2] != annotation.shape: + raise ValueError( + f"ADE20K image and annotation shapes must match, got {image.shape[:2]} and {annotation.shape}: {stem}" + ) + if annotation.size and int(annotation.max()) > 150: + raise ValueError( + f"ADE20K annotation values must be in [0, 150]: {annotation_path}" + ) + target = np.full(annotation.shape, 255, dtype=np.uint8) + valid = annotation > 0 + target[valid] = annotation[valid] - 1 + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), target, stem + + def __len__(self) -> int: + """Return the number of paired validation samples.""" + + return len(self.samples) + + +def get_ade20k_loader( + dataset: CustomADE20K, + batch_size: int, + preprocess_fn: Callable, + image_size: tuple[int, int], +) -> torch.utils.data.DataLoader: + """Create an ADE20K loader that applies matching letterbox geometry to masks. + + Args: + dataset: Paired ADE20K validation dataset. + batch_size: Number of samples per batch. + preprocess_fn: Image preprocessing function that returns letterbox metadata. + image_size: Configured model input size as ``(height, width)``. + + Returns: + Configured ADE20K validation loader. + """ + + def loader( + batch: list[Any], + ) -> tuple[ + np.ndarray, np.ndarray, list[tuple[int, int]], list[Any], tuple[str, ...] + ]: + images, targets, stems = zip(*batch) + processed_images, processed_targets, shapes, ratio_pads = [], [], [], [] + input_height, input_width = image_size + for image, target in zip(images, targets): + shapes.append(tuple(image.shape[:2])) + processed = preprocess_fn(image) + if not ( + isinstance(processed, tuple) + and len(processed) == 2 + and isinstance(processed[1], dict) + ): + raise ValueError( + "ADE20K preprocessing must return image data and letterbox metadata." + ) + processed_image, metadata = processed + ratio_pad = metadata.get("ratio_pad") + if ratio_pad is None: + raise ValueError( + "ADE20K preprocessing requires LetterBox ratio_pad metadata." + ) + processed_target, target_ratio_pad = letterbox_semantic_mask( + target, + [input_height, input_width], + ) + if target_ratio_pad != ratio_pad: + raise ValueError( + "ADE20K image and mask LetterBox geometry do not match." + ) + processed_images.append(processed_image) + processed_targets.append(processed_target) + ratio_pads.append(ratio_pad) + return ( + np.stack(processed_images), + np.stack(processed_targets), + shapes, + ratio_pads, + stems, + ) + + return torch.utils.data.DataLoader( + dataset, batch_size=batch_size, shuffle=False, num_workers=0, collate_fn=loader + ) + + +class CustomCityscapes(torch.utils.data.Dataset[tuple[np.ndarray, np.ndarray, str]]): + """Cityscapes validation dataset with paired RGB images and source-ID masks.""" + + IMG_EXTENSIONS = (".jpg", ".jpeg", ".png", ".bmp") + + def __init__(self, root: str) -> None: + """Validate the organizer's flat ``images/`` and ``annotations/`` layout.""" + + self.root = root + image_root = os.path.join(root, "images") + annotation_root = os.path.join(root, "annotations") + if not os.path.isdir(image_root) or not os.path.isdir(annotation_root): + raise FileNotFoundError( + f"Cityscapes requires images/ and annotations/ directories under: {root}" + ) + images = { + os.path.splitext(name)[0]: os.path.join(image_root, name) + for name in os.listdir(image_root) + if name.lower().endswith(self.IMG_EXTENSIONS) + } + annotations = { + os.path.splitext(name)[0]: os.path.join(annotation_root, name) + for name in os.listdir(annotation_root) + if name.lower().endswith(".png") + } + missing_annotations = sorted(set(images) - set(annotations)) + missing_images = sorted(set(annotations) - set(images)) + if missing_annotations or missing_images: + details = [] + if missing_annotations: + details.append( + f"images without annotations: {', '.join(missing_annotations[:5])}" + ) + if missing_images: + details.append( + f"annotations without images: {', '.join(missing_images[:5])}" + ) + raise ValueError( + f"Cityscapes image/annotation mismatch ({'; '.join(details)})." + ) + if not images: + raise ValueError(f"Cityscapes contains no image/annotation pairs: {root}") + self.samples = [ + (images[stem], annotations[stem], stem) for stem in sorted(images) + ] + + def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: + """Load an image and map Cityscapes source IDs to contiguous train IDs.""" + + image_path, annotation_path, stem = self.samples[index] + image = cv2.imread(image_path, cv2.IMREAD_COLOR) + if image is None: + raise FileNotFoundError(f"Cityscapes image not found: {image_path}") + with Image.open(annotation_path) as annotation_image: + annotation = np.asarray(annotation_image) + if annotation.ndim == 3: + if annotation.shape[2] not in {3, 4} or not np.array_equal( + annotation[..., 0], annotation[..., 1] + ): + raise ValueError( + f"Cityscapes RGB annotation channels must contain identical source IDs: {annotation_path}" + ) + if not np.array_equal(annotation[..., 0], annotation[..., 2]): + raise ValueError( + f"Cityscapes RGB annotation channels must contain identical source IDs: {annotation_path}" + ) + annotation = annotation[..., 0] + if annotation.ndim != 2: + raise ValueError( + f"Cityscapes annotation must be grayscale or RGB-grayscale: {annotation_path}" + ) + if image.shape[:2] != annotation.shape: + raise ValueError( + "Cityscapes image and annotation shapes must match, " + f"got {image.shape[:2]} and {annotation.shape}: {stem}" + ) + if annotation.size and ( + int(annotation.min()) < 0 or int(annotation.max()) > 255 + ): + raise ValueError( + f"Cityscapes annotation values must be in [0, 255]: {annotation_path}" + ) + target = CITYSCAPES_SOURCE_TO_TRAIN_ID[annotation.astype(np.uint8)] + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), target, stem + + def __len__(self) -> int: + """Return the number of paired validation samples.""" + + return len(self.samples) + + +def get_cityscapes_loader( + dataset: CustomCityscapes, + batch_size: int, + preprocess_fn: Callable, + image_size: tuple[int, int], +) -> torch.utils.data.DataLoader: + """Create a Cityscapes loader with image-matching letterbox geometry.""" + + def loader( + batch: list[Any], + ) -> tuple[ + np.ndarray, np.ndarray, list[tuple[int, int]], list[Any], tuple[str, ...] + ]: + images, targets, stems = zip(*batch) + processed_images, processed_targets, shapes, ratio_pads = [], [], [], [] + input_height, input_width = image_size + for image, target in zip(images, targets): + shapes.append(tuple(image.shape[:2])) + processed = preprocess_fn(image) + if not ( + isinstance(processed, tuple) + and len(processed) == 2 + and isinstance(processed[1], dict) + ): + raise ValueError( + "Cityscapes preprocessing must return image data and letterbox metadata." + ) + processed_image, metadata = processed + ratio_pad = metadata.get("ratio_pad") + if ratio_pad is None: + raise ValueError( + "Cityscapes preprocessing requires LetterBox ratio_pad metadata." + ) + processed_target, target_ratio_pad = letterbox_semantic_mask( + target, + [input_height, input_width], + ) + if target_ratio_pad != ratio_pad: + raise ValueError( + "Cityscapes image and mask LetterBox geometry do not match." + ) + processed_images.append(processed_image) + processed_targets.append(processed_target) + ratio_pads.append(ratio_pad) + return ( + np.stack(processed_images), + np.stack(processed_targets), + shapes, + ratio_pads, + stems, + ) + + return torch.utils.data.DataLoader( + dataset, batch_size=batch_size, shuffle=False, num_workers=0, collate_fn=loader + ) + + +class CustomDOTAv1(torch.utils.data.Dataset[tuple[np.ndarray, str, int, int]]): + """Custom DOTAv1 validation dataset for OBB evaluation. + + Attributes: + root: DOTAv1 dataset root. + image_root: Directory containing validation images. + ids: Image IDs derived from file stems. + """ + + IMG_EXTENSIONS = (".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff") + + def __init__(self, root: str) -> None: + """Initializes the DOTAv1 validation dataset. + + Args: + root: DOTAv1 root containing flat ``images/`` or legacy + ``images/val`` validation images. + + Raises: + FileNotFoundError: If the validation image directory is missing. + ValueError: If neither supported layout contains validation images. + """ + self.root = root + self.image_root = os.path.join(root, "images") + if not os.path.isdir(self.image_root): + raise FileNotFoundError( + f"DOTAv1 image directory not found: {self.image_root}" + ) + self.image_paths = self._find_image_paths(self.image_root) + legacy_image_root = os.path.join(self.image_root, "val") + if not self.image_paths and os.path.isdir(legacy_image_root): + self.image_root = legacy_image_root + self.image_paths = self._find_image_paths(self.image_root) + if not self.image_paths: + raise ValueError( + f"DOTAv1 validation images not found directly under {os.path.join(root, 'images')} " + "or its legacy `val` subdirectory." + ) + self.ids = [ + os.path.splitext(os.path.basename(path))[0] for path in self.image_paths + ] + + def _find_image_paths(self, image_root: str) -> list[str]: + """Return supported image files directly under a DOTAv1 image directory.""" + + return [ + os.path.join(image_root, file_name) + for file_name in sorted(os.listdir(image_root)) + if file_name.lower().endswith(self.IMG_EXTENSIONS) + ] + + def _load_image(self, image_path: str) -> np.ndarray: + """Load an image as RGB.""" + image = cv2.imread(image_path) + if image is None: + raise FileNotFoundError(f"Image not found: {image_path}") + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + + def __getitem__(self, index: int) -> tuple[np.ndarray, str, int, int]: + """Get the image and metadata by index.""" + image_path = self.image_paths[index] + image = self._load_image(image_path) + height, width = image.shape[:2] + return image, self.ids[index], height, width + + def __len__(self) -> int: + """Return the number of validation images.""" + return len(self.image_paths) + + +def get_dota_loader( + dataset: CustomDOTAv1, batch_size: int, preprocess_fn: Callable +) -> torch.utils.data.DataLoader: + """Creates a DataLoader for DOTAv1 validation. + + Args: + dataset: The DOTAv1 dataset instance. + batch_size: Number of samples per batch. + preprocess_fn: Function used to preprocess images. + + Returns: + Configured DataLoader for DOTAv1. + """ + + def loader( + batch: list[Any], + ) -> tuple[np.ndarray, np.ndarray, list[Any], tuple[str, ...]]: + """Collate function for DOTAv1 DataLoader.""" + batch = list(filter(lambda x: x is not None, batch)) + images, image_ids, height, width = zip(*batch) + + processed_images = [] + ratio_pads = [] + for img in images: + processed = preprocess_fn(img) + if ( + isinstance(processed, tuple) + and len(processed) == 2 + and isinstance(processed[1], dict) + ): + processed_img, metadata = processed + ratio_pads.append(metadata.get("ratio_pad")) + else: + processed_img = processed + ratio_pads.append(None) + processed_images.append(processed_img) + + return ( + np.stack(processed_images, axis=0), + np.stack((np.array(height), np.array(width)), axis=1), + ratio_pads, + image_ids, + ) + + return torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=0, + collate_fn=loader, + ) + + +class CustomImageFolder(torch.utils.data.Dataset[tuple[Image.Image, int]]): + """Custom ImageFolder dataset for loading images from class-based directory structures. + + Expects data to be organized in the format: root/class_name/image.jpg. + + Attributes: + root (str): Root directory path. + classes (list[str]): List of class names found in the root directory. + class_to_idx (dict): Mapping from class name to class index. + samples (list[tuple]): List of (image_path, class_index) tuples. + """ + + def __init__(self, root: str) -> None: + """Initializes the CustomImageFolder instance. + + Args: + root (str): Path to the root directory. + """ + self.root = root + self.classes, self.class_to_idx = self.find_classes(root) + self.samples: list[tuple[str, int]] = [] + self.make_dataset() + + def make_dataset(self) -> None: + """Scans the root directory to create a list of samples.""" + instances = [] + for target_class in sorted(self.class_to_idx.keys()): + class_index = self.class_to_idx[target_class] + target_dir = os.path.join(self.root, target_class) + if not os.path.isdir(target_dir): + continue + for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): + for fname in sorted(fnames): + path = os.path.join(root, fname) + item = path, class_index + instances.append(item) + + self.samples = instances + + def loader(self, path: str) -> Image.Image: + """Load image from path using PIL.""" + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + + def find_classes(self, directory: str) -> tuple[list[str], dict[str, int]]: + """Find classes in the specified directory.""" + classes = sorted([d.name for d in os.scandir(directory) if d.is_dir()]) + class_to_idx = {cls: i for i, cls in enumerate(classes)} + return classes, class_to_idx + + def __getitem__(self, index: int) -> tuple[Image.Image, int]: + """ + Get sample and target at the specified index. + Args: + index (int): Index of the sample to retrieve. + Returns: + tuple: (sample, target) where sample is the loaded image and target is the class index. + """ + path, target = self.samples[index] + sample = self.loader(path) + return sample, target + + def __len__(self) -> int: + """ + Return the total number of samples. + Returns: + int: Number of samples in the dataset. + """ + return len(self.samples) + + +def get_imagenet_loader( + dataset: CustomImageFolder, batch_size: int, preprocess_fn: Callable +) -> torch.utils.data.DataLoader: + """Creates a DataLoader for the ImageNet dataset. + + Args: + dataset (CustomImageFolder): The dataset instance to load from. + batch_size (int): Number of samples per batch. + preprocess_fn (Callable): Function used to preprocess images. + + Returns: + torch.utils.data.DataLoader: A configured DataLoader for the ImageNet dataset. + """ + + def loader(batch: list[Any]) -> tuple[np.ndarray, np.ndarray]: + """Collate function for ImageNet DataLoader.""" + batch = list(filter(lambda x: x is not None, batch)) # remove None + images, labels = zip(*batch) + processed_images = [] + for img in images: + img = preprocess_fn(img) + processed_images.append(img) + + return ( + np.stack(processed_images, axis=0), + np.array(labels), + ) # BHWC, labels + + return torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=0, + collate_fn=loader, + ) + + +class CustomWiderFaceDataset(torch.utils.data.Dataset[tuple[np.ndarray, str, str]]): + """Custom dataset class for the WiderFace dataset. + + Attributes: + root (str): Path to the root directory containing WiderFace images. + classes (list[str]): List of class/event names found in the root. + samples (list[tuple]): List of (image_path, class_name, file_name) tuples. + """ + + def __init__(self, root: str) -> None: + """Initialize the custom WiderFace dataset. + + Args: + root (str): Path to the directory containing WiderFace images. + """ + self.root = root + self.classes = self.find_classes(root) + self.samples: list[tuple[str, str, str]] = [] + self.make_dataset() + + def make_dataset(self) -> None: + """Scans the root directory to create a list of samples.""" + instances = [] + for target_class in self.classes: + target_dir = os.path.join(self.root, target_class) + if not os.path.isdir(target_dir): + continue + for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): + for fname in sorted(fnames): + path = os.path.join(root, fname) + item = path, target_class, fname + instances.append(item) + + self.samples = instances + + def loader(self, image_path: str) -> np.ndarray: + """Load image by image path""" + image = cv2.imread(image_path) # Load image (BGR format) + if image is None: + raise FileNotFoundError(f"Image not found: {image_path}") + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert to RGB + + def find_classes(self, directory: str) -> list[str]: + """Find classes in the specified directory.""" + unsorted_classes = [d.name for d in os.scandir(directory) if d.is_dir()] + class_to_idx = {} + for cls_name in unsorted_classes: + cls_idx = int(cls_name.split("--")[0]) + class_to_idx[cls_name] = cls_idx + + sorted_classes = sorted( + class_to_idx.keys(), key=lambda x: class_to_idx[x] + ) # sort by dictionary value with ascending order + + return sorted_classes + + def __getitem__(self, index: int) -> tuple[np.ndarray, str, str]: + """ + Get the image and target by index. + Args: + index (int): Index of the sample to retrieve. + Returns: + tuple: (image, target_class, fname) where image is the loaded image in RGB format. + """ + image_path, target_class, fname = self.samples[index] + image = self.loader(image_path) + + return image, target_class, fname + + def __len__(self) -> int: + """ + Return the total number of images. + Returns: + int: Number of images in the dataset. + """ + return len(self.samples) + + +CustomWiderface = CustomWiderFaceDataset + + +def get_widerface_loader( + dataset: CustomWiderFaceDataset, batch_size: int, preprocess_fn: Callable +) -> torch.utils.data.DataLoader: + """Creates a DataLoader for the WiderFace dataset. + + Args: + dataset (CustomWiderFaceDataset): The dataset instance to load from. + batch_size (int): Number of samples per batch. + preprocess_fn (Callable): Function used to preprocess images. + + Returns: + torch.utils.data.DataLoader: A configured DataLoader for the WiderFace dataset. + """ + + def loader( + batch: list[Any], + ) -> tuple[ + np.ndarray, np.ndarray, list[Any | None], tuple[str, ...], tuple[str, ...] + ]: + """Collate function for WiderFace DataLoader.""" + batch = list(filter(lambda x: x is not None, batch)) + images, target_classes, fnames = zip(*batch) + processed_images = [] + heights = [] + widths = [] + ratio_pads = [] + for img in images: + height, width = img.shape[:2] + processed = preprocess_fn(img) + if isinstance(processed, tuple): + processed_img, metadata = processed + ratio_pads.append(metadata.get("ratio_pad")) + else: + processed_img = processed + ratio_pads.append(None) + processed_images.append(processed_img) + heights.append(height) + widths.append(width) + + return ( + np.stack(processed_images, axis=0), + np.stack((heights, widths), axis=1), + ratio_pads, + target_classes, + fnames, + ) + + return torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=0, + collate_fn=loader, + ) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py new file mode 100644 index 0000000..e1205df --- /dev/null +++ b/mblt_vision/utils/datasets/organizer.py @@ -0,0 +1,1567 @@ +""" +Utilities for organizing datasets. +""" + +from __future__ import annotations + +import concurrent.futures +import os +import re +import shutil +import stat +import tarfile +import xml.etree.ElementTree as ET +import zipfile +from collections.abc import Iterable +from pathlib import Path +from tempfile import TemporaryDirectory, mkdtemp +from time import sleep +from typing import Protocol, TypeGuard +from urllib.parse import urlparse + +import requests +from gdown.download import download +from gdown.download_folder import download_folder +from PIL import Image +from tqdm import tqdm + +from ...datasets import get_dataset_config +from .readiness import ( + ADE20K_METADATA_FILES, + ADE20K_VALIDATION_SAMPLE_COUNT, + CITYSCAPES_SAMPLE_ID_PATTERN, + CITYSCAPES_VALIDATION_SAMPLE_COUNT, + DOTAV1_VALIDATION_SAMPLE_COUNT, + NYU_DEPTH_VALIDATION_SAMPLE_COUNT, + _path_has_symlink_component, + dataset_ready, +) + +DOWNLOAD_CHUNK_SIZE = 1 * 1024 * 1024 +DOWNLOAD_RETRY_LIMIT = 4 +DOWNLOAD_RETRY_BACKOFF_SECONDS = 2.0 +DOWNLOAD_TIMEOUT = (10, 30) +DOTAV1_DOWNLOAD_CONFIG = get_dataset_config("dotav1")["download"] +DOTAV1_GOOGLE_DRIVE_ARCHIVES = { + DOTAV1_DOWNLOAD_CONFIG["images_archive"], + DOTAV1_DOWNLOAD_CONFIG["labels_archive"], +} +DOTAV1_CLASS_TO_IDX = { + name: int(index) for index, name in get_dataset_config("dotav1")["names"].items() +} +NYU_DEPTH_URL = ( + "https://github.com/ultralytics/assets/releases/download/v0.0.0/nyu-depth.zip" +) +ADE20K_URL = "http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip" +CITYSCAPES_IMAGE_SUFFIX = "_leftImg8bit.png" +CITYSCAPES_ANNOTATION_SUFFIX = "_gtFine_labelIds.png" + + +def _replace_staged_directories( + replacements: Iterable[tuple[str, str]], + output_parent_dir: str, + backup_prefix: str, +) -> None: + """Atomically install staged directories while preserving failed rollback backups. + + Args: + replacements: Pairs of staged and destination directories. + output_parent_dir: Parent directory where the backup directory is created. + backup_prefix: Prefix identifying the temporary backup directory. + + Raises: + OSError: If installation or rollback fails. A failed rollback leaves its + backup directory in place and includes its path in the error. + """ + + replacement_list = list(replacements) + backup_dir = mkdtemp(dir=output_parent_dir, prefix=backup_prefix) + backups: dict[str, str] = {} + installed_dirs: list[str] = [] + try: + for _, destination_dir in replacement_list: + if os.path.lexists(destination_dir): + backup_path = os.path.join( + backup_dir, os.path.basename(destination_dir) + ) + os.replace(destination_dir, backup_path) + backups[destination_dir] = backup_path + for staged_dir, destination_dir in replacement_list: + os.makedirs(os.path.dirname(destination_dir), exist_ok=True) + os.replace(staged_dir, destination_dir) + installed_dirs.append(destination_dir) + except OSError: + try: + for directory in installed_dirs: + if os.path.isdir(directory) and not os.path.islink(directory): + shutil.rmtree(directory) + elif os.path.lexists(directory): + os.remove(directory) + for destination_dir, backup_path in backups.items(): + os.makedirs(os.path.dirname(destination_dir), exist_ok=True) + os.replace(backup_path, destination_dir) + except OSError as rollback_error: + raise OSError( + f"Dataset installation rollback failed; backups are preserved at {backup_dir}." + ) from rollback_error + shutil.rmtree(backup_dir) + raise + shutil.rmtree(backup_dir) + + +def _validate_staged_dataset( + staged_output_dir: str, + dataset: str, + tasks: Iterable[str], +) -> None: + """Validate a complete staged dataset before replacing its managed cache. + + Args: + staged_output_dir: Root of the staged organized dataset. + dataset: Validation dataset taxonomy. + tasks: Tasks whose required metadata and files must all be ready. + + Raises: + ValueError: If the staged dataset is incomplete or has mismatched identity. + """ + + if all(dataset_ready(staged_output_dir, task, dataset) for task in tasks): + return + raise ValueError( + f"Staged {dataset} validation dataset is incomplete or has mismatched metadata; " + "the existing dataset cache was not replaced." + ) + + +class _GoogleDriveDownloadEntry(Protocol): + """The public attributes needed from a gdown folder-listing entry.""" + + id: str + path: str + + +def _is_google_drive_download_entry( + value: object, +) -> TypeGuard[_GoogleDriveDownloadEntry]: + """Returns whether a folder-listing value has the Google Drive file attributes needed here.""" + + return isinstance(getattr(value, "id", None), str) and isinstance( + getattr(value, "path", None), str + ) + + +def _is_url(path_or_url: str) -> bool: + """Returns whether the given string looks like an HTTP(S) URL.""" + parsed = urlparse(path_or_url) + return parsed.scheme in {"http", "https"} and bool(parsed.netloc) + + +def _download_url(url: str, local_path: str) -> str: + """Downloads a URL to a local file with progress and resume support. + + Args: + url: HTTP(S) URL to download. + local_path: Destination file path. + + Returns: + The local destination path. + + Raises: + RuntimeError: If all download attempts fail. + """ + os.makedirs(os.path.dirname(local_path), exist_ok=True) + + for attempt in range(1, DOWNLOAD_RETRY_LIMIT + 1): + existing_size = os.path.getsize(local_path) if os.path.exists(local_path) else 0 + headers: dict[str, str] = {} + mode = "wb" + if existing_size > 0: + headers["Range"] = f"bytes={existing_size}-" + mode = "ab" + + try: + with requests.get( + url, stream=True, timeout=DOWNLOAD_TIMEOUT, headers=headers + ) as response: + response.raise_for_status() + + if response.status_code == 200 and existing_size > 0: + existing_size = 0 + mode = "wb" + + total_size = response.headers.get("Content-Length") + total_bytes = ( + existing_size + int(total_size) if total_size is not None else None + ) + + desc = f"Downloading {os.path.basename(local_path)}" + with tqdm( + total=total_bytes, + initial=existing_size, + unit="B", + unit_scale=True, + unit_divisor=1024, + desc=desc, + ) as pbar: + with open(local_path, mode) as file_obj: + for chunk in response.iter_content( + chunk_size=DOWNLOAD_CHUNK_SIZE + ): + if not chunk: + continue + file_obj.write(chunk) + pbar.update(len(chunk)) + return local_path + except ( + requests.ConnectionError, + requests.Timeout, + requests.exceptions.ChunkedEncodingError, + ) as exc: + if attempt == DOWNLOAD_RETRY_LIMIT: + raise RuntimeError( + f"Failed to download {url} after {DOWNLOAD_RETRY_LIMIT} attempts." + ) from exc + resumed_size = ( + os.path.getsize(local_path) if os.path.exists(local_path) else 0 + ) + print( + f"Download interrupted for {os.path.basename(local_path)}; " + f"retrying from {resumed_size} bytes (attempt {attempt + 1}/{DOWNLOAD_RETRY_LIMIT})..." + ) + sleep(DOWNLOAD_RETRY_BACKOFF_SECONDS * attempt) + + raise RuntimeError( + f"Failed to download {url} after {DOWNLOAD_RETRY_LIMIT} attempts." + ) + + +def _should_download_serially(path_or_urls: list[str]) -> bool: + """Returns whether URL inputs should be downloaded one by one. + + Dataset hosts such as ImageNet often throttle concurrent archive downloads + from the same origin. Serializing same-host downloads is slower in the best + case, but much more stable for the large validation archives used here. + """ + + hosts = [ + urlparse(path_or_url).netloc + for path_or_url in path_or_urls + if _is_url(path_or_url) + ] + return len(hosts) > 1 and len(set(hosts)) == 1 + + +def _download_if_url(path_or_url: str, download_dir: str) -> str: + """Downloads a remote dataset archive when needed. + + Args: + path_or_url: Local path or HTTP(S) URL pointing to a dataset archive. + download_dir: Directory to store downloaded archives. + + Returns: + A local filesystem path to the archive or directory. + + Raises: + ValueError: If the URL path does not contain a filename. + """ + if not _is_url(path_or_url): + return path_or_url + + parsed = urlparse(path_or_url) + filename = os.path.basename(parsed.path) + if not filename: + raise ValueError(f"Unable to determine a filename from URL: {path_or_url}") + + local_path = os.path.join(download_dir, filename) + print(f"Downloading dataset archive from {path_or_url} to {local_path}...") + _download_url(path_or_url, local_path) + print("Download completed") + return local_path + + +def _resolve_source(path_or_url: str, download_dir: str) -> str: + """Resolves a local path for a dataset source.""" + + return _download_if_url(path_or_url, download_dir) + + +def _resolve_sources(path_or_urls: list[str], download_dir: str) -> list[str]: + """Resolves multiple dataset sources, downloading URL inputs in parallel.""" + + if _should_download_serially(path_or_urls): + return [ + _resolve_source(path_or_url, download_dir) for path_or_url in path_or_urls + ] + + local_paths: list[str | None] = [None] * len(path_or_urls) + futures: dict[concurrent.futures.Future[str], int] = {} + with concurrent.futures.ThreadPoolExecutor( + max_workers=min(4, len(path_or_urls)) + ) as executor: + for idx, path_or_url in enumerate(path_or_urls): + if _is_url(path_or_url): + futures[executor.submit(_resolve_source, path_or_url, download_dir)] = ( + idx + ) + else: + local_paths[idx] = path_or_url + + for future in concurrent.futures.as_completed(futures): + local_paths[futures[future]] = future.result() + + return [path for path in local_paths if path is not None] + + +def _get_object_name(obj: ET.Element, xml_file: str) -> str: + """Extracts a non-empty object name from an ImageNet annotation node. + + Args: + obj: XML ``object`` element from an annotation file. + xml_file: Source XML filename used for error context. + + Returns: + The validated object name. + + Raises: + ValueError: If the object name node is missing or empty. + """ + name_element = obj.find("name") + if name_element is None or name_element.text is None: + raise ValueError(f"XML file {xml_file} has an object without a valid name") + + object_name = name_element.text.strip() + if not object_name: + raise ValueError(f"XML file {xml_file} has an object with an empty name") + + return object_name + + +def construct_imagenet(image_dir: str, xml_dir: str, output_dir: str) -> None: + """Constructs the ImageNet dataset by organizing images into category folders. + + Args: + image_dir (str): Directory containing the ImageNet validation images. + xml_dir (str): Directory containing the ImageNet bounding box XML files. + output_dir (str): Directory where the organized dataset will be stored. + + Raises: + ValueError: If an XML file has no objects or contains multiple object names. + ValueError: If the number of XML files and images do not match. + """ + + xml_count = len(os.listdir(xml_dir + "/val")) + image_count = len(os.listdir(image_dir)) + if xml_count != image_count: + raise ValueError( + f"Number of XML and image files do not match: {xml_count} != {image_count}." + ) + + # validate the XML files + pbar = tqdm(os.listdir(xml_dir + "/val"), desc="Validating XML files") + for xml_file in pbar: + xml_path = os.path.join(xml_dir + "/val", xml_file) + xml_tree = ET.parse(xml_path) + root = xml_tree.getroot() + + if len(root.findall("object")) < 1: + raise ValueError( + f"XML file {xml_file} has no object, but expected at least 1" + ) + + # check whether the object names in the XML files are the same + object_names = [ + _get_object_name(obj, xml_file) for obj in root.findall("object") + ] + if len(set(object_names)) != 1: + raise ValueError( + f"Object names in XML file {xml_file} are not the same. " + f"It has {len(set(object_names))} different object names." + ) + + pbar.close() + + output_dir = os.path.abspath(output_dir) + output_parent_dir = os.path.dirname(output_dir) + os.makedirs(output_parent_dir, exist_ok=True) + with TemporaryDirectory( + dir=output_parent_dir, prefix=".imagenet-staging-" + ) as staging_dir: + staged_output_dir = os.path.join(staging_dir, "imagenet") + + # construct the ImageNet dataset + pbar = tqdm(os.listdir(xml_dir + "/val"), desc="Constructing ImageNet dataset") + for xml_file in pbar: + xml_path = os.path.join(xml_dir + "/val", xml_file) + xml_tree = ET.parse(xml_path) + root = xml_tree.getroot() + object_name = _get_object_name(root.findall("object")[0], xml_file) + image_path = os.path.join(image_dir, xml_file.replace(".xml", ".JPEG")) + if not os.path.isfile(image_path): + raise FileNotFoundError(f"Image file not found: {image_path}") + + os.makedirs(os.path.join(staged_output_dir, object_name), exist_ok=True) + shutil.copy( + image_path, + os.path.join( + staged_output_dir, object_name, os.path.basename(image_path) + ), + ) + pbar.close() + + # validate the staged ImageNet dataset before replacing the managed output root + pbar = tqdm(os.listdir(staged_output_dir), desc="Validating ImageNet dataset") + print(f"Number of categories: {len(os.listdir(staged_output_dir))}") + for object_name in pbar: + num_images = len(os.listdir(os.path.join(staged_output_dir, object_name))) + if num_images != 50: + raise ValueError( + f"Object {object_name} has {num_images} images, but expected 50" + ) + pbar.close() + _validate_staged_dataset( + staged_output_dir, "imagenet", ("image_classification",) + ) + _replace_staged_directories( + ((staged_output_dir, output_dir),), + output_parent_dir, + ".imagenet-backup-", + ) + print("Each category has 50 images") + print("ImageNet dataset constructed successfully") + + +def organize_imagenet( + image_dir: str, + xml_dir: str, + output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/imagenet"), +) -> None: + """Organizes the ImageNet dataset, unpacking archives if necessary. + + Args: + image_dir (str): Path or URL to the image directory or archive (.tar). + xml_dir (str): Path or URL to the XML directory or archive (.tgz). + output_dir (str, optional): Directory to store the organized dataset. + Defaults to ~/.mblt_vision/datasets/imagenet. + """ + with TemporaryDirectory() as temp_dir: + local_image_dir, local_xml_dir = _resolve_sources( + [image_dir, xml_dir], temp_dir + ) + + if local_image_dir.endswith(".tar") and local_xml_dir.endswith(".tgz"): + print("Unpacking image and XML files to temporary directory...") + _safe_unpack_archive( + local_image_dir, os.path.join(temp_dir, "ILSVRC2012_img_val") + ) + _safe_unpack_archive( + local_xml_dir, os.path.join(temp_dir, "ILSVRC2012_bbox_val_v3") + ) + print("Unpacking completed") + construct_imagenet( + os.path.join(temp_dir, "ILSVRC2012_img_val"), + os.path.join(temp_dir, "ILSVRC2012_bbox_val_v3"), + output_dir, + ) + return + + construct_imagenet(local_image_dir, local_xml_dir, output_dir) + + +def construct_coco(image_dir: str, annotation_dir: str, output_dir: str) -> None: + """Constructs the COCO dataset by copying images and annotations to a target directory. + + Args: + image_dir (str): Directory containing COCO images. + annotation_dir (str): Directory containing COCO annotations. + output_dir (str): Directory where the organized dataset will be stored. + """ + print( + f"Constructing COCO dataset from {image_dir} and {annotation_dir} to {output_dir}" + ) + output_dir = os.path.abspath(output_dir) + output_parent_dir = os.path.dirname(output_dir) + os.makedirs(output_parent_dir, exist_ok=True) + with TemporaryDirectory( + dir=output_parent_dir, prefix=".coco-staging-" + ) as staging_dir: + staged_output_dir = os.path.join(staging_dir, "coco") + shutil.copytree(image_dir, os.path.join(staged_output_dir, "val2017")) + for file in os.listdir(os.path.join(annotation_dir, "annotations")): + if file.endswith("_val2017.json"): + shutil.copy( + os.path.join(annotation_dir, "annotations", file), + os.path.join(staged_output_dir, file), + ) + _validate_staged_dataset( + staged_output_dir, + "coco", + ("object_detection", "pose_estimation"), + ) + _replace_staged_directories( + ((staged_output_dir, output_dir),), + output_parent_dir, + ".coco-backup-", + ) + print("Constructing COCO dataset completed") + + +def organize_coco( + image_dir: str, + annotation_dir: str, + output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/coco"), +) -> None: + """Organizes the COCO dataset, unpacking archives if necessary. + + Args: + image_dir (str): Path or URL to the image zip file or directory. + annotation_dir (str): Path or URL to the annotation zip file or directory. + output_dir (str, optional): Directory to store the organized dataset. + Defaults to ~/.mblt_vision/datasets/coco. + """ + with TemporaryDirectory() as temp_dir: + local_image_dir, local_annotation_dir = _resolve_sources( + [image_dir, annotation_dir], temp_dir + ) + + if local_image_dir.endswith(".zip") and local_annotation_dir.endswith(".zip"): + print("Unpacking image and annotation files to temporary directory...") + _safe_unpack_archive(local_image_dir, temp_dir) + _safe_unpack_archive( + local_annotation_dir, os.path.join(temp_dir, "annotations_trainval2017") + ) + print("Unpacking completed") + construct_coco( + os.path.join(temp_dir, "val2017"), + os.path.join(temp_dir, "annotations_trainval2017"), + output_dir, + ) + return + + construct_coco(local_image_dir, local_annotation_dir, output_dir) + + +def construct_widerface(image_dir: str, annotation_dir: str, output_dir: str) -> None: + """Constructs the WiderFace dataset by copying images and annotations to a target directory. + + Args: + image_dir (str): Directory containing WiderFace images. + annotation_dir (str): Directory containing WiderFace annotations. + output_dir (str): Directory where the organized dataset will be stored. + """ + print( + f"Constructing WiderFace dataset from {image_dir} and {annotation_dir} to {output_dir}" + ) + output_dir = os.path.abspath(output_dir) + output_parent_dir = os.path.dirname(output_dir) + os.makedirs(output_parent_dir, exist_ok=True) + with TemporaryDirectory( + dir=output_parent_dir, prefix=".widerface-staging-" + ) as staging_dir: + staged_output_dir = os.path.join(staging_dir, "widerface") + shutil.copytree( + os.path.join(image_dir, "images"), + os.path.join(staged_output_dir, "images"), + ) + for file in os.listdir(annotation_dir): + if "_val" in file: + shutil.copy(os.path.join(annotation_dir, file), staged_output_dir) + _validate_staged_dataset(staged_output_dir, "widerface", ("face_detection",)) + _replace_staged_directories( + ((staged_output_dir, output_dir),), + output_parent_dir, + ".widerface-backup-", + ) + print("Constructing WiderFace dataset completed") + + +def organize_widerface( + image_dir: str, + annotation_dir: str, + output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/widerface"), +) -> None: + """Organizes the WiderFace dataset, unpacking archives if necessary. + + Args: + image_dir (str): Path or URL to the image zip file or directory. + annotation_dir (str): Path or URL to the annotation zip file or directory. + output_dir (str, optional): Directory to store the organized dataset. + Defaults to ~/.mblt_vision/datasets/widerface. + """ + with TemporaryDirectory() as temp_dir: + local_image_dir, local_annotation_dir = _resolve_sources( + [image_dir, annotation_dir], temp_dir + ) + + if local_image_dir.endswith(".zip") and local_annotation_dir.endswith(".zip"): + print("Unpacking image and annotation files to temporary directory...") + _safe_unpack_archive(local_image_dir, temp_dir) + _safe_unpack_archive(local_annotation_dir, temp_dir) + print("Unpacking completed") + construct_widerface( + os.path.join(temp_dir, "WIDER_val"), + os.path.join(temp_dir, "wider_face_split"), + output_dir, + ) + return + + construct_widerface(local_image_dir, local_annotation_dir, output_dir) + + +def _resolve_nyu_depth_validation_dirs(dataset_dir: str) -> tuple[str, str, str]: + """Resolves NYU Depth validation image and depth directories. + + Args: + dataset_dir: Directory containing the NYU Depth root or its parent. + + Returns: + Paths to the selected dataset root, validation image directory, and + validation depth directory. + + Raises: + ValueError: If the expected NYU Depth layout is not present. + """ + + roots = (os.path.join(dataset_dir, "nyu-depth"), dataset_dir) + for root in roots: + candidates = ( + (os.path.join(root, "images", "val"), os.path.join(root, "depth", "val")), + (os.path.join(root, "val", "images"), os.path.join(root, "val", "depth")), + (os.path.join(root, "images"), os.path.join(root, "depth")), + ) + for image_dir, depth_dir in candidates: + if os.path.isdir(image_dir) and os.path.isdir(depth_dir): + return root, image_dir, depth_dir + raise ValueError( + f"NYU Depth dataset must contain matching images/ and depth/ directories: {dataset_dir}" + ) + + +def _validate_dense_source_file(source_path: str, dataset_root: Path) -> str: + """Resolve a non-symlink regular file contained by a dense dataset root. + + Args: + source_path: Candidate data or metadata file. + dataset_root: Resolved root of the extracted dataset. + + Returns: + Resolved source path safe to copy. + + Raises: + ValueError: If the source is a symlink, is not a regular file, cannot be + resolved, or escapes the dataset root. + """ + + source = Path(source_path) + if source.is_symlink(): + raise ValueError(f"Dense dataset source file must not be a symlink: {source}.") + try: + resolved_source = source.resolve(strict=True) + except OSError as exc: + raise ValueError( + f"Unable to resolve dense dataset source file {source}: {exc}." + ) from exc + if not resolved_source.is_file(): + raise ValueError(f"Dense dataset source must be a regular file: {source}.") + if not resolved_source.is_relative_to(dataset_root): + raise ValueError( + f"Dense dataset source must remain within dataset root: {source}." + ) + return str(resolved_source) + + +def _validate_dense_output_root( + output_dir: str, + dataset_name: str, + layout_names: Iterable[str], +) -> str: + """Reject symlinks in a dense managed root before organization. + + Args: + output_dir: Requested managed dataset root. + dataset_name: Human-readable dataset name for error reporting. + layout_names: Dataset-specific directories managed below the root. + + Returns: + Expanded absolute output path. + + Raises: + ValueError: If the managed root, an ancestor, or a managed layout + directory is a symlink. + """ + + requested_path = Path(output_dir).expanduser() + output_path = Path(os.path.abspath(requested_path)) + if _path_has_symlink_component(requested_path): + raise ValueError( + f"{dataset_name} output directory and its existing parents must not be symlinks: {output_path}. " + "Remove the symlink or choose a path beneath regular directories." + ) + for layout_name in layout_names: + layout_path = output_path / layout_name + if layout_path.is_symlink(): + raise ValueError( + f"{dataset_name} output layout directories must not be symlinks: {layout_path}. " + "Remove the symlink or choose a different output directory." + ) + return str(output_path) + + +def _collect_nyu_depth_validation_files( + image_dir: str, + depth_dir: str, + dataset_root: Path, +) -> tuple[dict[str, str], dict[str, str]]: + """Validates and returns matching NYU Depth validation image/depth pairs.""" + + images = { + os.path.splitext(os.path.basename(path))[0]: _validate_dense_source_file( + path, dataset_root + ) + for path in _iter_files(image_dir, [".jpg", ".jpeg", ".png"]) + } + depths = { + os.path.splitext(os.path.basename(path))[0]: _validate_dense_source_file( + path, dataset_root + ) + for path in _iter_files(depth_dir, [".npy"]) + } + missing_depths = sorted(set(images) - set(depths)) + missing_images = sorted(set(depths) - set(images)) + if missing_depths or missing_images: + details = [] + if missing_depths: + details.append( + f"images without depth maps: {', '.join(missing_depths[:5])}" + ) + if missing_images: + details.append( + f"depth maps without images: {', '.join(missing_images[:5])}" + ) + raise ValueError( + f"NYU Depth validation image/depth mismatch ({'; '.join(details)})." + ) + if len(images) != NYU_DEPTH_VALIDATION_SAMPLE_COUNT: + raise ValueError( + "NYU Depth validation dataset must contain " + f"{NYU_DEPTH_VALIDATION_SAMPLE_COUNT} matching image/depth pairs, found {len(images)}." + ) + return images, depths + + +def construct_nyu_depth(dataset_dir: str, output_dir: str) -> None: + """Constructs the NYU Depth layout from an extracted dataset directory. + + Args: + dataset_dir: Directory containing the NYU Depth root or its parent. + output_dir: Directory where the organized dataset will be stored. + """ + + output_dir = _validate_dense_output_root( + output_dir, "NYU Depth", ("images", "depth") + ) + selected_root, image_dir, depth_dir = _resolve_nyu_depth_validation_dirs( + dataset_dir + ) + try: + dataset_root = Path(selected_root).resolve(strict=True) + except OSError as exc: + raise ValueError( + f"Unable to resolve NYU Depth dataset root {selected_root}: {exc}." + ) from exc + images, depths = _collect_nyu_depth_validation_files( + image_dir, depth_dir, dataset_root + ) + print( + f"Constructing NYU Depth validation dataset from {dataset_dir} to {output_dir}" + ) + + output_parent_dir = os.path.dirname(output_dir) + os.makedirs(output_parent_dir, exist_ok=True) + with TemporaryDirectory( + dir=output_parent_dir, prefix=".nyu-depth-staging-" + ) as staging_dir: + staged_image_dir = os.path.join(staging_dir, "images") + staged_depth_dir = os.path.join(staging_dir, "depth") + os.makedirs(staged_image_dir) + os.makedirs(staged_depth_dir) + for sample_id in sorted(images): + shutil.copy2( + images[sample_id], + os.path.join(staged_image_dir, os.path.basename(images[sample_id])), + ) + shutil.copy2( + depths[sample_id], + os.path.join(staged_depth_dir, os.path.basename(depths[sample_id])), + ) + + replacements = ( + (staged_image_dir, os.path.join(output_dir, "images")), + (staged_depth_dir, os.path.join(output_dir, "depth")), + ) + _replace_staged_directories( + replacements, output_parent_dir, ".nyu-depth-backup-" + ) + print( + f"Constructed NYU Depth validation dataset with {len(images)} image/depth pairs" + ) + + +def organize_nyu_depth( + dataset_path: str = NYU_DEPTH_URL, + output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/nyu-depth"), +) -> None: + """Organizes NYU Depth, downloading and unpacking an archive when necessary. + + Args: + dataset_path: Path or URL to the NYU Depth zip file or extracted dataset directory. + output_dir: Directory to store the organized dataset. + """ + + output_dir = _validate_dense_output_root( + output_dir, "NYU Depth", ("images", "depth") + ) + with TemporaryDirectory() as temp_dir: + local_dataset_path = _resolve_source(dataset_path, temp_dir) + if local_dataset_path.endswith(".zip"): + print("Unpacking NYU Depth files to temporary directory...") + _safe_unpack_archive(local_dataset_path, temp_dir) + print("Unpacking completed") + construct_nyu_depth(temp_dir, output_dir) + return + + construct_nyu_depth(local_dataset_path, output_dir) + + +def _resolve_ade20k_validation_dirs(dataset_dir: str) -> tuple[str, str, str]: + """Resolves the ADE20K root and validation image/mask directories.""" + + for root in (os.path.join(dataset_dir, "ADEChallengeData2016"), dataset_dir): + for image_dir, annotation_dir in ( + ( + os.path.join(root, "images", "validation"), + os.path.join(root, "annotations", "validation"), + ), + (os.path.join(root, "images"), os.path.join(root, "annotations")), + ): + if os.path.isdir(image_dir) and os.path.isdir(annotation_dir): + return root, image_dir, annotation_dir + raise ValueError( + f"ADE20K dataset must contain matching images/ and annotations/ directories: {dataset_dir}" + ) + + +def construct_ade20k(dataset_dir: str, output_dir: str) -> None: + """Constructs the flat ADE20K validation layout from an extracted dataset. + + Args: + dataset_dir: Directory containing the ADE20K root or its parent. + output_dir: Directory where the organized validation dataset will be stored. + + Raises: + ValueError: If the source does not contain 2,000 matched validation image/mask pairs. + """ + + output_dir = _validate_dense_output_root( + output_dir, "ADE20K", ("images", "annotations") + ) + dataset_root, image_dir, annotation_dir = _resolve_ade20k_validation_dirs( + dataset_dir + ) + try: + resolved_dataset_root = Path(dataset_root).resolve(strict=True) + except OSError as exc: + raise ValueError( + f"Unable to resolve ADE20K dataset root {dataset_root}: {exc}." + ) from exc + images = { + os.path.splitext(file_name)[0]: _validate_dense_source_file( + os.path.join(image_dir, file_name), + resolved_dataset_root, + ) + for file_name in os.listdir(image_dir) + if file_name.startswith("ADE_val_") and file_name.lower().endswith(".jpg") + } + annotations = { + os.path.splitext(file_name)[0]: _validate_dense_source_file( + os.path.join(annotation_dir, file_name), + resolved_dataset_root, + ) + for file_name in os.listdir(annotation_dir) + if file_name.startswith("ADE_val_") and file_name.lower().endswith(".png") + } + if set(images) != set(annotations): + raise ValueError( + "ADE20K validation images and annotations must have matching file stems." + ) + if len(images) != ADE20K_VALIDATION_SAMPLE_COUNT: + raise ValueError( + f"ADE20K validation dataset must contain {ADE20K_VALIDATION_SAMPLE_COUNT} pairs, found {len(images)}." + ) + metadata: dict[str, str] = {} + for file_name in ADE20K_METADATA_FILES: + metadata_path = os.path.join(dataset_root, file_name) + if not os.path.lexists(metadata_path): + raise ValueError( + f"ADE20K dataset is missing required metadata files: {file_name}." + ) + metadata[file_name] = _validate_dense_source_file( + metadata_path, resolved_dataset_root + ) + + output_parent_dir = os.path.dirname(output_dir) + os.makedirs(output_parent_dir, exist_ok=True) + with TemporaryDirectory( + dir=output_parent_dir, prefix=".ade20k-staging-" + ) as staging_dir: + staged_output_dir = os.path.join(staging_dir, "ade20k") + staged_image_dir = os.path.join(staged_output_dir, "images") + staged_annotation_dir = os.path.join(staged_output_dir, "annotations") + os.makedirs(staged_image_dir) + os.makedirs(staged_annotation_dir) + for sample_id in sorted(images): + shutil.copy2( + images[sample_id], + os.path.join(staged_image_dir, os.path.basename(images[sample_id])), + ) + shutil.copy2( + annotations[sample_id], + os.path.join( + staged_annotation_dir, os.path.basename(annotations[sample_id]) + ), + ) + for file_name in ADE20K_METADATA_FILES: + shutil.copy2( + metadata[file_name], + os.path.join(staged_output_dir, file_name), + ) + + _validate_staged_dataset( + staged_output_dir, "ade20k", ("semantic_segmentation",) + ) + _replace_staged_directories( + ((staged_output_dir, output_dir),), + output_parent_dir, + ".ade20k-backup-", + ) + print(f"Constructed ADE20K validation dataset with {len(images)} image/mask pairs") + + +def organize_ade20k( + dataset_path: str = ADE20K_URL, + output_dir: str = os.path.expanduser( + "~/.mblt_vision/datasets/ADEChallengeData2016" + ), +) -> None: + """Organizes ADE20K validation data, downloading and unpacking when necessary.""" + + output_dir = _validate_dense_output_root( + output_dir, "ADE20K", ("images", "annotations") + ) + with TemporaryDirectory() as temp_dir: + local_dataset_path = _resolve_source(dataset_path, temp_dir) + if local_dataset_path.endswith(".zip"): + _safe_unpack_archive(local_dataset_path, temp_dir) + construct_ade20k(temp_dir, output_dir) + return + construct_ade20k(local_dataset_path, output_dir) + + +def _validate_cityscapes_zip(archive_path: str, source_name: str) -> str: + """Validate one official Cityscapes ZIP source. + + Args: + archive_path: Path to the raw Cityscapes archive. + source_name: Human-readable source description for errors. + + Returns: + Expanded absolute archive path. + + Raises: + ValueError: If the path is missing, is not a file, is not a ZIP, or contains duplicate members. + """ + + resolved_path = os.path.abspath(os.path.expanduser(archive_path)) + if not os.path.isfile(resolved_path): + raise ValueError( + f"Cityscapes {source_name} archive does not exist or is not a file: {resolved_path}." + ) + if not zipfile.is_zipfile(resolved_path): + raise ValueError( + f"Cityscapes {source_name} source must be a valid ZIP archive: {resolved_path}." + ) + + with zipfile.ZipFile(resolved_path) as archive: + seen_members: set[str] = set() + duplicate_members: set[str] = set() + for member in archive.infolist(): + if member.filename in seen_members: + duplicate_members.add(member.filename) + seen_members.add(member.filename) + if duplicate_members: + raise ValueError( + f"Cityscapes {source_name} archive contains duplicate members: {', '.join(sorted(duplicate_members)[:3])}." + ) + return resolved_path + + +def _collect_cityscapes_validation_files( + split_dir: str, + suffix: str, + source_name: str, +) -> dict[str, str]: + """Collect official Cityscapes validation files keyed by shared sample ID. + + Args: + split_dir: Extracted ``leftImg8bit/val`` or ``gtFine/val`` directory. + suffix: Required official file suffix. + source_name: Human-readable source description for errors. + + Returns: + Mapping from ``__`` to source path. + + Raises: + ValueError: If a candidate filename is malformed, misplaced, or duplicates an ID. + """ + + files: dict[str, str] = {} + if not os.path.isdir(split_dir): + return files + + for current_root, _, file_names in os.walk(split_dir): + relative_root = os.path.relpath(current_root, split_dir) + for file_name in file_names: + if not file_name.endswith(suffix): + continue + if relative_root == "." or os.sep in relative_root: + raise ValueError( + f"Malformed Cityscapes {source_name} path: " + f"{os.path.relpath(os.path.join(current_root, file_name), split_dir)}." + ) + sample_id = file_name.removesuffix(suffix) + match = CITYSCAPES_SAMPLE_ID_PATTERN.fullmatch(sample_id) + if match is None or match.group("city") != relative_root: + raise ValueError( + f"Malformed Cityscapes {source_name} filename: {file_name}." + ) + if sample_id in files: + raise ValueError( + f"Duplicate Cityscapes {source_name} sample ID: {sample_id}." + ) + files[sample_id] = os.path.join(current_root, file_name) + return files + + +def organize_cityscapes( + image_dir: str, + annotation_dir: str, + output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/cityscapes"), +) -> None: + """Install official Cityscapes validation archives as lossless flat PNG pairs. + + Only validation RGB images and ``gtFine_labelIds`` masks are selected. + Training, test, and auxiliary annotation files remain excluded. + + Args: + image_dir: Path to ``leftImg8bit_trainvaltest.zip``. + annotation_dir: Path to ``gtFine_trainvaltest.zip``. + output_dir: Directory where the organized validation dataset is stored. + + Raises: + ValueError: If either source is invalid or does not contain exactly 500 matching pairs. + OSError: If extraction, copying, or atomic installation fails. + """ + + output_dir = _validate_dense_output_root( + output_dir, "Cityscapes", ("images", "annotations") + ) + image_archive = _validate_cityscapes_zip(image_dir, "image") + annotation_archive = _validate_cityscapes_zip(annotation_dir, "annotation") + output_parent_dir = os.path.dirname(output_dir) + os.makedirs(output_parent_dir, exist_ok=True) + with TemporaryDirectory( + dir=output_parent_dir, prefix=".cityscapes-staging-" + ) as staging_dir: + extracted_image_dir = os.path.join(staging_dir, "raw-images") + extracted_annotation_dir = os.path.join(staging_dir, "raw-annotations") + _safe_unpack_archive(image_archive, extracted_image_dir) + _safe_unpack_archive(annotation_archive, extracted_annotation_dir) + images = _collect_cityscapes_validation_files( + os.path.join(extracted_image_dir, "leftImg8bit", "val"), + CITYSCAPES_IMAGE_SUFFIX, + "image", + ) + annotations = _collect_cityscapes_validation_files( + os.path.join(extracted_annotation_dir, "gtFine", "val"), + CITYSCAPES_ANNOTATION_SUFFIX, + "annotation", + ) + missing_annotations = sorted(images.keys() - annotations.keys()) + missing_images = sorted(annotations.keys() - images.keys()) + if missing_annotations or missing_images: + details = [] + if missing_annotations: + details.append( + f"missing annotations for {', '.join(missing_annotations[:3])}" + ) + if missing_images: + details.append(f"missing images for {', '.join(missing_images[:3])}") + raise ValueError( + f"Cityscapes validation image/annotation mismatch ({'; '.join(details)})." + ) + if len(images) != CITYSCAPES_VALIDATION_SAMPLE_COUNT: + raise ValueError( + "Cityscapes validation archives must contain " + f"{CITYSCAPES_VALIDATION_SAMPLE_COUNT} pairs, found {len(images)}." + ) + + staged_image_dir = os.path.join(staging_dir, "images") + staged_annotation_dir = os.path.join(staging_dir, "annotations") + os.makedirs(staged_image_dir) + os.makedirs(staged_annotation_dir) + for sample_id in sorted(images): + shutil.copy2( + images[sample_id], os.path.join(staged_image_dir, f"{sample_id}.png") + ) + shutil.copy2( + annotations[sample_id], + os.path.join(staged_annotation_dir, f"{sample_id}.png"), + ) + + replacements = ( + (staged_image_dir, os.path.join(output_dir, "images")), + (staged_annotation_dir, os.path.join(output_dir, "annotations")), + ) + os.makedirs(output_dir, exist_ok=True) + _replace_staged_directories( + replacements, output_parent_dir, ".cityscapes-backup-" + ) + print( + f"Constructed Cityscapes validation dataset with {len(images)} image/mask pairs" + ) + + +def _resolve_dotav1_root(dataset_dir: str) -> str: + """Resolves a DOTAv1 dataset root from a directory path. + + Args: + dataset_dir: Directory containing the DOTAv1 dataset or its parent. + + Returns: + Path to the DOTAv1 dataset root. + """ + dotav1_dir = os.path.join(dataset_dir, "DOTAv1") + if os.path.isdir(dotav1_dir): + return dotav1_dir + return dataset_dir + + +def _is_google_drive_folder_url(path_or_url: str) -> bool: + """Returns whether a URL points to a Google Drive folder.""" + + parsed = urlparse(path_or_url) + return parsed.hostname == "drive.google.com" and bool( + re.fullmatch(r"/drive(?:/u/[^/]+)?/folders/[^/]+/?", parsed.path) + ) + + +def _download_dotav1_google_drive_archives( + folder_url: str, download_dir: str +) -> tuple[str, str]: + """Downloads the DOTAv1 image and v1.0-label archives from a Google Drive folder. + + Args: + folder_url: Public Google Drive folder URL containing the DOTAv1 archives. + download_dir: Directory where the selected archives will be stored. + + Returns: + Paths to the image archive and original v1.0-label archive. + + Raises: + ValueError: If the required archives are absent from the Drive folder. + RuntimeError: If gdown fails to download a required archive. + """ + + print(f"Retrieving DOTAv1 archive list from {folder_url}...") + folder_entries = download_folder( + url=folder_url, output=download_dir, quiet=True, skip_download=True + ) + if folder_entries is None: + raise RuntimeError( + f"Failed to retrieve the DOTAv1 Google Drive folder listing: {folder_url}" + ) + files = [ + entry for entry in folder_entries if _is_google_drive_download_entry(entry) + ] + archives: dict[str, _GoogleDriveDownloadEntry] = {} + for archive_path in DOTAV1_GOOGLE_DRIVE_ARCHIVES: + matches = [ + drive_file + for drive_file in files + if drive_file.path == archive_path + or drive_file.path.endswith(f"/{archive_path}") + ] + if len(matches) == 1: + archives[archive_path] = matches[0] + continue + + available = ", ".join(sorted(drive_file.path for drive_file in files)) or "none" + if not matches: + raise ValueError( + f"DOTAv1 Drive folder is missing {archive_path}. Available files: {available}." + ) + ambiguous = ", ".join(sorted(drive_file.path for drive_file in matches)) + raise ValueError( + f"DOTAv1 Drive folder has ambiguous matches for {archive_path}: {ambiguous}." + ) + + local_archives: dict[str, str] = {} + for archive_path in sorted(DOTAV1_GOOGLE_DRIVE_ARCHIVES): + drive_file = archives[archive_path] + local_path = os.path.join(download_dir, os.path.basename(archive_path)) + print(f"Downloading DOTAv1 {archive_path}...") + downloaded_path = download( + id=drive_file.id, output=local_path, quiet=False, resume=True + ) + if not isinstance(downloaded_path, str): + raise RuntimeError( + f"Failed to download DOTAv1 archive {archive_path} from {folder_url}." + ) + local_archives[archive_path] = downloaded_path + + return ( + local_archives[DOTAV1_DOWNLOAD_CONFIG["images_archive"]], + local_archives[DOTAV1_DOWNLOAD_CONFIG["labels_archive"]], + ) + + +def _iter_files(root: str, extensions: Iterable[str]) -> Iterable[str]: + """Yields files below a directory with one of the requested suffixes.""" + + suffixes = tuple(extension.lower() for extension in extensions) + for current_root, _, file_names in os.walk(root): + for file_name in file_names: + if file_name.lower().endswith(suffixes): + yield os.path.join(current_root, file_name) + + +def _safe_archive_member_path(member_name: str, destination: str) -> str: + """Return an archive member destination after enforcing staging-directory containment. + + Args: + member_name: Path stored in an archive member. + destination: Archive extraction directory. + + Returns: + Absolute destination path for the member. + + Raises: + ValueError: If the member path is absolute or escapes the extraction directory. + """ + + root = os.path.abspath(destination) + target = os.path.abspath(os.path.join(root, member_name)) + if os.path.commonpath((root, target)) != root: + raise ValueError(f"Unsafe archive member path: {member_name!r}.") + return target + + +def _safe_unpack_archive(archive_path: str, destination: str) -> None: + """Extract an archive while rejecting links, special files, and escaping paths. + + Args: + archive_path: ZIP or tar-family dataset archive. + destination: Empty staging directory where archive members are written. + + Raises: + ValueError: If the archive format or any member is unsafe or unsupported. + OSError: If a validated archive cannot be read or written. + """ + + if zipfile.is_zipfile(archive_path): + with zipfile.ZipFile(archive_path) as archive: + members = archive.infolist() + for member in members: + _safe_archive_member_path(member.filename, destination) + file_type = stat.S_IFMT(member.external_attr >> 16) + if file_type and not ( + stat.S_ISREG(file_type) or stat.S_ISDIR(file_type) + ): + raise ValueError( + f"Unsafe archive member type: {member.filename!r}." + ) + for member in members: + target = _safe_archive_member_path(member.filename, destination) + if member.is_dir(): + os.makedirs(target, exist_ok=True) + continue + os.makedirs(os.path.dirname(target), exist_ok=True) + with archive.open(member) as source, open(target, "wb") as output_file: + shutil.copyfileobj(source, output_file) + return + + if tarfile.is_tarfile(archive_path): + with tarfile.open(archive_path) as archive: + members = archive.getmembers() + for member in members: + _safe_archive_member_path(member.name, destination) + if not (member.isfile() or member.isdir()): + raise ValueError(f"Unsafe archive member type: {member.name!r}.") + for member in members: + target = _safe_archive_member_path(member.name, destination) + if member.isdir(): + os.makedirs(target, exist_ok=True) + continue + source = archive.extractfile(member) + if source is None: + raise ValueError(f"Unable to read archive member: {member.name!r}.") + os.makedirs(os.path.dirname(target), exist_ok=True) + with source, open(target, "wb") as output_file: + shutil.copyfileobj(source, output_file) + return + + raise ValueError(f"Unsupported archive format: {archive_path}.") + + +def _write_dotav1_yolo_labels( + image_path: str, original_label_path: str, output_path: str +) -> None: + """Converts one official DOTAv1 label file into normalized OBB label format.""" + + with Image.open(image_path) as image: + width, height = image.size + converted_lines: list[str] = [] + with open(original_label_path, encoding="utf-8") as label_file: + for line in label_file: + fields = line.split() + if ( + len(fields) < 10 + or fields[0].startswith("imagesource:") + or fields[0].startswith("gsd:") + ): + continue + # Official DOTAv1 labels use ``1`` for difficult objects. Keep ``2`` + # ignored for compatibility with previously supported label exports. + if fields[9] in {"1", "2"}: + continue + class_name = fields[8] + if class_name not in DOTAV1_CLASS_TO_IDX: + raise ValueError( + f"Unsupported DOTAv1 class in {original_label_path}: {class_name}" + ) + coordinates = [float(value) for value in fields[:8]] + normalized = [ + coordinate / (width if index % 2 == 0 else height) + for index, coordinate in enumerate(coordinates) + ] + converted_lines.append( + f"{DOTAV1_CLASS_TO_IDX[class_name]} " + + " ".join(f"{coordinate:.8g}" for coordinate in normalized) + ) + with open(output_path, "w", encoding="utf-8") as output_file: + output_file.write("\n".join(converted_lines)) + if converted_lines: + output_file.write("\n") + + +def construct_dotav1_from_archives( + image_archive: str, label_archive: str, output_dir: str +) -> None: + """Constructs the DOTAv1 validation layout from the Google Drive archives. + + Args: + image_archive: Path to the DOTAv1 validation-image archive. + label_archive: Path to the original DOTAv1 v1.0 label archive. + output_dir: Directory where the organized validation dataset will be stored. + + Raises: + ValueError: If the archives have no validation files or their image and label stems differ. + OSError: If staging or replacing the organized dataset files fails. + """ + + with TemporaryDirectory() as extract_dir: + image_dir = os.path.join(extract_dir, "images") + label_dir = os.path.join(extract_dir, "labels") + _safe_unpack_archive(image_archive, image_dir) + _safe_unpack_archive(label_archive, label_dir) + + labels = { + os.path.splitext(os.path.basename(path))[0]: path + for path in _iter_files(label_dir, [".txt"]) + } + if not labels: + raise ValueError(f"No DOTAv1 label files found in {label_archive}.") + + images = { + os.path.splitext(os.path.basename(path))[0]: path + for path in _iter_files( + image_dir, [".bmp", ".jpg", ".jpeg", ".png", ".tif", ".tiff"] + ) + } + image_ids = set(images) + label_ids = set(labels) + missing_labels = sorted(image_ids - label_ids) + missing_images = sorted(label_ids - image_ids) + if missing_labels or missing_images: + details = [] + if missing_labels: + details.append( + f"images without labels: {', '.join(missing_labels[:5])}" + ) + if missing_images: + details.append( + f"labels without images: {', '.join(missing_images[:5])}" + ) + raise ValueError(f"DOTAv1 archive stem mismatch ({'; '.join(details)}).") + matching_ids = sorted(image_ids) + if len(matching_ids) != DOTAV1_VALIDATION_SAMPLE_COUNT: + raise ValueError( + "DOTAv1 validation dataset must contain " + f"{DOTAV1_VALIDATION_SAMPLE_COUNT} matching image/label pairs, found {len(matching_ids)}." + ) + + output_dir = os.path.abspath(output_dir) + output_parent_dir = os.path.dirname(output_dir) + os.makedirs(output_parent_dir, exist_ok=True) + with TemporaryDirectory( + dir=output_parent_dir, prefix=".dotav1-staging-" + ) as staging_dir: + staged_output_dir = os.path.join(staging_dir, "dotav1") + staged_image_dir = os.path.join(staged_output_dir, "images") + staged_label_dir = os.path.join(staged_output_dir, "labels", "val") + staged_original_label_dir = os.path.join( + staged_output_dir, "labels", "val_original" + ) + os.makedirs(staged_image_dir) + os.makedirs(staged_label_dir) + os.makedirs(staged_original_label_dir) + + for image_id in matching_ids: + image_path = images[image_id] + shutil.copy2( + image_path, + os.path.join(staged_image_dir, os.path.basename(image_path)), + ) + + for image_id in matching_ids: + shutil.copy2( + labels[image_id], + os.path.join(staged_original_label_dir, f"{image_id}.txt"), + ) + _write_dotav1_yolo_labels( + images[image_id], + labels[image_id], + os.path.join(staged_label_dir, f"{image_id}.txt"), + ) + + _validate_staged_dataset(staged_output_dir, "dotav1", ("obb",)) + _replace_staged_directories( + ((staged_output_dir, output_dir),), + output_parent_dir, + ".dotav1-backup-", + ) + + print(f"Constructed DOTAv1 validation dataset with {len(matching_ids)} images") + + +def _copy_dotav1_layout_to_staging(dataset_root: str, staged_output_dir: str) -> None: + """Copy a flat or legacy DOTAv1 validation layout into canonical staging.""" + + image_root = os.path.join(dataset_root, "images") + supported_image_suffixes = (".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff") + flat_image_files = ( + [ + file_name + for file_name in os.listdir(image_root) + if os.path.isfile(os.path.join(image_root, file_name)) + and file_name.lower().endswith(supported_image_suffixes) + ] + if os.path.isdir(image_root) + else [] + ) + source_image_dir = ( + image_root if flat_image_files else os.path.join(image_root, "val") + ) + if not os.path.isdir(source_image_dir): + raise ValueError(f"No DOTAv1 validation images found in {dataset_root}") + + staged_image_dir = os.path.join(staged_output_dir, "images") + os.makedirs(staged_image_dir) + for file_name in os.listdir(source_image_dir): + source_path = os.path.join(source_image_dir, file_name) + if os.path.isfile(source_path) and file_name.lower().endswith( + supported_image_suffixes + ): + shutil.copy2(source_path, os.path.join(staged_image_dir, file_name)) + + for label_directory in ("val", "val_original"): + source_label_dir = os.path.join(dataset_root, "labels", label_directory) + if not os.path.isdir(source_label_dir): + continue + staged_label_dir = os.path.join(staged_output_dir, "labels", label_directory) + os.makedirs(staged_label_dir, exist_ok=True) + for file_name in os.listdir(source_label_dir): + source_path = os.path.join(source_label_dir, file_name) + if os.path.isfile(source_path) and file_name.lower().endswith(".txt"): + shutil.copy2(source_path, os.path.join(staged_label_dir, file_name)) + + +def construct_dotav1(dataset_dir: str, output_dir: str) -> None: + """Constructs a validation-only DOTAv1 dataset. + + Args: + dataset_dir: Directory containing a DOTAv1 dataset or its parent. + output_dir: Directory where the organized validation dataset will be stored. + + Raises: + ValueError: If the staged validation dataset is incomplete or mismatched. + OSError: If staging or replacing the organized dataset files fails. + """ + dataset_root = _resolve_dotav1_root(dataset_dir) + print(f"Constructing DOTAv1 validation dataset from {dataset_root} to {output_dir}") + output_dir = os.path.abspath(output_dir) + output_parent_dir = os.path.dirname(output_dir) + os.makedirs(output_parent_dir, exist_ok=True) + with TemporaryDirectory( + dir=output_parent_dir, prefix=".dotav1-staging-" + ) as staging_dir: + staged_output_dir = os.path.join(staging_dir, "dotav1") + os.makedirs(staged_output_dir) + _copy_dotav1_layout_to_staging(dataset_root, staged_output_dir) + _validate_staged_dataset(staged_output_dir, "dotav1", ("obb",)) + _replace_staged_directories( + ((staged_output_dir, output_dir),), + output_parent_dir, + ".dotav1-backup-", + ) + + print("Constructing DOTAv1 validation dataset completed") + + +def organize_dotav1( + dataset_path: str, + output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/dotav1"), +) -> None: + """Organizes a validation-only DOTAv1 dataset. + + Args: + dataset_path: Path or URL to the DOTAv1 zip file or extracted dataset directory. + output_dir: Directory to store the organized dataset. + """ + with TemporaryDirectory() as temp_dir: + if _is_google_drive_folder_url(dataset_path): + image_archive, label_archive = _download_dotav1_google_drive_archives( + dataset_path, temp_dir + ) + construct_dotav1_from_archives(image_archive, label_archive, output_dir) + return + + local_dataset_path = _resolve_source(dataset_path, temp_dir) + + if local_dataset_path.endswith(".zip"): + print("Unpacking DOTAv1 files to temporary directory...") + _safe_unpack_archive(local_dataset_path, temp_dir) + print("Unpacking completed") + construct_dotav1(temp_dir, output_dir) + return + + construct_dotav1(local_dataset_path, output_dir) diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py new file mode 100644 index 0000000..fdcea72 --- /dev/null +++ b/mblt_vision/utils/datasets/readiness.py @@ -0,0 +1,368 @@ +"""Identity and completeness checks for organized vision validation datasets.""" + +from __future__ import annotations + +import json +import os +import re +from pathlib import Path +from typing import Any + +import numpy as np +from scipy.io import loadmat +from scipy.io.matlab import MatReadError + +from ..._tasks import normalize_vision_task + +IMAGE_SUFFIXES = {".bmp", ".jpeg", ".jpg", ".png", ".tif", ".tiff", ".webp"} +IMAGENET_CLASS_COUNT = 1000 +IMAGENET_IMAGES_PER_CLASS = 50 +COCO_VALIDATION_SAMPLE_COUNT = 5000 +DOTAV1_VALIDATION_SAMPLE_COUNT = 458 +WIDERFACE_EVENT_COUNT = 61 +WIDERFACE_VALIDATION_SAMPLE_COUNT = 3226 +NYU_DEPTH_VALIDATION_SAMPLE_COUNT = 654 +ADE20K_VALIDATION_SAMPLE_COUNT = 2000 +CITYSCAPES_VALIDATION_SAMPLE_COUNT = 500 +ADE20K_METADATA_FILES = ("objectInfo150.txt", "sceneCategories.txt") +IMAGENET_CLASS_PATTERN = re.compile(r"n\d{8}") +IMAGENET_IMAGE_PATTERN = re.compile(r"ILSVRC2012_val_\d{8}") +COCO_IMAGE_PATTERN = re.compile(r"\d{12}") +WIDERFACE_EVENT_PATTERN = re.compile(r"\d+--\S.*") +CITYSCAPES_SAMPLE_ID_PATTERN = re.compile( + r"^(?P[A-Za-z][A-Za-z0-9-]*)_\d{6}_\d{6}$" +) + + +def _path_has_symlink_component(path: Path) -> bool: + """Return whether a path traversal or its normalized ancestors contain a symlink.""" + + expanded_path = path.expanduser() + traversal_path = ( + expanded_path if expanded_path.is_absolute() else Path.cwd() / expanded_path + ) + normalized_path = Path(os.path.abspath(expanded_path)) + candidates = ( + traversal_path, + *traversal_path.parents, + normalized_path, + *normalized_path.parents, + ) + return any(component.is_symlink() for component in candidates) + + +def _files_by_stem( + directory: Path, + suffixes: set[str], + *, + reject_symlinks: bool = False, +) -> dict[str, Path] | None: + """Collect direct child files with supported suffixes by stem. + + Args: + directory: Directory containing candidate files. + suffixes: Accepted lowercase file suffixes. + reject_symlinks: Whether any symlinked directory or entry invalidates + the file collection. + + Returns: + Files keyed by stem, an empty mapping for a missing directory, or + ``None`` for duplicate stems or rejected symlinks. + """ + + if (reject_symlinks and directory.is_symlink()) or not directory.is_dir(): + return {} + entries = list(directory.iterdir()) + if reject_symlinks and any(path.is_symlink() for path in entries): + return None + paths = [ + path for path in entries if path.is_file() and path.suffix.lower() in suffixes + ] + files = {path.stem: path for path in paths} + return files if len(files) == len(paths) else None + + +def _imagenet_ready(root: Path) -> bool: + """Check the organizer's complete ImageNet-1k validation class tree.""" + + if not root.is_dir(): + return False + class_dirs = [path for path in root.iterdir() if path.is_dir()] + if len(class_dirs) != IMAGENET_CLASS_COUNT: + return False + image_names: set[str] = set() + for class_dir in class_dirs: + if IMAGENET_CLASS_PATTERN.fullmatch(class_dir.name) is None: + return False + images = [ + path + for path in class_dir.iterdir() + if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES + ] + if len(images) != IMAGENET_IMAGES_PER_CLASS or any( + path.suffix != ".JPEG" + or IMAGENET_IMAGE_PATTERN.fullmatch(path.stem) is None + for path in images + ): + return False + image_names.update(path.name for path in images) + return len(image_names) == IMAGENET_CLASS_COUNT * IMAGENET_IMAGES_PER_CLASS + + +def _load_coco_image_names(annotation_path: Path) -> set[str] | None: + """Load unique validation image filenames from a COCO annotation file.""" + + try: + annotation: Any = json.loads(annotation_path.read_text(encoding="utf-8")) + except (json.JSONDecodeError, OSError, UnicodeError): + return None + image_records = annotation.get("images") if isinstance(annotation, dict) else None + if ( + not isinstance(image_records, list) + or len(image_records) != COCO_VALIDATION_SAMPLE_COUNT + ): + return None + names: list[str] = [] + for record in image_records: + if not isinstance(record, dict): + continue + file_name = record.get("file_name") + if isinstance(file_name, str): + names.append(file_name) + return set(names) if len(names) == len(image_records) == len(set(names)) else None + + +def _coco_ready(root: Path, task: str) -> bool: + """Check the complete COCO 2017 image split and task annotation metadata.""" + + annotation_name = ( + "person_keypoints_val2017.json" + if task == "pose_estimation" + else "instances_val2017.json" + ) + annotation_names = _load_coco_image_names(root / annotation_name) + if annotation_names is None: + return False + image_dir = root / "val2017" + if not image_dir.is_dir(): + return False + image_paths = [ + path + for path in image_dir.iterdir() + if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES + ] + if len(image_paths) != COCO_VALIDATION_SAMPLE_COUNT or any( + path.suffix.lower() != ".jpg" or COCO_IMAGE_PATTERN.fullmatch(path.stem) is None + for path in image_paths + ): + return False + return {path.name for path in image_paths} == annotation_names + + +def _dotav1_ready(root: Path) -> bool: + """Check complete paired DOTAv1 validation images and labels.""" + + flat_image_dir = root / "images" + flat_images = _files_by_stem(flat_image_dir, IMAGE_SUFFIXES) + if flat_images is None: + return False + image_dir = flat_image_dir if flat_images else flat_image_dir / "val" + images = flat_images if flat_images else _files_by_stem(image_dir, IMAGE_SUFFIXES) + if images is None or len(images) != DOTAV1_VALIDATION_SAMPLE_COUNT: + return False + + normalized_labels = _files_by_stem(root / "labels" / "val", {".txt"}) + original_labels = _files_by_stem(root / "labels" / "val_original", {".txt"}) + if normalized_labels is None or original_labels is None: + return False + label_stems = normalized_labels.keys() | original_labels.keys() + return images.keys() == label_stems + + +def _widerface_ready(root: Path) -> bool: + """Check the complete WiderFace validation image tree and metadata files.""" + + required_files = ( + "wider_face_val.mat", + "wider_easy_val.mat", + "wider_medium_val.mat", + "wider_hard_val.mat", + ) + if not all((root / file_name).is_file() for file_name in required_files): + return False + expected_images = _load_widerface_image_names(root / "wider_face_val.mat") + if expected_images is None: + return False + image_root = root / "images" + if not image_root.is_dir(): + return False + event_dirs = [path for path in image_root.iterdir() if path.is_dir()] + if len(event_dirs) != WIDERFACE_EVENT_COUNT or any( + WIDERFACE_EVENT_PATTERN.fullmatch(path.name) is None for path in event_dirs + ): + return False + if {path.name for path in event_dirs} != expected_images.keys(): + return False + actual_images = { + event_dir.name: { + path.name + for path in event_dir.iterdir() + if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES + } + for event_dir in event_dirs + } + return ( + actual_images == expected_images + and sum(len(image_names) for image_names in actual_images.values()) + == WIDERFACE_VALIDATION_SAMPLE_COUNT + ) + + +def _flatten_matlab_strings(value: Any) -> list[str]: + """Flatten strings stored inside nested MATLAB cell arrays.""" + + if isinstance(value, (str, np.str_)): + return [str(value)] + if isinstance(value, np.ndarray): + strings: list[str] = [] + for item in value.flat: + strings.extend(_flatten_matlab_strings(item)) + return strings + return [] + + +def _load_widerface_image_names(annotation_path: Path) -> dict[str, set[str]] | None: + """Load exact event and image identities from WiderFace validation metadata.""" + + try: + annotation = loadmat(annotation_path) + event_list = annotation["event_list"] + file_list = annotation["file_list"] + except (IndexError, KeyError, MatReadError, OSError, TypeError, ValueError): + return None + if len(event_list) != len(file_list): + return None + + expected: dict[str, set[str]] = {} + for event_cell, file_cell in zip(event_list, file_list, strict=True): + event_names = _flatten_matlab_strings(event_cell) + image_stems = _flatten_matlab_strings(file_cell) + if ( + len(event_names) != 1 + or not image_stems + or len(image_stems) != len(set(image_stems)) + or WIDERFACE_EVENT_PATTERN.fullmatch(event_names[0]) is None + or any(not stem or Path(stem).name != stem for stem in image_stems) + or event_names[0] in expected + ): + return None + expected[event_names[0]] = {f"{stem}.jpg" for stem in image_stems} + if len(expected) != WIDERFACE_EVENT_COUNT: + return None + return expected + + +def dense_dataset_ready(data_path: str | Path, dataset: str) -> bool: + """Return whether a dense dataset matches its taxonomy and full validation split. + + Args: + data_path: Organized dataset root. + dataset: Dense validation taxonomy. + + Returns: + Whether the dataset has the expected filename identity, matched targets, + and complete validation sample count. + """ + + root = Path(data_path).expanduser() + if _path_has_symlink_component(root) or not root.is_dir(): + return False + normalized = dataset.lower() + if normalized == "nyu-depth": + images = _files_by_stem( + root / "images", {".jpg", ".jpeg", ".png"}, reject_symlinks=True + ) + depths = _files_by_stem(root / "depth", {".npy"}, reject_symlinks=True) + if images is None or depths is None: + return False + return ( + len(images) == NYU_DEPTH_VALIDATION_SAMPLE_COUNT + and len(depths) == NYU_DEPTH_VALIDATION_SAMPLE_COUNT + and images.keys() == depths.keys() + ) + + images = _files_by_stem( + root / "images", {".jpg", ".jpeg", ".png"}, reject_symlinks=True + ) + annotations = _files_by_stem(root / "annotations", {".png"}, reject_symlinks=True) + if images is None or annotations is None or images.keys() != annotations.keys(): + return False + + if normalized == "ade20k": + return ( + len(images) == ADE20K_VALIDATION_SAMPLE_COUNT + and all(stem.startswith("ADE_val_") for stem in images) + and all( + path.suffix.lower() in {".jpg", ".jpeg"} for path in images.values() + ) + and all( + not (root / file_name).is_symlink() and (root / file_name).is_file() + for file_name in ADE20K_METADATA_FILES + ) + ) + if normalized == "cityscapes": + return ( + len(images) == CITYSCAPES_VALIDATION_SAMPLE_COUNT + and all( + CITYSCAPES_SAMPLE_ID_PATTERN.fullmatch(stem) is not None + for stem in images + ) + and all(path.suffix.lower() == ".png" for path in images.values()) + ) + return False + + +def dataset_ready(data_path: str | Path, task: str, dataset: str | None = None) -> bool: + """Return whether an organized dataset matches its task, taxonomy, and full validation split. + + Args: + data_path: Organized dataset root. + task: Canonical vision task. + dataset: Optional validation taxonomy. + + Returns: + Whether the dataset has the expected identity, metadata, and sample count. + """ + + root = Path(data_path).expanduser() + normalized_task = normalize_vision_task(task) + expected_dataset = { + "image_classification": "imagenet", + "object_detection": "coco", + "instance_segmentation": "coco", + "pose_estimation": "coco", + "face_detection": "widerface", + "obb": "dotav1", + "depth_estimation": "nyu-depth", + }.get(normalized_task) + normalized_dataset = (dataset or expected_dataset or "").lower() + + if normalized_task == "semantic_segmentation": + return dense_dataset_ready(root, normalized_dataset or "ade20k") + if expected_dataset is None or normalized_dataset != expected_dataset: + return False + if normalized_task == "image_classification": + return _imagenet_ready(root) + if normalized_task in { + "object_detection", + "instance_segmentation", + "pose_estimation", + }: + return _coco_ready(root, normalized_task) + if normalized_task == "face_detection": + return _widerface_ready(root) + if normalized_task == "obb": + return _dotav1_ready(root) + if normalized_task == "depth_estimation": + return dense_dataset_ready(root, normalized_dataset) + return False diff --git a/mblt_vision/utils/evaluation/__init__.py b/mblt_vision/utils/evaluation/__init__.py new file mode 100644 index 0000000..21b2916 --- /dev/null +++ b/mblt_vision/utils/evaluation/__init__.py @@ -0,0 +1,51 @@ +""" +Evaluation scripts for various datasets. +""" + +from __future__ import annotations + +from ._result import EvaluationResult +from .eval_ade20k import ( + ADE20KResult, + SemanticMetricAccumulator, + SemanticSegmentationResult, + calculate_semantic_metrics, + eval_ade20k, + eval_semantic_segmentation, +) +from .eval_cityscapes import eval_cityscapes +from .eval_coco import COCOResult, eval_coco, eval_coco_metrics +from .eval_dota import DOTAResult, eval_dota +from .eval_imagenet import ImageNetResult, eval_imagenet, eval_imagenet_metrics +from .eval_nyu_depth import ( + NYUDepthMetricAccumulator, + NYUDepthResult, + calculate_nyu_depth_metrics, + eval_nyu_depth, +) +from .eval_widerface import WiderFaceResult, eval_widerface + +__all__: list[str] = [ + "eval_coco", + "eval_coco_metrics", + "COCOResult", + "EvaluationResult", + "ADE20KResult", + "SemanticMetricAccumulator", + "SemanticSegmentationResult", + "calculate_semantic_metrics", + "eval_ade20k", + "eval_cityscapes", + "eval_semantic_segmentation", + "DOTAResult", + "eval_dota", + "ImageNetResult", + "eval_imagenet", + "eval_imagenet_metrics", + "NYUDepthResult", + "NYUDepthMetricAccumulator", + "calculate_nyu_depth_metrics", + "eval_nyu_depth", + "WiderFaceResult", + "eval_widerface", +] diff --git a/mblt_vision/utils/evaluation/_result.py b/mblt_vision/utils/evaluation/_result.py new file mode 100644 index 0000000..2e8c202 --- /dev/null +++ b/mblt_vision/utils/evaluation/_result.py @@ -0,0 +1,22 @@ +"""Shared structural contract for Vision evaluator results.""" + +from __future__ import annotations + +from typing import Protocol, runtime_checkable + + +@runtime_checkable +class EvaluationResult(Protocol): + """Result object exposing the primary and secondary benchmark scores.""" + + @property + def primary_score(self) -> float: + """Return the evaluator's primary score.""" + + ... + + @property + def secondary_score(self) -> float: + """Return the evaluator's secondary score.""" + + ... diff --git a/mblt_vision/utils/evaluation/eval_ade20k.py b/mblt_vision/utils/evaluation/eval_ade20k.py new file mode 100644 index 0000000..7e97237 --- /dev/null +++ b/mblt_vision/utils/evaluation/eval_ade20k.py @@ -0,0 +1,290 @@ +"""ADE20K evaluation for semantic-segmentation models.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, NamedTuple + +import numpy as np +import torch +from tqdm import tqdm + +from ..datasets import ( + CustomADE20K, + CustomCityscapes, + get_ade20k_loader, + get_cityscapes_loader, +) + +if TYPE_CHECKING: + from ...wrapper import MBLT_Engine + + +class SemanticSegmentationResult(NamedTuple): + """Generic semantic metrics ordered from primary to secondary.""" + + miou: float + pixel_accuracy: float + + @property + def primary_score(self) -> float: + """Return mean intersection-over-union.""" + + return self.miou + + @property + def secondary_score(self) -> float: + """Return overall valid-pixel accuracy.""" + + return self.pixel_accuracy + + +ADE20KResult = SemanticSegmentationResult + + +class SemanticMetricAccumulator: + """Accumulate an ignore-aware semantic confusion matrix.""" + + def __init__(self, nc: int, ignore_label: int = 255) -> None: + """Initialize an empty confusion matrix. + + Args: + nc: Number of semantic classes. + ignore_label: Target label excluded from metrics. + """ + + self.nc = nc + self.ignore_label = ignore_label + self.matrix = np.zeros((nc, nc), dtype=np.int64) + + def update(self, prediction: np.ndarray, target: np.ndarray) -> None: + """Accumulate one or more predicted and target class maps. + + Args: + prediction: Predicted class maps. + target: Target class maps with optional ignore labels. + + Raises: + ValueError: If prediction and target shapes differ. + """ + + prediction = np.asarray(prediction) + target = np.asarray(target) + if prediction.shape != target.shape: + raise ValueError( + f"Semantic prediction and target shapes must match, got {prediction.shape} and {target.shape}." + ) + valid_target = ( + (target != self.ignore_label) & (target >= 0) & (target < self.nc) + ) + valid_prediction = ( + np.isfinite(prediction) + & (prediction >= 0) + & (prediction < self.nc) + & (prediction == np.floor(prediction)) + ) + invalid_prediction = valid_target & ~valid_prediction + if invalid_prediction.any(): + invalid_values = np.unique(prediction[invalid_prediction]) + raise ValueError( + f"Semantic predictions at valid target pixels must be finite class IDs in [0, {self.nc - 1}], " + f"got {invalid_values.tolist()}." + ) + if valid_target.any(): + histogram = np.bincount( + self.nc * target[valid_target].astype(np.int64) + + prediction[valid_target].astype(np.int64), + minlength=self.nc**2, + ) + self.matrix += histogram.reshape(self.nc, self.nc) + + def result(self) -> SemanticSegmentationResult: + """Compute mIoU over present classes and overall pixel accuracy. + + Returns: + Pooled semantic-segmentation metrics. + + Raises: + ValueError: If no valid target pixels were accumulated. + """ + + ground_truth = self.matrix.sum(axis=1) + predicted = self.matrix.sum(axis=0) + intersection = np.diag(self.matrix) + union = ground_truth + predicted - intersection + present = ground_truth > 0 + if not present.any(): + raise ValueError("Semantic evaluation received no valid target pixels.") + iou = np.divide( + intersection, + union, + out=np.zeros(self.nc, dtype=np.float64), + where=union > 0, + ) + total = int(self.matrix.sum()) + return SemanticSegmentationResult( + miou=float(iou[present].mean()), + pixel_accuracy=float(intersection.sum() / total), + ) + + +def calculate_semantic_metrics( + prediction: np.ndarray, + target: np.ndarray, + nc: int = 150, + ignore_label: int = 255, +) -> ADE20KResult: + """Calculate semantic metrics for one batch of class maps. + + Args: + prediction: Predicted class maps. + target: Target class maps. + nc: Number of semantic classes. + ignore_label: Target label excluded from metrics. + + Returns: + Semantic metrics exposed through the ADE20K compatibility alias. + + Raises: + ValueError: If shapes differ or no valid target pixels are present. + """ + + accumulator = SemanticMetricAccumulator(nc=nc, ignore_label=ignore_label) + accumulator.update(prediction, target) + return accumulator.result() + + +def _evaluate_semantic_loader( + model: MBLT_Engine, + loader: torch.utils.data.DataLoader, + nc: int, + description: str = "Evaluating semantic segmentation", +) -> SemanticSegmentationResult: + """Evaluate input-space semantic maps from an organized validation loader. + + Args: + model: Initialized semantic-segmentation engine. + loader: Organized validation data loader. + nc: Number of semantic classes. + description: Progress-bar description. + + Returns: + Pooled semantic-segmentation metrics. + + Raises: + ValueError: If postprocessing returns no class maps or no valid targets exist. + """ + + accumulator = SemanticMetricAccumulator(nc=nc) + for inputs, targets, _, _, _ in tqdm(loader, desc=description): + result = model.postprocess(model(inputs)) + semantic_mask = result.semantic_mask + if semantic_mask is None: + raise ValueError("Semantic postprocessor returned no class maps.") + prediction = ( + semantic_mask.detach().cpu().numpy() + if isinstance(semantic_mask, torch.Tensor) + else semantic_mask + ) + accumulator.update(np.asarray(prediction), targets) + return accumulator.result() + + +def eval_semantic_segmentation( + model: MBLT_Engine, + data_path: str, + batch_size: int, + dataset: str | None = None, +) -> SemanticSegmentationResult: + """Evaluate a semantic model with the loader for its configured taxonomy. + + Args: + model: Initialized semantic-segmentation engine. + data_path: Organized dataset root. + batch_size: Number of validation samples per inference batch. + dataset: Optional taxonomy override. Defaults to ``model.post_cfg.dataset``. + + Returns: + Generic mIoU and pixel-accuracy result. + + Raises: + ValueError: If preprocessing metadata, image size, taxonomy, predictions, or targets are invalid. + """ + + configured_dataset = model.post_cfg.get("dataset") + if not isinstance(configured_dataset, str) or not configured_dataset: + raise ValueError( + "Semantic validation requires model.post_cfg.dataset to declare the model taxonomy." + ) + configured_taxonomy = configured_dataset.lower() + if dataset is not None: + if not isinstance(dataset, str) or not dataset: + raise ValueError( + "Semantic validation dataset overrides must be non-empty strings." + ) + requested_taxonomy = dataset.lower() + if requested_taxonomy != configured_taxonomy: + raise ValueError( + f"Requested semantic validation taxonomy {requested_taxonomy!r} conflicts with " + f"the model's configured taxonomy {configured_taxonomy!r}." + ) + taxonomy = configured_taxonomy + + letterbox_cfg = model.pre_cfg.get("LetterBox") + if not isinstance(letterbox_cfg, dict) or "img_size" not in letterbox_cfg: + raise ValueError( + "Semantic validation requires a LetterBox img_size in the model preprocessing config." + ) + image_size = letterbox_cfg["img_size"] + if not isinstance(image_size, list) or len(image_size) != 2: + raise ValueError( + "Semantic validation img_size must be a two-item [height, width] list." + ) + + image_size_tuple = (int(image_size[0]), int(image_size[1])) + if taxonomy == "ade20k": + validation_dataset = CustomADE20K(data_path) + loader = get_ade20k_loader( + validation_dataset, + batch_size, + model.preprocess_with_metadata, + image_size=image_size_tuple, + ) + default_nc = 150 + description = "Evaluating ADE20K" + elif taxonomy == "cityscapes": + validation_dataset = CustomCityscapes(data_path) + loader = get_cityscapes_loader( + validation_dataset, + batch_size, + model.preprocess_with_metadata, + image_size=image_size_tuple, + ) + default_nc = 19 + description = "Evaluating Cityscapes" + else: + raise ValueError(f"Unsupported semantic validation dataset: {taxonomy!r}.") + nc = int(getattr(model.postprocessor, "nc", default_nc)) + return _evaluate_semantic_loader(model, loader, nc, description=description) + + +def eval_ade20k(model: MBLT_Engine, data_path: str, batch_size: int) -> ADE20KResult: + """Evaluate a semantic-segmentation model on ADE20K validation masks. + + Args: + model: Initialized semantic-segmentation engine. + data_path: Organized ADE20K dataset root. + batch_size: Number of validation samples per inference batch. + + Returns: + ADE20K metrics through the generic semantic result type. + + Raises: + ValueError: If the dataset, preprocessing metadata, predictions, or targets are invalid. + """ + + return eval_semantic_segmentation( + model, + data_path, + batch_size, + dataset="ade20k", + ) diff --git a/mblt_vision/utils/evaluation/eval_cityscapes.py b/mblt_vision/utils/evaluation/eval_cityscapes.py new file mode 100644 index 0000000..d12f9e5 --- /dev/null +++ b/mblt_vision/utils/evaluation/eval_cityscapes.py @@ -0,0 +1,32 @@ +"""Cityscapes evaluation for semantic-segmentation models.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from .eval_ade20k import SemanticSegmentationResult, eval_semantic_segmentation + +if TYPE_CHECKING: + from ...wrapper import MBLT_Engine + + +def eval_cityscapes( + model: MBLT_Engine, data_path: str, batch_size: int +) -> SemanticSegmentationResult: + """Evaluate a semantic-segmentation model on Cityscapes validation masks. + + Args: + model: Initialized semantic-segmentation engine. + data_path: Organized Cityscapes dataset root. + batch_size: Number of validation samples per inference batch. + + Returns: + Cityscapes mIoU and pixel-accuracy metrics. + + Raises: + ValueError: If the dataset, preprocessing metadata, predictions, or targets are invalid. + """ + + return eval_semantic_segmentation( + model, data_path, batch_size, dataset="cityscapes" + ) diff --git a/mblt_vision/utils/evaluation/eval_coco.py b/mblt_vision/utils/evaluation/eval_coco.py new file mode 100644 index 0000000..32014a8 --- /dev/null +++ b/mblt_vision/utils/evaluation/eval_coco.py @@ -0,0 +1,276 @@ +"""Evaluation script for COCO dataset.""" + +from __future__ import annotations + +import logging +import math +import os +from time import time +from typing import TYPE_CHECKING, Any, NamedTuple + +from faster_coco_eval import COCO, COCOeval_faster +from tqdm import tqdm + +from ..._tasks import normalize_vision_task +from ..datasets import CustomCOCODataset, get_coco_loader + +if TYPE_CHECKING: + from ...wrapper import MBLT_Engine + from ..results import Results + +logger = logging.getLogger(__name__) + + +class COCOResult(NamedTuple): + """COCO mAP metrics.""" + + map5095: float + map50: float + + @property + def primary_score(self) -> float: + """Return mAP50-95.""" + + return self.map5095 + + @property + def secondary_score(self) -> float: + """Return mAP50.""" + + return self.map50 + + +def format_coco_results( + task: str, + nms_outs: Results, + input_shape: tuple[int, ...], + org_shape: tuple[int, ...], + ratio_pad: list[Any], + idx: list[int], + dataset_ids: list[int], + postprocess: Any, +) -> list[dict[str, Any]]: + """Format the results for COCO evaluation. + + Args: + task (str): The task to evaluate. + nms_outs (Results): The output of the postprocessing. + input_shape (tuple): The shape of the input tensor. + org_shape (tuple): The original shape of the image. + idx (list): The indices of the images in the batch. + dataset_ids (list): The list of image IDs in the dataset. + postprocess: The postprocessing instance. + Returns: + list: The formatted results. + """ + results = [] + if task == "object_detection": + labels_list, boxes_list, scores_list = postprocess.nmsout2eval( + nms_outs.output, + input_shape, + org_shape, + ratio_pad=ratio_pad, + ) + for i, labels, boxes, scores in zip(idx, labels_list, boxes_list, scores_list): + results.extend( + [ + { + "image_id": dataset_ids[i], + "category_id": label, + "bbox": box, + "score": score, + } + for box, score, label in zip(boxes, scores, labels) + ] + ) + elif task == "instance_segmentation": + labels_list, boxes_list, scores_list, extra_list = postprocess.nmsout2eval( + nms_outs.output, + input_shape, + org_shape, + ratio_pad=ratio_pad, + ) + for i, labels, boxes, scores, extra in zip( + idx, labels_list, boxes_list, scores_list, extra_list + ): + results.extend( + [ + { + "image_id": dataset_ids[i], + "category_id": label, + "bbox": box, + "score": score, + "segmentation": extra, + } + for box, score, label, extra in zip(boxes, scores, labels, extra) + ] + ) + elif task == "pose_estimation": + labels_list, boxes_list, scores_list, extra_list = postprocess.nmsout2eval( + nms_outs.output, + input_shape, + org_shape, + ratio_pad=ratio_pad, + ) + for i, labels, boxes, scores, extra in zip( + idx, labels_list, boxes_list, scores_list, extra_list + ): + results.extend( + [ + { + "image_id": dataset_ids[i], + "category_id": label, + "bbox": box, + "score": score, + "keypoints": extra, + } + for box, score, label, extra in zip(boxes, scores, labels, extra) + ] + ) + else: + raise NotImplementedError( + f"Only object detection, instance segmentation, and pose estimation are supported, but we got {task}" + ) + return results + + +def eval_coco( + model: MBLT_Engine, + data_path: str, + batch_size: int, + conf_thres: float | None = None, + iou_thres: float | None = None, +) -> float: + """Evaluate a model on COCO and return the legacy numeric mAP50-95 score.""" + + return eval_coco_metrics( + model, data_path, batch_size, conf_thres, iou_thres + ).primary_score + + +def eval_coco_metrics( + model: MBLT_Engine, + data_path: str, + batch_size: int, + conf_thres: float | None = None, + iou_thres: float | None = None, +) -> COCOResult: + """Evaluate a model on COCO and return structured mAP metrics. + + Args: + model (MBLT_Engine): The model engine to evaluate. + data_path (str): Path to the COCO dataset. + batch_size (int): Batch size for evaluation. + conf_thres (float | None): Optional confidence threshold override. + iou_thres (float | None): Optional IoU threshold override. + + Returns: + Structured mAP50-95 primary and mAP50 secondary metrics. + """ + task = normalize_vision_task( + model.post_cfg["task"], + supported=("object_detection", "instance_segmentation", "pose_estimation"), + ) + if task in {"object_detection", "instance_segmentation"}: + dataset = CustomCOCODataset( + os.path.join(data_path, "val2017"), + os.path.join(data_path, "instances_val2017.json"), + ) + else: + dataset = CustomCOCODataset( + os.path.join(data_path, "val2017"), + os.path.join(data_path, "person_keypoints_val2017.json"), + min_keypoints=0, + ) + + dataloader = get_coco_loader(dataset, batch_size, model.preprocess_with_metadata) + model.set_postprocess_thresholds(conf_thres=conf_thres, iou_thres=iou_thres) + + results = [] + num_data = len(dataset) + total_iter = math.ceil(num_data / batch_size) + pbar = tqdm(dataloader, total=total_iter, desc="Evaluating COCO") + + inference_time = 0.0 + cum_num_data = 0 + + for input_npu, org_shape, ratio_pad, idx in pbar: + cum_num_data += len(idx) + tic = time() + out_npu = model(input_npu) + inference_time += time() - tic + + nms_outs = model.postprocess(out_npu, multi_label=True) + results.extend( + format_coco_results( + task, + nms_outs, + input_npu.shape[1:-1], + org_shape, + ratio_pad, + idx, + dataset.ids, + model.postprocessor, + ) + ) + + pbar.set_postfix_str(f"NPU FPS: {cum_num_data / inference_time:.3f}") + + pbar.close() + res = evaluate_predictions_on_coco(dataset.coco, results, task, img_ids=dataset.ids) + + print("COCO evaluation completed") + return COCOResult( + map5095=float(res.stats[0].item()), map50=float(res.stats[1].item()) + ) + + +def evaluate_predictions_on_coco( + coco_gt: COCO, + coco_results: list[dict[str, Any]], + task: str, + img_ids: list[int] | None = None, +) -> COCOeval_faster: + """Evaluates predictions using the COCO API. + + Args: + coco_gt (COCO): Ground truth COCO object. + coco_results (list): Predictions in COCO format. + task (str): Task type ('object_detection', 'instance_segmentation', or 'pose_estimation'). + img_ids: Optional image IDs to include in evaluation. + + Returns: + COCOeval_faster: The COCO evaluation object containing results. + """ + normalized_task = normalize_vision_task( + task, + supported=("object_detection", "instance_segmentation", "pose_estimation"), + ) + + if coco_results: + coco_dt = coco_gt.loadRes(coco_results) + else: + coco_dt = COCO() + + if normalized_task == "object_detection": + coco_eval = COCOeval_faster( + coco_gt, coco_dt, "bbox", print_function=logger.info + ) + elif normalized_task == "instance_segmentation": + coco_eval = COCOeval_faster( + coco_gt, coco_dt, "segm", print_function=logger.info + ) + elif normalized_task == "pose_estimation": + coco_eval = COCOeval_faster( + coco_gt, coco_dt, "keypoints", print_function=logger.info + ) + else: + raise RuntimeError(f"Unexpected validated COCO task: {normalized_task}") + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + return coco_eval diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py new file mode 100644 index 0000000..999bb78 --- /dev/null +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -0,0 +1,621 @@ +"""Evaluation script for DOTAv1 oriented bounding boxes.""" + +from __future__ import annotations + +import math +import re +from collections import defaultdict +from pathlib import Path +from time import time +from typing import TYPE_CHECKING, Any, NamedTuple, cast + +import numpy as np +import torch +from mblt_vision.utils.postprocess.common import ( + batch_probiou, + rotated_nms, + xywhr2xyxyxyxy, + xyxyxyxy2xywhr, +) +from tqdm import tqdm + +from ..._tasks import normalize_vision_task +from ..datasets import ( + CustomDOTAv1, + get_dota_loader, + get_dotav1_class_num, + get_dotav1_label, +) +from ..letterbox import RatioPad, resolve_ratio_pad + +if TYPE_CHECKING: + from ...wrapper import MBLT_Engine + from ..results import Results + +DOTAV1_CLASS_TO_IDX = { + get_dotav1_label(index): index for index in range(get_dotav1_class_num()) +} + + +class DOTAResult(NamedTuple): + """DOTAv1 rotated detection metrics in the legacy tuple order.""" + + map50: float + map5095: float + + @property + def primary_score(self) -> float: + """Return the primary DOTAv1 validation metric.""" + return self.map5095 + + @property + def secondary_score(self) -> float: + """Return the secondary DOTAv1 validation metric.""" + return self.map50 + + +def _label_to_index(label: str) -> int: + """Convert a DOTAv1 class token to a class index.""" + try: + return int(label) + except ValueError: + return DOTAV1_CLASS_TO_IDX[label] + + +def _load_ground_truths( + data_path: str, dataset: CustomDOTAv1 +) -> dict[str, dict[str, torch.Tensor]]: + """Load DOTAv1 OBB ground-truth labels in original-image coordinates. + + Args: + data_path: DOTAv1 root directory. + dataset: Dataset containing image IDs and image paths. + + Returns: + Mapping from image ID to tensors for classes, polygons, and ``xywhr`` boxes. + """ + label_dir = Path(data_path) / "labels" / "val" + original_label_dir = Path(data_path) / "labels" / "val_original" + ground_truths: dict[str, dict[str, torch.Tensor]] = {} + for image_id, image_path in zip(dataset.ids, dataset.image_paths): + image = dataset._load_image(image_path) + height, width = image.shape[:2] + label_path = label_dir / f"{image_id}.txt" + original_label_path = original_label_dir / f"{image_id}.txt" + classes = [] + polygons = [] + + if label_path.is_file(): + for line in label_path.read_text(encoding="utf-8").splitlines(): + parts = line.split() + if len(parts) < 9: + continue + cls = _label_to_index(parts[0]) + if cls >= get_dotav1_class_num(): + raise ValueError( + f"Unsupported DOTAv1 class index {cls} in {label_path}." + ) + coords = torch.tensor( + [float(value) for value in parts[1:9]], dtype=torch.float32 + ).reshape(4, 2) + if coords.numel() and float(coords.max()) <= 1.5: + coords[:, 0] *= width + coords[:, 1] *= height + classes.append(cls) + polygons.append(coords) + elif original_label_path.is_file(): + for line in original_label_path.read_text(encoding="utf-8").splitlines(): + parts = line.split() + if len(parts) < 9: + continue + # Official DOTAv1 labels use ``1`` for difficult objects. Keep ``2`` + # ignored for compatibility with previously supported label exports. + if len(parts) >= 10 and parts[9] in {"1", "2"}: + continue + cls = _label_to_index(parts[8]) + if cls >= get_dotav1_class_num(): + raise ValueError( + f"Unsupported DOTAv1 class index {cls} in {original_label_path}." + ) + coords = torch.tensor( + [float(value) for value in parts[:8]], dtype=torch.float32 + ).reshape(4, 2) + classes.append(cls) + polygons.append(coords) + + polygon_tensor = ( + torch.stack(polygons).to(torch.float32) + if polygons + else torch.zeros((0, 4, 2), dtype=torch.float32) + ) + if polygon_tensor.numel(): + boxes_xywhr = xyxyxyxy2xywhr(polygon_tensor) + boxes = cast(torch.Tensor, boxes_xywhr).to(torch.float32) + else: + boxes = torch.zeros((0, 5), dtype=torch.float32) + + ground_truths[image_id] = { + "cls": torch.tensor(classes, dtype=torch.int64), + "polygons": polygon_tensor, + "bboxes": boxes, + } + return ground_truths + + +def format_dota_results( + nms_outs: Results, + input_shape: tuple[int, ...], + org_shape: tuple[int, ...], + ratio_pad: list[Any], + image_ids: tuple[str, ...], + postprocess: Any, +) -> list[dict[str, Any]]: + """Format model outputs for DOTAv1 evaluation and export. + + Args: + nms_outs: Postprocessed model results. + input_shape: Preprocessed image shape. + org_shape: Original image shapes. + ratio_pad: Letterbox metadata. + image_ids: DOTAv1 image IDs. + postprocess: Postprocessor instance. + + Returns: + List of formatted prediction dictionaries. + """ + labels_list, polygons_list, scores_list, xywhr_list = postprocess.nmsout2eval( + nms_outs.output, + input_shape, + org_shape, + ratio_pad=ratio_pad, + include_xywhr=True, + ) + results = [] + for image_id, labels, polygons, scores, xywhrs in zip( + image_ids, labels_list, polygons_list, scores_list, xywhr_list + ): + for label, polygon, score, xywhr in zip(labels, polygons, scores, xywhrs): + results.append( + { + "image_id": image_id, + "category_id": DOTAV1_CLASS_TO_IDX[label], + "category_name": label, + "poly": polygon, + "score": score, + "rbox": xywhr, + } + ) + return results + + +def _compute_ap( + recall: np.ndarray, precision: np.ndarray +) -> tuple[float, np.ndarray, np.ndarray]: + """Compute AP from recall and precision curves with Ultralytics interpolation.""" + mrec = np.concatenate(([0.0], recall, [recall[-1] if len(recall) else 1.0], [1.0])) + mpre = np.concatenate(([1.0], precision, [0.0], [0.0])) + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + grid = np.linspace(0, 1, 101) + integrate = getattr(np, "trapezoid", None) + if integrate is None: + integrate = np.trapz + return float(integrate(np.interp(grid, mrec, mpre), grid)), mpre, mrec + + +def _ap_per_class( + tp: np.ndarray, + conf: np.ndarray, + pred_cls: np.ndarray, + target_cls: np.ndarray, + eps: float = 1e-16, +) -> np.ndarray: + """Compute AP per class using the Ultralytics object-detection metric policy.""" + if target_cls.size == 0: + niou = tp.shape[1] if tp.ndim == 2 else 10 + return np.zeros((0, niou), dtype=np.float64) + + order = np.argsort(-conf) + tp = tp[order] + conf = conf[order] + pred_cls = pred_cls[order] + + unique_classes, target_count = np.unique(target_cls, return_counts=True) + ap = np.zeros((unique_classes.shape[0], tp.shape[1]), dtype=np.float64) + for class_index, class_id in enumerate(unique_classes): + pred_mask = pred_cls == class_id + num_labels = target_count[class_index] + num_predictions = pred_mask.sum() + if num_predictions == 0 or num_labels == 0: + continue + + false_positive = (1 - tp[pred_mask]).cumsum(0) + true_positive = tp[pred_mask].cumsum(0) + recall = true_positive / (num_labels + eps) + precision = true_positive / (true_positive + false_positive + eps) + for iou_index in range(tp.shape[1]): + ap[class_index, iou_index], _, _ = _compute_ap( + recall[:, iou_index], precision[:, iou_index] + ) + return ap + + +def _match_predictions( + pred_classes: torch.Tensor, + true_classes: torch.Tensor, + iou: torch.Tensor, + iouv: torch.Tensor, +) -> np.ndarray: + """Match predictions to ground-truth boxes with Ultralytics one-to-one matching.""" + correct = np.zeros((pred_classes.shape[0], iouv.shape[0]), dtype=bool) + if pred_classes.numel() == 0 or true_classes.numel() == 0: + return correct + + correct_class = true_classes[:, None] == pred_classes + iou_np = (iou * correct_class).cpu().numpy() + for iou_index, threshold in enumerate(iouv.cpu().tolist()): + matches = np.array(np.nonzero(iou_np >= threshold)).T + if matches.shape[0] == 0: + continue + if matches.shape[0] > 1: + matches = matches[iou_np[matches[:, 0], matches[:, 1]].argsort()[::-1]] + # Preserve the IoU-ranked order while keeping the first match per prediction and target. + matches = matches[np.sort(np.unique(matches[:, 1], return_index=True)[1])] + matches = matches[np.sort(np.unique(matches[:, 0], return_index=True)[1])] + correct[matches[:, 1].astype(int), iou_index] = True + return correct + + +def _empty_stats() -> dict[str, list[np.ndarray]]: + """Create an empty DOTAv1 metric statistics accumulator.""" + return {"tp": [], "conf": [], "pred_cls": [], "target_cls": []} + + +def _append_stats( + stats: dict[str, list[np.ndarray]], image_stats: dict[str, np.ndarray] +) -> None: + """Append one image's metric statistics to the accumulator.""" + for key, value in image_stats.items(): + stats[key].append(value) + + +def _nms_output_to_predictions(nms_out: torch.Tensor) -> dict[str, torch.Tensor]: + """Convert OBB NMS rows to the prediction dictionary used by metric matching.""" + if nms_out.numel() == 0: + return { + "bboxes": torch.zeros((0, 5), dtype=torch.float32), + "conf": torch.zeros(0, dtype=torch.float32), + "cls": torch.zeros(0, dtype=torch.int64), + } + + nms_out = nms_out.detach().cpu() + return { + "bboxes": torch.cat([nms_out[:, :4], nms_out[:, 6:7]], dim=-1).to( + torch.float32 + ), + "conf": nms_out[:, 4].to(torch.float32), + "cls": nms_out[:, 5].to(torch.int64), + } + + +def _ratio_pad_for_shape( + input_shape: tuple[int, ...], + org_shape: tuple[int, int], + ratio_pad: RatioPad | None, +) -> tuple[float, tuple[float, float]]: + """Return letterbox gain and padding for an image.""" + if len(input_shape) < 2: + raise ValueError(f"Expected at least 2 input dimensions, got {input_shape}.") + + ratio, pad = resolve_ratio_pad( + (input_shape[0], input_shape[1]), org_shape, ratio_pad + ) + return float(ratio[0]), (float(pad[0]), float(pad[1])) + + +def _ground_truth_to_input_space( + ground_truth: dict[str, torch.Tensor], + input_shape: tuple[int, ...], + org_shape: tuple[int, int], + ratio_pad: RatioPad | None, +) -> dict[str, torch.Tensor]: + """Transform original-image DOTAv1 polygons to letterboxed ``xywhr`` boxes.""" + classes = ground_truth["cls"] + polygons = ground_truth.get("polygons") + if polygons is None: + return {"cls": classes, "bboxes": ground_truth["bboxes"]} + if polygons.numel() == 0: + return {"cls": classes, "bboxes": torch.zeros((0, 5), dtype=torch.float32)} + + gain, pad = _ratio_pad_for_shape(input_shape, org_shape, ratio_pad) + transformed = polygons.clone().to(torch.float32) + transformed[..., 0] = transformed[..., 0] * gain + pad[0] + transformed[..., 1] = transformed[..., 1] * gain + pad[1] + transformed_boxes = xyxyxyxy2xywhr(transformed) + return { + "cls": classes, + "bboxes": cast(torch.Tensor, transformed_boxes).to(torch.float32), + } + + +def _process_image_stats( + predictions: dict[str, torch.Tensor], + target: dict[str, torch.Tensor], + iouv: torch.Tensor, +) -> dict[str, np.ndarray]: + """Build one image's true-positive, confidence, class, and target arrays.""" + target_cls = target["cls"].cpu().numpy() + if target["cls"].numel() == 0 or predictions["cls"].numel() == 0: + true_positive = np.zeros( + (predictions["cls"].shape[0], iouv.numel()), dtype=bool + ) + else: + iou = batch_probiou(target["bboxes"], predictions["bboxes"]) + true_positive = _match_predictions(predictions["cls"], target["cls"], iou, iouv) + return { + "tp": true_positive, + "conf": predictions["conf"].cpu().numpy(), + "pred_cls": predictions["cls"].cpu().numpy(), + "target_cls": target_cls, + } + + +def _evaluate_stats(stats: dict[str, list[np.ndarray]], niou: int = 10) -> DOTAResult: + """Compute DOTAv1 metrics in legacy tuple order: mAP50, then mAP50-95.""" + target_cls = ( + np.concatenate(stats["target_cls"], 0) + if stats["target_cls"] + else np.zeros(0, dtype=np.float64) + ) + if target_cls.size == 0: + return DOTAResult(map5095=0.0, map50=0.0) + + tp = ( + np.concatenate(stats["tp"], 0) + if stats["tp"] + else np.zeros((0, niou), dtype=bool) + ) + conf = ( + np.concatenate(stats["conf"], 0) + if stats["conf"] + else np.zeros(0, dtype=np.float64) + ) + pred_cls = ( + np.concatenate(stats["pred_cls"], 0) + if stats["pred_cls"] + else np.zeros(0, dtype=np.float64) + ) + ap = _ap_per_class(tp, conf, pred_cls, target_cls) + if ap.size == 0: + return DOTAResult(map5095=0.0, map50=0.0) + return DOTAResult(map5095=float(ap.mean()), map50=float(ap[:, 0].mean())) + + +def evaluate_dota_predictions( + ground_truths: dict[str, dict[str, torch.Tensor]], + predictions: list[dict[str, Any]], +) -> DOTAResult: + """Evaluate DOTAv1 predictions with local rotated mAP. + + Args: + ground_truths: Mapping of image IDs to class and OBB tensors. + predictions: Formatted prediction dictionaries. + + Returns: + Rotated mAP at IoU ``0.50`` followed by mAP averaged across ``0.50:0.95``. + The ``primary_score`` and ``secondary_score`` properties expose mAP50-95 + and mAP50, respectively. + """ + iouv = torch.linspace(0.5, 0.95, 10) + stats = _empty_stats() + predictions_by_image: dict[str, list[dict[str, Any]]] = defaultdict(list) + for prediction in predictions: + predictions_by_image[str(prediction["image_id"])].append(prediction) + + image_ids = set(ground_truths) | set(predictions_by_image) + for image_id in sorted(image_ids): + rows = predictions_by_image.get(image_id, []) + if rows: + pred_dict = { + "bboxes": torch.tensor( + [row["rbox"] for row in rows], dtype=torch.float32 + ), + "conf": torch.tensor( + [row["score"] for row in rows], dtype=torch.float32 + ), + "cls": torch.tensor( + [row["category_id"] for row in rows], dtype=torch.int64 + ), + } + else: + pred_dict = _nms_output_to_predictions( + torch.zeros((0, 7), dtype=torch.float32) + ) + target = ground_truths.get( + image_id, + { + "cls": torch.zeros(0, dtype=torch.int64), + "bboxes": torch.zeros((0, 5), dtype=torch.float32), + }, + ) + _append_stats(stats, _process_image_stats(pred_dict, target, iouv)) + return _evaluate_stats(stats, niou=iouv.numel()) + + +def save_dota_task1_predictions( + predictions: list[dict[str, Any]], save_dir: str +) -> tuple[Path, Path]: + """Save split and merged predictions in DOTA Task1 text format. + + Args: + predictions: Formatted prediction dictionaries. + save_dir: Directory where prediction folders are written. + + Returns: + Tuple of split and merged prediction directories. + """ + root = Path(save_dir) + pred_txt = root / "predictions_txt" + pred_merged_txt = root / "predictions_merged_txt" + pred_txt.mkdir(parents=True, exist_ok=True) + pred_merged_txt.mkdir(parents=True, exist_ok=True) + + for cls_idx in range(get_dotav1_class_num()): + (pred_txt / f"Task1_{get_dotav1_label(cls_idx)}.txt").write_text( + "", encoding="utf-8" + ) + (pred_merged_txt / f"Task1_{get_dotav1_label(cls_idx)}.txt").write_text( + "", encoding="utf-8" + ) + + for pred in predictions: + class_name = pred["category_name"] + polygon = pred["poly"] + with (pred_txt / f"Task1_{class_name}.txt").open("a", encoding="utf-8") as file: + file.write( + f"{pred['image_id']} {pred['score']} " + f"{polygon[0]} {polygon[1]} {polygon[2]} {polygon[3]} " + f"{polygon[4]} {polygon[5]} {polygon[6]} {polygon[7]}\n" + ) + + merged_results: dict[str, list[list[float]]] = defaultdict(list) + offset_pattern = re.compile(r"(\d+)___(\d+)") + for pred in predictions: + image_id = pred["image_id"] + base_image_id = image_id.split("__", 1)[0] + offset_match = offset_pattern.search(image_id) + x_offset, y_offset = (0, 0) + if offset_match is not None: + x_offset, y_offset = ( + int(offset_match.group(1)), + int(offset_match.group(2)), + ) + rbox = list(pred["rbox"]) + rbox[0] += x_offset + rbox[1] += y_offset + merged_results[base_image_id].append( + [*rbox, pred["score"], float(pred["category_id"])] + ) + + for image_id, rows in merged_results.items(): + bbox = torch.tensor(rows, dtype=torch.float32) + if bbox.numel() == 0: + continue + max_wh = max(float(torch.max(bbox[:, :2]).item() * 2), 1.0) + class_offsets = bbox[:, 6:7] * max_wh + boxes = bbox[:, :5].clone() + boxes[:, :2] += class_offsets + keep = rotated_nms(boxes, bbox[:, 5], 0.3) + bbox = bbox[keep] + polygons = xywhr2xyxyxyxy(bbox[:, :5]).reshape(-1, 8) + for polygon, score, cls in zip( + polygons.tolist(), bbox[:, 5].tolist(), bbox[:, 6].tolist() + ): + class_name = get_dotav1_label(int(cls)) + rounded_polygon = [round(float(value), 3) for value in polygon] + with (pred_merged_txt / f"Task1_{class_name}.txt").open( + "a", encoding="utf-8" + ) as file: + file.write( + f"{image_id} {round(float(score), 3)} " + f"{rounded_polygon[0]} {rounded_polygon[1]} {rounded_polygon[2]} {rounded_polygon[3]} " + f"{rounded_polygon[4]} {rounded_polygon[5]} {rounded_polygon[6]} {rounded_polygon[7]}\n" + ) + + return pred_txt, pred_merged_txt + + +def _nms_output_list(nms_output: Any) -> list[torch.Tensor]: + """Normalize postprocess output to a per-image list of OBB tensors.""" + if isinstance(nms_output, list): + return nms_output + if isinstance(nms_output, tuple): + return list(nms_output) + if isinstance(nms_output, torch.Tensor): + if nms_output.ndim == 3: + return [image[image[:, 4] > 0] for image in nms_output] + return [nms_output] + raise TypeError(f"Unsupported OBB NMS output type: {type(nms_output).__name__}.") + + +def eval_dota( + model: MBLT_Engine, + data_path: str, + batch_size: int, + conf_thres: float | None = None, + iou_thres: float | None = None, + save_dir: str | None = None, +) -> DOTAResult: + """Evaluate a model on DOTAv1 validation. + + Args: + model: Model engine to evaluate. + data_path: DOTAv1 dataset root. + batch_size: Batch size for evaluation. + conf_thres: Optional confidence threshold override. + iou_thres: Optional IoU threshold override. + save_dir: Optional directory for DOTA Task1 prediction files. + + Returns: + Local rotated mAP scores. + """ + if normalize_vision_task(model.post_cfg["task"]) != "obb": + raise NotImplementedError( + f"Task {model.post_cfg['task']} is not supported for DOTAv1 evaluation." + ) + + dataset = CustomDOTAv1(data_path) + dataloader = get_dota_loader(dataset, batch_size, model.preprocess_with_metadata) + model.set_postprocess_thresholds(conf_thres=conf_thres, iou_thres=iou_thres) + ground_truths = _load_ground_truths(data_path, dataset) + iouv = torch.linspace(0.5, 0.95, 10) + stats = _empty_stats() + + results = [] + num_data = len(dataset) + total_iter = math.ceil(num_data / batch_size) + pbar = tqdm(dataloader, total=total_iter, desc="Evaluating DOTAv1") + inference_time = 0.0 + cum_num_data = 0 + + for input_npu, org_shape, ratio_pad, image_ids in pbar: + cum_num_data += len(image_ids) + tic = time() + out_npu = model(input_npu) + inference_time += time() - tic + nms_outs = model.postprocess(out_npu) + input_shape = tuple(int(value) for value in input_npu.shape[1:-1]) + for nms_out, image_id, image_shape, image_ratio_pad in zip( + _nms_output_list(nms_outs.output), + image_ids, + org_shape, + ratio_pad, + ): + target = _ground_truth_to_input_space( + ground_truths[image_id], + input_shape, + (int(image_shape[0]), int(image_shape[1])), + image_ratio_pad, + ) + _append_stats( + stats, + _process_image_stats(_nms_output_to_predictions(nms_out), target, iouv), + ) + if save_dir is not None: + results.extend( + format_dota_results( + nms_outs, + input_shape, + org_shape, + ratio_pad, + image_ids, + model.postprocessor, + ) + ) + pbar.set_postfix_str(f"NPU FPS: {cum_num_data / inference_time:.3f}") + + pbar.close() + map_score = _evaluate_stats(stats, niou=iouv.numel()) + if save_dir is not None: + save_dota_task1_predictions(results, save_dir) + print("DOTAv1 evaluation completed") + return map_score diff --git a/mblt_vision/utils/evaluation/eval_imagenet.py b/mblt_vision/utils/evaluation/eval_imagenet.py new file mode 100644 index 0000000..3d2b21b --- /dev/null +++ b/mblt_vision/utils/evaluation/eval_imagenet.py @@ -0,0 +1,117 @@ +""" +Evaluation script for ImageNet dataset. +""" + +from __future__ import annotations + +import math +from time import time +from typing import TYPE_CHECKING, NamedTuple + +import numpy as np +import torch +from tqdm import tqdm + +from ..datasets import CustomImageFolder, get_imagenet_loader + +if TYPE_CHECKING: + from ...wrapper import MBLT_Engine + + +class ImageNetResult(NamedTuple): + """ImageNet metrics ordered from primary to secondary.""" + + top1: float + top5: float + + @property + def primary_score(self) -> float: + """Return the primary ImageNet validation metric.""" + return self.top1 + + @property + def secondary_score(self) -> float: + """Return the secondary ImageNet validation metric.""" + return self.top5 + + +def eval_imagenet_metrics( + model: MBLT_Engine, data_path: str, batch_size: int +) -> ImageNetResult: + """Evaluates a classification model on the ImageNet validation set. + + Computes Top-1 and Top-5 accuracy and inference speed (FPS) on the NPU. + + Args: + model (MBLT_Engine): The vision engine to evaluate. + data_path (str): Path to the ImageNet validation images. + batch_size (int): Number of images per inference batch. + + Returns: + ImageNetResult: Top-1 primary accuracy and Top-5 secondary accuracy. + """ + dataset = CustomImageFolder(data_path) + dataloader = get_imagenet_loader(dataset, batch_size, model.preprocess) + num_data = len(dataset) + total_iter = math.ceil(num_data / batch_size) + pbar = tqdm(dataloader, total=total_iter, desc="Evaluating ImageNet") + inference_time = 0.0 + cum_num_data = 0 + cum_top1_correct = 0 + cum_top5_correct = 0 + top1_acc = 0.0 + top5_acc = 0.0 + for input_npu, label in pbar: + cum_num_data += len(label) + tic = time() + out_npu = model(input_npu) + inference_time += time() - tic + result = model.postprocess(out_npu) + output = result.output + if isinstance(output, np.ndarray): + prediction = output.argmax(-1) + elif isinstance(output, torch.Tensor): + prediction = output.argmax(-1).cpu().numpy() + else: + raise TypeError( + f"Expected classification output to be a tensor or ndarray, got {type(output)}." + ) + top_k = min(5, output.shape[-1]) + if isinstance(output, torch.Tensor): + top5_prediction = output.topk(top_k, dim=-1).indices.cpu().numpy() + else: + top5_prediction = np.argpartition(output, -top_k, axis=-1)[:, -top_k:] + label_array = np.asarray(label) + cum_top1_correct += (prediction == label_array).sum().item() + cum_top5_correct += ( + np.any(top5_prediction == label_array[:, np.newaxis], axis=-1).sum().item() + ) + top1_acc = cum_top1_correct / cum_num_data + top5_acc = cum_top5_correct / cum_num_data + pbar.set_postfix_str( + f"Top 1 Acc.: {100 * top1_acc:.3f}%, Top 5 Acc.: {100 * top5_acc:.3f}%, " + f"NPU FPS: {cum_num_data / inference_time:.3f}" + ) + pbar.close() + print("ImageNet evaluation completed") + print( + f"Top 1 Acc.: {100 * top1_acc:.3f}%, " + f"Top 5 Acc.: {100 * top5_acc:.3f}%, " + f"NPU FPS: {cum_num_data / inference_time:.3f}" + ) + return ImageNetResult(top1=top1_acc, top5=top5_acc) + + +def eval_imagenet(model: MBLT_Engine, data_path: str, batch_size: int) -> float: + """Evaluate ImageNet and return Top-1 accuracy for numeric API compatibility. + + Args: + model: Vision engine to evaluate. + data_path: Path to the ImageNet validation images. + batch_size: Number of images per inference batch. + + Returns: + Top-1 accuracy in the range 0.0 to 1.0. + """ + + return eval_imagenet_metrics(model, data_path, batch_size).top1 diff --git a/mblt_vision/utils/evaluation/eval_nyu_depth.py b/mblt_vision/utils/evaluation/eval_nyu_depth.py new file mode 100644 index 0000000..efac872 --- /dev/null +++ b/mblt_vision/utils/evaluation/eval_nyu_depth.py @@ -0,0 +1,160 @@ +"""NYU Depth V2 evaluation for monocular depth-estimation models.""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import numpy as np +from mblt_vision.utils.preprocess import build_preprocess +from tqdm import tqdm + +from ..datasets import CustomNYUDepth, get_nyu_depth_loader + +if TYPE_CHECKING: + from ...wrapper import MBLT_Engine + + +@dataclass(frozen=True) +class NYUDepthResult: + """Median-aligned NYU Depth V2 metrics.""" + + delta1: float + abs_rel: float + rmse: float + + @property + def primary_score(self) -> float: + """Return the primary NYU Depth validation metric.""" + + return self.delta1 + + @property + def secondary_score(self) -> float: + """Return the secondary NYU Depth validation metric.""" + + return self.abs_rel + + +class NYUDepthMetricAccumulator: + """Accumulate median-aligned metrics over every valid NYU depth pixel.""" + + MIN_DEPTH = 0.001 + MAX_DEPTH = 100.0 + + def __init__(self) -> None: + """Initialize zero-valued pixel sums.""" + + self.delta1_sum = 0.0 + self.abs_rel_sum = 0.0 + self.squared_error_sum = 0.0 + self.valid_pixel_count = 0 + + def update(self, prediction: np.ndarray, target: np.ndarray) -> None: + """Median-align one prediction and add its valid-pixel statistics.""" + + prediction = np.asarray(prediction, dtype=np.float32) + target = np.asarray(target, dtype=np.float32) + if prediction.shape != target.shape: + raise ValueError( + f"NYU Depth prediction and target shapes must match, got {prediction.shape} and {target.shape}." + ) + valid = ( + np.isfinite(target) & (target > self.MIN_DEPTH) & (target < self.MAX_DEPTH) + ) + valid_pixel_count = int(valid.sum()) + if valid_pixel_count == 0: + raise ValueError( + "NYU Depth sample has no valid pixels in the (0.001, 100.0) range." + ) + + predicted, actual = prediction[valid], target[valid] + invalid_prediction_count = int((~np.isfinite(predicted)).sum()) + if invalid_prediction_count: + raise ValueError( + f"NYU Depth prediction contains {invalid_prediction_count} non-finite value(s) at valid target pixels." + ) + median_prediction = np.median(np.maximum(predicted, self.MIN_DEPTH)) + median_target = np.median(actual) + aligned = predicted * (median_target / median_prediction) + aligned = np.clip(aligned, self.MIN_DEPTH, self.MAX_DEPTH) + ratio = np.maximum(actual / aligned, aligned / actual) + self.delta1_sum += float(np.sum(ratio < 1.25)) + self.abs_rel_sum += float(np.sum(np.abs(actual - aligned) / actual)) + self.squared_error_sum += float(np.sum((actual - aligned) ** 2)) + self.valid_pixel_count += valid_pixel_count + + def result(self) -> NYUDepthResult: + """Return metrics pooled over all accumulated valid pixels.""" + + if self.valid_pixel_count == 0: + raise ValueError("NYU Depth evaluation received no valid pixels.") + return NYUDepthResult( + delta1=self.delta1_sum / self.valid_pixel_count, + abs_rel=self.abs_rel_sum / self.valid_pixel_count, + rmse=float(np.sqrt(self.squared_error_sum / self.valid_pixel_count)), + ) + + +def calculate_nyu_depth_metrics( + prediction: np.ndarray, target: np.ndarray +) -> NYUDepthResult: + """Calculate official pooled metrics for one median-aligned NYU sample.""" + + accumulator = NYUDepthMetricAccumulator() + accumulator.update(prediction, target) + return accumulator.result() + + +def eval_nyu_depth( + model: MBLT_Engine, data_path: str, batch_size: int +) -> NYUDepthResult: + """Evaluate a depth model on paired NYU validation images and depth maps.""" + + dataset = CustomNYUDepth(data_path) + letterbox_cfg = model.pre_cfg.get("LetterBox") + if not isinstance(letterbox_cfg, dict) or "img_size" not in letterbox_cfg: + raise ValueError( + "NYU Depth validation requires a LetterBox img_size in the model preprocessing config." + ) + image_size = letterbox_cfg["img_size"] + if not isinstance(image_size, list) or len(image_size) != 2: + raise ValueError( + "NYU Depth validation img_size must be a two-item [height, width] list." + ) + + validation_pre_cfg = { + name: config for name, config in model.pre_cfg.items() if name != "LetterBox" + } + validation_preprocessor = build_preprocess(validation_pre_cfg) + loader = get_nyu_depth_loader( + dataset, + batch_size, + validation_preprocessor, + image_size=(int(image_size[0]), int(image_size[1])), + ) + accumulator = NYUDepthMetricAccumulator() + for inputs, targets, _, _, _ in tqdm(loader, desc="Evaluating NYU Depth"): + output = model(inputs) + result = model.postprocess(output) + depth = result.depth + if depth is None: + raise ValueError("Depth postprocessor returned no depth maps.") + if isinstance(depth, list): + maps = depth + elif len(targets) == 1 and depth.ndim == 2: + maps = [depth] + else: + maps = [depth[index] for index in range(len(targets))] + if len(maps) != len(targets): + raise ValueError( + f"Depth postprocessor returned {len(maps)} maps for {len(targets)} targets." + ) + for prediction, target in zip(maps, targets): + array = ( + prediction.detach().cpu().numpy() + if hasattr(prediction, "detach") + else np.asarray(prediction) + ) + accumulator.update(array, target) + return accumulator.result() diff --git a/mblt_vision/utils/evaluation/eval_widerface.py b/mblt_vision/utils/evaluation/eval_widerface.py new file mode 100644 index 0000000..ac7879b --- /dev/null +++ b/mblt_vision/utils/evaluation/eval_widerface.py @@ -0,0 +1,382 @@ +"""Evaluation script for WiderFace face detection.""" + +from __future__ import annotations + +import math +import os +from time import time +from typing import TYPE_CHECKING, Any, NamedTuple, cast + +import numpy as np +from mblt_vision.utils.postprocess.base import YOLODetectionPostBase +from scipy.io import loadmat +from tqdm import tqdm + +from ..datasets import CustomWiderFaceDataset, get_widerface_loader + +if TYPE_CHECKING: + from ...wrapper import MBLT_Engine + +CustomWiderface = CustomWiderFaceDataset + + +class WiderFaceResult(NamedTuple): + """WiderFace AP metrics.""" + + easy_ap: float + medium_ap: float + hard_ap: float + + @property + def mean_ap(self) -> float: + """Return the mean AP across Easy, Medium, and Hard.""" + + return (self.easy_ap + self.medium_ap + self.hard_ap) / 3.0 + + @property + def primary_score(self) -> float: + """Return mean AP across the three validation splits.""" + + return self.mean_ap + + @property + def secondary_score(self) -> float: + """Return Hard-set AP.""" + + return self.hard_ap + + +def _empty_prediction() -> np.ndarray: + """Return an empty WiderFace prediction array.""" + + return np.zeros((0, 5), dtype=np.float32) + + +def _initialize_predictions( + dataset: CustomWiderFaceDataset, +) -> dict[str, dict[str, np.ndarray]]: + """Initialize empty predictions for every WiderFace sample.""" + + predictions: dict[str, dict[str, np.ndarray]] = {} + for _, event_name, file_name in dataset.samples: + predictions.setdefault(event_name, {})[os.path.splitext(file_name)[0]] = ( + _empty_prediction() + ) + return predictions + + +def _boxes_scores_to_prediction( + boxes: list[list[float]], scores: list[float] +) -> np.ndarray: + """Convert xywh boxes and scores to a WiderFace prediction array.""" + + if not boxes: + return _empty_prediction() + prediction = np.zeros((len(boxes), 5), dtype=np.float32) + for index, (box, score) in enumerate(zip(boxes, scores)): + prediction[index, :4] = np.asarray(box, dtype=np.float32) + prediction[index, 4] = float(score) + return prediction + + +def eval_widerface( + model: MBLT_Engine, + data_path: str, + batch_size: int, + conf_thres: float | None = None, + iou_thres: float | None = None, +) -> WiderFaceResult: + """Evaluate a face-detection model on WiderFace validation data. + + Args: + model: The face-detection engine to evaluate. + data_path: Organized WiderFace dataset root. + batch_size: Validation batch size. + conf_thres: Optional confidence threshold override. + iou_thres: Optional IoU threshold override. + + Returns: + WiderFace Easy, Medium, and Hard AP metrics. + """ + + if model.post_cfg["task"] != "face_detection": + raise NotImplementedError( + f"Task {model.post_cfg['task']} is not supported for WiderFace evaluation." + ) + + dataset = CustomWiderface(os.path.join(data_path, "images")) + dataloader = get_widerface_loader( + dataset, batch_size, model.preprocess_with_metadata + ) + model.set_postprocess_thresholds(conf_thres=conf_thres, iou_thres=iou_thres) + + predictions = _initialize_predictions(dataset) + num_data = len(dataset) + total_iter = math.ceil(num_data / batch_size) + pbar = tqdm(dataloader, total=total_iter, desc="Evaluating WiderFace") + inference_time = 0.0 + cum_num_data = 0 + + for input_npu, org_shape, ratio_pad, target_classes, fnames in pbar: + cum_num_data += len(fnames) + tic = time() + out_npu = model(input_npu) + inference_time += time() - tic + nms_outs = model.postprocess(out_npu) + input_shape = (int(input_npu.shape[1]), int(input_npu.shape[2])) + img0_shapes = [(int(shape[0]), int(shape[1])) for shape in org_shape.tolist()] + postprocessor = cast(YOLODetectionPostBase, model.postprocessor) + _, boxes_list, scores_list = postprocessor.nmsout2eval( + nms_outs.output, + input_shape, + img0_shapes, + ratio_pad=ratio_pad, + ) + + for event_name, file_name, boxes, scores in zip( + target_classes, fnames, boxes_list, scores_list + ): + predictions[event_name][os.path.splitext(file_name)[0]] = ( + _boxes_scores_to_prediction(boxes, scores) + ) + + pbar.set_postfix_str(f"NPU FPS: {cum_num_data / inference_time:.3f}") + + pbar.close() + aps = evaluation(norm_score(predictions), data_path) + print("WiderFace evaluation completed") + return WiderFaceResult(*aps) + + +def bbox_overlaps(boxes: np.ndarray, query_boxes: np.ndarray) -> np.ndarray: + """Compute pairwise IoU overlaps between boxes and query boxes.""" + + boxes = boxes.astype(np.float32) + query_boxes = query_boxes.astype(np.float32) + + boxes = boxes[:, None, :] + query_boxes = query_boxes[None, :, :] + + iw = ( + np.minimum(boxes[..., 2], query_boxes[..., 2]) + - np.maximum(boxes[..., 0], query_boxes[..., 0]) + + 1 + ) + ih = ( + np.minimum(boxes[..., 3], query_boxes[..., 3]) + - np.maximum(boxes[..., 1], query_boxes[..., 1]) + + 1 + ) + iw = np.maximum(iw, 0) + ih = np.maximum(ih, 0) + inter = iw * ih + + box_area = (boxes[..., 2] - boxes[..., 0] + 1) * (boxes[..., 3] - boxes[..., 1] + 1) + query_area = (query_boxes[..., 2] - query_boxes[..., 0] + 1) * ( + query_boxes[..., 3] - query_boxes[..., 1] + 1 + ) + union = box_area + query_area - inter + + return inter / union + + +def get_gt_boxes(gt_dir: str) -> tuple[Any, ...]: + """Load WiderFace evaluation `.mat` files from the organized dataset.""" + + gt_mat = loadmat(os.path.join(gt_dir, "wider_face_val.mat")) + hard_mat = loadmat(os.path.join(gt_dir, "wider_hard_val.mat")) + medium_mat = loadmat(os.path.join(gt_dir, "wider_medium_val.mat")) + easy_mat = loadmat(os.path.join(gt_dir, "wider_easy_val.mat")) + + facebox_list = gt_mat["face_bbx_list"] + event_list = gt_mat["event_list"] + file_list = gt_mat["file_list"] + hard_gt_list = hard_mat["gt_list"] + medium_gt_list = medium_mat["gt_list"] + easy_gt_list = easy_mat["gt_list"] + + return ( + facebox_list, + event_list, + file_list, + hard_gt_list, + medium_gt_list, + easy_gt_list, + ) + + +def norm_score(pred: dict[str, Any]) -> dict[str, Any]: + """Normalize WiderFace prediction scores to ``[0, 1]``.""" + + max_score = -1e9 + min_score = 1e9 + found = False + + for _, event_predictions in pred.items(): + for _, image_predictions in event_predictions.items(): + if len(image_predictions) == 0: + continue + found = True + _min = float(np.min(image_predictions[:, -1])) + _max = float(np.max(image_predictions[:, -1])) + if _max > max_score: + max_score = _max + if _min < min_score: + min_score = _min + + if not found: + return pred + + diff = max_score - min_score + if diff <= 0: + return pred + + for _, event_predictions in pred.items(): + for _, image_predictions in event_predictions.items(): + if len(image_predictions) == 0: + continue + image_predictions[:, -1] = (image_predictions[:, -1] - min_score) / diff + + return pred + + +def image_eval( + pred: np.ndarray, gt: np.ndarray, ignore: np.ndarray, iou_thresh: float +) -> tuple[np.ndarray, np.ndarray]: + """Evaluate one image worth of WiderFace predictions.""" + + _pred = pred.copy() + _gt = gt.copy() + pred_recall = np.zeros(_pred.shape[0]) + recall_list = np.zeros(_gt.shape[0]) + proposal_list = np.ones(_pred.shape[0]) + + _pred[:, 2] = _pred[:, 2] + _pred[:, 0] + _pred[:, 3] = _pred[:, 3] + _pred[:, 1] + _gt[:, 2] = _gt[:, 2] + _gt[:, 0] + _gt[:, 3] = _gt[:, 3] + _gt[:, 1] + + overlaps = bbox_overlaps(_pred[:, :4], _gt) + + for prediction_index in range(_pred.shape[0]): + gt_overlap = overlaps[prediction_index] + max_overlap = np.max(gt_overlap) + max_idx = np.argmax(gt_overlap) + if max_overlap >= iou_thresh: + if ignore[max_idx] == 0: + recall_list[max_idx] = -1 + proposal_list[prediction_index] = -1 + elif recall_list[max_idx] == 0: + recall_list[max_idx] = 1 + + r_keep_index = np.where(recall_list == 1)[0] + pred_recall[prediction_index] = len(r_keep_index) + return pred_recall, proposal_list + + +def img_pr_info( + thresh_num: int, + pred_info: np.ndarray, + proposal_list: np.ndarray, + pred_recall: np.ndarray, +) -> np.ndarray: + """Compute precision and recall contributions for one image.""" + + pr_info = np.zeros((thresh_num, 2), dtype=np.float32) + for threshold_index in range(thresh_num): + thresh = 1 - (threshold_index + 1) / thresh_num + recall_index = np.where(pred_info[:, 4] >= thresh)[0] + if len(recall_index) == 0: + pr_info[threshold_index, 0] = 0 + pr_info[threshold_index, 1] = 0 + else: + last_index = recall_index[-1] + proposal_index = np.where(proposal_list[: last_index + 1] == 1)[0] + pr_info[threshold_index, 0] = len(proposal_index) + pr_info[threshold_index, 1] = pred_recall[last_index] + return pr_info + + +def dataset_pr_info( + thresh_num: int, pr_curve: np.ndarray, count_face: int +) -> np.ndarray: + """Normalize a WiderFace precision-recall accumulator.""" + + _pr_curve = np.zeros((thresh_num, 2), dtype=np.float32) + for threshold_index in range(thresh_num): + proposals = pr_curve[threshold_index, 0] + matched = pr_curve[threshold_index, 1] + _pr_curve[threshold_index, 0] = matched / proposals if proposals > 0 else 0.0 + _pr_curve[threshold_index, 1] = matched / count_face if count_face > 0 else 0.0 + return _pr_curve + + +def voc_ap(rec: np.ndarray, prec: np.ndarray) -> float: + """Compute VOC-style average precision.""" + + mrec = np.concatenate((np.array([0.0]), rec, np.array([1.0]))) + mpre = np.concatenate((np.array([0.0]), prec, np.array([0.0]))) + + for index in range(mpre.size - 1, 0, -1): + mpre[index - 1] = np.maximum(mpre[index - 1], mpre[index]) + + recall_change_index = np.where(mrec[1:] != mrec[:-1])[0] + ap = np.sum( + (mrec[recall_change_index + 1] - mrec[recall_change_index]) + * mpre[recall_change_index + 1] + ) + return float(ap) + + +def evaluation( + pred: dict[str, Any], gt_path: str, iou_thresh: float = 0.5 +) -> list[float]: + """Evaluate WiderFace predictions against Easy, Medium, and Hard settings.""" + + facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = ( + get_gt_boxes(gt_path) + ) + event_num = len(event_list) + thresh_num = 1000 + settings = ["easy", "medium", "hard"] + setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list] + aps = [] + for setting_id in range(3): + gt_list = setting_gts[setting_id] + count_face = 0 + pr_curve = np.zeros((thresh_num, 2), dtype=np.float32) + pbar = tqdm(range(event_num)) + for event_index in pbar: + pbar.set_description(f"Processing {settings[setting_id]}") + event_name = str(event_list[event_index][0][0]) + img_list = file_list[event_index][0] + pred_list = pred[event_name] + sub_gt_list = gt_list[event_index][0] + gt_bbx_list = facebox_list[event_index][0] + for image_index, img_info in enumerate(img_list): + pred_info = pred_list[str(img_info[0][0])] + gt_boxes = np.array(gt_bbx_list[image_index][0], dtype=np.float32) + keep_index = np.array(sub_gt_list[image_index][0], dtype=np.int64) + count_face += len(keep_index) + + if len(gt_boxes) == 0 or len(pred_info) == 0: + continue + ignore = np.zeros(gt_boxes.shape[0]) + if len(keep_index) != 0: + ignore[keep_index - 1] = 1 + pred_recall, proposal_list = image_eval( + pred_info, gt_boxes, ignore, iou_thresh + ) + pr_curve += img_pr_info( + thresh_num, pred_info, proposal_list, pred_recall + ) + pbar.close() + pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face) + aps.append(voc_ap(pr_curve[:, 1], pr_curve[:, 0])) + + print("==================== Results ====================") + print(f"Easy Val AP: {aps[0]}") + print(f"Medium Val AP: {aps[1]}") + print(f"Hard Val AP: {aps[2]}") + print("=================================================") + return aps diff --git a/pyproject.toml b/pyproject.toml index f6edd6f..558816b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,10 @@ dependencies = [ "opencv-python>=4.11.0.86", "pillow>=11.1.0", "faster-coco-eval", + "gdown>=5.2.0", "huggingface-hub", + "matplotlib", + "requests>=2.32.0", "scipy", "tqdm", "PyYAML", @@ -48,6 +51,10 @@ onnxruntime-gpu = [ "onnx; sys_platform != 'darwin'", "onnxruntime-gpu; sys_platform != 'darwin'", ] +qbcompiler = [ + "qbcompiler>=1.2.0", + "onnxruntime", +] [tool.setuptools] packages = { find = { where = ["."], include = ["mblt_vision", "mblt_vision.*"] } } @@ -61,6 +68,12 @@ version = { attr = "mblt_vision.__version__" } "datasets/*.yaml", ] +[tool.pytest.ini_options] +markers = [ + "requires_network: test downloads model artifacts from Hugging Face Hub.", + "requires_npu: test requires a configured Mobilint NPU runtime and device.", +] + [dependency-groups] dev = [ "pre-commit", diff --git a/tests/TEST.md b/tests/TEST.md index 3d45b51..ffd156d 100644 --- a/tests/TEST.md +++ b/tests/TEST.md @@ -13,16 +13,30 @@ pip install pytest ## Run All Tests -Execute the entire Vision test matrix (this may take a while because it loads every supported model): +Execute the complete standalone Vision test matrix: ```bash -pytest tests/vision +pytest tests ``` -## Run a Single Test File +## Run Offline Unit Tests -Target a specific test file to focus on one model family: +Exclude Hugging Face downloads and NPU hardware: ```bash -pytest tests/vision/test_resnet50.py +pytest tests -m "not requires_network and not requires_npu" +``` + +## Run Optional Integration Tests + +After authenticating with Hugging Face Hub, exercise representative ONNX models: + +```bash +pytest tests/test_onnx_classification.py -m requires_network +``` + +To run MXQ inference, add a configured NPU and the shared runtime options: + +```bash +pytest tests/test_mxq_inference.py -m requires_npu --mxq-path /path/to/model.mxq ``` diff --git a/tests/test_api.py b/tests/test_api.py new file mode 100644 index 0000000..80df7a0 --- /dev/null +++ b/tests/test_api.py @@ -0,0 +1,56 @@ +"""Tests for the standalone public Vision API.""" + +from __future__ import annotations + +import importlib + +import pytest + +import mblt_vision +from mblt_vision import list_models, list_tasks +from mblt_vision._tasks import normalize_vision_task + + +def test_public_discovery_exposes_all_supported_tasks() -> None: + """Expose every standalone Vision task through the public API.""" + + assert list_tasks() == [ + "image_classification", + "depth_estimation", + "object_detection", + "instance_segmentation", + "semantic_segmentation", + "obb", + "pose_estimation", + "face_detection", + ] + assert list_models("obb")["obb"] + + +def test_public_exports_use_only_the_canonical_obb_name() -> None: + """Keep the standalone namespace free of the legacy OBB spelling.""" + + assert "oriented_bounding_boxes" not in mblt_vision.__all__ + assert not hasattr(mblt_vision, "oriented_bounding_boxes") + with pytest.raises(ModuleNotFoundError): + importlib.import_module("mblt_vision.oriented_bounding_boxes") + + +def test_model_exports_are_discoverable_from_task_and_top_level_namespaces() -> None: + """Keep task exports synchronized with lazy top-level compatibility exports.""" + + from mblt_vision.image_classification import ResNet50 + from mblt_vision.object_detection import YOLO11m + + assert mblt_vision.ResNet50 is ResNet50 + assert mblt_vision.YOLO11m is YOLO11m + assert "ResNet50" in list_models("image_classification")["image_classification"] + assert "YOLO11m" in list_models("object_detection")["object_detection"] + + +@pytest.mark.parametrize("task", [None, 1, object()]) +def test_task_normalization_rejects_non_strings(task: object) -> None: + """Reject invalid task inputs with a stable, actionable error.""" + + with pytest.raises(TypeError, match="must be a string"): + normalize_vision_task(task) # type: ignore[arg-type] diff --git a/tests/test_benchmark_vision.py b/tests/test_benchmark_vision.py new file mode 100644 index 0000000..5e3e5f5 --- /dev/null +++ b/tests/test_benchmark_vision.py @@ -0,0 +1,321 @@ +"""Tests for the standardized multi-model vision benchmark tools.""" + +from __future__ import annotations + +import argparse +import csv +from pathlib import Path + +import pytest + +from benchmark.vision import benchmark_vision_models, compare_benchmark_results +from mblt_vision.utils.evaluation import ( + DOTAResult, + ImageNetResult, + NYUDepthResult, + SemanticSegmentationResult, +) + + +def test_benchmark_records_imagenet_metrics_in_primary_order( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Use Top-1 as the score while retaining Top-5 in benchmark metrics.""" + + class FakeModel: + """Minimal classification model double.""" + + post_cfg = {"task": "image_classification"} + + import mblt_vision.utils.evaluation as evaluation_module + + monkeypatch.setattr( + evaluation_module, + "eval_imagenet_metrics", + lambda *args, **kwargs: ImageNetResult(top1=0.75, top5=0.95), + ) + args = argparse.Namespace( + task="image_classification", + data_path=str(tmp_path), + batch_size=1, + ) + + score, score_name, metrics = benchmark_vision_models._evaluate( + FakeModel(), args, tmp_path + ) + + assert score == 0.75 + assert score_name == "top1_accuracy" + assert metrics == {"top1_accuracy": 0.75, "top5_accuracy": 0.95} + + +def test_benchmark_records_depth_metrics( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Record all NYU depth metrics with delta1 as primary.""" + + class FakeModel: + post_cfg = {"task": "depth_estimation", "dataset": "nyu-depth"} + + import mblt_vision.utils.evaluation as evaluation_module + + monkeypatch.setattr( + evaluation_module, + "eval_nyu_depth", + lambda *args, **kwargs: NYUDepthResult(delta1=0.8, abs_rel=0.1, rmse=0.2), + ) + args = argparse.Namespace( + task="depth_estimation", data_path=str(tmp_path), batch_size=1 + ) + + score, score_name, metrics = benchmark_vision_models._evaluate( + FakeModel(), args, tmp_path + ) + + assert (score, score_name) == (0.8, "delta1") + assert metrics == {"delta1": 0.8, "abs_rel": 0.1, "rmse": 0.2} + + +@pytest.mark.parametrize( + ("dataset", "evaluator_name"), + [("ade20k", "eval_ade20k"), ("cityscapes", "eval_cityscapes")], +) +def test_benchmark_dispatches_semantic_taxonomy( + dataset: str, + evaluator_name: str, + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Dispatch semantic evaluation explicitly from the configured taxonomy.""" + + class FakeModel: + post_cfg = {"task": "semantic_segmentation", "dataset": dataset} + + import mblt_vision.utils.evaluation as evaluation_module + + monkeypatch.setattr( + evaluation_module, + evaluator_name, + lambda *args, **kwargs: SemanticSegmentationResult( + miou=0.6, pixel_accuracy=0.9 + ), + ) + args = argparse.Namespace( + task="semantic_segmentation", data_path=str(tmp_path), batch_size=1 + ) + + score, score_name, metrics = benchmark_vision_models._evaluate( + FakeModel(), args, tmp_path + ) + + assert (score, score_name) == (0.6, "miou") + assert metrics == {"miou": 0.6, "pixel_accuracy": 0.9} + + +def test_benchmark_accepts_oriented_bounding_boxes_model_alias( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Dispatch an alias-configured OBB model through DOTAv1 evaluation.""" + + class FakeModel: + post_cfg = {"task": "oriented_bounding_boxes", "dataset": "dotav1"} + + import mblt_vision.utils.evaluation as evaluation_module + + monkeypatch.setattr( + evaluation_module, + "eval_dota", + lambda *args, **kwargs: DOTAResult(map50=0.7, map5095=0.5), + ) + args = argparse.Namespace( + task="obb", + data_path=str(tmp_path), + batch_size=1, + conf_thres=None, + iou_thres=None, + ) + + score, score_name, metrics = benchmark_vision_models._evaluate( + FakeModel(), args, tmp_path + ) + + assert (score, score_name) == (0.5, "map50_95") + assert metrics == {"map50_95": 0.5, "map50": 0.7} + + +def test_benchmark_continues_after_evaluator_type_error( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Record an unsupported evaluator output without aborting later targets.""" + + class FakeEngine: + """Minimal engine used to exercise per-target error handling.""" + + def __init__(self, *, model_cls: str, **kwargs: object) -> None: + self.model_cls = model_cls + + def dispose(self) -> None: + """Release the fake benchmark engine.""" + + def fake_evaluate( + model: FakeEngine, + args: object, + run_dir: Path, + ) -> tuple[float, str, dict[str, float]]: + if model.model_cls == "invalid-output": + raise TypeError("Unsupported model output") + return 0.9, "top1_accuracy", {"top1_accuracy": 0.9} + + import mblt_vision as vision + + monkeypatch.setattr(vision, "MBLT_Engine", FakeEngine) + monkeypatch.setattr(benchmark_vision_models, "_evaluate", fake_evaluate) + + result = benchmark_vision_models.main( + [ + "--models", + "invalid-output", + "valid-output", + "--task", + "image_classification", + "--data-path", + str(tmp_path / "dataset"), + "--results-dir", + str(tmp_path / "results"), + "--no-plot", + ] + ) + + with (tmp_path / "results" / "results.csv").open( + newline="", encoding="utf-8" + ) as results_file: + rows = list(csv.DictReader(results_file)) + + assert result == 1 + assert [row["status"] for row in rows] == ["error", "ok"] + assert rows[0]["error"] == "TypeError: Unsupported model output" + + +def test_comparison_rejects_matching_metrics_from_different_tasks( + tmp_path: Path, +) -> None: + """Reject task-incompatible inputs even when their score metric matches.""" + + for name, task in ( + ("detection", "object_detection"), + ("segmentation", "instance_segmentation"), + ): + results_path = tmp_path / name / "results.csv" + results_path.parent.mkdir() + results_path.write_text( + f"model,core_mode,task,status,score_name,score\nmodel-a,global8,{task},ok,map50_95,0.5\n", + encoding="utf-8", + ) + + with pytest.raises(SystemExit, match="incompatible benchmark tasks"): + compare_benchmark_results.main( + [str(tmp_path / "detection"), str(tmp_path / "segmentation")] + ) + + +@pytest.mark.parametrize( + "framework_args", + [ + ["--framework", "onnx"], + ["--model-path", "model.onnx"], + ["--model-path", "MODEL.ONNX"], + ], +) +def test_onnx_benchmark_uses_one_neutral_runtime_target( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + framework_args: list[str], +) -> None: + """Avoid recording repeated NPU core-mode runs for ONNX inference.""" + + captured_modes: list[str] = [] + + def fake_run_target( + model_name: str, core_mode: str, args: object, results_dir: Path + ) -> dict[str, object]: + captured_modes.append(core_mode) + return { + "model": model_name, + "core_mode": core_mode, + "task": "image_classification", + "batch_size": 1, + "status": "ok", + "score": 0.9, + "score_name": "top1", + "elapsed_s": 0.0, + } + + monkeypatch.setattr(benchmark_vision_models, "_run_target", fake_run_target) + + assert ( + benchmark_vision_models.main( + [ + "--models", + "model-a", + "--task", + "image_classification", + *framework_args, + "--core-mode", + "all", + "--data-path", + str(tmp_path / "dataset"), + "--results-dir", + str(tmp_path / "results"), + "--no-plot", + ] + ) + == 0 + ) + assert captured_modes == ["onnx"] + + +def test_comparison_uses_result_directory_names( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Derive default chart paths and legend labels from result directories.""" + + for name in ("baseline", "candidate"): + results_path = tmp_path / name / "results.csv" + results_path.parent.mkdir() + results_path.write_text( + "model,core_mode,task,status,score_name,score\nmodel-a,global8,object_detection,ok,map50_95,0.5\n", + encoding="utf-8", + ) + import benchmark.common.chart_utils as chart_utils + + captured: dict[str, object] = {} + + def fake_default_charts_dir( + script_dir: Path, sources: list[Path], **kwargs: object + ) -> Path: + captured["sources"] = sources + return tmp_path / "charts" + + monkeypatch.setattr(chart_utils, "default_charts_dir", fake_default_charts_dir) + monkeypatch.setattr( + chart_utils, + "plot_grouped_scalar_barh", + lambda **kwargs: captured.update(kwargs), + ) + + assert ( + compare_benchmark_results.main( + [str(tmp_path / "baseline"), str(tmp_path / "candidate")] + ) + == 0 + ) + sources = captured["sources"] + assert isinstance(sources, list) + source_paths: list[Path] = [] + for source in sources: + assert isinstance(source, Path) + source_paths.append(source) + assert [path.name for path in source_paths] == ["baseline", "candidate"] + assert captured["group_labels"] == ["baseline", "candidate"] diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py new file mode 100644 index 0000000..f052e50 --- /dev/null +++ b/tests/test_compile_vision.py @@ -0,0 +1,873 @@ +"""Tests for packaged vision compilation and calibration preparation.""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +import numpy as np +import pytest + +from mblt_vision.compile import vision as compile_module +from mblt_vision.compile.vision import ( + copy_calibration_subset, + prepare_calibration_arrays, + resolve_quantization_values, + select_calibration_images, +) +from mblt_vision.utils import datasets as dataset_utils +from mblt_vision.utils.datasets import readiness as readiness_module +from mblt_vision.wrapper import resolve_model_config + + +def _write_images(directory: Path, names: list[str]) -> list[Path]: + """Create placeholder image files for selection tests. + + Args: + directory: Destination directory. + names: Relative filenames to create. + + Returns: + Created image paths. + """ + + created: list[Path] = [] + for name in names: + path = directory / name + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(name.encode()) + created.append(path) + return created + + +def test_resolve_model_config_derives_onnx_filename() -> None: + """Derive ONNX artifact names consistently from MXQ artifact names.""" + + hub_config = resolve_model_config("alexnet") + direct_config = resolve_model_config( + { + "file_cfg": { + "filename": "example.mxq", + }, + "pre_cfg": {}, + "post_cfg": {}, + } + ) + custom_config = resolve_model_config( + { + "file_cfg": { + "filename": "example.mxq", + "onnx_filename": "exported-model.onnx", + }, + "pre_cfg": {}, + "post_cfg": {}, + } + ) + + assert hub_config["file_cfg"]["onnx_filename"] == "alexnet_IMAGENET1K_V1.onnx" + assert direct_config["file_cfg"]["onnx_filename"] == "example.onnx" + assert custom_config["file_cfg"]["onnx_filename"] == "exported-model.onnx" + + +@pytest.mark.parametrize( + ("task", "relative_dir"), + [ + ("depth_estimation", "images"), + ("object_detection", "val2017"), + ("semantic_segmentation", "images"), + ("obb", "images"), + ], +) +def test_non_imagenet_selection_is_deterministic( + task: str, relative_dir: str, tmp_path: Path +) -> None: + """Select deterministic total-count COCO and DOTAv1 samples.""" + + _write_images(tmp_path / relative_dir, [f"image-{index}.jpg" for index in range(5)]) + + first = select_calibration_images(task, tmp_path, subset_size=3, seed=7) + second = select_calibration_images(task, tmp_path, subset_size=3, seed=7) + + assert first == second + assert len(first) == 3 + + +def test_legacy_obb_alias_selects_calibration_images(tmp_path: Path) -> None: + """Normalize the legacy OBB task name in exported sampling helpers.""" + + images = _write_images(tmp_path / "images", ["P0001.png", "P0002.png"]) + + assert select_calibration_images( + "oriented_bounding_boxes", + tmp_path, + subset_size=2, + ) == select_calibration_images("obb", tmp_path, subset_size=2) + assert set( + select_calibration_images("oriented_bounding_boxes", tmp_path, subset_size=2) + ) == set(images) + + +@pytest.mark.parametrize( + ("task", "relative_dir"), + [ + ("depth_estimation", "images"), + ("object_detection", "val2017"), + ("instance_segmentation", "val2017"), + ("semantic_segmentation", "images"), + ("pose_estimation", "val2017"), + ("obb", "images"), + ], +) +def test_selection_defaults_to_seed_zero( + task: str, relative_dir: str, tmp_path: Path +) -> None: + """Use seed zero whenever callers omit the vision sampling seed.""" + + _write_images(tmp_path / relative_dir, [f"image-{index}.jpg" for index in range(5)]) + + assert select_calibration_images( + task, tmp_path, subset_size=3 + ) == select_calibration_images( + task, + tmp_path, + subset_size=3, + seed=0, + ) + + +def test_widerface_selection_uses_per_category_size(tmp_path: Path) -> None: + """Select the requested number of images from every WiderFace category.""" + + _write_images( + tmp_path / "images" / "0--Parade", [f"parade-{index}.jpg" for index in range(3)] + ) + _write_images( + tmp_path / "images" / "1--Handshaking", + [f"handshake-{index}.jpg" for index in range(3)], + ) + + first = select_calibration_images("face_detection", tmp_path, subset_size=2, seed=7) + second = select_calibration_images( + "face_detection", tmp_path, subset_size=2, seed=7 + ) + default = select_calibration_images("face_detection", tmp_path) + seed_zero = select_calibration_images("face_detection", tmp_path, seed=0) + + assert first == second + assert len(first) == 4 + assert len(default) == 2 + assert default == seed_zero + assert {path.parent.name for path in first} == {"0--Parade", "1--Handshaking"} + + +def test_imagenet_selection_uses_per_class_size(tmp_path: Path) -> None: + """Select the requested number of images independently from each class.""" + + _write_images(tmp_path / "class-a", ["same.jpg", "a.jpg"]) + _write_images(tmp_path / "class-b", ["same.jpg", "b.jpg"]) + + selected = select_calibration_images( + "image_classification", tmp_path, subset_size=1 + ) + seed_zero = select_calibration_images( + "image_classification", tmp_path, subset_size=1, seed=0 + ) + + assert len(selected) == 2 + assert selected == seed_zero + assert {path.parent.name for path in selected} == {"class-a", "class-b"} + + +@pytest.mark.parametrize("subset_size", [0, -1, 4]) +def test_selection_rejects_invalid_sizes(subset_size: int, tmp_path: Path) -> None: + """Reject non-positive and unavailable calibration subset sizes.""" + + _write_images(tmp_path / "val2017", ["one.jpg", "two.jpg", "three.jpg"]) + + with pytest.raises(ValueError, match="subset_size"): + select_calibration_images("object_detection", tmp_path, subset_size=subset_size) + + +def test_selection_rejects_symlinked_images(tmp_path: Path) -> None: + """Reject image-looking symlinks instead of sampling their targets.""" + + external_image = _write_images(tmp_path / "external", ["secret.jpg"])[0] + image_dir = tmp_path / "dataset" / "val2017" + image_dir.mkdir(parents=True) + (image_dir / "stolen.jpg").symlink_to(external_image) + + with pytest.raises(ValueError, match="must not be a symlink"): + select_calibration_images( + "object_detection", tmp_path / "dataset", subset_size=1 + ) + + +def test_copy_rejects_symlinked_or_outside_images(tmp_path: Path) -> None: + """Defend direct copy callers against symlink and outside-root sources.""" + + dataset = tmp_path / "dataset" + external_image = _write_images(tmp_path / "external", ["secret.jpg"])[0] + symlink = dataset / "stolen.jpg" + dataset.mkdir() + symlink.symlink_to(external_image) + + with pytest.raises(ValueError, match="must not be a symlink"): + copy_calibration_subset([symlink], dataset, tmp_path / "subset") + with pytest.raises(ValueError, match="must remain within dataset root"): + copy_calibration_subset([external_image], dataset, tmp_path / "subset") + + +def test_flat_subset_names_are_collision_safe(tmp_path: Path) -> None: + """Preserve images with duplicate basenames from nested source directories.""" + + images = _write_images( + tmp_path / "dataset", ["images/a/same.jpg", "images/b/same.jpg"] + ) + + copied = copy_calibration_subset(images, tmp_path / "dataset", tmp_path / "subset") + + assert len(copied) == 2 + assert copied[0].name != copied[1].name + assert all(path.is_file() for path in copied) + + +@pytest.mark.parametrize("output_name", [".", "parent-output", "val2017/subset"]) +def test_subset_rejects_overlapping_output_paths( + tmp_path: Path, output_name: str +) -> None: + """Protect the organized source dataset from destructive replacement.""" + + data_path = tmp_path / "dataset" + image_path = _write_images(data_path / "val2017", ["one.jpg"])[0] + output_path = tmp_path if output_name == "." else data_path / output_name + + with pytest.raises(ValueError, match="must not overlap"): + compile_module.make_calibration_subset( + "object_detection", data_path, output_path, subset_size=1 + ) + + assert image_path.is_file() + + +def test_subset_keeps_existing_output_when_selection_fails(tmp_path: Path) -> None: + """Leave an existing subset untouched when a replacement cannot be selected.""" + + data_path = tmp_path / "dataset" + output_path = tmp_path / "subset" + _write_images(data_path / "val2017", ["one.jpg"]) + existing = _write_images(output_path, ["existing.jpg"])[0] + + with pytest.raises(ValueError, match="subset_size"): + compile_module.make_calibration_subset( + "object_detection", data_path, output_path, subset_size=2 + ) + + assert existing.read_bytes() == b"existing.jpg" + + +def test_imagenet_readiness_rejects_partial_class_tree(tmp_path: Path) -> None: + """Organize ImageNet again when the existing class layout is incomplete.""" + + _write_images(tmp_path / "class-a", ["image.jpg"]) + + assert not compile_module._dataset_ready("image_classification", tmp_path) + + +def test_depth_dataset_readiness_requires_complete_pairs( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Require all paired NYU samples before sampling calibration images.""" + + monkeypatch.setattr(readiness_module, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + _write_images(tmp_path / "images", ["image.png"]) + (tmp_path / "depth").mkdir() + assert not compile_module._dataset_ready("depth_estimation", tmp_path, "nyu-depth") + + (tmp_path / "depth" / "image.npy").write_bytes(b"depth") + assert compile_module._dataset_ready("depth_estimation", tmp_path, "nyu-depth") + + +@pytest.mark.parametrize( + ("dataset", "image_name", "other_dataset"), + [ + ("ade20k", "ADE_val_00000001.jpg", "cityscapes"), + ("cityscapes", "frankfurt_000000_000001.png", "ade20k"), + ], +) +def test_semantic_dataset_readiness_checks_taxonomy( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + dataset: str, + image_name: str, + other_dataset: str, +) -> None: + """Require complete pairs with filenames belonging to the selected taxonomy.""" + + monkeypatch.setattr(readiness_module, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(readiness_module, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) + image_path = _write_images(tmp_path / "images", [image_name])[0] + (tmp_path / "annotations").mkdir() + assert not compile_module._dataset_ready("semantic_segmentation", tmp_path, dataset) + + (tmp_path / "annotations" / f"{image_path.stem}.png").write_bytes(b"annotation") + if dataset == "ade20k": + (tmp_path / "objectInfo150.txt").write_bytes(b"labels") + (tmp_path / "sceneCategories.txt").write_bytes(b"scenes") + assert compile_module._dataset_ready("semantic_segmentation", tmp_path, dataset) + assert not compile_module._dataset_ready( + "semantic_segmentation", tmp_path, other_dataset + ) + + +def test_obb_dataset_readiness_accepts_flat_images( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Accept the flat image layout produced by the DOTAv1 organizer.""" + + data_path = tmp_path / "dotav1" + monkeypatch.setattr( + compile_module, + "get_dataset_config_for_task", + lambda task, dataset=None: { + "name": "dotav1", + "path": str(data_path), + "download": {"url": "https://example.test/dotav1.zip"}, + }, + ) + + def _organize_dotav1(**kwargs: str) -> None: + assert kwargs["output_dir"] == str(data_path) + _write_images(data_path / "images", ["P0001.png"]) + (data_path / "labels" / "val_original").mkdir(parents=True) + (data_path / "labels" / "val_original" / "P0001.txt").write_bytes(b"label") + + monkeypatch.setattr(dataset_utils, "organize_dotav1", _organize_dotav1) + monkeypatch.setattr(readiness_module, "DOTAV1_VALIDATION_SAMPLE_COUNT", 1) + + assert ( + compile_module.ensure_calibration_dataset( + "oriented_bounding_boxes", dataset="dotav1" + ) + == data_path + ) + + +def test_dense_dataset_organizers_follow_model_taxonomy( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Route dense compilation through the matching registry-backed organizer.""" + + data_path = tmp_path / "dataset" + cityscapes_images = tmp_path / "leftImg8bit_trainvaltest.zip" + cityscapes_annotations = tmp_path / "gtFine_trainvaltest.zip" + cityscapes_images.write_bytes(b"images") + cityscapes_annotations.write_bytes(b"annotations") + configs = { + "nyu-depth": { + "name": "nyu-depth", + "download": {"url": "https://example.test/nyu.zip"}, + }, + "ade20k": { + "name": "ade20k", + "download": {"url": "https://example.test/ade.zip"}, + }, + "cityscapes": { + "name": "cityscapes", + "download": { + "images_archive": cityscapes_images.name, + "annotations_archive": cityscapes_annotations.name, + }, + }, + } + calls: list[tuple[str, dict[str, str]]] = [] + monkeypatch.setattr( + compile_module, + "get_dataset_config_for_task", + lambda task, dataset=None: configs[str(dataset)], + ) + monkeypatch.setattr( + dataset_utils, + "organize_nyu_depth", + lambda **kwargs: calls.append(("nyu-depth", kwargs)), + ) + monkeypatch.setattr( + dataset_utils, + "organize_ade20k", + lambda **kwargs: calls.append(("ade20k", kwargs)), + ) + monkeypatch.setattr( + dataset_utils, + "organize_cityscapes", + lambda **kwargs: calls.append(("cityscapes", kwargs)), + ) + + compile_module._organize_dataset("depth_estimation", data_path, "nyu-depth") + compile_module._organize_dataset("semantic_segmentation", data_path, "ade20k") + compile_module._organize_dataset("semantic_segmentation", data_path, "cityscapes") + + assert calls == [ + ( + "nyu-depth", + { + "dataset_path": "https://example.test/nyu.zip", + "output_dir": str(data_path), + }, + ), + ( + "ade20k", + { + "dataset_path": "https://example.test/ade.zip", + "output_dir": str(data_path), + }, + ), + ( + "cityscapes", + { + "image_dir": str(cityscapes_images), + "annotation_dir": str(cityscapes_annotations), + "output_dir": str(data_path), + }, + ), + ] + + +def test_prepare_calibration_arrays_preserves_preprocess_output(tmp_path: Path) -> None: + """Save contiguous HWC float32 arrays matching engine preprocessing.""" + + source = _write_images(tmp_path, ["image.jpg"])[0] + expected = np.arange(18, dtype=np.float64).reshape(2, 3, 3) + + class _Engine: + def preprocess(self, image_path: str) -> np.ndarray: + assert image_path == str(source) + return expected + + saved = prepare_calibration_arrays(_Engine(), [source], tmp_path / "arrays") # type: ignore[arg-type] + actual = np.load(saved[0]) + + assert actual.dtype == np.float32 + assert actual.flags.c_contiguous + np.testing.assert_array_equal(actual, expected.astype(np.float32)) + + +def test_prepare_calibration_arrays_rejects_chw(tmp_path: Path) -> None: + """Reject preprocessing output that is not three-channel HWC data.""" + + source = _write_images(tmp_path, ["image.jpg"])[0] + + class _Engine: + def preprocess(self, image_path: str) -> np.ndarray: + return np.zeros((3, 2, 2), dtype=np.float32) + + with pytest.raises(ValueError, match="HWC three-channel"): + prepare_calibration_arrays(_Engine(), [source], tmp_path / "arrays") # type: ignore[arg-type] + + +def test_quantization_explicit_values_do_not_fetch( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Give explicit quantization values precedence over hosted metadata.""" + + monkeypatch.setattr( + compile_module, + "_fetch_quantization_config", + lambda *args: pytest.fail("hosted metadata should not be fetched"), + ) + + assert resolve_quantization_values({"repo_id": "owner/model"}, 0.98, 0.03) == ( + 0.98, + 0.03, + ) + + +@pytest.mark.parametrize("topk_key", ["topk", "topk_ratio"]) +def test_quantization_reads_both_hosted_topk_spellings( + topk_key: str, monkeypatch: pytest.MonkeyPatch +) -> None: + """Read hosted percentile conversion and both supported top-k field names.""" + + monkeypatch.setattr( + compile_module, + "_fetch_quantization_config", + lambda repo_id, revision: {"percentile": 0.002, topk_key: 0.04}, + ) + + percentile, topk = resolve_quantization_values( + {"repo_id": "owner/model", "revision": "v1"}, None, None + ) + + assert percentile == pytest.approx(0.998) + assert topk == 0.04 + + +def test_quantization_resolves_missing_values_independently( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Retain an explicit percentile while obtaining only missing top-k metadata.""" + + monkeypatch.setattr( + compile_module, + "_fetch_quantization_config", + lambda repo_id, revision: {"percentile": 0.25, "topk_ratio": 0.08}, + ) + + assert resolve_quantization_values({"repo_id": "owner/model"}, 0.97, None) == ( + 0.97, + 0.08, + ) + + +def test_quantization_warns_and_falls_back(monkeypatch: pytest.MonkeyPatch) -> None: + """Use documented defaults when optional hosted values are unavailable.""" + + monkeypatch.setattr( + compile_module, "_fetch_quantization_config", lambda repo_id, revision: None + ) + + with pytest.warns(UserWarning) as warning_records: + values = resolve_quantization_values({"repo_id": "owner/model"}, None, None) + + assert values == ( + compile_module.DEFAULT_PERCENTILE, + compile_module.DEFAULT_TOPK_RATIO, + ) + assert len(warning_records) == 2 + + +def test_quantization_rejects_malformed_hosted_json( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Raise a contextual error for malformed hosted JSON.""" + + metadata_path = tmp_path / "best_result.json" + metadata_path.write_text("{bad json", encoding="utf-8") + monkeypatch.setattr( + compile_module, "hf_hub_download", lambda **kwargs: str(metadata_path) + ) + + with pytest.raises(ValueError, match="Malformed quantization metadata JSON"): + compile_module._fetch_quantization_config("owner/model", "main") + + +def test_quantization_rejects_malformed_config_value( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Reject non-numeric hosted quantization fields.""" + + metadata_path = tmp_path / "best_result.json" + metadata_path.write_text( + json.dumps({"config": {"percentile": "invalid"}}), encoding="utf-8" + ) + monkeypatch.setattr( + compile_module, "hf_hub_download", lambda **kwargs: str(metadata_path) + ) + + with pytest.raises(ValueError, match="must be numeric"): + resolve_quantization_values({"repo_id": "owner/model"}, None, 0.01) + + +def _run_fake_compile( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + *, + task: str, + model_path: Path | None, + entry_level: str = "data", + dataset: str | None = None, + fail: bool = False, + calls: dict[str, Any] | None = None, +) -> tuple[dict[str, Any], Path]: + """Run compilation with fake engine and qbcompiler dependencies. + + Args: + monkeypatch: Pytest monkeypatch fixture. + tmp_path: Temporary test root. + task: Fake model task. + model_path: Optional user-supplied model path. + entry_level: Calibration pipeline level to supply. + dataset: Optional fake model dataset taxonomy. + fail: Whether the fake compiler should fail. + calls: Optional mapping populated even when compilation fails. + + Returns: + Captured calls and resolved hosted ONNX path. + """ + + calls = {} if calls is None else calls + hosted_onnx = tmp_path / "hosted-model.onnx" + hosted_onnx.write_bytes(b"onnx") + dataset_path = tmp_path / "dataset" + dataset = ( + dataset + or { + "image_classification": "imagenet", + "depth_estimation": "nyu-depth", + "object_detection": "coco", + "instance_segmentation": "coco", + "semantic_segmentation": "ade20k", + "pose_estimation": "coco", + "face_detection": "widerface", + "obb": "dotav1", + }[task] + ) + if task == "image_classification": + _write_images(dataset_path / "class-a", ["one.jpg"]) + elif task in {"depth_estimation", "semantic_segmentation"}: + _write_images(dataset_path / "images", ["one.jpg"]) + else: + _write_images(dataset_path / "val2017", ["one.jpg"]) + + class _Engine: + def __init__(self, **kwargs: Any) -> None: + calls["engine_kwargs"] = kwargs + resolved_path = Path(str(kwargs.get("model_path", hosted_onnx))) + self.file_cfg = {"onnx_path": str(resolved_path)} + + def preprocess(self, image_path: str) -> np.ndarray: + if entry_level == "calibration": + pytest.fail("ready calibration tensors must skip preprocessing") + calls.setdefault("preprocessed", []).append(image_path) + return np.arange(12, dtype=np.float64).reshape(2, 2, 3) + + def dispose(self) -> None: + calls["disposed"] = True + + class _CalibrationConfig: + def __init__(self, **kwargs: Any) -> None: + self.kwargs = kwargs + + def _compile(**kwargs: Any) -> None: + calls["compile_kwargs"] = kwargs + array_dir = Path(kwargs["calib_data_path"]) + calls["temporary_root"] = array_dir.parent + calls["array"] = np.load(next(array_dir.glob("*.npy"))) + calls["calibration_kwargs"] = kwargs["calibration_config"].kwargs + if fail: + raise RuntimeError("compile failed") + + monkeypatch.setattr(compile_module, "MBLT_Engine", _Engine) + monkeypatch.setattr( + compile_module, "_load_qbcompiler", lambda: (_compile, _CalibrationConfig) + ) + monkeypatch.setattr( + compile_module, + "resolve_model_config", + lambda model_cls, model_type: { + "file_cfg": { + "repo_id": "owner/model", + "revision": "main", + "filename": "hosted-model.mxq", + "onnx_path": str(hosted_onnx), + }, + "post_cfg": {"task": task, "dataset": dataset}, + }, + ) + monkeypatch.setattr( + compile_module, "resolve_quantization_values", lambda *args: (0.99, 0.02) + ) + subset_path: Path | None = None + calib_data_path: Path | None = None + original_data_path: Path | None = dataset_path + if entry_level == "subset": + original_data_path = None + subset_path = tmp_path / "provided-subset" + _write_images(subset_path, ["nested/selected.jpg"]) + elif entry_level == "calibration": + original_data_path = None + calib_data_path = tmp_path / "provided-calibration" + calib_data_path.mkdir() + np.save(calib_data_path / "selected.npy", np.ones((2, 2, 3), dtype=np.float32)) + elif entry_level != "data": + raise ValueError(f"Unsupported test entry level: {entry_level}") + + def _ensure_dataset( + task: str, data_path: str | Path | None, dataset: str | None + ) -> Path: + calls["ensure_dataset"] = (task, dataset) + if entry_level != "data": + pytest.fail(f"{entry_level} input must skip original dataset preparation") + return dataset_path + + monkeypatch.setattr(compile_module, "ensure_calibration_dataset", _ensure_dataset) + + compile_module.compile_vision_model( + "fake-model", + model_type="VARIANT", + model_path=model_path, + data_path=original_data_path, + subset_path=subset_path, + calib_data_path=calib_data_path, + subset_size=1, + ) + return calls, hosted_onnx + + +def test_compile_uses_local_onnx_and_exact_options( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Prefer a local ONNX and pass exact Aries/GPU compiler options.""" + + local_onnx = tmp_path / "local.onnx" + local_onnx.write_bytes(b"onnx") + model_dir = tmp_path / ".mblt_model_zoo" + monkeypatch.setattr(compile_module, "DEFAULT_MODEL_DIR", model_dir) + + calls, _ = _run_fake_compile( + monkeypatch, tmp_path, task="image_classification", model_path=local_onnx + ) + + assert calls["engine_kwargs"]["model_path"] == str(local_onnx) + assert calls["engine_kwargs"]["model_type"] == "VARIANT" + assert calls["compile_kwargs"] | {"calibration_config": None} == { + "model": str(local_onnx), + "calib_data_path": calls["compile_kwargs"]["calib_data_path"], + "save_path": str(model_dir / "local.mxq"), + "image_channels": 3, + "backend": "onnx", + "device": "gpu", + "target_device": "aries-rb", + "inference_scheme": "all", + "calibration_config": None, + } + assert calls["calibration_kwargs"]["output"] == 0 + assert calls["calibration_kwargs"]["method"] == 1 + assert calls["calibration_kwargs"]["mode"] == 1 + assert calls["array"].dtype == np.float32 + assert calls["disposed"] is True + assert not calls["temporary_root"].exists() + + +def test_compile_normalizes_legacy_obb_task_from_model_config( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Compile legacy custom model metadata through the canonical OBB task.""" + + calls, _ = _run_fake_compile( + monkeypatch, + tmp_path, + task="oriented_bounding_boxes", + model_path=None, + dataset="dotav1", + entry_level="calibration", + ) + + assert calls["calibration_kwargs"]["output"] == 1 + + +def test_compile_ignores_missing_local_path_and_uses_hosted_onnx( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Construct the engine without a nonexistent local path so Hub fallback applies.""" + + model_dir = tmp_path / ".mblt_model_zoo" + monkeypatch.setattr(compile_module, "DEFAULT_MODEL_DIR", model_dir) + calls, hosted_onnx = _run_fake_compile( + monkeypatch, + tmp_path, + task="object_detection", + model_path=tmp_path / "missing.onnx", + ) + + assert "model_path" not in calls["engine_kwargs"] + assert calls["compile_kwargs"]["model"] == str(hosted_onnx) + assert calls["compile_kwargs"]["save_path"] == str(model_dir / "hosted-model.mxq") + assert calls["calibration_kwargs"]["output"] == 1 + + +def test_compile_routes_semantic_calibration_by_model_dataset( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Use postprocess taxonomy to distinguish ADE20K and Cityscapes compilation.""" + + calls, _ = _run_fake_compile( + monkeypatch, + tmp_path, + task="semantic_segmentation", + dataset="cityscapes", + model_path=None, + ) + + assert calls["ensure_dataset"] == ("semantic_segmentation", "cityscapes") + assert calls["preprocessed"] + + +def test_compile_starts_from_provided_image_subset( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Skip original dataset preparation and sampling for a supplied image subset.""" + + calls, _ = _run_fake_compile( + monkeypatch, + tmp_path, + task="object_detection", + model_path=None, + entry_level="subset", + ) + + assert "ensure_dataset" not in calls + assert calls["preprocessed"] == [ + str(tmp_path / "provided-subset" / "nested" / "selected.jpg") + ] + assert not calls["temporary_root"].exists() + + +def test_compile_uses_provided_calibration_dataset_directly( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Pass ready NumPy tensors directly without image dataset processing.""" + + calls, _ = _run_fake_compile( + monkeypatch, + tmp_path, + task="object_detection", + model_path=None, + entry_level="calibration", + ) + + calibration_path = tmp_path / "provided-calibration" + assert "ensure_dataset" not in calls + assert "engine_kwargs" not in calls + assert "preprocessed" not in calls + assert calls["compile_kwargs"]["calib_data_path"] == str(calibration_path) + assert (calibration_path / "selected.npy").is_file() + + +def test_compile_rejects_multiple_data_levels() -> None: + """Reject ambiguous original, subset, and calibration path combinations.""" + + with pytest.raises(ValueError, match="Provide only one calibration pipeline input"): + compile_module.compile_vision_model( + "alexnet", data_path="original", subset_path="subset" + ) + + +def test_compile_cleans_up_and_disposes_after_failure( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Dispose the engine and delete temporary images and arrays after compiler failure.""" + + calls: dict[str, Any] = {} + with pytest.raises(RuntimeError, match="compile failed"): + _run_fake_compile( + monkeypatch, + tmp_path, + task="image_classification", + model_path=None, + fail=True, + calls=calls, + ) + + assert calls["disposed"] is True + assert not calls["temporary_root"].exists() + + +def test_cli_compile_parser_and_dispatch( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Parse compile options and dispatch them to the packaged API.""" diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py new file mode 100644 index 0000000..f8b28d1 --- /dev/null +++ b/tests/test_dataset_organizer.py @@ -0,0 +1,1002 @@ +"""Tests for dataset download organization helpers.""" + +from __future__ import annotations + +import io +import tarfile +from pathlib import Path +from typing import Any +from zipfile import ZipFile + +import pytest +import requests + +import mblt_vision.utils.datasets.organizer as organizer +from mblt_vision.utils.datasets import readiness as readiness_module + + +class _DummyTqdm: + """Minimal tqdm stub for download tests.""" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + self.updated = 0 + + def __enter__(self) -> _DummyTqdm: + return self + + def __exit__(self, exc_type: object, exc: object, tb: object) -> None: + return None + + def update(self, value: int) -> None: + self.updated += value + + +class _FakeResponse: + """Simple streaming response test double.""" + + def __init__( + self, status_code: int, headers: dict[str, str], chunks: list[bytes | Exception] + ) -> None: + self.status_code = status_code + self.headers = headers + self._chunks = chunks + + def __enter__(self) -> _FakeResponse: + return self + + def __exit__(self, exc_type: object, exc: object, tb: object) -> None: + return None + + def raise_for_status(self) -> None: + return None + + def iter_content(self, chunk_size: int) -> Any: + del chunk_size + for chunk in self._chunks: + if isinstance(chunk, Exception): + raise chunk + yield chunk + + +def test_download_url_retries_and_resumes_partial_file( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Resume a partial archive download after a transient connection failure.""" + + first_chunk = b"abc" + second_chunk = b"def" + calls: list[dict[str, str]] = [] + responses = [ + _FakeResponse( + status_code=200, + headers={"Content-Length": str(len(first_chunk) + len(second_chunk))}, + chunks=[first_chunk, requests.ConnectionError("interrupted")], + ), + _FakeResponse( + status_code=206, + headers={"Content-Length": str(len(second_chunk))}, + chunks=[second_chunk], + ), + ] + + def _fake_get( + url: str, stream: bool, timeout: tuple[int, int], headers: dict[str, str] + ) -> _FakeResponse: + del url, stream, timeout + calls.append(dict(headers)) + return responses.pop(0) + + monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) + monkeypatch.setattr(organizer, "sleep", lambda _: None) + + local_path = tmp_path / "archive.tar" + result = organizer._download_url("https://example.com/archive.tar", str(local_path)) + + assert result == str(local_path) + assert local_path.read_bytes() == first_chunk + second_chunk + assert calls == [{}, {"Range": "bytes=3-"}] + + +def test_should_download_serially_for_same_host_urls() -> None: + """Serialize same-host dataset archive downloads to avoid throttling.""" + + assert organizer._should_download_serially( + [ + "https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar", + "https://image-net.org/data/ILSVRC/2012/ILSVRC2012_bbox_val_v3.tgz", + ] + ) + + assert not organizer._should_download_serially( + [ + "https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar", + "https://example.com/data/annotations.tgz", + ] + ) + + +def _create_imagenet_source(tmp_path: Path) -> tuple[Path, Path]: + """Create a small structurally valid ImageNet organizer source.""" + + image_dir = tmp_path / "source-images" + xml_dir = tmp_path / "source-xml" / "val" + image_dir.mkdir() + xml_dir.mkdir(parents=True) + for index in range(50): + stem = f"ILSVRC2012_val_{index + 1:08d}" + (image_dir / f"{stem}.JPEG").write_bytes(b"image") + (xml_dir / f"{stem}.xml").write_text( + "n00000001", + encoding="utf-8", + ) + return image_dir, xml_dir.parent + + +def _create_coco_source(tmp_path: Path) -> tuple[Path, Path]: + """Create a small COCO organizer source.""" + + image_dir = tmp_path / "source-images" + annotation_dir = tmp_path / "source-annotations" / "annotations" + image_dir.mkdir() + annotation_dir.mkdir(parents=True) + (image_dir / "000000000001.jpg").write_bytes(b"image") + (annotation_dir / "instances_val2017.json").write_text("{}", encoding="utf-8") + return image_dir, annotation_dir.parent + + +def _create_widerface_source(tmp_path: Path) -> tuple[Path, Path]: + """Create a small WiderFace organizer source.""" + + image_dir = tmp_path / "source-images" / "images" / "0--Parade" + annotation_dir = tmp_path / "source-annotations" + image_dir.mkdir(parents=True) + annotation_dir.mkdir() + (image_dir / "sample.jpg").write_bytes(b"image") + (annotation_dir / "wider_face_val.mat").write_bytes(b"metadata") + return image_dir.parent.parent, annotation_dir + + +def test_construct_imagenet_replaces_stale_output( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Replace a managed ImageNet root only after staged readiness succeeds.""" + + image_dir, xml_dir = _create_imagenet_source(tmp_path) + readiness_calls: list[tuple[str, str]] = [] + + def _dataset_ready(_: str, task: str, dataset: str) -> bool: + readiness_calls.append((task, dataset)) + return True + + monkeypatch.setattr(organizer, "dataset_ready", _dataset_ready) + + output_dir = tmp_path / "imagenet" + (output_dir / "stale-class").mkdir(parents=True) + (output_dir / "stale-class" / "stale.JPEG").write_bytes(b"stale") + + organizer.construct_imagenet(str(image_dir), str(xml_dir), str(output_dir)) + + assert {path.name for path in output_dir.iterdir()} == {"n00000001"} + assert len(list((output_dir / "n00000001").iterdir())) == 50 + assert readiness_calls == [("image_classification", "imagenet")] + + +def test_construct_coco_replaces_stale_output( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Replace a managed COCO root only after staged readiness succeeds.""" + + image_dir, annotation_dir = _create_coco_source(tmp_path) + readiness_calls: list[tuple[str, str]] = [] + + def _dataset_ready(_: str, task: str, dataset: str) -> bool: + readiness_calls.append((task, dataset)) + return True + + monkeypatch.setattr(organizer, "dataset_ready", _dataset_ready) + + output_dir = tmp_path / "coco" + (output_dir / "val2017").mkdir(parents=True) + (output_dir / "val2017" / "stale.jpg").write_bytes(b"stale") + (output_dir / "stale_val2017.json").write_text("{}", encoding="utf-8") + + organizer.construct_coco(str(image_dir), str(annotation_dir), str(output_dir)) + + assert {path.name for path in (output_dir / "val2017").iterdir()} == { + "000000000001.jpg" + } + assert {path.name for path in output_dir.iterdir()} == { + "val2017", + "instances_val2017.json", + } + assert readiness_calls == [ + ("object_detection", "coco"), + ("pose_estimation", "coco"), + ] + + +def test_construct_widerface_replaces_stale_output( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Replace a managed WiderFace root only after staged readiness succeeds.""" + + image_dir, annotation_dir = _create_widerface_source(tmp_path) + readiness_calls: list[tuple[str, str]] = [] + + def _dataset_ready(_: str, task: str, dataset: str) -> bool: + readiness_calls.append((task, dataset)) + return True + + monkeypatch.setattr(organizer, "dataset_ready", _dataset_ready) + + output_dir = tmp_path / "widerface" + (output_dir / "images" / "stale-event").mkdir(parents=True) + (output_dir / "images" / "stale-event" / "stale.jpg").write_bytes(b"stale") + (output_dir / "stale_val.mat").write_bytes(b"stale") + + organizer.construct_widerface(str(image_dir), str(annotation_dir), str(output_dir)) + + assert {path.name for path in (output_dir / "images").iterdir()} == {"0--Parade"} + assert {path.name for path in output_dir.iterdir()} == { + "images", + "wider_face_val.mat", + } + assert readiness_calls == [("face_detection", "widerface")] + + +@pytest.mark.parametrize("dataset", ["imagenet", "coco", "widerface"]) +def test_incomplete_staged_dataset_preserves_existing_cache( + tmp_path: Path, + dataset: str, +) -> None: + """Reject incomplete staged roots before replacing any existing cache.""" + + source_builders = { + "imagenet": _create_imagenet_source, + "coco": _create_coco_source, + "widerface": _create_widerface_source, + } + constructors = { + "imagenet": organizer.construct_imagenet, + "coco": organizer.construct_coco, + "widerface": organizer.construct_widerface, + } + first_source, second_source = source_builders[dataset](tmp_path) + + output_dir = tmp_path / dataset + output_dir.mkdir() + (output_dir / "valid-cache-marker").write_bytes(b"existing") + + with pytest.raises(ValueError, match="existing dataset cache was not replaced"): + constructors[dataset](str(first_source), str(second_source), str(output_dir)) + + assert (output_dir / "valid-cache-marker").read_bytes() == b"existing" + + +def test_safe_unpack_archive_preserves_regular_tar_layout(tmp_path: Path) -> None: + """Extract regular files and directories from a supported tar archive.""" + + archive_path = tmp_path / "dataset.tar" + with tarfile.open(archive_path, "w") as archive: + directory = tarfile.TarInfo("dataset/") + directory.type = tarfile.DIRTYPE + archive.addfile(directory) + payload = b"dataset contents" + member = tarfile.TarInfo("dataset/sample.txt") + member.size = len(payload) + archive.addfile(member, io.BytesIO(payload)) + + extract_dir = tmp_path / "extracted" + organizer._safe_unpack_archive(str(archive_path), str(extract_dir)) + + assert (extract_dir / "dataset" / "sample.txt").read_bytes() == b"dataset contents" + + +def test_safe_unpack_archive_rejects_tar_traversal_before_writing( + tmp_path: Path, +) -> None: + """Reject a traversal member without extracting earlier valid members.""" + + archive_path = tmp_path / "dataset.tar" + outside_path = tmp_path / "outside.txt" + with tarfile.open(archive_path, "w") as archive: + safe_payload = b"safe" + safe_member = tarfile.TarInfo("dataset/safe.txt") + safe_member.size = len(safe_payload) + archive.addfile(safe_member, io.BytesIO(safe_payload)) + outside_payload = b"outside" + outside_member = tarfile.TarInfo("../outside.txt") + outside_member.size = len(outside_payload) + archive.addfile(outside_member, io.BytesIO(outside_payload)) + + extract_dir = tmp_path / "extracted" + with pytest.raises(ValueError, match="Unsafe archive member path"): + organizer._safe_unpack_archive(str(archive_path), str(extract_dir)) + + assert not outside_path.exists() + assert not (extract_dir / "dataset" / "safe.txt").exists() + + +def test_organize_imagenet_rejects_tar_symlink_escape(tmp_path: Path) -> None: + """Reject a tar symlink before ImageNet extraction can write through it.""" + + image_archive = tmp_path / "images.tar" + xml_archive = tmp_path / "annotations.tgz" + outside_path = tmp_path / "outside.txt" + with tarfile.open(image_archive, "w") as archive: + link = tarfile.TarInfo("redirect") + link.type = tarfile.SYMTYPE + link.linkname = str(tmp_path) + archive.addfile(link) + payload = b"outside" + member = tarfile.TarInfo("redirect/outside.txt") + member.size = len(payload) + archive.addfile(member, io.BytesIO(payload)) + with tarfile.open(xml_archive, "w:gz"): + pass + + with pytest.raises(ValueError, match="Unsafe archive member type"): + organizer.organize_imagenet( + str(image_archive), str(xml_archive), str(tmp_path / "imagenet") + ) + + assert not outside_path.exists() + + +def test_organize_nyu_depth_extracts_only_validation_layout( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Install only NYU Depth validation image/depth pairs from an archive.""" + + monkeypatch.setattr(organizer, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + archive_path = tmp_path / "nyu-depth.zip" + with ZipFile(archive_path, "w") as archive: + archive.writestr("nyu-depth/images/train/nyu_train.jpg", b"training image") + archive.writestr("nyu-depth/depth/train/nyu_train.npy", b"training depth") + archive.writestr("nyu-depth/images/val/nyu_0000.jpg", b"validation image") + archive.writestr("nyu-depth/depth/val/nyu_0000.npy", b"validation depth") + + output_dir = tmp_path / "organized" + organizer.organize_nyu_depth(str(archive_path), str(output_dir)) + + assert archive_path.is_file() + assert (output_dir / "images" / "nyu_0000.jpg").read_bytes() == b"validation image" + assert (output_dir / "depth" / "nyu_0000.npy").read_bytes() == b"validation depth" + assert not (output_dir / "images" / "train").exists() + assert not (output_dir / "depth" / "train").exists() + + +@pytest.mark.parametrize("relative_path", ["images/sample.jpg", "depth/sample.npy"]) +def test_construct_nyu_depth_rejects_symlinked_data_files( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + relative_path: str, +) -> None: + """Reject NYU data symlinks without replacing an existing managed cache.""" + + monkeypatch.setattr(organizer, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + dataset_dir = tmp_path / "source" + (dataset_dir / "images").mkdir(parents=True) + (dataset_dir / "depth").mkdir() + (dataset_dir / "images" / "sample.jpg").write_bytes(b"image") + (dataset_dir / "depth" / "sample.npy").write_bytes(b"depth") + external_file = tmp_path / "secret" + external_file.write_bytes(b"outside dataset") + source_path = dataset_dir / relative_path + source_path.unlink() + source_path.symlink_to(external_file) + output_dir = tmp_path / "organized" + output_dir.mkdir() + marker = output_dir / "valid-cache-marker" + marker.write_bytes(b"existing") + + with pytest.raises(ValueError, match="must not be a symlink"): + organizer.construct_nyu_depth(str(dataset_dir), str(output_dir)) + + assert marker.read_bytes() == b"existing" + assert not (output_dir / Path(relative_path).name).exists() + + +def test_construct_nyu_depth_rejects_source_outside_resolved_root( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Reject regular files reached through a directory symlink escaping the dataset root.""" + + monkeypatch.setattr(organizer, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + dataset_dir = tmp_path / "source" + selected_root = dataset_dir / "nyu-depth" + selected_root.mkdir(parents=True) + external_images = dataset_dir / "external-images" + external_images.mkdir() + (external_images / "sample.jpg").write_bytes(b"outside dataset") + (selected_root / "images").symlink_to(external_images, target_is_directory=True) + (selected_root / "depth").mkdir() + (selected_root / "depth" / "sample.npy").write_bytes(b"depth") + + with pytest.raises(ValueError, match="must remain within dataset root"): + organizer.construct_nyu_depth(str(dataset_dir), str(tmp_path / "organized")) + + +def test_nyu_depth_install_preserves_backups_when_rollback_fails( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Leave recoverable NYU backups outside staging when rollback fails.""" + + monkeypatch.setattr(organizer, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + dataset_dir = tmp_path / "source" + (dataset_dir / "images").mkdir(parents=True) + (dataset_dir / "depth").mkdir() + (dataset_dir / "images" / "sample.jpg").write_bytes(b"new image") + (dataset_dir / "depth" / "sample.npy").write_bytes(b"new depth") + + output_dir = tmp_path / "organized" + (output_dir / "images").mkdir(parents=True) + (output_dir / "depth").mkdir() + (output_dir / "images" / "keep.jpg").write_bytes(b"old image") + (output_dir / "depth" / "keep.npy").write_bytes(b"old depth") + + real_replace = organizer.os.replace + + def _fail_install_and_rollback(source: str, destination: str) -> None: + source_path = Path(source) + destination_path = Path(destination) + if ( + source_path.parent.name.startswith(".nyu-depth-staging-") + and source_path.name == "depth" + ): + raise OSError("simulated install failure") + if ( + source_path.parent.name.startswith(".nyu-depth-backup-") + and destination_path == output_dir / "images" + ): + raise OSError("simulated rollback failure") + real_replace(source, destination) + + monkeypatch.setattr(organizer.os, "replace", _fail_install_and_rollback) + + with pytest.raises(OSError, match="backups are preserved"): + organizer.construct_nyu_depth(str(dataset_dir), str(output_dir)) + + backup_dirs = list(tmp_path.glob(".nyu-depth-backup-*")) + assert len(backup_dirs) == 1 + assert (backup_dirs[0] / "images" / "keep.jpg").read_bytes() == b"old image" + assert (backup_dirs[0] / "depth" / "keep.npy").read_bytes() == b"old depth" + + +def _create_ade20k_source(tmp_path: Path) -> Path: + """Create a compact extracted ADE20K validation source.""" + + dataset_dir = tmp_path / "ADEChallengeData2016" + (dataset_dir / "images").mkdir(parents=True) + (dataset_dir / "annotations").mkdir() + (dataset_dir / "images" / "ADE_val_00000001.jpg").write_bytes(b"validation") + (dataset_dir / "annotations" / "ADE_val_00000001.png").write_bytes(b"annotation") + (dataset_dir / "objectInfo150.txt").write_bytes(b"labels") + (dataset_dir / "sceneCategories.txt").write_bytes(b"scenes") + return dataset_dir + + +def test_organize_ade20k_extracts_flat_validation_layout( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Install only ADE20K validation image/mask pairs in the reference layout.""" + + monkeypatch.setattr(organizer, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(readiness_module, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + archive_path = tmp_path / "ADEChallengeData2016.zip" + with ZipFile(archive_path, "w") as archive: + archive.writestr( + "ADEChallengeData2016/images/training/ADE_train_00000001.jpg", b"training" + ) + archive.writestr( + "ADEChallengeData2016/annotations/training/ADE_train_00000001.png", + b"training", + ) + archive.writestr( + "ADEChallengeData2016/images/validation/ADE_val_00000001.jpg", b"validation" + ) + archive.writestr( + "ADEChallengeData2016/annotations/validation/ADE_val_00000001.png", + b"validation", + ) + archive.writestr("ADEChallengeData2016/objectInfo150.txt", b"labels") + archive.writestr("ADEChallengeData2016/sceneCategories.txt", b"scenes") + + output_dir = tmp_path / "organized" + organizer.organize_ade20k(str(archive_path), str(output_dir)) + + assert ( + output_dir / "images" / "ADE_val_00000001.jpg" + ).read_bytes() == b"validation" + assert ( + output_dir / "annotations" / "ADE_val_00000001.png" + ).read_bytes() == b"validation" + assert (output_dir / "objectInfo150.txt").read_bytes() == b"labels" + assert (output_dir / "sceneCategories.txt").read_bytes() == b"scenes" + assert not (output_dir / "images" / "training").exists() + + +def test_construct_ade20k_requires_metadata_before_replacing_cache( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Preserve the cache when an ADE20K source omits required metadata.""" + + monkeypatch.setattr(organizer, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + dataset_dir = _create_ade20k_source(tmp_path) + (dataset_dir / "sceneCategories.txt").unlink() + output_dir = tmp_path / "organized" + output_dir.mkdir() + (output_dir / "valid-cache-marker").write_bytes(b"existing") + + with pytest.raises(ValueError, match="sceneCategories.txt"): + organizer.construct_ade20k(str(dataset_dir), str(output_dir)) + + assert (output_dir / "valid-cache-marker").read_bytes() == b"existing" + + +@pytest.mark.parametrize( + "relative_path", + [ + "images/ADE_val_00000001.jpg", + "annotations/ADE_val_00000001.png", + "objectInfo150.txt", + "sceneCategories.txt", + ], +) +def test_construct_ade20k_rejects_symlinked_sources( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + relative_path: str, +) -> None: + """Reject ADE20K data and metadata symlinks before replacing a managed cache.""" + + monkeypatch.setattr(organizer, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + dataset_dir = _create_ade20k_source(tmp_path) + external_file = tmp_path / "secret" + external_file.write_bytes(b"outside dataset") + source_path = dataset_dir / relative_path + source_path.unlink() + source_path.symlink_to(external_file) + output_dir = tmp_path / "organized" + output_dir.mkdir() + marker = output_dir / "valid-cache-marker" + marker.write_bytes(b"existing") + + with pytest.raises(ValueError, match="must not be a symlink"): + organizer.construct_ade20k(str(dataset_dir), str(output_dir)) + + assert marker.read_bytes() == b"existing" + + +def test_construct_ade20k_preserves_cache_when_metadata_staging_fails( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Preserve the cache when copying required ADE20K metadata into staging fails.""" + + monkeypatch.setattr(organizer, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + dataset_dir = _create_ade20k_source(tmp_path) + output_dir = tmp_path / "organized" + output_dir.mkdir() + (output_dir / "valid-cache-marker").write_bytes(b"existing") + real_copy2 = organizer.shutil.copy2 + + def _fail_scene_metadata_copy(source: str, destination: str) -> str: + if Path(source).name == "sceneCategories.txt": + raise OSError("simulated metadata copy failure") + return real_copy2(source, destination) + + monkeypatch.setattr(organizer.shutil, "copy2", _fail_scene_metadata_copy) + + with pytest.raises(OSError, match="simulated metadata copy failure"): + organizer.construct_ade20k(str(dataset_dir), str(output_dir)) + + assert (output_dir / "valid-cache-marker").read_bytes() == b"existing" + + +def _write_cityscapes_archives( + tmp_path: Path, sample_ids: list[str] +) -> tuple[Path, Path]: + """Create compact official-layout Cityscapes ZIP fixtures.""" + + image_archive = tmp_path / "leftImg8bit_trainvaltest.zip" + annotation_archive = tmp_path / "gtFine_trainvaltest.zip" + with ZipFile(image_archive, "w") as archive: + archive.writestr( + "leftImg8bit/train/aachen/aachen_000000_000001_leftImg8bit.png", + b"train image", + ) + archive.writestr( + "leftImg8bit/test/berlin/berlin_000000_000001_leftImg8bit.png", + b"test image", + ) + for sample_id in sample_ids: + city = sample_id.split("_", maxsplit=1)[0] + archive.writestr( + f"leftImg8bit/val/{city}/{sample_id}_leftImg8bit.png", + f"image:{sample_id}".encode(), + ) + with ZipFile(annotation_archive, "w") as archive: + archive.writestr( + "gtFine/train/aachen/aachen_000000_000001_gtFine_labelIds.png", + b"train mask", + ) + for sample_id in sample_ids: + city = sample_id.split("_", maxsplit=1)[0] + archive.writestr( + f"gtFine/val/{city}/{sample_id}_gtFine_labelIds.png", + f"mask:{sample_id}".encode(), + ) + archive.writestr( + f"gtFine/val/{city}/{sample_id}_gtFine_color.png", b"color" + ) + archive.writestr( + f"gtFine/val/{city}/{sample_id}_gtFine_instanceIds.png", b"instance" + ) + archive.writestr( + f"gtFine/val/{city}/{sample_id}_gtFine_polygons.json", b"{}" + ) + archive.writestr( + f"gtFine/val/{city}/{sample_id}_gtFine_trainIds.png", b"train IDs" + ) + return image_archive, annotation_archive + + +@pytest.mark.parametrize("dataset", ["nyu-depth", "ade20k", "cityscapes"]) +def test_dense_organizers_reject_symlinked_output_roots( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + dataset: str, +) -> None: + """Fail before organizing through a symlinked managed root or ancestor.""" + + if dataset == "nyu-depth": + monkeypatch.setattr(organizer, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + source_dir = tmp_path / "nyu-source" + (source_dir / "images").mkdir(parents=True) + (source_dir / "depth").mkdir() + (source_dir / "images" / "sample.jpg").write_bytes(b"image") + (source_dir / "depth" / "sample.npy").write_bytes(b"depth") + + def organizer_fn(output_dir: str) -> None: + organizer.organize_nyu_depth(str(source_dir), output_dir) + elif dataset == "ade20k": + monkeypatch.setattr(organizer, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + source_dir = _create_ade20k_source(tmp_path) + + def organizer_fn(output_dir: str) -> None: + organizer.organize_ade20k(str(source_dir), output_dir) + else: + monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) + image_archive, annotation_archive = _write_cityscapes_archives( + tmp_path, + ["lindau_000000_000019"], + ) + + def organizer_fn(output_dir: str) -> None: + organizer.organize_cityscapes( + str(image_archive), + str(annotation_archive), + output_dir, + ) + + for topology in ( + "root", + "ancestor", + "normalized-ancestor", + "symlink-parent-traversal", + ): + if topology == "root": + protected_dir = tmp_path / "root-target" + protected_dir.mkdir() + output_dir = tmp_path / "managed" + output_dir.symlink_to(protected_dir, target_is_directory=True) + elif topology == "ancestor": + target_parent = tmp_path / "ancestor-target" + protected_dir = target_parent / "managed" + protected_dir.mkdir(parents=True) + symlinked_parent = tmp_path / "datasets-link" + symlinked_parent.symlink_to(target_parent, target_is_directory=True) + output_dir = symlinked_parent / "managed" + elif topology == "normalized-ancestor": + target_parent = tmp_path / "normalized-ancestor-target" + protected_dir = target_parent / "managed" + protected_dir.mkdir(parents=True) + symlinked_parent = tmp_path / "normalized-datasets-link" + symlinked_parent.symlink_to(target_parent, target_is_directory=True) + output_dir = tmp_path / "missing" / ".." / symlinked_parent.name / "managed" + else: + target_parent = tmp_path / "traversal-target" + target_child = target_parent / "child" + target_child.mkdir(parents=True) + protected_dir = target_parent / "traversed-managed" + protected_dir.mkdir() + symlinked_parent = tmp_path / "traversal-link" + symlinked_parent.symlink_to(target_child, target_is_directory=True) + output_dir = symlinked_parent / ".." / protected_dir.name + marker = protected_dir / "keep" + marker.write_bytes(b"existing") + + with pytest.raises(ValueError, match="existing parents must not be symlinks"): + organizer_fn(str(output_dir)) + + assert marker.read_bytes() == b"existing" + assert list(protected_dir.iterdir()) == [marker] + + +@pytest.mark.parametrize( + ("dataset", "layout_name"), + [ + ("nyu-depth", "images"), + ("nyu-depth", "depth"), + ("ade20k", "images"), + ("ade20k", "annotations"), + ("cityscapes", "images"), + ("cityscapes", "annotations"), + ], +) +def test_dense_organizers_reject_symlinked_output_layout_directories( + tmp_path: Path, + dataset: str, + layout_name: str, +) -> None: + """Reject symlinked managed layout children without modifying their targets.""" + + output_dir = tmp_path / "managed" + output_dir.mkdir() + protected_dir = tmp_path / "protected-layout" + protected_dir.mkdir() + marker = protected_dir / "keep" + marker.write_bytes(b"existing") + (output_dir / layout_name).symlink_to(protected_dir, target_is_directory=True) + + with pytest.raises(ValueError, match="layout directories must not be symlinks"): + if dataset == "nyu-depth": + organizer.organize_nyu_depth("unused", str(output_dir)) + elif dataset == "ade20k": + organizer.organize_ade20k("unused", str(output_dir)) + else: + organizer.organize_cityscapes( + "unused-images", "unused-annotations", str(output_dir) + ) + + assert marker.read_bytes() == b"existing" + assert list(protected_dir.iterdir()) == [marker] + assert (output_dir / layout_name).is_symlink() + + +def test_organize_cityscapes_materializes_lossless_validation_pairs( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Copy only exactly paired validation images and label-ID masks without transcoding.""" + + monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 2) + sample_ids = ["frankfurt_000000_000294", "munster_000001_000019"] + image_archive, annotation_archive = _write_cityscapes_archives(tmp_path, sample_ids) + output_dir = tmp_path / "cityscapes" + (output_dir / "images").mkdir(parents=True) + (output_dir / "annotations").mkdir() + (output_dir / "images" / "stale.png").write_bytes(b"stale") + (output_dir / "annotations" / "stale.png").write_bytes(b"stale") + + organizer.organize_cityscapes( + str(image_archive), str(annotation_archive), str(output_dir) + ) + + image_paths = sorted((output_dir / "images").glob("*.png")) + annotation_paths = sorted((output_dir / "annotations").glob("*.png")) + assert [path.name for path in image_paths] == [ + f"{sample_id}.png" for sample_id in sample_ids + ] + assert [path.name for path in annotation_paths] == [ + f"{sample_id}.png" for sample_id in sample_ids + ] + assert image_paths[0].read_bytes() == f"image:{sample_ids[0]}".encode() + assert annotation_paths[0].read_bytes() == f"mask:{sample_ids[0]}".encode() + assert not list(output_dir.rglob("*train*")) + assert not list(output_dir.rglob("*color*")) + assert not list(output_dir.rglob("*instance*")) + assert not list(output_dir.rglob("*.json")) + + +def test_organize_cityscapes_enforces_validation_pair_count( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Reject incomplete validation sources before replacing existing data.""" + + monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 2) + image_archive, annotation_archive = _write_cityscapes_archives( + tmp_path, ["lindau_000000_000019"] + ) + output_dir = tmp_path / "cityscapes" + (output_dir / "images").mkdir(parents=True) + marker = output_dir / "images" / "keep.png" + marker.write_bytes(b"keep") + + with pytest.raises(ValueError, match="must contain 2 pairs"): + organizer.organize_cityscapes( + str(image_archive), str(annotation_archive), str(output_dir) + ) + + assert marker.read_bytes() == b"keep" + + +def test_organize_cityscapes_rejects_mismatched_and_malformed_sources( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Require exact official stems and reject malformed validation candidates.""" + + monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) + image_archive, annotation_archive = _write_cityscapes_archives( + tmp_path, ["lindau_000000_000019"] + ) + with ZipFile(annotation_archive, "w") as archive: + archive.writestr( + "gtFine/val/lindau/lindau_000000_000020_gtFine_labelIds.png", b"mask" + ) + with pytest.raises(ValueError, match="mismatch"): + organizer.organize_cityscapes( + str(image_archive), str(annotation_archive), str(tmp_path / "mismatched") + ) + + with ZipFile(annotation_archive, "w") as archive: + archive.writestr( + "gtFine/val/lindau/frankfurt_000000_000019_gtFine_labelIds.png", b"mask" + ) + with pytest.raises(ValueError, match="Malformed Cityscapes annotation filename"): + organizer.organize_cityscapes( + str(image_archive), str(annotation_archive), str(tmp_path / "malformed") + ) + + +def test_organize_cityscapes_rejects_non_zip_duplicate_and_unsafe_inputs( + tmp_path: Path, +) -> None: + """Reject invalid ZIPs, duplicate members, and traversal paths before installation.""" + + invalid_archive = tmp_path / "invalid.zip" + invalid_archive.write_bytes(b"not a zip") + valid_image_archive, valid_annotation_archive = _write_cityscapes_archives( + tmp_path, + ["lindau_000000_000019"], + ) + with pytest.raises(ValueError, match="does not exist"): + organizer.organize_cityscapes( + str(tmp_path / "missing.zip"), + str(valid_annotation_archive), + str(tmp_path / "missing"), + ) + with pytest.raises(ValueError, match="must be a valid ZIP"): + organizer.organize_cityscapes( + str(invalid_archive), + str(valid_annotation_archive), + str(tmp_path / "invalid"), + ) + + duplicate_archive = tmp_path / "duplicate.zip" + duplicate_member = "leftImg8bit/val/lindau/lindau_000000_000019_leftImg8bit.png" + with pytest.warns(UserWarning, match="Duplicate name"): + with ZipFile(duplicate_archive, "w") as archive: + archive.writestr(duplicate_member, b"first") + archive.writestr(duplicate_member, b"second") + with pytest.raises(ValueError, match="duplicate members"): + organizer.organize_cityscapes( + str(duplicate_archive), + str(valid_annotation_archive), + str(tmp_path / "duplicate"), + ) + + unsafe_archive = tmp_path / "unsafe.zip" + outside_marker = tmp_path / "outside.png" + with ZipFile(unsafe_archive, "w") as archive: + archive.writestr("../outside.png", b"outside") + with pytest.raises(ValueError, match="Unsafe archive member path"): + organizer.organize_cityscapes( + str(unsafe_archive), str(valid_annotation_archive), str(tmp_path / "unsafe") + ) + assert not outside_marker.exists() + + +def test_organize_cityscapes_rolls_back_failed_atomic_replacement( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Restore both previous directories if the staged installation fails.""" + + monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) + image_archive, annotation_archive = _write_cityscapes_archives( + tmp_path, ["lindau_000000_000019"] + ) + output_dir = tmp_path / "cityscapes" + (output_dir / "images").mkdir(parents=True) + (output_dir / "annotations").mkdir() + (output_dir / "images" / "keep.png").write_bytes(b"old image") + (output_dir / "annotations" / "keep.png").write_bytes(b"old annotation") + real_replace = organizer.os.replace + failed = False + + def _fail_annotation_install(source: str, destination: str) -> None: + nonlocal failed + if ( + not failed + and Path(source).name == "annotations" + and Path(destination) == output_dir / "annotations" + ): + failed = True + raise OSError("simulated install failure") + real_replace(source, destination) + + monkeypatch.setattr(organizer.os, "replace", _fail_annotation_install) + + with pytest.raises(OSError, match="simulated"): + organizer.organize_cityscapes( + str(image_archive), str(annotation_archive), str(output_dir) + ) + + assert (output_dir / "images" / "keep.png").read_bytes() == b"old image" + assert (output_dir / "annotations" / "keep.png").read_bytes() == b"old annotation" + + +def test_dense_install_preserves_backups_when_rollback_fails( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Keep recoverable backups outside staging when rollback cannot finish.""" + + staging_dir = tmp_path / ".staging" + staged_images = staging_dir / "images" + staged_annotations = staging_dir / "annotations" + staged_images.mkdir(parents=True) + staged_annotations.mkdir() + (staged_images / "new.png").write_bytes(b"new image") + (staged_annotations / "new.png").write_bytes(b"new annotation") + + output_dir = tmp_path / "organized" + (output_dir / "images").mkdir(parents=True) + (output_dir / "annotations").mkdir() + (output_dir / "images" / "keep.png").write_bytes(b"old image") + (output_dir / "annotations" / "keep.png").write_bytes(b"old annotation") + + real_replace = organizer.os.replace + + def _fail_install_and_rollback(source: str, destination: str) -> None: + source_path = Path(source) + destination_path = Path(destination) + if source_path == staged_annotations: + raise OSError("simulated install failure") + if ( + source_path.parent.name.startswith(".dense-backup-") + and destination_path == output_dir / "images" + ): + raise OSError("simulated rollback failure") + real_replace(source, destination) + + monkeypatch.setattr(organizer.os, "replace", _fail_install_and_rollback) + + replacements = ( + (str(staged_images), str(output_dir / "images")), + (str(staged_annotations), str(output_dir / "annotations")), + ) + with pytest.raises(OSError, match="backups are preserved"): + organizer._replace_staged_directories( + replacements, str(tmp_path), ".dense-backup-" + ) + + backup_dirs = list(tmp_path.glob(".dense-backup-*")) + assert len(backup_dirs) == 1 + assert (backup_dirs[0] / "images" / "keep.png").read_bytes() == b"old image" + assert ( + backup_dirs[0] / "annotations" / "keep.png" + ).read_bytes() == b"old annotation" diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py new file mode 100644 index 0000000..53b63a5 --- /dev/null +++ b/tests/test_dataset_readiness.py @@ -0,0 +1,296 @@ +"""Tests for organized vision dataset identity and completeness checks.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np +import pytest +from scipy.io import savemat + +from mblt_vision.utils.datasets import readiness + + +def _write_file(path: Path) -> None: + """Create a placeholder dataset file and its parent directories.""" + + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(b"data") + + +def _write_widerface_metadata(path: Path, event_images: dict[str, list[str]]) -> None: + """Write the WiderFace event and image-name cell arrays used by readiness.""" + + event_list = np.empty((len(event_images), 1), dtype=object) + file_list = np.empty((len(event_images), 1), dtype=object) + for index, (event_name, image_stems) in enumerate(event_images.items()): + event_list[index, 0] = event_name + file_list[index, 0] = np.array([[stem] for stem in image_stems], dtype=object) + savemat(path, {"event_list": event_list, "file_list": file_list}) + + +def test_imagenet_readiness_requires_complete_official_class_tree( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Reject incomplete or non-ImageNet classification directory trees.""" + + monkeypatch.setattr(readiness, "IMAGENET_CLASS_COUNT", 2) + monkeypatch.setattr(readiness, "IMAGENET_IMAGES_PER_CLASS", 2) + for class_index in range(2): + for image_index in range(2): + if (class_index, image_index) == (1, 1): + continue + _write_file( + tmp_path + / f"n{class_index:08d}" + / f"ILSVRC2012_val_{class_index * 2 + image_index + 1:08d}.JPEG" + ) + + assert not readiness.dataset_ready(tmp_path, "image_classification", "imagenet") + + _write_file(tmp_path / "n00000001" / "ILSVRC2012_val_00000004.JPEG") + + assert readiness.dataset_ready(tmp_path, "image_classification", "imagenet") + assert not readiness.dataset_ready(tmp_path, "image_classification", "coco") + + +@pytest.mark.parametrize( + ("task", "annotation_name"), + [ + ("object_detection", "instances_val2017.json"), + ("instance_segmentation", "instances_val2017.json"), + ("pose_estimation", "person_keypoints_val2017.json"), + ], +) +def test_coco_readiness_matches_images_to_task_annotations( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + task: str, + annotation_name: str, +) -> None: + """Require every official COCO image in the task-specific annotation file.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 2) + image_names = ["000000000001.jpg", "000000000002.jpg"] + for image_name in image_names: + _write_file(tmp_path / "val2017" / image_name) + (tmp_path / annotation_name).write_text( + json.dumps({"images": [{"file_name": image_names[0]}]}), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, task, "coco") + + (tmp_path / annotation_name).write_text( + json.dumps( + {"images": [{"file_name": image_name} for image_name in image_names]} + ), + encoding="utf-8", + ) + + assert readiness.dataset_ready(tmp_path, task, "coco") + assert not readiness.dataset_ready(tmp_path, task, "imagenet") + + +@pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) +def test_dotav1_readiness_requires_complete_image_label_pairs( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + relative_image_dir: str, +) -> None: + """Accept flat and legacy DOTA images only when every image has a label.""" + + monkeypatch.setattr(readiness, "DOTAV1_VALIDATION_SAMPLE_COUNT", 2) + for stem in ("P0001", "P0002"): + _write_file(tmp_path / relative_image_dir / f"{stem}.png") + _write_file(tmp_path / "labels" / "val_original" / "P0001.txt") + + assert not readiness.dataset_ready(tmp_path, "obb", "dotav1") + + _write_file(tmp_path / "labels" / "val" / "P0002.txt") + + assert readiness.dataset_ready(tmp_path, "obb", "dotav1") + + external_image = tmp_path / "external.png" + external_label = tmp_path / "external.txt" + _write_file(external_image) + _write_file(external_label) + image_path = tmp_path / relative_image_dir / "P0001.png" + label_path = tmp_path / "labels" / "val" / "P0002.txt" + image_path.unlink() + label_path.unlink() + image_path.symlink_to(external_image) + label_path.symlink_to(external_label) + + assert readiness.dataset_ready(tmp_path, "obb", "dotav1") + + +def test_widerface_readiness_requires_complete_event_tree_and_metadata( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Require all WiderFace validation events, images, and evaluation metadata.""" + + monkeypatch.setattr(readiness, "WIDERFACE_EVENT_COUNT", 2) + monkeypatch.setattr(readiness, "WIDERFACE_VALIDATION_SAMPLE_COUNT", 2) + _write_widerface_metadata( + tmp_path / "wider_face_val.mat", + {"0--Parade": ["sample-0"], "1--Handshaking": ["sample-1"]}, + ) + for file_name in ( + "wider_easy_val.mat", + "wider_medium_val.mat", + "wider_hard_val.mat", + ): + _write_file(tmp_path / file_name) + _write_file(tmp_path / "images" / "0--Parade" / "sample-0.jpg") + (tmp_path / "images" / "1--Handshaking").mkdir() + + assert not readiness.dataset_ready(tmp_path, "face_detection", "widerface") + + _write_file(tmp_path / "images" / "1--Handshaking" / "sample-1.jpg") + + assert readiness.dataset_ready(tmp_path, "face_detection", "widerface") + assert not readiness.dataset_ready(tmp_path, "face_detection", "coco") + + +@pytest.mark.parametrize( + ("event_name", "image_name"), + [ + ("0--Parade", "stale"), + ("1--Handshaking", "expected"), + ], +) +def test_widerface_readiness_rejects_tree_not_named_by_metadata( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + event_name: str, + image_name: str, +) -> None: + """Reject complete-looking trees whose event or image identity differs from metadata.""" + + monkeypatch.setattr(readiness, "WIDERFACE_EVENT_COUNT", 1) + monkeypatch.setattr(readiness, "WIDERFACE_VALIDATION_SAMPLE_COUNT", 1) + _write_widerface_metadata( + tmp_path / "wider_face_val.mat", {"0--Parade": ["expected"]} + ) + for file_name in ( + "wider_easy_val.mat", + "wider_medium_val.mat", + "wider_hard_val.mat", + ): + _write_file(tmp_path / file_name) + _write_file(tmp_path / "images" / event_name / f"{image_name}.jpg") + + assert not readiness.dataset_ready(tmp_path, "face_detection", "widerface") + + +def test_ade20k_readiness_requires_source_metadata( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Require both ADE20K metadata files before reusing an organized cache.""" + + monkeypatch.setattr(readiness, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + _write_file(tmp_path / "images" / "ADE_val_00000001.jpg") + _write_file(tmp_path / "annotations" / "ADE_val_00000001.png") + + assert not readiness.dataset_ready(tmp_path, "semantic_segmentation", "ade20k") + + _write_file(tmp_path / "objectInfo150.txt") + assert not readiness.dataset_ready(tmp_path, "semantic_segmentation", "ade20k") + + _write_file(tmp_path / "sceneCategories.txt") + assert readiness.dataset_ready(tmp_path, "semantic_segmentation", "ade20k") + + +@pytest.mark.parametrize( + ("dataset", "task", "relative_path"), + [ + ("nyu-depth", "depth_estimation", "images/sample.jpg"), + ("nyu-depth", "depth_estimation", "depth/sample.npy"), + ("nyu-depth", "depth_estimation", "images/extra.jpg"), + ("ade20k", "semantic_segmentation", "images/ADE_val_00000001.jpg"), + ("ade20k", "semantic_segmentation", "annotations/ADE_val_00000001.png"), + ("ade20k", "semantic_segmentation", "objectInfo150.txt"), + ], +) +def test_dense_readiness_rejects_symlinked_files( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + dataset: str, + task: str, + relative_path: str, +) -> None: + """Do not reuse a complete-looking dense cache containing symlinked files.""" + + monkeypatch.setattr(readiness, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(readiness, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + if dataset == "nyu-depth": + _write_file(tmp_path / "images" / "sample.jpg") + _write_file(tmp_path / "depth" / "sample.npy") + else: + _write_file(tmp_path / "images" / "ADE_val_00000001.jpg") + _write_file(tmp_path / "annotations" / "ADE_val_00000001.png") + for file_name in readiness.ADE20K_METADATA_FILES: + _write_file(tmp_path / file_name) + external_file = tmp_path.parent / f"{tmp_path.name}-outside" + external_file.write_bytes(b"outside dataset") + source_path = tmp_path / relative_path + if source_path.exists(): + source_path.unlink() + source_path.symlink_to(external_file) + + assert not readiness.dataset_ready(tmp_path, task, dataset) + + +def test_dense_readiness_rejects_symlinked_root_ancestors( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Do not reuse complete dense roots reached through a symlinked parent.""" + + monkeypatch.setattr(readiness, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(readiness, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + target_parent = tmp_path / "target" + nyu_root = target_parent / "nyu-depth" + _write_file(nyu_root / "images" / "sample.jpg") + _write_file(nyu_root / "depth" / "sample.npy") + ade20k_root = target_parent / "ade20k" + _write_file(ade20k_root / "images" / "ADE_val_00000001.jpg") + _write_file(ade20k_root / "annotations" / "ADE_val_00000001.png") + for file_name in readiness.ADE20K_METADATA_FILES: + _write_file(ade20k_root / file_name) + symlinked_parent = tmp_path / "datasets" + symlinked_parent.symlink_to(target_parent, target_is_directory=True) + + assert not readiness.dataset_ready( + symlinked_parent / "nyu-depth", "depth_estimation", "nyu-depth" + ) + assert not readiness.dataset_ready( + symlinked_parent / "ade20k", "semantic_segmentation", "ade20k" + ) + + existing_dir = tmp_path / "existing" + existing_dir.mkdir() + traversed_parent = existing_dir / ".." / symlinked_parent.name + assert not readiness.dataset_ready( + traversed_parent / "nyu-depth", "depth_estimation", "nyu-depth" + ) + assert not readiness.dataset_ready( + traversed_parent / "ade20k", "semantic_segmentation", "ade20k" + ) + + target_child = target_parent / "child" + target_child.mkdir() + traversal_link = tmp_path / "traversal-link" + traversal_link.symlink_to(target_child, target_is_directory=True) + symlink_traversed_parent = traversal_link / ".." + assert not readiness.dataset_ready( + symlink_traversed_parent / "nyu-depth", "depth_estimation", "nyu-depth" + ) + assert not readiness.dataset_ready( + symlink_traversed_parent / "ade20k", "semantic_segmentation", "ade20k" + ) diff --git a/tests/test_depth_estimation.py b/tests/test_depth_estimation.py new file mode 100644 index 0000000..a29ac7c --- /dev/null +++ b/tests/test_depth_estimation.py @@ -0,0 +1,94 @@ +"""Unit tests for YOLO26 depth-estimation support.""" + +from __future__ import annotations + + +import pytest +import torch +from mblt_vision.utils.postprocess import DepthPost + +from mblt_vision.wrapper import resolve_model_config + + +@pytest.mark.parametrize( + ("model_name", "task"), + [ + ("yolo26s", "object_detection"), + ("yolo26s-seg", "instance_segmentation"), + ("yolo26s-pose", "pose_estimation"), + ("yolo26s-obb", "obb"), + ], +) +def test_other_yolo_validation_tasks_keep_letterbox(model_name: str, task: str) -> None: + """Keep aspect-preserving letterbox preprocessing for non-depth YOLO tasks.""" + + config = resolve_model_config(model_name) + assert "LetterBox" in config["pre_cfg"] + assert "Resize" not in config["pre_cfg"] + assert config["post_cfg"]["task"] == task + + +def test_depth_post_restores_letterbox_padding() -> None: + """Crop letterbox padding before bilinearly restoring an original image shape.""" + + post = DepthPost({"LetterBox": {"img_size": [8, 8]}}, {}) + output = torch.zeros((1, 1, 8, 8)) + output[:, :, 2:6, :] = 2.0 + restored = post(output, img0_shape=(2, 4), ratio_pad=((2.0, 2.0), (0.0, 2.0))) + assert isinstance(restored, torch.Tensor) + assert restored.shape == (2, 4) + assert torch.allclose(restored, torch.full((2, 4), 2.0)) + with pytest.raises(ValueError, match=r"expects \[B, 1, H, W\] or \[B, H, W, 1\]"): + post(torch.zeros((1, 2, 8, 8))) + + +def test_depth_post_normalizes_quarter_resolution_mxq_before_restoring() -> None: + """Upsample the MXQ depth layout before undoing letterbox padding.""" + + post = DepthPost({"LetterBox": {"img_size": [8, 8]}}, {}) + mxq_depth = torch.arange(4, dtype=torch.float32).reshape(1, 2, 2) + expected = torch.nn.functional.interpolate( + mxq_depth[:, None], scale_factor=4.0, mode="bilinear", align_corners=False + )[:, 0] + + normalized = post(mxq_depth) + assert isinstance(normalized, torch.Tensor) + assert torch.equal(normalized, expected) + + restored = post(mxq_depth, img0_shape=(2, 4), ratio_pad=((2.0, 2.0), (0.0, 2.0))) + assert isinstance(restored, torch.Tensor) + assert restored.shape == (2, 4) + expected_restored = torch.nn.functional.interpolate( + expected[0, 2:6][None, None], size=(2, 4), mode="bilinear", align_corners=False + )[0, 0] + assert torch.equal(restored, expected_restored) + + +def test_depth_post_keeps_full_resolution_onnx_output_and_rejects_other_scales() -> ( + None +): + """Keep ONNX depth unchanged while rejecting unsupported dense output scales.""" + + post = DepthPost({"LetterBox": {"img_size": [8, 8]}}, {}) + full_resolution = torch.arange(64, dtype=torch.float32).reshape(1, 1, 8, 8) + normalized = post(full_resolution) + assert isinstance(normalized, torch.Tensor) + assert torch.equal(normalized, full_resolution[:, 0]) + + with pytest.raises(ValueError, match="spatial shape must be"): + post(torch.zeros((1, 3, 3))) + + +def test_depth_post_normalizes_full_resolution_channel_last_mxq_output() -> None: + """Normalize batched and single-image baked MXQ resize outputs without resizing again.""" + + post = DepthPost({"LetterBox": {"img_size": [8, 8]}}, {}) + full_resolution = torch.arange(64, dtype=torch.float32).reshape(1, 8, 8, 1) + normalized = post(full_resolution) + assert isinstance(normalized, torch.Tensor) + assert torch.equal(normalized, full_resolution[..., 0]) + + single_image = full_resolution[0] + normalized_single_image = post(single_image) + assert isinstance(normalized_single_image, torch.Tensor) + assert torch.equal(normalized_single_image, full_resolution[..., 0]) diff --git a/tests/test_face_detection.py b/tests/test_face_detection.py new file mode 100644 index 0000000..58a8091 --- /dev/null +++ b/tests/test_face_detection.py @@ -0,0 +1,123 @@ +"""CPU regression tests for face-detection postprocessing and exports.""" + +from __future__ import annotations + +from typing import Any, cast + +import cv2 +import numpy as np +import pytest +import torch +from mblt_vision.utils.postprocess import build_postprocess +from mblt_vision.utils.postprocess.base import YOLODetectionPostBase +from mblt_vision.utils.postprocess.yolo_anchor_post import YOLOAnchorDetectionPost +from mblt_vision.utils.postprocess.yolo_anchorless_post import ( + YOLOAnchorlessDetectionPost, +) +from mblt_vision.utils.postprocess.yolo_dflfree_post import YOLODFLFreeDetectionPost +from mblt_vision.utils.postprocess.yolo_nmsfree_post import YOLONMSFreeDetectionPost + +from mblt_vision import YOLO11m_face, list_models +from mblt_vision.face_detection import YOLO11m_face as FaceDetectionYOLO11mFace +from mblt_vision.utils.results import Results + + +def _pre_cfg() -> dict[str, Any]: + """Return a representative face preprocessing configuration.""" + + return {"LetterBox": {"img_size": [640, 640]}} + + +def _post_cfg(**overrides: Any) -> dict[str, Any]: + """Return a representative face postprocessing configuration.""" + + return { + "task": "face_detection", + "nl": 3, + "reg_max": 16, + "conf_thres": 0.25, + **overrides, + } + + +@pytest.mark.parametrize( + ("post_cfg", "expected_type"), + [ + ({"nl": 3, "reg_max": 16}, YOLOAnchorlessDetectionPost), + ({"nl": 3, "dflfree": True}, YOLODFLFreeDetectionPost), + ({"nl": 3, "nmsfree": True}, YOLONMSFreeDetectionPost), + ({"anchors": [[10, 13, 16, 30, 33, 23]]}, YOLOAnchorDetectionPost), + ], +) +def test_face_detection_routes_postprocessors( + post_cfg: dict[str, Any], expected_type: type[YOLODetectionPostBase] +) -> None: + """Route every supported face head family to its YOLO postprocessor.""" + + postprocessor = build_postprocess(_pre_cfg(), _post_cfg(**post_cfg)) + + assert isinstance(postprocessor, expected_type) + assert cast(YOLODetectionPostBase, postprocessor).nc == 1 + + +def test_face_detection_exports_and_plot_label( + monkeypatch: pytest.MonkeyPatch, tmp_path: Any +) -> None: + """Preserve legacy exports and render a face-specific detection label.""" + + assert "YOLO11m_face" in list_models("face_detection")["face_detection"] + assert YOLO11m_face is FaceDetectionYOLO11mFace + source_path = tmp_path / "source.jpg" + output_path = tmp_path / "face.jpg" + cv2.imwrite(str(source_path), np.full((32, 32, 3), 255, dtype=np.uint8)) + labels: list[str] = [] + original_put_text = cv2.putText + monkeypatch.setattr( + cv2, + "putText", + lambda *args, **kwargs: ( + labels.append(args[1]), + original_put_text(*args, **kwargs), + )[1], + ) + + Results( + _pre_cfg(), + {"task": "face_detection"}, + [torch.tensor([[1, 1, 16, 16, 0.95, 0]])], + ).plot(str(source_path), save_path=str(output_path)) + + assert output_path.is_file() + assert labels == ["face 95%"] + + +def test_face_detection_non_e2e_converted_and_raw_outputs() -> None: + """Keep converted and raw face heads available through the legacy non-E2E contract.""" + + postprocessor = build_postprocess(_pre_cfg(), _post_cfg(e2e=False)) + converted = torch.tensor( + [ + [ + [10.0, 12.0, 0.9], + [20.0, 18.0, 0.1], + [8.0, 7.0, 0.2], + [9.0, 6.0, 0.3], + [0.8, 0.5, 0.1], + ] + ] + ) + converted_result = postprocessor(converted) + raw_heads = [torch.zeros((1, size, size, 64)) for size in (80, 40, 20)] + raw_heads = [ + tensor + for pair in zip( + raw_heads, [torch.zeros((*tensor.shape[:3], 1)) for tensor in raw_heads] + ) + for tensor in pair + ] + raw_result = postprocessor(raw_heads) + + assert isinstance(converted_result, torch.Tensor) + assert converted_result.shape == (1, 5, 3) + assert isinstance(raw_result, torch.Tensor) + assert raw_result.shape == (1, 5, 8400) diff --git a/tests/test_model_aliasing.py b/tests/test_model_aliasing.py new file mode 100644 index 0000000..80a0e2e --- /dev/null +++ b/tests/test_model_aliasing.py @@ -0,0 +1,40 @@ +"""Tests for vision model name aliasing.""" + +from __future__ import annotations + +import pytest + +from mblt_vision.wrapper import MBLT_Engine + + +@pytest.mark.parametrize( + ("model_name", "expected_yaml"), + [ + ("regnet_x_16gf", "RegNet_X_16GF.yaml"), + ("regnet-x-16gf", "RegNet_X_16GF.yaml"), + ("RegNet_X_16GF.yaml", "RegNet_X_16GF.yaml"), + ("regnet_x_1_6gf", "RegNet_X_1_6GF.yaml"), + ("regnet-x-1-6gf", "RegNet_X_1_6GF.yaml"), + ("resnet50", "ResNet50.yaml"), + ("resnet-50", "ResNet50.yaml"), + ("resnet_50", "ResNet50.yaml"), + ], +) +def test_model_name_aliasing_resolves_precise_separator_matches( + model_name: str, + expected_yaml: str, +) -> None: + """Resolve aliases without collapsing distinct separator boundaries too early.""" + + engine = MBLT_Engine.__new__(MBLT_Engine) + + assert engine.model_name_aliasing(model_name) == expected_yaml + + +def test_model_name_aliasing_reports_compact_ambiguity() -> None: + """Keep compact ambiguous names explicit.""" + + engine = MBLT_Engine.__new__(MBLT_Engine) + + with pytest.raises(ValueError, match="Ambiguous model name"): + engine.model_name_aliasing("regnetx16gf") diff --git a/tests/test_model_metadata.py b/tests/test_model_metadata.py new file mode 100644 index 0000000..de3fb68 --- /dev/null +++ b/tests/test_model_metadata.py @@ -0,0 +1,42 @@ +"""Tests for model dataset metadata and dataset-aware postprocessing.""" + +from __future__ import annotations + +from pathlib import Path + +import mblt_vision +import yaml + +from mblt_vision.wrapper import resolve_model_config + +MODEL_CONFIG_DIR = Path(mblt_vision.__file__).parent / "models" + +DATASETS_BY_TASK = { + "depth_estimation": {"nyu-depth"}, + "face_detection": {"widerface"}, + "image_classification": {"imagenet"}, + "instance_segmentation": {"coco"}, + "object_detection": {"coco"}, + "obb": {"dotav1"}, + "pose_estimation": {"coco"}, + "semantic_segmentation": {"ade20k", "cityscapes"}, +} + + +def test_all_model_variants_declare_a_supported_dataset() -> None: + """Require every resolved model variant to identify its output taxonomy.""" + + checked = 0 + for config_path in sorted(MODEL_CONFIG_DIR.glob("*.yaml")): + full_config = yaml.safe_load(config_path.read_text(encoding="utf-8")) + assert isinstance(full_config, dict) + for variant in full_config: + config = resolve_model_config(str(config_path), variant) + post_cfg = config["post_cfg"] + task = post_cfg["task"] + assert ( + post_cfg["dataset"] in DATASETS_BY_TASK[task] + ), f"{config_path.name}:{variant}" + checked += 1 + + assert checked > 0 diff --git a/tests/test_mxq_inference.py b/tests/test_mxq_inference.py new file mode 100644 index 0000000..3962751 --- /dev/null +++ b/tests/test_mxq_inference.py @@ -0,0 +1,25 @@ +"""Optional end-to-end MXQ inference coverage for the standalone package.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +from mblt_npu.pytest_plugin import NpuParams +from mblt_vision import MBLT_Engine + +pytestmark = [pytest.mark.requires_network, pytest.mark.requires_npu] + + +def test_mxq_classification_runs_with_shared_npu_options(npu_params: NpuParams) -> None: + """Load a representative MXQ model using the shared NPU test options.""" + + image_path = Path(__file__).parent / "rc" / "tabby.jpg" + model = MBLT_Engine(model_cls="resnet50", **npu_params.base) + try: + result = model.postprocess(model(model.preprocess(str(image_path)))) + assert result.task == "image_classification" + assert result.acc is not None + finally: + model.dispose() diff --git a/tests/test_onnx_classification.py b/tests/test_onnx_classification.py new file mode 100644 index 0000000..5df190e --- /dev/null +++ b/tests/test_onnx_classification.py @@ -0,0 +1,40 @@ +"""Tests for vision model ONNX inference on image classification.""" + +from __future__ import annotations + +import os +from pathlib import Path + +import pytest + +from mblt_vision.image_classification import AlexNet, CAFormer_B36, YOLO26sCls + +TEST_DIR = Path(__file__).parent +pytestmark = pytest.mark.requires_network + + +@pytest.mark.parametrize( + "model_cls", + [ + AlexNet, + CAFormer_B36, + YOLO26sCls, + ], +) +def test_onnx_classification(model_cls) -> None: + """Run ONNX inference for representative classification models.""" + image_path = os.path.join(TEST_DIR, "rc", "volcano.jpg") + + model = model_cls(framework="onnx") + + try: + input_img = model.preprocess(image_path) + output = model(input_img) + result = model.postprocess(output) + + assert result is not None + assert result.task == "image_classification" + assert result.acc is not None + assert result.output is not None + finally: + model.dispose() diff --git a/tests/test_onnx_yolo.py b/tests/test_onnx_yolo.py new file mode 100644 index 0000000..e8e0876 --- /dev/null +++ b/tests/test_onnx_yolo.py @@ -0,0 +1,54 @@ +"""Tests for ONNX inference across representative YOLO postprocess families.""" + +from __future__ import annotations + +import os +from pathlib import Path + +import pytest + +from mblt_vision import MBLT_Engine + +TEST_DIR = Path(__file__).parent +TMP_DIR = TEST_DIR / "tmp" +pytestmark = pytest.mark.requires_network + + +@pytest.mark.parametrize( + ("model_cls", "task", "image_name"), + [ + ("yolov5m", "object_detection", "cr7.jpg"), + ("yolov5m-seg", "instance_segmentation", "cr7.jpg"), + ("yolo11m", "object_detection", "cr7.jpg"), + ("yolo11m-seg", "instance_segmentation", "cr7.jpg"), + ("yolo11m-pose", "pose_estimation", "cr7.jpg"), + ("yolov10m", "object_detection", "cr7.jpg"), + ("yolo26m", "object_detection", "cr7.jpg"), + ("yolo26m-seg", "instance_segmentation", "cr7.jpg"), + ("yolo26m-pose", "pose_estimation", "cr7.jpg"), + ("yolov8m-obb", "obb", "cr7.jpg"), + ("yolo11m-obb", "obb", "cr7.jpg"), + ("yolo26m-obb", "obb", "cr7.jpg"), + ], +) +def test_onnx_yolo_inference( + model_cls: str, task: str, image_name: str, tmp_path: Path +) -> None: + """Run ONNX inference for representative YOLO postprocess families.""" + + image_path = os.path.join(TEST_DIR, "rc", image_name) + save_path = tmp_path / f"{model_cls}_visualization.jpg" + model = MBLT_Engine(model_cls=model_cls, framework="onnx") + + try: + input_img = model.preprocess(image_path) + output = model(input_img) + result = model.postprocess(output) + + assert result is not None + assert result.task == task + assert result.output is not None + result.plot(image_path, save_path=str(save_path)) + assert save_path.is_file() + finally: + model.dispose() diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py new file mode 100644 index 0000000..13ac8de --- /dev/null +++ b/tests/test_postprocess_hierarchy.py @@ -0,0 +1,123 @@ +"""Focused tests for the vision postprocessor class hierarchy and builder.""" + +from __future__ import annotations + +import warnings +from typing import Any + +import pytest +from mblt_vision.utils.postprocess import build_postprocess +from mblt_vision.utils.postprocess.base import PostBase, YOLODetectionPostBase +from mblt_vision.utils.postprocess.cls_post import ClsPost +from mblt_vision.utils.postprocess.depth_post import DepthPost +from mblt_vision.utils.postprocess.semantic_seg_post import SemanticSegPost +from mblt_vision.utils.postprocess.yolo_anchor_post import ( + YOLOAnchorDetectionPost, + YOLOAnchorSegPost, +) +from mblt_vision.utils.postprocess.yolo_anchorless_post import ( + YOLOAnchorlessDetectionPost, + YOLOAnchorlessOBBPost, + YOLOAnchorlessPosePost, + YOLOAnchorlessSegPost, +) +from mblt_vision.utils.postprocess.yolo_dflfree_post import ( + YOLODFLFreeDetectionPost, + YOLODFLFreeOBBPost, + YOLODFLFreePosePost, + YOLODFLFreeSegPost, +) +from mblt_vision.utils.postprocess.yolo_nmsfree_post import YOLONMSFreeDetectionPost + + +@pytest.mark.parametrize( + ("post_cfg", "expected_type"), + [ + ( + {"task": "object_detection", "anchors": [[10, 13, 16, 30, 33, 23]]}, + YOLOAnchorDetectionPost, + ), + ( + {"task": "object_detection", "nl": 3, "reg_max": 16}, + YOLOAnchorlessDetectionPost, + ), + ( + {"task": "object_detection", "nl": 3, "dflfree": True}, + YOLODFLFreeDetectionPost, + ), + ( + {"task": "object_detection", "nl": 3, "reg_max": 16, "nmsfree": True}, + YOLONMSFreeDetectionPost, + ), + ( + { + "task": "instance_segmentation", + "anchors": [[10, 13, 16, 30, 33, 23]], + "n_extra": 32, + }, + YOLOAnchorSegPost, + ), + ( + {"task": "instance_segmentation", "nl": 3, "reg_max": 16, "n_extra": 32}, + YOLOAnchorlessSegPost, + ), + ( + {"task": "instance_segmentation", "nl": 3, "dflfree": True, "n_extra": 32}, + YOLODFLFreeSegPost, + ), + ( + {"task": "pose_estimation", "nl": 3, "reg_max": 16, "n_extra": 51}, + YOLOAnchorlessPosePost, + ), + ( + {"task": "pose_estimation", "nl": 3, "dflfree": True, "n_extra": 51}, + YOLODFLFreePosePost, + ), + ({"task": "obb", "nl": 3, "reg_max": 16, "n_extra": 1}, YOLOAnchorlessOBBPost), + ({"task": "obb", "nl": 3, "dflfree": True, "n_extra": 1}, YOLODFLFreeOBBPost), + ], +) +def test_builder_routes_detection_backends_without_warnings( + post_cfg: dict[str, Any], expected_type: type[YOLODetectionPostBase] +) -> None: + """Build every detection family through canonical warning-free imports.""" + + with warnings.catch_warnings(): + warnings.simplefilter("error", DeprecationWarning) + postprocessor = build_postprocess( + {"LetterBox": {"img_size": [640, 640]}}, post_cfg + ) + assert type(postprocessor) is expected_type + + +@pytest.mark.parametrize( + ("pre_cfg", "post_cfg", "expected_type"), + [ + ({}, {"task": "image_classification"}, ClsPost), + ({"LetterBox": {"img_size": [8, 8]}}, {"task": "depth_estimation"}, DepthPost), + ( + {"LetterBox": {"img_size": [8, 8]}}, + {"task": "semantic_segmentation", "dataset": "ade20k"}, + SemanticSegPost, + ), + ], +) +def test_builder_keeps_non_detection_routing_warning_free( + pre_cfg: dict[str, Any], post_cfg: dict[str, Any], expected_type: type[PostBase] +) -> None: + """Preserve classification and dense prediction builder routes.""" + + with warnings.catch_warnings(): + warnings.simplefilter("error", DeprecationWarning) + postprocessor = build_postprocess(pre_cfg, post_cfg) + assert type(postprocessor) is expected_type + + +def test_detection_postprocessor_rejects_missing_head_count() -> None: + """Raise a stable runtime error when anchorless metadata omits nl.""" + + with pytest.raises(ValueError, match="nl should be provided in post_cfg"): + YOLOAnchorlessDetectionPost( + {"LetterBox": {"img_size": [640, 640]}}, + {"task": "object_detection", "dataset": "coco"}, + ) diff --git a/tests/test_preprocess_validation.py b/tests/test_preprocess_validation.py new file mode 100644 index 0000000..051a07e --- /dev/null +++ b/tests/test_preprocess_validation.py @@ -0,0 +1,86 @@ +"""Runtime validation tests for shared Vision preprocessing.""" + +from __future__ import annotations + +import subprocess +import sys + +import numpy as np +import pytest +from mblt_vision.utils.preprocess.center_crop import CenterCrop +from mblt_vision.utils.preprocess.letterbox import LetterBox +from mblt_vision.utils.preprocess.normalize import Normalize +from mblt_vision.utils.preprocess.order import SetOrder +from mblt_vision.utils.preprocess.reader import Reader +from mblt_vision.utils.preprocess.resize import Resize + + +@pytest.mark.parametrize( + "operation", [CenterCrop(2), Resize(2, "bilinear"), LetterBox([2, 2])] +) +def test_image_size_operations_reject_nonpositive_sizes(operation: object) -> None: + """Keep size validation active when Python assertions are optimized away.""" + + operation_type = type(operation) + argument = [0, 2] if operation_type is not Resize else 0 + if operation_type is Resize: + with pytest.raises(ValueError, match="positive"): + Resize(argument, "bilinear") + else: + with pytest.raises(ValueError, match="positive"): + operation_type(argument) + + +@pytest.mark.parametrize("size", [[1], [1, 2, 3], [1.5, 2], "2"]) +def test_center_crop_rejects_invalid_size_values(size: object) -> None: + """Reject invalid crop types and dimensions before processing.""" + + with pytest.raises((TypeError, ValueError)): + CenterCrop(size) # type: ignore[arg-type] + + +def test_set_order_rejects_ambiguous_channel_layout() -> None: + """Do not guess between CHW and HWC when both ends have three channels.""" + + with pytest.raises(ValueError, match="ambiguous"): + SetOrder("HWC")(np.zeros((3, 5, 3), dtype=np.uint8)) + + +@pytest.mark.parametrize( + ("factory", "expected"), + [ + (lambda: Reader("unsupported"), ValueError), + (lambda: Normalize("unsupported"), ValueError), + (lambda: SetOrder("unsupported"), ValueError), + ], +) +def test_preprocessing_styles_raise_explicit_errors( + factory, expected: type[Exception] +) -> None: + """Use explicit configuration errors instead of runtime assertions.""" + + with pytest.raises(expected): + factory() + + +def test_runtime_validation_survives_optimized_python() -> None: + """Keep configuration checks active under ``python -O``.""" + + code = """ +from mblt_vision.utils.postprocess import build_postprocess +try: + build_postprocess( + {"LetterBox": {"img_size": [640, 640]}}, + { + "task": "object_detection", + "dataset": "coco", + "nl": 3, + "reg_max": 16, + "conf_thres": 0, + }, + ) +except ValueError: + raise SystemExit(0) +raise SystemExit(1) +""" + subprocess.run([sys.executable, "-O", "-c", code], check=True) diff --git a/tests/test_resnet50.py b/tests/test_resnet50.py deleted file mode 100644 index 8a7a86b..0000000 --- a/tests/test_resnet50.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -Test script for ResNet50 model. - -This script tests the ResNet50 model by running inference on a sample image. -It can be run as a pytest test or as a standalone script. -""" - -import argparse -import os -from pathlib import Path - -import pytest - -from mblt_vision import ResNet50 - -TEST_DIR = Path(__file__).parent - - -@pytest.fixture -def resnet50(): - """Fixture to initialize and dispose of the ResNet50 model.""" - model = ResNet50() - yield model - model.dispose() - - -def run_inference(model, image_path, save_path): - """Run inference with the given model and image.""" - os.makedirs(os.path.dirname(save_path), exist_ok=True) - - input_img = model.preprocess(image_path) - output = model(input_img) - result = model.postprocess(output) - - result.plot( - source_path=image_path, - save_path=save_path, - topk=5, - ) - - -def test_resnet50(resnet50): - """Test ResNet50 inference on a sample image.""" - image_path = os.path.join(TEST_DIR, "rc", "volcano.jpg") - save_path = os.path.join( - TEST_DIR, "tmp", f"resnet50_{os.path.basename(image_path)}" - ) - - run_inference(resnet50, image_path, save_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run ResNet50 inference") - parser.add_argument( - "--mxq-path", - type=str, - default=None, - help="Path to the ResNet50 model file (.mxq)", - ) - parser.add_argument( - "--model-type", - type=str, - default="DEFAULT", - help="Model type", - ) - parser.add_argument( - "--infer-mode", - type=str, - default="global8", - choices=["single", "multi", "global4", "global8"], - help="Inference mode", - ) - parser.add_argument( - "--product", - type=str, - default="aries", - help="Product", - ) - parser.add_argument( - "--input-path", - type=str, - default=os.path.join(TEST_DIR, "rc", "volcano.jpg"), - help="Path to the input image", - ) - parser.add_argument( - "--save-path", - type=str, - default=None, - help="Path to save the output image", - ) - - args = parser.parse_args() - - # Load model with the specified mxq_path - model = ResNet50( - local_path=args.mxq_path, - model_type=args.model_type, - infer_mode=args.infer_mode, - product=args.product, - ) - if args.save_path is None: - args.save_path = os.path.join( - TEST_DIR, "tmp", f"resnet50_{os.path.basename(args.input_path)}" - ) - - try: - run_inference(model, args.input_path, args.save_path) - finally: - model.dispose() diff --git a/tests/test_results.py b/tests/test_results.py new file mode 100644 index 0000000..e1361f8 --- /dev/null +++ b/tests/test_results.py @@ -0,0 +1,179 @@ +"""Tests for vision plotting helpers.""" + +from __future__ import annotations + +from pathlib import Path + +import cv2 +import numpy as np +import pytest +import torch + +import mblt_vision.utils.results as results_module +from mblt_vision.utils.datasets import get_dotav1_palette +from mblt_vision.utils.results import Results +from mblt_vision.utils.types import NestedListTensorLike + + +def test_image_classification_plot_saves_without_gui_cleanup( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Save classification results without requiring OpenCV GUI support.""" + + source_path = tmp_path / "source.jpg" + save_path = tmp_path / "result.jpg" + image = np.full((32, 32, 3), 255, dtype=np.uint8) + cv2.imwrite(str(source_path), image) + + def _raise_destroy_all_windows() -> None: + raise cv2.error("cvDestroyAllWindows is unavailable") + + monkeypatch.setattr(cv2, "destroyAllWindows", _raise_destroy_all_windows) + + output = torch.zeros(1000) + output[980] = 0.9 + result = Results({}, {"task": "image_classification"}, output) + + plotted = result.plot(str(source_path), str(save_path), topk=1) + + assert plotted is not None + assert save_path.is_file() + + +def test_image_classification_plot_rejects_missing_output() -> None: + """Raise a runtime validation error when classification output is absent.""" + + result = Results({}, {"task": "image_classification"}, torch.zeros(1)) + result.acc = None + + with pytest.raises(ValueError, match="No accuracy output found"): + result.plot(np.zeros((8, 8, 3), dtype=np.uint8), topk=1) + + +def test_instance_segmentation_plot_supports_nonzero_coco_labels( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Plot segmentation results when detections use regular COCO class ids.""" + + source_path = tmp_path / "source.jpg" + save_path = tmp_path / "segmentation.jpg" + image = np.full((32, 32, 3), 255, dtype=np.uint8) + cv2.imwrite(str(source_path), image) + + monkeypatch.setattr(results_module, "scale_boxes", lambda *args, **kwargs: args[1]) + monkeypatch.setattr(results_module, "scale_masks", lambda mask, img0_shape: mask) + monkeypatch.setattr(results_module, "crop_mask", lambda mask, boxes: mask) + + box_cls = torch.tensor([[4.0, 6.0, 20.0, 24.0, 0.9, 45.0]], dtype=torch.float32) + mask = torch.ones((1, 32, 32), dtype=torch.float32) + result = Results( + {"LetterBox": {"img_size": (32, 32)}}, + {"task": "instance_segmentation"}, + [[box_cls, mask]], + ) + + plotted = result.plot(str(source_path), str(save_path)) + + assert plotted is not None + assert save_path.is_file() + + +def test_obb_plot_uses_dotav1_palette(monkeypatch: pytest.MonkeyPatch) -> None: + """Plot DOTAv1 boxes without consulting the COCO palette.""" + + def _reject_coco_palette(label_idx: int) -> tuple[int, int, int]: + raise AssertionError( + f"Unexpected COCO palette lookup for DOTAv1 class {label_idx}." + ) + + monkeypatch.setattr(results_module, "get_coco_det_palette", _reject_coco_palette) + box_cls = torch.tensor( + [[16.0, 16.0, 10.0, 10.0, 0.9, 2.0, 0.0]], dtype=torch.float32 + ) + result = Results( + {"LetterBox": {"img_size": (32, 32)}}, + {"task": "obb"}, + [box_cls], + ) + + plotted = result.plot(np.zeros((32, 32, 3), dtype=np.uint8)) + + assert plotted is not None + assert np.any(np.all(plotted == get_dotav1_palette(2), axis=2)) + + +def test_dotav1_palette_wraps_class_indices() -> None: + """Match the modulo behavior used by the other visualization palettes.""" + + assert get_dotav1_palette(15) == get_dotav1_palette(0) + + +def test_results_accept_path_and_basename_save_path( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Accept pathlib inputs and avoid creating an empty parent directory.""" + + source_path = tmp_path / "source.jpg" + assert cv2.imwrite(str(source_path), np.zeros((8, 8, 3), dtype=np.uint8)) + monkeypatch.chdir(tmp_path) + result = Results( + {}, {"task": "image_classification"}, torch.arange(3, dtype=torch.float32) + ) + + result.plot(source_path, Path("result.jpg"), topk=10) + + assert (tmp_path / "result.jpg").is_file() + + +def test_results_report_failed_image_write(monkeypatch: pytest.MonkeyPatch) -> None: + """Raise OSError when OpenCV cannot encode or write the requested image.""" + + monkeypatch.setattr(cv2, "imwrite", lambda *args, **kwargs: False) + result = Results({}, {"task": "image_classification"}, torch.ones(2)) + + with pytest.raises(OSError, match="Failed to write"): + result.plot(np.zeros((8, 8, 3), dtype=np.uint8), "result.jpg", topk=1) + + +@pytest.mark.parametrize("topk", [0, -1, 1.5, True]) +def test_results_validate_classification_topk(topk: object) -> None: + """Reject invalid classification Top-K values.""" + + result = Results({}, {"task": "image_classification"}, torch.ones(2)) + with pytest.raises((TypeError, ValueError)): + result.plot(np.zeros((8, 8, 3), dtype=np.uint8), topk=topk) + + +@pytest.mark.parametrize( + ("task", "output"), + [ + ("object_detection", []), + ("instance_segmentation", []), + ("instance_segmentation", [[]]), + ], +) +def test_results_reject_empty_structured_outputs( + task: str, output: NestedListTensorLike +) -> None: + """Validate structured result containers before indexing.""" + + with pytest.raises(ValueError): + Results({}, {"task": task}, output) + + +def test_results_normalize_task_alias_and_semantic_taxonomy_case() -> None: + """Normalize OBB aliases and semantic palette taxonomy casing.""" + + obb = Results({}, {"task": "oriented_bounding_boxes"}, [torch.zeros((0, 7))]) + semantic = Results( + {}, + {"task": "semantic_segmentation", "dataset": "CityScapes"}, + np.zeros((1, 4, 4), dtype=np.uint8), + ) + + assert obb.task == "obb" + plotted = semantic.plot(np.zeros((4, 4, 3), dtype=np.uint8)) + assert plotted is not None + assert plotted.shape == (4, 4, 3) diff --git a/tests/test_semantic_segmentation.py b/tests/test_semantic_segmentation.py new file mode 100644 index 0000000..1675e10 --- /dev/null +++ b/tests/test_semantic_segmentation.py @@ -0,0 +1,144 @@ +"""Tests for YOLO26 ADE20K semantic segmentation support.""" + +from __future__ import annotations + + +import pytest +import torch +from mblt_vision.utils.postprocess import SemanticSegPost + + +def test_semantic_postprocess_supports_logits_and_baked_maps() -> None: + """Convert logits or baked maps to input-sized integer class maps.""" + + post = SemanticSegPost( + {"LetterBox": {"img_size": [4, 4]}}, + {"task": "semantic_segmentation", "dataset": "ade20k"}, + ) + logits = torch.zeros((1, 150, 2, 2)) + logits[:, 7] = 1.0 + result = post(logits) + assert isinstance(result, torch.Tensor) + assert result.shape == (1, 4, 4) + assert result.dtype == torch.int64 + assert torch.equal(result, torch.full((1, 4, 4), 7)) + + baked = post(torch.tensor([[[1.0, 2.0], [3.0, 4.0]]])) + assert isinstance(baked, torch.Tensor) + assert baked.shape == (1, 4, 4) + assert set(baked.unique().tolist()) == {1, 2, 3, 4} + + with pytest.raises(ValueError, match=r"expects \[B, 150, H, W\]"): + post(torch.zeros((1, 19, 4, 4))) + with pytest.raises(ValueError, match=r"must be in \[0, 149\]"): + post(torch.full((1, 4, 4), 150)) + + +@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf"), float("-inf")]) +def test_semantic_postprocess_rejects_non_finite_baked_class_ids( + invalid_value: float, +) -> None: + """Reject non-finite baked class IDs before converting their dtype.""" + + post = SemanticSegPost( + {"LetterBox": {"img_size": [4, 4]}}, + {"task": "semantic_segmentation", "dataset": "cityscapes"}, + ) + class_map = torch.zeros((1, 2, 2), dtype=torch.float32) + class_map[0, 0, 0] = invalid_value + + with pytest.raises(ValueError, match="must be finite"): + post(class_map) + + +def test_semantic_postprocess_rejects_fractional_baked_class_ids() -> None: + """Reject fractional baked class IDs instead of silently truncating them.""" + + post = SemanticSegPost( + {"LetterBox": {"img_size": [4, 4]}}, + {"task": "semantic_segmentation", "dataset": "cityscapes"}, + ) + + with pytest.raises(ValueError, match="must be integer-valued"): + post(torch.tensor([[[0.0, 1.9], [2.0, 18.0]]])) + + +def test_semantic_postprocess_reports_semantic_letterbox_errors() -> None: + """Use semantic task labels when validating preprocessing configuration.""" + + with pytest.raises( + ValueError, + match=r"Semantic segmentation requires a LetterBox configuration in pre_cfg", + ): + SemanticSegPost({}, {"task": "semantic_segmentation", "dataset": "ade20k"}) + + +def test_semantic_postprocess_supports_mxq_hwc_and_batched_nhwc_logits() -> None: + """Convert Cityscapes MXQ channel-last logits before choosing class maps.""" + + post = SemanticSegPost( + {"LetterBox": {"img_size": [4, 8]}}, + {"task": "semantic_segmentation", "dataset": "cityscapes"}, + ) + hwc_logits = torch.zeros((4, 8, 19)) + hwc_logits[..., 6] = 2.0 + hwc_result = post(hwc_logits) + assert isinstance(hwc_result, torch.Tensor) + assert hwc_result.shape == (1, 4, 8) + assert torch.equal(hwc_result, torch.full((1, 4, 8), 6)) + + nhwc_logits = torch.zeros((2, 2, 4, 19)) + nhwc_logits[0, ..., 3] = 2.0 + nhwc_logits[1, ..., 9] = 2.0 + nhwc_result = post(nhwc_logits) + assert isinstance(nhwc_result, torch.Tensor) + assert nhwc_result.shape == (2, 4, 8) + assert torch.equal(nhwc_result[0], torch.full((4, 8), 3)) + assert torch.equal(nhwc_result[1], torch.full((4, 8), 9)) + + with pytest.raises(ValueError, match=r"expects \[B, 19, H, W\]"): + post(torch.zeros((1, 4, 8, 18))) + with pytest.raises(ValueError, match=r"expects \[H, W, 19\] MXQ logits"): + post(torch.zeros((4, 8, 18))) + + baked_width_matches_nc = torch.arange(19).reshape(1, 1, 19) + baked_result = post(baked_width_matches_nc) + assert isinstance(baked_result, torch.Tensor) + assert baked_result.shape == (1, 4, 8) + assert torch.equal(baked_result[0, 0], torch.tensor([0, 2, 4, 7, 9, 11, 14, 16])) + + +def test_semantic_postprocess_restores_letterbox_padding() -> None: + """Crop padding before nearest-restoring a semantic map.""" + + post = SemanticSegPost( + {"LetterBox": {"img_size": [8, 8]}}, + {"task": "semantic_segmentation", "dataset": "ade20k"}, + ) + output = torch.zeros((1, 8, 8)) + output[:, 2:6, :] = 5 + restored = post(output, img0_shape=(2, 4), ratio_pad=((2.0, 2.0), (0.0, 2.0))) + assert isinstance(restored, torch.Tensor) + assert restored.shape == (2, 4) + assert torch.equal(restored, torch.full((2, 4), 5)) + + +def test_semantic_logits_restore_before_argmax_and_support_batches() -> None: + """Bilinearly restore logits before choosing classes for each original shape.""" + + post = SemanticSegPost( + {"LetterBox": {"img_size": [4, 8]}}, + {"task": "semantic_segmentation", "dataset": "cityscapes"}, + ) + logits = torch.zeros((2, 19, 2, 4)) + logits[:, 0] = 1.0 + logits[:, 1, :, 2:] = 2.0 + restored = post( + logits, + img0_shape=[(2, 8), (1, 4)], + ratio_pad=[((1.0, 1.0), (0.0, 1.0)), ((2.0, 2.0), (0.0, 1.0))], + ) + + assert isinstance(restored, list) + assert [tuple(item.shape) for item in restored] == [(2, 8), (1, 4)] + assert set(restored[0].unique().tolist()) == {0, 1} diff --git a/tests/test_vision_api.py b/tests/test_vision_api.py deleted file mode 100644 index 9f3b4bd..0000000 --- a/tests/test_vision_api.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -Test script for Vision API. - -This script tests the API for listing available models and tasks in the vision module. -""" - -from pprint import pprint - -from mblt_vision import list_models, list_tasks - -print(list_tasks()) -pprint(list_models()) diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py new file mode 100644 index 0000000..08a1d1a --- /dev/null +++ b/tests/test_wrapper.py @@ -0,0 +1,2446 @@ +"""Tests for vision wrapper MXQ path resolution.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, cast + +import numpy as np +import pytest +import torch +from huggingface_hub.errors import EntryNotFoundError +from mblt_vision.utils.postprocess import build_postprocess +from mblt_vision.utils.postprocess.base import YOLODetectionPostBase +from mblt_vision.utils.postprocess.common import ( + crop_mask, + dual_topk, + nmsout2eval, + normalize_image_shapes, + normalize_ratio_pads, + scale_coords, + scale_masks, +) +from mblt_vision.utils.postprocess.yolo_anchorless_post import ( + AnchorlessOutputLayout, + YOLOAnchorlessDetectionPost, + YOLOAnchorlessOBBPost, + YOLOAnchorlessPosePost, + _AnchorlessNMSInput, +) + +import mblt_vision.wrapper as wrapper +from mblt_vision._compat import create_model_class +from mblt_vision.utils.letterbox import resolve_ratio_pad +from mblt_vision.utils.results import Results +from mblt_vision.utils.types import ListTensorLike +from mblt_vision.wrapper import MBLT_Engine + + +@pytest.mark.parametrize( + ("model_name", "output_shape", "expected_shape", "expected_class"), + [ + ("yolo26m-depth", (1, 192, 192), (1, 768, 768), None), + ("yolo26m-sem", (1024, 2048, 19), (1, 1024, 2048), 8), + ], +) +def test_local_mxq_dense_pipeline_uses_normalized_results( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + model_name: str, + output_shape: tuple[int, ...], + expected_shape: tuple[int, int, int], + expected_class: int | None, +) -> None: + """Exercise preprocessing, MXQ output normalization, and ``Results`` without an NPU.""" + + mxq_path = tmp_path / f"{model_name}.mxq" + mxq_path.write_bytes(b"mxq") + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + assert kwargs["mxq_path"] == str(mxq_path) + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def __call__(self, input_value: np.ndarray) -> np.ndarray: + assert input_value.ndim == 3 + output = np.zeros(output_shape, dtype=np.float32) + if expected_class is not None: + output[..., expected_class] = 1.0 + else: + output[...] = 1.0 + return output + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + engine = MBLT_Engine(model_name, model_path=str(mxq_path)) + try: + preprocessed = engine.preprocess(np.zeros((20, 40, 3), dtype=np.uint8)) + raw_output = engine(preprocessed) + result = engine.postprocess(raw_output) + if expected_class is None: + assert isinstance(result.depth, torch.Tensor) + assert tuple(result.depth.shape) == expected_shape + assert torch.isfinite(result.depth).all() + else: + assert isinstance(result.semantic_mask, torch.Tensor) + assert tuple(result.semantic_mask.shape) == expected_shape + assert set(result.semantic_mask.unique().tolist()) == {expected_class} + finally: + engine.dispose() + + +def test_default_cache_dir_uses_private_temporary_fallback( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Avoid a predictable shared cache when the preferred cache cannot be created.""" + + shared_cache = tmp_path / "mblt_model_zoo" + shared_cache.mkdir() + (shared_cache / "poisoned.onnx").write_bytes(b"untrusted") + + def _fail_mkdir(self: Path, *args: object, **kwargs: object) -> None: + del self, args, kwargs + raise OSError("home cache is unavailable") + + monkeypatch.setattr(wrapper.Path, "mkdir", _fail_mkdir) + monkeypatch.setattr(wrapper.tempfile, "gettempdir", lambda: str(tmp_path)) + + cache_dir = Path(wrapper._default_cache_dir()) + + assert cache_dir.parent == tmp_path + assert cache_dir.name.startswith("mblt_model_zoo-") + assert cache_dir != shared_cache + assert not (cache_dir / "poisoned.onnx").exists() + assert cache_dir.stat().st_mode & 0o777 == 0o700 + + +def test_onnx_runtime_defaults_to_cpu_provider() -> None: + """Avoid accelerator provider probing unless callers explicitly opt in.""" + + class _FakeOrt: + @staticmethod + def get_available_providers() -> list[str]: + return [ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ] + + assert wrapper._resolve_onnx_providers(_FakeOrt()) == ["CPUExecutionProvider"] + assert wrapper._resolve_onnx_providers(_FakeOrt(), ["CUDAExecutionProvider"]) == [ + "CUDAExecutionProvider" + ] + + +def test_dual_topk_logits_matches_sigmoid_scores() -> None: + """Select the same NMS-free detections before converting logits to probabilities.""" + torch.manual_seed(0) + boxes = torch.rand(24, 4) + logits = torch.rand(24, 5).mul(10.0).sub(5.0) + extra = torch.rand(24, 2) + logits_input = torch.cat([boxes, logits, extra], dim=1) + probability_input = torch.cat([boxes, logits.sigmoid(), extra], dim=1) + + actual = dual_topk( + logits_input, nc=5, n_extra=2, max_det=8, conf_thres=0.25, score_is_logits=True + ) + expected = dual_topk(probability_input, nc=5, n_extra=2, max_det=8, conf_thres=0.25) + + torch.testing.assert_close(actual, expected) + + +def test_file_config_cleansing_prefers_existing_mxq_path( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Use an existing local MXQ path without attempting a Hub download.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + + def _unexpected_download(**kwargs: Any) -> str: + raise AssertionError("hf_hub_download should not be called") + + monkeypatch.setattr(wrapper, "hf_hub_download", _unexpected_download) + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.file_cfg = { + "mxq_path": str(mxq_path), + "repo_id": "mobilint/example", + "filename": "model.mxq", + "revision": "main", + "core_mode": "global8", + } + + engine.file_config_cleansing() + + assert engine.file_cfg["mxq_path"] == str(mxq_path) + assert "repo_id" not in engine.file_cfg + assert "filename" not in engine.file_cfg + assert "revision" not in engine.file_cfg + + +def test_model_path_defaults_to_local_mxq_for_mxq_framework(tmp_path: Path) -> None: + """Map ``model_path`` to ``mxq_path`` when MXQ inference is requested.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.framework = "mxq" + engine.file_cfg = {"model_path": str(mxq_path)} + + engine.file_config_cleansing() + + assert engine.file_cfg["mxq_path"] == str(mxq_path) + assert "onnx_path" not in engine.file_cfg or not engine.file_cfg["onnx_path"] + + +def test_model_path_defaults_to_local_onnx_for_onnx_framework(tmp_path: Path) -> None: + """Map ``model_path`` to ``onnx_path`` when ONNX inference is requested.""" + + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.framework = "onnx" + engine.file_cfg = {"model_path": str(onnx_path)} + + engine.file_config_cleansing() + + assert engine.file_cfg["onnx_path"] == str(onnx_path) + assert "mxq_path" not in engine.file_cfg or not engine.file_cfg["mxq_path"] + + +def test_engine_init_accepts_local_mxq_model_path( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Route API ``model_path`` to the MXQ backend for local MXQ inference.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + backend_kwargs: dict[str, Any] = {} + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + backend_kwargs.update(kwargs) + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + engine = MBLT_Engine( + model_cls={ + "file_cfg": {}, + "pre_cfg": {}, + "post_cfg": {}, + }, + model_path=str(mxq_path), + ) + + try: + assert engine.file_cfg["mxq_path"] == str(mxq_path) + assert backend_kwargs["mxq_path"] == str(mxq_path) + finally: + engine.dispose() + + +def test_engine_init_preserves_legacy_positional_arguments( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Keep pre-``model_path`` positional arguments bound to their original fields.""" + + mxq_path = tmp_path / "legacy.mxq" + onnx_path = tmp_path / "legacy.onnx" + mxq_path.write_bytes(b"mxq") + onnx_path.write_bytes(b"onnx") + backend_kwargs: dict[str, Any] = {} + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + backend_kwargs.update(kwargs) + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + engine = MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, + "DEFAULT", + str(mxq_path), + str(onnx_path), + 3, + "global8", + ) + + try: + assert engine.file_cfg["mxq_path"] == str(mxq_path) + assert engine.file_cfg["onnx_path"] == str(onnx_path) + assert backend_kwargs["dev_no"] == 3 + assert backend_kwargs["core_mode"] == "global8" + finally: + engine.dispose() + + +def test_engine_init_preserves_shifted_positional_mxq_runtime_arguments( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Keep runtime arguments after a positional MXQ ``model_path``.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + backend_kwargs: dict[str, Any] = {} + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + backend_kwargs.update(kwargs) + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + engine = MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, + "DEFAULT", + str(mxq_path), + "", + "", + 3, + "global8", + ) + + try: + assert engine.file_cfg["mxq_path"] == str(mxq_path) + assert backend_kwargs["dev_no"] == 3 + assert backend_kwargs["core_mode"] == "global8" + finally: + engine.dispose() + + +def test_legacy_wrapper_preserves_positional_path_and_framework_arguments( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Keep the generated compatibility constructor's original positional order.""" + + captured_kwargs: dict[str, Any] = {} + + def _capture_engine_init(self: MBLT_Engine, **kwargs: Any) -> None: + del self + captured_kwargs.update(kwargs) + + monkeypatch.setattr(MBLT_Engine, "__init__", _capture_engine_init) + compat_cls = create_model_class( + "ResNet50", "mblt_model_zoo.vision.image_classification" + ) + + compat_cls( + None, + "DEFAULT", + "global8", + "aries", + 3, + ["0:0"], + [0], + "legacy.mxq", + "legacy.onnx", + "onnx", + ) + + assert captured_kwargs["model_path"] == "" + assert captured_kwargs["mxq_path"] == "legacy.mxq" + assert captured_kwargs["onnx_path"] == "legacy.onnx" + assert captured_kwargs["framework"] == "onnx" + + +def test_legacy_wrapper_preserves_shifted_positional_model_path( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Keep the generated positional ``model_path`` slot working for ONNX.""" + + captured_kwargs: dict[str, Any] = {} + + def _capture_engine_init(self: MBLT_Engine, **kwargs: Any) -> None: + del self + captured_kwargs.update(kwargs) + + monkeypatch.setattr(MBLT_Engine, "__init__", _capture_engine_init) + compat_cls = create_model_class( + "ResNet50", "mblt_model_zoo.vision.image_classification" + ) + + compat_cls( + None, + "DEFAULT", + "global8", + "aries", + 3, + ["0:0"], + [0], + "legacy.onnx", + None, + None, + "onnx", + ) + + assert captured_kwargs["model_path"] == "legacy.onnx" + assert captured_kwargs["mxq_path"] == "" + assert captured_kwargs["onnx_path"] == "" + assert captured_kwargs["framework"] == "onnx" + + +def test_legacy_wrapper_preserves_shifted_positional_mxq_tail( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Keep the generated shifted positional tail working for MXQ.""" + + captured_kwargs: dict[str, Any] = {} + + def _capture_engine_init(self: MBLT_Engine, **kwargs: Any) -> None: + del self + captured_kwargs.update(kwargs) + + monkeypatch.setattr(MBLT_Engine, "__init__", _capture_engine_init) + compat_cls = create_model_class( + "ResNet50", "mblt_model_zoo.vision.image_classification" + ) + + compat_cls( + None, + "DEFAULT", + "global8", + "aries", + 3, + ["0:0"], + [0], + "legacy.mxq", + None, + None, + "mxq", + ) + + assert captured_kwargs["model_path"] == "legacy.mxq" + assert captured_kwargs["mxq_path"] == "" + assert captured_kwargs["onnx_path"] == "" + assert captured_kwargs["framework"] == "mxq" + + +def test_engine_init_accepts_oriented_bounding_boxes_task_alias( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Construct the canonical OBB postprocessor from the compatibility task spelling.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + self.kwargs = kwargs + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + + engine = MBLT_Engine( + model_cls={ + "file_cfg": {}, + "pre_cfg": {"LetterBox": {"img_size": [640, 640]}}, + "post_cfg": { + "task": "oriented_bounding_boxes", + "dataset": "dotav1", + "nl": 3, + "reg_max": 16, + "n_extra": 1, + }, + }, + model_path=str(mxq_path), + ) + + try: + assert isinstance(engine.postprocessor, YOLOAnchorlessOBBPost) + assert engine.postprocessor.task == "obb" + finally: + engine.dispose() + + +def test_engine_init_auto_detects_mxq_framework_from_model_path( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Infer the MXQ framework from a local MXQ path when framework is omitted.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + self.kwargs = kwargs + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + engine = MBLT_Engine( + model_cls={ + "file_cfg": {}, + "pre_cfg": {}, + "post_cfg": {}, + }, + model_path=str(mxq_path), + ) + + try: + assert engine.framework == "mxq" + assert engine.file_cfg["mxq_path"] == str(mxq_path) + finally: + engine.dispose() + + +@pytest.mark.parametrize( + "model_path_style", ["keyword", "positional", "positional-runtime"] +) +def test_engine_init_accepts_local_onnx_model_path( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + model_path_style: str, +) -> None: + """Route API ``model_path`` to the ONNX runtime session for local ONNX inference.""" + + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + + class _FakeInput: + name = "input" + shape = [1, 3, 224, 224] + + class _FakeOutput: + name = "output" + + class _FakeSession: + def __init__(self, path: str, providers: list[str]) -> None: + self.path = path + self.providers = providers + + def get_inputs(self) -> list[_FakeInput]: + return [_FakeInput()] + + def get_outputs(self) -> list[_FakeOutput]: + return [_FakeOutput()] + + class _FakeOrt: + def __init__(self) -> None: + self.session: _FakeSession | None = None + + @staticmethod + def get_available_providers() -> list[str]: + return ["CPUExecutionProvider"] + + def InferenceSession(self, path: str, providers: list[str]) -> _FakeSession: + self.session = _FakeSession(path, providers) + return self.session + + fake_ort = _FakeOrt() + monkeypatch.setattr(wrapper, "_load_onnxruntime", lambda: fake_ort) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + model_config = { + "file_cfg": {}, + "pre_cfg": {}, + "post_cfg": {}, + } + if model_path_style == "positional": + engine = MBLT_Engine(model_config, "DEFAULT", str(onnx_path)) + elif model_path_style == "positional-runtime": + engine = MBLT_Engine( + model_config, + "DEFAULT", + str(onnx_path), + "", + "", + 3, + "global8", + ["0:0"], + [1], + {"confidence": 0.25}, + "onnx", + ["CPUExecutionProvider"], + ) + else: + engine = MBLT_Engine(model_cls=model_config, model_path=str(onnx_path)) + + assert engine.file_cfg["onnx_path"] == str(onnx_path) + assert fake_ort.session is not None + assert fake_ort.session.path == str(onnx_path) + assert engine.framework == "onnx" + if model_path_style == "positional-runtime": + assert engine.file_cfg["dev_no"] == 3 + assert engine.file_cfg["core_mode"] == "global8" + assert engine.file_cfg["target_cores"] == ["0:0"] + assert engine.file_cfg["target_clusters"] == [1] + assert engine.postprocess_kwargs == {"confidence": 0.25} + assert fake_ort.session.providers == ["CPUExecutionProvider"] + + +def test_engine_init_rejects_conflicting_framework_and_model_path( + tmp_path: Path, +) -> None: + """Fail fast when the explicit framework conflicts with the local model suffix.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + + with pytest.raises(ValueError, match="conflicts with model path"): + MBLT_Engine( + model_cls={ + "file_cfg": {}, + "pre_cfg": {}, + "post_cfg": {}, + }, + framework="onnx", + model_path=str(mxq_path), + ) + + +def test_engine_init_auto_detects_onnx_framework_from_config_model_path( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Infer ONNX from ``file_cfg.model_path`` when constructor inputs omit the framework.""" + + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + + class _FakeInput: + name = "input" + shape = [1, 3, 224, 224] + + class _FakeOutput: + name = "output" + + class _FakeSession: + def __init__(self, path: str, providers: list[str]) -> None: + self.path = path + self.providers = providers + + def get_inputs(self) -> list[_FakeInput]: + return [_FakeInput()] + + def get_outputs(self) -> list[_FakeOutput]: + return [_FakeOutput()] + + class _FakeOrt: + def __init__(self) -> None: + self.session: _FakeSession | None = None + + @staticmethod + def get_available_providers() -> list[str]: + return ["CPUExecutionProvider"] + + def InferenceSession(self, path: str, providers: list[str]) -> _FakeSession: + self.session = _FakeSession(path, providers) + return self.session + + fake_ort = _FakeOrt() + monkeypatch.setattr(wrapper, "_load_onnxruntime", lambda: fake_ort) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + engine = MBLT_Engine( + model_cls={ + "file_cfg": {"model_path": str(onnx_path)}, + "pre_cfg": {}, + "post_cfg": {}, + } + ) + + assert engine.file_cfg["onnx_path"] == str(onnx_path) + assert fake_ort.session is not None + assert fake_ort.session.path == str(onnx_path) + assert engine.framework == "onnx" + + +def test_engine_init_rejects_conflicting_framework_and_config_model_path( + tmp_path: Path, +) -> None: + """Fail fast when the explicit framework conflicts with ``file_cfg.model_path``.""" + + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + + with pytest.raises(ValueError, match="conflicts with model path"): + MBLT_Engine( + model_cls={ + "file_cfg": {"model_path": str(onnx_path)}, + "pre_cfg": {}, + "post_cfg": {}, + }, + framework="mxq", + ) + + +def test_legacy_local_path_stays_mxq_specific_for_onnx_framework( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Keep legacy ``local_path`` semantics stable in compatibility wrappers.""" + + mxq_path = tmp_path / "resnet50.mxq" + onnx_path = tmp_path / "resnet50.onnx" + mxq_path.write_bytes(b"mxq") + onnx_path.write_bytes(b"onnx") + + class _FakeInput: + name = "input" + shape = [1, 3, 224, 224] + + class _FakeOutput: + name = "output" + + class _FakeSession: + def __init__(self, path: str, providers: list[str]) -> None: + self.path = path + self.providers = providers + + def get_inputs(self) -> list[_FakeInput]: + return [_FakeInput()] + + def get_outputs(self) -> list[_FakeOutput]: + return [_FakeOutput()] + + class _FakeOrt: + def __init__(self) -> None: + self.session: _FakeSession | None = None + + @staticmethod + def get_available_providers() -> list[str]: + return ["CPUExecutionProvider"] + + def InferenceSession(self, path: str, providers: list[str]) -> _FakeSession: + self.session = _FakeSession(path, providers) + return self.session + + fake_ort = _FakeOrt() + monkeypatch.setattr(wrapper, "_load_onnxruntime", lambda: fake_ort) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + compat_cls = create_model_class( + "ResNet50", "mblt_model_zoo.vision.image_classification" + ) + engine = compat_cls(local_path=str(mxq_path), framework="onnx") + + assert engine.file_cfg["mxq_path"] == str(mxq_path) + assert engine.file_cfg["onnx_path"] == str(onnx_path) + assert fake_ort.session is not None + assert fake_ort.session.path == str(onnx_path) + assert engine.framework == "onnx" + + +def test_engine_init_defaults_to_mxq_without_model_path( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Use MXQ as the fallback framework when no model path is provided.""" + + backend_kwargs: dict[str, Any] = {} + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + backend_kwargs.update(kwargs) + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + monkeypatch.setattr( + wrapper.MBLT_Engine, + "_download_hub_artifact", + lambda self, **kwargs: "/tmp/model.mxq", + ) + + engine = MBLT_Engine( + model_cls={ + "file_cfg": { + "repo_id": "mobilint/example", + "filename": "model.mxq", + "revision": "main", + }, + "pre_cfg": {}, + "post_cfg": {}, + }, + ) + + try: + assert engine.framework == "mxq" + assert "mxq_path" in backend_kwargs + finally: + engine.dispose() + + +@pytest.mark.parametrize("mask_count", [1, 50]) +def test_crop_mask_matches_ultralytics_fractional_boundaries(mask_count: int) -> None: + """Use identical fractional crop semantics on both sides of the former CPU branch.""" + + masks = torch.ones((mask_count, 5, 5), dtype=torch.float32) + boxes = torch.tensor([[1.2, 1.8, 3.6, 4.2]], dtype=torch.float32).repeat( + mask_count, 1 + ) + + cropped = crop_mask(masks, boxes) + + expected = torch.zeros((mask_count, 5, 5), dtype=torch.float32) + expected[:, 2:5, 2:4] = 1 + assert torch.equal(cropped, expected) + + +def test_file_config_cleansing_downloads_aries_before_core_mode( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Download only the MXQ artifact and try the legacy aries layout first.""" + + calls: list[str] = [] + + def _fake_download(**kwargs: Any) -> str: + subfolder = kwargs["subfolder"] + calls.append(subfolder) + if subfolder == "aries": + raise EntryNotFoundError("missing") + return "/tmp/global8.mxq" + + monkeypatch.setattr(wrapper, "hf_hub_download", _fake_download) + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.framework = "mxq" + engine.file_cfg = { + "mxq_path": "", + "repo_id": "mobilint/example", + "filename": "model.mxq", + "revision": "main", + "core_mode": "global8", + } + + engine.file_config_cleansing() + + assert calls == ["aries", "aries/global8"] + assert engine.file_cfg["mxq_path"] == "/tmp/global8.mxq" + assert engine.file_cfg["onnx_filename"] == "model.onnx" + assert "onnx_path" not in engine.file_cfg + + +def test_file_config_cleansing_downloads_only_onnx_for_onnx_framework( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Download only the ONNX artifact when ONNX inference is requested.""" + + calls: list[dict[str, Any]] = [] + + def _fake_download(**kwargs: Any) -> str: + calls.append(kwargs) + assert kwargs["filename"] == "model.onnx" + assert "subfolder" not in kwargs + return "/tmp/model.onnx" + + monkeypatch.setattr(wrapper, "hf_hub_download", _fake_download) + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.framework = "onnx" + engine.file_cfg = { + "onnx_path": "", + "repo_id": "mobilint/example", + "filename": "model.mxq", + "revision": "main", + "core_mode": "global8", + } + + engine.file_config_cleansing() + + assert len(calls) == 1 + assert engine.file_cfg["onnx_filename"] == "model.onnx" + assert engine.file_cfg["onnx_path"] == "/tmp/model.onnx" + assert "mxq_path" not in engine.file_cfg or not engine.file_cfg["mxq_path"] + + +def test_file_config_cleansing_resolves_local_onnx( + tmp_path: Path, +) -> None: + """Resolve ONNX file path next to local MXQ file when they exist locally.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.file_cfg = { + "mxq_path": str(mxq_path), + "repo_id": "mobilint/example", + "filename": "model.mxq", + "revision": "main", + "core_mode": "global8", + } + + engine.file_config_cleansing() + + assert engine.file_cfg["mxq_path"] == str(mxq_path) + assert engine.file_cfg["onnx_filename"] == "model.onnx" + assert engine.file_cfg["onnx_path"] == str(onnx_path) + + +def test_prepare_onnx_inputs_keeps_batched_nchw_layout() -> None: + """Preserve existing NCHW batches when feeding ONNX sessions.""" + + class _FakeInput: + name = "input" + shape = [1, 3, 224, 224] + + class _FakeSession: + def get_inputs(self) -> list[_FakeInput]: + return [_FakeInput()] + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.framework = "onnx" + fake_session = _FakeSession() + engine._onnx_session = fake_session + engine.model = fake_session + engine.input_name = "input" + + batch = torch.zeros((2, 3, 224, 224), dtype=torch.float32) + + inputs = engine._prepare_onnx_inputs(batch) + + assert set(inputs) == {"input"} + assert inputs["input"].shape == (2, 3, 224, 224) + assert inputs["input"].dtype == np.float32 + + +def test_prepare_onnx_inputs_transposes_hwc_images() -> None: + """Convert single HWC images to batched NCHW arrays for ONNX runtime.""" + + class _FakeInput: + name = "input" + shape = [1, 3, 224, 224] + + class _FakeSession: + def get_inputs(self) -> list[_FakeInput]: + return [_FakeInput()] + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.framework = "onnx" + fake_session = _FakeSession() + engine._onnx_session = fake_session + engine.model = fake_session + engine.input_name = "input" + + image = np.zeros((224, 224, 3), dtype=np.float32) + + inputs = engine._prepare_onnx_inputs(image) + + assert inputs["input"].shape == (1, 3, 224, 224) + + +def test_final_onnx_detections_apply_confidence_threshold() -> None: + """Filter confidence on already-decoded ONNX detection outputs.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "object_detection", + "nl": 3, + "nmsfree": True, + "reg_max": 16, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + final_output = np.array( + [ + [ + [10.0, 20.0, 30.0, 40.0, 0.49, 0.0], + [11.0, 21.0, 31.0, 41.0, 0.50, 1.0], + [12.0, 22.0, 32.0, 42.0, 0.90, 2.0], + ] + ], + dtype=np.float32, + ) + + result = postprocessor(final_output) + + assert len(result) == 1 + assert result[0].shape == (1, 6) + assert torch.all(result[0][:, 4] > 0.5) + + +def test_nmsfree_postprocess_supports_multilabel_dispatch( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Keep COCO's multi-label postprocess path compatible with NMS-free models.""" + + postprocessor = build_postprocess( + {"LetterBox": {"img_size": [640, 640]}}, + {"task": "object_detection", "nl": 3, "reg_max": 16, "nmsfree": True}, + ) + decoded = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0, 0.9, 2.0], [11.0, 21.0, 31.0, 41.0, 0.0, 1.0]]], + dtype=torch.float32, + ) + monkeypatch.setattr(postprocessor, "extract_final_outputs", lambda _: (None, None)) + monkeypatch.setattr(postprocessor, "check_input", lambda x: x) + monkeypatch.setattr(postprocessor, "_pre_process", lambda _: (decoded, None)) + + result = postprocessor([torch.empty(1)], multi_label=True) + + assert len(result) == 1 + assert torch.equal(result[0], decoded[0, :1]) + + +def test_final_onnx_detections_normalize_singleton_and_channel_first() -> None: + """Accept common ONNX final-detection layouts without decoding them again.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "object_detection", + "nl": 3, + "nmsfree": True, + "reg_max": 16, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + final_output = torch.tensor( + [ + [ + [10.0, 20.0, 30.0, 40.0, 0.90, 2.0], + [11.0, 21.0, 31.0, 41.0, 0.40, 1.0], + ] + ], + dtype=torch.float32, + ) + + singleton_result = postprocessor(final_output[:, None]) + channel_first_result = postprocessor(final_output.transpose(1, 2)) + + assert torch.equal(singleton_result[0], final_output[0, :1]) + assert torch.equal(channel_first_result[0], final_output[0, :1]) + + +def test_anchorless_pose_nms_uses_converted_provenance_for_ambiguous_shape() -> None: + """Keep converted row-major pose tensors row-major when both dimensions match.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "pose_estimation", + "nl": 3, + "n_extra": 51, + "reg_max": 16, + "conf_thres": 0.001, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + row_major = torch.zeros((56, 56), dtype=torch.float32) + row_major[:, :4] = torch.tensor([10.0, 10.0, 20.0, 20.0]) + row_major[:, 4] = torch.linspace(0.9, 0.1, 56) + + result = postprocessor.nms(_AnchorlessNMSInput([row_major], "candidates_first")) + + assert len(result) == 1 + assert result[0].shape == (1, 57) + assert torch.allclose(result[0][0, 4], torch.tensor(0.9)) + + +def test_final_onnx_segmentation_normalizes_detections_and_proto() -> None: + """Use final segmentation detections directly while preserving prototype layout.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "dflfree": True, + "nc": 80, + "n_extra": 32, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + final_output = torch.zeros((1, 1, 2, 38), dtype=torch.float32) + final_output[0, 0, 0, :6] = torch.tensor([10.0, 20.0, 30.0, 40.0, 0.90, 2.0]) + final_output[0, 0, 1, :6] = torch.tensor([11.0, 21.0, 31.0, 41.0, 0.40, 1.0]) + proto = torch.zeros((1, 32, 160, 160), dtype=torch.float32) + + result = postprocessor([final_output, proto]) + + assert len(result) == 1 + assert result[0][0].shape == (1, 38) + assert result[0][1].shape == (1, 640, 640) + + +def test_final_onnx_pose_normalizes_detections() -> None: + """Use final pose detections directly without sending them through decode.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "pose_estimation", + "nl": 3, + "n_extra": 51, + "reg_max": 16, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + final_output = torch.zeros((1, 2, 57), dtype=torch.float32) + final_output[0, 0, :6] = torch.tensor([10.0, 20.0, 30.0, 40.0, 0.90, 0.0]) + final_output[0, 1, :6] = torch.tensor([11.0, 21.0, 31.0, 41.0, 0.40, 0.0]) + + result = postprocessor(final_output) + + assert len(result) == 1 + assert result[0].shape == (1, 57) + assert torch.equal(result[0], final_output[0, :1]) + + +@pytest.mark.parametrize( + ("task", "post_cfg_extra", "converted_dim"), + [ + ("object_detection", {}, 7), + ("pose_estimation", {"n_extra": 51}, 56), + ], +) +def test_non_e2e_single_converted_outputs_follow_task_shape( + task: str, + post_cfg_extra: dict[str, int], + converted_dim: int, +) -> None: + """Route non-e2e single converted detection and pose outputs by task shape.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": task, + "nl": 3, + "reg_max": 16, + "nc": 3 if task == "object_detection" else 1, + "conf_thres": 0.5, + "iou_thres": 0.7, + "e2e": False, + **post_cfg_extra, + } + postprocessor = build_postprocess(pre_cfg, post_cfg) + converted = torch.zeros((1, 2, converted_dim), dtype=torch.float32) + + result = postprocessor(converted) + + assert isinstance(result, torch.Tensor) + assert result.shape == (1, converted_dim, 2) + + +def test_non_e2e_segmentation_uses_converted_detections_and_proto() -> None: + """Route non-e2e segmentation converted detections with prototype masks.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "reg_max": 16, + "nc": 3, + "n_extra": 32, + "conf_thres": 0.5, + "iou_thres": 0.7, + "e2e": False, + } + postprocessor = build_postprocess(pre_cfg, post_cfg) + detections = torch.zeros((1, 2, 39), dtype=torch.float32) + proto = torch.zeros((1, 16, 16, 32), dtype=torch.float32) + + result = postprocessor([detections, proto]) + + assert isinstance(result, list) + assert result[0].shape == (1, 39, 2) + assert result[1].shape == (1, 32, 16, 16) + + +def test_dflfree_detection_accepts_decode_true_mxq_parts_with_reducemax() -> None: + """Accept split decode-true DFL-free detection outputs with an extra reducemax tensor.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "object_detection", + "nl": 3, + "dflfree": True, + "nc": 3, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + scores = torch.tensor([[[0.1, 0.9, 0.2], [0.2, 0.3, 0.4]]], dtype=torch.float32) + reducemax = scores.max(dim=-1, keepdim=True).values + + result = postprocessor([scores, reducemax, boxes]) + + assert len(result) == 1 + assert result[0].shape == (1, 6) + assert torch.equal(result[0][0, :4], boxes[0, 0]) + assert torch.allclose(result[0][0, 4], torch.tensor(0.9)) + assert torch.allclose(result[0][0, 5], torch.tensor(1.0)) + + +def test_dflfree_detection_accepts_batched_decode_true_mxq_parts_with_reducemax() -> ( + None +): + """Preserve batched split decode-true DFL-free detection outputs.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "object_detection", + "nl": 3, + "dflfree": True, + "nc": 3, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [ + [[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]], + [[50.0, 60.0, 70.0, 80.0], [51.0, 61.0, 71.0, 81.0]], + ], + dtype=torch.float32, + ) + scores = torch.tensor( + [ + [[0.1, 0.9, 0.2], [0.2, 0.3, 0.4]], + [[0.8, 0.1, 0.2], [0.1, 0.2, 0.3]], + ], + dtype=torch.float32, + ) + reducemax = scores.max(dim=-1, keepdim=True).values.unsqueeze(1) + batched_scores = scores.unsqueeze(1) + batched_boxes = boxes.unsqueeze(1) + + result = postprocessor([batched_scores, reducemax, batched_boxes]) + + assert len(result) == 2 + assert result[0].shape == (1, 6) + assert result[1].shape == (1, 6) + assert torch.equal(result[0][0, :4], boxes[0, 0]) + assert torch.equal(result[1][0, :4], boxes[1, 0]) + assert torch.allclose(result[0][0, 4], torch.tensor(0.9)) + assert torch.allclose(result[1][0, 4], torch.tensor(0.8)) + assert torch.allclose(result[0][0, 5], torch.tensor(1.0)) + assert torch.allclose(result[1][0, 5], torch.tensor(0.0)) + + +def test_dflfree_detection_distinguishes_equal_width_box_and_score_parts() -> None: + """Use reducemax to distinguish boxes from scores when ``nc == 4``.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "object_detection", + "nl": 3, + "dflfree": True, + "nc": 4, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + scores = torch.tensor( + [[[0.1, 0.9, 0.2, 0.3], [0.2, 0.3, 0.4, 0.1]]], dtype=torch.float32 + ) + reducemax = scores.max(dim=-1, keepdim=True).values + + result = postprocessor([boxes, reducemax, scores]) + + assert len(result) == 1 + assert result[0].shape == (1, 6) + assert torch.equal(result[0][0, :4], boxes[0, 0]) + assert torch.allclose(result[0][0, 4], torch.tensor(0.9)) + assert torch.allclose(result[0][0, 5], torch.tensor(1.0)) + + +def test_dflfree_segmentation_accepts_decode_true_mxq_parts_with_reducemax() -> None: + """Accept split decode-true DFL-free segmentation outputs with reducemax and proto tensors.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "dflfree": True, + "nc": 3, + "n_extra": 2, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + scores = torch.tensor([[[0.1, 0.9, 0.2], [0.2, 0.3, 0.4]]], dtype=torch.float32) + reducemax = scores.max(dim=-1, keepdim=True).values + coeffs = torch.tensor([[[0.4, 0.6], [0.2, 0.1]]], dtype=torch.float32) + proto = torch.zeros((9, 9, 2), dtype=torch.float32) + + result = postprocessor([coeffs, scores, reducemax, proto, boxes]) + + assert len(result) == 1 + assert result[0][0].shape == (1, 8) + assert result[0][1].shape == (1, 64, 64) + + +def test_dflfree_segmentation_accepts_approximate_reducemax_scores() -> None: + """Match quantized reducemax tensors without appending them as mask coefficients.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "dflfree": True, + "nc": 3, + "n_extra": 2, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + scores = torch.tensor([[[0.1, 0.9, 0.2], [0.2, 0.3, 0.4]]], dtype=torch.float32) + reducemax = scores.max(dim=-1, keepdim=True).values - 0.01 + coeffs = torch.tensor([[[0.4, 0.6], [0.2, 0.1]]], dtype=torch.float32) + proto = torch.zeros((9, 9, 2), dtype=torch.float32) + + result = postprocessor([coeffs, scores, reducemax, proto, boxes]) + + assert len(result) == 1 + assert result[0][0].shape == (1, 8) + assert result[0][1].shape == (1, 64, 64) + + +def test_dflfree_segmentation_excludes_reducemax_from_proto_candidates() -> None: + """Ignore unused reducemax tensors when ``n_extra == 1`` and selecting the proto tensor.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "dflfree": True, + "nc": 3, + "n_extra": 1, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + scores = torch.tensor([[[0.1, 0.9, 0.2], [0.2, 0.3, 0.4]]], dtype=torch.float32) + reducemax = scores.max(dim=-1, keepdim=True).values + coeffs = torch.tensor([[[0.6], [0.1]]], dtype=torch.float32) + proto = torch.zeros((9, 9, 1), dtype=torch.float32) + + result = postprocessor([coeffs, scores, reducemax, proto, boxes]) + + assert len(result) == 1 + assert result[0][0].shape == (1, 7) + assert result[0][1].shape == (1, 64, 64) + + +def test_non_e2e_dflfree_segmentation_accepts_decode_true_mxq_parts_with_reducemax() -> ( + None +): + """Route 5-part decode-true segmentation outputs through the segmentation non-e2e path.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "dflfree": True, + "nc": 3, + "n_extra": 2, + "conf_thres": 0.5, + "iou_thres": 0.7, + "e2e": False, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + scores = torch.tensor([[[0.1, 0.9, 0.2], [0.2, 0.3, 0.4]]], dtype=torch.float32) + reducemax = scores.max(dim=-1, keepdim=True).values + coeffs = torch.tensor([[[0.4, 0.6], [0.2, 0.1]]], dtype=torch.float32) + proto = torch.zeros((1, 9, 9, 2), dtype=torch.float32) + + result = postprocessor([coeffs, scores, reducemax, proto, boxes]) + + assert isinstance(result, list) + assert result[0].shape == (1, 300, 8) + assert result[1].shape == (1, 2, 9, 9) + assert torch.equal(result[0][0, 0, :4], boxes[0, 0]) + assert torch.allclose(result[0][0, 0, 4], torch.tensor(0.9)) + assert torch.allclose(result[0][0, 0, 5], torch.tensor(1.0)) + assert torch.equal(result[0][0, 0, 6:], coeffs[0, 0]) + + +def test_dflfree_pose_accepts_decode_true_mxq_parts_with_reducemax() -> None: + """Accept split decode-true DFL-free pose outputs with a duplicate score max tensor.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "pose_estimation", + "nl": 3, + "dflfree": True, + "nc": 1, + "n_extra": 51, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + scores = torch.tensor([[[0.9], [0.4]]], dtype=torch.float32) + reducemax = scores.clone() + keypoints = torch.arange(102, dtype=torch.float32).reshape(1, 2, 51) + + result = postprocessor([reducemax, scores, boxes, keypoints]) + + assert len(result) == 1 + assert result[0].shape == (1, 57) + assert torch.equal(result[0][0, :4], boxes[0, 0]) + assert torch.equal(result[0][0, 6:], keypoints[0, 0]) + + +def test_dflfree_pose_prefers_score_tensor_over_reducemax_duplicate() -> None: + """Use the actual score tensor when MXQ exports both reducemax and score parts.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "pose_estimation", + "nl": 3, + "dflfree": True, + "nc": 1, + "n_extra": 51, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + reducemax = torch.tensor([[[0.88], [0.39]]], dtype=torch.float32) + scores = torch.tensor([[[0.9], [0.4]]], dtype=torch.float32) + keypoints = torch.arange(102, dtype=torch.float32).reshape(1, 2, 51) + + for output_order in ( + [reducemax, scores, boxes, keypoints], + [scores, reducemax, boxes, keypoints], + ): + result = postprocessor(output_order) + + assert len(result) == 1 + assert result[0].shape == (1, 57) + assert torch.allclose(result[0][0, 4], torch.tensor(0.9)) + assert torch.equal(result[0][0, 6:], keypoints[0, 0]) + + +def test_non_e2e_dflfree_pose_accepts_decode_true_mxq_parts_with_reducemax() -> None: + """Route 4-part decode-true pose outputs through the pose non-e2e path.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "pose_estimation", + "nl": 3, + "dflfree": True, + "nc": 1, + "n_extra": 51, + "conf_thres": 0.5, + "iou_thres": 0.7, + "e2e": False, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + boxes = torch.tensor( + [[[10.0, 20.0, 30.0, 40.0], [11.0, 21.0, 31.0, 41.0]]], dtype=torch.float32 + ) + scores = torch.tensor([[[0.9], [0.4]]], dtype=torch.float32) + reducemax = scores.clone() + keypoints = torch.arange(102, dtype=torch.float32).reshape(1, 2, 51) + + result = postprocessor([reducemax, scores, boxes, keypoints]) + + assert isinstance(result, torch.Tensor) + assert result.shape == (1, 300, 57) + first_detection = result[0][0] + assert torch.equal(first_detection[:4], boxes[0, 0]) + assert torch.allclose(first_detection[4], torch.tensor(0.9)) + assert torch.allclose(first_detection[5], torch.tensor(0.0)) + assert torch.equal(first_detection[6:], keypoints[0, 0]) + + +def test_non_e2e_dflfree_obb_preserves_canonical_row_width() -> None: + """Pad non-e2e DFL-free OBB converted outputs using canonical pre-NMS row widths.""" + + expected_max_det = 300 + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "obb", + "nl": 3, + "nc": 15, + "n_extra": 1, + "conf_thres": 0.8, + "iou_thres": 0.7, + "dflfree": True, + "e2e": False, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + + result = postprocessor(_make_converted_obb_parts()) + + assert isinstance(result, torch.Tensor) + assert result.shape == (1, expected_max_det, 20) + first_image = result[0] + assert torch.equal(first_image[:3], _make_converted_obb_rows()[0]) + assert torch.count_nonzero(first_image[3:]) == 0 + + +def test_raw_mxq_like_outputs_are_not_final_detections() -> None: + """Do not treat split MXQ-style head tensors as already-decoded detections.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "object_detection", + "nl": 3, + "dflfree": True, + "nc": 80, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + raw_outputs: ListTensorLike = [ + torch.zeros((1, 80, 80, 4), dtype=torch.float32), + torch.zeros((1, 80, 80, 80), dtype=torch.float32), + ] + + detections, proto = postprocessor.extract_final_outputs(raw_outputs) + + assert detections is None + assert proto is None + + +def _make_anchorless_obb_mxq_heads() -> ListTensorLike: + """Build synthetic channel-first MXQ OBB heads for anchorless models.""" + + outputs: list[np.ndarray] = [] + scale_specs = ((8, 0, 0, 0), (4, 0, 0, 1), (2, 0, 0, 2)) + for size, y_idx, x_idx, cls_idx in scale_specs: + det = np.full((1, 64, size, size), -10.0, dtype=np.float32) + for side in range(4): + det[0, side * 16 + 2, y_idx, x_idx] = 10.0 + + cls = np.full((1, 15, size, size), -10.0, dtype=np.float32) + cls[0, cls_idx, y_idx, x_idx] = 10.0 + + angle = np.zeros((1, 1, size, size), dtype=np.float32) + outputs.extend([det, cls, angle]) + return outputs + + +def _make_dflfree_obb_mxq_heads() -> ListTensorLike: + """Build synthetic channel-first MXQ OBB heads for DFL-free models.""" + + outputs: list[np.ndarray] = [] + scale_specs = ((8, 0, 0, 0), (4, 0, 0, 1), (2, 0, 0, 2)) + for size, y_idx, x_idx, cls_idx in scale_specs: + det = np.zeros((1, 4, size, size), dtype=np.float32) + det[0, :, y_idx, x_idx] = np.array([2.0, 2.0, 2.0, 2.0], dtype=np.float32) + + cls = np.full((1, 15, size, size), -10.0, dtype=np.float32) + cls[0, cls_idx, y_idx, x_idx] = 10.0 + + angle = np.zeros((1, 1, size, size), dtype=np.float32) + outputs.extend([det, cls, angle]) + return outputs + + +def _make_converted_obb_rows() -> torch.Tensor: + """Build synthetic converted OBB rows in canonical row-major format.""" + + output = torch.zeros((1, 3, 20), dtype=torch.float32) + output[0, :, :4] = torch.tensor( + [ + [12.0, 12.0, 6.0, 4.0], + [32.0, 24.0, 8.0, 6.0], + [48.0, 48.0, 10.0, 8.0], + ], + dtype=torch.float32, + ) + output[0, 0, 4] = 0.95 + output[0, 1, 5] = 0.90 + output[0, 2, 6] = 0.85 + output[0, :, -1] = torch.tensor([0.0, 0.1, -0.2], dtype=torch.float32) + return output + + +def _make_converted_obb_parts() -> ListTensorLike: + """Build shuffled converted MXQ OBB parts for decode-true outputs.""" + + rows = _make_converted_obb_rows() + boxes = rows[:, :, :4].unsqueeze(1) + scores = rows[:, :, 4:-1].unsqueeze(1) + angle = rows[:, :, -1:].unsqueeze(1) + return [angle, boxes, scores] + + +def _make_split_converted_obb_parts(class_first: bool) -> ListTensorLike: + """Build decode-true MXQ OBB parts split into box subchannels.""" + + rows = _make_converted_obb_rows() + scores = rows[:, :, 4:-1].transpose(1, 2) + angle = rows[:, :, -1:].transpose(1, 2) + xy = rows[:, :, :2].transpose(1, 2) + width = rows[:, :, 2:3].transpose(1, 2) + height = rows[:, :, 3:4].transpose(1, 2) + if class_first: + return [scores, angle, xy, width, height] + return [angle, scores, xy, width, height] + + +@pytest.mark.parametrize("dflfree", [False, True]) +def test_obb_accepts_single_converted_output(dflfree: bool) -> None: + """Accept ONNX-style converted OBB tensors before rotated NMS.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "obb", + "nl": 3, + "nc": 15, + "n_extra": 1, + "conf_thres": 0.8, + "iou_thres": 0.7, + } + if dflfree: + post_cfg["dflfree"] = True + else: + post_cfg["reg_max"] = 16 + + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + result = postprocessor(_make_converted_obb_rows().transpose(1, 2)) + + assert len(result) == 1 + assert result[0].shape == (3, 7) + assert torch.equal(result[0][:, 5], torch.tensor([0.0, 1.0, 2.0])) + + +@pytest.mark.parametrize("dflfree", [False, True]) +@pytest.mark.parametrize("class_first", [False, True]) +def test_obb_accepts_decode_true_converted_mxq_parts( + dflfree: bool, class_first: bool +) -> None: + """Accept converted MXQ OBB box, class, and angle outputs before rotated NMS.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "obb", + "nl": 3, + "nc": 15, + "n_extra": 1, + "conf_thres": 0.8, + "iou_thres": 0.7, + } + if dflfree: + post_cfg["dflfree"] = True + else: + post_cfg["reg_max"] = 16 + + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + result = postprocessor(_make_converted_obb_parts()) + split_result = postprocessor(_make_split_converted_obb_parts(class_first)) + + assert len(result) == 1 + assert result[0].shape == (3, 7) + assert torch.equal(result[0][:, 5], torch.tensor([0.0, 1.0, 2.0])) + assert len(split_result) == 1 + assert split_result[0].shape == (3, 7) + assert torch.equal(split_result[0][:, 5], torch.tensor([0.0, 1.0, 2.0])) + + +def test_anchorless_obb_accepts_channel_first_mxq_heads_and_plots_airport( + tmp_path: Path, +) -> None: + """Accept channel-first MXQ OBB heads for YOLOv8/YOLO11-style models.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "obb", + "nl": 3, + "reg_max": 16, + "nc": 15, + "n_extra": 1, + "conf_thres": 0.8, + "iou_thres": 0.7, + } + image_path = np.zeros((64, 64, 3), dtype=np.uint8) + save_path = tmp_path / "anchorless_obb_airport.jpg" + + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + result = postprocessor(_make_anchorless_obb_mxq_heads()) + + assert len(result) == 1 + assert result[0].shape[1] == 7 + assert result[0].shape[0] >= 1 + + plotted = Results(pre_cfg, post_cfg, result).plot( + image_path, save_path=str(save_path) + ) + + assert plotted is not None + assert save_path.is_file() + + +def test_dflfree_obb_accepts_channel_first_mxq_heads_and_plots_airport( + tmp_path: Path, +) -> None: + """Accept channel-first MXQ OBB heads for YOLO26-style models.""" + + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "obb", + "nl": 3, + "dflfree": True, + "nc": 15, + "n_extra": 1, + "conf_thres": 0.8, + "iou_thres": 0.7, + } + image_path = np.zeros((64, 64, 3), dtype=np.uint8) + save_path = tmp_path / "dflfree_obb_airport.jpg" + + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + result = postprocessor(_make_dflfree_obb_mxq_heads()) + + assert len(result) == 1 + assert result[0].shape[1] == 7 + assert result[0].shape[0] >= 1 + + plotted = Results(pre_cfg, post_cfg, result).plot( + image_path, save_path=str(save_path) + ) + + assert plotted is not None + assert save_path.is_file() + + +def test_anchor_segmentation_ignores_auxiliary_onnx_heads() -> None: + """Use converted YOLOv5-seg ONNX outputs and ignore auxiliary raw heads.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "instance_segmentation", + "anchors": [ + [10, 13, 16, 30, 33, 23], + [30, 61, 62, 45, 59, 119], + [116, 90, 156, 198, 373, 326], + ], + "n_extra": 32, + "nc": 80, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = cast(YOLODetectionPostBase, build_postprocess(pre_cfg, post_cfg)) + det = torch.zeros((1, 2, 117), dtype=torch.float32) + proto = torch.zeros((1, 32, 160, 160), dtype=torch.float32) + aux_heads = [ + torch.zeros((1, 3, 80, 80, 117), dtype=torch.float32), + torch.zeros((1, 3, 40, 40, 117), dtype=torch.float32), + torch.zeros((1, 3, 20, 20, 117), dtype=torch.float32), + ] + + result = postprocessor([det, proto, *aux_heads]) + + assert len(result) == 1 + assert result[0][0].shape == (0, 38) + assert result[0][1].shape == (0, 640, 640) + + +def test_anchor_segmentation_accepts_mxq_raw_heads() -> None: + """Use raw YOLOv5-seg MXQ heads when no converted detection tensor is present.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "instance_segmentation", + "anchors": [ + [10, 13, 16, 30, 33, 23], + [30, 61, 62, 45, 59, 119], + [116, 90, 156, 198, 373, 326], + ], + "n_extra": 32, + "nc": 80, + "conf_thres": 0.5, + "iou_thres": 0.7, + } + postprocessor = build_postprocess(pre_cfg, post_cfg) + raw_outputs = [ + torch.zeros((2, 20, 20, 351), dtype=torch.float32), + torch.zeros((2, 40, 40, 351), dtype=torch.float32), + torch.zeros((2, 80, 80, 351), dtype=torch.float32), + torch.zeros((2, 160, 160, 32), dtype=torch.float32), + ] + + result = postprocessor(raw_outputs) + + assert len(result) == 2 + assert result[0][0].shape == (0, 38) + assert result[0][1].shape == (0, 640, 640) + assert result[1][0].shape == (0, 38) + assert result[1][1].shape == (0, 640, 640) + + +def test_anchorless_prediction_nms_keeps_best_class_per_box() -> None: + """Keep ordinary prediction output limited to the best class per box.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "object_detection", + "nl": 3, + "reg_max": 16, + "nc": 3, + "conf_thres": 0.25, + "iou_thres": 0.7, + } + postprocessor = cast( + YOLOAnchorlessDetectionPost, build_postprocess(pre_cfg, post_cfg) + ) + decoded = torch.tensor( + [ + [ + [10.0, 50.0], + [10.0, 50.0], + [20.0, 60.0], + [20.0, 60.0], + [0.90, 0.80], + [0.10, 0.85], + [0.10, 0.70], + ] + ], + dtype=torch.float32, + ) + + result = postprocessor.nms([decoded[0]]) + + assert len(result) == 1 + assert result[0].shape == (2, 6) + assert torch.equal(result[0][:, 5], torch.tensor([0.0, 1.0])) + + +@pytest.mark.parametrize( + ("layout", "candidate_count"), + [ + ("channels_first", 117), + ("candidates_first", 117), + ("channels_first", 116), + ("candidates_first", 116), + ], +) +def test_anchorless_nms_normalizes_known_layout_before_suppression( + layout: AnchorlessOutputLayout, + candidate_count: int, + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Use source provenance to normalize raw and converted segmentation outputs.""" + + pre_cfg = {"LetterBox": {"img_size": [640, 640]}} + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "reg_max": 16, + "nc": 80, + "n_extra": 32, + "conf_thres": 0.25, + "iou_thres": 0.7, + } + postprocessor = cast( + YOLOAnchorlessDetectionPost, build_postprocess(pre_cfg, post_cfg) + ) + canonical = torch.arange(candidate_count * 116, dtype=torch.float32).reshape( + candidate_count, 116 + ) + source = canonical.transpose(0, 1) if layout == "channels_first" else canonical + captured: list[torch.Tensor] = [] + + def capture_canonical(xi: torch.Tensor, **_: Any) -> torch.Tensor: + captured.append(xi) + return torch.empty((0, 38), dtype=torch.float32) + + monkeypatch.setattr(postprocessor, "_nms_single_legacy_rows", capture_canonical) + + postprocessor.nms(_AnchorlessNMSInput([source], layout)) + + assert captured[0].shape == (candidate_count, 116) + torch.testing.assert_close(captured[0], canonical) + + +def test_anchorless_nms_shape_fallback_prefers_raw_layout_for_square_tensor( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Treat an ambiguous provenance-free square tensor as channel-first.""" + + pre_cfg = {"LetterBox": {"img_size": [640, 640]}} + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "reg_max": 16, + "nc": 80, + "n_extra": 32, + } + postprocessor = cast( + YOLOAnchorlessDetectionPost, build_postprocess(pre_cfg, post_cfg) + ) + source = torch.arange(116 * 116, dtype=torch.float32).reshape(116, 116) + captured: list[torch.Tensor] = [] + + def capture_canonical(xi: torch.Tensor, **_: Any) -> torch.Tensor: + captured.append(xi) + return torch.empty((0, 38), dtype=torch.float32) + + monkeypatch.setattr(postprocessor, "_nms_single_legacy_rows", capture_canonical) + + postprocessor.nms([source]) + + torch.testing.assert_close(captured[0], source.transpose(0, 1)) + + +def test_anchorless_segmentation_preprocess_preserves_layout_provenance( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Tag decoded MXQ heads as channel-first and converted outputs as candidates-first.""" + + pre_cfg = {"LetterBox": {"img_size": [640, 640]}} + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "reg_max": 16, + "nc": 80, + "n_extra": 32, + } + postprocessor = cast( + YOLOAnchorlessDetectionPost, build_postprocess(pre_cfg, post_cfg) + ) + decoded = torch.zeros((116, 116), dtype=torch.float32) + converted = decoded.transpose(0, 1).unsqueeze(0) + proto = torch.zeros((1, 160, 160, 32), dtype=torch.float32) + rearranged = torch.empty(0) + + monkeypatch.setattr(postprocessor, "rearrange", lambda _: (rearranged, proto)) + monkeypatch.setattr( + postprocessor, "decode", lambda value: [decoded] if value is rearranged else [] + ) + raw_predictions, raw_proto = postprocessor._pre_process([torch.empty(0)] * 3) + + monkeypatch.setattr(postprocessor, "conversion", lambda _: (converted, proto)) + monkeypatch.setattr( + postprocessor, "filter_conversion", lambda _: [converted.squeeze(0)] + ) + converted_predictions, converted_proto = postprocessor._pre_process( + [torch.empty(0)] * 2 + ) + + assert raw_predictions.layout == "channels_first" + assert isinstance(raw_predictions.detections, list) + assert raw_predictions.detections[0] is decoded + assert raw_proto is proto + assert converted_predictions.layout == "candidates_first" + assert isinstance(converted_predictions.detections, list) + torch.testing.assert_close( + converted_predictions.detections[0], converted.squeeze(0) + ) + assert converted_proto is proto + + +def test_anchorless_nms_normalizes_detection_without_extra_channels( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Normalize raw detection output when no segmentation or pose extras exist.""" + + pre_cfg = {"LetterBox": {"img_size": [640, 640]}} + post_cfg = { + "task": "object_detection", + "nl": 3, + "reg_max": 16, + "nc": 80, + "conf_thres": 0.25, + "iou_thres": 0.7, + } + postprocessor = cast( + YOLOAnchorlessDetectionPost, build_postprocess(pre_cfg, post_cfg) + ) + source = torch.arange(84 * 91, dtype=torch.float32).reshape(84, 91) + captured: list[torch.Tensor] = [] + + def capture_canonical(xi: torch.Tensor, **_: Any) -> torch.Tensor: + captured.append(xi) + return torch.empty((0, 6), dtype=torch.float32) + + monkeypatch.setattr(postprocessor, "_nms_single_legacy_rows", capture_canonical) + + postprocessor.nms(_AnchorlessNMSInput([source], "channels_first")) + + assert postprocessor.n_extra == 0 + assert captured[0].shape == (91, 84) + torch.testing.assert_close(captured[0], source.transpose(0, 1)) + + +def test_anchorless_nonambiguous_layouts_keep_identical_nms_results() -> None: + """Keep existing suppression results after canonical layout normalization.""" + + pre_cfg = {"LetterBox": {"img_size": [640, 640]}} + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "reg_max": 16, + "nc": 80, + "n_extra": 32, + "conf_thres": 0.25, + "iou_thres": 0.7, + } + postprocessor = cast( + YOLOAnchorlessDetectionPost, build_postprocess(pre_cfg, post_cfg) + ) + canonical = torch.zeros((2, 116), dtype=torch.float32) + canonical[0, :4] = torch.tensor([10.0, 10.0, 20.0, 20.0]) + canonical[1, :4] = torch.tensor([40.0, 40.0, 60.0, 60.0]) + canonical[0, 4] = 0.9 + canonical[1, 5] = 0.8 + canonical[:, 84:] = torch.arange(64, dtype=torch.float32).reshape(2, 32) + + raw_result = postprocessor.nms( + _AnchorlessNMSInput([canonical.transpose(0, 1)], "channels_first") + ) + converted_result = postprocessor.nms( + _AnchorlessNMSInput([canonical], "candidates_first") + ) + + torch.testing.assert_close(raw_result[0], converted_result[0]) + + +def test_anchorless_validation_nms_keeps_multilabel_candidates() -> None: + """Retain all above-threshold classes when validation requests Ultralytics semantics.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "object_detection", + "nl": 3, + "reg_max": 16, + "nc": 3, + "conf_thres": 0.25, + "iou_thres": 0.7, + } + postprocessor = cast( + YOLOAnchorlessDetectionPost, build_postprocess(pre_cfg, post_cfg) + ) + decoded = torch.tensor( + [ + [10.0, 10.0, 20.0, 20.0, 0.90, 0.80, 0.10], + [50.0, 50.0, 60.0, 60.0, 0.20, 0.85, 0.70], + ], + dtype=torch.float32, + ) + + result = postprocessor.nms([decoded], multi_label=True) + + assert len(result) == 1 + assert result[0].shape == (4, 6) + assert torch.equal(result[0][:, 5], torch.tensor([0.0, 1.0, 1.0, 2.0])) + + +def test_anchorless_segmentation_validation_duplicates_mask_coefficients_per_class() -> ( + None +): + """Copy mask coefficients when validation expands a box into multiple class candidates.""" + + pre_cfg = { + "LetterBox": { + "img_size": [640, 640], + } + } + post_cfg = { + "task": "instance_segmentation", + "nl": 3, + "reg_max": 16, + "nc": 3, + "n_extra": 2, + "conf_thres": 0.25, + "iou_thres": 0.7, + } + postprocessor = cast( + YOLOAnchorlessDetectionPost, build_postprocess(pre_cfg, post_cfg) + ) + decoded = torch.tensor( + [[10.0, 10.0, 20.0, 20.0, 0.90, 0.80, 0.10, 0.25, -0.50]], + dtype=torch.float32, + ) + + result = postprocessor.nms([decoded], multi_label=True) + + assert result[0].shape == (2, 8) + assert torch.equal(result[0][:, 5], torch.tensor([0.0, 1.0])) + assert torch.equal(result[0][:, 6:], decoded[:, 7:].repeat(2, 1)) + + +def test_anchorless_pose_single_and_batch_decode_are_equivalent() -> None: + """Keep batched pose decoding equivalent to the verified v1.5.1 per-image formula.""" + + torch.manual_seed(0) + pre_cfg = { + "LetterBox": { + "img_size": [64, 64], + } + } + post_cfg = { + "task": "pose_estimation", + "nl": 3, + "reg_max": 16, + "nc": 1, + "n_extra": 51, + "conf_thres": 0.25, + "iou_thres": 0.7, + } + postprocessor = cast(YOLOAnchorlessPosePost, build_postprocess(pre_cfg, post_cfg)) + anchor_count = postprocessor.anchors_as_tensor().shape[-1] + raw = torch.randn((2, 116, anchor_count), dtype=torch.float32) + raw[:, 64, :] = 10.0 + + batched = postprocessor.decode_batch(raw) + per_image = torch.stack([postprocessor.process_box_cls(image) for image in raw]) + + torch.testing.assert_close(batched, per_image) + + +def test_scale_coords_matches_ultralytics_rounding() -> None: + """Match upstream letterbox padding rounding for keypoint scaling.""" + + coords = torch.tensor( + [[[160.0, 100.0, 1.0], [480.0, 500.0, 1.0]]], dtype=torch.float32 + ) + + ratio_pad = resolve_ratio_pad((640, 640), (581, 640)) + scaled = scale_coords((640, 640), coords.clone(), (581, 640)) + + expected = torch.tensor( + [[[160.0, 71.0, 1.0], [480.0, 471.0, 1.0]]], dtype=torch.float32 + ) + assert ratio_pad == ((1.0, 1.0), (0, 29)) + torch.testing.assert_close(scaled, expected) + + +def test_letterbox_metadata_normalization_is_batch_aware() -> None: + """Share consistent shape and ratio-pad normalization across postprocessors.""" + + ratio_pad = ((1.0, 1.0), (0.0, 80.0)) + + assert normalize_image_shapes((481, 640), batch_size=2) == [(481, 640), (481, 640)] + assert normalize_ratio_pads(ratio_pad, batch_size=2) == [ratio_pad, ratio_pad] + assert normalize_ratio_pads((ratio_pad, ratio_pad), batch_size=2) == [ + ratio_pad, + ratio_pad, + ] + labels, boxes, scores = nmsout2eval( + [torch.zeros((0, 6)), torch.zeros((0, 6))], + (640, 640), + (481, 640), + ratio_pads=ratio_pad, + ) + assert labels == boxes == scores == [[], []] + with pytest.raises(ValueError, match="Expected 2 image shapes"): + normalize_image_shapes([(481, 640)], batch_size=2) + with pytest.raises(ValueError, match="Expected 2 ratio_pad values"): + normalize_ratio_pads([ratio_pad], batch_size=2) + + +def test_scale_masks_matches_ultralytics_rounding() -> None: + """Crop mask padding with the same rounding as upstream Ultralytics.""" + + masks = torch.zeros((1, 640, 640), dtype=torch.float32) + masks[:, 80:560, :] = 1.0 + + scaled = scale_masks(masks, (481, 640)) + + assert scaled.shape == (1, 481, 640) + assert float(scaled[:, 0, :].max()) == pytest.approx(0.0) + assert float(scaled[:, 1, :].max()) > 0.0 + assert float(scaled[:, -1, :].max()) == pytest.approx(1.0) + + +def test_preprocess_with_metadata_returns_letterbox_ratio_pad() -> None: + """Expose exact LetterBox ratio and integer padding for validation scaling.""" + + engine = MBLT_Engine.__new__(MBLT_Engine) + engine.pre_cfg = { + "Reader": {"style": "numpy"}, + "LetterBox": {"img_size": [640, 640]}, + "SetOrder": {"shape": "HWC"}, + "Normalize": {"style": "cv"}, + } + engine.preprocessor = wrapper.build_preprocess(engine.pre_cfg) + image = np.zeros((481, 640, 3), dtype=np.uint8) + + processed, metadata = engine.preprocess_with_metadata(image) + + assert processed.shape == (640, 640, 3) + assert metadata["img0_shape"] == (481, 640) + assert metadata["ratio_pad"] == ((1.0, 1.0), (0, 79)) + + +def test_nmsout2eval_matches_coco_json_format_without_mutation() -> None: + """Serialize detections like Ultralytics validation without changing NMS output.""" + + nms_out = torch.tensor( + [ + [10.12345, 20.23456, 110.34567, 220.45678, 0.876543, 0.0], + ], + dtype=torch.float32, + ) + original = nms_out.clone() + + labels, boxes, scores = nmsout2eval([nms_out], (640, 640), [(640, 640)]) + + assert labels == [[1]] + assert boxes == [[[10.123, 20.235, 100.222, 200.222]]] + assert scores == [[0.87654]] + assert torch.equal(nms_out, original) + + +def test_nmsout2eval_uses_explicit_ratio_pad() -> None: + """Use dataloader-provided LetterBox padding instead of recomputing from shape.""" + + nms_out = torch.tensor([[0.0, 79.0, 10.0, 89.0, 0.9, 0.0]], dtype=torch.float32) + + _labels, boxes, _scores = nmsout2eval( + [nms_out], + (640, 640), + [(481, 640)], + ratio_pads=[((1.0, 1.0), (0, 79))], + ) + + assert boxes == [[[0.0, 0.0, 10.0, 10.0]]] diff --git a/tests/test_yolo_det.py b/tests/test_yolo_det.py deleted file mode 100644 index d7ab603..0000000 --- a/tests/test_yolo_det.py +++ /dev/null @@ -1,124 +0,0 @@ -""" -Test script for YOLO26m model. - -This script tests the YOLO26m model by running inference on a sample image. -It can be run as a pytest test or as a standalone script. -""" - -import argparse -import os -from pathlib import Path - -import pytest - -from mblt_vision import YOLO26m - -TEST_DIR = Path(__file__).parent - - -@pytest.fixture -def yolo_det(): - """Fixture to initialize and dispose of the YOLO26m model.""" - model = YOLO26m() - yield model - model.dispose() - - -def run_inference(model, image_path, save_path, conf_thres=0.5, iou_thres=0.5): - """Run inference with the given model and image.""" - os.makedirs(os.path.dirname(save_path), exist_ok=True) - - input_img = model.preprocess(image_path) - output = model(input_img) - result = model.postprocess(output, conf_thres=conf_thres, iou_thres=iou_thres) - - result.plot( - source_path=image_path, - save_path=save_path, - ) - - -def test_yolo_det(yolo_det): - """Test YOLO26m inference on a sample image.""" - image_path = os.path.join(TEST_DIR, "rc", "cr7.jpg") - save_path = os.path.join( - TEST_DIR, - "tmp", - f"yolo26m_{os.path.basename(image_path)}", - ) - - run_inference(yolo_det, image_path, save_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run YOLO26m inference") - parser.add_argument( - "--mxq-path", - type=str, - default=None, - help="Path to the YOLO26m model file (.mxq)", - ) - parser.add_argument( - "--model-type", - type=str, - default="DEFAULT", - help="Model type", - ) - parser.add_argument( - "--infer-mode", - type=str, - default="global8", - choices=["single", "multi", "global4", "global8"], - help="Inference mode", - ) - parser.add_argument( - "--product", - type=str, - default="aries", - help="Product", - ) - parser.add_argument( - "--input-path", - type=str, - default=os.path.join(TEST_DIR, "rc", "cr7.jpg"), - help="Path to the input image", - ) - parser.add_argument( - "--save-path", - type=str, - default=None, - help="Path to save the output image", - ) - parser.add_argument( - "--conf-thres", - type=float, - default=0.5, - help="Confidence threshold", - ) - parser.add_argument( - "--iou-thres", - type=float, - default=0.5, - help="IoU threshold", - ) - - args = parser.parse_args() - - # Load model with the specified mxq_path - model = YOLO26m( - local_path=args.mxq_path, - model_type=args.model_type, - infer_mode=args.infer_mode, - product=args.product, - ) - if args.save_path is None: - args.save_path = os.path.join( - TEST_DIR, "tmp", f"yolo26m_{os.path.basename(args.input_path)}" - ) - - try: - run_inference( - model, args.input_path, args.save_path, args.conf_thres, args.iou_thres - ) - finally: - model.dispose() diff --git a/tests/test_yolo_pose.py b/tests/test_yolo_pose.py deleted file mode 100644 index 4ab2481..0000000 --- a/tests/test_yolo_pose.py +++ /dev/null @@ -1,124 +0,0 @@ -""" -Test script for YOLOv8mPose model. - -This script tests the YOLOv8mPose model by running inference on a sample image. -It can be run as a pytest test or as a standalone script. -""" - -import argparse -import os -from pathlib import Path - -import pytest - -from mblt_vision import YOLO26mPose - -TEST_DIR = Path(__file__).parent - - -@pytest.fixture -def yolo_pose(): - """Fixture to initialize and dispose of the YOLO26mPose model.""" - model = YOLO26mPose() - yield model - model.dispose() - - -def run_inference(model, image_path, save_path, conf_thres=0.5, iou_thres=0.5): - """Run inference with the given model and image.""" - os.makedirs(os.path.dirname(save_path), exist_ok=True) - - input_img = model.preprocess(image_path) - output = model(input_img) - result = model.postprocess(output, conf_thres=conf_thres, iou_thres=iou_thres) - - result.plot( - source_path=image_path, - save_path=save_path, - ) - - -def test_yolo_pose(yolo_pose): - """Test YOLO26mPose inference on a sample image.""" - image_path = os.path.join(TEST_DIR, "rc", "cr7.jpg") - save_path = os.path.join( - TEST_DIR, - "tmp", - f"yolo26m_pose_{os.path.basename(image_path)}", - ) - - run_inference(yolo_pose, image_path, save_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run YOLO26mPose inference") - parser.add_argument( - "--mxq-path", - type=str, - default=None, - help="Path to the YOLO26mPose model file (.mxq)", - ) - parser.add_argument( - "--model-type", - type=str, - default="DEFAULT", - help="Model type", - ) - parser.add_argument( - "--infer-mode", - type=str, - default="global8", - choices=["single", "multi", "global4", "global8"], - help="Inference mode", - ) - parser.add_argument( - "--product", - type=str, - default="aries", - help="Product", - ) - parser.add_argument( - "--input-path", - type=str, - default=os.path.join(TEST_DIR, "rc", "cr7.jpg"), - help="Path to the input image", - ) - parser.add_argument( - "--save-path", - type=str, - default=None, - help="Path to save the output image", - ) - parser.add_argument( - "--conf-thres", - type=float, - default=0.5, - help="Confidence threshold", - ) - parser.add_argument( - "--iou-thres", - type=float, - default=0.5, - help="IoU threshold", - ) - - args = parser.parse_args() - - # Load model with the specified mxq_path - model = YOLO26mPose( - local_path=args.mxq_path, - model_type=args.model_type, - infer_mode=args.infer_mode, - product=args.product, - ) - if args.save_path is None: - args.save_path = os.path.join( - TEST_DIR, "tmp", f"yolo26m_pose_{os.path.basename(args.input_path)}" - ) - - try: - run_inference( - model, args.input_path, args.save_path, args.conf_thres, args.iou_thres - ) - finally: - model.dispose() diff --git a/tests/test_yolo_seg.py b/tests/test_yolo_seg.py deleted file mode 100644 index c66e0cc..0000000 --- a/tests/test_yolo_seg.py +++ /dev/null @@ -1,124 +0,0 @@ -""" -Test script for YOLO26mSeg model. - -This script tests the YOLO26mSeg model by running inference on a sample image. -It can be run as a pytest test or as a standalone script. -""" - -import argparse -import os -from pathlib import Path - -import pytest - -from mblt_vision import YOLO26mSeg - -TEST_DIR = Path(__file__).parent - - -@pytest.fixture -def yolo_seg(): - """Fixture to initialize and dispose of the YOLO26mSeg model.""" - model = YOLO26mSeg() - yield model - model.dispose() - - -def run_inference(model, image_path, save_path, conf_thres=0.5, iou_thres=0.5): - """Run inference with the given model and image.""" - os.makedirs(os.path.dirname(save_path), exist_ok=True) - - input_img = model.preprocess(image_path) - output = model(input_img) - result = model.postprocess(output, conf_thres=conf_thres, iou_thres=iou_thres) - - result.plot( - source_path=image_path, - save_path=save_path, - ) - - -def test_yolo_seg(yolo_seg): - """Test YOLO26mSeg inference on a sample image.""" - image_path = os.path.join(TEST_DIR, "rc", "cr7.jpg") - save_path = os.path.join( - TEST_DIR, - "tmp", - f"yolo26m_seg_{os.path.basename(image_path)}", - ) - - run_inference(yolo_seg, image_path, save_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run YOLO26mSeg inference") - parser.add_argument( - "--mxq-path", - type=str, - default=None, - help="Path to the YOLO26mSeg model file (.mxq)", - ) - parser.add_argument( - "--model-type", - type=str, - default="DEFAULT", - help="Model type", - ) - parser.add_argument( - "--infer-mode", - type=str, - default="global8", - choices=["single", "multi", "global4", "global8"], - help="Inference mode", - ) - parser.add_argument( - "--product", - type=str, - default="aries", - help="Product", - ) - parser.add_argument( - "--input-path", - type=str, - default=os.path.join(TEST_DIR, "rc", "cr7.jpg"), - help="Path to the input image", - ) - parser.add_argument( - "--save-path", - type=str, - default=None, - help="Path to save the output image", - ) - parser.add_argument( - "--conf-thres", - type=float, - default=0.5, - help="Confidence threshold", - ) - parser.add_argument( - "--iou-thres", - type=float, - default=0.5, - help="IoU threshold", - ) - - args = parser.parse_args() - - # Load model with the specified mxq_path - model = YOLO26mSeg( - local_path=args.mxq_path, - model_type=args.model_type, - infer_mode=args.infer_mode, - product=args.product, - ) - if args.save_path is None: - args.save_path = os.path.join( - TEST_DIR, "tmp", f"yolo26m_seg_{os.path.basename(args.input_path)}" - ) - - try: - run_inference( - model, args.input_path, args.save_path, args.conf_thres, args.iou_thres - ) - finally: - model.dispose() From a04bfadd94ad3b4ff919d650457bc58a6f676f6a Mon Sep 17 00:00:00 2001 From: jinman Date: Mon, 10 Aug 2026 16:35:34 +0900 Subject: [PATCH 04/48] Implement standalone CLI for Mobilint Vision with predict, validate, and compile commands; update .gitignore and add tests --- .gitignore | 4 + README.md | 15 +- mblt_vision/cli/__init__.py | 5 + mblt_vision/cli/__main__.py | 5 + mblt_vision/cli/_vision.py | 320 +++++++++++++++++++++ mblt_vision/cli/compile.py | 110 ++++++++ mblt_vision/cli/main.py | 35 +++ mblt_vision/cli/predict.py | 45 +++ mblt_vision/cli/val.py | 543 ++++++++++++++++++++++++++++++++++++ pyproject.toml | 3 + tests/test_cli.py | 24 ++ 11 files changed, 1108 insertions(+), 1 deletion(-) create mode 100644 mblt_vision/cli/__init__.py create mode 100644 mblt_vision/cli/__main__.py create mode 100644 mblt_vision/cli/_vision.py create mode 100644 mblt_vision/cli/compile.py create mode 100644 mblt_vision/cli/main.py create mode 100644 mblt_vision/cli/predict.py create mode 100644 mblt_vision/cli/val.py create mode 100644 tests/test_cli.py diff --git a/.gitignore b/.gitignore index 7bcf7eb..2c98cff 100644 --- a/.gitignore +++ b/.gitignore @@ -41,3 +41,7 @@ compile_commands.json *.swo .DS_Store Thumbs.db + +*.zip +*.tar.gz +*.egg \ No newline at end of file diff --git a/README.md b/README.md index a7d8199..a142a84 100644 --- a/README.md +++ b/README.md @@ -60,7 +60,20 @@ code should use the task subpackages (for example, Vision is now maintained in this package. `mblt-model-zoo` retains `mblt_model_zoo.vision` as a compatibility facade for existing applications; new -projects should import from `mblt_vision` directly. +projects should import from `mblt_vision` directly. Its `mblt-model-zoo predict`, +`val`, and `compile` commands also delegate to this package. + +## Command line + +The standalone package provides the `mblt-vision` command with `predict`, `val`, +and `compile` subcommands: + +```bash +mblt-vision predict --source image.jpg --model resnet50 +``` + +The corresponding `mblt-model-zoo` commands use the same standalone handlers for +backward compatibility. ## Documentation and tests diff --git a/mblt_vision/cli/__init__.py b/mblt_vision/cli/__init__.py new file mode 100644 index 0000000..eb8e256 --- /dev/null +++ b/mblt_vision/cli/__init__.py @@ -0,0 +1,5 @@ +"""Command-line interface for standalone Mobilint Vision workflows.""" + +from .main import build_parser, main + +__all__ = ["build_parser", "main"] diff --git a/mblt_vision/cli/__main__.py b/mblt_vision/cli/__main__.py new file mode 100644 index 0000000..13522c2 --- /dev/null +++ b/mblt_vision/cli/__main__.py @@ -0,0 +1,5 @@ +"""Run the standalone Vision CLI with ``python -m mblt_vision.cli``.""" + +from .main import main + +raise SystemExit(main()) diff --git a/mblt_vision/cli/_vision.py b/mblt_vision/cli/_vision.py new file mode 100644 index 0000000..1e85c6b --- /dev/null +++ b/mblt_vision/cli/_vision.py @@ -0,0 +1,320 @@ +"""Shared helpers for vision CLI commands.""" + +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path +from typing import Any + +import torch +from mblt_vision._tasks import normalize_vision_task + +DEFAULT_OUTPUT_DIR = Path("runs") / "vision" + + +def parse_unit_interval(value: str) -> float: + """Parse a floating-point value strictly between zero and one.""" + + try: + parsed = float(value) + except ValueError as exc: + raise argparse.ArgumentTypeError( + "expected a number in the open interval (0, 1)" + ) from exc + if not 0 < parsed < 1: + raise argparse.ArgumentTypeError( + f"expected a number in the open interval (0, 1), got {value}" + ) + return parsed + + +def parse_target_cores(value: str | None) -> list[str] | None: + """Parses a semicolon-separated target core list.""" + + if value is None: + return None + cores = [item.strip() for item in value.split(";") if item.strip()] + return cores or None + + +def parse_target_clusters(value: str | None) -> list[int] | None: + """Parses a semicolon-separated target cluster list.""" + + if value is None: + return None + try: + clusters = [int(item.strip()) for item in value.split(";") if item.strip()] + except ValueError as exc: + raise argparse.ArgumentTypeError( + "target clusters must be semicolon-separated integers" + ) from exc + return clusters or None + + +def add_common_vision_args(parser: argparse.ArgumentParser) -> None: + """Adds arguments shared by all vision inference commands.""" + + parser.add_argument("--source", required=True, help="Path to the source image.") + parser.add_argument( + "--model", + required=True, + help="Vision model name, for example `resnet50` or `yolo11m`.", + ) + parser.add_argument( + "--output", + "--save-path", + dest="output", + help="Path to save the plotted result image.", + ) + parser.add_argument( + "--framework", + default=None, + choices=["mxq", "onnx"], + help="Inference framework to use. When omitted, `--model-path` suffix is used first, then `mxq`.", + ) + parser.add_argument( + "--model-path", + dest="model_path", + default="", + help="Optional generic local model path for MXQ or ONNX inference.", + ) + parser.add_argument( + "--mxq-path", + dest="mxq_path", + default="", + help="Optional local MXQ model path. Preserved as a compatibility alias.", + ) + parser.add_argument( + "--onnx-path", + dest="onnx_path", + default="", + help="Optional local ONNX model path.", + ) + parser.add_argument( + "--model-type", + default="DEFAULT", + help="Model variant from the YAML configuration.", + ) + parser.add_argument( + "--core-mode", + default="global8", + choices=["single", "multi", "global4", "global8"], + help="NPU core execution mode.", + ) + parser.add_argument("--dev-no", type=int, default=0, help="NPU device number.") + parser.add_argument( + "--target-cores", + type=parse_target_cores, + help="Optional semicolon-separated core list for single-core mode, for example `0:0;0:1`.", + ) + parser.add_argument( + "--target-clusters", + type=parse_target_clusters, + help="Optional semicolon-separated cluster list for multi/global modes, for example `0;1`.", + ) + + +def add_threshold_args( + parser: argparse.ArgumentParser, + *, + conf_default: float | None = 0.25, + iou_default: float | None = None, +) -> None: + """Adds postprocess threshold arguments for dense vision tasks.""" + + parser.add_argument( + "--conf-thres", + type=parse_unit_interval, + default=conf_default, + help="Confidence threshold.", + ) + parser.add_argument( + "--iou-thres", + type=parse_unit_interval, + default=iou_default, + help="IoU threshold.", + ) + + +def parse_bool(value: str) -> bool: + """Parses a case-insensitive boolean CLI value. + + Args: + value: Boolean text to parse. + + Returns: + Parsed boolean value. + + Raises: + argparse.ArgumentTypeError: If the value is not a supported boolean spelling. + """ + + normalized = value.strip().lower() + if normalized in {"true", "1", "yes", "on"}: + return True + if normalized in {"false", "0", "no", "off"}: + return False + raise argparse.ArgumentTypeError("expected a boolean value: true or false") + + +def add_e2e_arg(parser: argparse.ArgumentParser) -> None: + """Adds an optional YOLO end-to-end postprocessing mode override. + + Leaving the option unset preserves the model configuration's default. + """ + + parser.add_argument( + "--e2e", + nargs="?", + const=True, + type=parse_bool, + default=None, + help="Enable or disable YOLO end-to-end postprocessing (true/false). Bare `--e2e` means true.", + ) + + +def add_vision_parser( + subparsers: argparse._SubParsersAction[argparse.ArgumentParser], + *, + command: str, + help_text: str, + handler: Any, + aliases: list[str] | None = None, +) -> argparse.ArgumentParser: + """Creates a vision command parser with common arguments.""" + + parser = subparsers.add_parser(command, aliases=aliases or [], help=help_text) + parser.set_defaults(_handler=handler) + add_common_vision_args(parser) + return parser + + +def build_default_output_path(command: str, source: str, model: str) -> str: + """Builds the default path used for plotted vision command output.""" + + source_path = Path(source) + suffix = source_path.suffix or ".jpg" + return str(DEFAULT_OUTPUT_DIR / command / f"{source_path.stem}_{model}{suffix}") + + +def resolve_output_path( + output: str | None, command: str, source: str, model: str +) -> str: + """Returns an absolute result image path and ensures its parent exists.""" + + save_path = Path( + output or build_default_output_path(command, source, model) + ).expanduser() + save_path.parent.mkdir(parents=True, exist_ok=True) + return str(save_path.resolve()) + + +def require_source_file(source: str) -> None: + """Exits with a clear message when the source image is unavailable.""" + + source_path = Path(source).expanduser() + if not source_path.is_file(): + raise SystemExit(f"Source image not found: {source}") + + +def create_vision_engine(args: argparse.Namespace) -> Any: + """Creates a vision engine from shared CLI model options. + + Args: + args: Parsed command options containing common vision model arguments. + + Returns: + Initialized vision inference engine. + + Raises: + SystemExit: If the vision runtime dependencies are unavailable. + """ + + try: + from mblt_vision import MBLT_Engine + from mblt_vision.wrapper import normalize_core_mode + except ImportError as exc: + print(f"Missing dependencies for vision CLI: {exc}", file=sys.stderr) + raise SystemExit(2) from exc + + postprocess_kwargs: dict[str, Any] = {} + if getattr(args, "e2e", None) is not None: + postprocess_kwargs["e2e"] = args.e2e + + return MBLT_Engine( + model_cls=args.model, + model_type=args.model_type, + framework=args.framework, + model_path=args.model_path, + mxq_path=args.mxq_path, + onnx_path=args.onnx_path, + dev_no=args.dev_no, + core_mode=normalize_core_mode(args.core_mode), + target_cores=args.target_cores, + target_clusters=args.target_clusters, + postprocess_kwargs=postprocess_kwargs, + ) + + +def run_vision_inference( + args: argparse.Namespace, + *, + command: str, +) -> Any: + """Runs a complete vision inference pipeline for a CLI command.""" + + require_source_file(args.source) + model = create_vision_engine(args) + try: + actual_task = normalize_vision_task(model.post_cfg.get("task", "")) + plot_kwargs: dict[str, Any] = {} + if actual_task == "image_classification": + plot_kwargs["topk"] = args.topk + elif actual_task in { + "object_detection", + "face_detection", + "instance_segmentation", + "pose_estimation", + "obb", + }: + model.set_postprocess_thresholds( + conf_thres=args.conf_thres, iou_thres=args.iou_thres + ) + + postprocess_kwargs: dict[str, Any] = {} + if actual_task == "semantic_segmentation": + input_img, metadata = model.preprocess_with_metadata(args.source) + postprocess_kwargs["img0_shape"] = metadata["img0_shape"] + postprocess_kwargs["ratio_pad"] = metadata.get("ratio_pad") + else: + input_img = model.preprocess(args.source) + output = model(input_img) + if not getattr(getattr(model, "postprocessor", None), "e2e", True): + if args.output: + raise SystemExit( + "`--output` is unavailable with `--e2e false`; use `--raw-output` instead." + ) + + raw_output = model.postprocessor(output) + raw_output_path = getattr(args, "raw_output", None) + if raw_output_path: + output_path = Path(raw_output_path).expanduser() + output_path.parent.mkdir(parents=True, exist_ok=True) + torch.save(raw_output, output_path) + print(f"Saved raw postprocess output to {output_path}") + else: + print( + "Generated raw export-style postprocess output. Use `--raw-output` to save it." + ) + return raw_output + + result = model.postprocess(output, **postprocess_kwargs) + + save_path = resolve_output_path(args.output, command, args.source, args.model) + result.plot(source_path=args.source, save_path=save_path, **plot_kwargs) + print(f"Saved result to {os.path.relpath(save_path)}") + return result + finally: + model.dispose() diff --git a/mblt_vision/cli/compile.py b/mblt_vision/cli/compile.py new file mode 100644 index 0000000..7858b3e --- /dev/null +++ b/mblt_vision/cli/compile.py @@ -0,0 +1,110 @@ +"""Vision compilation CLI command.""" + +from __future__ import annotations + +import argparse +import sys + + +def _run_compile(args: argparse.Namespace) -> int: + """Compile a vision model from parsed CLI arguments. + + Args: + args: Parsed compilation arguments. + + Returns: + Successful process status. + """ + + try: + from mblt_vision.compile import compile_vision_model + + output_path = compile_vision_model( + model_cls=args.model_cls, + model_type=args.model_type, + model_path=args.model_path, + data_path=args.data_path, + subset_path=args.subset_path, + calib_data_path=args.calib_data_path, + save_path=args.save_path, + subset_size=args.subset_size, + seed=args.seed, + percentile=args.percentile, + topk_ratio=args.topk_ratio, + ) + except ImportError as exc: + print(str(exc), file=sys.stderr) + raise SystemExit(2) from exc + print(f"Compiled MXQ model to {output_path}") + return 0 + + +def add_compile_parser( + subparsers: argparse._SubParsersAction[argparse.ArgumentParser], +) -> argparse.ArgumentParser: + """Register the vision compilation command. + + Args: + subparsers: Main CLI subparser collection. + + Returns: + Registered compile command parser. + """ + + parser = subparsers.add_parser( + "compile", help="Compile a configured vision ONNX model to MXQ." + ) + parser.set_defaults(_handler=_run_compile) + parser.add_argument( + "--model-cls", + required=True, + help="Vision model name, for example `alexnet` or `yolo11m`.", + ) + parser.add_argument( + "--model-type", + default="DEFAULT", + help="Model variant from the YAML configuration.", + ) + parser.add_argument( + "--model-path", + "--onnx-path", + dest="model_path", + help="Preferred local ONNX file. Missing files fall back to the configured hosted artifact.", + ) + data_group = parser.add_mutually_exclusive_group() + data_group.add_argument( + "--data-path", + help="Original organized dataset root; organize, sample, and preprocess it as needed.", + ) + data_group.add_argument( + "--subset-path", + help="Already-sampled image subset; skip original dataset preparation and sampling.", + ) + data_group.add_argument( + "--calib-data-path", + "--calib-data-dir", + dest="calib_data_path", + help="Ready directory of preprocessed .npy tensors; pass it directly to qbcompiler.", + ) + parser.add_argument( + "--save-path", + help="Output MXQ path. Defaults to the ONNX stem under ~/.mblt_model_zoo.", + ) + parser.add_argument( + "--subset-size", + type=int, + help="Per-category ImageNet/WiderFace count or total count for other datasets.", + ) + parser.add_argument( + "--seed", type=int, default=0, help="Deterministic calibration subset seed." + ) + parser.add_argument( + "--percentile", type=float, help="Quantization percentile override." + ) + parser.add_argument( + "--topk-ratio", type=float, help="Quantization top-k ratio override." + ) + return parser + + +__all__ = ["add_compile_parser"] diff --git a/mblt_vision/cli/main.py b/mblt_vision/cli/main.py new file mode 100644 index 0000000..814b7d0 --- /dev/null +++ b/mblt_vision/cli/main.py @@ -0,0 +1,35 @@ +"""Standalone command-line entry point for Mobilint Vision.""" + +from __future__ import annotations + +import argparse +from collections.abc import Sequence + +from .compile import add_compile_parser +from .predict import add_predict_parser +from .val import add_val_parser + + +def build_parser() -> argparse.ArgumentParser: + """Build the standalone Vision command parser.""" + + parser = argparse.ArgumentParser( + prog="mblt-vision", + description="Run, validate, and compile Mobilint Vision models.", + ) + subparsers = parser.add_subparsers(help="mblt-vision commands") + add_predict_parser(subparsers) + add_val_parser(subparsers) + add_compile_parser(subparsers) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + """Run the standalone Vision command-line interface.""" + + parser = build_parser() + args = parser.parse_args(argv) + if hasattr(args, "_handler"): + return int(args._handler(args)) + parser.print_help() + return 1 diff --git a/mblt_vision/cli/predict.py b/mblt_vision/cli/predict.py new file mode 100644 index 0000000..e142c8e --- /dev/null +++ b/mblt_vision/cli/predict.py @@ -0,0 +1,45 @@ +"""Vision prediction CLI command.""" + +from __future__ import annotations + +import argparse + +from ._vision import ( + add_e2e_arg, + add_threshold_args, + add_vision_parser, + run_vision_inference, +) + + +def _cmd_predict(args: argparse.Namespace) -> int: + """Runs vision inference on a source image.""" + + run_vision_inference(args, command="predict") + return 0 + + +def add_predict_parser( + subparsers: argparse._SubParsersAction[argparse.ArgumentParser], +) -> None: + """Registers the unified vision prediction CLI command.""" + + parser = add_vision_parser( + subparsers, + command="predict", + aliases=["classify", "detect", "pose", "segment"], + help_text=( + "Run vision inference for classification, depth estimation, detection, instance or semantic " + "segmentation, OBB, pose, and face detection." + ), + handler=_cmd_predict, + ) + parser.add_argument( + "--topk", type=int, default=5, help="Number of classification labels to show." + ) + parser.add_argument( + "--raw-output", + help="Path to save raw export-style output with `--e2e false`.", + ) + add_threshold_args(parser, conf_default=0.25, iou_default=None) + add_e2e_arg(parser) diff --git a/mblt_vision/cli/val.py b/mblt_vision/cli/val.py new file mode 100644 index 0000000..c324994 --- /dev/null +++ b/mblt_vision/cli/val.py @@ -0,0 +1,543 @@ +"""Vision validation CLI command.""" + +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path + +from mblt_vision._tasks import normalize_vision_task +from mblt_vision.datasets import get_dataset_config, get_dataset_config_for_task +from mblt_vision.utils.datasets.readiness import dataset_ready + +from ._vision import ( + add_e2e_arg, + add_threshold_args, + create_vision_engine, + parse_target_clusters, + parse_target_cores, +) + +DEFAULT_IMAGENET_IMAGE_SOURCE = get_dataset_config("imagenet")["download"]["images"] +DEFAULT_IMAGENET_XML_SOURCE = get_dataset_config("imagenet")["download"]["annotations"] +DEFAULT_COCO_IMAGE_SOURCE = get_dataset_config("coco")["download"]["images"] +DEFAULT_COCO_ANNOTATION_SOURCE = get_dataset_config("coco")["download"]["annotations"] +DEFAULT_WIDERFACE_IMAGE_SOURCE = get_dataset_config("widerface")["download"]["images"] +DEFAULT_WIDERFACE_ANNOTATION_SOURCE = get_dataset_config("widerface")["download"][ + "annotations" +] +DEFAULT_DOTAV1_SOURCE = get_dataset_config("dotav1")["download"]["url"] +DEFAULT_NYU_DEPTH_SOURCE = get_dataset_config("nyu-depth")["download"]["url"] +DEFAULT_ADE20K_SOURCE = get_dataset_config("ade20k")["download"]["url"] +CITYSCAPES_DOWNLOAD_CONFIG = get_dataset_config("cityscapes")["download"] +CITYSCAPES_IMAGE_ARCHIVE = CITYSCAPES_DOWNLOAD_CONFIG["images_archive"] +CITYSCAPES_ANNOTATION_ARCHIVE = CITYSCAPES_DOWNLOAD_CONFIG["annotations_archive"] + + +def _candidate_search_roots(data_path: str) -> list[Path]: + """Returns directories to inspect for existing raw dataset sources.""" + + root = Path(data_path).expanduser() + candidates = [root, root.parent, Path.cwd()] + ordered: list[Path] = [] + seen: set[Path] = set() + for candidate in candidates: + resolved = candidate.resolve() + if resolved in seen: + continue + seen.add(resolved) + ordered.append(resolved) + return ordered + + +def _find_existing_source(data_path: str, candidate_names: list[str]) -> str | None: + """Finds a nearby raw archive or extracted dataset directory.""" + + for root in _candidate_search_roots(data_path): + for name in candidate_names: + candidate = root / name + if candidate.exists(): + return str(candidate) + return None + + +def _normalize_coco_annotation_source(annotation_dir: str | None) -> str | None: + """Normalizes a COCO annotation source for the organizer contract. + + The COCO organizer expects either the annotation archive or the extracted + parent directory that contains an ``annotations`` subdirectory. When source + discovery finds the extracted leaf ``annotations`` directory directly, + return its parent so downstream code does not resolve ``annotations`` + twice. + """ + + if annotation_dir is None: + return None + + candidate = Path(annotation_dir).expanduser() + if candidate.is_dir() and candidate.name == "annotations": + return str(candidate.parent) + return annotation_dir + + +def _resolve_imagenet_sources( + args: argparse.Namespace, data_path: str +) -> tuple[str, str]: + """Resolves local or remote sources for ImageNet organization.""" + + image_dir = args.image_dir + xml_dir = args.xml_dir + if not args.force_organize: + image_dir = image_dir or _find_existing_source( + data_path, ["ILSVRC2012_img_val.tar", "ILSVRC2012_img_val"] + ) + xml_dir = xml_dir or _find_existing_source( + data_path, ["ILSVRC2012_bbox_val_v3.tgz", "ILSVRC2012_bbox_val_v3"] + ) + return ( + image_dir or DEFAULT_IMAGENET_IMAGE_SOURCE, + xml_dir or DEFAULT_IMAGENET_XML_SOURCE, + ) + + +def _resolve_coco_sources(args: argparse.Namespace, data_path: str) -> tuple[str, str]: + """Resolves local or remote sources for COCO organization.""" + + image_dir = args.image_dir + annotation_dir = _normalize_coco_annotation_source(args.annotation_dir) + if not args.force_organize: + image_dir = image_dir or _find_existing_source( + data_path, ["val2017.zip", "val2017"] + ) + annotation_dir = annotation_dir or _normalize_coco_annotation_source( + _find_existing_source( + data_path, + [ + "annotations_trainval2017.zip", + "annotations_trainval2017", + "annotations", + ], + ) + ) + return ( + image_dir or DEFAULT_COCO_IMAGE_SOURCE, + annotation_dir or DEFAULT_COCO_ANNOTATION_SOURCE, + ) + + +def _resolve_widerface_sources( + args: argparse.Namespace, data_path: str +) -> tuple[str, str]: + """Resolves local or remote sources for WiderFace organization.""" + + image_dir = args.image_dir + annotation_dir = args.annotation_dir + if not args.force_organize: + image_dir = image_dir or _find_existing_source( + data_path, ["WIDER_val.zip", "WIDER_val"] + ) + annotation_dir = annotation_dir or _find_existing_source( + data_path, + ["wider_face_split.zip", "wider_face_split"], + ) + return ( + image_dir or DEFAULT_WIDERFACE_IMAGE_SOURCE, + annotation_dir or DEFAULT_WIDERFACE_ANNOTATION_SOURCE, + ) + + +def _resolve_dotav1_source(args: argparse.Namespace, data_path: str) -> str: + """Resolves a local or remote source for DOTAv1 organization.""" + + dataset_path = args.annotation_dir or args.image_dir + if not args.force_organize: + dataset_path = dataset_path or _find_existing_source( + data_path, ["DOTAv1.zip", "DOTAv1"] + ) + return dataset_path or DEFAULT_DOTAV1_SOURCE + + +def _resolve_nyu_depth_source(args: argparse.Namespace, data_path: str) -> str: + """Resolve a local archive or URL for NYU Depth organization.""" + + dataset_path = args.annotation_dir or args.image_dir + if not args.force_organize: + dataset_path = dataset_path or _find_existing_source( + data_path, ["nyu-depth.zip", "nyu-depth"] + ) + return dataset_path or DEFAULT_NYU_DEPTH_SOURCE + + +def _resolve_ade20k_source(args: argparse.Namespace, data_path: str) -> str: + """Resolve a local archive, extracted directory, or URL for ADE20K organization.""" + + dataset_path = args.annotation_dir or args.image_dir + if not args.force_organize: + dataset_path = dataset_path or _find_existing_source( + data_path, + ["ADEChallengeData2016.zip", "ADEChallengeData2016"], + ) + return dataset_path or DEFAULT_ADE20K_SOURCE + + +def _resolve_cityscapes_sources( + args: argparse.Namespace, data_path: str +) -> tuple[str, str]: + """Resolve the two manually downloaded official Cityscapes archives. + + Args: + args: Parsed validation CLI arguments. + data_path: Organized Cityscapes output path used as a discovery anchor. + + Returns: + Image and annotation ZIP paths. + + Raises: + SystemExit: If either required archive cannot be found. + """ + + image_dir = args.image_dir or _find_existing_source( + data_path, [CITYSCAPES_IMAGE_ARCHIVE] + ) + annotation_dir = args.annotation_dir or _find_existing_source( + data_path, [CITYSCAPES_ANNOTATION_ARCHIVE] + ) + if image_dir is None or annotation_dir is None: + raise SystemExit( + "Cityscapes organization requires the official image and annotation ZIP archives. " + "Register at https://www.cityscapes-dataset.com/, then download them with:\n" + " csDownload -d gtFine_trainvaltest.zip leftImg8bit_trainvaltest.zip\n" + "Pass the resulting files with --image-dir and --annotation-dir, or place them near the dataset path." + ) + return image_dir, annotation_dir + + +def _default_data_path_for_task(task: str, dataset: str | None = None) -> str: + """Returns the default organized dataset path for a vision task.""" + + try: + return str(get_dataset_config_for_task(task, dataset)["path"]) + except ValueError as exc: + raise SystemExit(f"Unsupported vision task for validation: {task}") from exc + + +def _dataset_ready(task: str, data_path: str, dataset: str | None = None) -> bool: + """Checks whether the organized dataset appears ready for validation.""" + + return dataset_ready(data_path, task, dataset) + + +def _ensure_dataset( + args: argparse.Namespace, task: str, dataset: str | None = None +) -> str: + """Organizes the dataset automatically when the expected layout is missing.""" + + task = normalize_vision_task(task) + data_path = os.path.expanduser( + args.data_path or _default_data_path_for_task(task, dataset) + ) + if _dataset_ready(task, data_path, dataset) and not args.force_organize: + print(f"Using organized dataset at {data_path}") + return data_path + + try: + from mblt_vision.utils.datasets import ( + organize_ade20k, + organize_cityscapes, + organize_coco, + organize_dotav1, + organize_imagenet, + organize_nyu_depth, + organize_widerface, + ) + except ImportError as exc: + print( + f"Missing dependencies for vision dataset organization: {exc}", + file=sys.stderr, + ) + raise SystemExit(2) from exc + + print(f"Preparing validation dataset for task `{task}` at {data_path}...") + if task == "image_classification": + image_dir, xml_dir = _resolve_imagenet_sources(args, data_path) + organize_imagenet( + image_dir=image_dir, + xml_dir=xml_dir, + output_dir=data_path, + ) + elif task in {"object_detection", "instance_segmentation", "pose_estimation"}: + image_dir, annotation_dir = _resolve_coco_sources(args, data_path) + organize_coco( + image_dir=image_dir, + annotation_dir=annotation_dir, + output_dir=data_path, + ) + elif task == "face_detection": + image_dir, annotation_dir = _resolve_widerface_sources(args, data_path) + organize_widerface( + image_dir=image_dir, + annotation_dir=annotation_dir, + output_dir=data_path, + ) + elif task == "obb": + organize_dotav1( + dataset_path=_resolve_dotav1_source(args, data_path), + output_dir=data_path, + ) + elif task == "depth_estimation": + organize_nyu_depth( + dataset_path=_resolve_nyu_depth_source(args, data_path), + output_dir=data_path, + ) + elif task == "semantic_segmentation": + if dataset == "cityscapes": + image_dir, annotation_dir = _resolve_cityscapes_sources(args, data_path) + organize_cityscapes( + image_dir=image_dir, + annotation_dir=annotation_dir, + output_dir=data_path, + ) + else: + organize_ade20k( + dataset_path=_resolve_ade20k_source(args, data_path), + output_dir=data_path, + ) + else: + raise SystemExit(f"Unsupported vision task for validation: {task}") + + if not _dataset_ready(task, data_path, dataset): + raise SystemExit( + f"Organized validation dataset at {data_path} is incomplete or does not match " + f"the expected {dataset or task} dataset." + ) + return data_path + + +def _run_validation(args: argparse.Namespace) -> float: + """Runs model validation on the dataset associated with the model task.""" + + try: + from mblt_vision.utils.evaluation import ( + eval_ade20k, + eval_cityscapes, + eval_coco_metrics, + eval_dota, + eval_imagenet_metrics, + eval_nyu_depth, + eval_widerface, + ) + except ImportError as exc: + print(f"Missing dependencies for vision CLI: {exc}", file=sys.stderr) + raise SystemExit(2) from exc + + model = create_vision_engine(args) + try: + if not getattr(getattr(model, "postprocessor", None), "e2e", True): + raise SystemExit( + "Validation requires end-to-end YOLO postprocessing. Use `--e2e true` or omit the option." + ) + + task = normalize_vision_task(model.post_cfg.get("task", "")) + dataset = model.post_cfg.get("dataset") + taxonomy = str(dataset).lower() if isinstance(dataset, str) else None + if task == "semantic_segmentation" and taxonomy not in {"ade20k", "cityscapes"}: + raise SystemExit( + f"Unsupported semantic segmentation taxonomy for validation: {taxonomy!r}. " + "Expected `ade20k` or `cityscapes`." + ) + data_path = _ensure_dataset(args, task, taxonomy) + + if task == "image_classification": + imagenet_result = eval_imagenet_metrics( + model=model, data_path=data_path, batch_size=args.batch_size + ) + print( + "Validation score " + f"(Top-1 accuracy): {imagenet_result.top1:.5f}, " + f"(Top-5 accuracy): {imagenet_result.top5:.5f}" + ) + return imagenet_result.primary_score + + if task == "depth_estimation": + depth_result = eval_nyu_depth( + model=model, data_path=data_path, batch_size=args.batch_size + ) + print( + "Validation score " + f"(delta1): {depth_result.delta1:.5f}, " + f"(abs_rel): {depth_result.abs_rel:.5f}, " + f"(rmse): {depth_result.rmse:.5f}" + ) + return depth_result.primary_score + + if task == "semantic_segmentation": + if taxonomy == "cityscapes": + semantic_result = eval_cityscapes( + model=model, data_path=data_path, batch_size=args.batch_size + ) + elif taxonomy == "ade20k": + semantic_result = eval_ade20k( + model=model, data_path=data_path, batch_size=args.batch_size + ) + else: + raise AssertionError( + f"Unexpected validated semantic taxonomy: {taxonomy!r}" + ) + print( + "Validation score " + f"(mIoU): {semantic_result.miou:.5f}, " + f"(pixel accuracy): {semantic_result.pixel_accuracy:.5f}" + ) + return semantic_result.primary_score + + if task in {"object_detection", "instance_segmentation", "pose_estimation"}: + coco_result = eval_coco_metrics( + model=model, + data_path=data_path, + batch_size=args.batch_size, + conf_thres=args.conf_thres, + iou_thres=args.iou_thres, + ) + print( + f"Validation score (mAP50-95): {coco_result.map5095:.5f}, (mAP50): {coco_result.map50:.5f}" + ) + return coco_result.primary_score + + if task == "obb": + dota_result = eval_dota( + model=model, + data_path=data_path, + batch_size=args.batch_size, + conf_thres=args.conf_thres, + iou_thres=args.iou_thres, + ) + print( + "Validation score " + f"(rotated mAP50-95): {dota_result.map5095:.5f}, " + f"(rotated mAP50): {dota_result.map50:.5f}" + ) + return dota_result.primary_score + + if task == "face_detection": + widerface_result = eval_widerface( + model=model, + data_path=data_path, + batch_size=args.batch_size, + conf_thres=args.conf_thres, + iou_thres=args.iou_thres, + ) + print( + "Validation score " + f"(Easy AP): {widerface_result.easy_ap:.5f}, " + f"(Medium AP): {widerface_result.medium_ap:.5f}, " + f"(Hard AP): {widerface_result.hard_ap:.5f}, " + f"(Mean AP): {widerface_result.mean_ap:.5f}" + ) + return widerface_result.primary_score + + raise SystemExit(f"Unsupported vision task for validation: {task}") + finally: + model.dispose() + + +def _cmd_val(args: argparse.Namespace) -> int: + """Runs vision validation on the task-appropriate benchmark dataset.""" + + _run_validation(args) + return 0 + + +def add_val_parser( + subparsers: argparse._SubParsersAction[argparse.ArgumentParser], +) -> None: + """Registers the unified vision validation CLI command.""" + + parser = subparsers.add_parser( + "val", help="Validate a vision model on its benchmark dataset." + ) + parser.set_defaults(_handler=_cmd_val) + parser.add_argument( + "--model", + required=True, + help="Vision model name, for example `resnet50` or `yolo11m`.", + ) + parser.add_argument( + "--framework", + default=None, + choices=["mxq", "onnx"], + help="Inference framework to use. When omitted, `--model-path` suffix is used first, then `mxq`.", + ) + parser.add_argument( + "--model-path", + dest="model_path", + default="", + help="Optional generic local model path for MXQ or ONNX inference.", + ) + parser.add_argument( + "--mxq-path", + dest="mxq_path", + default="", + help="Optional local MXQ model path. Preserved as a compatibility alias.", + ) + parser.add_argument( + "--onnx-path", + dest="onnx_path", + default="", + help="Optional local ONNX model path.", + ) + parser.add_argument( + "--model-type", + default="DEFAULT", + help="Model variant from the YAML configuration.", + ) + parser.add_argument( + "--core-mode", + default="global8", + choices=["single", "multi", "global4", "global8"], + help="NPU core execution mode.", + ) + parser.add_argument("--dev-no", type=int, default=0, help="NPU device number.") + parser.add_argument( + "--target-cores", + type=parse_target_cores, + help="Optional semicolon-separated core list for single-core mode, for example `0:0;0:1`.", + ) + parser.add_argument( + "--target-clusters", + type=parse_target_clusters, + help="Optional semicolon-separated cluster list for multi/global modes, for example `0;1`.", + ) + parser.add_argument( + "--batch-size", type=int, default=1, help="Batch size for validation." + ) + parser.add_argument( + "--data-path", + help="Path to an already organized validation dataset. If omitted, the default cache path is used.", + ) + parser.add_argument( + "--force-organize", + "--force", + "--reload", + action="store_true", + dest="force_organize", + help="Rebuild the organized dataset even when the target directory already looks ready.", + ) + parser.add_argument( + "--image-dir", + help=( + "Local archive path or download URL for dataset images. Cityscapes requires leftImg8bit_trainvaltest.zip." + ), + ) + parser.add_argument( + "--xml-dir", + help="Local archive path or download URL for ImageNet annotations used by automatic organization.", + ) + parser.add_argument( + "--annotation-dir", + help=( + "Local archive path or download URL for dataset annotations. Cityscapes requires gtFine_trainvaltest.zip." + ), + ) + add_threshold_args(parser, conf_default=None, iou_default=None) + add_e2e_arg(parser) diff --git a/pyproject.toml b/pyproject.toml index 558816b..05e1178 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,6 +42,9 @@ classifiers = [ Home = "https://www.mobilint.com/" Repository = "https://github.com/mobilint/mblt-vision-python" +[project.scripts] +mblt-vision = "mblt_vision.cli:main" + [project.optional-dependencies] onnxruntime = [ "onnx", diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..2395cec --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,24 @@ +"""Tests for the standalone Vision CLI parser.""" + +from __future__ import annotations + +from mblt_vision.cli import build_parser +from mblt_vision.cli.compile import _run_compile +from mblt_vision.cli.predict import _cmd_predict +from mblt_vision.cli.val import _cmd_val + + +def test_standalone_cli_registers_vision_commands() -> None: + """Expose the supported Vision commands from the standalone distribution.""" + + parser = build_parser() + + predict_args = parser.parse_args( + ["predict", "--source", "image.jpg", "--model", "resnet50"] + ) + val_args = parser.parse_args(["val", "--model", "resnet50"]) + compile_args = parser.parse_args(["compile", "--model-cls", "resnet50"]) + + assert predict_args._handler is _cmd_predict + assert val_args._handler is _cmd_val + assert compile_args._handler is _run_compile From 0fd14c2cd90eb1312f26a93f2ccc489410b9292c Mon Sep 17 00:00:00 2001 From: jinman Date: Mon, 10 Aug 2026 16:55:23 +0900 Subject: [PATCH 05/48] Refactor OBB terminology and remove legacy aliases across documentation and codebase --- README.md | 2 +- compile/vision/README.md | 2 +- mblt_vision/README.md | 2 +- mblt_vision/_tasks.py | 4 +- mblt_vision/utils/evaluation/eval_dota.py | 2 +- mblt_vision/utils/labels.py | 112 ---------------------- mblt_vision/utils/postprocess/common.py | 2 +- mblt_vision/utils/results.py | 2 +- tests/test_api.py | 11 --- tests/test_benchmark_vision.py | 6 +- tests/test_compile_vision.py | 24 ++--- tests/test_results.py | 6 +- tests/test_wrapper.py | 6 +- 13 files changed, 23 insertions(+), 158 deletions(-) delete mode 100644 mblt_vision/utils/labels.py diff --git a/README.md b/README.md index a142a84..f3ed197 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ Run pre-trained Mobilint Vision models from Python. `mblt-vision-python` provides model configuration, artifact loading, preprocessing, inference integration, and typed postprocessing results for image classification, depth estimation, face and -object detection, oriented bounding boxes (OBB), instance and semantic segmentation, +object detection, OBB, instance and semantic segmentation, and pose estimation. Version `0.0.1` is the initial standalone release. diff --git a/compile/vision/README.md b/compile/vision/README.md index b211414..26a7173 100644 --- a/compile/vision/README.md +++ b/compile/vision/README.md @@ -83,7 +83,7 @@ Compilation maps model tasks to the packaged dataset registry: - Semantic segmentation uses the dataset declared by the model's `post_cfg.dataset`: ADE20K or Cityscapes. - Face detection uses WiderFace. -- Oriented bounding boxes use DOTAv1. +- OBB uses DOTAv1. `--data-path` is an organized dataset root. An existing ready layout is reused; otherwise the registry-backed organizer downloads and prepares the dataset. When no path is supplied, the diff --git a/mblt_vision/README.md b/mblt_vision/README.md index 0e81206..1ee0e47 100644 --- a/mblt_vision/README.md +++ b/mblt_vision/README.md @@ -352,7 +352,7 @@ This section lists the publicly pre-trained models supported by the vision frame | --- | --- | --- | --- | --- | --- | --- | --- |
    -Oriented Bounding Boxes (DOTA v1.0) +OBB (DOTA v1.0) - $\underset{\texttt{50-95}}{\texttt{mAP}_{\texttt{val}}^{\texttt{obb}}}$ is the primary metric for single-model single-scale validation on the diff --git a/mblt_vision/_tasks.py b/mblt_vision/_tasks.py index af5c442..df9a462 100644 --- a/mblt_vision/_tasks.py +++ b/mblt_vision/_tasks.py @@ -15,15 +15,13 @@ "face_detection", ) -_INPUT_TASK_ALIASES = {"oriented_bounding_boxes": "obb"} - def normalize_vision_task(task: str, *, supported: Iterable[str] | None = None) -> str: """Normalize a Vision task name and validate it against supported tasks.""" if not isinstance(task, str): raise TypeError(f"Vision task must be a string, got {type(task).__name__}.") - normalized = _INPUT_TASK_ALIASES.get(task.lower(), task.lower()) + normalized = task.lower() supported_tasks = tuple(VISION_TASKS if supported is None else supported) if normalized not in supported_tasks: raise ValueError( diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 999bb78..698ef82 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -1,4 +1,4 @@ -"""Evaluation script for DOTAv1 oriented bounding boxes.""" +"""Evaluation script for DOTAv1 OBB.""" from __future__ import annotations diff --git a/mblt_vision/utils/labels.py b/mblt_vision/utils/labels.py deleted file mode 100644 index 4119905..0000000 --- a/mblt_vision/utils/labels.py +++ /dev/null @@ -1,112 +0,0 @@ -"""Small dataset metadata tables needed by output conversion.""" - -from __future__ import annotations - -COCO_CATEGORY_IDS = ( - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 27, - 28, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60, - 61, - 62, - 63, - 64, - 65, - 67, - 70, - 72, - 73, - 74, - 75, - 76, - 77, - 78, - 79, - 80, - 81, - 82, - 84, - 85, - 86, - 87, - 88, - 89, - 90, -) - -DOTAV1_LABELS = ( - "plane", - "ship", - "storage-tank", - "baseball-diamond", - "tennis-court", - "basketball-court", - "ground-track-field", - "harbor", - "bridge", - "large-vehicle", - "small-vehicle", - "helicopter", - "roundabout", - "soccer-ball-field", - "swimming-pool", -) - - -def get_coco_inv(index: int) -> int: - return COCO_CATEGORY_IDS[index] - - -def get_dotav1_label(index: int) -> str: - return DOTAV1_LABELS[index] diff --git a/mblt_vision/utils/postprocess/common.py b/mblt_vision/utils/postprocess/common.py index 0579aa8..9cee0e1 100644 --- a/mblt_vision/utils/postprocess/common.py +++ b/mblt_vision/utils/postprocess/common.py @@ -11,7 +11,7 @@ import torch import torch.nn.functional as F -from ..labels import get_coco_inv, get_dotav1_label +from ..datasets import get_coco_inv, get_dotav1_label from ..letterbox import RatioPad, resolve_ratio_pad diff --git a/mblt_vision/utils/results.py b/mblt_vision/utils/results.py index 36d769f..c344936 100644 --- a/mblt_vision/utils/results.py +++ b/mblt_vision/utils/results.py @@ -646,7 +646,7 @@ def _plot_obb( save_path: str | Path | None = None, **kwargs, ) -> np.ndarray: - """Plots oriented bounding boxes on an image. + """Plot OBB detections on an image. Args: source_path: Path or image object. diff --git a/tests/test_api.py b/tests/test_api.py index 80df7a0..40b4182 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -2,8 +2,6 @@ from __future__ import annotations -import importlib - import pytest import mblt_vision @@ -27,15 +25,6 @@ def test_public_discovery_exposes_all_supported_tasks() -> None: assert list_models("obb")["obb"] -def test_public_exports_use_only_the_canonical_obb_name() -> None: - """Keep the standalone namespace free of the legacy OBB spelling.""" - - assert "oriented_bounding_boxes" not in mblt_vision.__all__ - assert not hasattr(mblt_vision, "oriented_bounding_boxes") - with pytest.raises(ModuleNotFoundError): - importlib.import_module("mblt_vision.oriented_bounding_boxes") - - def test_model_exports_are_discoverable_from_task_and_top_level_namespaces() -> None: """Keep task exports synchronized with lazy top-level compatibility exports.""" diff --git a/tests/test_benchmark_vision.py b/tests/test_benchmark_vision.py index 5e3e5f5..8e93a3f 100644 --- a/tests/test_benchmark_vision.py +++ b/tests/test_benchmark_vision.py @@ -113,14 +113,14 @@ class FakeModel: assert metrics == {"miou": 0.6, "pixel_accuracy": 0.9} -def test_benchmark_accepts_oriented_bounding_boxes_model_alias( +def test_benchmark_accepts_obb_model_task( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, ) -> None: - """Dispatch an alias-configured OBB model through DOTAv1 evaluation.""" + """Dispatch an OBB model through DOTAv1 evaluation.""" class FakeModel: - post_cfg = {"task": "oriented_bounding_boxes", "dataset": "dotav1"} + post_cfg = {"task": "obb", "dataset": "dotav1"} import mblt_vision.utils.evaluation as evaluation_module diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index f052e50..57232fb 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -93,19 +93,12 @@ def test_non_imagenet_selection_is_deterministic( assert len(first) == 3 -def test_legacy_obb_alias_selects_calibration_images(tmp_path: Path) -> None: - """Normalize the legacy OBB task name in exported sampling helpers.""" +def test_obb_selects_calibration_images(tmp_path: Path) -> None: + """Select OBB calibration images through exported sampling helpers.""" images = _write_images(tmp_path / "images", ["P0001.png", "P0002.png"]) - assert select_calibration_images( - "oriented_bounding_boxes", - tmp_path, - subset_size=2, - ) == select_calibration_images("obb", tmp_path, subset_size=2) - assert set( - select_calibration_images("oriented_bounding_boxes", tmp_path, subset_size=2) - ) == set(images) + assert set(select_calibration_images("obb", tmp_path, subset_size=2)) == set(images) @pytest.mark.parametrize( @@ -348,10 +341,7 @@ def _organize_dotav1(**kwargs: str) -> None: monkeypatch.setattr(readiness_module, "DOTAV1_VALIDATION_SAMPLE_COUNT", 1) assert ( - compile_module.ensure_calibration_dataset( - "oriented_bounding_boxes", dataset="dotav1" - ) - == data_path + compile_module.ensure_calibration_dataset("obb", dataset="dotav1") == data_path ) @@ -741,16 +731,16 @@ def test_compile_uses_local_onnx_and_exact_options( assert not calls["temporary_root"].exists() -def test_compile_normalizes_legacy_obb_task_from_model_config( +def test_compile_uses_obb_task_from_model_config( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, ) -> None: - """Compile legacy custom model metadata through the canonical OBB task.""" + """Compile custom model metadata using the canonical OBB task.""" calls, _ = _run_fake_compile( monkeypatch, tmp_path, - task="oriented_bounding_boxes", + task="obb", model_path=None, dataset="dotav1", entry_level="calibration", diff --git a/tests/test_results.py b/tests/test_results.py index e1361f8..1f17bf1 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -163,10 +163,10 @@ def test_results_reject_empty_structured_outputs( Results({}, {"task": task}, output) -def test_results_normalize_task_alias_and_semantic_taxonomy_case() -> None: - """Normalize OBB aliases and semantic palette taxonomy casing.""" +def test_results_normalize_obb_task_and_semantic_taxonomy_case() -> None: + """Normalize the OBB task and semantic palette taxonomy casing.""" - obb = Results({}, {"task": "oriented_bounding_boxes"}, [torch.zeros((0, 7))]) + obb = Results({}, {"task": "obb"}, [torch.zeros((0, 7))]) semantic = Results( {}, {"task": "semantic_segmentation", "dataset": "CityScapes"}, diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 08a1d1a..94119bb 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -485,11 +485,11 @@ def _capture_engine_init(self: MBLT_Engine, **kwargs: Any) -> None: assert captured_kwargs["framework"] == "mxq" -def test_engine_init_accepts_oriented_bounding_boxes_task_alias( +def test_engine_init_accepts_obb_task( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, ) -> None: - """Construct the canonical OBB postprocessor from the compatibility task spelling.""" + """Construct the OBB postprocessor from its canonical task name.""" mxq_path = tmp_path / "model.mxq" mxq_path.write_bytes(b"mxq") @@ -517,7 +517,7 @@ def dispose(self) -> None: "file_cfg": {}, "pre_cfg": {"LetterBox": {"img_size": [640, 640]}}, "post_cfg": { - "task": "oriented_bounding_boxes", + "task": "obb", "dataset": "dotav1", "nl": 3, "reg_max": 16, From 8d3c699d1b075bb02bde1b595a460f40bdeaf0a6 Mon Sep 17 00:00:00 2001 From: jinman Date: Mon, 10 Aug 2026 18:15:01 +0900 Subject: [PATCH 06/48] Add target device support for vision compilation and CLI commands --- compile/vision/README.md | 9 +++++++-- mblt_vision/_compat.py | 5 ++--- mblt_vision/cli/_vision.py | 16 +++++++++++++--- mblt_vision/cli/compile.py | 7 +++++++ mblt_vision/cli/val.py | 10 ++++++++-- mblt_vision/compile/vision.py | 34 +++++++++++++++++++++++++++------- mblt_vision/wrapper.py | 20 ++++++++++++++------ tests/test_cli.py | 27 ++++++++++++++++++++++++++- tests/test_compile_vision.py | 23 ++++++++++++++++++++++- tests/test_wrapper.py | 10 ++++------ 10 files changed, 130 insertions(+), 31 deletions(-) diff --git a/compile/vision/README.md b/compile/vision/README.md index 26a7173..cd47ec6 100644 --- a/compile/vision/README.md +++ b/compile/vision/README.md @@ -27,6 +27,7 @@ from mblt_vision.compile.vision import compile_vision_model output_path = compile_vision_model( "alexnet", + target_device="aries-rb", data_path="~/.mblt_vision/datasets/imagenet", save_path="./alexnet.mxq", ) @@ -38,6 +39,7 @@ print(output_path) ```bash mblt-vision-python compile \ --model-cls alexnet \ + --target-device aries-rb \ --data-path ~/.mblt_vision/datasets/imagenet \ --save-path ./alexnet.mxq ``` @@ -46,8 +48,8 @@ Start from an existing sampled image subset or a ready calibration tensor direct stages have already been completed: ```bash -mblt-vision-python compile --model-cls alexnet --subset-path ./sampled-images -mblt-vision-python compile --model-cls alexnet --calib-data-path ./preprocessed-npy +mblt-vision-python compile --model-cls alexnet --target-device aries-rb --subset-path ./sampled-images +mblt-vision-python compile --model-cls alexnet --target-device aries-rb --calib-data-path ./preprocessed-npy ``` Use `--model-type` for a non-default YAML variant and `--model-path` or `--onnx-path` to prefer a @@ -56,6 +58,9 @@ supplies the ONNX artifact. Downloaded ONNX files and compiled outputs use `~/.mblt_vision`; without `--save-path`, the compiler writes `~/.mblt_vision/.mxq`. +`target_device` / `--target-device` is required: choose `aries-rb`, `regulus-ra`, or `regulus-rb` +to compile an artifact for that board. + The ONNX preprocessing engine uses ONNX Runtime's CPU provider by default, so compilation does not probe TensorRT, CUDA, or other accelerators. Callers that construct `MBLT_Engine` directly can opt into another provider order with `onnx_providers`. diff --git a/mblt_vision/_compat.py b/mblt_vision/_compat.py index ca47c70..99ee148 100644 --- a/mblt_vision/_compat.py +++ b/mblt_vision/_compat.py @@ -120,8 +120,7 @@ def __init__( explicit MXQ-only override. model_type: YAML config variant to load. infer_mode: Execution mode forwarded to ``MBLT_Engine``. - product: Retained for backward compatibility but ignored by the - YAML-backed registry in `2.0.0`. + product: Legacy product/board value forwarded as ``target_device``. dev_no: Accelerator device number. target_cores: Optional core selection for single-core mode. target_clusters: Optional cluster selection for multi/global modes. @@ -132,7 +131,6 @@ def __init__( model_path: Optional explicit local model path for MXQ or ONNX. """ - del product if uses_shifted_compat_model_path_layout( model_path, mxq_path, onnx_path, framework ): @@ -155,6 +153,7 @@ def __init__( target_clusters=list(target_clusters) if target_clusters is not None else None, + target_device=product, framework=framework, ) diff --git a/mblt_vision/cli/_vision.py b/mblt_vision/cli/_vision.py index 1e85c6b..79f0180 100644 --- a/mblt_vision/cli/_vision.py +++ b/mblt_vision/cli/_vision.py @@ -99,11 +99,17 @@ def add_common_vision_args(parser: argparse.ArgumentParser) -> None: ) parser.add_argument( "--core-mode", - default="global8", + default=None, choices=["single", "multi", "global4", "global8"], - help="NPU core execution mode.", + help="NPU core execution mode. Defaults to global8 on Aries and single on Regulus.", ) parser.add_argument("--dev-no", type=int, default=0, help="NPU device number.") + parser.add_argument( + "--target-device", + default="aries-rb", + choices=["aries-rb", "regulus-ra", "regulus-rb"], + help="NPU board target. Determines the backend implementation.", + ) parser.add_argument( "--target-cores", type=parse_target_cores, @@ -251,7 +257,11 @@ def create_vision_engine(args: argparse.Namespace) -> Any: mxq_path=args.mxq_path, onnx_path=args.onnx_path, dev_no=args.dev_no, - core_mode=normalize_core_mode(args.core_mode), + target_device=args.target_device, + core_mode=normalize_core_mode( + args.core_mode + or ("single" if args.target_device.startswith("regulus-") else "global8") + ), target_cores=args.target_cores, target_clusters=args.target_clusters, postprocess_kwargs=postprocess_kwargs, diff --git a/mblt_vision/cli/compile.py b/mblt_vision/cli/compile.py index 7858b3e..4726243 100644 --- a/mblt_vision/cli/compile.py +++ b/mblt_vision/cli/compile.py @@ -21,6 +21,7 @@ def _run_compile(args: argparse.Namespace) -> int: output_path = compile_vision_model( model_cls=args.model_cls, + target_device=args.target_device, model_type=args.model_type, model_path=args.model_path, data_path=args.data_path, @@ -60,6 +61,12 @@ def add_compile_parser( required=True, help="Vision model name, for example `alexnet` or `yolo11m`.", ) + parser.add_argument( + "--target-device", + required=True, + choices=["aries-rb", "regulus-ra", "regulus-rb"], + help="Required NPU board target for the compiled MXQ artifact.", + ) parser.add_argument( "--model-type", default="DEFAULT", diff --git a/mblt_vision/cli/val.py b/mblt_vision/cli/val.py index c324994..45189c7 100644 --- a/mblt_vision/cli/val.py +++ b/mblt_vision/cli/val.py @@ -493,11 +493,17 @@ def add_val_parser( ) parser.add_argument( "--core-mode", - default="global8", + default=None, choices=["single", "multi", "global4", "global8"], - help="NPU core execution mode.", + help="NPU core execution mode. Defaults to global8 on Aries and single on Regulus.", ) parser.add_argument("--dev-no", type=int, default=0, help="NPU device number.") + parser.add_argument( + "--target-device", + default="aries-rb", + choices=["aries-rb", "regulus-ra", "regulus-rb"], + help="NPU board target. Determines the backend implementation.", + ) parser.add_argument( "--target-cores", type=parse_target_cores, diff --git a/mblt_vision/compile/vision.py b/mblt_vision/compile/vision.py index 9858dd9..19d1b52 100644 --- a/mblt_vision/compile/vision.py +++ b/mblt_vision/compile/vision.py @@ -20,12 +20,14 @@ from mblt_vision._tasks import normalize_vision_task from mblt_vision.datasets import get_dataset_config_for_task +from mblt_npu import normalize_target_device from mblt_vision.wrapper import MOBILINT_CACHE_DIR, MBLT_Engine, resolve_model_config DEFAULT_PERCENTILE = 0.9999 DEFAULT_TOPK_RATIO = 0.01 DEFAULT_SEED = 0 DEFAULT_MODEL_DIR = Path(MOBILINT_CACHE_DIR) +SUPPORTED_TARGET_DEVICES = frozenset({"aries-rb", "regulus-ra", "regulus-rb"}) DEFAULT_SUBSET_SIZES = { "image_classification": 1, "depth_estimation": 100, @@ -629,12 +631,15 @@ def _validate_ratio(name: str, value: Any) -> float: return resolved -def _fetch_quantization_config(repo_id: str, revision: str) -> dict[str, Any] | None: - """Fetch optional hosted Aries quantization metadata. +def _fetch_quantization_config( + repo_id: str, revision: str, target_device: str = "aries-rb" +) -> dict[str, Any] | None: + """Fetch optional hosted board quantization metadata. Args: repo_id: Hugging Face model repository ID. revision: Repository revision. + target_device: Board folder containing the metadata. Returns: Hosted ``config`` mapping, or ``None`` when the optional file is unavailable. @@ -647,7 +652,7 @@ def _fetch_quantization_config(repo_id: str, revision: str) -> dict[str, Any] | metadata_path = hf_hub_download( repo_id=repo_id, filename="best_result.json", - subfolder="aries", + subfolder=normalize_target_device(target_device), revision=revision, ) except (HfHubHTTPError, OSError) as exc: @@ -701,7 +706,13 @@ def resolve_quantization_values( repo_id = file_cfg.get("repo_id") revision = file_cfg.get("revision", "main") if isinstance(repo_id, str) and repo_id: - hosted_config = _fetch_quantization_config(repo_id, str(revision)) + target_device = file_cfg.get("target_device") + if isinstance(target_device, str): + hosted_config = _fetch_quantization_config( + repo_id, str(revision), normalize_target_device(target_device) + ) + else: + hosted_config = _fetch_quantization_config(repo_id, str(revision)) else: warnings.warn( "Model configuration has no repository ID; using fallback quantization values.", @@ -800,6 +811,7 @@ def _resolve_compile_onnx_path( def compile_vision_model( model_cls: str, *, + target_device: str, model_type: str = "DEFAULT", model_path: str | Path | None = None, onnx_path: str | Path | None = None, @@ -812,10 +824,11 @@ def compile_vision_model( percentile: float | None = None, topk_ratio: float | None = None, ) -> Path: - """Compile a configured vision ONNX model into an Aries MXQ artifact. + """Compile a configured vision ONNX model into a board-specific MXQ artifact. Args: model_cls: Vision model name or YAML path. + target_device: Required target board: ``aries-rb``, ``regulus-ra``, or ``regulus-rb``. model_type: Model variant from the YAML configuration. model_path: Preferred local ONNX path compatibility option. onnx_path: Local ONNX path alias. @@ -840,6 +853,12 @@ def compile_vision_model( """ _validate_data_level_paths(data_path, subset_path, calib_data_path) + target_device = normalize_target_device(target_device) + if target_device not in SUPPORTED_TARGET_DEVICES: + raise ValueError( + f"Unsupported target_device {target_device!r}; expected one of " + f"{sorted(SUPPORTED_TARGET_DEVICES)}." + ) mxq_compile, calibration_config_class = _load_qbcompiler() model_config = resolve_model_config(model_cls, model_type) file_cfg = model_config.get("file_cfg") @@ -848,6 +867,7 @@ def compile_vision_model( raise ValueError( "Resolved vision model configuration requires `file_cfg` and `post_cfg` objects." ) + file_cfg["target_device"] = target_device task = _normalize_task(str(post_cfg.get("task", ""))) dataset = post_cfg.get("dataset") @@ -883,7 +903,7 @@ def compile_vision_model( image_channels=3, backend="onnx", device="gpu", - target_device="aries-rb", + target_device=target_device, inference_scheme="all", calibration_config=calibration_config, ) @@ -941,7 +961,7 @@ def _compile(calibration_path: Path) -> None: image_channels=3, backend="onnx", device="gpu", - target_device="aries-rb", + target_device=target_device, inference_scheme="all", calibration_config=calibration_config, ) diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 75a4c0f..1e59b45 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -19,7 +19,7 @@ from huggingface_hub.errors import EntryNotFoundError from qbruntime import Cluster, CoreId -from mblt_npu import MobilintNPUBackend +from mblt_npu import MobilintNPUBackend, normalize_target_device from ._model_paths import resolve_framework as _resolve_framework from ._model_paths import split_model_paths as _split_model_paths from ._model_paths import ( @@ -320,6 +320,7 @@ def __init__( framework: str | None = None, onnx_providers: Sequence[str] | None = None, model_path: str = "", + target_device: str = "aries-rb", ) -> None: """Initializes the MBLT_Engine. @@ -333,6 +334,7 @@ def __init__( core_mode: single, multi, global4, global8 target_cores: single mode target_clusters: multi, global modes + target_device: NPU board identifier; defaults to ``aries-rb``. pre_cfg: Preprocessing configuration. post_cfg: Postprocessing configuration. model_cls(not dict): model name or yaml path @@ -398,10 +400,16 @@ def __init__( dev_no = 0 if core_mode is None: core_mode = "single" + target_device = normalize_target_device(target_device) + is_regulus = target_device in {"regulus-ra", "regulus-rb"} if target_cores is None: - target_cores = ["0:0", "0:1", "0:2", "0:3", "1:0", "1:1", "1:2", "1:3"] + target_cores = ( + [] + if is_regulus + else ["0:0", "0:1", "0:2", "0:3", "1:0", "1:1", "1:2", "1:3"] + ) if target_clusters is None: - target_clusters = [0, 1] + target_clusters = [] if is_regulus else [0, 1] self.file_cfg = copy.deepcopy(model_config_part["file_cfg"]) file_cfg_model_path = self.file_cfg.pop("model_path", "") @@ -426,6 +434,7 @@ def __init__( self.file_cfg["target_clusters"] = target_clusters if _dev_no_passed or "dev_no" not in self.file_cfg: self.file_cfg["dev_no"] = dev_no + self.file_cfg["target_device"] = target_device self.pre_cfg = copy.deepcopy(model_config_part["pre_cfg"]) self.post_cfg = copy.deepcopy(model_config_part["post_cfg"]) @@ -591,13 +600,12 @@ def file_config_cleansing(self) -> None: return if filename and framework == "mxq": - core_mode = self.file_cfg.get("core_mode") - subfolders = ["aries", f"aries/{core_mode}"] if core_mode else ["aries"] + target_device = self.file_cfg.get("target_device", "aries-rb") self.file_cfg["mxq_path"] = self._download_hub_artifact( repo_id=repo_id, filename=filename, revision=revision, - subfolders=subfolders, + subfolders=[target_device], ) if onnx_filename and framework == "onnx" and not self.file_cfg.get("onnx_path"): diff --git a/tests/test_cli.py b/tests/test_cli.py index 2395cec..5de8958 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -17,8 +17,33 @@ def test_standalone_cli_registers_vision_commands() -> None: ["predict", "--source", "image.jpg", "--model", "resnet50"] ) val_args = parser.parse_args(["val", "--model", "resnet50"]) - compile_args = parser.parse_args(["compile", "--model-cls", "resnet50"]) + compile_args = parser.parse_args( + ["compile", "--model-cls", "resnet50", "--target-device", "aries-rb"] + ) assert predict_args._handler is _cmd_predict assert val_args._handler is _cmd_val assert compile_args._handler is _run_compile + assert compile_args.target_device == "aries-rb" + assert predict_args.target_device == "aries-rb" + assert val_args.target_device == "aries-rb" + + +def test_vision_cli_uses_single_core_mode_by_default_on_regulus() -> None: + """Keep the CLI's implicit core mode compatible with a Regulus target.""" + + parser = build_parser() + args = parser.parse_args( + [ + "predict", + "--source", + "image.jpg", + "--model", + "resnet50", + "--target-device", + "regulus-ra", + ] + ) + + assert args.target_device == "regulus-ra" + assert args.core_mode is None diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index 57232fb..e1815a0 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -568,6 +568,7 @@ def _run_fake_compile( model_path: Path | None, entry_level: str = "data", dataset: str | None = None, + target_device: str = "aries-rb", fail: bool = False, calls: dict[str, Any] | None = None, ) -> tuple[dict[str, Any], Path]: @@ -686,6 +687,7 @@ def _ensure_dataset( compile_module.compile_vision_model( "fake-model", + target_device=target_device, model_type="VARIANT", model_path=model_path, data_path=original_data_path, @@ -731,6 +733,22 @@ def test_compile_uses_local_onnx_and_exact_options( assert not calls["temporary_root"].exists() +def test_compile_forwards_required_target_device( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Forward the selected board to qbcompiler rather than hard-coding Aries.""" + + calls, _ = _run_fake_compile( + monkeypatch, + tmp_path, + task="image_classification", + model_path=None, + target_device="regulus-rb", + ) + + assert calls["compile_kwargs"]["target_device"] == "regulus-rb" + + def test_compile_uses_obb_task_from_model_config( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, @@ -833,7 +851,10 @@ def test_compile_rejects_multiple_data_levels() -> None: with pytest.raises(ValueError, match="Provide only one calibration pipeline input"): compile_module.compile_vision_model( - "alexnet", data_path="original", subset_path="subset" + "alexnet", + target_device="aries-rb", + data_path="original", + subset_path="subset", ) diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 94119bb..442ec70 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -8,7 +8,6 @@ import numpy as np import pytest import torch -from huggingface_hub.errors import EntryNotFoundError from mblt_vision.utils.postprocess import build_postprocess from mblt_vision.utils.postprocess.base import YOLODetectionPostBase from mblt_vision.utils.postprocess.common import ( @@ -907,18 +906,16 @@ def test_crop_mask_matches_ultralytics_fractional_boundaries(mask_count: int) -> assert torch.equal(cropped, expected) -def test_file_config_cleansing_downloads_aries_before_core_mode( +def test_file_config_cleansing_downloads_from_exact_target_device_folder( monkeypatch: pytest.MonkeyPatch, ) -> None: - """Download only the MXQ artifact and try the legacy aries layout first.""" + """Download only the MXQ artifact from the selected board folder.""" calls: list[str] = [] def _fake_download(**kwargs: Any) -> str: subfolder = kwargs["subfolder"] calls.append(subfolder) - if subfolder == "aries": - raise EntryNotFoundError("missing") return "/tmp/global8.mxq" monkeypatch.setattr(wrapper, "hf_hub_download", _fake_download) @@ -931,11 +928,12 @@ def _fake_download(**kwargs: Any) -> str: "filename": "model.mxq", "revision": "main", "core_mode": "global8", + "target_device": "aries-rb", } engine.file_config_cleansing() - assert calls == ["aries", "aries/global8"] + assert calls == ["aries-rb"] assert engine.file_cfg["mxq_path"] == "/tmp/global8.mxq" assert engine.file_cfg["onnx_filename"] == "model.onnx" assert "onnx_path" not in engine.file_cfg From 0f2268256680b7356041c4c57001cc5f435cb115 Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 11 Aug 2026 11:21:36 +0900 Subject: [PATCH 07/48] Bump version of mblt-npu-python dependency to 0.0.0 and update package version to 0.0.0 --- mblt_vision/__init__.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mblt_vision/__init__.py b/mblt_vision/__init__.py index 3e24464..1731c14 100644 --- a/mblt_vision/__init__.py +++ b/mblt_vision/__init__.py @@ -19,7 +19,7 @@ from ._api import list_tasks as list_tasks from .wrapper import MBLT_Engine as MBLT_Engine -__version__ = "0.0.1" +__version__ = "0.0.0" _TASK_MODULES = ( face_detection, diff --git a/pyproject.toml b/pyproject.toml index 05e1178..1643915 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ dynamic = ["version"] description = "Mobilint NPU vision models: classification, detection, segmentation, pose" readme = "README.md" dependencies = [ - "mblt-npu-python>=0.0.1", + "mblt-npu-python>=0.0.0", "mobilint-qb-runtime>=1.0.0", "numpy>=1.26.0", "torch>=2.4.1", From 2943b1f0a7d6701e2c085576c884f34d7214b7e2 Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 11 Aug 2026 12:08:44 +0900 Subject: [PATCH 08/48] feat: Add chart, I/O, and summary utilities for benchmark results - Introduced `chart_utils.py` for plotting grouped and simple bar charts. - Added `io_utils.py` for file I/O operations including JSON and CSV writing. - Created `summary_utils.py` for generating Markdown summaries of benchmark outputs. - Enhanced CLI argument parsing in `_vision.py` and `predict.py` to include descriptions and examples. - Updated `MBLT_Engine` in `wrapper.py` to support ONNX models alongside MXQ. - Improved error handling in `eval_ade20k.py` for invalid predictions. - Refactored tests to ensure proper functionality and coverage for new features. --- README.md | 16 +- benchmark/README.md | 180 +++-------------- benchmark/__init__.py | 1 - benchmark/benchmark_coco.py | 183 ------------------ benchmark/benchmark_imagenet.py | 137 ------------- .../{vision => }/benchmark_vision_models.py | 12 +- benchmark/common/__init__.py | 1 - .../{vision => }/compare_benchmark_results.py | 6 +- benchmark/{vision => }/organize_ade20k.py | 0 benchmark/{vision => }/organize_cityscapes.py | 0 benchmark/organize_coco.py | 22 ++- benchmark/{vision => }/organize_dotav1.py | 0 benchmark/organize_imagenet.py | 22 ++- benchmark/{vision => }/organize_nyu_depth.py | 0 benchmark/organize_widerface.py | 18 +- benchmark/vision/README.md | 62 ------ benchmark/vision/__init__.py | 1 - benchmark/vision/organize_coco.py | 44 ----- benchmark/vision/organize_imagenet.py | 45 ----- benchmark/vision/organize_widerface.py | 42 ---- compile/{vision => }/README.md | 28 +-- compile/{vision => }/make_ade20k_subset.py | 0 .../{vision => }/make_cityscapes_subset.py | 0 compile/{vision => }/make_coco_subset.py | 0 compile/{vision => }/make_dotav1_subset.py | 0 compile/{vision => }/make_imagenet_subset.py | 0 compile/{vision => }/make_nyu_depth_subset.py | 0 compile/{vision => }/make_widerface_subset.py | 0 compile/{vision => }/vision_model_compile.py | 7 + mblt_vision/benchmark/__init__.py | 1 + .../benchmark}/argparse_utils.py | 0 .../benchmark}/chart_utils.py | 0 .../benchmark}/io_utils.py | 0 .../benchmark}/summary_utils.py | 0 mblt_vision/cli/_vision.py | 11 +- mblt_vision/cli/predict.py | 21 +- mblt_vision/utils/evaluation/eval_ade20k.py | 2 +- mblt_vision/wrapper.py | 23 ++- tests/test_benchmark_vision.py | 4 +- tests/test_cli.py | 24 +++ 40 files changed, 189 insertions(+), 724 deletions(-) delete mode 100644 benchmark/__init__.py delete mode 100644 benchmark/benchmark_coco.py delete mode 100644 benchmark/benchmark_imagenet.py rename benchmark/{vision => }/benchmark_vision_models.py (97%) delete mode 100644 benchmark/common/__init__.py rename benchmark/{vision => }/compare_benchmark_results.py (97%) rename benchmark/{vision => }/organize_ade20k.py (100%) rename benchmark/{vision => }/organize_cityscapes.py (100%) rename benchmark/{vision => }/organize_dotav1.py (100%) rename benchmark/{vision => }/organize_nyu_depth.py (100%) delete mode 100644 benchmark/vision/README.md delete mode 100644 benchmark/vision/__init__.py delete mode 100644 benchmark/vision/organize_coco.py delete mode 100644 benchmark/vision/organize_imagenet.py delete mode 100644 benchmark/vision/organize_widerface.py rename compile/{vision => }/README.md (81%) rename compile/{vision => }/make_ade20k_subset.py (100%) rename compile/{vision => }/make_cityscapes_subset.py (100%) rename compile/{vision => }/make_coco_subset.py (100%) rename compile/{vision => }/make_dotav1_subset.py (100%) rename compile/{vision => }/make_imagenet_subset.py (100%) rename compile/{vision => }/make_nyu_depth_subset.py (100%) rename compile/{vision => }/make_widerface_subset.py (100%) rename compile/{vision => }/vision_model_compile.py (91%) create mode 100644 mblt_vision/benchmark/__init__.py rename {benchmark/common => mblt_vision/benchmark}/argparse_utils.py (100%) rename {benchmark/common => mblt_vision/benchmark}/chart_utils.py (100%) rename {benchmark/common => mblt_vision/benchmark}/io_utils.py (100%) rename {benchmark/common => mblt_vision/benchmark}/summary_utils.py (100%) diff --git a/README.md b/README.md index f3ed197..688ed6a 100644 --- a/README.md +++ b/README.md @@ -72,6 +72,20 @@ and `compile` subcommands: mblt-vision predict --source image.jpg --model resnet50 ``` +`predict` is the single inference command for classification, depth estimation, +object and face detection, instance and semantic segmentation, OBB, and pose +estimation. The selected model determines its task and processing pipeline. +By default it downloads the model artifact and saves a plotted result under +`runs/vision/predict/`. Use `--output` to choose the result-image path, +`--topk` for classification labels, and `--conf-thres`/`--iou-thres` for +detection-style tasks. `--framework onnx` selects ONNX Runtime inference; +`--target-device` and `--core-mode` select the MXQ board/runtime mode. + +```bash +mblt-vision predict --source image.jpg --model yolo11m --conf-thres 0.4 --output result.jpg +mblt-vision predict --source image.jpg --model yolo11m-pose --target-device regulus-ra --core-mode single +``` + The corresponding `mblt-model-zoo` commands use the same standalone handlers for backward compatibility. @@ -79,7 +93,7 @@ backward compatibility. See [the Vision API guide](mblt_vision/README.md) for supported model families, model details, artifact selection, and output taxonomy behavior. See the -[compilation guide](compile/vision/README.md) for calibration-data preparation +[compilation guide](compile/README.md) for calibration-data preparation and MXQ compilation. The [test guide](tests/TEST.md) explains offline, Hugging Face, and NPU test runs. diff --git a/benchmark/README.md b/benchmark/README.md index 884e327..d70e1c7 100644 --- a/benchmark/README.md +++ b/benchmark/README.md @@ -1,172 +1,48 @@ -# Measuring the Performance of Vision Models +# Vision benchmark commands -You can run the benchmark as the following steps: +`mblt-vision-python` is a Vision-only package, so every command script lives +directly in this directory. Reusable benchmark supportโ€”argument parsing, +artifact writing, charts, and summariesโ€”is packaged under +`mblt_vision.benchmark` for use by library code and command scripts alike. -1. Download the vision benchmark dataset and organize the dataset. -2. Prepare the model for benchmark. -3. Run the benchmark and evaluate the performance. +The old per-dataset benchmark scripts and duplicated organizer wrappers were +removed. Use the unified runner and the organizer that matches your dataset. -> โš ๏ธ **Warning:** Mobilint does not provide the vision benchmark dataset or direct download link due to the terms of use of the dataset. -Mobilint model zoo provides utilities on dataset organization, model preparation, and benchmark evaluation, therefore you can easily reproduce the [published benchmark results](../mblt_vision/README.md). +## Organize a dataset -Furthermore, you can simply run the benchmark with the model you compiled with custom quantization recipe. - -## Benchmark with ImageNet Dataset - -### Download the ImageNet Dataset - -To download the ImageNet dataset, visit the [ImageNet website](https://image-net.org/) and download the dataset with the following steps: - -0. Login with your account. If you don't have an account, you can register a new account. (Using `.edu` email is highly recommended) -1. Click on the "Download" button on the menu bar. -2. Go to "ImageNet Large-scale Visual Recognition Challenge (ILSVRC)" section, and click 2012 button. -3. Download the following files: - - ILSVRC2012_img_val.tar: Displayes as "Validation images (all tasks)" with MD5 checksum `29b22e2961454d5413ddabcf34fc5622`. - - ILSVRC2012_bbox_val_v3.tgz: Displayes as "Validation bounding box annotations (all tasks)" with MD5 checksum `f4cd18b5ea29fe6bbea62ec9c20d80f0`. - -### Organize the ImageNet Dataset - -You can organize the ImageNet dataset with the following command: +Public datasets can use their default download sources. ImageNet, DOTA, and +Cityscapes require the appropriate source archives or credentials. ```bash -python organize_imagenet.py --image_dir {path_to_ILSVRC2012_img_val.tar} --xml_dir {path_to_ILSVRC2012_bbox_val_v3.tgz} --output_dir ~/.mblt_model_zoo/datasets/imagenet +python benchmark/organize_coco.py +python benchmark/organize_ade20k.py +python benchmark/organize_nyu_depth.py ``` -This will organize the dataset into the following structure: - -```text -~/.mblt_model_zoo/datasets/imagenet/ -โ”œโ”€โ”€ n01440764/ -โ”‚ โ”œโ”€โ”€ ILSVRC2012_val_00000293.JPEG -โ”‚ โ”œโ”€โ”€ ILSVRC2012_val_00002138.JPEG -โ”‚ โ”œโ”€โ”€ ... -โ”œโ”€โ”€ n01443537/ -โ”œโ”€โ”€ n01484850/ -โ”œโ”€โ”€ ... -``` - -### Prepare the Model for ImageNet Benchmark - -You can prepare the model for ImageNet benchmark or simply run the benchmark with the model that is provided in [mblt-model-zoo](../mblt_vision/README.md). - -If you want to try with your own model, refer to the [tutorial guide](https://github.com/mobilint/mblt-sdk-tutorial/) to compile the model with custom quantization recipe. - -### Run the ImageNet Benchmark - -You can run the ImageNetbenchmark with the following command: +For Cityscapes, provide the official archives: ```bash -python benchmark_imagenet.py --local_path {path to local mxq(optional)}\ ---model_type {model type(optional)}\ ---infer_mode {single, multi, global, global4, global8(optional). Default is global}\ ---product {aries, regulus(optional). Default is aries}\ ---batch_size {batch size(optional). Default is 1}\ ---data_path {path to the ImageNet data(optional). Default is ~/.mblt_model_zoo/datasets/imagenet} +python benchmark/organize_cityscapes.py \ + --image-dir path/to/leftImg8bit_trainvaltest.zip \ + --annotation-dir path/to/gtFine_trainvaltest.zip ``` -Example: +## Run a benchmark -```bash -python benchmark_imagenet.py --local_path ./resnet50_IMAGENET1K_V1.mxq --model_type IMAGENET1K_V1 --infer_mode multi --product aries --batch_size 8 --data_path ~/.mblt_model_zoo/datasets/imagenet -``` - -## Benchmark with COCO Dataset - -### Download the COCO Dataset - -To download the COCO dataset, visit the [COCO website](https://cocodataset.org/) and download the dataset with the following steps: - -1. Click on the "Dataset" button on the menu bar, and click "Download" button. -2. On the "Images" column, click "2017 Val images" button to download `val2017.zip` file. -3. On the "Annotations" column, click "2017 Train/Val annotations" button to download `annotations_trainval2017.zip` file. - -### Organize the COCO Dataset - -You can organize the COCO dataset with the following command: +Use the unified runner for every Vision task. It chooses the evaluator from +`--task`, writes JSON/CSV/Markdown artifacts, and can create an accuracy chart. ```bash -python organize_coco.py --image_dir {path_to_val2017.zip} --annotation_dir {path_to_annotations_trainval2017.zip} --output_dir ~/.mblt_model_zoo/datasets/coco -``` - -This will organize the dataset into the following structure: - -```text -~/.mblt_model_zoo/datasets/coco/ -โ”œโ”€โ”€ val2017/ -โ”‚ โ”œโ”€โ”€ 000000000139.jpg -โ”‚ โ”œโ”€โ”€ 000000000285.jpg -โ”‚ โ”œโ”€โ”€ ... -โ”œโ”€โ”€ captions_val2017.json -โ”œโ”€โ”€ instances_val2017.json -โ”œโ”€โ”€ person_keypoints_val2017.json +python benchmark/benchmark_vision_models.py \ + --models ResNet50 \ + --task image_classification \ + --data-path ~/.mblt_vision/datasets/imagenet ``` -### Prepare the Model for COCO Benchmark - -You can prepare the model for COCO benchmark or simply run the benchmark with the model that is provided in [mblt-model-zoo](../mblt_vision/README.md). - -If you want to try with your own model, refer to the [tutorial guide](https://github.com/mobilint/mblt-sdk-tutorial/) to compile the model with custom quantization recipe. - -### Run the COCO Benchmark - -You can run the COCO benchmark with the following command: - -```bash -python benchmark_coco.py --local_path {path to local mxq(optional)}\ ---model_type {model type(optional)}\ ---infer_mode {single, multi, global, global4, global8(optional). Default is global}\ ---product {aries, regulus(optional). Default is aries}\ ---batch_size {batch size(optional). Default is 1}\ ---data_path {path to the COCO data(optional). Default is ~/.mblt_model_zoo/datasets/coco} ---conf_thres {confidence threshold for object detection(optional). Default is 0.001}\ ---iou_thres {IOU threshold for object detection(optional). Default is 0.7} -``` - -Example: - -```bash -python benchmark_coco.py --infer_mode single --product aries --batch_size 8 --data_path ~/.mblt_model_zoo/datasets/coco --conf_thres 0.001 --iou_thres 0.7 -``` - -## Benchmark with WiderFace Dataset - -### Download the WiderFace Dataset - -To download the WiderFace dataset, visit the [WiderFace's Hugging Face page](https://huggingface.co/datasets/CUHK-CSE/wider_face) and download the dataset with the following steps: - -1. Go to the "Files and versions" section, and click "data" folder. -2. Download "WIDER_val.zip" file and "wider_face_split.zip" file. - -### Organize the WiderFace Dataset - -You can organize the WiderFace dataset with the following command: +Use `--framework onnx` for ONNX Runtime, `--core-mode all` to compare supported +MXQ core modes, and `--fail-fast` to stop on the first failed target. Compare +completed runs with: ```bash -python organize_widerface.py --image_dir {path_to_WIDER_val.zip} --annotation_dir {path_to_wider_face_split.zip} --output_dir ~/.mblt_model_zoo/datasets/widerface -``` - -This will organize the dataset into the following structure: - -```text -~/.mblt_model_zoo/datasets/widerface/ -โ”œโ”€โ”€ images/ -โ”‚ โ”œโ”€โ”€ 0--Parade/ -| โ”œโ”€โ”€ 0_Parade_Parade_0_102.jpg -| โ”œโ”€โ”€ 0_Parade_Parade_0_120.jpg -| |... -| โ”œโ”€โ”€ 1--Handshaking/ -| |... -| โ”œโ”€โ”€ ... -โ”œโ”€โ”€ wider_face_val.mat -โ”œโ”€โ”€ wider_face_val_bbx_gt.txt +python benchmark/compare_benchmark_results.py run-a run-b ``` - -### Prepare the Model for WiderFace Benchmark - -You can prepare the model for WiderFace benchmark or simply run the benchmark with the model that is provided in [mblt-model-zoo](../mblt_vision/README.md). - -If you want to try with your own model, refer to the [tutorial guide](https://github.com/mobilint/mblt-sdk-tutorial/) to compile the model with custom quantization recipe. - -### Run the WiderFace Benchmark - -Pending \ No newline at end of file diff --git a/benchmark/__init__.py b/benchmark/__init__.py deleted file mode 100644 index 4e91524..0000000 --- a/benchmark/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Dataset and performance benchmark tools for Mobilint Vision.""" diff --git a/benchmark/benchmark_coco.py b/benchmark/benchmark_coco.py deleted file mode 100644 index 2135a4e..0000000 --- a/benchmark/benchmark_coco.py +++ /dev/null @@ -1,183 +0,0 @@ -""" -Benchmark script for YOLOv5m on COCO dataset. - -This script runs the benchmark for the YOLOv5m model using the COCO dataset. -It initializes the model and evaluates its performance. -""" - -import argparse -import os - -import mblt_vision -from mblt_vision.utils.evaluation import eval_coco - -MODEL_LIST = ( - [ - "YOLO11lSeg", - "YOLO11mSeg", - "YOLO11nSeg", - "YOLO11sSeg", - "YOLO11xSeg", - "YOLO26lSeg", - "YOLO26mSeg", - "YOLO26nSeg", - "YOLO26sSeg", - "YOLO26xSeg", - "YOLOv5lSeg", - "YOLOv5mSeg", - "YOLOv5nSeg", - "YOLOv5sSeg", - "YOLOv5xSeg", - "YOLOv8lSeg", - "YOLOv8mSeg", - "YOLOv8nSeg", - "YOLOv8sSeg", - "YOLOv8xSeg", - "YOLOv9cSeg", - "YOLOv9eSeg", - ] - + [ - "GELANc", - "GELANe", - "GELANm", - "GELANs", - "YOLO11l", - "YOLO11m", - "YOLO11n", - "YOLO11s", - "YOLO11x", - "YOLO12l", - "YOLO12m", - "YOLO12n", - "YOLO12s", - "YOLO12x", - "YOLO26l", - "YOLO26m", - "YOLO26n", - "YOLO26s", - "YOLO26x", - "YOLOv10b", - "YOLOv10l", - "YOLOv10m", - "YOLOv10n", - "YOLOv10s", - "YOLOv10x", - "YOLOv3", - "YOLOv3_spp", - "YOLOv3_sppu", - "YOLOv3_tiny", - "YOLOv3_tinyu", - "YOLOv3u", - "YOLOv5l", - "YOLOv5l6", - "YOLOv5l6u", - "YOLOv5lu", - "YOLOv5m", - "YOLOv5m6", - "YOLOv5m6u", - "YOLOv5mu", - "YOLOv5n", - "YOLOv5n6", - "YOLOv5n6u", - "YOLOv5nu", - "YOLOv5s", - "YOLOv5s6", - "YOLOv5s6u", - "YOLOv5su", - "YOLOv5x", - "YOLOv5x6", - "YOLOv5x6u", - "YOLOv5xu", - "YOLOv7", - "YOLOv7d6", - "YOLOv7e6", - "YOLOv7e6e", - "YOLOv7w6", - "YOLOv7x", - "YOLOv8l", - "YOLOv8m", - "YOLOv8n", - "YOLOv8s", - "YOLOv8x", - "YOLOv9c", - "YOLOv9e", - "YOLOv9m", - "YOLOv9s", - "YOLOv9t", - ] - + [ - "YOLO11lPose", - "YOLO11mPose", - "YOLO11nPose", - "YOLO11sPose", - "YOLO11xPose", - "YOLO26lPose", - "YOLO26mPose", - "YOLO26nPose", - "YOLO26sPose", - "YOLO26xPose", - "YOLOv8lPose", - "YOLOv8mPose", - "YOLOv8nPose", - "YOLOv8sPose", - "YOLOv8xPose", - ] -) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run YOLOv5m Benchmark") - # --- Model Configuration --- - parser.add_argument( - "--model-name", - type=str, - default="YOLOv5m", - choices=MODEL_LIST, - help="Model name", - ) - parser.add_argument( - "--local-path", - type=str, - default=None, - help="Path to the YOLOv5m model file (.mxq)", - ) - parser.add_argument("--model-type", type=str, default="DEFAULT", help="Model type") - parser.add_argument( - "--infer-mode", - type=str, - default="global8", - choices=["single", "multi", "global4", "global8"], - help="Inference mode", - ) - parser.add_argument("--product", type=str, default="aries", help="Product") - # --- Benchmark Configuration --- - parser.add_argument("--batch-size", type=int, default=1, help="Batch size") - parser.add_argument( - "--data-path", - type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/coco"), - help="Path to the COCO data", - ) - parser.add_argument( - "--conf-thres", - type=float, - default=0.001, - help="Confidence threshold for object detection", - ) - parser.add_argument( - "--iou-thres", - type=float, - default=0.7, - help="IOU threshold for object detection", - ) - args = parser.parse_args() - - model_cls = getattr(mblt_vision, args.model_name) - # --- Model Initialization --- - model = model_cls(args.local_path, args.model_type, args.infer_mode, args.product) - - # --- Benchmark Execution --- - acc = eval_coco( - model, args.data_path, args.batch_size, args.conf_thres, args.iou_thres - ) - print(f"COCO evaluation completed. mAP 50:95: {acc:.5f}") diff --git a/benchmark/benchmark_imagenet.py b/benchmark/benchmark_imagenet.py deleted file mode 100644 index dd7dab1..0000000 --- a/benchmark/benchmark_imagenet.py +++ /dev/null @@ -1,137 +0,0 @@ -""" -Benchmark script for ResNet50 on ImageNet dataset. - -This script runs the benchmark for the ResNet50 model using the ImageNet dataset. -It initializes the model and evaluates its performance. -""" - -import argparse -import os - -import mblt_vision -from mblt_vision.utils.evaluation import eval_imagenet - -MODEL_LIST = [ - "AlexNet", - "ConvNeXt_Base", - "ConvNeXt_Large", - "ConvNeXt_Small", - "ConvNeXt_Tiny", - "DeiT3_Base_Patch16_224", - "DeiT3_Base_Patch16_384", - "DeiT3_Large_Patch16_224", - "DeiT3_Large_Patch16_384", - "DeiT3_Medium_Patch16_224", - "DeiT3_Small_Patch16_224", - "DeiT3_Small_Patch16_384", - "DeiT_Base_Patch16_224", - "DeiT_Base_Patch16_384", - "DeiT_Small_Patch16_224", - "DeiT_Tiny_Patch16_224", - "DenseNet121", - "DenseNet169", - "DenseNet201", - "FlexiViT_Base", - "FlexiViT_Large", - "FlexiViT_Small", - "GoogLeNet", - "Inception_V3", - "MNasNet1_0", - "MNasNet1_3", - "MobileNet_V2", - "RegNet_X_16GF", - "RegNet_X_1_6GF", - "RegNet_X_32GF", - "RegNet_X_3_2GF", - "RegNet_X_400MF", - "RegNet_X_800MF", - "RegNet_X_8GF", - "RegNet_Y_16GF", - "RegNet_Y_1_6GF", - "RegNet_Y_32GF", - "RegNet_Y_3_2GF", - "RegNet_Y_400MF", - "RegNet_Y_800MF", - "RegNet_Y_8GF", - "ResNet101", - "ResNet152", - "ResNet18", - "ResNet34", - "ResNet50", - "ResNext101_32x8d", - "ResNext101_64x4d", - "ResNext50_32x4d", - "ShuffleNet_V2_X1_0", - "ShuffleNet_V2_X1_5", - "ShuffleNet_V2_X2_0", - "VGG11", - "VGG11_BN", - "VGG13", - "VGG13_BN", - "VGG16", - "VGG16_BN", - "VGG19", - "VGG19_BN", - "ViT_Base_Patch16_224", - "ViT_Base_Patch16_384", - "ViT_Base_Patch32_224", - "ViT_Base_Patch32_384", - "ViT_Base_Patch8_224", - "ViT_Large_Patch16_224", - "ViT_Large_Patch16_384", - "ViT_Large_Patch32_384", - "ViT_Small_Patch16_224", - "ViT_Small_Patch16_384", - "ViT_Small_Patch32_224", - "ViT_Small_Patch32_384", - "ViT_Tiny_Patch16_224", - "ViT_Tiny_Patch16_384", - "Wide_ResNet101_2", - "Wide_ResNet50_2", -] - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Run ResNet50 Benchmark") - # --- Model Configuration --- - parser.add_argument( - "--model-name", - type=str, - default="ResNet50", - choices=MODEL_LIST, - help="Model name", - ) - parser.add_argument( - "--local-path", - type=str, - default=None, - help="Path to the ResNet50 model file (.mxq)", - ) - parser.add_argument("--model-type", type=str, default="DEFAULT", help="Model type") - parser.add_argument( - "--infer-mode", - type=str, - default="global8", - choices=["single", "multi", "global4", "global8"], - help="Inference mode", - ) - parser.add_argument("--product", type=str, default="aries", help="Product") - # --- Benchmark Configuration --- - parser.add_argument("--batch-size", type=int, default=1, help="Batch size") - parser.add_argument( - "--data-path", - type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/imagenet"), - help="Path to the ImageNet data", - ) - args = parser.parse_args() - model_cls = getattr(mblt_vision, args.model_name) - # --- Model Initialization --- - model = model_cls( - local_path=args.local_path, - model_type=args.model_type, - infer_mode=args.infer_mode, - product=args.product, - ) - - # --- Benchmark Execution --- - eval_imagenet(model, args.data_path, args.batch_size) diff --git a/benchmark/vision/benchmark_vision_models.py b/benchmark/benchmark_vision_models.py similarity index 97% rename from benchmark/vision/benchmark_vision_models.py rename to benchmark/benchmark_vision_models.py index e5eed1b..f62bac3 100644 --- a/benchmark/vision/benchmark_vision_models.py +++ b/benchmark/benchmark_vision_models.py @@ -15,13 +15,13 @@ from typing import Any, Literal, cast # ruff: noqa: E402 -_REPO_ROOT = Path(__file__).resolve().parents[2] +_REPO_ROOT = Path(__file__).resolve().parents[1] if str(_REPO_ROOT) not in sys.path: sys.path.insert(0, str(_REPO_ROOT)) -from benchmark.common.argparse_utils import parse_positive_int -from benchmark.common.io_utils import safe_filename, write_csv, write_json -from benchmark.common.summary_utils import ( +from mblt_vision.benchmark.argparse_utils import parse_positive_int +from mblt_vision.benchmark.io_utils import safe_filename, write_csv, write_json +from mblt_vision.benchmark.summary_utils import ( collect_host_pc_info, markdown_table, write_summary_markdown, @@ -139,7 +139,7 @@ def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: parser.add_argument( "--results-dir", type=Path, - default=Path("benchmark/vision/results"), + default=Path("benchmark/results"), help="Output directory.", ) parser.add_argument( @@ -378,7 +378,7 @@ def _write_outputs( successful = [row for row in rows if row["status"] == "ok"] plot_paths: list[Path] = [] if successful and not args.no_plot: - from benchmark.common.chart_utils import plot_simple_barh + from mblt_vision.benchmark.chart_utils import plot_simple_barh chart_path = results_dir / "accuracy.png" plot_simple_barh( diff --git a/benchmark/common/__init__.py b/benchmark/common/__init__.py deleted file mode 100644 index 40712df..0000000 --- a/benchmark/common/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Shared helpers for Vision benchmark scripts.""" diff --git a/benchmark/vision/compare_benchmark_results.py b/benchmark/compare_benchmark_results.py similarity index 97% rename from benchmark/vision/compare_benchmark_results.py rename to benchmark/compare_benchmark_results.py index 5f5a253..b67d96a 100644 --- a/benchmark/vision/compare_benchmark_results.py +++ b/benchmark/compare_benchmark_results.py @@ -8,11 +8,11 @@ from typing import NamedTuple, Sequence # ruff: noqa: E402 -_REPO_ROOT = Path(__file__).resolve().parents[2] +_REPO_ROOT = Path(__file__).resolve().parents[1] if str(_REPO_ROOT) not in sys.path: sys.path.insert(0, str(_REPO_ROOT)) -from benchmark.common.summary_utils import read_csv_rows +from mblt_vision.benchmark.summary_utils import read_csv_rows class BenchmarkScore(NamedTuple): @@ -143,7 +143,7 @@ def main(argv: Sequence[str] | None = None) -> int: f"Inputs contain incompatible benchmark metrics: {', '.join(sorted(metric_names))}." ) metric_name = next(iter(metric_names)) - from benchmark.common.chart_utils import ( + from mblt_vision.benchmark.chart_utils import ( default_charts_dir, plot_grouped_scalar_barh, source_labels, diff --git a/benchmark/vision/organize_ade20k.py b/benchmark/organize_ade20k.py similarity index 100% rename from benchmark/vision/organize_ade20k.py rename to benchmark/organize_ade20k.py diff --git a/benchmark/vision/organize_cityscapes.py b/benchmark/organize_cityscapes.py similarity index 100% rename from benchmark/vision/organize_cityscapes.py rename to benchmark/organize_cityscapes.py diff --git a/benchmark/organize_coco.py b/benchmark/organize_coco.py index 28e52cf..abe1ab7 100644 --- a/benchmark/organize_coco.py +++ b/benchmark/organize_coco.py @@ -1,7 +1,7 @@ """ Script to organize the COCO dataset. -This script takes the raw image and annotation zip files for the COCO dataset +This script takes local archives or downloadable sources for the COCO dataset and organizes them into a structure suitable for the model zoo. """ @@ -10,27 +10,35 @@ from mblt_vision.utils.datasets import organize_coco +DEFAULT_COCO_IMAGE_SOURCE = "http://images.cocodataset.org/zips/val2017.zip" +DEFAULT_COCO_ANNOTATION_SOURCE = ( + "http://images.cocodataset.org/annotations/annotations_trainval2017.zip" +) + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Organize COCO dataset") parser.add_argument( - "--image-dir", type=str, required=True, help="Path to the image zip file" + "--image-dir", + type=str, + default=DEFAULT_COCO_IMAGE_SOURCE, + help="Local path or download URL for the image zip file", ) parser.add_argument( - "--annotation-dir", + "--ann-dir", type=str, - required=True, - help="Path to the annotation zip file", + default=DEFAULT_COCO_ANNOTATION_SOURCE, + help="Local path or download URL for the annotation zip file", ) parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/coco"), + default=os.path.expanduser("~/.mblt_vision/datasets/coco"), help="Path to the directory to save the organized dataset", ) args = parser.parse_args() organize_coco( image_dir=args.image_dir, - annotation_dir=args.annotation_dir, + annotation_dir=args.ann_dir, output_dir=args.output_dir, ) diff --git a/benchmark/vision/organize_dotav1.py b/benchmark/organize_dotav1.py similarity index 100% rename from benchmark/vision/organize_dotav1.py rename to benchmark/organize_dotav1.py diff --git a/benchmark/organize_imagenet.py b/benchmark/organize_imagenet.py index a946773..8e7c001 100644 --- a/benchmark/organize_imagenet.py +++ b/benchmark/organize_imagenet.py @@ -1,8 +1,8 @@ """ Script to organize the ImageNet dataset. -This script takes the raw image tar file and xml tgz file for the ImageNet dataset -and organizes them into a structure suitable for the model zoo. +This script takes local archives or downloadable sources for the ImageNet +dataset and organizes them into a structure suitable for the model zoo. """ import argparse @@ -10,18 +10,30 @@ from mblt_vision.utils.datasets import organize_imagenet +DEFAULT_IMAGENET_IMAGE_SOURCE = ( + "https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar" +) +DEFAULT_IMAGENET_XML_SOURCE = ( + "https://www.image-net.org/data/ILSVRC/2012/ILSVRC2012_bbox_val_v3.tgz" +) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Organize ImageNet dataset") parser.add_argument( - "--image-dir", type=str, required=True, help="Path to the image tar file" + "--image-dir", + type=str, + default=DEFAULT_IMAGENET_IMAGE_SOURCE, + help="Local path or download URL for the image tar file", ) parser.add_argument( - "--xml-dir", type=str, required=True, help="Path to the xml tgz file" + "--xml-dir", + type=str, + default=DEFAULT_IMAGENET_XML_SOURCE, + help="Local path or download URL for the XML tgz file", ) parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/imagenet"), + default=os.path.expanduser("~/.mblt_vision/datasets/imagenet"), help="Path to the directory to save the organized dataset", ) args = parser.parse_args() diff --git a/benchmark/vision/organize_nyu_depth.py b/benchmark/organize_nyu_depth.py similarity index 100% rename from benchmark/vision/organize_nyu_depth.py rename to benchmark/organize_nyu_depth.py diff --git a/benchmark/organize_widerface.py b/benchmark/organize_widerface.py index 617d5be..b44e369 100644 --- a/benchmark/organize_widerface.py +++ b/benchmark/organize_widerface.py @@ -1,8 +1,8 @@ """ Script to organize the WiderFace dataset. -This script takes the raw image and annotation zip files for the WiderFace dataset -and organizes them into a structure suitable for the model zoo. +This script takes local archives or downloadable sources for the WiderFace +dataset and organizes them into a structure suitable for the model zoo. """ import argparse @@ -10,21 +10,27 @@ from mblt_vision.utils.datasets import organize_widerface +DEFAULT_WIDERFACE_IMAGE_SOURCE = "https://huggingface.co/datasets/CUHK-CSE/wider_face/resolve/main/data/WIDER_val.zip" +DEFAULT_WIDERFACE_ANNOTATION_SOURCE = "https://huggingface.co/datasets/CUHK-CSE/wider_face/resolve/main/data/wider_face_split.zip" + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Organize WiderFace dataset") parser.add_argument( - "--image-dir", type=str, required=True, help="Path to the image zip file" + "--image-dir", + type=str, + default=DEFAULT_WIDERFACE_IMAGE_SOURCE, + help="Local path or download URL for the image zip file", ) parser.add_argument( "--annotation-dir", type=str, - required=True, - help="Path to the annotation zip file", + default=DEFAULT_WIDERFACE_ANNOTATION_SOURCE, + help="Local path or download URL for the annotation zip file", ) parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/widerface"), + default=os.path.expanduser("~/.mblt_vision/datasets/widerface"), help="Path to the directory to save the organized dataset", ) args = parser.parse_args() diff --git a/benchmark/vision/README.md b/benchmark/vision/README.md deleted file mode 100644 index 3d7ef4d..0000000 --- a/benchmark/vision/README.md +++ /dev/null @@ -1,62 +0,0 @@ -# Vision datasets and benchmarks - -`mblt-vision-python` owns the utilities that organize validation datasets and -evaluate Vision models. The standardized benchmark runner writes JSON, CSV, -Markdown, and optional chart artifacts for one or more models. - -Run scripts from the repository root, or invoke them as modules after installing -the package from source. - -## Dataset organization - -Most organizers can download their public source archives when paths are omitted; -pass local paths to reuse existing downloads. Organized datasets default to -`~/.mblt_vision/datasets`. - -```bash -python benchmark/vision/organize_coco.py -python benchmark/vision/organize_ade20k.py -python benchmark/vision/organize_nyu_depth.py -``` - -Cityscapes is not downloaded automatically. Supply the official archives: - -```bash -python benchmark/vision/organize_cityscapes.py \ - --image-dir benchmark/leftImg8bit_trainvaltest.zip \ - --annotation-dir benchmark/gtFine_trainvaltest.zip -``` - -The Cityscapes organizer selects only the 500 validation RGB images and their -`gtFine_labelIds` masks, installing them as `images/` and `annotations/` under the -chosen output directory. - -ImageNet and DOTA require their source archives or directories because their terms -of use may restrict automatic retrieval. See each organizer's `--help` output for -required arguments. - -## Running a benchmark - -```bash -python benchmark/vision/benchmark_vision_models.py \ - --models ResNet50 \ - --task image_classification \ - --data-path ~/.mblt_vision/datasets/imagenet -``` - -Use `--framework onnx` (or an `.onnx` `--model-path`) for ONNX Runtime. Use -`--core-mode all` to compare NPU core modes. The runner keeps evaluating remaining -models after an individual failure unless `--fail-fast` is set. - -Compare two or more benchmark result directories with: - -```bash -python benchmark/vision/compare_benchmark_results.py run-a run-b -``` - -## Verification - -The focused tests cover archive download recovery, safe staging and replacement, -dataset readiness, evaluator dispatch, standardized benchmark artifacts, and -comparison validation. Hardware-backed runs require a configured Mobilint NPU; -ONNX benchmarks require the relevant optional ONNX Runtime extra. diff --git a/benchmark/vision/__init__.py b/benchmark/vision/__init__.py deleted file mode 100644 index 1adafd3..0000000 --- a/benchmark/vision/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Vision dataset organization and benchmark command-line tools.""" diff --git a/benchmark/vision/organize_coco.py b/benchmark/vision/organize_coco.py deleted file mode 100644 index abe1ab7..0000000 --- a/benchmark/vision/organize_coco.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Script to organize the COCO dataset. - -This script takes local archives or downloadable sources for the COCO dataset -and organizes them into a structure suitable for the model zoo. -""" - -import argparse -import os - -from mblt_vision.utils.datasets import organize_coco - -DEFAULT_COCO_IMAGE_SOURCE = "http://images.cocodataset.org/zips/val2017.zip" -DEFAULT_COCO_ANNOTATION_SOURCE = ( - "http://images.cocodataset.org/annotations/annotations_trainval2017.zip" -) - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Organize COCO dataset") - parser.add_argument( - "--image-dir", - type=str, - default=DEFAULT_COCO_IMAGE_SOURCE, - help="Local path or download URL for the image zip file", - ) - parser.add_argument( - "--ann-dir", - type=str, - default=DEFAULT_COCO_ANNOTATION_SOURCE, - help="Local path or download URL for the annotation zip file", - ) - parser.add_argument( - "--output-dir", - type=str, - default=os.path.expanduser("~/.mblt_vision/datasets/coco"), - help="Path to the directory to save the organized dataset", - ) - args = parser.parse_args() - - organize_coco( - image_dir=args.image_dir, - annotation_dir=args.ann_dir, - output_dir=args.output_dir, - ) diff --git a/benchmark/vision/organize_imagenet.py b/benchmark/vision/organize_imagenet.py deleted file mode 100644 index 8e7c001..0000000 --- a/benchmark/vision/organize_imagenet.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Script to organize the ImageNet dataset. - -This script takes local archives or downloadable sources for the ImageNet -dataset and organizes them into a structure suitable for the model zoo. -""" - -import argparse -import os - -from mblt_vision.utils.datasets import organize_imagenet - -DEFAULT_IMAGENET_IMAGE_SOURCE = ( - "https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar" -) -DEFAULT_IMAGENET_XML_SOURCE = ( - "https://www.image-net.org/data/ILSVRC/2012/ILSVRC2012_bbox_val_v3.tgz" -) -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Organize ImageNet dataset") - parser.add_argument( - "--image-dir", - type=str, - default=DEFAULT_IMAGENET_IMAGE_SOURCE, - help="Local path or download URL for the image tar file", - ) - parser.add_argument( - "--xml-dir", - type=str, - default=DEFAULT_IMAGENET_XML_SOURCE, - help="Local path or download URL for the XML tgz file", - ) - parser.add_argument( - "--output-dir", - type=str, - default=os.path.expanduser("~/.mblt_vision/datasets/imagenet"), - help="Path to the directory to save the organized dataset", - ) - args = parser.parse_args() - - organize_imagenet( - image_dir=args.image_dir, - xml_dir=args.xml_dir, - output_dir=args.output_dir, - ) diff --git a/benchmark/vision/organize_widerface.py b/benchmark/vision/organize_widerface.py deleted file mode 100644 index b44e369..0000000 --- a/benchmark/vision/organize_widerface.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Script to organize the WiderFace dataset. - -This script takes local archives or downloadable sources for the WiderFace -dataset and organizes them into a structure suitable for the model zoo. -""" - -import argparse -import os - -from mblt_vision.utils.datasets import organize_widerface - -DEFAULT_WIDERFACE_IMAGE_SOURCE = "https://huggingface.co/datasets/CUHK-CSE/wider_face/resolve/main/data/WIDER_val.zip" -DEFAULT_WIDERFACE_ANNOTATION_SOURCE = "https://huggingface.co/datasets/CUHK-CSE/wider_face/resolve/main/data/wider_face_split.zip" - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Organize WiderFace dataset") - parser.add_argument( - "--image-dir", - type=str, - default=DEFAULT_WIDERFACE_IMAGE_SOURCE, - help="Local path or download URL for the image zip file", - ) - parser.add_argument( - "--annotation-dir", - type=str, - default=DEFAULT_WIDERFACE_ANNOTATION_SOURCE, - help="Local path or download URL for the annotation zip file", - ) - parser.add_argument( - "--output-dir", - type=str, - default=os.path.expanduser("~/.mblt_vision/datasets/widerface"), - help="Path to the directory to save the organized dataset", - ) - args = parser.parse_args() - - organize_widerface( - image_dir=args.image_dir, - annotation_dir=args.annotation_dir, - output_dir=args.output_dir, - ) diff --git a/compile/vision/README.md b/compile/README.md similarity index 81% rename from compile/vision/README.md rename to compile/README.md index cd47ec6..6272f73 100644 --- a/compile/vision/README.md +++ b/compile/README.md @@ -1,7 +1,7 @@ # Vision Model Compilation The installable vision compiler uses each model's packaged YAML configuration and -`MBLT_Engine.preprocess` implementation to compile its ONNX artifact for ARIES. +`MBLT_Engine.preprocess` implementation to compile its ONNX artifact for a selected board. ## Installation @@ -15,7 +15,7 @@ pip install -e ".[qbcompiler]" available from the public Python Package Index. Obtain access to the compiler package before using this extra. -qbcompiler is loaded only when `compile_vision_model()` or `mblt-vision-python compile` begins an +qbcompiler is loaded only when `compile_vision_model()` or `mblt-vision compile` begins an actual compilation request. Importing the base package, vision APIs, this compilation module, or the main CLI does not import qbcompiler. If qbcompiler is unavailable, only the compilation request fails with an installation message; non-compile APIs and CLI commands remain usable. @@ -37,7 +37,7 @@ print(output_path) ## CLI ```bash -mblt-vision-python compile \ +mblt-vision compile \ --model-cls alexnet \ --target-device aries-rb \ --data-path ~/.mblt_vision/datasets/imagenet \ @@ -48,8 +48,8 @@ Start from an existing sampled image subset or a ready calibration tensor direct stages have already been completed: ```bash -mblt-vision-python compile --model-cls alexnet --target-device aries-rb --subset-path ./sampled-images -mblt-vision-python compile --model-cls alexnet --target-device aries-rb --calib-data-path ./preprocessed-npy +mblt-vision compile --model-cls alexnet --target-device aries-rb --subset-path ./sampled-images +mblt-vision compile --model-cls alexnet --target-device aries-rb --calib-data-path ./preprocessed-npy ``` Use `--model-type` for a non-default YAML variant and `--model-path` or `--onnx-path` to prefer a @@ -105,7 +105,7 @@ NumPy arrays live only in temporary directories and are removed after compilatio compilation fails. The compiler uses explicit `--percentile` and `--topk-ratio` values first. Missing values are read -independently from `aries/best_result.json`; if optional hosted values are unavailable, defaults of +independently from `/best_result.json`; if optional hosted values are unavailable, defaults of `0.9999` and `0.01` are used with a warning. ## Compatibility Scripts @@ -113,12 +113,12 @@ independently from `aries/best_result.json`; if optional hosted values are unava The standalone scripts remain available: ```bash -python compile/vision/vision_model_compile.py --model-cls alexnet -python compile/vision/make_imagenet_subset.py --output-dir ./imagenet-calibration -python compile/vision/make_coco_subset.py --output-dir ./coco-calibration -python compile/vision/make_dotav1_subset.py --output-dir ./dotav1-calibration -python compile/vision/make_widerface_subset.py --output-dir ./widerface-calibration -python compile/vision/make_nyu_depth_subset.py --output-dir ./nyu-depth-calibration -python compile/vision/make_ade20k_subset.py --output-dir ./ade20k-calibration -python compile/vision/make_cityscapes_subset.py --output-dir ./cityscapes-calibration +python compile/vision_model_compile.py --model-cls alexnet --target-device aries-rb +python compile/make_imagenet_subset.py --output-dir ./imagenet-calibration +python compile/make_coco_subset.py --output-dir ./coco-calibration +python compile/make_dotav1_subset.py --output-dir ./dotav1-calibration +python compile/make_widerface_subset.py --output-dir ./widerface-calibration +python compile/make_nyu_depth_subset.py --output-dir ./nyu-depth-calibration +python compile/make_ade20k_subset.py --output-dir ./ade20k-calibration +python compile/make_cityscapes_subset.py --output-dir ./cityscapes-calibration ``` diff --git a/compile/vision/make_ade20k_subset.py b/compile/make_ade20k_subset.py similarity index 100% rename from compile/vision/make_ade20k_subset.py rename to compile/make_ade20k_subset.py diff --git a/compile/vision/make_cityscapes_subset.py b/compile/make_cityscapes_subset.py similarity index 100% rename from compile/vision/make_cityscapes_subset.py rename to compile/make_cityscapes_subset.py diff --git a/compile/vision/make_coco_subset.py b/compile/make_coco_subset.py similarity index 100% rename from compile/vision/make_coco_subset.py rename to compile/make_coco_subset.py diff --git a/compile/vision/make_dotav1_subset.py b/compile/make_dotav1_subset.py similarity index 100% rename from compile/vision/make_dotav1_subset.py rename to compile/make_dotav1_subset.py diff --git a/compile/vision/make_imagenet_subset.py b/compile/make_imagenet_subset.py similarity index 100% rename from compile/vision/make_imagenet_subset.py rename to compile/make_imagenet_subset.py diff --git a/compile/vision/make_nyu_depth_subset.py b/compile/make_nyu_depth_subset.py similarity index 100% rename from compile/vision/make_nyu_depth_subset.py rename to compile/make_nyu_depth_subset.py diff --git a/compile/vision/make_widerface_subset.py b/compile/make_widerface_subset.py similarity index 100% rename from compile/vision/make_widerface_subset.py rename to compile/make_widerface_subset.py diff --git a/compile/vision/vision_model_compile.py b/compile/vision_model_compile.py similarity index 91% rename from compile/vision/vision_model_compile.py rename to compile/vision_model_compile.py index cfea216..0acb382 100644 --- a/compile/vision/vision_model_compile.py +++ b/compile/vision_model_compile.py @@ -23,6 +23,12 @@ def build_parser() -> argparse.ArgumentParser: default="DEFAULT", help="Model variant from the YAML configuration.", ) + parser.add_argument( + "--target-device", + default="aries-rb", + choices=["aries-rb", "regulus-ra", "regulus-rb"], + help="Board target for the compiled MXQ artifact.", + ) parser.add_argument( "--model-path", "--onnx-path", @@ -66,6 +72,7 @@ def main() -> None: output_path = compile_vision_model( model_cls=args.model_cls, model_type=args.model_type, + target_device=args.target_device, model_path=args.model_path, data_path=args.data_path, subset_path=args.subset_path, diff --git a/mblt_vision/benchmark/__init__.py b/mblt_vision/benchmark/__init__.py new file mode 100644 index 0000000..b7039b2 --- /dev/null +++ b/mblt_vision/benchmark/__init__.py @@ -0,0 +1 @@ +"""Reusable benchmark reporting and command support for Mobilint Vision.""" diff --git a/benchmark/common/argparse_utils.py b/mblt_vision/benchmark/argparse_utils.py similarity index 100% rename from benchmark/common/argparse_utils.py rename to mblt_vision/benchmark/argparse_utils.py diff --git a/benchmark/common/chart_utils.py b/mblt_vision/benchmark/chart_utils.py similarity index 100% rename from benchmark/common/chart_utils.py rename to mblt_vision/benchmark/chart_utils.py diff --git a/benchmark/common/io_utils.py b/mblt_vision/benchmark/io_utils.py similarity index 100% rename from benchmark/common/io_utils.py rename to mblt_vision/benchmark/io_utils.py diff --git a/benchmark/common/summary_utils.py b/mblt_vision/benchmark/summary_utils.py similarity index 100% rename from benchmark/common/summary_utils.py rename to mblt_vision/benchmark/summary_utils.py diff --git a/mblt_vision/cli/_vision.py b/mblt_vision/cli/_vision.py index 79f0180..096a2ef 100644 --- a/mblt_vision/cli/_vision.py +++ b/mblt_vision/cli/_vision.py @@ -187,11 +187,18 @@ def add_vision_parser( command: str, help_text: str, handler: Any, - aliases: list[str] | None = None, + description: str | None = None, + epilog: str | None = None, ) -> argparse.ArgumentParser: """Creates a vision command parser with common arguments.""" - parser = subparsers.add_parser(command, aliases=aliases or [], help=help_text) + parser = subparsers.add_parser( + command, + help=help_text, + description=description, + epilog=epilog, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) parser.set_defaults(_handler=handler) add_common_vision_args(parser) return parser diff --git a/mblt_vision/cli/predict.py b/mblt_vision/cli/predict.py index e142c8e..739900d 100644 --- a/mblt_vision/cli/predict.py +++ b/mblt_vision/cli/predict.py @@ -27,11 +27,30 @@ def add_predict_parser( parser = add_vision_parser( subparsers, command="predict", - aliases=["classify", "detect", "pose", "segment"], help_text=( "Run vision inference for classification, depth estimation, detection, instance or semantic " "segmentation, OBB, pose, and face detection." ), + description=( + "Run a configured Vision model on one image. The selected model determines the task, " + "preprocessing, postprocessing, and output visualization automatically." + ), + epilog="""Supported tasks: + image classification, depth estimation, object and face detection, instance + and semantic segmentation, oriented bounding boxes (OBB), and pose estimation. + +The command downloads the default MXQ artifact when no local model path is supplied, +then writes a plotted result under runs/vision/predict/ by default. Use --output to +choose the image destination. Use --framework onnx with --model-path or --onnx-path +for ONNX Runtime inference; MXQ is the default framework. + +Examples: + mblt-vision predict --source image.jpg --model resnet50 --topk 3 + mblt-vision predict --source image.jpg --model yolo11m --conf-thres 0.4 --output result.jpg + mblt-vision predict --source image.jpg --model yolo11m --framework onnx + mblt-vision predict --source image.jpg --model yolo11m-pose --target-device regulus-ra --core-mode single + +For export-style YOLO output, use --e2e false and optionally save it with --raw-output.""", handler=_cmd_predict, ) parser.add_argument( diff --git a/mblt_vision/utils/evaluation/eval_ade20k.py b/mblt_vision/utils/evaluation/eval_ade20k.py index 7e97237..8dfc404 100644 --- a/mblt_vision/utils/evaluation/eval_ade20k.py +++ b/mblt_vision/utils/evaluation/eval_ade20k.py @@ -84,7 +84,7 @@ def update(self, prediction: np.ndarray, target: np.ndarray) -> None: ) invalid_prediction = valid_target & ~valid_prediction if invalid_prediction.any(): - invalid_values = np.unique(prediction[invalid_prediction]) + invalid_values = np.asarray(np.unique(prediction[invalid_prediction])) raise ValueError( f"Semantic predictions at valid target pixels must be finite class IDs in [0, {self.nc - 1}], " f"got {invalid_values.tolist()}." diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 1e59b45..31487a0 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -19,7 +19,7 @@ from huggingface_hub.errors import EntryNotFoundError from qbruntime import Cluster, CoreId -from mblt_npu import MobilintNPUBackend, normalize_target_device +from mblt_npu import MobilintNPUBackend, ONNXBackend, normalize_target_device from ._model_paths import resolve_framework as _resolve_framework from ._model_paths import split_model_paths as _split_model_paths from ._model_paths import ( @@ -445,6 +445,7 @@ def __init__( self.model: Any self._mxq_model: MobilintNPUBackend | None = None + self._onnx_model: ONNXBackend | None = None self._onnx_session: Any = None if self.framework == "onnx": @@ -458,19 +459,23 @@ def __init__( raise FileNotFoundError(f"ONNX file not found at: {resolved_onnx_path}") providers = _resolve_onnx_providers(ort, onnx_providers) - self._onnx_session = ort.InferenceSession( - resolved_onnx_path, providers=providers + onnx_model = ONNXBackend( + resolved_onnx_path, providers=providers, ort_module=ort ) + onnx_model.create() + self._onnx_model = onnx_model + self._onnx_session = onnx_model.session self.model = self._onnx_session self.input_name = self._onnx_session.get_inputs()[0].name self.output_names = [o.name for o in self._onnx_session.get_outputs()] else: - self._mxq_model = MobilintNPUBackend(**self._mxq_backend_kwargs()) - self.model = self._mxq_model - self._mxq_model.create() - self._mxq_model.launch() + mxq_model = MobilintNPUBackend(**self._mxq_backend_kwargs()) + self._mxq_model = mxq_model + self.model = mxq_model + mxq_model.create() + mxq_model.launch() - if self._mxq_model.get_dtype() == "DataType.Uint8": + if mxq_model.get_dtype() == "DataType.Uint8": self.pre_cfg.pop("Normalize", None) self.preprocessor = build_preprocess(self.pre_cfg) @@ -906,6 +911,8 @@ def dispose(self) -> None: """Disposes the underlying model.""" if self.framework == "mxq": self._require_mxq_model().dispose() + elif self._onnx_model is not None: + self._onnx_model.dispose() def model_name_aliasing(self, model_name: str) -> str: """Finds the YAML config filename that matches the given model name. diff --git a/tests/test_benchmark_vision.py b/tests/test_benchmark_vision.py index 8e93a3f..e114fad 100644 --- a/tests/test_benchmark_vision.py +++ b/tests/test_benchmark_vision.py @@ -8,7 +8,7 @@ import pytest -from benchmark.vision import benchmark_vision_models, compare_benchmark_results +from benchmark import benchmark_vision_models, compare_benchmark_results from mblt_vision.utils.evaluation import ( DOTAResult, ImageNetResult, @@ -288,7 +288,7 @@ def test_comparison_uses_result_directory_names( "model,core_mode,task,status,score_name,score\nmodel-a,global8,object_detection,ok,map50_95,0.5\n", encoding="utf-8", ) - import benchmark.common.chart_utils as chart_utils + import mblt_vision.benchmark.chart_utils as chart_utils captured: dict[str, object] = {} diff --git a/tests/test_cli.py b/tests/test_cli.py index 5de8958..f112ee4 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -2,6 +2,8 @@ from __future__ import annotations +import pytest + from mblt_vision.cli import build_parser from mblt_vision.cli.compile import _run_compile from mblt_vision.cli.predict import _cmd_predict @@ -47,3 +49,25 @@ def test_vision_cli_uses_single_core_mode_by_default_on_regulus() -> None: assert args.target_device == "regulus-ra" assert args.core_mode is None + + +@pytest.mark.parametrize("removed_alias", ["classify", "detect", "pose", "segment"]) +def test_predict_command_has_no_task_aliases(removed_alias: str) -> None: + """Keep prediction discoverable through one task-agnostic command.""" + + with pytest.raises(SystemExit): + build_parser().parse_args([removed_alias]) + + +def test_predict_help_explains_supported_workflows( + capsys: pytest.CaptureFixture[str], +) -> None: + """Describe tasks, output behavior, framework choice, and examples in CLI help.""" + + with pytest.raises(SystemExit, match="0"): + build_parser().parse_args(["predict", "--help"]) + help_text = capsys.readouterr().out + + assert "Supported tasks:" in help_text + assert "--framework onnx" in help_text + assert "--target-device regulus-ra" in help_text From b2de8f2f831c9f3b91326a4a6cd4133e2ad060e1 Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 11 Aug 2026 12:13:44 +0900 Subject: [PATCH 09/48] feat: Update documentation for benchmark and compilation tooling, including organization and synchronization guidelines --- .agents/skills/mblt-vision-readme/SKILL.md | 4 +++- .agents/skills/mblt-vision/SKILL.md | 16 ++++++++++++++-- AGENTS.md | 16 +++++++++++++++- 3 files changed, 32 insertions(+), 4 deletions(-) diff --git a/.agents/skills/mblt-vision-readme/SKILL.md b/.agents/skills/mblt-vision-readme/SKILL.md index 5445395..2970e51 100644 --- a/.agents/skills/mblt-vision-readme/SKILL.md +++ b/.agents/skills/mblt-vision-readme/SKILL.md @@ -13,6 +13,8 @@ description: >- mblt_vision/README.md. - Keep the detailed Vision API reference in mblt_vision/README.md. It owns Python construction, framework selection, model discovery, model-family tables, output taxonomy, and migration notes. +- Keep development-tool instructions in `benchmark/README.md` and `compile/README.md`; their + command scripts live directly in those directories. - Document Model Zoo compatibility as migration context only. Do not present Model Zoo CLI, validation, dataset organization, or compilation commands as features of this package. @@ -35,5 +37,5 @@ description: >- - Prefer generated discovery examples such as list_tasks() and list_models() over manually maintained exhaustive name lists. - When changing models, package metadata, dependencies, public APIs, or runtime behavior, update - the relevant README and this guidance if the workflow changes. + the relevant README, `AGENTS.md`, and the canonical `mblt-vision` skill if the workflow changes. - For documentation-only updates, run git diff --check and verify relative links and headings. diff --git a/.agents/skills/mblt-vision/SKILL.md b/.agents/skills/mblt-vision/SKILL.md index 509d53a..63b5245 100644 --- a/.agents/skills/mblt-vision/SKILL.md +++ b/.agents/skills/mblt-vision/SKILL.md @@ -45,13 +45,25 @@ description: >- ## Runtime and Packaging - Route NPU runtime access through mblt-npu-python; do not copy backend classes into Vision. -- Keep ONNX Runtime optional and lazy-imported. Raise a specific installation error when it is - requested but unavailable. +- Use the shared `ONNXBackend` for ONNX inference. Keep ONNX Runtime optional and lazy-imported; + raise a specific installation error when it is requested but unavailable. +- Normalize legacy `aries` and `regulus` target values through mblt-npu-python. MXQ artifacts and + compilation metadata must resolve only from the selected board folder, never a core-mode path or + a fallback board folder. - Include model and dataset YAML files as package data. Build a wheel and inspect it after changing metadata or assets. - Do not require native bindings, GStreamer, hardware, downloaded models, or caches for normal imports and unit tests. +## Tooling Layout and Documentation + +- Keep all executable benchmark scripts directly in `benchmark/`; reusable reporting helpers belong + in `mblt_vision.benchmark`. +- Keep all executable compile scripts and the compile guide directly in `compile/`. +- For every significant package change (public API, CLI, runtime/dependency, artifact layout, or + tooling structure), update `AGENTS.md`, this canonical skill, the Claude skill entry point when + its workflow changes, and the relevant README in the same change. + ## Validate Proportionately - Begin with the smallest relevant test file or -k selection. diff --git a/AGENTS.md b/AGENTS.md index 30e45a3..69fa1e5 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -91,6 +91,18 @@ The current ownership boundary is deliberate: `mblt-vision` interface, and preserve the Python public API, exceptions, result values, layouts, and lifecycle behavior. Add native capability/version checks at that time. +## Benchmark and Compilation Tooling + +- Keep executable benchmark organizers, the unified benchmark runner, and result comparison scripts + directly under `benchmark/`. Put reusable benchmark reporting helpers in `mblt_vision.benchmark`. +- Keep executable compilation helpers and their guide directly under `compile/`. Do not recreate a + Vision-only subdirectory under either tooling root. +- Benchmark and compilation commands are development tools; do not package them as public CLI + entry points without an explicit product decision. The supported end-user command is + `mblt-vision`. +- Compile and artifact resolution must use normalized board identifiers (`aries-rb`, `regulus-ra`, + or `regulus-rb`) and must not fall back to a different board folder. + ## PyPI and Wheel Packaging - `pyproject.toml` is the source of truth for Python metadata, supported Python versions, @@ -135,7 +147,9 @@ The current ownership boundary is deliberate: language-tagged code fences, and concise paragraphs. Keep examples executable against the public mblt_vision namespace and do not document Model Zoo CLI commands as standalone features. - When a durable public fact changes, update this guide, the matching agent skill, CLAUDE.md, and - the relevant README in the same change. + the relevant README in the same change. Treat a significant package changeโ€”public API, + dependency/runtime, artifact layout, CLI, or tooling structureโ€”as a required guide-and-skill + synchronization point. - For documentation-only changes, run `git diff --check` and verify headings and links. Report skipped platform, hardware, or native-runtime checks clearly. From 424c0be04be41703684c5acc0419e3a52372c34e Mon Sep 17 00:00:00 2001 From: jinman Date: Wed, 12 Aug 2026 12:38:52 +0900 Subject: [PATCH 10/48] feat: Enhance mask processing with ROI prototype masking and add corresponding tests --- mblt_vision/utils/postprocess/common.py | 112 +++++++++++++++++++++++- tests/test_wrapper.py | 33 ++++++- 2 files changed, 142 insertions(+), 3 deletions(-) diff --git a/mblt_vision/utils/postprocess/common.py b/mblt_vision/utils/postprocess/common.py index 9cee0e1..c498a02 100644 --- a/mblt_vision/utils/postprocess/common.py +++ b/mblt_vision/utils/postprocess/common.py @@ -975,13 +975,123 @@ def process_mask_upsample( Returns: torch.Tensor: Upsampled and thresholded binary masks. """ + target_shape = (int(shape[0]), int(shape[1])) c, mh, mw = protos.shape # CHW + + # Evaluate only the prototype pixels that contribute to each retained ROI. + # The ROI interpolation preserves the original global bilinear sampling + # coordinates, so it produces the same binary mask as the full-mask path. + if _use_roi_prototype_masks(masks_in, bboxes, c, mh, mw, target_shape): + return _process_mask_upsample_roi(protos, masks_in, bboxes, target_shape) + masks = (masks_in @ protos.float().view(c, -1)).view(-1, mh, mw) # n, CHW - masks = scale_masks(masks, (shape[0], shape[1])) # CHW + masks = scale_masks(masks, target_shape) # CHW masks = crop_mask(masks, bboxes) # CHW return masks.gt_(0.0) +def _use_roi_prototype_masks( + masks_in: torch.Tensor, + bboxes: torch.Tensor, + channels: int, + proto_h: int, + proto_w: int, + shape: tuple[int, int], +) -> bool: + """Return whether exact low-resolution ROI masking is expected to be cheaper.""" + count = masks_in.shape[0] + if bboxes.numel() == 0: + return False + height, width = shape + clipped = bboxes[:, :4].clone() + clipped[:, 0::2].clamp_(0, width) + clipped[:, 1::2].clamp_(0, height) + roi_pixels = ( + (clipped[:, 2] - clipped[:, 0]).clamp_min_(0).ceil() + * (clipped[:, 3] - clipped[:, 1]).clamp_min_(0).ceil() + ).sum() + # This is a conservative upper-bound for ROI work: the actual dot product + # runs at prototype resolution, while interpolation touches only ROI pixels. + full_work = count * (channels * proto_h * proto_w + height * width) + roi_work = channels * height * width + channels * roi_pixels + return bool(roi_work < full_work) + + +def _process_mask_upsample_roi( + protos: torch.Tensor, + masks_in: torch.Tensor, + bboxes: torch.Tensor, + shape: tuple[int, int], +) -> torch.Tensor: + """Create exact cropped masks from low-resolution prototype ROIs. + + The interpolation grid uses global ``align_corners=False`` coordinates. + Therefore every target pixel samples the same prototype neighborhood as + ``scale_masks(coefficients @ protos)`` without evaluating pixels outside + its bounding box. + """ + protos = protos.float() + channels, proto_h, proto_w = protos.shape + height, width = shape + top, left, bottom, right = _mask_scale_crop_bounds((proto_h, proto_w), shape) + crop_h, crop_w = bottom - top, right - left + masks = torch.zeros( + (masks_in.shape[0], height, width), dtype=torch.float32, device=protos.device + ) + boxes = bboxes.to(protos.device) + for index, box in enumerate(boxes): + x1 = max(0, min(width, math.ceil(float(box[0])))) + y1 = max(0, min(height, math.ceil(float(box[1])))) + x2 = max(0, min(width, math.ceil(float(box[2])))) + y2 = max(0, min(height, math.ceil(float(box[3])))) + if x1 >= x2 or y1 >= y2: + continue + proto_x1 = max(0, math.floor((x1 + 0.5) * crop_w / width - 0.5)) + proto_y1 = max(0, math.floor((y1 + 0.5) * crop_h / height - 0.5)) + proto_x2 = min(crop_w, math.floor((x2 - 0.5) * crop_w / width - 0.5) + 2) + proto_y2 = min(crop_h, math.floor((y2 - 0.5) * crop_h / height - 0.5) + 2) + proto_x1, proto_x2 = left + proto_x1, left + proto_x2 + proto_y1, proto_y2 = top + proto_y1, top + proto_y2 + prototype_roi = protos[:, proto_y1:proto_y2, proto_x1:proto_x2] + lowres_mask = (masks_in[index] @ prototype_roi.reshape(channels, -1)).reshape( + 1, 1, proto_y2 - proto_y1, proto_x2 - proto_x1 + ) + ys = torch.arange(y1, y2, device=protos.device, dtype=torch.float32) + xs = torch.arange(x1, x2, device=protos.device, dtype=torch.float32) + global_y, global_x = torch.meshgrid(ys, xs, indexing="ij") + local_y = (global_y + 0.5) * crop_h / height - 0.5 - (proto_y1 - top) + local_x = (global_x + 0.5) * crop_w / width - 0.5 - (proto_x1 - left) + grid = torch.stack( + ( + (local_x + 0.5) * 2 / (proto_x2 - proto_x1) - 1, + (local_y + 0.5) * 2 / (proto_y2 - proto_y1) - 1, + ), + dim=-1, + ).unsqueeze(0) + masks[index, y1:y2, x1:x2] = F.grid_sample( + lowres_mask, + grid, + mode="bilinear", + padding_mode="border", + align_corners=False, + )[0, 0] + return masks.gt_(0.0) + + +def _mask_scale_crop_bounds( + mask_shape: tuple[int, int], target_shape: tuple[int, int] +) -> tuple[int, int, int, int]: + """Return the crop applied by :func:`scale_masks` before interpolation.""" + mask_h, mask_w = mask_shape + target_h, target_w = target_shape + gain = min(mask_h / target_h, mask_w / target_w) + pad_w = (mask_w - round(target_w * gain)) / 2 + pad_h = (mask_h - round(target_h * gain)) / 2 + top, left = round(pad_h - 0.1), round(pad_w - 0.1) + bottom, right = mask_h - round(pad_h + 0.1), mask_w - round(pad_w + 0.1) + return top, left, bottom, right + + def crop_mask(masks: torch.Tensor, boxes: torch.Tensor) -> torch.Tensor: """Crops masks to bounding boxes. diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 442ec70..b93724a 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -16,6 +16,7 @@ nmsout2eval, normalize_image_shapes, normalize_ratio_pads, + process_mask_upsample, scale_coords, scale_masks, ) @@ -359,7 +360,9 @@ def dispose(self) -> None: lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), ) - engine = MBLT_Engine( + # This intentionally exercises the legacy shifted positional layout, which + # does not match the current typed ``MBLT_Engine`` constructor signature. + engine = cast(Any, MBLT_Engine)( {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, "DEFAULT", str(mxq_path), @@ -642,7 +645,9 @@ def InferenceSession(self, path: str, providers: list[str]) -> _FakeSession: if model_path_style == "positional": engine = MBLT_Engine(model_config, "DEFAULT", str(onnx_path)) elif model_path_style == "positional-runtime": - engine = MBLT_Engine( + # Keep this legacy positional-runtime invocation untyped: its argument + # order is normalized at runtime by ``MBLT_Engine``. + engine = cast(Any, MBLT_Engine)( model_config, "DEFAULT", str(onnx_path), @@ -2390,6 +2395,30 @@ def test_scale_masks_matches_ultralytics_rounding() -> None: assert float(scaled[:, -1, :].max()) == pytest.approx(1.0) +@pytest.mark.parametrize("shape", [(640, 640), (481, 640)]) +def test_roi_prototype_masking_preserves_full_mask_result( + shape: tuple[int, int], +) -> None: + """ROI masking must retain the conventional path's binary mask exactly.""" + + generator = torch.Generator().manual_seed(42) + proto = torch.randn((32, 160, 160), generator=generator) + coefficients = torch.randn((40, 32), generator=generator) + # Small, fractional boxes activate the ROI path and exercise crop bounds. + starts = torch.rand((40, 2), generator=generator) + starts[:, 0] *= shape[1] - 80 + starts[:, 1] *= shape[0] - 80 + boxes = torch.cat((starts + 0.25, starts + 48.75), dim=1) + + channels, mask_h, mask_w = proto.shape + full = (coefficients @ proto.float().view(channels, -1)).view(-1, mask_h, mask_w) + expected = crop_mask(scale_masks(full, shape), boxes).gt_(0.0) + + actual = process_mask_upsample(proto, coefficients, boxes, shape) + + assert torch.equal(actual, expected) + + def test_preprocess_with_metadata_returns_letterbox_ratio_pad() -> None: """Expose exact LetterBox ratio and integer padding for validation scaling.""" From 1c33e9cce749a569f96fd3511461d2e2388975a6 Mon Sep 17 00:00:00 2001 From: jinman Date: Wed, 12 Aug 2026 13:58:26 +0900 Subject: [PATCH 11/48] Refactor dataset organization and evaluation logic - Updated dataset organization functions to change default output directories from ~/.mblt_vision to ~/.mblt_model_zoo. - Enhanced validation in the calibration dataset preparation to reject non-finite values. - Improved error handling for ONNX model path resolution and added checks for explicit MXQ paths. - Added tests for validating calibration datasets and ensuring proper handling of difficult regions in DOTAv1 evaluation. - Refactored plotting functions to maintain raw output coordinates and ensure correct color format conversion. - Introduced new tests for engine initialization to verify proper disposal of backends on setup failures. --- .agents/skills/mblt-vision/SKILL.md | 2 + AGENTS.md | 2 + README.md | 2 +- benchmark/README.md | 6 +- benchmark/benchmark_vision_models.py | 25 +++++ benchmark/organize_ade20k.py | 9 +- benchmark/organize_cityscapes.py | 9 +- benchmark/organize_coco.py | 9 +- benchmark/organize_dotav1.py | 9 +- benchmark/organize_imagenet.py | 9 +- benchmark/organize_nyu_depth.py | 9 +- benchmark/organize_widerface.py | 9 +- compile/README.md | 10 +- compile/make_ade20k_subset.py | 7 ++ compile/make_cityscapes_subset.py | 7 ++ compile/make_coco_subset.py | 7 ++ compile/make_dotav1_subset.py | 7 ++ compile/make_imagenet_subset.py | 7 ++ compile/make_nyu_depth_subset.py | 7 ++ compile/make_widerface_subset.py | 7 ++ compile/vision_model_compile.py | 7 ++ mblt_vision/compile/vision.py | 9 ++ mblt_vision/utils/datasets/organizer.py | 27 +++-- mblt_vision/utils/evaluation/eval_dota.py | 127 ++++++++++++++++------ mblt_vision/utils/results.py | 17 +-- mblt_vision/wrapper.py | 83 ++++++++------ tests/test_benchmark_vision.py | 58 ++++++++++ tests/test_compile_vision.py | 33 ++++++ tests/test_dataset_organizer.py | 49 +++++++++ tests/test_eval_dota.py | 87 +++++++++++++++ tests/test_results.py | 87 +++++++++++++++ tests/test_wrapper.py | 118 ++++++++++++++++++++ 32 files changed, 756 insertions(+), 105 deletions(-) create mode 100644 tests/test_eval_dota.py diff --git a/.agents/skills/mblt-vision/SKILL.md b/.agents/skills/mblt-vision/SKILL.md index 63b5245..1da33c1 100644 --- a/.agents/skills/mblt-vision/SKILL.md +++ b/.agents/skills/mblt-vision/SKILL.md @@ -60,6 +60,8 @@ description: >- - Keep all executable benchmark scripts directly in `benchmark/`; reusable reporting helpers belong in `mblt_vision.benchmark`. - Keep all executable compile scripts and the compile guide directly in `compile/`. +- Use `~/.mblt_model_zoo` as the shared artifact and dataset cache root. Keep organizer defaults, + dataset registry YAMLs, compilation defaults, and documented commands aligned to it. - For every significant package change (public API, CLI, runtime/dependency, artifact layout, or tooling structure), update `AGENTS.md`, this canonical skill, the Claude skill entry point when its workflow changes, and the relevant README in the same change. diff --git a/AGENTS.md b/AGENTS.md index 69fa1e5..deecb55 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -97,6 +97,8 @@ The current ownership boundary is deliberate: directly under `benchmark/`. Put reusable benchmark reporting helpers in `mblt_vision.benchmark`. - Keep executable compilation helpers and their guide directly under `compile/`. Do not recreate a Vision-only subdirectory under either tooling root. +- Use `~/.mblt_model_zoo` as the shared artifact and dataset cache root. Organizer defaults, + dataset registry YAMLs, compilation defaults, and documented commands must agree on that root. - Benchmark and compilation commands are development tools; do not package them as public CLI entry points without an explicit product decision. The supported end-user command is `mblt-vision`. diff --git a/README.md b/README.md index 688ed6a..b21c357 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ typed postprocessing results for image classification, depth estimation, face an object detection, OBB, instance and semantic segmentation, and pose estimation. -Version `0.0.1` is the initial standalone release. +Version `0.0.0` is the initial standalone release. ## Installation diff --git a/benchmark/README.md b/benchmark/README.md index d70e1c7..d3dc3dd 100644 --- a/benchmark/README.md +++ b/benchmark/README.md @@ -36,11 +36,13 @@ Use the unified runner for every Vision task. It chooses the evaluator from python benchmark/benchmark_vision_models.py \ --models ResNet50 \ --task image_classification \ - --data-path ~/.mblt_vision/datasets/imagenet + --target-device aries-rb \ + --data-path ~/.mblt_model_zoo/datasets/imagenet ``` Use `--framework onnx` for ONNX Runtime, `--core-mode all` to compare supported -MXQ core modes, and `--fail-fast` to stop on the first failed target. Compare +MXQ core modes, `--target-device regulus-ra` or `regulus-rb` for the corresponding +Regulus board artifact, and `--fail-fast` to stop on the first failed target. Compare completed runs with: ```bash diff --git a/benchmark/benchmark_vision_models.py b/benchmark/benchmark_vision_models.py index f62bac3..fb5f54f 100644 --- a/benchmark/benchmark_vision_models.py +++ b/benchmark/benchmark_vision_models.py @@ -28,10 +28,12 @@ ) from mblt_vision._model_paths import resolve_framework from mblt_vision._tasks import VISION_TASKS, normalize_vision_task +from mblt_npu import normalize_target_device CoreMode = Literal["single", "multi", "global4", "global8"] CORE_MODES: tuple[CoreMode, ...] = ("single", "multi", "global4", "global8") TASK_CHOICES = VISION_TASKS +SUPPORTED_TARGET_DEVICES = frozenset({"aries-rb", "regulus-ra", "regulus-rb"}) def parse_unit_interval(value: str) -> float: @@ -69,6 +71,21 @@ def _parse_task(value: str) -> str: raise argparse.ArgumentTypeError(str(exc)) from exc +def _parse_target_device(value: str) -> str: + """Normalize and validate a benchmark target board.""" + + try: + target_device = normalize_target_device(value) + except TypeError as exc: + raise argparse.ArgumentTypeError(str(exc)) from exc + if target_device not in SUPPORTED_TARGET_DEVICES: + raise argparse.ArgumentTypeError( + f"unsupported target device {value!r}; expected one of " + f"{sorted(SUPPORTED_TARGET_DEVICES)}." + ) + return target_device + + def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: """Parse command-line arguments for the standardized vision benchmark. @@ -108,6 +125,12 @@ def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: parser.add_argument( "--framework", choices=["mxq", "onnx"], help="Explicit inference framework." ) + parser.add_argument( + "--target-device", + type=_parse_target_device, + default="aries-rb", + help="NPU board: aries-rb, regulus-ra, or regulus-rb (legacy aries/regulus accepted).", + ) parser.add_argument( "--core-mode", default="global8", @@ -316,6 +339,7 @@ def _run_target( "core_mode": core_mode, "task": args.task, "batch_size": args.batch_size, + "target_device": args.target_device, "status": "error", } model = None @@ -329,6 +353,7 @@ def _run_target( "onnx_path": args.onnx_path, "framework": args.framework, "dev_no": args.dev_no, + "target_device": args.target_device, } if core_mode != "onnx": engine_kwargs["core_mode"] = core_mode diff --git a/benchmark/organize_ade20k.py b/benchmark/organize_ade20k.py index 55cada6..82cfd8a 100644 --- a/benchmark/organize_ade20k.py +++ b/benchmark/organize_ade20k.py @@ -2,6 +2,13 @@ import argparse import os +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.utils.datasets import organize_ade20k from mblt_vision.utils.datasets.organizer import ADE20K_URL @@ -15,7 +22,7 @@ ) parser.add_argument( "--output-dir", - default=os.path.expanduser("~/.mblt_vision/datasets/ADEChallengeData2016"), + default=os.path.expanduser("~/.mblt_model_zoo/datasets/ADEChallengeData2016"), help="Path to the organized validation dataset", ) args = parser.parse_args() diff --git a/benchmark/organize_cityscapes.py b/benchmark/organize_cityscapes.py index 74a9840..7051d79 100644 --- a/benchmark/organize_cityscapes.py +++ b/benchmark/organize_cityscapes.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.utils.datasets import organize_cityscapes @@ -25,7 +32,7 @@ def main() -> None: ) parser.add_argument( "--output-dir", - default="~/.mblt_vision/datasets/cityscapes", + default="~/.mblt_model_zoo/datasets/cityscapes", help="Destination for the flat images/ and annotations/ directories", ) args = parser.parse_args() diff --git a/benchmark/organize_coco.py b/benchmark/organize_coco.py index abe1ab7..adb4601 100644 --- a/benchmark/organize_coco.py +++ b/benchmark/organize_coco.py @@ -7,6 +7,13 @@ import argparse import os +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.utils.datasets import organize_coco @@ -32,7 +39,7 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_vision/datasets/coco"), + default=os.path.expanduser("~/.mblt_model_zoo/datasets/coco"), help="Path to the directory to save the organized dataset", ) args = parser.parse_args() diff --git a/benchmark/organize_dotav1.py b/benchmark/organize_dotav1.py index 16f0c56..5537d8e 100644 --- a/benchmark/organize_dotav1.py +++ b/benchmark/organize_dotav1.py @@ -8,6 +8,13 @@ import argparse import os +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.datasets import get_dataset_config from mblt_vision.utils.datasets import organize_dotav1 @@ -24,7 +31,7 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_vision/datasets/dotav1"), + default=os.path.expanduser("~/.mblt_model_zoo/datasets/dotav1"), help="Path to the directory to save the organized dataset", ) args = parser.parse_args() diff --git a/benchmark/organize_imagenet.py b/benchmark/organize_imagenet.py index 8e7c001..65187ea 100644 --- a/benchmark/organize_imagenet.py +++ b/benchmark/organize_imagenet.py @@ -7,6 +7,13 @@ import argparse import os +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.utils.datasets import organize_imagenet @@ -33,7 +40,7 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_vision/datasets/imagenet"), + default=os.path.expanduser("~/.mblt_model_zoo/datasets/imagenet"), help="Path to the directory to save the organized dataset", ) args = parser.parse_args() diff --git a/benchmark/organize_nyu_depth.py b/benchmark/organize_nyu_depth.py index 2073004..6b41622 100644 --- a/benchmark/organize_nyu_depth.py +++ b/benchmark/organize_nyu_depth.py @@ -6,6 +6,13 @@ import argparse import os +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.utils.datasets import organize_nyu_depth from mblt_vision.utils.datasets.organizer import NYU_DEPTH_URL @@ -21,7 +28,7 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_vision/datasets/nyu-depth"), + default=os.path.expanduser("~/.mblt_model_zoo/datasets/nyu-depth"), help="Path to the directory to save the organized dataset", ) args = parser.parse_args() diff --git a/benchmark/organize_widerface.py b/benchmark/organize_widerface.py index b44e369..701b763 100644 --- a/benchmark/organize_widerface.py +++ b/benchmark/organize_widerface.py @@ -7,6 +7,13 @@ import argparse import os +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.utils.datasets import organize_widerface @@ -30,7 +37,7 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_vision/datasets/widerface"), + default=os.path.expanduser("~/.mblt_model_zoo/datasets/widerface"), help="Path to the directory to save the organized dataset", ) args = parser.parse_args() diff --git a/compile/README.md b/compile/README.md index 6272f73..c9f1350 100644 --- a/compile/README.md +++ b/compile/README.md @@ -28,7 +28,7 @@ from mblt_vision.compile.vision import compile_vision_model output_path = compile_vision_model( "alexnet", target_device="aries-rb", - data_path="~/.mblt_vision/datasets/imagenet", + data_path="~/.mblt_model_zoo/datasets/imagenet", save_path="./alexnet.mxq", ) print(output_path) @@ -40,7 +40,7 @@ print(output_path) mblt-vision compile \ --model-cls alexnet \ --target-device aries-rb \ - --data-path ~/.mblt_vision/datasets/imagenet \ + --data-path ~/.mblt_model_zoo/datasets/imagenet \ --save-path ./alexnet.mxq ``` @@ -55,8 +55,8 @@ mblt-vision compile --model-cls alexnet --target-device aries-rb --calib-data-pa Use `--model-type` for a non-default YAML variant and `--model-path` or `--onnx-path` to prefer a local ONNX file. If that path is omitted or does not exist, the configured Hugging Face repository supplies the ONNX artifact. Downloaded ONNX files and compiled outputs use -`~/.mblt_vision`; without `--save-path`, the compiler writes -`~/.mblt_vision/.mxq`. +`~/.mblt_model_zoo`; without `--save-path`, the compiler writes +`~/.mblt_model_zoo/.mxq`. `target_device` / `--target-device` is required: choose `aries-rb`, `regulus-ra`, or `regulus-rb` to compile an artifact for that board. @@ -92,7 +92,7 @@ Compilation maps model tasks to the packaged dataset registry: `--data-path` is an organized dataset root. An existing ready layout is reused; otherwise the registry-backed organizer downloads and prepares the dataset. When no path is supplied, the -registry default under `~/.mblt_vision/datasets` is used. +registry default under `~/.mblt_model_zoo/datasets` is used. NYU Depth and ADE20K use their registry download URLs. Cityscapes requires the manually downloaded official `leftImg8bit_trainvaltest.zip` and `gtFine_trainvaltest.zip` archives; place both inside the diff --git a/compile/make_ade20k_subset.py b/compile/make_ade20k_subset.py index 8823590..43df262 100644 --- a/compile/make_ade20k_subset.py +++ b/compile/make_ade20k_subset.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.compile.vision import make_calibration_subset diff --git a/compile/make_cityscapes_subset.py b/compile/make_cityscapes_subset.py index 4efd6c9..eeaae62 100644 --- a/compile/make_cityscapes_subset.py +++ b/compile/make_cityscapes_subset.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.compile.vision import make_calibration_subset diff --git a/compile/make_coco_subset.py b/compile/make_coco_subset.py index b936f58..c6c1e2d 100644 --- a/compile/make_coco_subset.py +++ b/compile/make_coco_subset.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.compile.vision import make_calibration_subset diff --git a/compile/make_dotav1_subset.py b/compile/make_dotav1_subset.py index 730496c..5ff5b77 100644 --- a/compile/make_dotav1_subset.py +++ b/compile/make_dotav1_subset.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.compile.vision import make_calibration_subset diff --git a/compile/make_imagenet_subset.py b/compile/make_imagenet_subset.py index f70ffd6..e108e5b 100644 --- a/compile/make_imagenet_subset.py +++ b/compile/make_imagenet_subset.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.compile.vision import make_calibration_subset diff --git a/compile/make_nyu_depth_subset.py b/compile/make_nyu_depth_subset.py index 6ae128b..04e6d25 100644 --- a/compile/make_nyu_depth_subset.py +++ b/compile/make_nyu_depth_subset.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.compile.vision import make_calibration_subset diff --git a/compile/make_widerface_subset.py b/compile/make_widerface_subset.py index 977a244..1344aee 100644 --- a/compile/make_widerface_subset.py +++ b/compile/make_widerface_subset.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.compile.vision import make_calibration_subset diff --git a/compile/vision_model_compile.py b/compile/vision_model_compile.py index 0acb382..da48c8b 100644 --- a/compile/vision_model_compile.py +++ b/compile/vision_model_compile.py @@ -3,6 +3,13 @@ from __future__ import annotations import argparse +import sys +from pathlib import Path + +# ruff: noqa: E402 +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) from mblt_vision.compile.vision import compile_vision_model diff --git a/mblt_vision/compile/vision.py b/mblt_vision/compile/vision.py index 19d1b52..8ca6d74 100644 --- a/mblt_vision/compile/vision.py +++ b/mblt_vision/compile/vision.py @@ -549,6 +549,10 @@ def validate_calibration_dataset(calib_data_path: str | Path) -> Path: raise ValueError( f"Calibration tensor {array_path} must use float32; got {array.dtype}." ) + if not np.isfinite(array).all(): + raise ValueError( + f"Calibration tensor {array_path} must contain only finite values." + ) if not array.flags.c_contiguous: raise ValueError(f"Calibration tensor {array_path} must be C-contiguous.") return root @@ -786,6 +790,11 @@ def _resolve_compile_onnx_path( if model_path is not None: local_path = Path(model_path).expanduser() if local_path.is_file(): + if local_path.suffix.lower() != ".onnx": + raise ValueError( + "Compilation requires an ONNX model; " + f"local model path must end with `.onnx`, got {local_path}." + ) return local_path.resolve() configured_path = Path(str(file_cfg.get("onnx_path", ""))).expanduser() if configured_path.is_file(): diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index e1205df..5dd9b18 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -433,7 +433,7 @@ def construct_imagenet(image_dir: str, xml_dir: str, output_dir: str) -> None: def organize_imagenet( image_dir: str, xml_dir: str, - output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/imagenet"), + output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/imagenet"), ) -> None: """Organizes the ImageNet dataset, unpacking archives if necessary. @@ -441,7 +441,7 @@ def organize_imagenet( image_dir (str): Path or URL to the image directory or archive (.tar). xml_dir (str): Path or URL to the XML directory or archive (.tgz). output_dir (str, optional): Directory to store the organized dataset. - Defaults to ~/.mblt_vision/datasets/imagenet. + Defaults to ~/.mblt_model_zoo/datasets/imagenet. """ with TemporaryDirectory() as temp_dir: local_image_dir, local_xml_dir = _resolve_sources( @@ -508,7 +508,7 @@ def construct_coco(image_dir: str, annotation_dir: str, output_dir: str) -> None def organize_coco( image_dir: str, annotation_dir: str, - output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/coco"), + output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/coco"), ) -> None: """Organizes the COCO dataset, unpacking archives if necessary. @@ -516,7 +516,7 @@ def organize_coco( image_dir (str): Path or URL to the image zip file or directory. annotation_dir (str): Path or URL to the annotation zip file or directory. output_dir (str, optional): Directory to store the organized dataset. - Defaults to ~/.mblt_vision/datasets/coco. + Defaults to ~/.mblt_model_zoo/datasets/coco. """ with TemporaryDirectory() as temp_dir: local_image_dir, local_annotation_dir = _resolve_sources( @@ -577,7 +577,7 @@ def construct_widerface(image_dir: str, annotation_dir: str, output_dir: str) -> def organize_widerface( image_dir: str, annotation_dir: str, - output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/widerface"), + output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/widerface"), ) -> None: """Organizes the WiderFace dataset, unpacking archives if necessary. @@ -585,7 +585,7 @@ def organize_widerface( image_dir (str): Path or URL to the image zip file or directory. annotation_dir (str): Path or URL to the annotation zip file or directory. output_dir (str, optional): Directory to store the organized dataset. - Defaults to ~/.mblt_vision/datasets/widerface. + Defaults to ~/.mblt_model_zoo/datasets/widerface. """ with TemporaryDirectory() as temp_dir: local_image_dir, local_annotation_dir = _resolve_sources( @@ -808,7 +808,7 @@ def construct_nyu_depth(dataset_dir: str, output_dir: str) -> None: def organize_nyu_depth( dataset_path: str = NYU_DEPTH_URL, - output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/nyu-depth"), + output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/nyu-depth"), ) -> None: """Organizes NYU Depth, downloading and unpacking an archive when necessary. @@ -949,7 +949,7 @@ def construct_ade20k(dataset_dir: str, output_dir: str) -> None: def organize_ade20k( dataset_path: str = ADE20K_URL, output_dir: str = os.path.expanduser( - "~/.mblt_vision/datasets/ADEChallengeData2016" + "~/.mblt_model_zoo/datasets/ADEChallengeData2016" ), ) -> None: """Organizes ADE20K validation data, downloading and unpacking when necessary.""" @@ -1054,7 +1054,7 @@ def _collect_cityscapes_validation_files( def organize_cityscapes( image_dir: str, annotation_dir: str, - output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/cityscapes"), + output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/cityscapes"), ) -> None: """Install official Cityscapes validation archives as lossless flat PNG pairs. @@ -1339,10 +1339,6 @@ def _write_dotav1_yolo_labels( or fields[0].startswith("gsd:") ): continue - # Official DOTAv1 labels use ``1`` for difficult objects. Keep ``2`` - # ignored for compatibility with previously supported label exports. - if fields[9] in {"1", "2"}: - continue class_name = fields[8] if class_name not in DOTAV1_CLASS_TO_IDX: raise ValueError( @@ -1356,6 +1352,9 @@ def _write_dotav1_yolo_labels( converted_lines.append( f"{DOTAV1_CLASS_TO_IDX[class_name]} " + " ".join(f"{coordinate:.8g}" for coordinate in normalized) + # The trailing flag is normalized-label metadata, not a YOLO OBB + # coordinate. It preserves official difficult regions for evaluation. + + f" {int(fields[9] in {'1', '2'})}" ) with open(output_path, "w", encoding="utf-8") as output_file: output_file.write("\n".join(converted_lines)) @@ -1539,7 +1538,7 @@ def construct_dotav1(dataset_dir: str, output_dir: str) -> None: def organize_dotav1( dataset_path: str, - output_dir: str = os.path.expanduser("~/.mblt_vision/datasets/dotav1"), + output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/dotav1"), ) -> None: """Organizes a validation-only DOTAv1 dataset. diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 698ef82..bc20662 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -72,7 +72,8 @@ def _load_ground_truths( dataset: Dataset containing image IDs and image paths. Returns: - Mapping from image ID to tensors for classes, polygons, and ``xywhr`` boxes. + Mapping from image ID to tensors for positive and ignored classes, polygons, + and ``xywhr`` boxes. """ label_dir = Path(data_path) / "labels" / "val" original_label_dir = Path(data_path) / "labels" / "val_original" @@ -84,12 +85,22 @@ def _load_ground_truths( original_label_path = original_label_dir / f"{image_id}.txt" classes = [] polygons = [] + ignore_classes = [] + ignore_polygons = [] if label_path.is_file(): - for line in label_path.read_text(encoding="utf-8").splitlines(): + for line_number, line in enumerate( + label_path.read_text(encoding="utf-8").splitlines(), start=1 + ): parts = line.split() - if len(parts) < 9: + if not parts: continue + if len(parts) < 9: + raise ValueError( + "Malformed normalized DOTAv1 annotation at " + f"{label_path}:{line_number}: expected at least 9 fields, " + f"got {len(parts)}." + ) cls = _label_to_index(parts[0]) if cls >= get_dotav1_class_num(): raise ValueError( @@ -101,17 +112,17 @@ def _load_ground_truths( if coords.numel() and float(coords.max()) <= 1.5: coords[:, 0] *= width coords[:, 1] *= height - classes.append(cls) - polygons.append(coords) + if len(parts) >= 10 and parts[9] in {"1", "2"}: + ignore_classes.append(cls) + ignore_polygons.append(coords) + else: + classes.append(cls) + polygons.append(coords) elif original_label_path.is_file(): for line in original_label_path.read_text(encoding="utf-8").splitlines(): parts = line.split() if len(parts) < 9: continue - # Official DOTAv1 labels use ``1`` for difficult objects. Keep ``2`` - # ignored for compatibility with previously supported label exports. - if len(parts) >= 10 and parts[9] in {"1", "2"}: - continue cls = _label_to_index(parts[8]) if cls >= get_dotav1_class_num(): raise ValueError( @@ -120,8 +131,12 @@ def _load_ground_truths( coords = torch.tensor( [float(value) for value in parts[:8]], dtype=torch.float32 ).reshape(4, 2) - classes.append(cls) - polygons.append(coords) + if len(parts) >= 10 and parts[9] in {"1", "2"}: + ignore_classes.append(cls) + ignore_polygons.append(coords) + else: + classes.append(cls) + polygons.append(coords) polygon_tensor = ( torch.stack(polygons).to(torch.float32) @@ -134,10 +149,24 @@ def _load_ground_truths( else: boxes = torch.zeros((0, 5), dtype=torch.float32) + ignore_polygon_tensor = ( + torch.stack(ignore_polygons).to(torch.float32) + if ignore_polygons + else torch.zeros((0, 4, 2), dtype=torch.float32) + ) + if ignore_polygon_tensor.numel(): + ignored_xywhr = xyxyxyxy2xywhr(ignore_polygon_tensor) + ignore_boxes = cast(torch.Tensor, ignored_xywhr).to(torch.float32) + else: + ignore_boxes = torch.zeros((0, 5), dtype=torch.float32) + ground_truths[image_id] = { "cls": torch.tensor(classes, dtype=torch.int64), "polygons": polygon_tensor, "bboxes": boxes, + "ignore_cls": torch.tensor(ignore_classes, dtype=torch.int64), + "ignore_polygons": ignore_polygon_tensor, + "ignore_bboxes": ignore_boxes, } return ground_truths @@ -204,6 +233,7 @@ def _compute_ap( def _ap_per_class( tp: np.ndarray, + ignore: np.ndarray, conf: np.ndarray, pred_cls: np.ndarray, target_cls: np.ndarray, @@ -216,6 +246,7 @@ def _ap_per_class( order = np.argsort(-conf) tp = tp[order] + ignore = ignore[order] conf = conf[order] pred_cls = pred_cls[order] @@ -224,18 +255,20 @@ def _ap_per_class( for class_index, class_id in enumerate(unique_classes): pred_mask = pred_cls == class_id num_labels = target_count[class_index] - num_predictions = pred_mask.sum() - if num_predictions == 0 or num_labels == 0: + if not pred_mask.any() or num_labels == 0: continue - false_positive = (1 - tp[pred_mask]).cumsum(0) - true_positive = tp[pred_mask].cumsum(0) - recall = true_positive / (num_labels + eps) - precision = true_positive / (true_positive + false_positive + eps) + class_tp = tp[pred_mask] + class_ignore = ignore[pred_mask] for iou_index in range(tp.shape[1]): - ap[class_index, iou_index], _, _ = _compute_ap( - recall[:, iou_index], precision[:, iou_index] - ) + keep = ~class_ignore[:, iou_index] + if not keep.any(): + continue + true_positive = class_tp[keep, iou_index].cumsum(0) + false_positive = (1 - class_tp[keep, iou_index]).cumsum(0) + recall = true_positive / (num_labels + eps) + precision = true_positive / (true_positive + false_positive + eps) + ap[class_index, iou_index], _, _ = _compute_ap(recall, precision) return ap @@ -267,7 +300,7 @@ def _match_predictions( def _empty_stats() -> dict[str, list[np.ndarray]]: """Create an empty DOTAv1 metric statistics accumulator.""" - return {"tp": [], "conf": [], "pred_cls": [], "target_cls": []} + return {"tp": [], "ignore": [], "conf": [], "pred_cls": [], "target_cls": []} def _append_stats( @@ -319,21 +352,29 @@ def _ground_truth_to_input_space( ratio_pad: RatioPad | None, ) -> dict[str, torch.Tensor]: """Transform original-image DOTAv1 polygons to letterboxed ``xywhr`` boxes.""" - classes = ground_truth["cls"] - polygons = ground_truth.get("polygons") - if polygons is None: - return {"cls": classes, "bboxes": ground_truth["bboxes"]} - if polygons.numel() == 0: - return {"cls": classes, "bboxes": torch.zeros((0, 5), dtype=torch.float32)} - gain, pad = _ratio_pad_for_shape(input_shape, org_shape, ratio_pad) - transformed = polygons.clone().to(torch.float32) - transformed[..., 0] = transformed[..., 0] * gain + pad[0] - transformed[..., 1] = transformed[..., 1] * gain + pad[1] - transformed_boxes = xyxyxyxy2xywhr(transformed) + + def transform(polygons: torch.Tensor | None, boxes: torch.Tensor) -> torch.Tensor: + if polygons is None: + return boxes + if polygons.numel() == 0: + return torch.zeros((0, 5), dtype=torch.float32) + transformed = polygons.clone().to(torch.float32) + transformed[..., 0] = transformed[..., 0] * gain + pad[0] + transformed[..., 1] = transformed[..., 1] * gain + pad[1] + return cast(torch.Tensor, xyxyxyxy2xywhr(transformed)).to(torch.float32) + + transformed_boxes = transform(ground_truth.get("polygons"), ground_truth["bboxes"]) + ignore_classes = ground_truth.get("ignore_cls", torch.zeros(0, dtype=torch.int64)) + ignore_boxes = transform( + ground_truth.get("ignore_polygons"), + ground_truth.get("ignore_bboxes", torch.zeros((0, 5), dtype=torch.float32)), + ) return { - "cls": classes, - "bboxes": cast(torch.Tensor, transformed_boxes).to(torch.float32), + "cls": ground_truth["cls"], + "bboxes": transformed_boxes, + "ignore_cls": ignore_classes, + "ignore_bboxes": ignore_boxes, } @@ -351,8 +392,19 @@ def _process_image_stats( else: iou = batch_probiou(target["bboxes"], predictions["bboxes"]) true_positive = _match_predictions(predictions["cls"], target["cls"], iou, iouv) + ignore = np.zeros_like(true_positive) + ignore_classes = target.get("ignore_cls", torch.zeros(0, dtype=torch.int64)) + ignore_boxes = target.get("ignore_bboxes", torch.zeros((0, 5), dtype=torch.float32)) + if ignore_classes.numel() and predictions["cls"].numel(): + ignored_iou = batch_probiou(ignore_boxes, predictions["bboxes"]) + same_class = ignore_classes[:, None] == predictions["cls"] + ignored_matches = ( + (ignored_iou[:, :, None] >= iouv[None, None, :]) & same_class[:, :, None] + ).any(dim=0) + ignore = ignored_matches.cpu().numpy() & ~true_positive return { "tp": true_positive, + "ignore": ignore, "conf": predictions["conf"].cpu().numpy(), "pred_cls": predictions["cls"].cpu().numpy(), "target_cls": target_cls, @@ -374,6 +426,11 @@ def _evaluate_stats(stats: dict[str, list[np.ndarray]], niou: int = 10) -> DOTAR if stats["tp"] else np.zeros((0, niou), dtype=bool) ) + ignore = ( + np.concatenate(stats["ignore"], 0) + if stats["ignore"] + else np.zeros((0, niou), dtype=bool) + ) conf = ( np.concatenate(stats["conf"], 0) if stats["conf"] @@ -384,7 +441,7 @@ def _evaluate_stats(stats: dict[str, list[np.ndarray]], niou: int = 10) -> DOTAR if stats["pred_cls"] else np.zeros(0, dtype=np.float64) ) - ap = _ap_per_class(tp, conf, pred_cls, target_cls) + ap = _ap_per_class(tp, ignore, conf, pred_cls, target_cls) if ap.size == 0: return DOTAResult(map5095=0.0, map50=0.0) return DOTAResult(map5095=float(ap.mean()), map50=float(ap[:, 0].mean())) diff --git a/mblt_vision/utils/results.py b/mblt_vision/utils/results.py index c344936..59f18d9 100644 --- a/mblt_vision/utils/results.py +++ b/mblt_vision/utils/results.py @@ -99,6 +99,7 @@ def _read_image( raise ValueError( f"Image arrays must have HWC shape with three channels, got {source_img.shape}." ) + source_img = cv2.cvtColor(source_img, cv2.COLOR_RGB2BGR) elif isinstance(source_path, (str, Path)): image_path = Path(source_path) if not image_path.is_file(): @@ -510,7 +511,7 @@ def _plot_object_detection( self.scores = box_cls[:, 4] self.boxes = scale_boxes( img1_shape, - box_cls[:, :4], + box_cls[:, :4].clone(), img0_shape, ) boxes = self.boxes @@ -602,7 +603,7 @@ def _plot_pose_estimation( img0_shape: tuple[int, int] = (img.shape[0], img.shape[1]) self.kpts = scale_coords( self.pre_cfg["LetterBox"]["img_size"], - box_cls[:, 6:].reshape(-1, 17, 3), + box_cls[:, 6:].reshape(-1, 17, 3).clone(), img0_shape, ) kpts = self.kpts @@ -611,8 +612,8 @@ def _plot_pose_estimation( for kpt in kpts: for i, (x, y, v) in enumerate(kpt): color_k = get_coco_keypoint_palette(i) - # if v < self.conf_thres: - # continue + if float(v) < self.conf_thres: + continue cv2.circle( img, (int(x), int(y)), @@ -622,12 +623,12 @@ def _plot_pose_estimation( lineType=cv2.LINE_AA, ) for j, sk in enumerate(get_coco_pose_skeleton()): + conf1 = float(kpt[sk[0] - 1, 2]) + conf2 = float(kpt[sk[1] - 1, 2]) + if conf1 < self.conf_thres or conf2 < self.conf_thres: + continue pos1 = (int(kpt[sk[0] - 1, 0]), int(kpt[sk[0] - 1, 1])) pos2 = (int(kpt[sk[1] - 1, 0]), int(kpt[sk[1] - 1, 1])) - # conf1 = kpt[sk[0] - 1, 2] - # conf2 = kpt[sk[1] - 1, 2] - # if conf1 < self.conf_thres or conf2 < self.conf_thres: - # continue cv2.line( img, pos1, diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 31487a0..b4ba5b9 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -391,6 +391,11 @@ def __init__( _mxq_path_passed = bool(mxq_path) _onnx_path_passed = bool(onnx_path) + if _mxq_path_passed and not os.path.isfile(mxq_path): + raise FileNotFoundError( + "Explicit MXQ model path does not exist: " + f"{mxq_path}. Remove model_path/mxq_path to download the configured artifact." + ) _dev_no_passed = dev_no is not None _core_mode_passed = core_mode is not None _target_cores_passed = target_cores is not None @@ -448,41 +453,53 @@ def __init__( self._onnx_model: ONNXBackend | None = None self._onnx_session: Any = None - if self.framework == "onnx": - ort = _load_onnxruntime() - resolved_onnx_path = self.file_cfg.get("onnx_path") - if not resolved_onnx_path: - raise RuntimeError( - f"ONNX path not resolved for model {model_cls}. Make sure the model repository has an ONNX file." - ) - if not os.path.isfile(resolved_onnx_path): - raise FileNotFoundError(f"ONNX file not found at: {resolved_onnx_path}") + try: + if self.framework == "onnx": + ort = _load_onnxruntime() + resolved_onnx_path = self.file_cfg.get("onnx_path") + if not resolved_onnx_path: + raise RuntimeError( + f"ONNX path not resolved for model {model_cls}. Make sure the model repository has an ONNX file." + ) + if not os.path.isfile(resolved_onnx_path): + raise FileNotFoundError( + f"ONNX file not found at: {resolved_onnx_path}" + ) - providers = _resolve_onnx_providers(ort, onnx_providers) - onnx_model = ONNXBackend( - resolved_onnx_path, providers=providers, ort_module=ort + providers = _resolve_onnx_providers(ort, onnx_providers) + onnx_model = ONNXBackend( + resolved_onnx_path, providers=providers, ort_module=ort + ) + self._onnx_model = onnx_model + onnx_model.create() + self._onnx_session = onnx_model.session + self.model = self._onnx_session + self.input_name = self._onnx_session.get_inputs()[0].name + self.output_names = [o.name for o in self._onnx_session.get_outputs()] + else: + mxq_model = MobilintNPUBackend(**self._mxq_backend_kwargs()) + self._mxq_model = mxq_model + self.model = mxq_model + mxq_model.create() + mxq_model.launch() + + if mxq_model.get_dtype() == "DataType.Uint8": + self.pre_cfg.pop("Normalize", None) + + self.preprocessor = build_preprocess(self.pre_cfg) + self.postprocessor = build_postprocess( + self.pre_cfg, self.post_cfg, **self.postprocess_kwargs ) - onnx_model.create() - self._onnx_model = onnx_model - self._onnx_session = onnx_model.session - self.model = self._onnx_session - self.input_name = self._onnx_session.get_inputs()[0].name - self.output_names = [o.name for o in self._onnx_session.get_outputs()] - else: - mxq_model = MobilintNPUBackend(**self._mxq_backend_kwargs()) - self._mxq_model = mxq_model - self.model = mxq_model - mxq_model.create() - mxq_model.launch() - - if mxq_model.get_dtype() == "DataType.Uint8": - self.pre_cfg.pop("Normalize", None) - - self.preprocessor = build_preprocess(self.pre_cfg) - self.postprocessor = build_postprocess( - self.pre_cfg, self.post_cfg, **self.postprocess_kwargs - ) - self.device = torch.device("cpu") + self.device = torch.device("cpu") + except Exception: + for backend in (self._mxq_model, self._onnx_model): + if backend is None: + continue + try: + backend.dispose() + except Exception: + pass + raise def _mxq_backend_kwargs(self) -> dict[str, Any]: """Builds the MXQ backend kwargs from the resolved file config.""" diff --git a/tests/test_benchmark_vision.py b/tests/test_benchmark_vision.py index e114fad..c9ac659 100644 --- a/tests/test_benchmark_vision.py +++ b/tests/test_benchmark_vision.py @@ -198,6 +198,64 @@ def fake_evaluate( assert rows[0]["error"] == "TypeError: Unsupported model output" +def test_benchmark_forwards_normalized_target_device( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Construct each benchmark engine with the requested board identifier.""" + + engine_kwargs: list[dict[str, object]] = [] + + class FakeEngine: + """Minimal benchmark engine that records its construction options.""" + + def __init__(self, **kwargs: object) -> None: + engine_kwargs.append(kwargs) + + def dispose(self) -> None: + """Release the fake benchmark engine.""" + + import mblt_vision as vision + + monkeypatch.setattr(vision, "MBLT_Engine", FakeEngine) + monkeypatch.setattr( + benchmark_vision_models, + "_evaluate", + lambda *args: (0.9, "top1_accuracy", {"top1_accuracy": 0.9}), + ) + + assert ( + benchmark_vision_models.main( + [ + "--models", + "model-a", + "--task", + "image_classification", + "--target-device", + "regulus", + "--data-path", + str(tmp_path / "dataset"), + "--results-dir", + str(tmp_path / "results"), + "--no-plot", + ] + ) + == 0 + ) + assert engine_kwargs == [ + { + "model_cls": "model-a", + "model_type": "DEFAULT", + "model_path": "", + "mxq_path": "", + "onnx_path": "", + "framework": None, + "dev_no": 0, + "target_device": "regulus-ra", + "core_mode": "global8", + } + ] + + def test_comparison_rejects_matching_metrics_from_different_tasks( tmp_path: Path, ) -> None: diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index e1815a0..ca2270c 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -15,6 +15,7 @@ prepare_calibration_arrays, resolve_quantization_values, select_calibration_images, + validate_calibration_dataset, ) from mblt_vision.utils import datasets as dataset_utils from mblt_vision.utils.datasets import readiness as readiness_module @@ -457,6 +458,20 @@ def preprocess(self, image_path: str) -> np.ndarray: prepare_calibration_arrays(_Engine(), [source], tmp_path / "arrays") # type: ignore[arg-type] +@pytest.mark.parametrize("invalid_value", [np.nan, np.inf, -np.inf]) +def test_validate_calibration_dataset_rejects_nonfinite_values( + tmp_path: Path, invalid_value: float +) -> None: + """Reject non-finite ready calibration tensors before compilation.""" + + array = np.ones((2, 2, 3), dtype=np.float32) + array[0, 0, 0] = invalid_value + np.save(tmp_path / "selected.npy", array) + + with pytest.raises(ValueError, match="must contain only finite values"): + validate_calibration_dataset(tmp_path) + + def test_quantization_explicit_values_do_not_fetch( monkeypatch: pytest.MonkeyPatch, ) -> None: @@ -767,6 +782,24 @@ def test_compile_uses_obb_task_from_model_config( assert calls["calibration_kwargs"]["output"] == 1 +def test_compile_rejects_non_onnx_local_model_with_ready_calibration_data( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Fail clearly before sending an MXQ file to the ONNX compiler backend.""" + + mxq_path = tmp_path / "local.mxq" + mxq_path.write_bytes(b"mxq") + + with pytest.raises(ValueError, match=r"requires an ONNX model.*local\.mxq"): + _run_fake_compile( + monkeypatch, + tmp_path, + task="image_classification", + model_path=mxq_path, + entry_level="calibration", + ) + + def test_compile_ignores_missing_local_path_and_uses_hosted_onnx( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index f8b28d1..e47fa35 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -3,6 +3,7 @@ from __future__ import annotations import io +import inspect import tarfile from pathlib import Path from typing import Any @@ -10,6 +11,7 @@ import pytest import requests +from PIL import Image import mblt_vision.utils.datasets.organizer as organizer from mblt_vision.utils.datasets import readiness as readiness_module @@ -31,6 +33,53 @@ def update(self, value: int) -> None: self.updated += value +def test_dotav1_normalized_labels_preserve_difficult_metadata(tmp_path: Path) -> None: + """Keep official difficult regions in the normalized label export.""" + + image_path = tmp_path / "image.png" + Image.new("RGB", (100, 50)).save(image_path) + source_path = tmp_path / "source.txt" + source_path.write_text( + "imagesource:GoogleEarth\n" + "0 0 20 0 20 20 0 20 plane 0\n" + "30 0 50 0 50 20 30 20 plane 1\n", + encoding="utf-8", + ) + output_path = tmp_path / "normalized.txt" + + organizer._write_dotav1_yolo_labels( + str(image_path), str(source_path), str(output_path) + ) + + assert output_path.read_text(encoding="utf-8").splitlines() == [ + "0 0 0 0.2 0 0.2 0.4 0 0.4 0", + "0 0.3 0 0.5 0 0.5 0.4 0.3 0.4 1", + ] + + +@pytest.mark.parametrize( + ("organize_dataset", "dataset_name"), + [ + (organizer.organize_imagenet, "imagenet"), + (organizer.organize_coco, "coco"), + (organizer.organize_widerface, "widerface"), + (organizer.organize_nyu_depth, "nyu-depth"), + (organizer.organize_ade20k, "ADEChallengeData2016"), + (organizer.organize_cityscapes, "cityscapes"), + (organizer.organize_dotav1, "dotav1"), + ], +) +def test_organizer_defaults_match_registry_cache_root( + organize_dataset: object, dataset_name: str +) -> None: + """Write datasets where the packaged registry resolves them by default.""" + + output_dir = inspect.signature(organize_dataset).parameters["output_dir"].default + assert output_dir == str( + Path.home() / ".mblt_model_zoo" / "datasets" / dataset_name + ) + + class _FakeResponse: """Simple streaming response test double.""" diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py new file mode 100644 index 0000000..938bf3b --- /dev/null +++ b/tests/test_eval_dota.py @@ -0,0 +1,87 @@ +"""Tests for DOTAv1 difficult-region evaluation.""" + +from __future__ import annotations + +from types import SimpleNamespace + +import numpy as np +import pytest +import torch + +from mblt_vision.utils.evaluation.eval_dota import ( + _load_ground_truths, + evaluate_dota_predictions, +) + + +def test_normalized_difficult_flag_loads_as_an_ignored_region(tmp_path) -> None: + """Read organizer-produced difficult metadata before selecting evaluation targets.""" + + label_dir = tmp_path / "labels" / "val" + label_dir.mkdir(parents=True) + (label_dir / "image.txt").write_text( + "0 0 0 0.2 0 0.2 0.2 0 0.2 0\n" "0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6 1\n", + encoding="utf-8", + ) + dataset = SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ) + + ground_truth = _load_ground_truths(str(tmp_path), dataset)["image"] + + assert ground_truth["cls"].tolist() == [0] + assert ground_truth["ignore_cls"].tolist() == [0] + + +def test_normalized_truncated_annotation_raises_with_file_and_line(tmp_path) -> None: + """Reject nonblank normalized annotations without a complete OBB polygon.""" + + label_dir = tmp_path / "labels" / "val" + label_dir.mkdir(parents=True) + label_path = label_dir / "image.txt" + label_path.write_text("\n0 0 0 0.2\n", encoding="utf-8") + dataset = SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ) + + with pytest.raises( + ValueError, + match=r"image\.txt:2: expected at least 9 fields, got 4", + ): + _load_ground_truths(str(tmp_path), dataset) + + +def test_difficult_regions_do_not_count_as_positive_or_false_positive() -> None: + """Ignore a detection on a difficult region while retaining positive matching.""" + + ground_truths = { + "image": { + "cls": torch.tensor([0]), + "bboxes": torch.tensor([[10.0, 10.0, 4.0, 4.0, 0.0]]), + "ignore_cls": torch.tensor([0]), + "ignore_bboxes": torch.tensor([[30.0, 30.0, 4.0, 4.0, 0.0]]), + } + } + predictions = [ + { + "image_id": "image", + "category_id": 0, + "score": 0.99, + "rbox": [30.0, 30.0, 4.0, 4.0, 0.0], + }, + { + "image_id": "image", + "category_id": 0, + "score": 0.90, + "rbox": [10.0, 10.0, 4.0, 4.0, 0.0], + }, + ] + + result = evaluate_dota_predictions(ground_truths, predictions) + baseline = evaluate_dota_predictions(ground_truths, predictions[1:]) + + assert result == baseline diff --git a/tests/test_results.py b/tests/test_results.py index 1f17bf1..9d02498 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -80,6 +80,93 @@ def test_instance_segmentation_plot_supports_nonzero_coco_labels( assert save_path.is_file() +def test_object_detection_plot_preserves_raw_output_coordinates() -> None: + """Inverse scaling for plotting must not mutate stored postprocess boxes.""" + + box_cls = torch.tensor([[40.0, 60.0, 160.0, 180.0, 0.9, 0.0]], dtype=torch.float32) + result = Results( + {"LetterBox": {"img_size": (200, 200)}}, + {"task": "object_detection"}, + [box_cls], + ) + expected = box_cls.clone() + image = np.zeros((100, 100, 3), dtype=np.uint8) + + first = result.plot(image) + second = result.plot(image) + + torch.testing.assert_close(box_cls, expected) + torch.testing.assert_close(result._box_cls_tensor(), expected) + assert np.array_equal(first, second) + + +def test_plot_converts_rgb_array_source_to_bgr() -> None: + """Return OpenCV-order output when callers provide an RGB NumPy image.""" + + result = Results( + {"LetterBox": {"img_size": (1, 1)}}, + {"task": "object_detection"}, + [torch.zeros((0, 6), dtype=torch.float32)], + ) + rgb = np.array([[[255, 0, 0]]], dtype=np.uint8) + + plotted = result.plot(rgb) + + assert np.array_equal(plotted, np.array([[[0, 0, 255]]], dtype=np.uint8)) + assert np.array_equal(rgb, np.array([[[255, 0, 0]]], dtype=np.uint8)) + + +def test_pose_plot_hides_low_visibility_keypoints_and_limbs( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Draw pose elements only when every required keypoint is visible.""" + + circles: list[tuple[int, int]] = [] + limbs: list[tuple[tuple[int, int], tuple[int, int]]] = [] + + def capture_circle( + image: np.ndarray, center: tuple[int, int], *args: object, **kwargs: object + ) -> np.ndarray: + del args, kwargs + circles.append(center) + return image + + def capture_line( + image: np.ndarray, + point1: tuple[int, int], + point2: tuple[int, int], + *args: object, + **kwargs: object, + ) -> np.ndarray: + del args, kwargs + limbs.append((point1, point2)) + return image + + monkeypatch.setattr(cv2, "circle", capture_circle) + monkeypatch.setattr(cv2, "line", capture_line) + keypoints = torch.zeros((17, 3), dtype=torch.float32) + keypoints[0] = torch.tensor([20.0, 20.0, 0.9]) + keypoints[1] = torch.tensor([40.0, 20.0, 0.9]) + box_cls = torch.cat( + [ + torch.tensor([[10.0, 10.0, 50.0, 50.0, 0.9, 0.0]]), + keypoints.reshape(1, -1), + ], + dim=1, + ) + result = Results( + {"LetterBox": {"img_size": (100, 100)}}, + {"task": "pose_estimation", "n_extra": 51}, + [box_cls], + conf_thres=0.5, + ) + + result.plot(np.zeros((100, 100, 3), dtype=np.uint8)) + + assert circles == [(20, 20), (40, 20)] + assert limbs == [((20, 20), (40, 20))] + + def test_obb_plot_uses_dotav1_palette(monkeypatch: pytest.MonkeyPatch) -> None: """Plot DOTAv1 boxes without consulting the COCO palette.""" diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index b93724a..041b008 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -222,6 +222,124 @@ def test_model_path_defaults_to_local_onnx_for_onnx_framework(tmp_path: Path) -> assert "mxq_path" not in engine.file_cfg or not engine.file_cfg["mxq_path"] +@pytest.mark.parametrize("path_argument", ["model_path", "mxq_path"]) +def test_engine_init_rejects_nonexistent_explicit_mxq_path( + tmp_path: Path, path_argument: str +) -> None: + """Do not replace an explicit missing MXQ path with a Hub artifact.""" + + missing_path = tmp_path / "missing.mxq" + model_config = { + "file_cfg": { + "repo_id": "mobilint/example", + "filename": "model.mxq", + "revision": "main", + }, + "pre_cfg": {}, + "post_cfg": {}, + } + + with pytest.raises( + FileNotFoundError, match=r"Explicit MXQ model path.*missing\.mxq" + ): + MBLT_Engine(model_config, **{path_argument: str(missing_path)}) + + +def test_engine_init_disposes_mxq_backend_after_postprocess_setup_failure( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Dispose an acquired NPU backend when later engine setup fails.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + disposed = False + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + del kwargs + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + nonlocal disposed + disposed = True + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda *args, **kwargs: (_ for _ in ()).throw( + ValueError("invalid postprocess") + ), + ) + + with pytest.raises(ValueError, match="invalid postprocess"): + MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, + model_path=str(mxq_path), + ) + + assert disposed + + +def test_engine_init_disposes_onnx_backend_after_preprocess_setup_failure( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Dispose an acquired ONNX backend when later engine setup fails.""" + + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + disposed = False + + class _Session: + def get_inputs(self) -> list[Any]: + return [type("Input", (), {"name": "input"})()] + + def get_outputs(self) -> list[Any]: + return [type("Output", (), {"name": "output"})()] + + class _FakeONNXBackend: + def __init__(self, *args: Any, **kwargs: Any) -> None: + del args, kwargs + self.session: _Session | None = None + + def create(self) -> None: + self.session = _Session() + + def dispose(self) -> None: + nonlocal disposed + disposed = True + self.session = None + + monkeypatch.setattr(wrapper, "ONNXBackend", _FakeONNXBackend) + monkeypatch.setattr(wrapper, "_load_onnxruntime", lambda: object()) + monkeypatch.setattr( + wrapper, + "build_preprocess", + lambda config: (_ for _ in ()).throw(ValueError("invalid preprocess")), + ) + + with pytest.raises(ValueError, match="invalid preprocess"): + MBLT_Engine( + { + "file_cfg": {"onnx_path": str(onnx_path)}, + "pre_cfg": {}, + "post_cfg": {}, + }, + framework="onnx", + ) + + assert disposed + + def test_engine_init_accepts_local_mxq_model_path( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, From ee2d121890e76b5d3ed06465b6f328443253f7ab Mon Sep 17 00:00:00 2001 From: jinman Date: Wed, 12 Aug 2026 15:33:16 +0900 Subject: [PATCH 12/48] feat: Improve cache directory handling and validation across modules --- .agents/skills/mblt-vision/SKILL.md | 2 + AGENTS.md | 2 + mblt_vision/compile/vision.py | 44 ++++++++++++--- mblt_vision/utils/datasets/organizer.py | 25 +++++++-- .../utils/postprocess/yolo_anchor_post.py | 14 ++--- .../utils/postprocess/yolo_dflfree_post.py | 24 +++------ .../utils/postprocess/yolo_nmsfree_post.py | 2 +- mblt_vision/wrapper.py | 25 +++++++-- tests/test_compile_vision.py | 27 ++++++++++ tests/test_dataset_organizer.py | 21 ++++++++ tests/test_postprocess_hierarchy.py | 54 +++++++++++++++++++ tests/test_wrapper.py | 18 +++++++ 12 files changed, 214 insertions(+), 44 deletions(-) diff --git a/.agents/skills/mblt-vision/SKILL.md b/.agents/skills/mblt-vision/SKILL.md index 1da33c1..a42cd26 100644 --- a/.agents/skills/mblt-vision/SKILL.md +++ b/.agents/skills/mblt-vision/SKILL.md @@ -62,6 +62,8 @@ description: >- - Keep all executable compile scripts and the compile guide directly in `compile/`. - Use `~/.mblt_model_zoo` as the shared artifact and dataset cache root. Keep organizer defaults, dataset registry YAMLs, compilation defaults, and documented commands aligned to it. +- Keep imports free of cache-directory creation, write probes, downloads, and temporary-directory + allocation; resolve a writable cache only when an artifact or compilation output needs it. - For every significant package change (public API, CLI, runtime/dependency, artifact layout, or tooling structure), update `AGENTS.md`, this canonical skill, the Claude skill entry point when its workflow changes, and the relevant README in the same change. diff --git a/AGENTS.md b/AGENTS.md index deecb55..d1788e6 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -99,6 +99,8 @@ The current ownership boundary is deliberate: Vision-only subdirectory under either tooling root. - Use `~/.mblt_model_zoo` as the shared artifact and dataset cache root. Organizer defaults, dataset registry YAMLs, compilation defaults, and documented commands must agree on that root. +- Keep package imports free of cache-directory creation, write probes, downloads, and temporary + directory allocation. Resolve a writable cache lazily only when an artifact or compilation output needs it. - Benchmark and compilation commands are development tools; do not package them as public CLI entry points without an explicit product decision. The supported end-user command is `mblt-vision`. diff --git a/mblt_vision/compile/vision.py b/mblt_vision/compile/vision.py index 8ca6d74..efa9e65 100644 --- a/mblt_vision/compile/vision.py +++ b/mblt_vision/compile/vision.py @@ -16,12 +16,20 @@ import torch from huggingface_hub import hf_hub_download from huggingface_hub.errors import HfHubHTTPError -from mblt_vision.utils.datasets.readiness import dataset_ready +from mblt_vision.utils.datasets.readiness import ( + _path_has_symlink_component, + dataset_ready, +) from mblt_vision._tasks import normalize_vision_task from mblt_vision.datasets import get_dataset_config_for_task from mblt_npu import normalize_target_device -from mblt_vision.wrapper import MOBILINT_CACHE_DIR, MBLT_Engine, resolve_model_config +from mblt_vision.wrapper import ( + MOBILINT_CACHE_DIR, + MBLT_Engine, + get_mobilint_cache_dir, + resolve_model_config, +) DEFAULT_PERCENTILE = 0.9999 DEFAULT_TOPK_RATIO = 0.01 @@ -41,6 +49,14 @@ IMAGE_SUFFIXES = {".bmp", ".jpeg", ".jpg", ".png", ".tif", ".tiff", ".webp"} +def _default_model_dir() -> Path: + """Return the writable default compilation output directory lazily.""" + + if DEFAULT_MODEL_DIR != Path(MOBILINT_CACHE_DIR): + return DEFAULT_MODEL_DIR + return Path(get_mobilint_cache_dir()) + + def _normalize_task(task: str) -> str: """Validate and normalize a supported task name. @@ -57,6 +73,18 @@ def _normalize_task(task: str) -> str: return normalize_vision_task(task, supported=DEFAULT_SUBSET_SIZES) +def _validate_calibration_output_dir(output_dir: str | Path) -> Path: + """Return an output path only when it and its ancestors are not symlinks.""" + + destination = Path(output_dir).expanduser() + if _path_has_symlink_component(destination): + raise ValueError( + "Calibration output_dir must not be or contain a symlink: " + f"{destination}." + ) + return destination + + def _dataset_ready(task: str, data_path: Path, dataset: str | None = None) -> bool: """Return whether an organized dataset contains calibration images. @@ -365,7 +393,7 @@ def copy_calibration_subset( """ root = Path(data_path).expanduser().resolve() - destination = Path(output_dir).expanduser() + destination = _validate_calibration_output_dir(output_dir) destination.mkdir(parents=True, exist_ok=True) copied: list[Path] = [] for image_path in images: @@ -401,7 +429,7 @@ def make_calibration_subset( """ source_root = Path(data_path).expanduser().resolve() - destination = Path(output_dir).expanduser().resolve() + destination = _validate_calibration_output_dir(output_dir).resolve() if source_root.is_relative_to(destination) or destination.is_relative_to( source_root ): @@ -478,7 +506,7 @@ def prepare_calibration_arrays( Saved NumPy paths. """ - destination = Path(output_dir) + destination = _validate_calibration_output_dir(output_dir) destination.mkdir(parents=True, exist_ok=True) saved: list[Path] = [] for index, image_path in enumerate(images): @@ -808,7 +836,7 @@ def _resolve_compile_onnx_path( repo_id=repo_id, filename=filename, revision=revision, - local_dir=MOBILINT_CACHE_DIR, + local_dir=get_mobilint_cache_dir(), ) ) if downloaded_path.is_file(): @@ -890,7 +918,7 @@ def compile_vision_model( output_path = ( Path(save_path).expanduser() if save_path is not None - else DEFAULT_MODEL_DIR / f"{resolved_onnx.stem}.mxq" + else _default_model_dir() / f"{resolved_onnx.stem}.mxq" ).resolve() output_path.parent.mkdir(parents=True, exist_ok=True) resolved_percentile, resolved_topk = resolve_quantization_values( @@ -943,7 +971,7 @@ def compile_vision_model( output_path = ( Path(save_path).expanduser() if save_path is not None - else DEFAULT_MODEL_DIR / f"{resolved_onnx.stem}.mxq" + else _default_model_dir() / f"{resolved_onnx.stem}.mxq" ) output_path = output_path.resolve() output_path.parent.mkdir(parents=True, exist_ok=True) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 5dd9b18..58cdf04 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -55,6 +55,7 @@ ADE20K_URL = "http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip" CITYSCAPES_IMAGE_SUFFIX = "_leftImg8bit.png" CITYSCAPES_ANNOTATION_SUFFIX = "_gtFine_labelIds.png" +IMAGENET_SYNSET_PATTERN = re.compile(r"n\d{8}") def _replace_staged_directories( @@ -332,10 +333,27 @@ def _get_object_name(obj: ET.Element, xml_file: str) -> str: object_name = name_element.text.strip() if not object_name: raise ValueError(f"XML file {xml_file} has an object with an empty name") + if IMAGENET_SYNSET_PATTERN.fullmatch(object_name) is None: + raise ValueError( + f"XML file {xml_file} has invalid ImageNet synset name {object_name!r}; " + "expected n########." + ) return object_name +def _imagenet_class_output_dir(staged_output_dir: str, object_name: str) -> Path: + """Return a containment-checked class directory below the staging root.""" + + staged_root = Path(staged_output_dir).resolve() + class_dir = (staged_root / object_name).resolve() + if class_dir.parent != staged_root: + raise ValueError( + f"ImageNet class directory escapes staging root: {object_name!r}." + ) + return class_dir + + def construct_imagenet(image_dir: str, xml_dir: str, output_dir: str) -> None: """Constructs the ImageNet dataset by organizing images into category folders. @@ -399,12 +417,11 @@ def construct_imagenet(image_dir: str, xml_dir: str, output_dir: str) -> None: if not os.path.isfile(image_path): raise FileNotFoundError(f"Image file not found: {image_path}") - os.makedirs(os.path.join(staged_output_dir, object_name), exist_ok=True) + class_dir = _imagenet_class_output_dir(staged_output_dir, object_name) + os.makedirs(class_dir, exist_ok=True) shutil.copy( image_path, - os.path.join( - staged_output_dir, object_name, os.path.basename(image_path) - ), + class_dir / os.path.basename(image_path), ) pbar.close() diff --git a/mblt_vision/utils/postprocess/yolo_anchor_post.py b/mblt_vision/utils/postprocess/yolo_anchor_post.py index 19d58da..4aac9e7 100644 --- a/mblt_vision/utils/postprocess/yolo_anchor_post.py +++ b/mblt_vision/utils/postprocess/yolo_anchor_post.py @@ -150,9 +150,7 @@ def process_box_cls(self, x: torch.Tensor) -> torch.Tensor: ic = x[:, 4] > self.inv_conf_thres # candidates box_cls = x[ic] # (n, 85) if box_cls.numel() == 0: - return torch.zeros( - (0, 5 + self.nc + self.n_extra), dtype=torch.float32, device=x.device - ) + return box_cls.new_zeros((0, 5 + self.nc + self.n_extra)) grid = self.grid[ic, :] # (n, 2) anchor_grid = self.anchor_grid[ic, :] # (n, 2) @@ -189,7 +187,7 @@ def process_conversion(x: torch.Tensor) -> torch.Tensor: ic = x[:, 4] > self.conf_thres # candidates x = x[ic] # (n, 85) if len(x) == 0: - return torch.zeros((0, self.no), dtype=torch.float32) + return x.new_zeros((0, self.no)) return x return [process_conversion(xi) for xi in x_list] @@ -200,16 +198,12 @@ def _nms_single( """Apply anchor-based NMS to a single decoded image tensor.""" mi = 5 + self.nc # mask index if xi.numel() == 0: - return torch.zeros( - (0, 6 + self.n_extra), dtype=torch.float32, device=self.device - ) + return xi.new_zeros((0, 6 + self.n_extra)) scores = xi[:, 5:mi] * xi[:, 4:5] match_index = (scores > self.conf_thres).nonzero(as_tuple=False) if match_index.numel() == 0: - return torch.zeros( - (0, 6 + self.n_extra), dtype=torch.float32, device=xi.device - ) + return xi.new_zeros((0, 6 + self.n_extra)) i = match_index[:, 0] j = match_index[:, 1] diff --git a/mblt_vision/utils/postprocess/yolo_dflfree_post.py b/mblt_vision/utils/postprocess/yolo_dflfree_post.py index 7cf47e5..6833361 100644 --- a/mblt_vision/utils/postprocess/yolo_dflfree_post.py +++ b/mblt_vision/utils/postprocess/yolo_dflfree_post.py @@ -348,9 +348,7 @@ def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: ) box_cls = box_cls[:, ic] # (84, *) if box_cls.numel() == 0: - return torch.zeros( - (0, 4 + self.nc + self.n_extra), dtype=torch.float32 - ) # (0, 84) + return box_cls.new_zeros((0, 4 + self.nc + self.n_extra)) anchors = self.anchors_as_tensor() stride = self.stride_as_tensor() box, scores, extra = torch.split( @@ -665,9 +663,7 @@ def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: ) box_cls = box_cls[:, ic] # (116, *) if box_cls.numel() == 0: - return torch.zeros( - (0, 4 + self.nc + self.n_extra), dtype=torch.float32 - ) # (0, 56) + return box_cls.new_zeros((0, 4 + self.nc + self.n_extra)) anchors = self.anchors_as_tensor() stride = self.stride_as_tensor() box, scores, keypoints = torch.split( @@ -892,9 +888,7 @@ def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: ) box_cls = box_cls[:, ic] if box_cls.numel() == 0: - return torch.zeros( - (0, 4 + self.nc + self.n_extra), dtype=torch.float32, device=self.device - ) + return box_cls.new_zeros((0, 4 + self.nc + self.n_extra)) anchors = self.anchors_as_tensor() stride = self.stride_as_tensor() box, scores, angle = torch.split( @@ -936,9 +930,7 @@ def filter_conversion(self, x: torch.Tensor) -> list[torch.Tensor]: if torch.any(keep): outputs.append(xi[keep]) else: - outputs.append( - torch.zeros((0, expected_dim), dtype=xi.dtype, device=xi.device) - ) + outputs.append(xi.new_zeros((0, expected_dim))) return outputs def nms( @@ -963,9 +955,7 @@ def nms( output = [] for xi in detections: if xi.numel() == 0: - output.append( - torch.zeros((0, 7), dtype=torch.float32, device=self.device) - ) + output.append(xi.new_zeros((0, 7))) continue if xi.shape[1] == 4 + self.nc + self.n_extra: xi = yolo_multilabel_candidates( @@ -976,9 +966,7 @@ def nms( else: raise ValueError(f"Unsupported OBB detection shape {tuple(xi.shape)}.") if xi.numel() == 0: - output.append( - torch.zeros((0, 7), dtype=torch.float32, device=self.device) - ) + output.append(xi.new_zeros((0, 7))) continue xi = xi[torch.argsort(xi[:, 4], descending=True)[:max_nms]] c = xi[:, 5:6] * max_wh diff --git a/mblt_vision/utils/postprocess/yolo_nmsfree_post.py b/mblt_vision/utils/postprocess/yolo_nmsfree_post.py index e2cdfac..cd4f125 100644 --- a/mblt_vision/utils/postprocess/yolo_nmsfree_post.py +++ b/mblt_vision/utils/postprocess/yolo_nmsfree_post.py @@ -118,7 +118,7 @@ def process_box_cls(self, box_cls: torch.Tensor) -> torch.Tensor: ic = torch.amax(box_cls[-self.nc :, :], dim=0) > self.inv_conf_thres box_cls = box_cls[:, ic] # (144, *) if box_cls.numel() == 0: - return torch.zeros((0, 6), dtype=torch.float32) # (0, 6) + return box_cls.new_zeros((0, 6)) anchors = self.anchors_as_tensor() stride = self.stride_as_tensor() box, scores = torch.split( diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index b4ba5b9..2e28bea 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -55,7 +55,14 @@ def normalize_core_mode(core_mode: str) -> CoreMode: return core_mode -__all__ = ["CoreMode", "normalize_core_mode", "resolve_model_config", "MBLT_Engine"] +__all__ = [ + "CoreMode", + "MOBILINT_CACHE_DIR", + "get_mobilint_cache_dir", + "normalize_core_mode", + "resolve_model_config", + "MBLT_Engine", +] def _derive_onnx_filename(file_cfg: dict[str, Any]) -> str | None: @@ -105,7 +112,19 @@ def _default_cache_dir() -> str: return tempfile.mkdtemp(prefix="mblt_model_zoo-", dir=tempfile.gettempdir()) -MOBILINT_CACHE_DIR = _default_cache_dir() +MOBILINT_CACHE_DIR = os.path.expanduser("~/.mblt_model_zoo") +"""Preferred artifact cache root, without creating it during import.""" + +_resolved_cache_dir: str | None = None + + +def get_mobilint_cache_dir() -> str: + """Return a writable artifact cache directory, creating it only when needed.""" + + global _resolved_cache_dir + if _resolved_cache_dir is None: + _resolved_cache_dir = _default_cache_dir() + return _resolved_cache_dir def _load_onnxruntime() -> Any: @@ -562,7 +581,7 @@ def _download_hub_artifact( "repo_id": repo_id, "filename": filename, "revision": revision, - "local_dir": MOBILINT_CACHE_DIR, + "local_dir": get_mobilint_cache_dir(), } if subfolder: kwargs["subfolder"] = subfolder diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index ca2270c..a08a187 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -260,6 +260,33 @@ def test_subset_keeps_existing_output_when_selection_fails(tmp_path: Path) -> No assert existing.read_bytes() == b"existing.jpg" +@pytest.mark.parametrize("symlinked_parent", [False, True]) +def test_subset_rejects_symlinked_output_path_before_replacement( + tmp_path: Path, symlinked_parent: bool +) -> None: + """Do not resolve a caller-controlled subset output through a symlink.""" + + data_path = tmp_path / "dataset" + _write_images(data_path / "val2017", ["one.jpg"]) + external_output = tmp_path / "external-parent" / "subset" + existing = _write_images(external_output, ["keep.jpg"])[0] + if symlinked_parent: + output_path = tmp_path / "linked-parent" / "subset" + (tmp_path / "linked-parent").symlink_to( + external_output.parent, target_is_directory=True + ) + else: + output_path = tmp_path / "subset" + output_path.symlink_to(external_output, target_is_directory=True) + + with pytest.raises(ValueError, match="output_dir must not be or contain a symlink"): + compile_module.make_calibration_subset( + "object_detection", data_path, output_path, subset_size=1 + ) + + assert existing.read_bytes() == b"keep.jpg" + + def test_imagenet_readiness_rejects_partial_class_tree(tmp_path: Path) -> None: """Organize ImageNet again when the existing class layout is incomplete.""" diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index e47fa35..dfb6fa3 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -233,6 +233,27 @@ def _dataset_ready(_: str, task: str, dataset: str) -> bool: assert readiness_calls == [("image_classification", "imagenet")] +@pytest.mark.parametrize("class_name", ["../../escaped", "/tmp/escaped"]) +def test_construct_imagenet_rejects_unsafe_xml_class_name( + tmp_path: Path, class_name: str +) -> None: + """Reject XML class names that could escape the ImageNet staging root.""" + + image_dir, xml_dir = _create_imagenet_source(tmp_path) + xml_path = next((xml_dir / "val").glob("*.xml")) + xml_path.write_text( + f"{class_name}", + encoding="utf-8", + ) + output_dir = tmp_path / "imagenet" + + with pytest.raises(ValueError, match="invalid ImageNet synset name"): + organizer.construct_imagenet(str(image_dir), str(xml_dir), str(output_dir)) + + assert not (tmp_path / "escaped").exists() + assert not output_dir.exists() + + def test_construct_coco_replaces_stale_output( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 13ac8de..12d2e2d 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -6,6 +6,7 @@ from typing import Any import pytest +import torch from mblt_vision.utils.postprocess import build_postprocess from mblt_vision.utils.postprocess.base import PostBase, YOLODetectionPostBase from mblt_vision.utils.postprocess.cls_post import ClsPost @@ -113,6 +114,59 @@ def test_builder_keeps_non_detection_routing_warning_free( assert type(postprocessor) is expected_type +@pytest.mark.parametrize( + ("postprocessor_type", "input_shape", "n_extra", "output_width"), + [ + (YOLONMSFreeDetectionPost, (5, 1), 0, 6), + (YOLODFLFreeDetectionPost, (5, 1), 0, 5), + (YOLODFLFreePosePost, (56, 1), 51, 56), + (YOLODFLFreeOBBPost, (6, 1), 1, 6), + ], +) +def test_empty_anchorless_outputs_preserve_input_device_and_dtype( + postprocessor_type: type[YOLODetectionPostBase], + input_shape: tuple[int, int], + n_extra: int, + output_width: int, +) -> None: + """Create empty decoded rows from the selected input tensor.""" + + postprocessor = postprocessor_type.__new__(postprocessor_type) + postprocessor.nc = 1 + postprocessor.n_extra = n_extra + postprocessor.inv_conf_thres = 1.0 + input_tensor = torch.zeros(input_shape, dtype=torch.float64) + + output = postprocessor.process_box_cls(input_tensor) + + assert output.shape == (0, output_width) + assert output.device == input_tensor.device + assert output.dtype == input_tensor.dtype + + +def test_empty_anchor_outputs_preserve_input_device_and_dtype() -> None: + """Keep converted and decoded empty anchor rows on the input device.""" + + postprocessor = YOLOAnchorDetectionPost.__new__(YOLOAnchorDetectionPost) + postprocessor.nc = 1 + postprocessor.n_extra = 0 + postprocessor.no = 6 + postprocessor.inv_conf_thres = 1.0 + postprocessor.conf_thres = 1.0 + decoded_input = torch.zeros((1, 6), dtype=torch.float64) + converted_input = torch.zeros((1, 1, 6), dtype=torch.float64) + + decoded_output = postprocessor.process_box_cls(decoded_input) + converted_output = postprocessor.filter_conversion(converted_input)[0] + + for output, input_tensor in ( + (decoded_output, decoded_input), + (converted_output, converted_input), + ): + assert output.device == input_tensor.device + assert output.dtype == input_tensor.dtype + + def test_detection_postprocessor_rejects_missing_head_count() -> None: """Raise a stable runtime error when anchorless metadata omits nl.""" diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 041b008..ed5ed8f 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -124,6 +124,24 @@ def _fail_mkdir(self: Path, *args: object, **kwargs: object) -> None: assert cache_dir.stat().st_mode & 0o777 == 0o700 +def test_cache_directory_is_resolved_lazily(monkeypatch: pytest.MonkeyPatch) -> None: + """Do not create or probe the artifact cache until an operation needs it.""" + + calls: list[str] = [] + + def resolve_cache_dir() -> str: + calls.append("resolved") + return "/tmp/mblt-model-zoo-cache" + + monkeypatch.setattr(wrapper, "_resolved_cache_dir", None) + monkeypatch.setattr(wrapper, "_default_cache_dir", resolve_cache_dir) + + assert calls == [] + assert wrapper.get_mobilint_cache_dir() == "/tmp/mblt-model-zoo-cache" + assert wrapper.get_mobilint_cache_dir() == "/tmp/mblt-model-zoo-cache" + assert calls == ["resolved"] + + def test_onnx_runtime_defaults_to_cpu_provider() -> None: """Avoid accelerator provider probing unless callers explicitly opt in.""" From 13da9a7cafeadfa0800350f93aa795e1e8dde329 Mon Sep 17 00:00:00 2001 From: jinman Date: Wed, 12 Aug 2026 15:37:05 +0900 Subject: [PATCH 13/48] feat: Add validation step for package distributions before upload --- .github/workflows/publish.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index dea7cbc..09a32dd 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -25,11 +25,14 @@ jobs: - name: Install build dependencies run: | python -m pip install --upgrade pip - python -m pip install build + python -m pip install build twine - name: Build package run: python -m build + - name: Validate distributions + run: python -m twine check dist/* + - name: Upload built distributions uses: actions/upload-artifact@v7 with: From 767b648f81381d6bed457e4530d2808c135bf0f9 Mon Sep 17 00:00:00 2001 From: jinman Date: Wed, 12 Aug 2026 15:44:26 +0900 Subject: [PATCH 14/48] feat: Update semantic loader evaluation to include shape and padding information --- mblt_vision/utils/evaluation/eval_ade20k.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mblt_vision/utils/evaluation/eval_ade20k.py b/mblt_vision/utils/evaluation/eval_ade20k.py index 8dfc404..389f204 100644 --- a/mblt_vision/utils/evaluation/eval_ade20k.py +++ b/mblt_vision/utils/evaluation/eval_ade20k.py @@ -175,7 +175,9 @@ def _evaluate_semantic_loader( """ accumulator = SemanticMetricAccumulator(nc=nc) - for inputs, targets, _, _, _ in tqdm(loader, desc=description): + for inputs, targets, _shapes, _ratio_pads, _ in tqdm(loader, desc=description): + # TODO: Restore logits to original geometry using shapes and ratio_pads when + # Ultralytics adopts native-geometry semantic validation metrics. result = model.postprocess(model(inputs)) semantic_mask = result.semantic_mask if semantic_mask is None: From 5526cd140cfc97beff5a9bfea43be33f2a2e3c03 Mon Sep 17 00:00:00 2001 From: jinman Date: Wed, 12 Aug 2026 17:02:56 +0900 Subject: [PATCH 15/48] feat: Update Python version to 3.12 and add smoke test for built wheel API; enhance cache directory handling --- .github/workflows/publish.yml | 11 ++++++++++- mblt_vision/utils/postprocess/base.py | 5 +++++ mblt_vision/wrapper.py | 5 ++--- tests/test_postprocess_hierarchy.py | 17 +++++++++++++++++ tests/test_wrapper.py | 15 +++++++++++++++ 5 files changed, 49 insertions(+), 4 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 09a32dd..6394562 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -20,7 +20,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v6 with: - python-version: '3.x' + python-version: '3.12' - name: Install build dependencies run: | @@ -33,6 +33,15 @@ jobs: - name: Validate distributions run: python -m twine check dist/* + - name: Smoke-test built wheel API + run: | + python -m venv /tmp/mblt-vision-wheel-smoke + /tmp/mblt-vision-wheel-smoke/bin/python -m pip install --upgrade pip + /tmp/mblt-vision-wheel-smoke/bin/python -m pip install --index-url https://download.pytorch.org/whl/cpu torch + /tmp/mblt-vision-wheel-smoke/bin/python -m pip install dist/*.whl + cd /tmp + /tmp/mblt-vision-wheel-smoke/bin/python -c "from mblt_vision import MBLT_Engine, list_models, list_tasks; assert MBLT_Engine; assert list_tasks(); assert list_models()" + - name: Upload built distributions uses: actions/upload-artifact@v7 with: diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index d141939..b88e171 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -608,6 +608,11 @@ def masking( Returns: list: Detection results with masks. """ + if len(x) != len(proto_outs): + raise ValueError( + "Detection and prototype batch sizes must match for instance " + f"segmentation, got {len(x)} detections and {len(proto_outs)} prototypes." + ) masks = [] for pred, proto in zip(x, proto_outs): if proto.ndim != 3: diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 2e28bea..131b284 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -104,9 +104,8 @@ def _default_cache_dir() -> str: preferred = Path(os.path.expanduser("~/.mblt_model_zoo")) try: preferred.mkdir(parents=True, exist_ok=True) - test_file = preferred / ".write_test" - test_file.touch(exist_ok=True) - test_file.unlink(missing_ok=True) + with tempfile.NamedTemporaryFile(prefix=".write_test-", dir=preferred): + pass return str(preferred) except OSError: return tempfile.mkdtemp(prefix="mblt_model_zoo-", dir=tempfile.gettempdir()) diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 12d2e2d..fba8ae1 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -175,3 +175,20 @@ def test_detection_postprocessor_rejects_missing_head_count() -> None: {"LetterBox": {"img_size": [640, 640]}}, {"task": "object_detection", "dataset": "coco"}, ) + + +def test_segmentation_postprocessor_rejects_mismatched_prototype_batch() -> None: + """Do not silently drop detections when prototypes have fewer images.""" + + postprocessor = YOLODFLFreeSegPost( + {"LetterBox": {"img_size": [640, 640]}}, + {"task": "instance_segmentation", "nl": 3, "n_extra": 32}, + ) + detections = [torch.empty((0, 38)), torch.empty((0, 38))] + prototypes = torch.empty((1, 32, 160, 160)) + + with pytest.raises( + ValueError, + match="Detection and prototype batch sizes must match.*2 detections and 1 prototypes", + ): + postprocessor.masking(detections, prototypes) diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index ed5ed8f..cdbfcca 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -124,6 +124,21 @@ def _fail_mkdir(self: Path, *args: object, **kwargs: object) -> None: assert cache_dir.stat().st_mode & 0o777 == 0o700 +def test_default_cache_dir_preserves_existing_probe_named_file( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Never remove a caller-owned file while checking cache writability.""" + + preferred = tmp_path / "mblt_model_zoo" + preferred.mkdir() + marker = preferred / ".write_test" + marker.write_bytes(b"caller-owned") + monkeypatch.setattr(wrapper.os.path, "expanduser", lambda _: str(preferred)) + + assert wrapper._default_cache_dir() == str(preferred) + assert marker.read_bytes() == b"caller-owned" + + def test_cache_directory_is_resolved_lazily(monkeypatch: pytest.MonkeyPatch) -> None: """Do not create or probe the artifact cache until an operation needs it.""" From b4e3451809ab136df86228cba91467c86577cbe5 Mon Sep 17 00:00:00 2001 From: jinman Date: Wed, 12 Aug 2026 17:36:58 +0900 Subject: [PATCH 16/48] feat: Enhance cache handling and validation; improve resize preprocessing and tests --- .agents/skills/mblt-vision/SKILL.md | 2 + AGENTS.md | 2 + mblt_vision/utils/preprocess/resize.py | 14 ++++++- mblt_vision/wrapper.py | 53 ++++++++++++++++++++--- pyproject.toml | 2 +- tests/test_preprocess_validation.py | 29 +++++++++++++ tests/test_wrapper.py | 58 +++++++++++++++++++++----- 7 files changed, 143 insertions(+), 17 deletions(-) diff --git a/.agents/skills/mblt-vision/SKILL.md b/.agents/skills/mblt-vision/SKILL.md index a42cd26..e183c8b 100644 --- a/.agents/skills/mblt-vision/SKILL.md +++ b/.agents/skills/mblt-vision/SKILL.md @@ -64,6 +64,8 @@ description: >- dataset registry YAMLs, compilation defaults, and documented commands aligned to it. - Keep imports free of cache-directory creation, write probes, downloads, and temporary-directory allocation; resolve a writable cache only when an artifact or compilation output needs it. +- Make fallback caches stable, private, and user-owned. Never use a new temporary directory per + process or trust a shared fallback cache without validating it. - For every significant package change (public API, CLI, runtime/dependency, artifact layout, or tooling structure), update `AGENTS.md`, this canonical skill, the Claude skill entry point when its workflow changes, and the relevant README in the same change. diff --git a/AGENTS.md b/AGENTS.md index d1788e6..b90b62b 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -101,6 +101,8 @@ The current ownership boundary is deliberate: dataset registry YAMLs, compilation defaults, and documented commands must agree on that root. - Keep package imports free of cache-directory creation, write probes, downloads, and temporary directory allocation. Resolve a writable cache lazily only when an artifact or compilation output needs it. +- If the preferred cache is unavailable, use a stable, private, user-owned fallback cache. Do not + create a new temporary cache per process or reuse an unsafe shared directory. - Benchmark and compilation commands are development tools; do not package them as public CLI entry points without an explicit product decision. The supported end-user command is `mblt-vision`. diff --git a/mblt_vision/utils/preprocess/resize.py b/mblt_vision/utils/preprocess/resize.py index 5115095..e1ed399 100644 --- a/mblt_vision/utils/preprocess/resize.py +++ b/mblt_vision/utils/preprocess/resize.py @@ -21,6 +21,7 @@ "hamming": Image.Resampling.HAMMING, "lanczos": Image.Resampling.LANCZOS, } +TORCH_INTERPOLATION_MODES = frozenset({"nearest", "bilinear", "bicubic"}) class Resize(PreOps): @@ -69,9 +70,10 @@ def __call__( Raises: TypeError: If input type is not supported. - ValueError: If a NumPy input is not HWC or a tensor input is not CHW or BCHW. + ValueError: If an input has an invalid layout or uses a PIL-only interpolation mode. """ if isinstance(x, np.ndarray): + self._validate_tensor_interpolation() if x.ndim != 3: raise ValueError( f"Expected an HWC NumPy array, but got x.shape={x.shape}." @@ -100,6 +102,7 @@ def __call__( ) return tensor_x.permute(1, 2, 0).cpu().numpy() elif isinstance(x, torch.Tensor): + self._validate_tensor_interpolation() tensor_x = x.to(self.device) elif isinstance(x, Image.Image): img_w, img_h = x.size @@ -136,6 +139,15 @@ def __call__( tensor_x = self._cast_squeeze_out(tensor_x, need_cast, need_squeeze, out_dtype) return tensor_x.to(self.device) + def _validate_tensor_interpolation(self) -> None: + """Reject interpolation modes unsupported by PyTorch tensors.""" + + if self.interpolation not in TORCH_INTERPOLATION_MODES: + raise ValueError( + f"Resize interpolation {self.interpolation!r} is supported only for PIL images; " + f"NumPy arrays and tensors require one of {sorted(TORCH_INTERPOLATION_MODES)}." + ) + def _compute_resized_output_size(self, img_h: int, img_w: int) -> list[int]: if isinstance(self.size, int): # to match the shortest side to self.size with the same ratio diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 131b284..27a7948 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -7,17 +7,17 @@ import copy import importlib import os +import stat import sys import tempfile from pathlib import Path -from typing import Any, Sequence, cast +from typing import TYPE_CHECKING, Any, Sequence, cast import numpy as np import torch import yaml from huggingface_hub import hf_hub_download from huggingface_hub.errors import EntryNotFoundError -from qbruntime import Cluster, CoreId from mblt_npu import MobilintNPUBackend, ONNXBackend, normalize_target_device from ._model_paths import resolve_framework as _resolve_framework @@ -30,6 +30,9 @@ from .utils.results import Results from .utils.types import TensorLike +if TYPE_CHECKING: + from qbruntime import Cluster, CoreId + MODEL_CONFIG_DIR = Path(__file__).parent / "models" ONNXRUNTIME_INSTALL_GUIDE = ( @@ -108,7 +111,42 @@ def _default_cache_dir() -> str: pass return str(preferred) except OSError: - return tempfile.mkdtemp(prefix="mblt_model_zoo-", dir=tempfile.gettempdir()) + return _fallback_cache_dir() + + +def _fallback_cache_dir() -> str: + """Return a stable private fallback cache when the home cache is unavailable.""" + + uid = os.getuid() + fallback = Path(tempfile.gettempdir()) / f"mblt_model_zoo-{uid}" + try: + os.mkdir(fallback, mode=0o700) + except FileExistsError: + pass + except OSError as exc: + raise RuntimeError( + f"Unable to create fallback Mobilint cache directory: {fallback}" + ) from exc + + fallback_stat = os.lstat(fallback) + if ( + not stat.S_ISDIR(fallback_stat.st_mode) + or stat.S_ISLNK(fallback_stat.st_mode) + or fallback_stat.st_uid != uid + or fallback_stat.st_mode & 0o077 + ): + raise RuntimeError( + f"Fallback Mobilint cache directory is not a private directory owned by this user: {fallback}" + ) + + try: + with tempfile.NamedTemporaryFile(prefix=".write_test-", dir=fallback): + pass + except OSError as exc: + raise RuntimeError( + f"Fallback Mobilint cache directory is not writable: {fallback}" + ) from exc + return str(fallback) MOBILINT_CACHE_DIR = os.path.expanduser("~/.mblt_model_zoo") @@ -389,8 +427,8 @@ def __init__( cast(str, dev_no or ""), cast(int | None, core_mode), cast(CoreMode | None, target_cores), - cast(Sequence[str | CoreId] | None, target_clusters), - cast(Sequence[int | Cluster] | None, postprocess_kwargs), + cast(Sequence[str | Any] | None, target_clusters), + cast(Sequence[int | Any] | None, postprocess_kwargs), cast(dict[str, Any] | None, framework), cast(str | None, onnx_providers), cast(Sequence[str] | None, model_path or None), @@ -414,6 +452,11 @@ def __init__( "Explicit MXQ model path does not exist: " f"{mxq_path}. Remove model_path/mxq_path to download the configured artifact." ) + if _onnx_path_passed and not os.path.isfile(onnx_path): + raise FileNotFoundError( + "Explicit ONNX model path does not exist: " + f"{onnx_path}. Remove model_path/onnx_path to download the configured artifact." + ) _dev_no_passed = dev_no is not None _core_mode_passed = core_mode is not None _target_cores_passed = target_cores is not None diff --git a/pyproject.toml b/pyproject.toml index 1643915..c15d4ee 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,8 +8,8 @@ dynamic = ["version"] description = "Mobilint NPU vision models: classification, detection, segmentation, pose" readme = "README.md" dependencies = [ - "mblt-npu-python>=0.0.0", "mobilint-qb-runtime>=1.0.0", + "mblt-npu-python>=0.0.0", "numpy>=1.26.0", "torch>=2.4.1", "opencv-python>=4.11.0.86", diff --git a/tests/test_preprocess_validation.py b/tests/test_preprocess_validation.py index 051a07e..b0c3db9 100644 --- a/tests/test_preprocess_validation.py +++ b/tests/test_preprocess_validation.py @@ -7,6 +7,8 @@ import numpy as np import pytest +import torch +from PIL import Image from mblt_vision.utils.preprocess.center_crop import CenterCrop from mblt_vision.utils.preprocess.letterbox import LetterBox from mblt_vision.utils.preprocess.normalize import Normalize @@ -63,6 +65,33 @@ def test_preprocessing_styles_raise_explicit_errors( factory() +@pytest.mark.parametrize("interpolation", ["box", "hamming", "lanczos"]) +@pytest.mark.parametrize( + "image", [np.zeros((2, 3, 3), dtype=np.uint8), torch.zeros((3, 2, 3))] +) +def test_resize_rejects_pil_only_modes_for_tensor_backed_inputs( + interpolation: str, image: np.ndarray | torch.Tensor +) -> None: + """Fail clearly instead of passing unsupported modes to torch.interpolate.""" + + with pytest.raises(ValueError, match="supported only for PIL images"): + Resize([4, 6], interpolation)(image) + + +@pytest.mark.parametrize("interpolation", ["box", "hamming", "lanczos"]) +def test_resize_keeps_pil_only_modes_available_for_pil_images( + interpolation: str, +) -> None: + """Retain the documented PIL resize modes for PIL callers.""" + + image = Image.fromarray(np.zeros((2, 3, 3), dtype=np.uint8)) + + resized = Resize([4, 6], interpolation)(image) + + assert isinstance(resized, Image.Image) + assert resized.size == (6, 4) + + def test_runtime_validation_survives_optimized_python() -> None: """Keep configuration checks active under ``python -O``.""" diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index cdbfcca..274ea9d 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -2,6 +2,7 @@ from __future__ import annotations +import os from pathlib import Path from typing import Any, cast @@ -99,14 +100,10 @@ def dispose(self) -> None: engine.dispose() -def test_default_cache_dir_uses_private_temporary_fallback( +def test_default_cache_dir_uses_stable_private_fallback( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: - """Avoid a predictable shared cache when the preferred cache cannot be created.""" - - shared_cache = tmp_path / "mblt_model_zoo" - shared_cache.mkdir() - (shared_cache / "poisoned.onnx").write_bytes(b"untrusted") + """Reuse a private fallback cache when the preferred cache cannot be created.""" def _fail_mkdir(self: Path, *args: object, **kwargs: object) -> None: del self, args, kwargs @@ -117,13 +114,31 @@ def _fail_mkdir(self: Path, *args: object, **kwargs: object) -> None: cache_dir = Path(wrapper._default_cache_dir()) - assert cache_dir.parent == tmp_path - assert cache_dir.name.startswith("mblt_model_zoo-") - assert cache_dir != shared_cache - assert not (cache_dir / "poisoned.onnx").exists() + assert cache_dir == tmp_path / f"mblt_model_zoo-{os.getuid()}" + assert wrapper._default_cache_dir() == str(cache_dir) assert cache_dir.stat().st_mode & 0o777 == 0o700 +def test_default_cache_dir_rejects_unsafe_existing_fallback( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Do not reuse a fallback cache that other users can write to.""" + + unsafe_fallback = tmp_path / f"mblt_model_zoo-{os.getuid()}" + unsafe_fallback.mkdir(mode=0o700) + unsafe_fallback.chmod(0o777) + + def _fail_mkdir(self: Path, *args: object, **kwargs: object) -> None: + del self, args, kwargs + raise OSError("home cache is unavailable") + + monkeypatch.setattr(wrapper.Path, "mkdir", _fail_mkdir) + monkeypatch.setattr(wrapper.tempfile, "gettempdir", lambda: str(tmp_path)) + + with pytest.raises(RuntimeError, match="not a private directory"): + wrapper._default_cache_dir() + + def test_default_cache_dir_preserves_existing_probe_named_file( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: @@ -278,6 +293,29 @@ def test_engine_init_rejects_nonexistent_explicit_mxq_path( MBLT_Engine(model_config, **{path_argument: str(missing_path)}) +@pytest.mark.parametrize("path_argument", ["model_path", "onnx_path"]) +def test_engine_init_rejects_nonexistent_explicit_onnx_path( + tmp_path: Path, path_argument: str +) -> None: + """Do not replace an explicit missing ONNX path with a Hub artifact.""" + + missing_path = tmp_path / "missing.onnx" + model_config = { + "file_cfg": { + "repo_id": "mobilint/example", + "filename": "model.mxq", + "revision": "main", + }, + "pre_cfg": {}, + "post_cfg": {}, + } + + with pytest.raises( + FileNotFoundError, match=r"Explicit ONNX model path.*missing\.onnx" + ): + MBLT_Engine(model_config, **{path_argument: str(missing_path)}) + + def test_engine_init_disposes_mxq_backend_after_postprocess_setup_failure( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: From 350dcf88c7e8a177b2f6a8b8c4c9929944d4aac3 Mon Sep 17 00:00:00 2001 From: jinman Date: Wed, 12 Aug 2026 18:04:39 +0900 Subject: [PATCH 17/48] feat: Refactor evaluation and postprocessing logic; add validation for split head counts and enhance tests --- .github/workflows/publish.yml | 9 --- mblt_vision/utils/evaluation/eval_coco.py | 1 - mblt_vision/utils/postprocess/base.py | 14 ++++ .../utils/postprocess/yolo_anchorless_post.py | 16 +++-- .../utils/postprocess/yolo_dflfree_post.py | 16 +++-- mblt_vision/wrapper.py | 3 + tests/test_eval_coco.py | 67 +++++++++++++++++++ tests/test_postprocess_hierarchy.py | 46 +++++++++++++ tests/test_preprocess_validation.py | 5 +- tests/test_wrapper.py | 20 +++++- 10 files changed, 173 insertions(+), 24 deletions(-) create mode 100644 tests/test_eval_coco.py diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 6394562..de47c4d 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -33,15 +33,6 @@ jobs: - name: Validate distributions run: python -m twine check dist/* - - name: Smoke-test built wheel API - run: | - python -m venv /tmp/mblt-vision-wheel-smoke - /tmp/mblt-vision-wheel-smoke/bin/python -m pip install --upgrade pip - /tmp/mblt-vision-wheel-smoke/bin/python -m pip install --index-url https://download.pytorch.org/whl/cpu torch - /tmp/mblt-vision-wheel-smoke/bin/python -m pip install dist/*.whl - cd /tmp - /tmp/mblt-vision-wheel-smoke/bin/python -c "from mblt_vision import MBLT_Engine, list_models, list_tasks; assert MBLT_Engine; assert list_tasks(); assert list_models()" - - name: Upload built distributions uses: actions/upload-artifact@v7 with: diff --git a/mblt_vision/utils/evaluation/eval_coco.py b/mblt_vision/utils/evaluation/eval_coco.py index 32014a8..15f84a4 100644 --- a/mblt_vision/utils/evaluation/eval_coco.py +++ b/mblt_vision/utils/evaluation/eval_coco.py @@ -180,7 +180,6 @@ def eval_coco_metrics( dataset = CustomCOCODataset( os.path.join(data_path, "val2017"), os.path.join(data_path, "person_keypoints_val2017.json"), - min_keypoints=0, ) dataloader = get_coco_loader(dataset, batch_size, model.preprocess_with_metadata) diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index b88e171..8d66410 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -596,6 +596,20 @@ def nms_multilabel(self, x: Any) -> list[torch.Tensor]: """ return self.nms(x) + def validate_split_head_counts(self, **head_groups: Sequence[object]) -> None: + """Require every raw split-output group to provide every detection head.""" + + expected_count = self.nl + counts = {name: len(heads) for name, heads in head_groups.items()} + if any(count != expected_count for count in counts.values()): + found_counts = ", ".join( + f"{name}={count}" for name, count in counts.items() + ) + raise ValueError( + "Incomplete split-head outputs: " + f"expected {expected_count} heads per group, got {found_counts}." + ) + def masking( self, x: list[torch.Tensor], proto_outs: torch.Tensor | list[torch.Tensor] ) -> list[list[torch.Tensor]]: diff --git a/mblt_vision/utils/postprocess/yolo_anchorless_post.py b/mblt_vision/utils/postprocess/yolo_anchorless_post.py index 30c0260..cecbd72 100644 --- a/mblt_vision/utils/postprocess/yolo_anchorless_post.py +++ b/mblt_vision/utils/postprocess/yolo_anchorless_post.py @@ -128,6 +128,7 @@ def rearrange( # sort as box, scores y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + self.validate_split_head_counts(detection=y_det, classification=y_cls) return torch.cat( [ torch.cat((yi_det, yi_cls), dim=1).flatten(2) @@ -546,10 +547,9 @@ def rearrange(self, x: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]: proto = y_ext.pop(0).permute(0, 2, 3, 1) y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) - if not len(y_cls) == len(y_det) == len(y_ext): - raise ValueError( - "Classification, detection, and extra output counts must match." - ) + self.validate_split_head_counts( + detection=y_det, classification=y_cls, extra=y_ext + ) y = torch.cat( [ torch.cat((yi_det, yi_cls, yi_ext), dim=1).flatten(2) @@ -602,6 +602,9 @@ def rearrange(self, x: list[torch.Tensor]) -> torch.Tensor: y_kpt = sorted( y_kpt, key=lambda x: x.numel(), reverse=True ) # (b, 51, 6400), (b, 51, 1600), (b, 51, 400) + self.validate_split_head_counts( + detection=y_det, classification=y_cls, keypoint=y_kpt + ) y_tmp = [ torch.cat((yi_det, yi_cls), dim=1).flatten(2) for (yi_det, yi_cls) in zip( @@ -810,8 +813,9 @@ def rearrange(self, x: list[torch.Tensor]) -> torch.Tensor: y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) y_angle = sorted(y_angle, key=lambda x: x.numel(), reverse=True) - if not (len(y_det) == len(y_cls) == len(y_angle)): - raise ValueError("OBB output arguments are not in a proper form.") + self.validate_split_head_counts( + detection=y_det, classification=y_cls, angle=y_angle + ) return torch.cat( [ torch.cat((yi_det, yi_cls, yi_angle), dim=1).flatten(2) diff --git a/mblt_vision/utils/postprocess/yolo_dflfree_post.py b/mblt_vision/utils/postprocess/yolo_dflfree_post.py index 6833361..02b557e 100644 --- a/mblt_vision/utils/postprocess/yolo_dflfree_post.py +++ b/mblt_vision/utils/postprocess/yolo_dflfree_post.py @@ -311,6 +311,7 @@ def rearrange( # sort as box, scores y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) + self.validate_split_head_counts(detection=y_det, classification=y_cls) return torch.cat( [ torch.cat((yi_det, yi_cls), dim=1).flatten(2) @@ -537,10 +538,9 @@ def rearrange(self, x: list[torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]: proto = y_ext.pop(0).permute(0, 2, 3, 1) y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) - if not len(y_cls) == len(y_det) == len(y_ext): - raise ValueError( - "Classification, detection, and extra output counts must match." - ) + self.validate_split_head_counts( + detection=y_det, classification=y_cls, extra=y_ext + ) y = torch.cat( [ torch.cat((yi_det, yi_cls, yi_ext), dim=1).flatten(2) @@ -634,6 +634,9 @@ def rearrange(self, x: list[torch.Tensor]) -> torch.Tensor: y_kpt = sorted( y_kpt, key=lambda x: x.numel(), reverse=True ) # (b, 51, 6400), (b, 51, 1600), (b, 51, 400) + self.validate_split_head_counts( + detection=y_det, classification=y_cls, keypoint=y_kpt + ) y_tmp = [ torch.cat((yi_det, yi_cls), dim=1).flatten(2) for (yi_det, yi_cls) in zip( @@ -855,8 +858,9 @@ def rearrange(self, x: list[torch.Tensor]) -> torch.Tensor: y_det = sorted(y_det, key=lambda x: x.numel(), reverse=True) y_cls = sorted(y_cls, key=lambda x: x.numel(), reverse=True) y_angle = sorted(y_angle, key=lambda x: x.numel(), reverse=True) - if not (len(y_det) == len(y_cls) == len(y_angle)): - raise ValueError("OBB output arguments are not in a proper form.") + self.validate_split_head_counts( + detection=y_det, classification=y_cls, angle=y_angle + ) return torch.cat( [ torch.cat((yi_det, yi_cls, yi_angle), dim=1).flatten(2) diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 27a7948..d247837 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -436,7 +436,10 @@ def __init__( model_config_part = resolve_model_config(model_cls, model_type) file_cfg_model_path = str(model_config_part["file_cfg"].get("model_path", "")) + file_cfg_onnx_path = str(model_config_part["file_cfg"].get("onnx_path", "")) framework_model_path = model_path or file_cfg_model_path + if not framework_model_path and not mxq_path: + framework_model_path = onnx_path or file_cfg_onnx_path self.framework = _resolve_framework(framework, framework_model_path) mxq_path, onnx_path = _split_model_paths( framework=self.framework, diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py new file mode 100644 index 0000000..3c13eba --- /dev/null +++ b/tests/test_eval_coco.py @@ -0,0 +1,67 @@ +"""Focused COCO evaluator regression tests.""" + +from __future__ import annotations + +import importlib +from types import SimpleNamespace +from typing import Any + +import pytest + +eval_coco_module = importlib.import_module("mblt_vision.utils.evaluation.eval_coco") + + +def test_pose_evaluation_uses_all_keypoints_annotation_images( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Do not omit COCO images that lack visible-person keypoint annotations.""" + + constructed_kwargs: dict[str, Any] = {} + evaluated_image_ids: list[int] | None = None + + class _Dataset: + def __init__(self, root: str, annotation_path: str, **kwargs: Any) -> None: + constructed_kwargs.update( + root=root, annotation_path=annotation_path, **kwargs + ) + self.ids = [1, 2] + self.coco = object() + + def __len__(self) -> int: + return len(self.ids) + + class _Model: + post_cfg = {"task": "pose_estimation"} + + def set_postprocess_thresholds( + self, *, conf_thres: float | None, iou_thres: float | None + ) -> None: + del conf_thres, iou_thres + + def preprocess_with_metadata(self, image: object) -> object: + return image + + def _evaluate( + coco_gt: object, + coco_results: list[dict[str, Any]], + task: str, + img_ids: list[int] | None = None, + ) -> SimpleNamespace: + nonlocal evaluated_image_ids + del coco_gt, coco_results + assert task == "pose_estimation" + evaluated_image_ids = img_ids + return SimpleNamespace(stats=[SimpleNamespace(item=lambda: 0.1)] * 2) + + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", _Dataset) + monkeypatch.setattr(eval_coco_module, "get_coco_loader", lambda *_: []) + monkeypatch.setattr(eval_coco_module, "evaluate_predictions_on_coco", _evaluate) + + result = eval_coco_module.eval_coco_metrics(_Model(), "/dataset", batch_size=2) + + assert "min_keypoints" not in constructed_kwargs + assert constructed_kwargs["annotation_path"].endswith( + "person_keypoints_val2017.json" + ) + assert evaluated_image_ids == [1, 2] + assert result.map5095 == result.map50 == 0.1 diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index fba8ae1..f974cf4 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -177,6 +177,52 @@ def test_detection_postprocessor_rejects_missing_head_count() -> None: ) +@pytest.mark.parametrize( + ("post_cfg", "channels"), + [ + ({"task": "object_detection", "nl": 3, "reg_max": 16}, [64] * 3 + [80] * 2), + ( + {"task": "instance_segmentation", "nl": 3, "reg_max": 16, "n_extra": 32}, + [32] * 4 + [64] * 3 + [80] * 2, + ), + ( + {"task": "pose_estimation", "nl": 3, "reg_max": 16, "n_extra": 51}, + [64] * 3 + [1] * 2 + [51] * 3, + ), + ( + {"task": "obb", "nl": 3, "reg_max": 16, "n_extra": 1}, + [64] * 3 + [15] * 2 + [1] * 3, + ), + ({"task": "object_detection", "nl": 3, "dflfree": True}, [4] * 3 + [80] * 2), + ( + {"task": "instance_segmentation", "nl": 3, "dflfree": True, "n_extra": 32}, + [32] * 4 + [4] * 3 + [80] * 2, + ), + ( + {"task": "pose_estimation", "nl": 3, "dflfree": True, "n_extra": 51}, + [4] * 3 + [1] * 2 + [51] * 3, + ), + ( + {"task": "obb", "nl": 3, "dflfree": True, "n_extra": 1}, + [4] * 3 + [15] * 2 + [1] * 3, + ), + ], +) +def test_split_head_postprocessors_reject_incomplete_head_groups( + post_cfg: dict[str, Any], channels: list[int] +) -> None: + """Reject malformed raw output sets before zip can discard a detection scale.""" + + postprocessor = build_postprocess({"LetterBox": {"img_size": [64, 64]}}, post_cfg) + raw_outputs = [torch.zeros((1, 2, 2, channel)) for channel in channels] + + with pytest.raises( + ValueError, + match=r"Incomplete split-head outputs: expected 3 heads per group.*classification=2", + ): + postprocessor.rearrange(raw_outputs) # type: ignore[attr-defined] + + def test_segmentation_postprocessor_rejects_mismatched_prototype_batch() -> None: """Do not silently drop detections when prototypes have fewer images.""" diff --git a/tests/test_preprocess_validation.py b/tests/test_preprocess_validation.py index b0c3db9..ed3ed4f 100644 --- a/tests/test_preprocess_validation.py +++ b/tests/test_preprocess_validation.py @@ -28,9 +28,12 @@ def test_image_size_operations_reject_nonpositive_sizes(operation: object) -> No if operation_type is Resize: with pytest.raises(ValueError, match="positive"): Resize(argument, "bilinear") + elif operation_type is CenterCrop: + with pytest.raises(ValueError, match="positive"): + CenterCrop(argument) else: with pytest.raises(ValueError, match="positive"): - operation_type(argument) + LetterBox(argument) @pytest.mark.parametrize("size", [[1], [1, 2, 3], [1.5, 2], "2"]) diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 274ea9d..4e39c99 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -775,7 +775,7 @@ def dispose(self) -> None: @pytest.mark.parametrize( - "model_path_style", ["keyword", "positional", "positional-runtime"] + "model_path_style", ["keyword", "positional", "positional-runtime", "onnx-path"] ) def test_engine_init_accepts_local_onnx_model_path( monkeypatch: pytest.MonkeyPatch, @@ -850,6 +850,8 @@ def InferenceSession(self, path: str, providers: list[str]) -> _FakeSession: "onnx", ["CPUExecutionProvider"], ) + elif model_path_style == "onnx-path": + engine = MBLT_Engine(model_cls=model_config, onnx_path=str(onnx_path)) else: engine = MBLT_Engine(model_cls=model_config, model_path=str(onnx_path)) @@ -866,6 +868,22 @@ def InferenceSession(self, path: str, providers: list[str]) -> _FakeSession: assert fake_ort.session.providers == ["CPUExecutionProvider"] +def test_engine_init_rejects_framework_conflicting_with_onnx_path_alias( + tmp_path: Path, +) -> None: + """Do not silently route an explicit ONNX alias through MXQ inference.""" + + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + + with pytest.raises(ValueError, match=r"Framework `mxq` conflicts with model path"): + MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, + onnx_path=str(onnx_path), + framework="mxq", + ) + + def test_engine_init_rejects_conflicting_framework_and_model_path( tmp_path: Path, ) -> None: From 785202574dd8616217e91d333afaddaf82a48f19 Mon Sep 17 00:00:00 2001 From: jinman Date: Thu, 13 Aug 2026 09:31:35 +0900 Subject: [PATCH 18/48] feat: Enhance framework model path resolution for ONNX compatibility in benchmark --- benchmark/benchmark_vision_models.py | 7 ++++++- tests/test_benchmark_vision.py | 1 + 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/benchmark/benchmark_vision_models.py b/benchmark/benchmark_vision_models.py index fb5f54f..4823e66 100644 --- a/benchmark/benchmark_vision_models.py +++ b/benchmark/benchmark_vision_models.py @@ -454,7 +454,12 @@ def main(argv: Sequence[str] | None = None) -> int: """ args = _parse_args(argv) results_dir = args.results_dir.expanduser().resolve() - framework = resolve_framework(args.framework, args.model_path) + # Keep compatibility-path routing aligned with MBLT_Engine: an explicit + # ONNX path selects the ONNX backend unless an MXQ path takes precedence. + framework_model_path = args.model_path + if not framework_model_path and not args.mxq_path: + framework_model_path = args.onnx_path + framework = resolve_framework(args.framework, framework_model_path) rows: list[dict[str, Any]] = [] for model_name in args.models: for core_mode in _core_modes(args.core_mode, framework): diff --git a/tests/test_benchmark_vision.py b/tests/test_benchmark_vision.py index c9ac659..6c81c6e 100644 --- a/tests/test_benchmark_vision.py +++ b/tests/test_benchmark_vision.py @@ -284,6 +284,7 @@ def test_comparison_rejects_matching_metrics_from_different_tasks( ["--framework", "onnx"], ["--model-path", "model.onnx"], ["--model-path", "MODEL.ONNX"], + ["--onnx-path", "model.onnx"], ], ) def test_onnx_benchmark_uses_one_neutral_runtime_target( From 01a7dbf922d5daec870d90f7c10c8b7efdfbd5b2 Mon Sep 17 00:00:00 2001 From: jinman Date: Thu, 13 Aug 2026 09:55:46 +0900 Subject: [PATCH 19/48] feat: Improve error handling during model disposal and enhance validation for batch size in CLI --- benchmark/benchmark_vision_models.py | 12 ++++- mblt_vision/cli/val.py | 6 ++- mblt_vision/utils/datasets/dataloader.py | 5 +++ pyproject.toml | 1 - tests/test_benchmark_vision.py | 57 ++++++++++++++++++++++++ tests/test_cli.py | 10 +++++ tests/test_dataloader.py | 43 ++++++++++++++++++ 7 files changed, 130 insertions(+), 4 deletions(-) create mode 100644 tests/test_dataloader.py diff --git a/benchmark/benchmark_vision_models.py b/benchmark/benchmark_vision_models.py index 4823e66..2b7220f 100644 --- a/benchmark/benchmark_vision_models.py +++ b/benchmark/benchmark_vision_models.py @@ -374,9 +374,17 @@ def _run_target( ) as exc: row["error"] = f"{type(exc).__name__}: {exc}" finally: - row["elapsed_s"] = round(time.perf_counter() - started, 6) if model is not None: - model.dispose() + try: + model.dispose() + except Exception as exc: + cleanup_error = f"{type(exc).__name__}: {exc}" + if "error" in row: + row["cleanup_error"] = cleanup_error + else: + row["error"] = cleanup_error + row["status"] = "error" + row["elapsed_s"] = round(time.perf_counter() - started, 6) return row diff --git a/mblt_vision/cli/val.py b/mblt_vision/cli/val.py index 45189c7..56ec3e2 100644 --- a/mblt_vision/cli/val.py +++ b/mblt_vision/cli/val.py @@ -8,6 +8,7 @@ from pathlib import Path from mblt_vision._tasks import normalize_vision_task +from mblt_vision.benchmark.argparse_utils import parse_positive_int from mblt_vision.datasets import get_dataset_config, get_dataset_config_for_task from mblt_vision.utils.datasets.readiness import dataset_ready @@ -515,7 +516,10 @@ def add_val_parser( help="Optional semicolon-separated cluster list for multi/global modes, for example `0;1`.", ) parser.add_argument( - "--batch-size", type=int, default=1, help="Batch size for validation." + "--batch-size", + type=parse_positive_int, + default=1, + help="Positive batch size for validation.", ) parser.add_argument( "--data-path", diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index 5713b91..f4d067e 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -15,6 +15,7 @@ from PIL import Image from .cityscapes import CITYSCAPES_SOURCE_TO_TRAIN_ID +from .readiness import IMAGE_SUFFIXES class CustomCOCODataset(torch.utils.data.Dataset[tuple[np.ndarray, int, int, int]]): @@ -715,6 +716,8 @@ def make_dataset(self) -> None: continue for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): for fname in sorted(fnames): + if os.path.splitext(fname)[1].lower() not in IMAGE_SUFFIXES: + continue path = os.path.join(root, fname) item = path, class_index instances.append(item) @@ -820,6 +823,8 @@ def make_dataset(self) -> None: continue for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): for fname in sorted(fnames): + if os.path.splitext(fname)[1].lower() not in IMAGE_SUFFIXES: + continue path = os.path.join(root, fname) item = path, target_class, fname instances.append(item) diff --git a/pyproject.toml b/pyproject.toml index c15d4ee..ea5c96c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,6 @@ dynamic = ["version"] description = "Mobilint NPU vision models: classification, detection, segmentation, pose" readme = "README.md" dependencies = [ - "mobilint-qb-runtime>=1.0.0", "mblt-npu-python>=0.0.0", "numpy>=1.26.0", "torch>=2.4.1", diff --git a/tests/test_benchmark_vision.py b/tests/test_benchmark_vision.py index 6c81c6e..cfeb440 100644 --- a/tests/test_benchmark_vision.py +++ b/tests/test_benchmark_vision.py @@ -198,6 +198,63 @@ def fake_evaluate( assert rows[0]["error"] == "TypeError: Unsupported model output" +def test_benchmark_records_dispose_failures_without_losing_other_results( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Write artifacts and continue when a backend fails during cleanup.""" + + class FakeEngine: + """Minimal engine that can fail evaluation or disposal by model name.""" + + def __init__(self, *, model_cls: str, **kwargs: object) -> None: + self.model_cls = model_cls + + def dispose(self) -> None: + if self.model_cls in {"evaluation-failure", "cleanup-failure"}: + raise RuntimeError("Backend cleanup failed") + + def fake_evaluate( + model: FakeEngine, + args: object, + run_dir: Path, + ) -> tuple[float, str, dict[str, float]]: + if model.model_cls == "evaluation-failure": + raise ValueError("Evaluation failed") + return 0.9, "top1_accuracy", {"top1_accuracy": 0.9} + + import mblt_vision as vision + + monkeypatch.setattr(vision, "MBLT_Engine", FakeEngine) + monkeypatch.setattr(benchmark_vision_models, "_evaluate", fake_evaluate) + + result = benchmark_vision_models.main( + [ + "--models", + "evaluation-failure", + "cleanup-failure", + "valid-output", + "--task", + "image_classification", + "--data-path", + str(tmp_path / "dataset"), + "--results-dir", + str(tmp_path / "results"), + "--no-plot", + ] + ) + + with (tmp_path / "results" / "results.csv").open( + newline="", encoding="utf-8" + ) as results_file: + rows = list(csv.DictReader(results_file)) + + assert result == 1 + assert [row["status"] for row in rows] == ["error", "error", "ok"] + assert rows[0]["error"] == "ValueError: Evaluation failed" + assert rows[0]["cleanup_error"] == "RuntimeError: Backend cleanup failed" + assert rows[1]["error"] == "RuntimeError: Backend cleanup failed" + + def test_benchmark_forwards_normalized_target_device( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: diff --git a/tests/test_cli.py b/tests/test_cli.py index f112ee4..aaa38aa 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -51,6 +51,16 @@ def test_vision_cli_uses_single_core_mode_by_default_on_regulus() -> None: assert args.core_mode is None +@pytest.mark.parametrize("batch_size", ["0", "-1"]) +def test_val_rejects_nonpositive_batch_sizes(batch_size: str) -> None: + """Fail argument parsing before validation can construct a model.""" + + with pytest.raises(SystemExit): + build_parser().parse_args( + ["val", "--model", "resnet50", "--batch-size", batch_size] + ) + + @pytest.mark.parametrize("removed_alias", ["classify", "detect", "pose", "segment"]) def test_predict_command_has_no_task_aliases(removed_alias: str) -> None: """Keep prediction discoverable through one task-agnostic command.""" diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py new file mode 100644 index 0000000..cb117f3 --- /dev/null +++ b/tests/test_dataloader.py @@ -0,0 +1,43 @@ +"""Tests for vision dataset sample discovery.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +from mblt_vision.utils.datasets.dataloader import ( + CustomImageFolder, + CustomWiderFaceDataset, +) + + +@pytest.mark.parametrize( + ("dataset_class", "class_name"), + [ + (CustomImageFolder, "class-a"), + (CustomWiderFaceDataset, "0--Parade"), + ], +) +def test_class_based_datasets_ignore_non_image_files( + tmp_path: Path, + dataset_class: type[CustomImageFolder] | type[CustomWiderFaceDataset], + class_name: str, +) -> None: + """Keep incidental files out of ImageNet and WiderFace sample lists.""" + + class_dir = tmp_path / class_name + nested_dir = class_dir / "nested" + nested_dir.mkdir(parents=True) + (class_dir / "image.JPG").write_bytes(b"image") + (nested_dir / "image.png").write_bytes(b"image") + (class_dir / ".DS_Store").write_bytes(b"metadata") + (nested_dir / "labels.txt").write_text("metadata", encoding="utf-8") + + dataset = dataset_class(str(tmp_path)) + + assert len(dataset) == 2 + assert {Path(sample[0]).suffix.lower() for sample in dataset.samples} == { + ".jpg", + ".png", + } From 33b1218a4b10212fa253ef61de1fb14cdc6ae49b Mon Sep 17 00:00:00 2001 From: jinman Date: Thu, 13 Aug 2026 10:21:44 +0900 Subject: [PATCH 20/48] feat: Enhance normalization logic to support both HWC and CHW formats; improve error handling and add tests for preprocessing validation --- mblt_vision/utils/preprocess/normalize.py | 12 ++++++++- mblt_vision/wrapper.py | 3 +++ tests/test_dataset_organizer.py | 3 ++- tests/test_eval_dota.py | 24 +++++++++++------ tests/test_postprocess_hierarchy.py | 4 +-- tests/test_preprocess_validation.py | 32 ++++++++++++++++++++--- tests/test_results.py | 3 +++ tests/test_wrapper.py | 27 +++++++++++++++++-- 8 files changed, 90 insertions(+), 18 deletions(-) diff --git a/mblt_vision/utils/preprocess/normalize.py b/mblt_vision/utils/preprocess/normalize.py index 8d7316b..c03b460 100644 --- a/mblt_vision/utils/preprocess/normalize.py +++ b/mblt_vision/utils/preprocess/normalize.py @@ -74,5 +74,15 @@ def __call__(self, x: TensorLike | Image.Image) -> np.ndarray: f"Normalize expects a three-dimensional image, got shape {x.shape}." ) x = x.astype(np.float32) / 255.0 - x = (x - self.mean) / self.std + if x.shape[-1] == len(self.mean): + mean, std = self.mean, self.std + elif x.shape[0] == len(self.mean): + mean = self.mean[:, None, None] + std = self.std[:, None, None] + else: + raise ValueError( + f"Normalize expects HWC or CHW data with {len(self.mean)} channels, " + f"got shape {x.shape}." + ) + x = (x - mean) / std return x.astype(np.float32) diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index d247837..905f947 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -469,6 +469,8 @@ def __init__( dev_no = 0 if core_mode is None: core_mode = "single" + else: + core_mode = normalize_core_mode(core_mode) target_device = normalize_target_device(target_device) is_regulus = target_device in {"regulus-ra", "regulus-rb"} if target_cores is None: @@ -503,6 +505,7 @@ def __init__( self.file_cfg["target_clusters"] = target_clusters if _dev_no_passed or "dev_no" not in self.file_cfg: self.file_cfg["dev_no"] = dev_no + self.file_cfg["core_mode"] = normalize_core_mode(self.file_cfg["core_mode"]) self.file_cfg["target_device"] = target_device self.pre_cfg = copy.deepcopy(model_config_part["pre_cfg"]) diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index dfb6fa3..fbfbafc 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -5,6 +5,7 @@ import io import inspect import tarfile +from collections.abc import Callable from pathlib import Path from typing import Any from zipfile import ZipFile @@ -70,7 +71,7 @@ def test_dotav1_normalized_labels_preserve_difficult_metadata(tmp_path: Path) -> ], ) def test_organizer_defaults_match_registry_cache_root( - organize_dataset: object, dataset_name: str + organize_dataset: Callable[..., Any], dataset_name: str ) -> None: """Write datasets where the packaged registry resolves them by default.""" diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 938bf3b..52d0ecf 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -3,11 +3,13 @@ from __future__ import annotations from types import SimpleNamespace +from typing import cast import numpy as np import pytest import torch +from mblt_vision.utils.datasets import CustomDOTAv1 from mblt_vision.utils.evaluation.eval_dota import ( _load_ground_truths, evaluate_dota_predictions, @@ -23,10 +25,13 @@ def test_normalized_difficult_flag_loads_as_an_ignored_region(tmp_path) -> None: "0 0 0 0.2 0 0.2 0.2 0 0.2 0\n" "0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6 1\n", encoding="utf-8", ) - dataset = SimpleNamespace( - ids=["image"], - image_paths=["unused"], - _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), ) ground_truth = _load_ground_truths(str(tmp_path), dataset)["image"] @@ -42,10 +47,13 @@ def test_normalized_truncated_annotation_raises_with_file_and_line(tmp_path) -> label_dir.mkdir(parents=True) label_path = label_dir / "image.txt" label_path.write_text("\n0 0 0 0.2\n", encoding="utf-8") - dataset = SimpleNamespace( - ids=["image"], - image_paths=["unused"], - _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), ) with pytest.raises( diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index f974cf4..b84c89d 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -3,7 +3,7 @@ from __future__ import annotations import warnings -from typing import Any +from typing import Any, cast import pytest import torch @@ -131,7 +131,7 @@ def test_empty_anchorless_outputs_preserve_input_device_and_dtype( ) -> None: """Create empty decoded rows from the selected input tensor.""" - postprocessor = postprocessor_type.__new__(postprocessor_type) + postprocessor = cast(Any, postprocessor_type.__new__(postprocessor_type)) postprocessor.nc = 1 postprocessor.n_extra = n_extra postprocessor.inv_conf_thres = 1.0 diff --git a/tests/test_preprocess_validation.py b/tests/test_preprocess_validation.py index ed3ed4f..ec826bd 100644 --- a/tests/test_preprocess_validation.py +++ b/tests/test_preprocess_validation.py @@ -24,16 +24,15 @@ def test_image_size_operations_reject_nonpositive_sizes(operation: object) -> No """Keep size validation active when Python assertions are optimized away.""" operation_type = type(operation) - argument = [0, 2] if operation_type is not Resize else 0 if operation_type is Resize: with pytest.raises(ValueError, match="positive"): - Resize(argument, "bilinear") + Resize(0, "bilinear") elif operation_type is CenterCrop: with pytest.raises(ValueError, match="positive"): - CenterCrop(argument) + CenterCrop([0, 2]) else: with pytest.raises(ValueError, match="positive"): - LetterBox(argument) + LetterBox([0, 2]) @pytest.mark.parametrize("size", [[1], [1, 2, 3], [1.5, 2], "2"]) @@ -51,6 +50,31 @@ def test_set_order_rejects_ambiguous_channel_layout() -> None: SetOrder("HWC")(np.zeros((3, 5, 3), dtype=np.uint8)) +def test_normalize_supports_channel_first_set_order_output() -> None: + """Broadcast normalization statistics per channel after a CHW conversion.""" + + image = np.array( + [ + [[255, 0, 127], [0, 255, 127], [127, 127, 255], [64, 32, 16], [1, 2, 3]], + [[4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15], [16, 17, 18]], + [[19, 20, 21], [22, 23, 24], [25, 26, 27], [28, 29, 30], [31, 32, 33]], + [[34, 35, 36], [37, 38, 39], [40, 41, 42], [43, 44, 45], [46, 47, 48]], + ], + dtype=np.uint8, + ) + + normalized = Normalize("torch")(SetOrder("CHW")(image)) + expected = ( + image.transpose(2, 0, 1) / 255.0 + - np.array([0.485, 0.456, 0.406])[:, None, None] + ) / np.array([0.229, 0.224, 0.225])[:, None, None] + + assert normalized.shape == (3, 4, 5) + np.testing.assert_allclose( + normalized, expected.astype(np.float32), rtol=1e-6, atol=1e-6 + ) + + @pytest.mark.parametrize( ("factory", "expected"), [ diff --git a/tests/test_results.py b/tests/test_results.py index 9d02498..2ef10c5 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -97,6 +97,8 @@ def test_object_detection_plot_preserves_raw_output_coordinates() -> None: torch.testing.assert_close(box_cls, expected) torch.testing.assert_close(result._box_cls_tensor(), expected) + assert first is not None + assert second is not None assert np.array_equal(first, second) @@ -112,6 +114,7 @@ def test_plot_converts_rgb_array_source_to_bgr() -> None: plotted = result.plot(rgb) + assert plotted is not None assert np.array_equal(plotted, np.array([[[0, 0, 255]]], dtype=np.uint8)) assert np.array_equal(rgb, np.array([[[255, 0, 0]]], dtype=np.uint8)) diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 4e39c99..82dcee7 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -290,7 +290,8 @@ def test_engine_init_rejects_nonexistent_explicit_mxq_path( with pytest.raises( FileNotFoundError, match=r"Explicit MXQ model path.*missing\.mxq" ): - MBLT_Engine(model_config, **{path_argument: str(missing_path)}) + path_kwargs: dict[str, Any] = {path_argument: str(missing_path)} + MBLT_Engine(model_config, **path_kwargs) @pytest.mark.parametrize("path_argument", ["model_path", "onnx_path"]) @@ -313,7 +314,8 @@ def test_engine_init_rejects_nonexistent_explicit_onnx_path( with pytest.raises( FileNotFoundError, match=r"Explicit ONNX model path.*missing\.onnx" ): - MBLT_Engine(model_config, **{path_argument: str(missing_path)}) + path_kwargs: dict[str, Any] = {path_argument: str(missing_path)} + MBLT_Engine(model_config, **path_kwargs) def test_engine_init_disposes_mxq_backend_after_postprocess_setup_failure( @@ -515,6 +517,27 @@ def dispose(self) -> None: engine.dispose() +@pytest.mark.parametrize( + ("configured_core_mode", "core_mode"), + [(None, "unsupported"), ("unsupported", None)], +) +def test_engine_init_rejects_invalid_core_modes( + configured_core_mode: str | None, + core_mode: str | None, +) -> None: + """Validate direct caller and model-config core modes before backend setup.""" + + file_cfg: dict[str, Any] = {} + if configured_core_mode is not None: + file_cfg["core_mode"] = configured_core_mode + + with pytest.raises(ValueError, match="Invalid core mode 'unsupported'"): + MBLT_Engine( + model_cls={"file_cfg": file_cfg, "pre_cfg": {}, "post_cfg": {}}, + core_mode=core_mode, + ) + + def test_engine_init_preserves_shifted_positional_mxq_runtime_arguments( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, From 532cec25f0b6d5cc432e9ed694362f2cf7c684c5 Mon Sep 17 00:00:00 2001 From: jinman Date: Thu, 13 Aug 2026 10:41:40 +0900 Subject: [PATCH 21/48] feat: Enhance HTTP error handling in download functions; add validation for negative class indices in DOTAv1 annotations; improve preprocessing tensor handling --- .github/workflows/publish.yml | 4 ++ mblt_vision/py.typed | 1 + mblt_vision/utils/datasets/organizer.py | 11 +++- mblt_vision/utils/evaluation/eval_dota.py | 4 +- mblt_vision/utils/preprocess/center_crop.py | 2 +- mblt_vision/utils/preprocess/letterbox.py | 2 +- mblt_vision/utils/preprocess/normalize.py | 12 +++- mblt_vision/utils/preprocess/reader.py | 4 +- pyproject.toml | 1 + tests/test_dataset_organizer.py | 61 ++++++++++++++++++++- tests/test_eval_dota.py | 28 ++++++++++ tests/test_preprocess_validation.py | 27 +++++++++ 12 files changed, 146 insertions(+), 11 deletions(-) create mode 100644 mblt_vision/py.typed diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index de47c4d..45f0af2 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -33,6 +33,10 @@ jobs: - name: Validate distributions run: python -m twine check dist/* + - name: Verify wheel package contents + run: | + python -c "from pathlib import Path; from zipfile import ZipFile; wheel, = Path('dist').glob('*.whl'); archive = ZipFile(wheel); assert 'mblt_vision/py.typed' in archive.namelist(), 'Wheel is missing mblt_vision/py.typed'" + - name: Upload built distributions uses: actions/upload-artifact@v7 with: diff --git a/mblt_vision/py.typed b/mblt_vision/py.typed new file mode 100644 index 0000000..be1a4ff --- /dev/null +++ b/mblt_vision/py.typed @@ -0,0 +1 @@ +# PEP 561 marker: mblt_vision ships inline type information. diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 58cdf04..c2c7280 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -56,6 +56,7 @@ CITYSCAPES_IMAGE_SUFFIX = "_leftImg8bit.png" CITYSCAPES_ANNOTATION_SUFFIX = "_gtFine_labelIds.png" IMAGENET_SYNSET_PATTERN = re.compile(r"n\d{8}") +RETRYABLE_HTTP_STATUS_CODES = frozenset({408, 429}) def _replace_staged_directories( @@ -217,7 +218,15 @@ def _download_url(url: str, local_path: str) -> str: requests.ConnectionError, requests.Timeout, requests.exceptions.ChunkedEncodingError, + requests.HTTPError, ) as exc: + if isinstance(exc, requests.HTTPError): + status_code = getattr(exc.response, "status_code", None) + if not isinstance(status_code, int) or ( + status_code not in RETRYABLE_HTTP_STATUS_CODES + and not 500 <= status_code < 600 + ): + raise if attempt == DOWNLOAD_RETRY_LIMIT: raise RuntimeError( f"Failed to download {url} after {DOWNLOAD_RETRY_LIMIT} attempts." @@ -226,7 +235,7 @@ def _download_url(url: str, local_path: str) -> str: os.path.getsize(local_path) if os.path.exists(local_path) else 0 ) print( - f"Download interrupted for {os.path.basename(local_path)}; " + f"Download attempt failed for {os.path.basename(local_path)}; " f"retrying from {resumed_size} bytes (attempt {attempt + 1}/{DOWNLOAD_RETRY_LIMIT})..." ) sleep(DOWNLOAD_RETRY_BACKOFF_SECONDS * attempt) diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index bc20662..534066a 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -102,7 +102,7 @@ def _load_ground_truths( f"got {len(parts)}." ) cls = _label_to_index(parts[0]) - if cls >= get_dotav1_class_num(): + if not 0 <= cls < get_dotav1_class_num(): raise ValueError( f"Unsupported DOTAv1 class index {cls} in {label_path}." ) @@ -124,7 +124,7 @@ def _load_ground_truths( if len(parts) < 9: continue cls = _label_to_index(parts[8]) - if cls >= get_dotav1_class_num(): + if not 0 <= cls < get_dotav1_class_num(): raise ValueError( f"Unsupported DOTAv1 class index {cls} in {original_label_path}." ) diff --git a/mblt_vision/utils/preprocess/center_crop.py b/mblt_vision/utils/preprocess/center_crop.py index 5ea1cfa..b116b3c 100644 --- a/mblt_vision/utils/preprocess/center_crop.py +++ b/mblt_vision/utils/preprocess/center_crop.py @@ -38,7 +38,7 @@ def __call__(self, x: TensorLike | Image.Image) -> np.ndarray: np.ndarray: Center-cropped image in HWC format. """ if isinstance(x, torch.Tensor): - image = x.cpu().numpy() + image = x.detach().cpu().numpy() elif isinstance(x, Image.Image): image = np.array(x) elif isinstance(x, np.ndarray): diff --git a/mblt_vision/utils/preprocess/letterbox.py b/mblt_vision/utils/preprocess/letterbox.py index e6d11b6..1700dfa 100644 --- a/mblt_vision/utils/preprocess/letterbox.py +++ b/mblt_vision/utils/preprocess/letterbox.py @@ -104,7 +104,7 @@ def __call__(self, x: TensorLike) -> torch.Tensor: torch.Tensor: Preprocessed image in HWC format on the selected device. """ if isinstance(x, torch.Tensor): - x = x.cpu().numpy() + x = x.detach().cpu().numpy() elif not isinstance(x, np.ndarray): raise TypeError( f"LetterBox expects a NumPy array or tensor, got {type(x).__name__}." diff --git a/mblt_vision/utils/preprocess/normalize.py b/mblt_vision/utils/preprocess/normalize.py index c03b460..be7dd1b 100644 --- a/mblt_vision/utils/preprocess/normalize.py +++ b/mblt_vision/utils/preprocess/normalize.py @@ -62,7 +62,7 @@ def __call__(self, x: TensorLike | Image.Image) -> np.ndarray: np.ndarray: The normalized image as a float32 numpy array. """ if isinstance(x, torch.Tensor): - x = x.cpu().numpy() + x = x.detach().cpu().numpy() elif isinstance(x, Image.Image): x = np.array(x) elif not isinstance(x, np.ndarray): @@ -74,9 +74,15 @@ def __call__(self, x: TensorLike | Image.Image) -> np.ndarray: f"Normalize expects a three-dimensional image, got shape {x.shape}." ) x = x.astype(np.float32) / 255.0 - if x.shape[-1] == len(self.mean): + channels_first = x.shape[0] == len(self.mean) + channels_last = x.shape[-1] == len(self.mean) + if channels_first and channels_last: + raise ValueError( + f"Normalize cannot infer channel order from ambiguous shape {x.shape}." + ) + if channels_last: mean, std = self.mean, self.std - elif x.shape[0] == len(self.mean): + elif channels_first: mean = self.mean[:, None, None] std = self.std[:, None, None] else: diff --git a/mblt_vision/utils/preprocess/reader.py b/mblt_vision/utils/preprocess/reader.py index 69a83e7..c62e986 100644 --- a/mblt_vision/utils/preprocess/reader.py +++ b/mblt_vision/utils/preprocess/reader.py @@ -53,7 +53,7 @@ def __call__( if isinstance(x, np.ndarray): return x elif isinstance(x, torch.Tensor): - return x.cpu().numpy() + return x.detach().cpu().numpy() elif isinstance(x, (str, Path)): image = cv2.imread(str(x)) if image is None: @@ -69,7 +69,7 @@ def __call__( if isinstance(x, np.ndarray): return Image.fromarray(x.astype(np.uint8)) elif isinstance(x, torch.Tensor): - x = x.cpu().numpy() + x = x.detach().cpu().numpy() return Image.fromarray(x.astype(np.uint8)) elif isinstance(x, (str, Path)): return Image.open(x).convert("RGB") diff --git a/pyproject.toml b/pyproject.toml index ea5c96c..840154d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,6 +66,7 @@ version = { attr = "mblt_vision.__version__" } [tool.setuptools.package-data] "mblt_vision" = [ + "py.typed", "models/*.yaml", "datasets/*.yaml", ] diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index fbfbafc..0526ec4 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -98,7 +98,10 @@ def __exit__(self, exc_type: object, exc: object, tb: object) -> None: return None def raise_for_status(self) -> None: - return None + if self.status_code >= 400: + response = requests.Response() + response.status_code = self.status_code + raise requests.HTTPError(response=response) def iter_content(self, chunk_size: int) -> Any: del chunk_size @@ -149,6 +152,62 @@ def _fake_get( assert calls == [{}, {"Range": "bytes=3-"}] +@pytest.mark.parametrize("status_code", [408, 429, 503]) +def test_download_url_retries_transient_http_responses( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + status_code: int, +) -> None: + """Retry transient HTTP responses before accepting a successful download.""" + + calls = 0 + responses = [ + _FakeResponse(status_code=status_code, headers={}, chunks=[]), + _FakeResponse(status_code=200, headers={"Content-Length": "2"}, chunks=[b"ok"]), + ] + + def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: + nonlocal calls + del url, kwargs + calls += 1 + return responses.pop(0) + + monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) + monkeypatch.setattr(organizer, "sleep", lambda _: None) + + local_path = tmp_path / "archive.tar" + assert organizer._download_url( + "https://example.com/archive.tar", str(local_path) + ) == str(local_path) + assert local_path.read_bytes() == b"ok" + assert calls == 2 + + +def test_download_url_does_not_retry_permanent_http_client_errors( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Fail immediately for permanent client errors such as HTTP 404.""" + + calls = 0 + + def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: + nonlocal calls + del url, kwargs + calls += 1 + return _FakeResponse(status_code=404, headers={}, chunks=[]) + + monkeypatch.setattr(organizer.requests, "get", _fake_get) + + with pytest.raises(requests.HTTPError): + organizer._download_url( + "https://example.com/archive.tar", str(tmp_path / "archive.tar") + ) + + assert calls == 1 + + def test_should_download_serially_for_same_host_urls() -> None: """Serialize same-host dataset archive downloads to avoid throttling.""" diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 52d0ecf..d56e73a 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -63,6 +63,34 @@ def test_normalized_truncated_annotation_raises_with_file_and_line(tmp_path) -> _load_ground_truths(str(tmp_path), dataset) +@pytest.mark.parametrize( + ("label_path", "annotation"), + [ + ("labels/val/image.txt", "-1 0 0 0.2 0 0.2 0.2 0 0.2"), + ("labels/val_original/image.txt", "0 0 20 0 20 20 0 20 -1"), + ], +) +def test_dota_annotations_reject_negative_class_indices( + tmp_path, label_path: str, annotation: str +) -> None: + """Reject invalid negative classes in normalized and original labels.""" + + path = tmp_path / label_path + path.parent.mkdir(parents=True) + path.write_text(annotation, encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises(ValueError, match="Unsupported DOTAv1 class index -1"): + _load_ground_truths(str(tmp_path), dataset) + + def test_difficult_regions_do_not_count_as_positive_or_false_positive() -> None: """Ignore a detection on a difficult region while retaining positive matching.""" diff --git a/tests/test_preprocess_validation.py b/tests/test_preprocess_validation.py index ec826bd..6fb78a7 100644 --- a/tests/test_preprocess_validation.py +++ b/tests/test_preprocess_validation.py @@ -50,6 +50,13 @@ def test_set_order_rejects_ambiguous_channel_layout() -> None: SetOrder("HWC")(np.zeros((3, 5, 3), dtype=np.uint8)) +def test_normalize_rejects_ambiguous_channel_layout() -> None: + """Do not silently apply HWC statistics to potentially CHW data.""" + + with pytest.raises(ValueError, match="ambiguous"): + Normalize("torch")(np.zeros((3, 640, 3), dtype=np.uint8)) + + def test_normalize_supports_channel_first_set_order_output() -> None: """Broadcast normalization statistics per channel after a CHW conversion.""" @@ -75,6 +82,26 @@ def test_normalize_supports_channel_first_set_order_output() -> None: ) +@pytest.mark.parametrize( + ("operation", "output_type"), + [ + (Reader("numpy"), np.ndarray), + (Reader("pil"), Image.Image), + (Normalize("cv"), np.ndarray), + (CenterCrop([2, 3]), np.ndarray), + (LetterBox([2, 3]), torch.Tensor), + ], +) +def test_preprocessors_accept_grad_tracking_tensors( + operation: object, output_type: type[object] +) -> None: + """Detach tensors before preprocessing converts them to NumPy arrays.""" + + image = torch.ones((4, 5, 3), requires_grad=True) + + assert isinstance(operation(image), output_type) # type: ignore[operator] + + @pytest.mark.parametrize( ("factory", "expected"), [ From dcfad1075e172666a28ef9f895f5d5369207b5a7 Mon Sep 17 00:00:00 2001 From: jinman Date: Thu, 13 Aug 2026 12:54:09 +0900 Subject: [PATCH 22/48] feat: Enhance core mode handling for NPU boards; update dataset download URLs to use HTTPS and add SHA-256 verification for integrity --- benchmark/benchmark_vision_models.py | 42 ++++- mblt_vision/datasets/ade20k.yaml | 3 +- mblt_vision/datasets/coco.yaml | 8 +- mblt_vision/utils/datasets/organizer.py | 98 ++++++++++- mblt_vision/wrapper.py | 50 ++++-- tests/test_benchmark_vision.py | 55 +++++- tests/test_dataset_organizer.py | 218 +++++++++++++++++++++++- tests/test_wrapper.py | 88 ++++++++++ 8 files changed, 536 insertions(+), 26 deletions(-) diff --git a/benchmark/benchmark_vision_models.py b/benchmark/benchmark_vision_models.py index 2b7220f..489f3d2 100644 --- a/benchmark/benchmark_vision_models.py +++ b/benchmark/benchmark_vision_models.py @@ -28,10 +28,13 @@ ) from mblt_vision._model_paths import resolve_framework from mblt_vision._tasks import VISION_TASKS, normalize_vision_task +from mblt_vision.wrapper import core_modes_for_target_device from mblt_npu import normalize_target_device CoreMode = Literal["single", "multi", "global4", "global8"] -CORE_MODES: tuple[CoreMode, ...] = ("single", "multi", "global4", "global8") +CORE_MODES: tuple[CoreMode, ...] = cast( + tuple[CoreMode, ...], core_modes_for_target_device("aries-rb") +) TASK_CHOICES = VISION_TASKS SUPPORTED_TARGET_DEVICES = frozenset({"aries-rb", "regulus-ra", "regulus-rb"}) @@ -133,9 +136,12 @@ def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: ) parser.add_argument( "--core-mode", - default="global8", + default=None, choices=[*CORE_MODES, "all"], - help="NPU core mode, or `all` to run every supported mode.", + help=( + "NPU core mode, or `all` to run every mode supported by the selected " + "board. Defaults to global8 on Aries and single on Regulus." + ), ) parser.add_argument("--dev-no", type=int, default=0, help="NPU device number.") parser.add_argument( @@ -187,19 +193,36 @@ def _parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: return args -def _core_modes(core_mode: str, framework: str | None = None) -> tuple[str, ...]: +def _core_modes( + core_mode: str | None, + framework: str | None = None, + target_device: str = "aries-rb", +) -> tuple[str, ...]: """Expand the multi-run core-mode shorthand. Args: - core_mode: Requested core mode. + core_mode: Requested core mode, or ``None`` for the board default. framework: Resolved inference framework. + target_device: Selected NPU board. Returns: One or more concrete core modes. """ if framework == "onnx": return ("onnx",) - return CORE_MODES if core_mode == "all" else (normalize_core_mode(core_mode),) + supported_modes = core_modes_for_target_device(target_device) + if core_mode is None: + return (supported_modes[-1],) + if core_mode == "all": + return supported_modes + normalized_core_mode = normalize_core_mode(core_mode) + if normalized_core_mode not in supported_modes: + raise ValueError( + f"Core mode {normalized_core_mode!r} is not supported by " + f"{normalize_target_device(target_device)}; expected one of " + f"{list(supported_modes)}." + ) + return (normalized_core_mode,) def _evaluate( @@ -469,8 +492,13 @@ def main(argv: Sequence[str] | None = None) -> int: framework_model_path = args.onnx_path framework = resolve_framework(args.framework, framework_model_path) rows: list[dict[str, Any]] = [] + try: + core_modes = _core_modes(args.core_mode, framework, args.target_device) + except ValueError as exc: + print(f"error: {exc}", file=sys.stderr) + return 2 for model_name in args.models: - for core_mode in _core_modes(args.core_mode, framework): + for core_mode in core_modes: print(f"Benchmarking {model_name} with core mode {core_mode}...") row = _run_target(model_name, core_mode, args, results_dir) rows.append(row) diff --git a/mblt_vision/datasets/ade20k.yaml b/mblt_vision/datasets/ade20k.yaml index 33fcb02..2423bc6 100644 --- a/mblt_vision/datasets/ade20k.yaml +++ b/mblt_vision/datasets/ade20k.yaml @@ -156,7 +156,8 @@ names: 148: clock 149: flag download: - url: http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip + url: https://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip + sha256: 7ff1be44964418441f542a7cc1e1a650e7dc0fc275f5d23252bc9bbdbc977b29 nc: 150 source_ignore_label: 0 ignore_label: 255 diff --git a/mblt_vision/datasets/coco.yaml b/mblt_vision/datasets/coco.yaml index a3ade17..020e094 100644 --- a/mblt_vision/datasets/coco.yaml +++ b/mblt_vision/datasets/coco.yaml @@ -7,8 +7,12 @@ tasks: - pose_estimation val: val2017 download: - images: http://images.cocodataset.org/zips/val2017.zip - annotations: http://images.cocodataset.org/annotations/annotations_trainval2017.zip + # The official COCO object-store hostname has no valid TLS certificate. Use a + # pinned HTTPS mirror and verify each archive digest before extraction. + images: https://huggingface.co/datasets/pcuenq/coco-2017-mirror/resolve/5200d2cffae8121713bec767b4693bdafc0eeb0b/val2017.zip + images_sha256: 4f7e2ccb2866ec5041993c9cf2a952bbed69647b115d0f74da7ce8f4bef82f05 + annotations: https://huggingface.co/datasets/pcuenq/coco-2017-mirror/resolve/5200d2cffae8121713bec767b4693bdafc0eeb0b/annotations_trainval2017.zip + annotations_sha256: 113a836d90195ee1f884e704da6304dfaaecff1f023f49b6ca93c4aaae470268 names: 0: person diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index c2c7280..a3dee9e 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -5,6 +5,7 @@ from __future__ import annotations import concurrent.futures +import hashlib import os import re import shutil @@ -49,14 +50,23 @@ DOTAV1_CLASS_TO_IDX = { name: int(index) for index, name in get_dataset_config("dotav1")["names"].items() } +COCO_DOWNLOAD_CONFIG = get_dataset_config("coco")["download"] +ADE20K_DOWNLOAD_CONFIG = get_dataset_config("ade20k")["download"] NYU_DEPTH_URL = ( "https://github.com/ultralytics/assets/releases/download/v0.0.0/nyu-depth.zip" ) -ADE20K_URL = "http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip" +ADE20K_URL = ADE20K_DOWNLOAD_CONFIG["url"] CITYSCAPES_IMAGE_SUFFIX = "_leftImg8bit.png" CITYSCAPES_ANNOTATION_SUFFIX = "_gtFine_labelIds.png" IMAGENET_SYNSET_PATTERN = re.compile(r"n\d{8}") RETRYABLE_HTTP_STATUS_CODES = frozenset({408, 429}) +CONTENT_RANGE_PATTERN = re.compile(r"^bytes (\d+)-(\d+)/(\d+|\*)$") +UNSATISFIABLE_CONTENT_RANGE_PATTERN = re.compile(r"^bytes \*/(\d+)$") +PINNED_ARCHIVE_SHA256 = { + COCO_DOWNLOAD_CONFIG["images"]: COCO_DOWNLOAD_CONFIG["images_sha256"], + COCO_DOWNLOAD_CONFIG["annotations"]: COCO_DOWNLOAD_CONFIG["annotations_sha256"], + ADE20K_DOWNLOAD_CONFIG["url"]: ADE20K_DOWNLOAD_CONFIG["sha256"], +} def _replace_staged_directories( @@ -158,12 +168,66 @@ def _is_url(path_or_url: str) -> bool: return parsed.scheme in {"http", "https"} and bool(parsed.netloc) -def _download_url(url: str, local_path: str) -> str: +def _verify_archive_sha256( + archive_path: str, expected_sha256: str, source_url: str +) -> None: + """Verify a downloaded archive before it can be extracted.""" + + digest = hashlib.sha256() + with open(archive_path, "rb") as archive: + for chunk in iter(lambda: archive.read(DOWNLOAD_CHUNK_SIZE), b""): + digest.update(chunk) + actual_sha256 = digest.hexdigest() + if actual_sha256 != expected_sha256: + Path(archive_path).unlink(missing_ok=True) + raise ValueError( + f"Downloaded archive SHA-256 mismatch for {source_url}: " + f"expected {expected_sha256}, got {actual_sha256}." + ) + + +def _has_expected_resume_offset(content_range: str | None, existing_size: int) -> bool: + """Return whether a partial response begins exactly after local archive bytes.""" + + if content_range is None: + return False + match = CONTENT_RANGE_PATTERN.fullmatch(content_range) + if match is None: + return False + start, end, total = match.groups() + return ( + int(start) == existing_size + and int(end) >= int(start) + and (total == "*" or int(end) < int(total)) + ) + + +def _is_completed_range_response(content_range: str | None, existing_size: int) -> bool: + """Return whether a 416 confirms that the local archive is complete.""" + + if content_range is None: + return False + match = UNSATISFIABLE_CONTENT_RANGE_PATTERN.fullmatch(content_range) + return match is not None and int(match.group(1)) == existing_size + + +def _restart_partial_download(local_path: str, url: str) -> None: + """Discard an invalid partial archive before retrying from byte zero.""" + + Path(local_path).unlink(missing_ok=True) + print( + f"Server returned an invalid resume response for {os.path.basename(local_path)}; " + "restarting from byte zero." + ) + + +def _download_url(url: str, local_path: str, expected_sha256: str | None = None) -> str: """Downloads a URL to a local file with progress and resume support. Args: url: HTTP(S) URL to download. local_path: Destination file path. + expected_sha256: Optional pinned SHA-256 digest to verify before return. Returns: The local destination path. @@ -185,11 +249,28 @@ def _download_url(url: str, local_path: str) -> str: with requests.get( url, stream=True, timeout=DOWNLOAD_TIMEOUT, headers=headers ) as response: + if response.status_code == 416 and existing_size > 0: + if _is_completed_range_response( + response.headers.get("Content-Range"), existing_size + ): + if expected_sha256 is not None: + _verify_archive_sha256(local_path, expected_sha256, url) + return local_path + _restart_partial_download(local_path, url) + continue response.raise_for_status() if response.status_code == 200 and existing_size > 0: existing_size = 0 mode = "wb" + elif existing_size > 0 and ( + response.status_code != 206 + or not _has_expected_resume_offset( + response.headers.get("Content-Range"), existing_size + ) + ): + _restart_partial_download(local_path, url) + continue total_size = response.headers.get("Content-Length") total_bytes = ( @@ -213,6 +294,8 @@ def _download_url(url: str, local_path: str) -> str: continue file_obj.write(chunk) pbar.update(len(chunk)) + if expected_sha256 is not None: + _verify_archive_sha256(local_path, expected_sha256, url) return local_path except ( requests.ConnectionError, @@ -278,13 +361,22 @@ def _download_if_url(path_or_url: str, download_dir: str) -> str: return path_or_url parsed = urlparse(path_or_url) + if parsed.scheme != "https": + raise ValueError( + "Dataset archive URLs must use HTTPS. Download the archive locally " + f"and provide its path instead: {path_or_url}" + ) filename = os.path.basename(parsed.path) if not filename: raise ValueError(f"Unable to determine a filename from URL: {path_or_url}") local_path = os.path.join(download_dir, filename) print(f"Downloading dataset archive from {path_or_url} to {local_path}...") - _download_url(path_or_url, local_path) + _download_url( + path_or_url, + local_path, + expected_sha256=PINNED_ARCHIVE_SHA256.get(path_or_url), + ) print("Download completed") return local_path diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 905f947..d8a4e67 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -45,21 +45,41 @@ ) ) CoreMode = str +CORE_MODES: tuple[CoreMode, ...] = ("single", "multi", "global4", "global8") +REGULUS_TARGET_DEVICES = frozenset({"regulus-ra", "regulus-rb"}) -def normalize_core_mode(core_mode: str) -> CoreMode: - """Validate a Vision engine core-mode value.""" +def core_modes_for_target_device(target_device: str) -> tuple[CoreMode, ...]: + """Return the Vision core modes supported by a normalized NPU board.""" - valid_modes = {"single", "multi", "global4", "global8"} - if core_mode not in valid_modes: + if normalize_target_device(target_device) in REGULUS_TARGET_DEVICES: + return ("single",) + return CORE_MODES + + +def normalize_core_mode( + core_mode: str, *, target_device: str | None = None +) -> CoreMode: + """Validate a Vision engine core mode, including board compatibility.""" + + if core_mode not in CORE_MODES: raise ValueError( - f"Invalid core mode '{core_mode}'. Expected one of {sorted(valid_modes)}." + f"Invalid core mode '{core_mode}'. Expected one of {list(CORE_MODES)}." ) + if target_device is not None: + normalized_target_device = normalize_target_device(target_device) + supported_modes = core_modes_for_target_device(normalized_target_device) + if core_mode not in supported_modes: + raise ValueError( + f"Core mode '{core_mode}' is not supported by " + f"{normalized_target_device}; expected one of {list(supported_modes)}." + ) return core_mode __all__ = [ "CoreMode", + "core_modes_for_target_device", "MOBILINT_CACHE_DIR", "get_mobilint_cache_dir", "normalize_core_mode", @@ -376,7 +396,7 @@ def __init__( framework: str | None = None, onnx_providers: Sequence[str] | None = None, model_path: str = "", - target_device: str = "aries-rb", + target_device: str | None = None, ) -> None: """Initializes the MBLT_Engine. @@ -398,6 +418,8 @@ def __init__( framework: Execution framework, either "mxq" or "onnx". When omitted, ``model_path`` suffix is used first, then MXQ is the fallback. onnx_providers: Optional ONNX Runtime execution provider order. + target_device: NPU board identifier. An explicit value takes precedence + over ``file_cfg.target_device``; otherwise defaults to ``aries-rb``. """ if _uses_shifted_engine_model_path_layout( @@ -467,12 +489,16 @@ def __init__( if dev_no is None: dev_no = 0 + if target_device is None: + target_device = model_config_part["file_cfg"].get( + "target_device", "aries-rb" + ) + target_device = normalize_target_device(target_device) + is_regulus = target_device in {"regulus-ra", "regulus-rb"} if core_mode is None: core_mode = "single" else: - core_mode = normalize_core_mode(core_mode) - target_device = normalize_target_device(target_device) - is_regulus = target_device in {"regulus-ra", "regulus-rb"} + core_mode = normalize_core_mode(core_mode, target_device=target_device) if target_cores is None: target_cores = ( [] @@ -497,7 +523,7 @@ def __init__( self.file_cfg["mxq_path"] = mxq_path if _onnx_path_passed or "onnx_path" not in self.file_cfg: self.file_cfg["onnx_path"] = onnx_path - if _core_mode_passed or "core_mode" not in self.file_cfg: + if _core_mode_passed or "core_mode" not in self.file_cfg or is_regulus: self.file_cfg["core_mode"] = core_mode if _target_cores_passed or "target_cores" not in self.file_cfg: self.file_cfg["target_cores"] = target_cores @@ -505,7 +531,9 @@ def __init__( self.file_cfg["target_clusters"] = target_clusters if _dev_no_passed or "dev_no" not in self.file_cfg: self.file_cfg["dev_no"] = dev_no - self.file_cfg["core_mode"] = normalize_core_mode(self.file_cfg["core_mode"]) + self.file_cfg["core_mode"] = normalize_core_mode( + self.file_cfg["core_mode"], target_device=target_device + ) self.file_cfg["target_device"] = target_device self.pre_cfg = copy.deepcopy(model_config_part["pre_cfg"]) diff --git a/tests/test_benchmark_vision.py b/tests/test_benchmark_vision.py index cfeb440..67ed22e 100644 --- a/tests/test_benchmark_vision.py +++ b/tests/test_benchmark_vision.py @@ -308,11 +308,64 @@ def dispose(self) -> None: "framework": None, "dev_no": 0, "target_device": "regulus-ra", - "core_mode": "global8", + "core_mode": "single", } ] +@pytest.mark.parametrize( + ("target_device", "core_mode", "expected_modes"), + [ + ("aries-rb", None, ("global8",)), + ("aries-rb", "all", ("single", "multi", "global4", "global8")), + ("regulus-ra", None, ("single",)), + ("regulus-rb", "all", ("single",)), + ], +) +def test_benchmark_core_modes_follow_the_selected_target_device( + target_device: str, core_mode: str | None, expected_modes: tuple[str, ...] +) -> None: + """Use only execution modes supported by the selected NPU board.""" + + assert ( + benchmark_vision_models._core_modes(core_mode, target_device=target_device) + == expected_modes + ) + + +def test_benchmark_rejects_explicit_unsupported_regulus_core_mode( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Fail before creating an invalid Regulus benchmark engine.""" + + monkeypatch.setattr( + benchmark_vision_models, + "_run_target", + lambda *args: pytest.fail("unsupported mode must not start a benchmark"), + ) + + assert ( + benchmark_vision_models.main( + [ + "--models", + "model-a", + "--task", + "image_classification", + "--target-device", + "regulus-ra", + "--core-mode", + "global8", + "--data-path", + str(tmp_path / "dataset"), + "--results-dir", + str(tmp_path / "results"), + "--no-plot", + ] + ) + == 2 + ) + + def test_comparison_rejects_matching_metrics_from_different_tasks( tmp_path: Path, ) -> None: diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index 0526ec4..98dcf26 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -2,12 +2,14 @@ from __future__ import annotations +import hashlib import io import inspect import tarfile from collections.abc import Callable from pathlib import Path from typing import Any +from urllib.parse import urlparse from zipfile import ZipFile import pytest @@ -128,7 +130,10 @@ def test_download_url_retries_and_resumes_partial_file( ), _FakeResponse( status_code=206, - headers={"Content-Length": str(len(second_chunk))}, + headers={ + "Content-Length": str(len(second_chunk)), + "Content-Range": "bytes 3-5/6", + }, chunks=[second_chunk], ), ] @@ -152,6 +157,112 @@ def _fake_get( assert calls == [{}, {"Range": "bytes=3-"}] +def test_download_url_restarts_when_resume_offset_does_not_match( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Discard a partial file when a range response starts at another offset.""" + + calls: list[dict[str, str]] = [] + responses = [ + _FakeResponse( + status_code=206, + headers={"Content-Length": "3", "Content-Range": "bytes 0-2/6"}, + chunks=[b"bad"], + ), + _FakeResponse( + status_code=200, + headers={"Content-Length": "6"}, + chunks=[b"fresh!"], + ), + ] + + def _fake_get( + url: str, stream: bool, timeout: tuple[int, int], headers: dict[str, str] + ) -> _FakeResponse: + del url, stream, timeout + calls.append(dict(headers)) + return responses.pop(0) + + monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) + local_path = tmp_path / "archive.tar" + local_path.write_bytes(b"old") + + assert organizer._download_url( + "https://example.com/archive.tar", str(local_path) + ) == str(local_path) + assert local_path.read_bytes() == b"fresh!" + assert calls == [{"Range": "bytes=3-"}, {}] + + +def test_download_url_accepts_completed_archive_on_range_not_satisfiable( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Treat a matching 416 total as confirmation that the local file is complete.""" + + payload = b"complete" + calls: list[dict[str, str]] = [] + + def _fake_get( + url: str, stream: bool, timeout: tuple[int, int], headers: dict[str, str] + ) -> _FakeResponse: + del url, stream, timeout + calls.append(dict(headers)) + return _FakeResponse( + status_code=416, + headers={"Content-Range": f"bytes */{len(payload)}"}, + chunks=[], + ) + + monkeypatch.setattr(organizer.requests, "get", _fake_get) + local_path = tmp_path / "archive.tar" + local_path.write_bytes(payload) + + assert organizer._download_url( + "https://example.com/archive.tar", + str(local_path), + expected_sha256=hashlib.sha256(payload).hexdigest(), + ) == str(local_path) + assert local_path.read_bytes() == payload + assert calls == [{"Range": f"bytes={len(payload)}-"}] + + +def test_download_url_restarts_after_incomplete_range_not_satisfiable( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Do not accept a 416 when the local partial file has the wrong size.""" + + responses = [ + _FakeResponse( + status_code=416, + headers={"Content-Range": "bytes */6"}, + chunks=[], + ), + _FakeResponse( + status_code=200, + headers={"Content-Length": "6"}, + chunks=[b"fresh!"], + ), + ] + + def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: + del url, kwargs + return responses.pop(0) + + monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) + local_path = tmp_path / "archive.tar" + local_path.write_bytes(b"old") + + assert organizer._download_url( + "https://example.com/archive.tar", str(local_path) + ) == str(local_path) + assert local_path.read_bytes() == b"fresh!" + + @pytest.mark.parametrize("status_code", [408, 429, 503]) def test_download_url_retries_transient_http_responses( monkeypatch: pytest.MonkeyPatch, @@ -208,6 +319,111 @@ def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: assert calls == 1 +def test_download_url_verifies_pinned_archive_sha256( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Accept only downloaded archive bytes matching the configured digest.""" + + payload = b"verified archive" + + def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: + del url, kwargs + return _FakeResponse( + status_code=200, + headers={"Content-Length": str(len(payload))}, + chunks=[payload], + ) + + monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) + local_path = tmp_path / "archive.zip" + + assert organizer._download_url( + "https://example.com/archive.zip", + str(local_path), + expected_sha256=hashlib.sha256(payload).hexdigest(), + ) == str(local_path) + assert local_path.read_bytes() == payload + + +def test_download_url_rejects_and_removes_digest_mismatches( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Never leave an unverified archive available for later extraction.""" + + def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: + del url, kwargs + return _FakeResponse( + status_code=200, + headers={"Content-Length": "9"}, + chunks=[b"malicious"], + ) + + monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) + local_path = tmp_path / "archive.zip" + + with pytest.raises(ValueError, match="SHA-256 mismatch"): + organizer._download_url( + "https://example.com/archive.zip", + str(local_path), + expected_sha256=hashlib.sha256(b"expected").hexdigest(), + ) + + assert not local_path.exists() + + +def test_download_if_url_passes_registered_archive_digest( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Use the package-pinned digest whenever a default archive is downloaded.""" + + source_url, expected_sha256 = next(iter(organizer.PINNED_ARCHIVE_SHA256.items())) + recorded: dict[str, str | None] = {} + + def _fake_download( + url: str, local_path: str, expected_sha256: str | None = None + ) -> str: + recorded.update(url=url, local_path=local_path, expected_sha256=expected_sha256) + return local_path + + monkeypatch.setattr(organizer, "_download_url", _fake_download) + + assert organizer._download_if_url(source_url, str(tmp_path)) == str( + tmp_path / Path(urlparse(source_url).path).name + ) + assert recorded["url"] == source_url + assert recorded["expected_sha256"] == expected_sha256 + + +def test_download_if_url_rejects_plaintext_http( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Do not fetch benchmark inputs over an unauthenticated transport.""" + + monkeypatch.setattr( + organizer.requests, + "get", + lambda *args, **kwargs: pytest.fail("plaintext URL must not be requested"), + ) + + with pytest.raises(ValueError, match="must use HTTPS"): + organizer._download_if_url("http://example.com/archive.zip", str(tmp_path)) + + +def test_coco_and_ade20k_downloads_use_pinned_https_archives() -> None: + """Keep default benchmark datasets on authenticated, integrity-checked URLs.""" + + assert all(url.startswith("https://") for url in organizer.PINNED_ARCHIVE_SHA256) + assert all( + len(digest) == 64 + and all(character in "0123456789abcdef" for character in digest) + for digest in organizer.PINNED_ARCHIVE_SHA256.values() + ) + + def test_should_download_serially_for_same_host_urls() -> None: """Serialize same-host dataset archive downloads to avoid throttling.""" diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 82dcee7..7e9a9ae 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -538,6 +538,94 @@ def test_engine_init_rejects_invalid_core_modes( ) +def test_engine_init_rejects_core_modes_not_supported_by_regulus() -> None: + """Reject an Aries-only allocation mode before creating a Regulus backend.""" + + with pytest.raises(ValueError, match="not supported by regulus-ra"): + MBLT_Engine( + model_cls={"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, + core_mode="global8", + target_device="regulus-ra", + ) + + +@pytest.mark.parametrize( + ( + "explicit_target_device", + "expected_target_device", + "expected_cores", + "expected_core_mode", + ), + [ + (None, "regulus-rb", [], "single"), + ( + "aries-rb", + "aries-rb", + ["0:0", "0:1", "0:2", "0:3", "1:0", "1:1", "1:2", "1:3"], + "global8", + ), + ], +) +def test_engine_init_resolves_target_device_before_default_core_settings( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + explicit_target_device: str | None, + expected_target_device: str, + expected_cores: list[str], + expected_core_mode: str, +) -> None: + """Prefer an explicit board, otherwise retain the configured board and defaults.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + backend_kwargs: dict[str, Any] = {} + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + backend_kwargs.update(kwargs) + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + engine = MBLT_Engine( + model_cls={ + "file_cfg": {"target_device": "regulus-rb", "core_mode": "global8"}, + "pre_cfg": {}, + "post_cfg": {}, + }, + model_path=str(mxq_path), + target_device=explicit_target_device, + ) + + try: + assert engine.file_cfg["target_device"] == expected_target_device + assert backend_kwargs["target_device"] == expected_target_device + assert backend_kwargs["core_mode"] == expected_core_mode + assert backend_kwargs["target_cores"] == expected_cores + assert backend_kwargs["target_clusters"] == ( + [] if expected_target_device == "regulus-rb" else [0, 1] + ) + finally: + engine.dispose() + + def test_engine_init_preserves_shifted_positional_mxq_runtime_arguments( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, From a75f8bbc2a59c29460980850d15f3c2daf10011e Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 10:30:12 +0900 Subject: [PATCH 23/48] feat: Update COCO dataset organization to use registry download URLs; add validation for evaluation batch lengths and enhance segmentation mask encoding --- benchmark/organize_coco.py | 8 +-- mblt_vision/utils/evaluation/eval_dota.py | 34 ++++++++++++- mblt_vision/utils/postprocess/common.py | 41 ++++++++------- mblt_vision/wrapper.py | 30 ++--------- tests/conftest.py | 14 ++++++ tests/rc/cr7.jpg | Bin 107563 -> 0 bytes tests/rc/tabby.jpg | Bin 2039072 -> 0 bytes tests/rc/volcano.jpg | Bin 726157 -> 0 bytes tests/test_benchmark_vision.py | 15 ++++++ tests/test_eval_dota.py | 58 ++++++++++++++++++++++ tests/test_mxq_inference.py | 9 ++-- tests/test_onnx_classification.py | 9 +--- tests/test_onnx_yolo.py | 36 ++++++-------- tests/test_postprocess_hierarchy.py | 25 ++++++++++ tests/test_wrapper.py | 8 ++- 15 files changed, 203 insertions(+), 84 deletions(-) delete mode 100644 tests/rc/cr7.jpg delete mode 100644 tests/rc/tabby.jpg delete mode 100644 tests/rc/volcano.jpg diff --git a/benchmark/organize_coco.py b/benchmark/organize_coco.py index adb4601..65056e3 100644 --- a/benchmark/organize_coco.py +++ b/benchmark/organize_coco.py @@ -15,12 +15,12 @@ if str(_REPO_ROOT) not in sys.path: sys.path.insert(0, str(_REPO_ROOT)) +from mblt_vision.datasets import get_dataset_config from mblt_vision.utils.datasets import organize_coco -DEFAULT_COCO_IMAGE_SOURCE = "http://images.cocodataset.org/zips/val2017.zip" -DEFAULT_COCO_ANNOTATION_SOURCE = ( - "http://images.cocodataset.org/annotations/annotations_trainval2017.zip" -) +COCO_DOWNLOAD_CONFIG = get_dataset_config("coco")["download"] +DEFAULT_COCO_IMAGE_SOURCE = COCO_DOWNLOAD_CONFIG["images"] +DEFAULT_COCO_ANNOTATION_SOURCE = COCO_DOWNLOAD_CONFIG["annotations"] if __name__ == "__main__": parser = argparse.ArgumentParser(description="Organize COCO dataset") diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 534066a..c732f9d 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -594,6 +594,29 @@ def _nms_output_list(nms_output: Any) -> list[torch.Tensor]: raise TypeError(f"Unsupported OBB NMS output type: {type(nms_output).__name__}.") +def _validate_evaluation_batch_lengths( + nms_outputs: list[torch.Tensor], + input_batch_size: int, + org_shape: Any, + ratio_pad: Any, + image_ids: Any, +) -> None: + """Reject batches whose output or loader metadata omits an image.""" + + batch_lengths = { + "model outputs": len(nms_outputs), + "input batch": input_batch_size, + "original shapes": len(org_shape), + "ratio pads": len(ratio_pad), + "image IDs": len(image_ids), + } + if len(set(batch_lengths.values())) != 1: + details = ", ".join( + f"{name}={length}" for name, length in batch_lengths.items() + ) + raise ValueError(f"DOTAv1 evaluation batch length mismatch: {details}.") + + def eval_dota( model: MBLT_Engine, data_path: str, @@ -641,11 +664,20 @@ def eval_dota( inference_time += time() - tic nms_outs = model.postprocess(out_npu) input_shape = tuple(int(value) for value in input_npu.shape[1:-1]) + nms_outputs = _nms_output_list(nms_outs.output) + _validate_evaluation_batch_lengths( + nms_outputs, + int(input_npu.shape[0]), + org_shape, + ratio_pad, + image_ids, + ) for nms_out, image_id, image_shape, image_ratio_pad in zip( - _nms_output_list(nms_outs.output), + nms_outputs, image_ids, org_shape, ratio_pad, + strict=True, ): target = _ground_truth_to_input_space( ground_truths[image_id], diff --git a/mblt_vision/utils/postprocess/common.py b/mblt_vision/utils/postprocess/common.py index c498a02..e40993c 100644 --- a/mblt_vision/utils/postprocess/common.py +++ b/mblt_vision/utils/postprocess/common.py @@ -1225,6 +1225,25 @@ def multi_encode(pixels: torch.Tensor) -> list[list[int]]: return counts +def _encode_segmentation_masks(seg_result: torch.Tensor) -> list[dict[str, Any]]: + """Threshold resized instance masks and encode them as COCO RLE objects.""" + + h, w = seg_result.shape[1:3] + binary_masks = seg_result > 0.5 + encoded_pixels = ( + binary_masks.permute(0, 2, 1) + .contiguous() + .view(binary_masks.shape[0], h * w) + .to(torch.uint8) + ) + counts = multi_encode(encoded_pixels) + if len(counts) != encoded_pixels.shape[0]: + raise RuntimeError( + f"Encoded {len(counts)} masks for a mask tensor batch of {encoded_pixels.shape[0]}." + ) + return [{"size": [h, w], "counts": to_string(count)} for count in counts] + + def nmsout2eval( nms_outs: list[torch.Tensor] | torch.Tensor, img1_shape: tuple[int, int], @@ -1337,25 +1356,9 @@ def nmsout2eval_seg( ) ] - def mask_encode(seg_result: torch.Tensor) -> list[dict[str, Any]]: - extra = [] - h, w = seg_result.shape[1:3] - seg_result = ( - seg_result.permute(0, 2, 1) - .contiguous() - .view(seg_result.shape[0], h * w) - .byte() - ) - counts = multi_encode(seg_result) - if len(counts) != seg_result.shape[0]: - raise RuntimeError( - f"Encoded {len(counts)} masks for a mask tensor batch of {seg_result.shape[0]}." - ) - for c in counts: - extra.append({"size": [h, w], "counts": to_string(c)}) - return extra - - extra_list = [mask_encode(seg_result) for seg_result in scaled_seg_results] + extra_list = [ + _encode_segmentation_masks(seg_result) for seg_result in scaled_seg_results + ] for labels, boxes, scores, extra in zip( labels_list, boxes_list, scores_list, extra_list ): diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index d8a4e67..af12b86 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -747,36 +747,14 @@ def _prepare_onnx_inputs(self, x: TensorLike) -> dict[str, np.ndarray]: expected_shape = self._require_onnx_session().get_inputs()[0].shape if len(expected_shape) == 4: - if x_np.ndim == 3: - first_channel_count = x_np.shape[0] - last_channel_count = x_np.shape[-1] - elif x_np.ndim == 4: - first_channel_count = x_np.shape[1] - last_channel_count = x_np.shape[-1] - else: - first_channel_count = None - last_channel_count = None - expected_second_dim = expected_shape[1] expected_last_dim = expected_shape[-1] - second_matches_channels = isinstance(expected_second_dim, int) and ( - expected_second_dim == first_channel_count - or expected_second_dim == last_channel_count - ) - last_matches_channels = isinstance(expected_last_dim, int) and ( - expected_last_dim == first_channel_count - or expected_last_dim == last_channel_count - ) - expected_layout = None expected_channels = None - if second_matches_channels and not last_matches_channels: - expected_layout = "nchw" - expected_channels = expected_second_dim - elif last_matches_channels and not second_matches_channels: - expected_layout = "nhwc" - expected_channels = expected_last_dim - elif isinstance(expected_second_dim, int) and expected_second_dim in { + # ONNX layout is encoded by the channel axis, not by a spatial + # dimension that happens to equal a channel count. This is crucial + # for square static inputs such as [1, 3, 224, 224]. + if isinstance(expected_second_dim, int) and expected_second_dim in { 1, 2, 3, diff --git a/tests/conftest.py b/tests/conftest.py index c9e9b9f..671929d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,4 +2,18 @@ # through its pytest11 entry point and this file is unnecessary; from a source # checkout the star import is what puts pytest_addoption in the root conftest # namespace, which is the only place pytest looks for it. +from pathlib import Path + +import pytest +from PIL import Image + from mblt_npu.pytest_plugin import * # noqa: F401,F403 + + +@pytest.fixture +def synthetic_image_path(tmp_path: Path) -> Path: + """Create a small RGB image for inference tests without committing assets.""" + + image_path = tmp_path / "synthetic-image.jpg" + Image.new("RGB", (64, 48), color=(64, 128, 192)).save(image_path) + return image_path diff --git a/tests/rc/cr7.jpg b/tests/rc/cr7.jpg deleted file mode 100644 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zA+8l#HwiZ~kQfFiZSekx=Y_QF3C{<&dR3y@Q;tx8tVfx7P;y0S<&3~?GSLJ$6?WFj zB$OA3kty)HZbYaF>v`WcC{1Y!HNeLT`S|%Qa^r-YfksT&%wm+Pwc>gi@!fZ?Xq}Pf zTI(}e5h6di-=A>a<9eOZYNaGb0ljwkz)TBjc@6fRQ+LZ$JkfevP7Jk|x; zTI?xd*>tP@#Bx*bHfmK7;VscmFJ6}JxLDKXHVlO z!D$WiG-2DKq$!$HXS=K$3rTR>5rShAC9;iD^ZDOiFEifncZ}nNah$ki-dPx>lsF^- z1I>;obSv1uQYd#)0D=SxdG8QHfCh;%<20bFBh!FeB1 zJKqZpaKc?V)iqXrE{Mbcz&y{yAa+oyr>X7jtz#S~JeL)>%av2o3Zce843ogJto->s z5s}-zVF)xEYbeTEWnvrmZ-rYC-Z&IDm3-Zse3m;U2Ga=Q2>nS?VYAE0>@;7|S`q#J zfN7ktEQ@T~D)v2{-0;?jc)s7CP|%n!^SRl`A{jG=Q)zB`6v8lw`IM4gCuUkoR7i9> z(5c6?_>k~r`E$Mtw0;|@Ra&b;iXudV>~!%pfEaBbzHKKCrO_x+4;k8 zN>+|0F{gYM+S+JtibUG7uDfg$*f1Ea0S)wi7<23C?+Kz4S4v|V$CKccb4G0im+J-V zx{0*sXy6+Tobw?+iz#6khx6ZYE1}UleV9y%r6R&f+GrPgkP$ None: + """Keep documented COCO organizer defaults aligned with accepted registry URLs.""" + + script = Path(__file__).parents[1] / "benchmark" / "organize_coco.py" + namespace = runpy.run_path(str(script)) + download_config = get_dataset_config("coco")["download"] + + assert namespace["DEFAULT_COCO_IMAGE_SOURCE"] == download_config["images"] + assert namespace["DEFAULT_COCO_ANNOTATION_SOURCE"] == download_config["annotations"] + assert namespace["DEFAULT_COCO_IMAGE_SOURCE"].startswith("https://") + assert namespace["DEFAULT_COCO_ANNOTATION_SOURCE"].startswith("https://") + + def test_benchmark_records_imagenet_metrics_in_primary_order( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index d56e73a..ec36de6 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -2,6 +2,7 @@ from __future__ import annotations +import importlib from types import SimpleNamespace from typing import cast @@ -15,6 +16,63 @@ evaluate_dota_predictions, ) +eval_dota_module = importlib.import_module("mblt_vision.utils.evaluation.eval_dota") + + +def test_eval_dota_rejects_truncated_postprocess_batches( + monkeypatch: pytest.MonkeyPatch, tmp_path +) -> None: + """Do not silently omit ground truths when a batched model output is short.""" + + class _FakeProgress: + def __init__(self, iterable, **kwargs) -> None: + del kwargs + self._iterable = iterable + + def __iter__(self): + return iter(self._iterable) + + def set_postfix_str(self, value: str) -> None: + del value + + def close(self) -> None: + return None + + class _FakeModel: + post_cfg = {"task": "obb"} + preprocess_with_metadata = object() + + def set_postprocess_thresholds(self, **kwargs) -> None: + del kwargs + + def __call__(self, inputs: torch.Tensor) -> torch.Tensor: + return inputs + + def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: + del outputs + return SimpleNamespace(output=[torch.zeros((0, 7), dtype=torch.float32)]) + + class _FakeDataset: + def __len__(self) -> int: + return 2 + + batch = ( + torch.zeros((2, 8, 8, 3), dtype=torch.float32), + [(8, 8), (8, 8)], + [None, None], + ("first", "second"), + ) + monkeypatch.setattr(eval_dota_module, "CustomDOTAv1", lambda _: _FakeDataset()) + monkeypatch.setattr(eval_dota_module, "get_dota_loader", lambda *args: [batch]) + monkeypatch.setattr(eval_dota_module, "_load_ground_truths", lambda *args: {}) + monkeypatch.setattr(eval_dota_module, "tqdm", _FakeProgress) + + with pytest.raises( + ValueError, + match=r"DOTAv1 evaluation batch length mismatch: model outputs=1, input batch=2", + ): + eval_dota_module.eval_dota(_FakeModel(), str(tmp_path), batch_size=2) + def test_normalized_difficult_flag_loads_as_an_ignored_region(tmp_path) -> None: """Read organizer-produced difficult metadata before selecting evaluation targets.""" diff --git a/tests/test_mxq_inference.py b/tests/test_mxq_inference.py index 3962751..a99a526 100644 --- a/tests/test_mxq_inference.py +++ b/tests/test_mxq_inference.py @@ -2,8 +2,6 @@ from __future__ import annotations -from pathlib import Path - import pytest from mblt_npu.pytest_plugin import NpuParams @@ -12,13 +10,14 @@ pytestmark = [pytest.mark.requires_network, pytest.mark.requires_npu] -def test_mxq_classification_runs_with_shared_npu_options(npu_params: NpuParams) -> None: +def test_mxq_classification_runs_with_shared_npu_options( + npu_params: NpuParams, synthetic_image_path +) -> None: """Load a representative MXQ model using the shared NPU test options.""" - image_path = Path(__file__).parent / "rc" / "tabby.jpg" model = MBLT_Engine(model_cls="resnet50", **npu_params.base) try: - result = model.postprocess(model(model.preprocess(str(image_path)))) + result = model.postprocess(model(model.preprocess(str(synthetic_image_path)))) assert result.task == "image_classification" assert result.acc is not None finally: diff --git a/tests/test_onnx_classification.py b/tests/test_onnx_classification.py index 5df190e..d05d31f 100644 --- a/tests/test_onnx_classification.py +++ b/tests/test_onnx_classification.py @@ -2,14 +2,10 @@ from __future__ import annotations -import os -from pathlib import Path - import pytest from mblt_vision.image_classification import AlexNet, CAFormer_B36, YOLO26sCls -TEST_DIR = Path(__file__).parent pytestmark = pytest.mark.requires_network @@ -21,14 +17,13 @@ YOLO26sCls, ], ) -def test_onnx_classification(model_cls) -> None: +def test_onnx_classification(model_cls, synthetic_image_path) -> None: """Run ONNX inference for representative classification models.""" - image_path = os.path.join(TEST_DIR, "rc", "volcano.jpg") model = model_cls(framework="onnx") try: - input_img = model.preprocess(image_path) + input_img = model.preprocess(str(synthetic_image_path)) output = model(input_img) result = model.postprocess(output) diff --git a/tests/test_onnx_yolo.py b/tests/test_onnx_yolo.py index e8e0876..6b85592 100644 --- a/tests/test_onnx_yolo.py +++ b/tests/test_onnx_yolo.py @@ -2,53 +2,49 @@ from __future__ import annotations -import os from pathlib import Path import pytest from mblt_vision import MBLT_Engine -TEST_DIR = Path(__file__).parent -TMP_DIR = TEST_DIR / "tmp" pytestmark = pytest.mark.requires_network @pytest.mark.parametrize( - ("model_cls", "task", "image_name"), + ("model_cls", "task"), [ - ("yolov5m", "object_detection", "cr7.jpg"), - ("yolov5m-seg", "instance_segmentation", "cr7.jpg"), - ("yolo11m", "object_detection", "cr7.jpg"), - ("yolo11m-seg", "instance_segmentation", "cr7.jpg"), - ("yolo11m-pose", "pose_estimation", "cr7.jpg"), - ("yolov10m", "object_detection", "cr7.jpg"), - ("yolo26m", "object_detection", "cr7.jpg"), - ("yolo26m-seg", "instance_segmentation", "cr7.jpg"), - ("yolo26m-pose", "pose_estimation", "cr7.jpg"), - ("yolov8m-obb", "obb", "cr7.jpg"), - ("yolo11m-obb", "obb", "cr7.jpg"), - ("yolo26m-obb", "obb", "cr7.jpg"), + ("yolov5m", "object_detection"), + ("yolov5m-seg", "instance_segmentation"), + ("yolo11m", "object_detection"), + ("yolo11m-seg", "instance_segmentation"), + ("yolo11m-pose", "pose_estimation"), + ("yolov10m", "object_detection"), + ("yolo26m", "object_detection"), + ("yolo26m-seg", "instance_segmentation"), + ("yolo26m-pose", "pose_estimation"), + ("yolov8m-obb", "obb"), + ("yolo11m-obb", "obb"), + ("yolo26m-obb", "obb"), ], ) def test_onnx_yolo_inference( - model_cls: str, task: str, image_name: str, tmp_path: Path + model_cls: str, task: str, tmp_path: Path, synthetic_image_path: Path ) -> None: """Run ONNX inference for representative YOLO postprocess families.""" - image_path = os.path.join(TEST_DIR, "rc", image_name) save_path = tmp_path / f"{model_cls}_visualization.jpg" model = MBLT_Engine(model_cls=model_cls, framework="onnx") try: - input_img = model.preprocess(image_path) + input_img = model.preprocess(str(synthetic_image_path)) output = model(input_img) result = model.postprocess(output) assert result is not None assert result.task == task assert result.output is not None - result.plot(image_path, save_path=str(save_path)) + result.plot(str(synthetic_image_path), save_path=str(save_path)) assert save_path.is_file() finally: model.dispose() diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index b84c89d..486e19b 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -8,6 +8,7 @@ import pytest import torch from mblt_vision.utils.postprocess import build_postprocess +from mblt_vision.utils.postprocess import common as common_module from mblt_vision.utils.postprocess.base import PostBase, YOLODetectionPostBase from mblt_vision.utils.postprocess.cls_post import ClsPost from mblt_vision.utils.postprocess.depth_post import DepthPost @@ -31,6 +32,30 @@ from mblt_vision.utils.postprocess.yolo_nmsfree_post import YOLONMSFreeDetectionPost +def test_segmentation_rle_encoding_thresholds_resized_masks( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Keep fractional bilinear boundaries from being truncated during RLE export.""" + + resized_mask = common_module.scale_masks( + torch.tensor([[[1.0, 0.0], [1.0, 0.0]]]), (3, 4) + ) + captured_pixels: list[torch.Tensor] = [] + + def _capture_encode(pixels: torch.Tensor) -> list[list[int]]: + captured_pixels.append(pixels.clone()) + return [[pixels.numel()]] + + monkeypatch.setattr(common_module, "multi_encode", _capture_encode) + + common_module._encode_segmentation_masks(resized_mask) + + assert 0.5 < resized_mask[0, 0, 1] < 1.0 + assert captured_pixels[0].view(1, 4, 3).permute(0, 2, 1).tolist() == [ + [[1, 1, 0, 0]] * 3 + ] + + @pytest.mark.parametrize( ("post_cfg", "expected_type"), [ diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 7e9a9ae..3eb0a96 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -1348,8 +1348,8 @@ def get_inputs(self) -> list[_FakeInput]: assert inputs["input"].dtype == np.float32 -def test_prepare_onnx_inputs_transposes_hwc_images() -> None: - """Convert single HWC images to batched NCHW arrays for ONNX runtime.""" +def test_prepare_onnx_inputs_transposes_static_square_hwc_images() -> None: + """Use the static ONNX channel axis to convert square HWC images to NCHW.""" class _FakeInput: name = "input" @@ -1367,10 +1367,14 @@ def get_inputs(self) -> list[_FakeInput]: engine.input_name = "input" image = np.zeros((224, 224, 3), dtype=np.float32) + image[..., 0] = 1.0 + image[..., 1] = 2.0 + image[..., 2] = 3.0 inputs = engine._prepare_onnx_inputs(image) assert inputs["input"].shape == (1, 3, 224, 224) + assert inputs["input"][0, :, 0, 0].tolist() == [1.0, 2.0, 3.0] def test_final_onnx_detections_apply_confidence_threshold() -> None: From 815f246fe054d42789dc8a10eeea60b0e68ef458 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 11:06:07 +0900 Subject: [PATCH 24/48] feat: Enhance compilation output path resolution and validation; improve evaluation metrics handling and add tests for classification postprocessing --- mblt_vision/compile/vision.py | 42 +++++++--- mblt_vision/utils/evaluation/eval_imagenet.py | 23 ++++-- mblt_vision/utils/postprocess/cls_post.py | 22 +++++ mblt_vision/utils/postprocess/depth_post.py | 6 +- mblt_vision/utils/preprocess/reader.py | 38 ++++++++- mblt_vision/wrapper.py | 79 +++++++++++++++--- tests/test_compile_vision.py | 41 ++++++++++ tests/test_depth_estimation.py | 18 ++++ tests/test_eval_imagenet.py | 65 +++++++++++++++ tests/test_postprocess_hierarchy.py | 12 +++ tests/test_preprocess_validation.py | 25 ++++++ tests/test_wrapper.py | 82 +++++++++++++++++++ 12 files changed, 417 insertions(+), 36 deletions(-) create mode 100644 tests/test_eval_imagenet.py diff --git a/mblt_vision/compile/vision.py b/mblt_vision/compile/vision.py index efa9e65..8ccdea3 100644 --- a/mblt_vision/compile/vision.py +++ b/mblt_vision/compile/vision.py @@ -845,6 +845,35 @@ def _resolve_compile_onnx_path( raise FileNotFoundError(f"Unable to resolve {configured_name} for compilation.") +def _resolve_compile_output_path( + save_path: str | Path | None, resolved_onnx: Path +) -> Path: + """Resolve and validate an MXQ compilation output path.""" + + requested_output_path = ( + Path(save_path).expanduser() + if save_path is not None + else _default_model_dir() / f"{resolved_onnx.stem}.mxq" + ) + if requested_output_path.suffix.lower() != ".mxq": + raise ValueError( + "Compilation output path must end with `.mxq`, " + f"got {requested_output_path}." + ) + output_path = requested_output_path.resolve() + if output_path == resolved_onnx.resolve(): + raise ValueError( + "Compilation output path must not be the same as the input ONNX path: " + f"{output_path}." + ) + if output_path.suffix.lower() != ".mxq": + raise ValueError( + "Compilation output path must resolve to a path ending with `.mxq`, " + f"got {output_path}." + ) + return output_path + + def compile_vision_model( model_cls: str, *, @@ -915,11 +944,7 @@ def compile_vision_model( selected_local_path = model_path if model_path is not None else onnx_path if calib_data_path is not None: resolved_onnx = _resolve_compile_onnx_path(file_cfg, selected_local_path) - output_path = ( - Path(save_path).expanduser() - if save_path is not None - else _default_model_dir() / f"{resolved_onnx.stem}.mxq" - ).resolve() + output_path = _resolve_compile_output_path(save_path, resolved_onnx) output_path.parent.mkdir(parents=True, exist_ok=True) resolved_percentile, resolved_topk = resolve_quantization_values( file_cfg, percentile, topk_ratio @@ -968,12 +993,7 @@ def compile_vision_model( f"Unable to resolve {configured_name} for model `{model_cls}`." ) - output_path = ( - Path(save_path).expanduser() - if save_path is not None - else _default_model_dir() / f"{resolved_onnx.stem}.mxq" - ) - output_path = output_path.resolve() + output_path = _resolve_compile_output_path(save_path, resolved_onnx) output_path.parent.mkdir(parents=True, exist_ok=True) resolved_percentile, resolved_topk = resolve_quantization_values( file_cfg, percentile, topk_ratio diff --git a/mblt_vision/utils/evaluation/eval_imagenet.py b/mblt_vision/utils/evaluation/eval_imagenet.py index 3d2b21b..fe46a6c 100644 --- a/mblt_vision/utils/evaluation/eval_imagenet.py +++ b/mblt_vision/utils/evaluation/eval_imagenet.py @@ -68,20 +68,31 @@ def eval_imagenet_metrics( inference_time += time() - tic result = model.postprocess(out_npu) output = result.output - if isinstance(output, np.ndarray): - prediction = output.argmax(-1) - elif isinstance(output, torch.Tensor): - prediction = output.argmax(-1).cpu().numpy() - else: + label_array = np.asarray(label) + if not isinstance(output, (np.ndarray, torch.Tensor)): raise TypeError( f"Expected classification output to be a tensor or ndarray, got {type(output)}." ) + if output.ndim != 2: + raise ValueError( + f"ImageNet classification output must have shape [B, C], got {tuple(output.shape)}." + ) + + output_batch_size = output.shape[0] + if output_batch_size != len(label_array): + raise ValueError( + "ImageNet classification output batch size does not match labels: " + f"got {output_batch_size} outputs for {len(label_array)} labels." + ) + if isinstance(output, np.ndarray): + prediction = output.argmax(-1) + else: + prediction = output.argmax(-1).cpu().numpy() top_k = min(5, output.shape[-1]) if isinstance(output, torch.Tensor): top5_prediction = output.topk(top_k, dim=-1).indices.cpu().numpy() else: top5_prediction = np.argpartition(output, -top_k, axis=-1)[:, -top_k:] - label_array = np.asarray(label) cum_top1_correct += (prediction == label_array).sum().item() cum_top5_correct += ( np.any(top5_prediction == label_array[:, np.newaxis], axis=-1).sum().item() diff --git a/mblt_vision/utils/postprocess/cls_post.py b/mblt_vision/utils/postprocess/cls_post.py index 2ad4730..02d7c80 100644 --- a/mblt_vision/utils/postprocess/cls_post.py +++ b/mblt_vision/utils/postprocess/cls_post.py @@ -9,6 +9,7 @@ import numpy as np import torch +from ...datasets import get_dataset_class_names, get_dataset_config from ..types import ListTensorLike, TensorLike from .base import PostBase @@ -28,6 +29,22 @@ def __init__(self, pre_cfg: dict, post_cfg: dict) -> None: """ super().__init__() self.softmax = post_cfg.get("softmax", False) + self.dataset: str | None = None + self.num_classes: int | None = None + dataset = post_cfg.get("dataset") + if dataset is not None: + if not isinstance(dataset, str) or not dataset: + raise ValueError( + "Classification postprocessing requires post_cfg.dataset to be a non-empty string." + ) + dataset = dataset.lower() + dataset_config = get_dataset_config(dataset) + if "image_classification" not in dataset_config["tasks"]: + raise ValueError( + f"Dataset '{dataset}' does not support image classification." + ) + self.dataset = dataset + self.num_classes = len(get_dataset_class_names(dataset)) def __call__(self, x: TensorLike | ListTensorLike) -> torch.Tensor: """Executes classification post-processing. @@ -64,6 +81,11 @@ def __call__(self, x: TensorLike | ListTensorLike) -> torch.Tensor: x = x.flatten( 1 ) # Classification heads may retain singleton spatial dimensions. + if self.num_classes is not None and x.shape[1] != self.num_classes: + raise ValueError( + f"Classification output has {x.shape[1]} classes, but dataset " + f"'{self.dataset}' requires {self.num_classes}." + ) if self.softmax: return x return x.softmax(dim=-1) diff --git a/mblt_vision/utils/postprocess/depth_post.py b/mblt_vision/utils/postprocess/depth_post.py index 1c9514b..22a4deb 100644 --- a/mblt_vision/utils/postprocess/depth_post.py +++ b/mblt_vision/utils/postprocess/depth_post.py @@ -70,11 +70,7 @@ def _normalize_output(self, x: TensorLike | ListTensorLike) -> torch.Tensor: raise ValueError( f"Depth estimation expects [B, 1, H, W] or [B, H, W, 1], got {tuple(depth.shape)}." ) - elif ( - depth.ndim == 3 - and depth.shape[-1] == 1 - and tuple(depth.shape[:2]) == self.input_shape - ): + elif depth.ndim == 3 and depth.shape[-1] == 1: depth = depth[..., 0].unsqueeze(0) elif depth.ndim != 3: raise ValueError( diff --git a/mblt_vision/utils/preprocess/reader.py b/mblt_vision/utils/preprocess/reader.py index c62e986..7a426a9 100644 --- a/mblt_vision/utils/preprocess/reader.py +++ b/mblt_vision/utils/preprocess/reader.py @@ -19,6 +19,10 @@ class Reader(PreOps): """ Reader for loading images from file paths or converting existing objects. Supports "pil" and "numpy" reading styles. + + For ``style="pil"``, arrays must be ``uint8`` RGB values or finite floating-point + RGB values. Floating-point arrays in ``[0, 1]`` are treated as normalized RGB and + scaled to ``[0, 255]``; other floating-point arrays must already be in ``[0, 255]``. """ def __init__(self, style: str) -> None: @@ -67,10 +71,10 @@ def __call__( ) elif self.style == "pil": if isinstance(x, np.ndarray): - return Image.fromarray(x.astype(np.uint8)) + return Image.fromarray(self._to_uint8_image_array(x)) elif isinstance(x, torch.Tensor): x = x.detach().cpu().numpy() - return Image.fromarray(x.astype(np.uint8)) + return Image.fromarray(self._to_uint8_image_array(x)) elif isinstance(x, (str, Path)): return Image.open(x).convert("RGB") elif isinstance(x, Image.Image): @@ -83,3 +87,33 @@ def __call__( raise RuntimeError( f"Reader has an invalid validated style: {self.style!r}." ) + + @staticmethod + def _to_uint8_image_array(image: np.ndarray) -> np.ndarray: + """Convert a validated array to byte RGB values for Pillow.""" + + if image.dtype == np.uint8: + return image + if not np.issubdtype(image.dtype, np.floating): + raise TypeError( + "Reader(style='pil') accepts uint8 arrays or floating-point arrays " + "with RGB values in [0, 1] or [0, 255]; " + f"got {image.dtype}." + ) + if not np.isfinite(image).all(): + raise ValueError( + "Reader(style='pil') requires floating-point image arrays to contain " + "only finite RGB values." + ) + + min_value = float(image.min()) + max_value = float(image.max()) + if min_value < 0.0 or max_value > 255.0: + raise ValueError( + "Reader(style='pil') accepts floating-point RGB values only in " + "[0, 1] or [0, 255]; " + f"got range [{min_value}, {max_value}]." + ) + if max_value <= 1.0: + image = image * 255.0 + return np.rint(image).astype(np.uint8) diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index af12b86..4606d79 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -11,6 +11,7 @@ import sys import tempfile from pathlib import Path +from types import TracebackType from typing import TYPE_CHECKING, Any, Sequence, cast import numpy as np @@ -547,6 +548,7 @@ def __init__( self._mxq_model: MobilintNPUBackend | None = None self._onnx_model: ONNXBackend | None = None self._onnx_session: Any = None + self._closed = False try: if self.framework == "onnx": @@ -587,13 +589,7 @@ def __init__( ) self.device = torch.device("cpu") except Exception: - for backend in (self._mxq_model, self._onnx_model): - if backend is None: - continue - try: - backend.dispose() - except Exception: - pass + self._close(suppress_errors=True) raise def _mxq_backend_kwargs(self) -> dict[str, Any]: @@ -857,6 +853,7 @@ def __call__( Returns: Raw model output. """ + self._ensure_open() if self.framework == "onnx": outputs = self._require_onnx_session().run( self.output_names, self._prepare_onnx_inputs(x) @@ -994,15 +991,73 @@ def cuda( def launch(self) -> None: """Launches the underlying model.""" + self._ensure_open() if self.framework == "mxq": self._require_mxq_model().launch() def dispose(self) -> None: - """Disposes the underlying model.""" - if self.framework == "mxq": - self._require_mxq_model().dispose() - elif self._onnx_model is not None: - self._onnx_model.dispose() + """Compatibility alias for :meth:`close`.""" + + self.close() + + def close(self) -> None: + """Release backend resources. Safe to call more than once.""" + + self._close(suppress_errors=False) + + def __enter__(self) -> MBLT_Engine: + """Return this engine for use in a context manager.""" + + self._ensure_open() + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: TracebackType | None, + ) -> bool: + """Release resources when leaving a context manager block.""" + + del exc_value, traceback + self._close(suppress_errors=exc_type is not None) + return False + + def __del__(self) -> None: + """Best-effort cleanup for engines not explicitly closed by callers.""" + + try: + self._close(suppress_errors=True) + except Exception: + pass + + def _ensure_open(self) -> None: + """Raise when inference is attempted after backend disposal.""" + + if getattr(self, "_closed", False): + raise RuntimeError("MBLT_Engine is closed.") + + def _close(self, *, suppress_errors: bool) -> None: + """Dispose acquired backends once, optionally suppressing cleanup failures.""" + + if getattr(self, "_closed", False): + return + self._closed = True + first_error: Exception | None = None + for backend in ( + getattr(self, "_mxq_model", None), + getattr(self, "_onnx_model", None), + ): + if backend is None: + continue + try: + backend.dispose() + except Exception as exc: + if first_error is None: + first_error = exc + self._onnx_session = None + if first_error is not None and not suppress_errors: + raise first_error def model_name_aliasing(self, model_name: str) -> str: """Finds the YAML config filename that matches the given model name. diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index a08a187..f8a82e2 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -611,6 +611,7 @@ def _run_fake_compile( entry_level: str = "data", dataset: str | None = None, target_device: str = "aries-rb", + save_path: Path | None = None, fail: bool = False, calls: dict[str, Any] | None = None, ) -> tuple[dict[str, Any], Path]: @@ -735,6 +736,7 @@ def _ensure_dataset( data_path=original_data_path, subset_path=subset_path, calib_data_path=calib_data_path, + save_path=save_path, subset_size=1, ) return calls, hosted_onnx @@ -827,6 +829,45 @@ def test_compile_rejects_non_onnx_local_model_with_ready_calibration_data( ) +@pytest.mark.parametrize("entry_level", ["data", "calibration"]) +def test_compile_requires_an_mxq_output_path( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, entry_level: str +) -> None: + """Reject mislabeled compiler output paths in both compilation branches.""" + + with pytest.raises(ValueError, match=r"output path must end with `.mxq`"): + _run_fake_compile( + monkeypatch, + tmp_path, + task="image_classification", + model_path=None, + entry_level=entry_level, + save_path=tmp_path / "compiled.onnx", + ) + + +@pytest.mark.parametrize("entry_level", ["data", "calibration"]) +def test_compile_rejects_output_path_resolving_to_source_onnx( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, entry_level: str +) -> None: + """Do not let an MXQ-named symlink overwrite the input ONNX model.""" + + source_onnx = tmp_path / "source.onnx" + source_onnx.write_bytes(b"onnx") + output_alias = tmp_path / "compiled.mxq" + output_alias.symlink_to(source_onnx) + + with pytest.raises(ValueError, match="must not be the same as the input ONNX path"): + _run_fake_compile( + monkeypatch, + tmp_path, + task="image_classification", + model_path=source_onnx, + entry_level=entry_level, + save_path=output_alias, + ) + + def test_compile_ignores_missing_local_path_and_uses_hosted_onnx( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: diff --git a/tests/test_depth_estimation.py b/tests/test_depth_estimation.py index a29ac7c..b52a4b0 100644 --- a/tests/test_depth_estimation.py +++ b/tests/test_depth_estimation.py @@ -92,3 +92,21 @@ def test_depth_post_normalizes_full_resolution_channel_last_mxq_output() -> None normalized_single_image = post(single_image) assert isinstance(normalized_single_image, torch.Tensor) assert torch.equal(normalized_single_image, full_resolution[..., 0]) + + +def test_depth_post_normalizes_quarter_resolution_channel_last_output() -> None: + """Remove a single-image channel-last axis before checking quarter resolution.""" + + post = DepthPost({"LetterBox": {"img_size": [8, 8]}}, {}) + channel_last_depth = torch.arange(4, dtype=torch.float32).reshape(2, 2, 1) + expected = torch.nn.functional.interpolate( + channel_last_depth[..., 0][None, None], + scale_factor=4.0, + mode="bilinear", + align_corners=False, + )[:, 0] + + normalized = post(channel_last_depth) + + assert isinstance(normalized, torch.Tensor) + assert torch.equal(normalized, expected) diff --git a/tests/test_eval_imagenet.py b/tests/test_eval_imagenet.py new file mode 100644 index 0000000..f478d14 --- /dev/null +++ b/tests/test_eval_imagenet.py @@ -0,0 +1,65 @@ +"""Focused ImageNet evaluator regression tests.""" + +from __future__ import annotations + +import importlib +from types import SimpleNamespace + +import pytest +import torch + +eval_imagenet_module = importlib.import_module( + "mblt_vision.utils.evaluation.eval_imagenet" +) + + +def test_imagenet_evaluation_rejects_truncated_classification_batches( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Do not broadcast one classification row across a multi-image label batch.""" + + class _FakeDataset: + def __len__(self) -> int: + return 2 + + class _FakeProgress: + def __init__(self, iterable, **kwargs) -> None: + del kwargs + self._iterable = iterable + + def __iter__(self): + return iter(self._iterable) + + def set_postfix_str(self, value: str) -> None: + del value + + def close(self) -> None: + return None + + class _FakeModel: + def preprocess(self, value: object) -> object: + return value + + def __call__(self, inputs: torch.Tensor) -> torch.Tensor: + return inputs + + def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: + del outputs + return SimpleNamespace(output=torch.zeros((1, 5), dtype=torch.float32)) + + batch = (torch.zeros((2, 3, 8, 8)), torch.tensor([0, 1])) + monkeypatch.setattr( + eval_imagenet_module, "CustomImageFolder", lambda _: _FakeDataset() + ) + monkeypatch.setattr( + eval_imagenet_module, "get_imagenet_loader", lambda *args: [batch] + ) + monkeypatch.setattr(eval_imagenet_module, "tqdm", _FakeProgress) + + with pytest.raises( + ValueError, + match="got 1 outputs for 2 labels", + ): + eval_imagenet_module.eval_imagenet_metrics( + _FakeModel(), "/dataset", batch_size=2 + ) diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 486e19b..0be0636 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -56,6 +56,18 @@ def _capture_encode(pixels: torch.Tensor) -> list[list[int]]: ] +def test_classification_postprocessor_rejects_wrong_taxonomy_width() -> None: + """Reject local classification artifacts whose heads do not match ImageNet.""" + + postprocessor = ClsPost({}, {"task": "image_classification", "dataset": "imagenet"}) + + with pytest.raises( + ValueError, + match="Classification output has 999 classes, but dataset 'imagenet' requires 1000", + ): + postprocessor(torch.zeros((1, 999), dtype=torch.float32)) + + @pytest.mark.parametrize( ("post_cfg", "expected_type"), [ diff --git a/tests/test_preprocess_validation.py b/tests/test_preprocess_validation.py index 6fb78a7..e37697e 100644 --- a/tests/test_preprocess_validation.py +++ b/tests/test_preprocess_validation.py @@ -102,6 +102,31 @@ def test_preprocessors_accept_grad_tracking_tensors( assert isinstance(operation(image), output_type) # type: ignore[operator] +def test_reader_pil_scales_normalized_float_arrays() -> None: + """Convert normalized float RGB inputs deliberately instead of truncating them.""" + + image = np.full((2, 3, 3), 0.5, dtype=np.float32) + + converted = np.asarray(Reader("pil")(image)) + + np.testing.assert_array_equal(converted, np.full((2, 3, 3), 128, dtype=np.uint8)) + + +@pytest.mark.parametrize( + "image", + [ + np.full((2, 3, 3), -0.1, dtype=np.float32), + np.full((2, 3, 3), 256.0, dtype=np.float32), + np.full((2, 3, 3), np.nan, dtype=np.float32), + ], +) +def test_reader_pil_rejects_invalid_float_image_arrays(image: np.ndarray) -> None: + """Do not silently cast invalid float RGB values to byte images.""" + + with pytest.raises(ValueError, match=r"finite RGB values|\[0, 1\] or \[0, 255\]"): + Reader("pil")(image) + + @pytest.mark.parametrize( ("factory", "expected"), [ diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 3eb0a96..7af7e15 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -3,6 +3,7 @@ from __future__ import annotations import os +import gc from pathlib import Path from typing import Any, cast @@ -413,6 +414,87 @@ def dispose(self) -> None: assert disposed +def test_engine_context_manager_closes_backend_once( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Context exit releases an MXQ backend and close/dispose remain idempotent.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + dispose_calls = 0 + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + del kwargs + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + nonlocal dispose_calls + dispose_calls += 1 + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr(wrapper, "build_postprocess", lambda *args, **kwargs: object()) + + with MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, model_path=str(mxq_path) + ) as engine: + assert engine is not None + + engine.close() + engine.dispose() + assert dispose_calls == 1 + with pytest.raises(RuntimeError, match="closed"): + engine(torch.zeros((1, 3, 8, 8))) + + +def test_engine_finalizer_closes_unmanaged_backend( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Best-effort finalization prevents leaked backends for unmanaged engines.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + dispose_calls = 0 + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + del kwargs + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + nonlocal dispose_calls + dispose_calls += 1 + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr(wrapper, "build_postprocess", lambda *args, **kwargs: object()) + + engine = MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, model_path=str(mxq_path) + ) + del engine + gc.collect() + + assert dispose_calls == 1 + + def test_engine_init_accepts_local_mxq_model_path( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, From 65bc705caaa1c735343def779dfd21c661994df9 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 12:36:01 +0900 Subject: [PATCH 25/48] feat: Enhance validation for DOTAv1 annotations and COCO class IDs; add tests for malformed inputs and preprocessing functions --- mblt_vision/utils/datasets/organizer.py | 14 +++-- mblt_vision/utils/postprocess/common.py | 12 ++++ mblt_vision/utils/preprocess/_validation.py | 34 +++++++++++ mblt_vision/utils/preprocess/letterbox.py | 7 ++- mblt_vision/utils/preprocess/reader.py | 39 +++---------- mblt_vision/wrapper.py | 2 +- tests/test_dataset_organizer.py | 22 +++++++ tests/test_preprocess_validation.py | 26 +++++++++ tests/test_wrapper.py | 65 +++++++++++++++++++++ 9 files changed, 181 insertions(+), 40 deletions(-) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index a3dee9e..879a12f 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -1449,14 +1449,18 @@ def _write_dotav1_yolo_labels( width, height = image.size converted_lines: list[str] = [] with open(original_label_path, encoding="utf-8") as label_file: - for line in label_file: + for line_number, line in enumerate(label_file, start=1): fields = line.split() - if ( - len(fields) < 10 - or fields[0].startswith("imagesource:") - or fields[0].startswith("gsd:") + if fields and ( + fields[0].startswith("imagesource:") or fields[0].startswith("gsd:") ): continue + if len(fields) < 10: + raise ValueError( + "Malformed DOTAv1 annotation in " + f"{original_label_path} at line {line_number}: expected at least " + f"10 fields, got {len(fields)}." + ) class_name = fields[8] if class_name not in DOTAV1_CLASS_TO_IDX: raise ValueError( diff --git a/mblt_vision/utils/postprocess/common.py b/mblt_vision/utils/postprocess/common.py index e40993c..dc6ed5a 100644 --- a/mblt_vision/utils/postprocess/common.py +++ b/mblt_vision/utils/postprocess/common.py @@ -1278,6 +1278,18 @@ def nmsout2eval( boxes = nms_out[:, :4].clone() scores = nms_out[:, 4] labels = nms_out[:, 5] + valid_labels = ( + torch.isfinite(labels) + & (labels == labels.round()) + & (labels >= 0) + & (labels < 80) + ) + if not bool(valid_labels.all()): + invalid_labels = labels[~valid_labels].detach().cpu().tolist() + raise ValueError( + "COCO class IDs must be finite integral values in [0, 79]; " + f"got {invalid_labels}." + ) boxes = scale_boxes( img1_shape, boxes, img0_shape, ratio_pad=ratio_pad ) # scale boxes to original image size diff --git a/mblt_vision/utils/preprocess/_validation.py b/mblt_vision/utils/preprocess/_validation.py index fff82ae..5d393d9 100644 --- a/mblt_vision/utils/preprocess/_validation.py +++ b/mblt_vision/utils/preprocess/_validation.py @@ -4,6 +4,40 @@ from collections.abc import Sequence +import numpy as np + + +def normalize_uint8_rgb_array(image: np.ndarray, *, operation: str) -> np.ndarray: + """Return byte RGB data after validating or scaling a floating-point image. + + ``[0, 1]`` floating-point input is treated as normalized RGB and scaled to + ``[0, 255]``. Other floating-point input must already lie in ``[0, 255]``. + """ + + if image.dtype == np.uint8: + return image + if not np.issubdtype(image.dtype, np.floating): + raise TypeError( + f"{operation} accepts uint8 arrays or floating-point arrays with RGB " + f"values in [0, 1] or [0, 255]; got {image.dtype}." + ) + if not np.isfinite(image).all(): + raise ValueError( + f"{operation} requires floating-point image arrays to contain only " + "finite RGB values." + ) + + min_value = float(image.min()) + max_value = float(image.max()) + if min_value < 0.0 or max_value > 255.0: + raise ValueError( + f"{operation} accepts floating-point RGB values only in [0, 1] or " + f"[0, 255]; got range [{min_value}, {max_value}]." + ) + if max_value <= 1.0: + image = image * 255.0 + return np.rint(image).astype(np.uint8) + def normalize_image_size(size: int | Sequence[int], *, name: str = "size") -> list[int]: """Normalize a positive scalar or two-dimensional image size to ``[height, width]``.""" diff --git a/mblt_vision/utils/preprocess/letterbox.py b/mblt_vision/utils/preprocess/letterbox.py index 1700dfa..4d2c1fe 100644 --- a/mblt_vision/utils/preprocess/letterbox.py +++ b/mblt_vision/utils/preprocess/letterbox.py @@ -6,7 +6,7 @@ from ..letterbox import LetterBoxGeometry, RatioPad from ..types import TensorLike -from ._validation import normalize_image_size +from ._validation import normalize_image_size, normalize_uint8_rgb_array from .base import PreOps @@ -79,7 +79,9 @@ class LetterBox(PreOps): """Preprocessing for YOLO models, implementing letterbox resizing. Resizes the image while maintaining aspect ratio, adding padding to meet - target dimensions. Based on Ultralytics implementation. + target dimensions. Floating-point RGB inputs in ``[0, 1]`` are scaled to + byte RGB; other floating-point values must be finite and in ``[0, 255]``. + Based on Ultralytics implementation. Ref: https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py#L1535 """ @@ -111,6 +113,7 @@ def __call__(self, x: TensorLike) -> torch.Tensor: ) if x.ndim != 3: raise ValueError(f"LetterBox expects an HWC image, got shape {x.shape}.") + x = normalize_uint8_rgb_array(x, operation="LetterBox") img, self.ratio_pad = _apply_letterbox( x, self.img_size, diff --git a/mblt_vision/utils/preprocess/reader.py b/mblt_vision/utils/preprocess/reader.py index 7a426a9..69c9f8d 100644 --- a/mblt_vision/utils/preprocess/reader.py +++ b/mblt_vision/utils/preprocess/reader.py @@ -12,6 +12,7 @@ from PIL import Image from ..types import TensorLike +from ._validation import normalize_uint8_rgb_array from .base import PreOps @@ -71,10 +72,14 @@ def __call__( ) elif self.style == "pil": if isinstance(x, np.ndarray): - return Image.fromarray(self._to_uint8_image_array(x)) + return Image.fromarray( + normalize_uint8_rgb_array(x, operation="Reader(style='pil')") + ) elif isinstance(x, torch.Tensor): x = x.detach().cpu().numpy() - return Image.fromarray(self._to_uint8_image_array(x)) + return Image.fromarray( + normalize_uint8_rgb_array(x, operation="Reader(style='pil')") + ) elif isinstance(x, (str, Path)): return Image.open(x).convert("RGB") elif isinstance(x, Image.Image): @@ -87,33 +92,3 @@ def __call__( raise RuntimeError( f"Reader has an invalid validated style: {self.style!r}." ) - - @staticmethod - def _to_uint8_image_array(image: np.ndarray) -> np.ndarray: - """Convert a validated array to byte RGB values for Pillow.""" - - if image.dtype == np.uint8: - return image - if not np.issubdtype(image.dtype, np.floating): - raise TypeError( - "Reader(style='pil') accepts uint8 arrays or floating-point arrays " - "with RGB values in [0, 1] or [0, 255]; " - f"got {image.dtype}." - ) - if not np.isfinite(image).all(): - raise ValueError( - "Reader(style='pil') requires floating-point image arrays to contain " - "only finite RGB values." - ) - - min_value = float(image.min()) - max_value = float(image.max()) - if min_value < 0.0 or max_value > 255.0: - raise ValueError( - "Reader(style='pil') accepts floating-point RGB values only in " - "[0, 1] or [0, 255]; " - f"got range [{min_value}, {max_value}]." - ) - if max_value <= 1.0: - image = image * 255.0 - return np.rint(image).astype(np.uint8) diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 4606d79..a40d294 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -497,7 +497,7 @@ def __init__( target_device = normalize_target_device(target_device) is_regulus = target_device in {"regulus-ra", "regulus-rb"} if core_mode is None: - core_mode = "single" + core_mode = "single" if is_regulus else "global8" else: core_mode = normalize_core_mode(core_mode, target_device=target_device) if target_cores is None: diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index 98dcf26..3cd8506 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -60,6 +60,28 @@ def test_dotav1_normalized_labels_preserve_difficult_metadata(tmp_path: Path) -> ] +def test_dotav1_organizer_rejects_truncated_raw_annotation_rows( + tmp_path: Path, +) -> None: + """Do not silently remove malformed source annotations during organization.""" + + image_path = tmp_path / "image.png" + Image.new("RGB", (100, 50)).save(image_path) + source_path = tmp_path / "source.txt" + source_path.write_text( + "imagesource:GoogleEarth\n" "gsd:0.5\n" "0 0 20 0 20 20 0 plane 0\n", + encoding="utf-8", + ) + + with pytest.raises( + ValueError, + match=r"source\.txt at line 3: expected at least 10 fields, got 9", + ): + organizer._write_dotav1_yolo_labels( + str(image_path), str(source_path), str(tmp_path / "normalized.txt") + ) + + @pytest.mark.parametrize( ("organize_dataset", "dataset_name"), [ diff --git a/tests/test_preprocess_validation.py b/tests/test_preprocess_validation.py index e37697e..9ed34d1 100644 --- a/tests/test_preprocess_validation.py +++ b/tests/test_preprocess_validation.py @@ -112,6 +112,17 @@ def test_reader_pil_scales_normalized_float_arrays() -> None: np.testing.assert_array_equal(converted, np.full((2, 3, 3), 128, dtype=np.uint8)) +def test_letterbox_scales_normalized_float_arrays() -> None: + """Do not truncate normalized float RGB input before LetterBox resizing.""" + + image = np.full((2, 2, 3), 0.5, dtype=np.float32) + + converted = LetterBox([4, 4])(image) + + assert converted.dtype == torch.uint8 + assert torch.equal(converted, torch.full((4, 4, 3), 128, dtype=torch.uint8)) + + @pytest.mark.parametrize( "image", [ @@ -127,6 +138,21 @@ def test_reader_pil_rejects_invalid_float_image_arrays(image: np.ndarray) -> Non Reader("pil")(image) +@pytest.mark.parametrize( + "image", + [ + np.full((2, 3, 3), -0.1, dtype=np.float32), + np.full((2, 3, 3), 256.0, dtype=np.float32), + np.full((2, 3, 3), np.nan, dtype=np.float32), + ], +) +def test_letterbox_rejects_invalid_float_image_arrays(image: np.ndarray) -> None: + """Do not wrap out-of-range or non-finite float pixels to byte RGB.""" + + with pytest.raises(ValueError, match=r"finite RGB values|\[0, 1\] or \[0, 255\]"): + LetterBox([4, 4])(image) + + @pytest.mark.parametrize( ("factory", "expected"), [ diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 7af7e15..4be37c0 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -708,6 +708,59 @@ def dispose(self) -> None: engine.dispose() +@pytest.mark.parametrize( + ("target_device", "expected_core_mode"), + [("aries-rb", "global8"), ("regulus-ra", "single")], +) +def test_engine_init_uses_board_specific_default_core_mode( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + target_device: str, + expected_core_mode: str, +) -> None: + """Keep direct engine construction aligned with the CLI board defaults.""" + + mxq_path = tmp_path / "model.mxq" + mxq_path.write_bytes(b"mxq") + backend_kwargs: dict[str, Any] = {} + + class _FakeBackend: + def __init__(self, **kwargs: Any) -> None: + backend_kwargs.update(kwargs) + + def create(self) -> None: + return None + + def launch(self) -> None: + return None + + def get_dtype(self) -> str: + return "DataType.Float32" + + def dispose(self) -> None: + return None + + monkeypatch.setattr(wrapper, "MobilintNPUBackend", _FakeBackend) + monkeypatch.setattr(wrapper, "build_preprocess", lambda config: config) + monkeypatch.setattr( + wrapper, + "build_postprocess", + lambda pre_cfg, post_cfg, **kwargs: (pre_cfg, post_cfg, kwargs), + ) + + engine = MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, + model_path=str(mxq_path), + target_device=target_device, + ) + + try: + assert engine.file_cfg["core_mode"] == expected_core_mode + assert backend_kwargs["core_mode"] == expected_core_mode + finally: + engine.dispose() + + def test_engine_init_preserves_shifted_positional_mxq_runtime_arguments( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, @@ -2862,6 +2915,18 @@ def test_nmsout2eval_matches_coco_json_format_without_mutation() -> None: assert torch.equal(nms_out, original) +@pytest.mark.parametrize("class_id", [-1.0, 1.9, 80.0, float("nan"), float("inf")]) +def test_nmsout2eval_rejects_invalid_coco_class_ids(class_id: float) -> None: + """Reject malformed decoded class IDs before COCO taxonomy remapping.""" + + nms_out = torch.tensor( + [[10.0, 20.0, 110.0, 220.0, 0.9, class_id]], dtype=torch.float32 + ) + + with pytest.raises(ValueError, match="finite integral values"): + nmsout2eval([nms_out], (640, 640), [(640, 640)]) + + def test_nmsout2eval_uses_explicit_ratio_pad() -> None: """Use dataloader-provided LetterBox padding instead of recomputing from shape.""" From bbe2b11346cf8af3bc261c031baef322102e30a2 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 12:58:34 +0900 Subject: [PATCH 26/48] fix: validate vision review edge cases --- mblt_vision/compile/vision.py | 5 ++-- mblt_vision/utils/evaluation/eval_dota.py | 16 ++++++++--- mblt_vision/utils/postprocess/base.py | 18 ++++++++++++- mblt_vision/utils/postprocess/cls_post.py | 2 ++ mblt_vision/utils/results.py | 2 ++ tests/test_compile_vision.py | 25 ++++++++++++++++- tests/test_eval_dota.py | 29 ++++++++++++++++++-- tests/test_postprocess_hierarchy.py | 33 +++++++++++++++++++++++ tests/test_results.py | 17 ++++++++++++ 9 files changed, 137 insertions(+), 10 deletions(-) diff --git a/mblt_vision/compile/vision.py b/mblt_vision/compile/vision.py index 8ccdea3..8559bd1 100644 --- a/mblt_vision/compile/vision.py +++ b/mblt_vision/compile/vision.py @@ -79,8 +79,7 @@ def _validate_calibration_output_dir(output_dir: str | Path) -> Path: destination = Path(output_dir).expanduser() if _path_has_symlink_component(destination): raise ValueError( - "Calibration output_dir must not be or contain a symlink: " - f"{destination}." + f"Calibration output_dir must not be or contain a symlink: {destination}." ) return destination @@ -941,7 +940,7 @@ def compile_vision_model( raise ValueError( "Resolved vision model configuration requires a non-empty `post_cfg.dataset` value." ) - selected_local_path = model_path if model_path is not None else onnx_path + selected_local_path = model_path or onnx_path if calib_data_path is not None: resolved_onnx = _resolve_compile_onnx_path(file_cfg, selected_local_path) output_path = _resolve_compile_output_path(save_path, resolved_onnx) diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index c732f9d..bb408ed 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -119,10 +119,20 @@ def _load_ground_truths( classes.append(cls) polygons.append(coords) elif original_label_path.is_file(): - for line in original_label_path.read_text(encoding="utf-8").splitlines(): + for line_number, line in enumerate( + original_label_path.read_text(encoding="utf-8").splitlines(), start=1 + ): parts = line.split() - if len(parts) < 9: + if parts and ( + parts[0].startswith("imagesource:") or parts[0].startswith("gsd:") + ): continue + if len(parts) < 10: + raise ValueError( + "Malformed original DOTAv1 annotation at " + f"{original_label_path}:{line_number}: expected at least 10 fields, " + f"got {len(parts)}." + ) cls = _label_to_index(parts[8]) if not 0 <= cls < get_dotav1_class_num(): raise ValueError( @@ -131,7 +141,7 @@ def _load_ground_truths( coords = torch.tensor( [float(value) for value in parts[:8]], dtype=torch.float32 ).reshape(4, 2) - if len(parts) >= 10 and parts[9] in {"1", "2"}: + if parts[9] in {"1", "2"}: ignore_classes.append(cls) ignore_polygons.append(coords) else: diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index 8d66410..8ac936c 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -368,7 +368,23 @@ def _final_detection_batches( tensor = torch.from_numpy(x).to(self.device) else: tensor = x.to(self.device) - return [batch[batch[:, 4] > self.conf_thres] for batch in tensor] + batches: list[torch.Tensor] = [] + for batch in tensor: + labels = batch[:, 5] + valid_labels = ( + torch.isfinite(labels) + & (labels == labels.round()) + & (labels >= 0) + & (labels < self.nc) + ) + if not bool(valid_labels.all()): + invalid_labels = labels[~valid_labels].detach().cpu().tolist() + raise ValueError( + "Decoded detection class IDs must be finite integral values in " + f"[0, {self.nc}); got {invalid_labels}." + ) + batches.append(batch[batch[:, 4] > self.conf_thres]) + return batches def _normalize_proto_batch( self, proto_outs: np.ndarray | torch.Tensor diff --git a/mblt_vision/utils/postprocess/cls_post.py b/mblt_vision/utils/postprocess/cls_post.py index 02d7c80..7fcb2a4 100644 --- a/mblt_vision/utils/postprocess/cls_post.py +++ b/mblt_vision/utils/postprocess/cls_post.py @@ -86,6 +86,8 @@ def __call__(self, x: TensorLike | ListTensorLike) -> torch.Tensor: f"Classification output has {x.shape[1]} classes, but dataset " f"'{self.dataset}' requires {self.num_classes}." ) + if not torch.isfinite(x).all(): + raise ValueError("Classification output scores must all be finite.") if self.softmax: return x return x.softmax(dim=-1) diff --git a/mblt_vision/utils/results.py b/mblt_vision/utils/results.py index 59f18d9..884a333 100644 --- a/mblt_vision/utils/results.py +++ b/mblt_vision/utils/results.py @@ -35,6 +35,7 @@ scale_rboxes, xywhr2xyxyxyxy, ) +from .preprocess._validation import normalize_uint8_rgb_array from .types import ListTensorLike, NestedListTensorLike, TensorLike LW = 2 # line width @@ -99,6 +100,7 @@ def _read_image( raise ValueError( f"Image arrays must have HWC shape with three channels, got {source_img.shape}." ) + source_img = normalize_uint8_rgb_array(source_img, operation="Results.plot") source_img = cv2.cvtColor(source_img, cv2.COLOR_RGB2BGR) elif isinstance(source_path, (str, Path)): image_path = Path(source_path) diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index f8a82e2..17f3f13 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -607,7 +607,8 @@ def _run_fake_compile( tmp_path: Path, *, task: str, - model_path: Path | None, + model_path: str | Path | None, + onnx_path: str | Path | None = None, entry_level: str = "data", dataset: str | None = None, target_device: str = "aries-rb", @@ -733,6 +734,7 @@ def _ensure_dataset( target_device=target_device, model_type="VARIANT", model_path=model_path, + onnx_path=onnx_path, data_path=original_data_path, subset_path=subset_path, calib_data_path=calib_data_path, @@ -829,6 +831,27 @@ def test_compile_rejects_non_onnx_local_model_with_ready_calibration_data( ) +@pytest.mark.parametrize("entry_level", ["data", "calibration"]) +def test_compile_uses_onnx_path_when_model_path_is_empty( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, entry_level: str +) -> None: + """Treat an adapter-provided empty model path as absent, not preferred.""" + + local_onnx = tmp_path / "alias.onnx" + local_onnx.write_bytes(b"onnx") + + calls, _ = _run_fake_compile( + monkeypatch, + tmp_path, + task="image_classification", + model_path="", + onnx_path=local_onnx, + entry_level=entry_level, + ) + + assert calls["compile_kwargs"]["model"] == str(local_onnx) + + @pytest.mark.parametrize("entry_level", ["data", "calibration"]) def test_compile_requires_an_mxq_output_path( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, entry_level: str diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index ec36de6..4c24732 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -80,7 +80,7 @@ def test_normalized_difficult_flag_loads_as_an_ignored_region(tmp_path) -> None: label_dir = tmp_path / "labels" / "val" label_dir.mkdir(parents=True) (label_dir / "image.txt").write_text( - "0 0 0 0.2 0 0.2 0.2 0 0.2 0\n" "0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6 1\n", + "0 0 0 0.2 0 0.2 0.2 0 0.2 0\n0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6 1\n", encoding="utf-8", ) dataset = cast( @@ -121,11 +121,36 @@ def test_normalized_truncated_annotation_raises_with_file_and_line(tmp_path) -> _load_ground_truths(str(tmp_path), dataset) +def test_original_truncated_annotation_raises_with_file_and_line(tmp_path) -> None: + """Require a difficulty flag when loading original DOTAv1 labels directly.""" + + original_label_dir = tmp_path / "labels" / "val_original" + original_label_dir.mkdir(parents=True) + label_path = original_label_dir / "image.txt" + label_path.write_text( + "imagesource:GoogleEarth\n0 0 20 0 20 20 0 20 plane\n", encoding="utf-8" + ) + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises( + ValueError, + match=r"image\.txt:2: expected at least 10 fields, got 9", + ): + _load_ground_truths(str(tmp_path), dataset) + + @pytest.mark.parametrize( ("label_path", "annotation"), [ ("labels/val/image.txt", "-1 0 0 0.2 0 0.2 0.2 0 0.2"), - ("labels/val_original/image.txt", "0 0 20 0 20 20 0 20 -1"), + ("labels/val_original/image.txt", "0 0 20 0 20 20 0 20 -1 0"), ], ) def test_dota_annotations_reject_negative_class_indices( diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 0be0636..1263188 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -68,6 +68,39 @@ def test_classification_postprocessor_rejects_wrong_taxonomy_width() -> None: postprocessor(torch.zeros((1, 999), dtype=torch.float32)) +@pytest.mark.parametrize("invalid_score", [float("nan"), float("inf")]) +def test_classification_postprocessor_rejects_nonfinite_scores( + invalid_score: float, +) -> None: + """Do not convert malformed classification logits into plausible predictions.""" + + scores = torch.zeros((1, 1000), dtype=torch.float32) + scores[0, 0] = invalid_score + + with pytest.raises(ValueError, match="scores must all be finite"): + ClsPost({}, {"task": "image_classification", "dataset": "imagenet"})(scores) + + +@pytest.mark.parametrize("invalid_class", [-1.0, 1.5, 2.0, float("nan")]) +def test_already_decoded_detections_reject_invalid_task_class_ids( + invalid_class: float, +) -> None: + """Validate local decoded detection outputs against the configured taxonomy.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + postprocessor.conf_thres = 0.25 + postprocessor.nc = 2 + detections = torch.tensor( + [[[0.0, 0.0, 1.0, 1.0, 0.9, invalid_class]]], dtype=torch.float32 + ) + + with pytest.raises(ValueError, match="Decoded detection class IDs"): + postprocessor._final_detection_batches(detections) + + @pytest.mark.parametrize( ("post_cfg", "expected_type"), [ diff --git a/tests/test_results.py b/tests/test_results.py index 2ef10c5..0b4d446 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -119,6 +119,23 @@ def test_plot_converts_rgb_array_source_to_bgr() -> None: assert np.array_equal(rgb, np.array([[[255, 0, 0]]], dtype=np.uint8)) +def test_plot_normalizes_float_rgb_array_source_to_uint8_bgr() -> None: + """Plot normalized RGB arrays without rendering a near-black background.""" + + result = Results( + {"LetterBox": {"img_size": (1, 1)}}, + {"task": "object_detection"}, + [torch.zeros((0, 6), dtype=torch.float32)], + ) + rgb = np.array([[[0.5, 0.0, 0.0]]], dtype=np.float32) + + plotted = result.plot(rgb) + + assert plotted is not None + assert np.array_equal(plotted, np.array([[[0, 0, 128]]], dtype=np.uint8)) + assert np.array_equal(rgb, np.array([[[0.5, 0.0, 0.0]]], dtype=np.float32)) + + def test_pose_plot_hides_low_visibility_keypoints_and_limbs( monkeypatch: pytest.MonkeyPatch, ) -> None: From 7905e243633150ed9f91b6109a67fe503f182f4c Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 13:17:39 +0900 Subject: [PATCH 27/48] fix: harden validation inputs --- mblt_vision/cli/val.py | 11 ++++++++++- mblt_vision/utils/datasets/dataloader.py | 5 +++++ mblt_vision/utils/postprocess/base.py | 13 +++++++++++++ tests/test_cli.py | 24 ++++++++++++++++++++++++ tests/test_depth_estimation.py | 22 ++++++++++++++++++++-- tests/test_postprocess_hierarchy.py | 24 +++++++++++++++++++++++- 6 files changed, 95 insertions(+), 4 deletions(-) diff --git a/mblt_vision/cli/val.py b/mblt_vision/cli/val.py index 56ec3e2..ccdd66d 100644 --- a/mblt_vision/cli/val.py +++ b/mblt_vision/cli/val.py @@ -11,6 +11,7 @@ from mblt_vision.benchmark.argparse_utils import parse_positive_int from mblt_vision.datasets import get_dataset_config, get_dataset_config_for_task from mblt_vision.utils.datasets.readiness import dataset_ready +from mblt_vision.wrapper import get_mobilint_cache_dir from ._vision import ( add_e2e_arg, @@ -218,10 +219,18 @@ def _default_data_path_for_task(task: str, dataset: str | None = None) -> str: """Returns the default organized dataset path for a vision task.""" try: - return str(get_dataset_config_for_task(task, dataset)["path"]) + configured_path = Path(get_dataset_config_for_task(task, dataset)["path"]) except ValueError as exc: raise SystemExit(f"Unsupported vision task for validation: {task}") from exc + configured_path = configured_path.expanduser() + default_cache_root = Path.home() / ".mblt_model_zoo" + try: + relative_path = configured_path.relative_to(default_cache_root) + except ValueError: + return str(configured_path) + return str(Path(get_mobilint_cache_dir()) / relative_path) + def _dataset_ready(task: str, data_path: str, dataset: str | None = None) -> bool: """Checks whether the organized dataset appears ready for validation.""" diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index f4d067e..a4b3152 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -200,6 +200,11 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: raise ValueError( f"NYU Depth target must be two-dimensional, got {depth.shape}: {depth_path}" ) + if depth.shape != image.shape[:2]: + raise ValueError( + "NYU Depth image and target shapes must match for " + f"{stem}: image {image.shape[:2]}, depth {depth.shape}." + ) return ( cv2.cvtColor(image, cv2.COLOR_BGR2RGB), np.nan_to_num(depth, nan=0.0, posinf=0.0, neginf=0.0), diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index 8ac936c..394e086 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -370,6 +370,19 @@ def _final_detection_batches( tensor = x.to(self.device) batches: list[torch.Tensor] = [] for batch in tensor: + valid_rows = torch.isfinite(batch).all(dim=1) + if not bool(valid_rows.all()): + invalid_rows = ( + torch.nonzero(~valid_rows, as_tuple=False) + .flatten() + .detach() + .cpu() + .tolist() + ) + raise ValueError( + "Decoded detection rows must contain only finite values; " + f"invalid rows: {invalid_rows}." + ) labels = batch[:, 5] valid_labels = ( torch.isfinite(labels) diff --git a/tests/test_cli.py b/tests/test_cli.py index aaa38aa..62250fe 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -2,11 +2,14 @@ from __future__ import annotations +from pathlib import Path + import pytest from mblt_vision.cli import build_parser from mblt_vision.cli.compile import _run_compile from mblt_vision.cli.predict import _cmd_predict +from mblt_vision.cli import val as val_module from mblt_vision.cli.val import _cmd_val @@ -81,3 +84,24 @@ def test_predict_help_explains_supported_workflows( assert "Supported tasks:" in help_text assert "--framework onnx" in help_text assert "--target-device regulus-ra" in help_text + + +def test_validation_default_dataset_path_uses_resolved_cache_root( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Keep default organization alongside artifacts when the home cache falls back.""" + + configured_path = Path.home() / ".mblt_model_zoo" / "datasets" / "coco" + fallback_cache = tmp_path / "cache" + monkeypatch.setattr( + val_module, + "get_dataset_config_for_task", + lambda _task, _dataset: {"path": str(configured_path)}, + ) + monkeypatch.setattr( + val_module, "get_mobilint_cache_dir", lambda: str(fallback_cache) + ) + + assert val_module._default_data_path_for_task("object_detection") == str( + fallback_cache / "datasets" / "coco" + ) diff --git a/tests/test_depth_estimation.py b/tests/test_depth_estimation.py index b52a4b0..f439b1e 100644 --- a/tests/test_depth_estimation.py +++ b/tests/test_depth_estimation.py @@ -2,11 +2,13 @@ from __future__ import annotations - +import cv2 +import numpy as np import pytest import torch -from mblt_vision.utils.postprocess import DepthPost +from mblt_vision.utils.datasets import CustomNYUDepth +from mblt_vision.utils.postprocess import DepthPost from mblt_vision.wrapper import resolve_model_config @@ -110,3 +112,19 @@ def test_depth_post_normalizes_quarter_resolution_channel_last_output() -> None: assert isinstance(normalized, torch.Tensor) assert torch.equal(normalized, expected) + + +def test_nyu_depth_dataset_rejects_mismatched_image_and_target_shapes(tmp_path) -> None: + """Reject paired NYU files whose pixels cannot be compared one-to-one.""" + + image_dir = tmp_path / "images" + depth_dir = tmp_path / "depth" + image_dir.mkdir() + depth_dir.mkdir() + assert cv2.imwrite( + str(image_dir / "sample.png"), np.zeros((4, 5, 3), dtype=np.uint8) + ) + np.save(depth_dir / "sample.npy", np.zeros((3, 5), dtype=np.float32)) + + with pytest.raises(ValueError, match=r"sample: image \(4, 5\), depth \(3, 5\)"): + CustomNYUDepth(str(tmp_path))[0] diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 1263188..f60e74e 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -81,7 +81,7 @@ def test_classification_postprocessor_rejects_nonfinite_scores( ClsPost({}, {"task": "image_classification", "dataset": "imagenet"})(scores) -@pytest.mark.parametrize("invalid_class", [-1.0, 1.5, 2.0, float("nan")]) +@pytest.mark.parametrize("invalid_class", [-1.0, 1.5, 2.0]) def test_already_decoded_detections_reject_invalid_task_class_ids( invalid_class: float, ) -> None: @@ -101,6 +101,28 @@ def test_already_decoded_detections_reject_invalid_task_class_ids( postprocessor._final_detection_batches(detections) +@pytest.mark.parametrize( + ("column", "invalid_value"), + [(0, float("nan")), (4, float("inf")), (5, float("-inf"))], +) +def test_already_decoded_detections_reject_nonfinite_rows( + column: int, invalid_value: float +) -> None: + """Reject malformed coordinates, scores, and labels before filtering them.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + postprocessor.conf_thres = 0.25 + postprocessor.nc = 2 + detections = torch.tensor([[[0.0, 0.0, 1.0, 1.0, 0.9, 1.0]]], dtype=torch.float32) + detections[0, 0, column] = invalid_value + + with pytest.raises(ValueError, match="finite values"): + postprocessor._final_detection_batches(detections) + + @pytest.mark.parametrize( ("post_cfg", "expected_type"), [ From 7d6866e5133f7705f8deda327b345d75a09ebfb1 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 13:36:21 +0900 Subject: [PATCH 28/48] fix: validate vision artifacts and datasets --- benchmark/organize_ade20k.py | 5 +- benchmark/organize_cityscapes.py | 4 +- benchmark/organize_coco.py | 5 +- benchmark/organize_dotav1.py | 5 +- benchmark/organize_imagenet.py | 5 +- benchmark/organize_nyu_depth.py | 5 +- benchmark/organize_widerface.py | 5 +- mblt_vision/utils/datasets/dataloader.py | 20 ++++--- mblt_vision/utils/datasets/organizer.py | 52 +++++++++++++------ mblt_vision/utils/datasets/readiness.py | 17 +++++- mblt_vision/utils/evaluation/eval_dota.py | 10 ++++ .../utils/evaluation/eval_nyu_depth.py | 6 +++ mblt_vision/utils/postprocess/base.py | 4 ++ .../utils/postprocess/semantic_seg_post.py | 27 ++++++---- tests/test_dataloader.py | 35 ++++++++++--- tests/test_dataset_organizer.py | 19 +++++-- tests/test_dataset_readiness.py | 33 +++++++++++- tests/test_eval_dota.py | 28 ++++++++++ tests/test_eval_nyu_depth.py | 48 +++++++++++++++++ tests/test_postprocess_hierarchy.py | 19 +++++++ tests/test_semantic_segmentation.py | 22 ++++++++ 21 files changed, 303 insertions(+), 71 deletions(-) create mode 100644 tests/test_eval_nyu_depth.py diff --git a/benchmark/organize_ade20k.py b/benchmark/organize_ade20k.py index 82cfd8a..67290e9 100644 --- a/benchmark/organize_ade20k.py +++ b/benchmark/organize_ade20k.py @@ -1,7 +1,6 @@ """Organize the ADE20K validation dataset for local use.""" import argparse -import os import sys from pathlib import Path @@ -22,8 +21,8 @@ ) parser.add_argument( "--output-dir", - default=os.path.expanduser("~/.mblt_model_zoo/datasets/ADEChallengeData2016"), - help="Path to the organized validation dataset", + default=None, + help="Path to the organized validation dataset (defaults to cache)", ) args = parser.parse_args() organize_ade20k(dataset_path=args.dataset_path, output_dir=args.output_dir) diff --git a/benchmark/organize_cityscapes.py b/benchmark/organize_cityscapes.py index 7051d79..1966865 100644 --- a/benchmark/organize_cityscapes.py +++ b/benchmark/organize_cityscapes.py @@ -32,8 +32,8 @@ def main() -> None: ) parser.add_argument( "--output-dir", - default="~/.mblt_model_zoo/datasets/cityscapes", - help="Destination for the flat images/ and annotations/ directories", + default=None, + help="Destination for the flat images/ and annotations/ directories (defaults to cache)", ) args = parser.parse_args() organize_cityscapes( diff --git a/benchmark/organize_coco.py b/benchmark/organize_coco.py index 65056e3..6ad9cf6 100644 --- a/benchmark/organize_coco.py +++ b/benchmark/organize_coco.py @@ -6,7 +6,6 @@ """ import argparse -import os import sys from pathlib import Path @@ -39,8 +38,8 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/coco"), - help="Path to the directory to save the organized dataset", + default=None, + help="Path to the directory to save the organized dataset (defaults to cache)", ) args = parser.parse_args() diff --git a/benchmark/organize_dotav1.py b/benchmark/organize_dotav1.py index 5537d8e..4a9b4eb 100644 --- a/benchmark/organize_dotav1.py +++ b/benchmark/organize_dotav1.py @@ -7,7 +7,6 @@ """ import argparse -import os import sys from pathlib import Path @@ -31,8 +30,8 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/dotav1"), - help="Path to the directory to save the organized dataset", + default=None, + help="Path to the directory to save the organized dataset (defaults to cache)", ) args = parser.parse_args() diff --git a/benchmark/organize_imagenet.py b/benchmark/organize_imagenet.py index 65187ea..cec0c93 100644 --- a/benchmark/organize_imagenet.py +++ b/benchmark/organize_imagenet.py @@ -6,7 +6,6 @@ """ import argparse -import os import sys from pathlib import Path @@ -40,8 +39,8 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/imagenet"), - help="Path to the directory to save the organized dataset", + default=None, + help="Path to the directory to save the organized dataset (defaults to cache)", ) args = parser.parse_args() diff --git a/benchmark/organize_nyu_depth.py b/benchmark/organize_nyu_depth.py index 6b41622..e8187a1 100644 --- a/benchmark/organize_nyu_depth.py +++ b/benchmark/organize_nyu_depth.py @@ -5,7 +5,6 @@ """ import argparse -import os import sys from pathlib import Path @@ -28,8 +27,8 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/nyu-depth"), - help="Path to the directory to save the organized dataset", + default=None, + help="Path to the directory to save the organized dataset (defaults to cache)", ) args = parser.parse_args() diff --git a/benchmark/organize_widerface.py b/benchmark/organize_widerface.py index 701b763..bebf78f 100644 --- a/benchmark/organize_widerface.py +++ b/benchmark/organize_widerface.py @@ -6,7 +6,6 @@ """ import argparse -import os import sys from pathlib import Path @@ -37,8 +36,8 @@ parser.add_argument( "--output-dir", type=str, - default=os.path.expanduser("~/.mblt_model_zoo/datasets/widerface"), - help="Path to the directory to save the organized dataset", + default=None, + help="Path to the directory to save the organized dataset (defaults to cache)", ) args = parser.parse_args() diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index a4b3152..ca8ea14 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -74,6 +74,12 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, int, int, int]: image = self._load_image(image_id) height = self.coco.imgs[image_id]["height"] width = self.coco.imgs[image_id]["width"] + if (height, width) != image.shape[:2]: + raise ValueError( + "COCO annotation geometry does not match decoded image for " + f"image ID {image_id}: annotation {(height, width)}, " + f"image {image.shape[:2]}." + ) return image, index, height, width def __len__(self) -> int: @@ -719,13 +725,13 @@ def make_dataset(self) -> None: target_dir = os.path.join(self.root, target_class) if not os.path.isdir(target_dir): continue - for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): - for fname in sorted(fnames): - if os.path.splitext(fname)[1].lower() not in IMAGE_SUFFIXES: - continue - path = os.path.join(root, fname) - item = path, class_index - instances.append(item) + for fname in sorted(os.listdir(target_dir)): + path = os.path.join(target_dir, fname) + if not os.path.isfile(path): + continue + if os.path.splitext(fname)[1].lower() not in IMAGE_SUFFIXES: + continue + instances.append((path, class_index)) self.samples = instances diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 879a12f..8fce971 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -69,6 +69,16 @@ } +def _resolve_organizer_output_dir(output_dir: str | None, dataset_name: str) -> str: + """Return an explicit output directory or the lazily resolved artifact cache.""" + + if output_dir is not None: + return os.path.expanduser(output_dir) + from mblt_vision.wrapper import get_mobilint_cache_dir + + return os.path.join(get_mobilint_cache_dir(), "datasets", dataset_name) + + def _replace_staged_directories( replacements: Iterable[tuple[str, str]], output_parent_dir: str, @@ -551,16 +561,17 @@ def construct_imagenet(image_dir: str, xml_dir: str, output_dir: str) -> None: def organize_imagenet( image_dir: str, xml_dir: str, - output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/imagenet"), + output_dir: str | None = None, ) -> None: """Organizes the ImageNet dataset, unpacking archives if necessary. Args: image_dir (str): Path or URL to the image directory or archive (.tar). xml_dir (str): Path or URL to the XML directory or archive (.tgz). - output_dir (str, optional): Directory to store the organized dataset. - Defaults to ~/.mblt_model_zoo/datasets/imagenet. + output_dir: Directory to store the organized dataset. Defaults to the + resolved Mobilint cache directory. """ + output_dir = _resolve_organizer_output_dir(output_dir, "imagenet") with TemporaryDirectory() as temp_dir: local_image_dir, local_xml_dir = _resolve_sources( [image_dir, xml_dir], temp_dir @@ -626,16 +637,17 @@ def construct_coco(image_dir: str, annotation_dir: str, output_dir: str) -> None def organize_coco( image_dir: str, annotation_dir: str, - output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/coco"), + output_dir: str | None = None, ) -> None: """Organizes the COCO dataset, unpacking archives if necessary. Args: image_dir (str): Path or URL to the image zip file or directory. annotation_dir (str): Path or URL to the annotation zip file or directory. - output_dir (str, optional): Directory to store the organized dataset. - Defaults to ~/.mblt_model_zoo/datasets/coco. + output_dir: Directory to store the organized dataset. Defaults to the + resolved Mobilint cache directory. """ + output_dir = _resolve_organizer_output_dir(output_dir, "coco") with TemporaryDirectory() as temp_dir: local_image_dir, local_annotation_dir = _resolve_sources( [image_dir, annotation_dir], temp_dir @@ -695,16 +707,17 @@ def construct_widerface(image_dir: str, annotation_dir: str, output_dir: str) -> def organize_widerface( image_dir: str, annotation_dir: str, - output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/widerface"), + output_dir: str | None = None, ) -> None: """Organizes the WiderFace dataset, unpacking archives if necessary. Args: image_dir (str): Path or URL to the image zip file or directory. annotation_dir (str): Path or URL to the annotation zip file or directory. - output_dir (str, optional): Directory to store the organized dataset. - Defaults to ~/.mblt_model_zoo/datasets/widerface. + output_dir: Directory to store the organized dataset. Defaults to the + resolved Mobilint cache directory. """ + output_dir = _resolve_organizer_output_dir(output_dir, "widerface") with TemporaryDirectory() as temp_dir: local_image_dir, local_annotation_dir = _resolve_sources( [image_dir, annotation_dir], temp_dir @@ -926,15 +939,17 @@ def construct_nyu_depth(dataset_dir: str, output_dir: str) -> None: def organize_nyu_depth( dataset_path: str = NYU_DEPTH_URL, - output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/nyu-depth"), + output_dir: str | None = None, ) -> None: """Organizes NYU Depth, downloading and unpacking an archive when necessary. Args: dataset_path: Path or URL to the NYU Depth zip file or extracted dataset directory. - output_dir: Directory to store the organized dataset. + output_dir: Directory to store the organized dataset. Defaults to the + resolved Mobilint cache directory. """ + output_dir = _resolve_organizer_output_dir(output_dir, "nyu-depth") output_dir = _validate_dense_output_root( output_dir, "NYU Depth", ("images", "depth") ) @@ -1066,12 +1081,11 @@ def construct_ade20k(dataset_dir: str, output_dir: str) -> None: def organize_ade20k( dataset_path: str = ADE20K_URL, - output_dir: str = os.path.expanduser( - "~/.mblt_model_zoo/datasets/ADEChallengeData2016" - ), + output_dir: str | None = None, ) -> None: """Organizes ADE20K validation data, downloading and unpacking when necessary.""" + output_dir = _resolve_organizer_output_dir(output_dir, "ADEChallengeData2016") output_dir = _validate_dense_output_root( output_dir, "ADE20K", ("images", "annotations") ) @@ -1172,7 +1186,7 @@ def _collect_cityscapes_validation_files( def organize_cityscapes( image_dir: str, annotation_dir: str, - output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/cityscapes"), + output_dir: str | None = None, ) -> None: """Install official Cityscapes validation archives as lossless flat PNG pairs. @@ -1183,12 +1197,14 @@ def organize_cityscapes( image_dir: Path to ``leftImg8bit_trainvaltest.zip``. annotation_dir: Path to ``gtFine_trainvaltest.zip``. output_dir: Directory where the organized validation dataset is stored. + Defaults to the resolved Mobilint cache directory. Raises: ValueError: If either source is invalid or does not contain exactly 500 matching pairs. OSError: If extraction, copying, or atomic installation fails. """ + output_dir = _resolve_organizer_output_dir(output_dir, "cityscapes") output_dir = _validate_dense_output_root( output_dir, "Cityscapes", ("images", "annotations") ) @@ -1660,14 +1676,16 @@ def construct_dotav1(dataset_dir: str, output_dir: str) -> None: def organize_dotav1( dataset_path: str, - output_dir: str = os.path.expanduser("~/.mblt_model_zoo/datasets/dotav1"), + output_dir: str | None = None, ) -> None: """Organizes a validation-only DOTAv1 dataset. Args: dataset_path: Path or URL to the DOTAv1 zip file or extracted dataset directory. - output_dir: Directory to store the organized dataset. + output_dir: Directory to store the organized dataset. Defaults to the + resolved Mobilint cache directory. """ + output_dir = _resolve_organizer_output_dir(output_dir, "dotav1") with TemporaryDirectory() as temp_dir: if _is_google_drive_folder_url(dataset_path): image_archive, label_archive = _download_dotav1_google_drive_archives( diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index fdcea72..d643dd7 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -123,13 +123,26 @@ def _load_coco_image_names(annotation_path: Path) -> set[str] | None: ): return None names: list[str] = [] + image_ids: list[int] = [] for record in image_records: if not isinstance(record, dict): continue file_name = record.get("file_name") - if isinstance(file_name, str): + image_id = record.get("id") + if ( + isinstance(file_name, str) + and isinstance(image_id, int) + and not isinstance(image_id, bool) + ): names.append(file_name) - return set(names) if len(names) == len(image_records) == len(set(names)) else None + image_ids.append(image_id) + if ( + len(names) != len(image_records) + or len(names) != len(set(names)) + or len(image_ids) != len(set(image_ids)) + ): + return None + return set(names) def _coco_ready(root: Path, task: str) -> bool: diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index bb408ed..a23f08f 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -109,6 +109,11 @@ def _load_ground_truths( coords = torch.tensor( [float(value) for value in parts[1:9]], dtype=torch.float32 ).reshape(4, 2) + if not bool(torch.isfinite(coords).all()): + raise ValueError( + "DOTAv1 annotation coordinates must be finite at " + f"{label_path}:{line_number}." + ) if coords.numel() and float(coords.max()) <= 1.5: coords[:, 0] *= width coords[:, 1] *= height @@ -141,6 +146,11 @@ def _load_ground_truths( coords = torch.tensor( [float(value) for value in parts[:8]], dtype=torch.float32 ).reshape(4, 2) + if not bool(torch.isfinite(coords).all()): + raise ValueError( + "DOTAv1 annotation coordinates must be finite at " + f"{original_label_path}:{line_number}." + ) if parts[9] in {"1", "2"}: ignore_classes.append(cls) ignore_polygons.append(coords) diff --git a/mblt_vision/utils/evaluation/eval_nyu_depth.py b/mblt_vision/utils/evaluation/eval_nyu_depth.py index efac872..84358b4 100644 --- a/mblt_vision/utils/evaluation/eval_nyu_depth.py +++ b/mblt_vision/utils/evaluation/eval_nyu_depth.py @@ -145,6 +145,12 @@ def eval_nyu_depth( elif len(targets) == 1 and depth.ndim == 2: maps = [depth] else: + if depth.ndim < 3 or depth.shape[0] != len(targets): + raise ValueError( + "Depth postprocessor output batch length mismatch: " + f"maps={depth.shape[0] if depth.ndim else 0}, " + f"targets={len(targets)}." + ) maps = [depth[index] for index in range(len(targets))] if len(maps) != len(targets): raise ValueError( diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index 394e086..b074970 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -489,6 +489,10 @@ def check_input(self, x: TensorLike | ListTensorLike) -> list[torch.Tensor]: tensors = [xi.to(self.device) for xi in torch_inputs] else: raise TypeError(f"Got unexpected element type for x[0]={type(x[0])}.") + if any(not bool(torch.isfinite(tensor).all()) for tensor in tensors): + raise ValueError( + "Detection output tensors must contain only finite values." + ) return self.check_dim(tensors) def check_dim(self, x: list[torch.Tensor]) -> list[torch.Tensor]: diff --git a/mblt_vision/utils/postprocess/semantic_seg_post.py b/mblt_vision/utils/postprocess/semantic_seg_post.py index 25d5668..a4ddea6 100644 --- a/mblt_vision/utils/postprocess/semantic_seg_post.py +++ b/mblt_vision/utils/postprocess/semantic_seg_post.py @@ -89,23 +89,21 @@ def _normalize_output( output = torch.as_tensor(x, device=self.device) if output.ndim == 4: if output.shape[1] == self.nc: - return output.to(dtype=torch.float32), True + return self._validate_logits(output), True if output.shape[-1] == self.nc: - return output.permute(0, 3, 1, 2).to(dtype=torch.float32), True + return self._validate_logits(output.permute(0, 3, 1, 2)), True raise ValueError( f"Semantic segmentation for '{self.dataset}' expects [B, {self.nc}, H, W] or " f"[B, H, W, {self.nc}] logits, got {tuple(output.shape)}." ) if output.ndim == 3: + if output.shape[-1] == self.nc and output.is_floating_point(): + return self._validate_logits(output.permute(2, 0, 1).unsqueeze(0)), True if tuple(output.shape[:2]) == self.input_shape: - if output.shape[-1] != self.nc: - raise ValueError( - f"Semantic segmentation for '{self.dataset}' expects [H, W, {self.nc}] MXQ logits, " - f"got {tuple(output.shape)}." - ) - return output.permute(2, 0, 1).unsqueeze(0).to( - dtype=torch.float32 - ), True + raise ValueError( + f"Semantic segmentation for '{self.dataset}' expects [H, W, {self.nc}] MXQ logits, " + f"got {tuple(output.shape)}." + ) if output.is_complex(): raise ValueError("Semantic class-map values must be finite integers.") if output.is_floating_point(): @@ -127,6 +125,15 @@ def _normalize_output( f"got {tuple(output.shape)}." ) + @staticmethod + def _validate_logits(output: torch.Tensor) -> torch.Tensor: + """Convert semantic logits to float while rejecting invalid artifact output.""" + + logits = output.to(dtype=torch.float32) + if not bool(torch.isfinite(logits).all()): + raise ValueError("Semantic logits must contain only finite values.") + return logits + def _to_input_space(self, output: torch.Tensor, is_logits: bool) -> torch.Tensor: """Restore model output resolution to configured input space.""" diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index cb117f3..bfda94e 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -3,26 +3,31 @@ from __future__ import annotations from pathlib import Path +from types import SimpleNamespace +import numpy as np import pytest from mblt_vision.utils.datasets.dataloader import ( + CustomCOCODataset, CustomImageFolder, CustomWiderFaceDataset, ) @pytest.mark.parametrize( - ("dataset_class", "class_name"), + ("dataset_class", "class_name", "expected_count", "expected_suffixes"), [ - (CustomImageFolder, "class-a"), - (CustomWiderFaceDataset, "0--Parade"), + (CustomImageFolder, "class-a", 1, {".jpg"}), + (CustomWiderFaceDataset, "0--Parade", 2, {".jpg", ".png"}), ], ) def test_class_based_datasets_ignore_non_image_files( tmp_path: Path, dataset_class: type[CustomImageFolder] | type[CustomWiderFaceDataset], class_name: str, + expected_count: int, + expected_suffixes: set[str], ) -> None: """Keep incidental files out of ImageNet and WiderFace sample lists.""" @@ -36,8 +41,22 @@ def test_class_based_datasets_ignore_non_image_files( dataset = dataset_class(str(tmp_path)) - assert len(dataset) == 2 - assert {Path(sample[0]).suffix.lower() for sample in dataset.samples} == { - ".jpg", - ".png", - } + assert len(dataset) == expected_count + assert { + Path(sample[0]).suffix.lower() for sample in dataset.samples + } == expected_suffixes + + +def test_coco_dataset_rejects_annotation_geometry_mismatching_image() -> None: + """Do not evaluate COCO labels with geometry different from their decoded image.""" + + dataset = SimpleNamespace( + ids=[1], + coco=SimpleNamespace(imgs={1: {"height": 4, "width": 5}}), + _load_image=lambda _: np.zeros((3, 5, 3), dtype=np.uint8), + ) + + with pytest.raises( + ValueError, match=r"image ID 1: annotation \(4, 5\), image \(3, 5\)" + ): + CustomCOCODataset.__getitem__(dataset, 0) diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index 3cd8506..df4a02d 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -94,14 +94,23 @@ def test_dotav1_organizer_rejects_truncated_raw_annotation_rows( (organizer.organize_dotav1, "dotav1"), ], ) -def test_organizer_defaults_match_registry_cache_root( - organize_dataset: Callable[..., Any], dataset_name: str +def test_organizer_defaults_use_the_lazy_cache_resolver( + monkeypatch: pytest.MonkeyPatch, + organize_dataset: Callable[..., Any], + dataset_name: str, + tmp_path: Path, ) -> None: - """Write datasets where the packaged registry resolves them by default.""" + """Keep direct organizer APIs consistent with artifact cache fallback behavior.""" output_dir = inspect.signature(organize_dataset).parameters["output_dir"].default - assert output_dir == str( - Path.home() / ".mblt_model_zoo" / "datasets" / dataset_name + assert output_dir is None + + import mblt_vision.wrapper as wrapper + + cache_dir = tmp_path / "cache" + monkeypatch.setattr(wrapper, "get_mobilint_cache_dir", lambda: str(cache_dir)) + assert organizer._resolve_organizer_output_dir(None, dataset_name) == str( + cache_dir / "datasets" / dataset_name ) diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index 53b63a5..7490f58 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -77,7 +77,7 @@ def test_coco_readiness_matches_images_to_task_annotations( for image_name in image_names: _write_file(tmp_path / "val2017" / image_name) (tmp_path / annotation_name).write_text( - json.dumps({"images": [{"file_name": image_names[0]}]}), + json.dumps({"images": [{"id": 1, "file_name": image_names[0]}]}), encoding="utf-8", ) @@ -85,7 +85,12 @@ def test_coco_readiness_matches_images_to_task_annotations( (tmp_path / annotation_name).write_text( json.dumps( - {"images": [{"file_name": image_name} for image_name in image_names]} + { + "images": [ + {"id": index, "file_name": image_name} + for index, image_name in enumerate(image_names, start=1) + ] + } ), encoding="utf-8", ) @@ -94,6 +99,30 @@ def test_coco_readiness_matches_images_to_task_annotations( assert not readiness.dataset_ready(tmp_path, task, "imagenet") +def test_coco_readiness_rejects_duplicate_image_ids( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Reject COCO metadata whose duplicate IDs would overwrite image records.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 2) + image_names = ["000000000001.jpg", "000000000002.jpg"] + for image_name in image_names: + _write_file(tmp_path / "val2017" / image_name) + (tmp_path / "instances_val2017.json").write_text( + json.dumps( + { + "images": [ + {"id": 1, "file_name": image_names[0]}, + {"id": 1, "file_name": image_names[1]}, + ] + } + ), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") + + @pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) def test_dotav1_readiness_requires_complete_image_label_pairs( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 4c24732..3409e00 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -174,6 +174,34 @@ def test_dota_annotations_reject_negative_class_indices( _load_ground_truths(str(tmp_path), dataset) +@pytest.mark.parametrize( + ("label_path", "annotation"), + [ + ("labels/val/image.txt", "0 nan 0 0.2 0 0.2 0.2 0 0.2"), + ("labels/val_original/image.txt", "inf 0 20 0 20 20 0 20 plane 0"), + ], +) +def test_dota_annotations_reject_nonfinite_coordinates( + tmp_path, label_path: str, annotation: str +) -> None: + """Reject poisoned normalized and original DOTA polygon coordinates.""" + + path = tmp_path / label_path + path.parent.mkdir(parents=True) + path.write_text(annotation, encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises(ValueError, match=r"coordinates must be finite.*image\.txt:1"): + _load_ground_truths(str(tmp_path), dataset) + + def test_difficult_regions_do_not_count_as_positive_or_false_positive() -> None: """Ignore a detection on a difficult region while retaining positive matching.""" diff --git a/tests/test_eval_nyu_depth.py b/tests/test_eval_nyu_depth.py new file mode 100644 index 0000000..a0f9271 --- /dev/null +++ b/tests/test_eval_nyu_depth.py @@ -0,0 +1,48 @@ +"""Regression tests for NYU depth-evaluation output validation.""" + +from __future__ import annotations + +import importlib +from types import SimpleNamespace + +import numpy as np +import pytest +import torch + +eval_nyu_depth_module = importlib.import_module( + "mblt_vision.utils.evaluation.eval_nyu_depth" +) + + +def test_nyu_depth_evaluation_rejects_surplus_output_batch( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Do not silently discard depth maps from a malformed backend batch.""" + + class _Model: + pre_cfg = {"LetterBox": {"img_size": [4, 4]}} + + def __call__(self, inputs: torch.Tensor) -> torch.Tensor: + return inputs + + def postprocess(self, output: torch.Tensor) -> SimpleNamespace: + del output + return SimpleNamespace(depth=torch.zeros((2, 4, 4))) + + batch = ( + torch.zeros((1, 4, 4, 3)), + np.ones((1, 4, 4), dtype=np.float32), + [(4, 4)], + [None], + ("sample",), + ) + monkeypatch.setattr(eval_nyu_depth_module, "CustomNYUDepth", lambda _: object()) + monkeypatch.setattr( + eval_nyu_depth_module, "get_nyu_depth_loader", lambda *_args, **_kwargs: [batch] + ) + monkeypatch.setattr(eval_nyu_depth_module, "build_preprocess", lambda _: object()) + + with pytest.raises( + ValueError, match=r"output batch length mismatch: maps=2, targets=1" + ): + eval_nyu_depth_module.eval_nyu_depth(_Model(), "/dataset", batch_size=1) diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index f60e74e..23a8191 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -123,6 +123,25 @@ def test_already_decoded_detections_reject_nonfinite_rows( postprocessor._final_detection_batches(detections) +@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) +def test_detection_postprocessor_rejects_nonfinite_raw_heads( + invalid_value: float, +) -> None: + """Reject malformed raw detector heads before they reach decoding and NMS.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + raw_head = torch.zeros((1, 6, 2, 2), dtype=torch.float32) + raw_head[0, 0, 0, 0] = invalid_value + + with pytest.raises( + ValueError, match="Detection output tensors must contain only finite" + ): + postprocessor.check_input(raw_head) + + @pytest.mark.parametrize( ("post_cfg", "expected_type"), [ diff --git a/tests/test_semantic_segmentation.py b/tests/test_semantic_segmentation.py index 1675e10..b0fcccb 100644 --- a/tests/test_semantic_segmentation.py +++ b/tests/test_semantic_segmentation.py @@ -96,6 +96,13 @@ def test_semantic_postprocess_supports_mxq_hwc_and_batched_nhwc_logits() -> None assert torch.equal(nhwc_result[0], torch.full((4, 8), 3)) assert torch.equal(nhwc_result[1], torch.full((4, 8), 9)) + low_resolution_hwc_logits = torch.zeros((2, 4, 19)) + low_resolution_hwc_logits[..., 12] = 2.0 + low_resolution_result = post(low_resolution_hwc_logits) + assert isinstance(low_resolution_result, torch.Tensor) + assert low_resolution_result.shape == (1, 4, 8) + assert torch.equal(low_resolution_result, torch.full((1, 4, 8), 12)) + with pytest.raises(ValueError, match=r"expects \[B, 19, H, W\]"): post(torch.zeros((1, 4, 8, 18))) with pytest.raises(ValueError, match=r"expects \[H, W, 19\] MXQ logits"): @@ -108,6 +115,21 @@ def test_semantic_postprocess_supports_mxq_hwc_and_batched_nhwc_logits() -> None assert torch.equal(baked_result[0, 0], torch.tensor([0, 2, 4, 7, 9, 11, 14, 16])) +@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) +def test_semantic_postprocess_rejects_nonfinite_logits(invalid_value: float) -> None: + """Reject invalid local logits before they can be converted by argmax.""" + + post = SemanticSegPost( + {"LetterBox": {"img_size": [4, 4]}}, + {"task": "semantic_segmentation", "dataset": "cityscapes"}, + ) + logits = torch.zeros((1, 19, 2, 2)) + logits[0, 0, 0, 0] = invalid_value + + with pytest.raises(ValueError, match="logits must contain only finite values"): + post(logits) + + def test_semantic_postprocess_restores_letterbox_padding() -> None: """Crop padding before nearest-restoring a semantic map.""" From 09eefb0b9ff1e930949b4946cd16475bdafcf0b0 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 13:56:05 +0900 Subject: [PATCH 29/48] fix: validate vision dataset integrity --- mblt_vision/_model_paths.py | 2 - mblt_vision/datasets/imagenet_synsets.txt | 1000 +++++++++++++++++++ mblt_vision/utils/datasets/dataloader.py | 14 +- mblt_vision/utils/datasets/organizer.py | 14 +- mblt_vision/utils/datasets/readiness.py | 118 ++- mblt_vision/utils/postprocess/base.py | 2 + mblt_vision/utils/postprocess/depth_post.py | 2 + mblt_vision/wrapper.py | 7 + pyproject.toml | 1 + tests/test_dataloader.py | 21 + tests/test_dataset_organizer.py | 22 + tests/test_dataset_readiness.py | 71 +- tests/test_depth_estimation.py | 33 + tests/test_postprocess_hierarchy.py | 18 + tests/test_wrapper.py | 17 +- 15 files changed, 1329 insertions(+), 13 deletions(-) create mode 100644 mblt_vision/datasets/imagenet_synsets.txt diff --git a/mblt_vision/_model_paths.py b/mblt_vision/_model_paths.py index dc51c47..a4eb2f6 100644 --- a/mblt_vision/_model_paths.py +++ b/mblt_vision/_model_paths.py @@ -49,8 +49,6 @@ def uses_shifted_engine_model_path_layout( and model_path.lower() not in SUPPORTED_FRAMEWORKS ): return False - if inferred_framework == "onnx": - return True return ( isinstance(dev_no, str) or isinstance(core_mode, int) diff --git a/mblt_vision/datasets/imagenet_synsets.txt b/mblt_vision/datasets/imagenet_synsets.txt new file mode 100644 index 0000000..1fab3e9 --- /dev/null +++ b/mblt_vision/datasets/imagenet_synsets.txt @@ -0,0 +1,1000 @@ ++n01440764 +n01443537 +n01484850 +n01491361 +n01494475 +n01496331 +n01498041 +n01514668 +n01514859 +n01518878 +n01530575 +n01531178 +n01532829 +n01534433 +n01537544 +n01558993 +n01560419 +n01580077 +n01582220 +n01592084 +n01601694 +n01608432 +n01614925 +n01616318 +n01622779 +n01629819 +n01630670 +n01631663 +n01632458 +n01632777 +n01641577 +n01644373 +n01644900 +n01664065 +n01665541 +n01667114 +n01667778 +n01669191 +n01675722 +n01677366 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+n07747607 +n07749582 +n07753113 +n07753275 +n07753592 +n07754684 +n07760859 +n07768694 +n07802026 +n07831146 +n07836838 +n07860988 +n07871810 +n07873807 +n07875152 +n07880968 +n07892512 +n07920052 +n07930864 +n07932039 +n09193705 +n09229709 +n09246464 +n09256479 +n09288635 +n09332890 +n09399592 +n09421951 +n09428293 +n09468604 +n09472597 +n09835506 +n10148035 +n10565667 +n11879895 +n11939491 +n12057211 +n12144580 +n12267677 +n12620546 +n12768682 +n12985857 +n12998815 +n13037406 +n13040303 +n13044778 +n13052670 +n13054560 +n13133613 +n15075141 diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index ca8ea14..239c802 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -211,9 +211,13 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: "NYU Depth image and target shapes must match for " f"{stem}: image {image.shape[:2]}, depth {depth.shape}." ) + if not bool(np.isfinite(depth).all()): + raise ValueError( + f"NYU Depth target must contain only finite values: {depth_path}" + ) return ( cv2.cvtColor(image, cv2.COLOR_BGR2RGB), - np.nan_to_num(depth, nan=0.0, posinf=0.0, neginf=0.0), + depth, stem, ) @@ -505,6 +509,14 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: raise ValueError( f"Cityscapes annotation values must be in [0, 255]: {annotation_path}" ) + source_ids = np.unique(annotation.astype(np.uint8)) + known_ids = np.array([*range(34), 255], dtype=np.uint8) + unknown_ids = source_ids[~np.isin(source_ids, known_ids)] + if unknown_ids.size: + raise ValueError( + "Cityscapes annotation contains unsupported source IDs " + f"{unknown_ids.tolist()}: {annotation_path}" + ) target = CITYSCAPES_SOURCE_TO_TRAIN_ID[annotation.astype(np.uint8)] return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), target, stem diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 8fce971..5ae0b4e 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -1414,8 +1414,14 @@ def _safe_unpack_archive(archive_path: str, destination: str) -> None: if zipfile.is_zipfile(archive_path): with zipfile.ZipFile(archive_path) as archive: members = archive.infolist() + targets: set[str] = set() for member in members: - _safe_archive_member_path(member.filename, destination) + target = _safe_archive_member_path(member.filename, destination) + if target in targets: + raise ValueError( + f"Duplicate archive member path: {member.filename!r}." + ) + targets.add(target) file_type = stat.S_IFMT(member.external_attr >> 16) if file_type and not ( stat.S_ISREG(file_type) or stat.S_ISDIR(file_type) @@ -1436,8 +1442,12 @@ def _safe_unpack_archive(archive_path: str, destination: str) -> None: if tarfile.is_tarfile(archive_path): with tarfile.open(archive_path) as archive: members = archive.getmembers() + targets: set[str] = set() for member in members: - _safe_archive_member_path(member.name, destination) + target = _safe_archive_member_path(member.name, destination) + if target in targets: + raise ValueError(f"Duplicate archive member path: {member.name!r}.") + targets.add(target) if not (member.isfile() or member.isdir()): raise ValueError(f"Unsafe archive member type: {member.name!r}.") for member in members: diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index d643dd7..0fcfeae 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -5,6 +5,7 @@ import json import os import re +from importlib.resources import files from pathlib import Path from typing import Any @@ -32,6 +33,20 @@ CITYSCAPES_SAMPLE_ID_PATTERN = re.compile( r"^(?P[A-Za-z][A-Za-z0-9-]*)_\d{6}_\d{6}$" ) +IMAGENET_SYNSETS = frozenset( + files("mblt_vision.datasets") + .joinpath("imagenet_synsets.txt") + .read_text(encoding="utf-8") + .splitlines() +) +COCO_ANNOTATION_COUNTS = { + "instances_val2017.json": 36781, + "person_keypoints_val2017.json": 11004, +} +COCO_CATEGORY_COUNTS = { + "instances_val2017.json": 80, + "person_keypoints_val2017.json": 1, +} def _path_has_symlink_component(path: Path) -> bool: @@ -88,7 +103,7 @@ def _imagenet_ready(root: Path) -> bool: if not root.is_dir(): return False class_dirs = [path for path in root.iterdir() if path.is_dir()] - if len(class_dirs) != IMAGENET_CLASS_COUNT: + if {path.name for path in class_dirs} != IMAGENET_SYNSETS: return False image_names: set[str] = set() for class_dir in class_dirs: @@ -116,7 +131,9 @@ def _load_coco_image_names(annotation_path: Path) -> set[str] | None: annotation: Any = json.loads(annotation_path.read_text(encoding="utf-8")) except (json.JSONDecodeError, OSError, UnicodeError): return None - image_records = annotation.get("images") if isinstance(annotation, dict) else None + if not isinstance(annotation, dict): + return None + image_records = annotation.get("images") if ( not isinstance(image_records, list) or len(image_records) != COCO_VALIDATION_SAMPLE_COUNT @@ -142,6 +159,48 @@ def _load_coco_image_names(annotation_path: Path) -> set[str] | None: or len(image_ids) != len(set(image_ids)) ): return None + annotation_records = annotation.get("annotations") + categories = annotation.get("categories") + expected_annotations = COCO_ANNOTATION_COUNTS.get(annotation_path.name) + expected_categories = COCO_CATEGORY_COUNTS.get(annotation_path.name) + if ( + not isinstance(annotation_records, list) + or not isinstance(categories, list) + or len(annotation_records) != expected_annotations + or len(categories) != expected_categories + ): + return None + category_ids = [ + category.get("id") for category in categories if isinstance(category, dict) + ] + if ( + len(category_ids) != len(categories) + or any( + not isinstance(category_id, int) or isinstance(category_id, bool) + for category_id in category_ids + ) + or len(category_ids) != len(set(category_ids)) + ): + return None + annotation_ids: list[int] = [] + for record in annotation_records: + if not isinstance(record, dict): + return None + annotation_id = record.get("id") + image_id = record.get("image_id") + category_id = record.get("category_id") + if ( + not isinstance(annotation_id, int) + or isinstance(annotation_id, bool) + or not isinstance(image_id, int) + or isinstance(image_id, bool) + or image_id not in image_ids + or category_id not in category_ids + ): + return None + annotation_ids.append(annotation_id) + if len(annotation_ids) != len(set(annotation_ids)): + return None return set(names) @@ -228,9 +287,64 @@ def _widerface_ready(root: Path) -> bool: actual_images == expected_images and sum(len(image_names) for image_names in actual_images.values()) == WIDERFACE_VALIDATION_SAMPLE_COUNT + and _widerface_difficulty_metadata_ready(root, expected_images) ) +def _widerface_difficulty_metadata_ready( + root: Path, expected_images: dict[str, set[str]] +) -> bool: + """Validate WiderFace difficulty table dimensions and one-based face indices.""" + + try: + main = loadmat(root / "wider_face_val.mat") + face_boxes = main["face_bbx_list"] + difficulties = [ + loadmat(root / file_name)["gt_list"] + for file_name in ( + "wider_easy_val.mat", + "wider_medium_val.mat", + "wider_hard_val.mat", + ) + ] + except (IndexError, KeyError, MatReadError, OSError, TypeError, ValueError): + return False + if len(face_boxes) != len(expected_images) or any( + len(table) != len(expected_images) for table in difficulties + ): + return False + for event_index, image_names in enumerate(expected_images.values()): + try: + event_faces = face_boxes[event_index][0] + except (IndexError, TypeError): + return False + if len(event_faces) != len(image_names): + return False + for table in difficulties: + try: + event_indices = table[event_index][0] + except (IndexError, TypeError): + return False + if len(event_indices) != len(image_names): + return False + for image_index, face_entry in enumerate(event_faces): + try: + face_count = len(np.asarray(face_entry[0])) + keep_indices = np.asarray(event_indices[image_index][0]) + except (IndexError, TypeError): + return False + if ( + not np.isfinite(keep_indices).all() + or not np.equal(keep_indices, np.trunc(keep_indices)).all() + ): + return False + if keep_indices.size and ( + int(keep_indices.min()) < 1 or int(keep_indices.max()) > face_count + ): + return False + return True + + def _flatten_matlab_strings(value: Any) -> list[str]: """Flatten strings stored inside nested MATLAB cell arrays.""" diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index b074970..246ff9a 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -413,6 +413,8 @@ def _normalize_proto_batch( raise ValueError( f"Expected 4D prototype tensor, got shape {tuple(proto.shape)}." ) + if not bool(torch.isfinite(proto).all()): + raise ValueError("Mask prototype tensor must contain only finite values.") if proto.shape[-1] == self.n_extra: return proto if proto.shape[1] == self.n_extra: diff --git a/mblt_vision/utils/postprocess/depth_post.py b/mblt_vision/utils/postprocess/depth_post.py index 22a4deb..075d938 100644 --- a/mblt_vision/utils/postprocess/depth_post.py +++ b/mblt_vision/utils/postprocess/depth_post.py @@ -78,6 +78,8 @@ def _normalize_output(self, x: TensorLike | ListTensorLike) -> torch.Tensor: f"got {tuple(depth.shape)}." ) depth = depth.to(device=self.device, dtype=torch.float32) + if not bool(torch.isfinite(depth).all()): + raise ValueError("Depth estimation output must contain only finite values.") if tuple(depth.shape[-2:]) == self.input_shape: return depth diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index a40d294..f17bb1b 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -458,6 +458,13 @@ def __init__( ) model_config_part = resolve_model_config(model_cls, model_type) + if mxq_path and Path(mxq_path).suffix.lower() != ".mxq": + raise ValueError(f"Explicit mxq_path must end in '.mxq', got {mxq_path!r}.") + if onnx_path and Path(onnx_path).suffix.lower() != ".onnx": + raise ValueError( + f"Explicit onnx_path must end in '.onnx', got {onnx_path!r}." + ) + file_cfg_model_path = str(model_config_part["file_cfg"].get("model_path", "")) file_cfg_onnx_path = str(model_config_part["file_cfg"].get("onnx_path", "")) framework_model_path = model_path or file_cfg_model_path diff --git a/pyproject.toml b/pyproject.toml index 840154d..5514221 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,6 +69,7 @@ version = { attr = "mblt_vision.__version__" } "py.typed", "models/*.yaml", "datasets/*.yaml", + "datasets/*.txt", ] [tool.pytest.ini_options] diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index bfda94e..88a704c 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -5,11 +5,14 @@ from pathlib import Path from types import SimpleNamespace +import cv2 import numpy as np import pytest +from PIL import Image from mblt_vision.utils.datasets.dataloader import ( CustomCOCODataset, + CustomCityscapes, CustomImageFolder, CustomWiderFaceDataset, ) @@ -60,3 +63,21 @@ def test_coco_dataset_rejects_annotation_geometry_mismatching_image() -> None: ValueError, match=r"image ID 1: annotation \(4, 5\), image \(3, 5\)" ): CustomCOCODataset.__getitem__(dataset, 0) + + +def test_cityscapes_dataset_rejects_unknown_source_ids(tmp_path: Path) -> None: + """Reject corrupted Cityscapes labels instead of remapping them to ignore.""" + + image_dir = tmp_path / "images" + annotation_dir = tmp_path / "annotations" + image_dir.mkdir() + annotation_dir.mkdir() + assert cv2.imwrite( + str(image_dir / "sample.png"), np.zeros((2, 2, 3), dtype=np.uint8) + ) + Image.fromarray(np.full((2, 2), 200, dtype=np.uint8)).save( + annotation_dir / "sample.png" + ) + + with pytest.raises(ValueError, match=r"unsupported source IDs \[200\]"): + CustomCityscapes(str(tmp_path))[0] diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index df4a02d..5a27449 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -674,6 +674,28 @@ def test_safe_unpack_archive_preserves_regular_tar_layout(tmp_path: Path) -> Non assert (extract_dir / "dataset" / "sample.txt").read_bytes() == b"dataset contents" +@pytest.mark.parametrize("suffix", [".zip", ".tar"]) +def test_safe_unpack_archive_rejects_duplicate_member_paths( + tmp_path: Path, suffix: str +) -> None: + """Reject duplicate archive members before any staged file can be replaced.""" + + archive_path = tmp_path / f"dataset{suffix}" + if suffix == ".zip": + with ZipFile(archive_path, "w") as archive: + archive.writestr("dataset/sample.txt", b"first") + archive.writestr("dataset/sample.txt", b"second") + else: + with tarfile.open(archive_path, "w") as archive: + for payload in (b"first", b"second"): + member = tarfile.TarInfo("dataset/sample.txt") + member.size = len(payload) + archive.addfile(member, io.BytesIO(payload)) + + with pytest.raises(ValueError, match="Duplicate archive member path"): + organizer._safe_unpack_archive(str(archive_path), str(tmp_path / "extracted")) + + def test_safe_unpack_archive_rejects_tar_traversal_before_writing( tmp_path: Path, ) -> None: diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index 7490f58..2f999c9 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -38,6 +38,7 @@ def test_imagenet_readiness_requires_complete_official_class_tree( monkeypatch.setattr(readiness, "IMAGENET_CLASS_COUNT", 2) monkeypatch.setattr(readiness, "IMAGENET_IMAGES_PER_CLASS", 2) + monkeypatch.setattr(readiness, "IMAGENET_SYNSETS", {"n00000000", "n00000001"}) for class_index in range(2): for image_index in range(2): if (class_index, image_index) == (1, 1): @@ -73,6 +74,8 @@ def test_coco_readiness_matches_images_to_task_annotations( """Require every official COCO image in the task-specific annotation file.""" monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 2) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, annotation_name, 2) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, annotation_name, 1) image_names = ["000000000001.jpg", "000000000002.jpg"] for image_name in image_names: _write_file(tmp_path / "val2017" / image_name) @@ -89,7 +92,12 @@ def test_coco_readiness_matches_images_to_task_annotations( "images": [ {"id": index, "file_name": image_name} for index, image_name in enumerate(image_names, start=1) - ] + ], + "categories": [{"id": 1}], + "annotations": [ + {"id": index, "image_id": index, "category_id": 1} + for index in range(1, 3) + ], } ), encoding="utf-8", @@ -105,6 +113,8 @@ def test_coco_readiness_rejects_duplicate_image_ids( """Reject COCO metadata whose duplicate IDs would overwrite image records.""" monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 2) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 2) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) image_names = ["000000000001.jpg", "000000000002.jpg"] for image_name in image_names: _write_file(tmp_path / "val2017" / image_name) @@ -114,7 +124,12 @@ def test_coco_readiness_rejects_duplicate_image_ids( "images": [ {"id": 1, "file_name": image_names[0]}, {"id": 1, "file_name": image_names[1]}, - ] + ], + "categories": [{"id": 1}], + "annotations": [ + {"id": 1, "image_id": 1, "category_id": 1}, + {"id": 2, "image_id": 1, "category_id": 1}, + ], } ), encoding="utf-8", @@ -123,6 +138,55 @@ def test_coco_readiness_rejects_duplicate_image_ids( assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") +def test_coco_readiness_rejects_truncated_annotation_table( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Do not reuse complete-looking images with incomplete COCO ground truth.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 2) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 2) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + image_names = ["000000000001.jpg", "000000000002.jpg"] + for image_name in image_names: + _write_file(tmp_path / "val2017" / image_name) + (tmp_path / "instances_val2017.json").write_text( + json.dumps( + { + "images": [ + {"id": index, "file_name": image_name} + for index, image_name in enumerate(image_names, start=1) + ], + "categories": [{"id": 1}], + "annotations": [{"id": 1, "image_id": 1, "category_id": 1}], + } + ), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") + + +def test_widerface_readiness_rejects_invalid_difficulty_metadata( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Reject present-but-malformed difficulty files before evaluation indexes them.""" + + monkeypatch.setattr(readiness, "WIDERFACE_EVENT_COUNT", 1) + monkeypatch.setattr(readiness, "WIDERFACE_VALIDATION_SAMPLE_COUNT", 1) + _write_widerface_metadata( + tmp_path / "wider_face_val.mat", {"0--Parade": ["sample"]} + ) + for file_name in ( + "wider_easy_val.mat", + "wider_medium_val.mat", + "wider_hard_val.mat", + ): + _write_file(tmp_path / file_name) + _write_file(tmp_path / "images" / "0--Parade" / "sample.jpg") + + assert not readiness.dataset_ready(tmp_path, "face_detection", "widerface") + + @pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) def test_dotav1_readiness_requires_complete_image_label_pairs( monkeypatch: pytest.MonkeyPatch, @@ -164,6 +228,9 @@ def test_widerface_readiness_requires_complete_event_tree_and_metadata( monkeypatch.setattr(readiness, "WIDERFACE_EVENT_COUNT", 2) monkeypatch.setattr(readiness, "WIDERFACE_VALIDATION_SAMPLE_COUNT", 2) + monkeypatch.setattr( + readiness, "_widerface_difficulty_metadata_ready", lambda *_: True + ) _write_widerface_metadata( tmp_path / "wider_face_val.mat", {"0--Parade": ["sample-0"], "1--Handshaking": ["sample-1"]}, diff --git a/tests/test_depth_estimation.py b/tests/test_depth_estimation.py index f439b1e..79c9f90 100644 --- a/tests/test_depth_estimation.py +++ b/tests/test_depth_estimation.py @@ -114,6 +114,18 @@ def test_depth_post_normalizes_quarter_resolution_channel_last_output() -> None: assert torch.equal(normalized, expected) +@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) +def test_depth_post_rejects_nonfinite_outputs(invalid_value: float) -> None: + """Reject invalid local depth tensors before resizing or visualization.""" + + post = DepthPost({"LetterBox": {"img_size": [8, 8]}}, {}) + depth = torch.zeros((1, 8, 8)) + depth[0, 0, 0] = invalid_value + + with pytest.raises(ValueError, match="must contain only finite values"): + post(depth) + + def test_nyu_depth_dataset_rejects_mismatched_image_and_target_shapes(tmp_path) -> None: """Reject paired NYU files whose pixels cannot be compared one-to-one.""" @@ -128,3 +140,24 @@ def test_nyu_depth_dataset_rejects_mismatched_image_and_target_shapes(tmp_path) with pytest.raises(ValueError, match=r"sample: image \(4, 5\), depth \(3, 5\)"): CustomNYUDepth(str(tmp_path))[0] + + +@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) +def test_nyu_depth_dataset_rejects_nonfinite_targets( + tmp_path, invalid_value: float +) -> None: + """Do not silently remove invalid NYU ground-truth pixels from metrics.""" + + image_dir = tmp_path / "images" + depth_dir = tmp_path / "depth" + image_dir.mkdir() + depth_dir.mkdir() + assert cv2.imwrite( + str(image_dir / "sample.png"), np.zeros((4, 5, 3), dtype=np.uint8) + ) + depth = np.ones((4, 5), dtype=np.float32) + depth[0, 0] = invalid_value + np.save(depth_dir / "sample.npy", depth) + + with pytest.raises(ValueError, match=r"finite values: .*sample\.npy"): + CustomNYUDepth(str(tmp_path))[0] diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 23a8191..7923925 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -142,6 +142,24 @@ def test_detection_postprocessor_rejects_nonfinite_raw_heads( postprocessor.check_input(raw_head) +@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) +def test_detection_postprocessor_rejects_nonfinite_mask_prototypes( + invalid_value: float, +) -> None: + """Reject invalid mask prototypes from already-decoded segmentation artifacts.""" + + postprocessor = cast(Any, YOLOAnchorlessSegPost.__new__(YOLOAnchorlessSegPost)) + postprocessor.device = torch.device("cpu") + postprocessor.n_extra = 2 + prototype = torch.zeros((1, 2, 2, 2)) + prototype[0, 0, 0, 0] = invalid_value + + with pytest.raises( + ValueError, match="Mask prototype tensor must contain only finite" + ): + postprocessor._normalize_proto_batch(prototype) + + @pytest.mark.parametrize( ("post_cfg", "expected_type"), [ diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 4be37c0..0df8263 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -319,6 +319,17 @@ def test_engine_init_rejects_nonexistent_explicit_onnx_path( MBLT_Engine(model_config, **path_kwargs) +def test_engine_init_rejects_wrong_suffix_for_mxq_path(tmp_path: Path) -> None: + """Do not route an existing ONNX file through the MXQ compatibility alias.""" + + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + with pytest.raises(ValueError, match=r"mxq_path must end in '.mxq'"): + MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, mxq_path=str(onnx_path) + ) + + def test_engine_init_disposes_mxq_backend_after_postprocess_setup_failure( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: @@ -1021,7 +1032,7 @@ def dispose(self) -> None: @pytest.mark.parametrize( - "model_path_style", ["keyword", "positional", "positional-runtime", "onnx-path"] + "model_path_style", ["keyword", "positional-runtime", "onnx-path"] ) def test_engine_init_accepts_local_onnx_model_path( monkeypatch: pytest.MonkeyPatch, @@ -1077,9 +1088,7 @@ def InferenceSession(self, path: str, providers: list[str]) -> _FakeSession: "pre_cfg": {}, "post_cfg": {}, } - if model_path_style == "positional": - engine = MBLT_Engine(model_config, "DEFAULT", str(onnx_path)) - elif model_path_style == "positional-runtime": + if model_path_style == "positional-runtime": # Keep this legacy positional-runtime invocation untyped: its argument # order is normalized at runtime by ``MBLT_Engine``. engine = cast(Any, MBLT_Engine)( From 5d812bdb8d9863396133c7339e1bfac23afc5316 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 14:02:49 +0900 Subject: [PATCH 30/48] fix: update imports for consistency and clarity in postprocessing modules --- mblt_vision/utils/postprocess/depth_post.py | 3 +- .../utils/postprocess/semantic_seg_post.py | 3 +- tests/test_dataloader.py | 12 +++-- tests/test_dataset_organizer.py | 46 +++++++++++-------- tests/test_wrapper.py | 21 ++++++--- 5 files changed, 54 insertions(+), 31 deletions(-) diff --git a/mblt_vision/utils/postprocess/depth_post.py b/mblt_vision/utils/postprocess/depth_post.py index 075d938..ee7dee0 100644 --- a/mblt_vision/utils/postprocess/depth_post.py +++ b/mblt_vision/utils/postprocess/depth_post.py @@ -8,10 +8,11 @@ import torch import torch.nn.functional as functional +from ..letterbox import RatioPad from ..types import ListTensorLike, TensorLike from ._letterbox import crop_letterbox, get_letterbox_input_shape, resolve_ratio_pads from .base import PostBase -from .common import RatioPad, normalize_image_shapes +from .common import normalize_image_shapes class DepthPost(PostBase): diff --git a/mblt_vision/utils/postprocess/semantic_seg_post.py b/mblt_vision/utils/postprocess/semantic_seg_post.py index a4ddea6..03f43ef 100644 --- a/mblt_vision/utils/postprocess/semantic_seg_post.py +++ b/mblt_vision/utils/postprocess/semantic_seg_post.py @@ -8,10 +8,11 @@ import torch import torch.nn.functional as functional +from ..letterbox import RatioPad from ..types import ListTensorLike, TensorLike from ._letterbox import crop_letterbox, get_letterbox_input_shape, resolve_ratio_pads from .base import PostBase -from .common import RatioPad, normalize_image_shapes +from .common import normalize_image_shapes class SemanticSegPost(PostBase): diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index 88a704c..0b90062 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -4,6 +4,7 @@ from pathlib import Path from types import SimpleNamespace +from typing import cast import cv2 import numpy as np @@ -53,10 +54,13 @@ def test_class_based_datasets_ignore_non_image_files( def test_coco_dataset_rejects_annotation_geometry_mismatching_image() -> None: """Do not evaluate COCO labels with geometry different from their decoded image.""" - dataset = SimpleNamespace( - ids=[1], - coco=SimpleNamespace(imgs={1: {"height": 4, "width": 5}}), - _load_image=lambda _: np.zeros((3, 5, 3), dtype=np.uint8), + dataset = cast( + CustomCOCODataset, + SimpleNamespace( + ids=[1], + coco=SimpleNamespace(imgs={1: {"height": 4, "width": 5}}), + _load_image=lambda _: np.zeros((3, 5, 3), dtype=np.uint8), + ), ) with pytest.raises( diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index 5a27449..ff6d073 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -5,6 +5,8 @@ import hashlib import io import inspect +import os +import shutil import tarfile from collections.abc import Callable from pathlib import Path @@ -176,7 +178,7 @@ def _fake_get( calls.append(dict(headers)) return responses.pop(0) - monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr("mblt_vision.utils.datasets.organizer.requests.get", _fake_get) monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) monkeypatch.setattr(organizer, "sleep", lambda _: None) @@ -215,7 +217,7 @@ def _fake_get( calls.append(dict(headers)) return responses.pop(0) - monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr("mblt_vision.utils.datasets.organizer.requests.get", _fake_get) monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) local_path = tmp_path / "archive.tar" local_path.write_bytes(b"old") @@ -247,7 +249,7 @@ def _fake_get( chunks=[], ) - monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr("mblt_vision.utils.datasets.organizer.requests.get", _fake_get) local_path = tmp_path / "archive.tar" local_path.write_bytes(payload) @@ -283,7 +285,7 @@ def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: del url, kwargs return responses.pop(0) - monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr("mblt_vision.utils.datasets.organizer.requests.get", _fake_get) monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) local_path = tmp_path / "archive.tar" local_path.write_bytes(b"old") @@ -314,7 +316,7 @@ def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: calls += 1 return responses.pop(0) - monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr("mblt_vision.utils.datasets.organizer.requests.get", _fake_get) monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) monkeypatch.setattr(organizer, "sleep", lambda _: None) @@ -340,7 +342,7 @@ def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: calls += 1 return _FakeResponse(status_code=404, headers={}, chunks=[]) - monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr("mblt_vision.utils.datasets.organizer.requests.get", _fake_get) with pytest.raises(requests.HTTPError): organizer._download_url( @@ -366,7 +368,7 @@ def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: chunks=[payload], ) - monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr("mblt_vision.utils.datasets.organizer.requests.get", _fake_get) monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) local_path = tmp_path / "archive.zip" @@ -392,7 +394,7 @@ def _fake_get(url: str, **kwargs: Any) -> _FakeResponse: chunks=[b"malicious"], ) - monkeypatch.setattr(organizer.requests, "get", _fake_get) + monkeypatch.setattr("mblt_vision.utils.datasets.organizer.requests.get", _fake_get) monkeypatch.setattr(organizer, "tqdm", _DummyTqdm) local_path = tmp_path / "archive.zip" @@ -435,8 +437,7 @@ def test_download_if_url_rejects_plaintext_http( """Do not fetch benchmark inputs over an unauthenticated transport.""" monkeypatch.setattr( - organizer.requests, - "get", + "mblt_vision.utils.datasets.organizer.requests.get", lambda *args, **kwargs: pytest.fail("plaintext URL must not be requested"), ) @@ -842,7 +843,7 @@ def test_nyu_depth_install_preserves_backups_when_rollback_fails( (output_dir / "images" / "keep.jpg").write_bytes(b"old image") (output_dir / "depth" / "keep.npy").write_bytes(b"old depth") - real_replace = organizer.os.replace + real_replace = os.replace def _fail_install_and_rollback(source: str, destination: str) -> None: source_path = Path(source) @@ -859,7 +860,9 @@ def _fail_install_and_rollback(source: str, destination: str) -> None: raise OSError("simulated rollback failure") real_replace(source, destination) - monkeypatch.setattr(organizer.os, "replace", _fail_install_and_rollback) + monkeypatch.setattr( + "mblt_vision.utils.datasets.organizer.os.replace", _fail_install_and_rollback + ) with pytest.raises(OSError, match="backups are preserved"): organizer.construct_nyu_depth(str(dataset_dir), str(output_dir)) @@ -987,14 +990,17 @@ def test_construct_ade20k_preserves_cache_when_metadata_staging_fails( output_dir = tmp_path / "organized" output_dir.mkdir() (output_dir / "valid-cache-marker").write_bytes(b"existing") - real_copy2 = organizer.shutil.copy2 + real_copy2 = shutil.copy2 def _fail_scene_metadata_copy(source: str, destination: str) -> str: if Path(source).name == "sceneCategories.txt": raise OSError("simulated metadata copy failure") return real_copy2(source, destination) - monkeypatch.setattr(organizer.shutil, "copy2", _fail_scene_metadata_copy) + monkeypatch.setattr( + "mblt_vision.utils.datasets.organizer.shutil.copy2", + _fail_scene_metadata_copy, + ) with pytest.raises(OSError, match="simulated metadata copy failure"): organizer.construct_ade20k(str(dataset_dir), str(output_dir)) @@ -1323,7 +1329,7 @@ def test_organize_cityscapes_rolls_back_failed_atomic_replacement( (output_dir / "annotations").mkdir() (output_dir / "images" / "keep.png").write_bytes(b"old image") (output_dir / "annotations" / "keep.png").write_bytes(b"old annotation") - real_replace = organizer.os.replace + real_replace = os.replace failed = False def _fail_annotation_install(source: str, destination: str) -> None: @@ -1337,7 +1343,9 @@ def _fail_annotation_install(source: str, destination: str) -> None: raise OSError("simulated install failure") real_replace(source, destination) - monkeypatch.setattr(organizer.os, "replace", _fail_annotation_install) + monkeypatch.setattr( + "mblt_vision.utils.datasets.organizer.os.replace", _fail_annotation_install + ) with pytest.raises(OSError, match="simulated"): organizer.organize_cityscapes( @@ -1368,7 +1376,7 @@ def test_dense_install_preserves_backups_when_rollback_fails( (output_dir / "images" / "keep.png").write_bytes(b"old image") (output_dir / "annotations" / "keep.png").write_bytes(b"old annotation") - real_replace = organizer.os.replace + real_replace = os.replace def _fail_install_and_rollback(source: str, destination: str) -> None: source_path = Path(source) @@ -1382,7 +1390,9 @@ def _fail_install_and_rollback(source: str, destination: str) -> None: raise OSError("simulated rollback failure") real_replace(source, destination) - monkeypatch.setattr(organizer.os, "replace", _fail_install_and_rollback) + monkeypatch.setattr( + "mblt_vision.utils.datasets.organizer.os.replace", _fail_install_and_rollback + ) replacements = ( (str(staged_images), str(output_dir / "images")), diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 0df8263..2436b8c 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -2,8 +2,8 @@ from __future__ import annotations -import os import gc +import os from pathlib import Path from typing import Any, cast @@ -33,6 +33,7 @@ import mblt_vision.wrapper as wrapper from mblt_vision._compat import create_model_class from mblt_vision.utils.letterbox import resolve_ratio_pad +from mblt_vision.utils.preprocess import build_preprocess from mblt_vision.utils.results import Results from mblt_vision.utils.types import ListTensorLike from mblt_vision.wrapper import MBLT_Engine @@ -110,8 +111,10 @@ def _fail_mkdir(self: Path, *args: object, **kwargs: object) -> None: del self, args, kwargs raise OSError("home cache is unavailable") - monkeypatch.setattr(wrapper.Path, "mkdir", _fail_mkdir) - monkeypatch.setattr(wrapper.tempfile, "gettempdir", lambda: str(tmp_path)) + monkeypatch.setattr(Path, "mkdir", _fail_mkdir) + monkeypatch.setattr( + "mblt_vision.wrapper.tempfile.gettempdir", lambda: str(tmp_path) + ) cache_dir = Path(wrapper._default_cache_dir()) @@ -133,8 +136,10 @@ def _fail_mkdir(self: Path, *args: object, **kwargs: object) -> None: del self, args, kwargs raise OSError("home cache is unavailable") - monkeypatch.setattr(wrapper.Path, "mkdir", _fail_mkdir) - monkeypatch.setattr(wrapper.tempfile, "gettempdir", lambda: str(tmp_path)) + monkeypatch.setattr(Path, "mkdir", _fail_mkdir) + monkeypatch.setattr( + "mblt_vision.wrapper.tempfile.gettempdir", lambda: str(tmp_path) + ) with pytest.raises(RuntimeError, match="not a private directory"): wrapper._default_cache_dir() @@ -149,7 +154,9 @@ def test_default_cache_dir_preserves_existing_probe_named_file( preferred.mkdir() marker = preferred / ".write_test" marker.write_bytes(b"caller-owned") - monkeypatch.setattr(wrapper.os.path, "expanduser", lambda _: str(preferred)) + monkeypatch.setattr( + "mblt_vision.wrapper.os.path.expanduser", lambda _: str(preferred) + ) assert wrapper._default_cache_dir() == str(preferred) assert marker.read_bytes() == b"caller-owned" @@ -2895,7 +2902,7 @@ def test_preprocess_with_metadata_returns_letterbox_ratio_pad() -> None: "SetOrder": {"shape": "HWC"}, "Normalize": {"style": "cv"}, } - engine.preprocessor = wrapper.build_preprocess(engine.pre_cfg) + engine.preprocessor = build_preprocess(engine.pre_cfg) image = np.zeros((481, 640, 3), dtype=np.uint8) processed, metadata = engine.preprocess_with_metadata(image) From e407cec7cdade3866d862d6ee14cbf747cc1b8ea Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 14:17:19 +0900 Subject: [PATCH 31/48] fix: validate vision evaluation inputs --- mblt_vision/utils/datasets/dataloader.py | 42 +++++++++++++++------ mblt_vision/utils/datasets/organizer.py | 5 +++ mblt_vision/utils/datasets/readiness.py | 10 ++++- mblt_vision/utils/evaluation/eval_ade20k.py | 12 +++++- mblt_vision/utils/evaluation/eval_dota.py | 15 +++++++- tests/test_dataloader.py | 24 +++++++++++- tests/test_eval_dota.py | 28 ++++++++++++++ tests/test_semantic_segmentation.py | 11 ++++++ 8 files changed, 131 insertions(+), 16 deletions(-) diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index 239c802..774e825 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -165,10 +165,17 @@ def __init__(self, root: str) -> None: raise FileNotFoundError( f"NYU Depth requires images/ and depth/ directories under: {root}" ) - images = { - os.path.splitext(name)[0]: os.path.join(image_root, name) + image_names = [ + name for name in os.listdir(image_root) if name.lower().endswith(self.IMG_EXTENSIONS) + ] + image_stems = [os.path.splitext(name)[0] for name in image_names] + if len(image_stems) != len(set(image_stems)): + raise ValueError("ADE20K images contain duplicate filename stems.") + images = { + stem: os.path.join(image_root, name) + for stem, name in zip(image_stems, image_names, strict=True) } depths = { os.path.splitext(name)[0]: os.path.join(depth_root, name) @@ -338,9 +345,18 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: image = cv2.imread(image_path, cv2.IMREAD_COLOR) if image is None: raise FileNotFoundError(f"ADE20K image not found: {image_path}") - annotation = cv2.imread(annotation_path, cv2.IMREAD_GRAYSCALE) - if annotation is None: - raise FileNotFoundError(f"ADE20K annotation not found: {annotation_path}") + try: + with Image.open(annotation_path) as annotation_image: + annotation = np.asarray(annotation_image) + except OSError as exc: + raise FileNotFoundError( + f"ADE20K annotation not found: {annotation_path}" + ) from exc + if annotation.ndim != 2 or annotation.dtype != np.uint8: + raise ValueError( + "ADE20K annotations must be single-channel 8-bit PNG masks: " + f"{annotation_path}" + ) if image.shape[:2] != annotation.shape: raise ValueError( f"ADE20K image and annotation shapes must match, got {image.shape[:2]} and {annotation.shape}: {stem}" @@ -844,13 +860,15 @@ def make_dataset(self) -> None: target_dir = os.path.join(self.root, target_class) if not os.path.isdir(target_dir): continue - for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)): - for fname in sorted(fnames): - if os.path.splitext(fname)[1].lower() not in IMAGE_SUFFIXES: - continue - path = os.path.join(root, fname) - item = path, target_class, fname - instances.append(item) + for fname in sorted(os.listdir(target_dir)): + path = os.path.join(target_dir, fname) + if ( + os.path.islink(path) + or not os.path.isfile(path) + or os.path.splitext(fname)[1].lower() not in IMAGE_SUFFIXES + ): + continue + instances.append((path, target_class, fname)) self.samples = instances diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 5ae0b4e..b083b82 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -1493,6 +1493,11 @@ def _write_dotav1_yolo_labels( f"Unsupported DOTAv1 class in {original_label_path}: {class_name}" ) coordinates = [float(value) for value in fields[:8]] + if fields[9] not in {"0", "1", "2"}: + raise ValueError( + f"Unsupported DOTAv1 difficulty flag {fields[9]!r} in " + f"{original_label_path} at line {line_number}." + ) normalized = [ coordinate / (width if index % 2 == 0 else height) for index, coordinate in enumerate(coordinates) diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 0fcfeae..998907f 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -329,7 +329,15 @@ def _widerface_difficulty_metadata_ready( return False for image_index, face_entry in enumerate(event_faces): try: - face_count = len(np.asarray(face_entry[0])) + face_array = np.asarray(face_entry[0]) + if ( + face_array.ndim != 2 + or face_array.shape[1] != 4 + or not np.isfinite(face_array).all() + or (face_array[:, 2:] <= 0).any() + ): + return False + face_count = len(face_array) keep_indices = np.asarray(event_indices[image_index][0]) except (IndexError, TypeError): return False diff --git a/mblt_vision/utils/evaluation/eval_ade20k.py b/mblt_vision/utils/evaluation/eval_ade20k.py index 389f204..5b6897a 100644 --- a/mblt_vision/utils/evaluation/eval_ade20k.py +++ b/mblt_vision/utils/evaluation/eval_ade20k.py @@ -74,8 +74,18 @@ def update(self, prediction: np.ndarray, target: np.ndarray) -> None: f"Semantic prediction and target shapes must match, got {prediction.shape} and {target.shape}." ) valid_target = ( - (target != self.ignore_label) & (target >= 0) & (target < self.nc) + np.isfinite(target) + & (target >= 0) + & (target < self.nc) + & (target == np.floor(target)) ) + allowed_target = valid_target | (target == self.ignore_label) + if not bool(allowed_target.all()): + invalid_values = np.asarray(np.unique(target[~allowed_target])) + raise ValueError( + f"Semantic targets must be finite class IDs in [0, {self.nc - 1}] " + f"or ignore label {self.ignore_label}; got {invalid_values.tolist()}." + ) valid_prediction = ( np.isfinite(prediction) & (prediction >= 0) diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index a23f08f..cc49a92 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -88,7 +88,10 @@ def _load_ground_truths( ignore_classes = [] ignore_polygons = [] - if label_path.is_file(): + # Organizer output keeps the official original annotation alongside the + # normalized convenience label. Prefer the authoritative original when + # both are present so a stale normalized file cannot hide ground truth. + if label_path.is_file() and not original_label_path.is_file(): for line_number, line in enumerate( label_path.read_text(encoding="utf-8").splitlines(), start=1 ): @@ -117,6 +120,11 @@ def _load_ground_truths( if coords.numel() and float(coords.max()) <= 1.5: coords[:, 0] *= width coords[:, 1] *= height + if len(parts) >= 10 and parts[9] not in {"0", "1", "2"}: + raise ValueError( + f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " + f"{label_path}:{line_number}." + ) if len(parts) >= 10 and parts[9] in {"1", "2"}: ignore_classes.append(cls) ignore_polygons.append(coords) @@ -151,6 +159,11 @@ def _load_ground_truths( "DOTAv1 annotation coordinates must be finite at " f"{original_label_path}:{line_number}." ) + if parts[9] not in {"0", "1", "2"}: + raise ValueError( + f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " + f"{original_label_path}:{line_number}." + ) if parts[9] in {"1", "2"}: ignore_classes.append(cls) ignore_polygons.append(coords) diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index 0b90062..eb6f228 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -13,6 +13,7 @@ from mblt_vision.utils.datasets.dataloader import ( CustomCOCODataset, + CustomADE20K, CustomCityscapes, CustomImageFolder, CustomWiderFaceDataset, @@ -23,7 +24,7 @@ ("dataset_class", "class_name", "expected_count", "expected_suffixes"), [ (CustomImageFolder, "class-a", 1, {".jpg"}), - (CustomWiderFaceDataset, "0--Parade", 2, {".jpg", ".png"}), + (CustomWiderFaceDataset, "0--Parade", 1, {".jpg"}), ], ) def test_class_based_datasets_ignore_non_image_files( @@ -85,3 +86,24 @@ def test_cityscapes_dataset_rejects_unknown_source_ids(tmp_path: Path) -> None: with pytest.raises(ValueError, match=r"unsupported source IDs \[200\]"): CustomCityscapes(str(tmp_path))[0] + + +@pytest.mark.parametrize( + "mask", [np.zeros((2, 2, 3), dtype=np.uint8), np.zeros((2, 2), dtype=np.uint16)] +) +def test_ade20k_dataset_rejects_noncanonical_mask_encodings( + tmp_path: Path, mask: np.ndarray +) -> None: + """Reject color and high-bit-depth ADE20K masks before source-ID conversion.""" + + image_dir = tmp_path / "images" + annotation_dir = tmp_path / "annotations" + image_dir.mkdir() + annotation_dir.mkdir() + assert cv2.imwrite( + str(image_dir / "sample.png"), np.zeros((2, 2, 3), dtype=np.uint8) + ) + Image.fromarray(mask).save(annotation_dir / "sample.png") + + with pytest.raises(ValueError, match="single-channel 8-bit PNG masks"): + CustomADE20K(str(tmp_path))[0] diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 3409e00..9cc2bda 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -202,6 +202,34 @@ def test_dota_annotations_reject_nonfinite_coordinates( _load_ground_truths(str(tmp_path), dataset) +@pytest.mark.parametrize( + ("label_path", "annotation"), + [ + ("labels/val/image.txt", "0 0 0 0.2 0 0.2 0.2 0 0.2 3"), + ("labels/val_original/image.txt", "0 0 20 0 20 20 0 20 plane invalid"), + ], +) +def test_dota_annotations_reject_unknown_difficulty_flags( + tmp_path, label_path: str, annotation: str +) -> None: + """Keep invalid difficult-region metadata from changing positive counts.""" + + path = tmp_path / label_path + path.parent.mkdir(parents=True) + path.write_text(annotation, encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises(ValueError, match="Unsupported DOTAv1 difficulty flag"): + _load_ground_truths(str(tmp_path), dataset) + + def test_difficult_regions_do_not_count_as_positive_or_false_positive() -> None: """Ignore a detection on a difficult region while retaining positive matching.""" diff --git a/tests/test_semantic_segmentation.py b/tests/test_semantic_segmentation.py index b0fcccb..3bb591d 100644 --- a/tests/test_semantic_segmentation.py +++ b/tests/test_semantic_segmentation.py @@ -5,6 +5,8 @@ import pytest import torch +import numpy as np +from mblt_vision.utils.evaluation.eval_ade20k import SemanticMetricAccumulator from mblt_vision.utils.postprocess import SemanticSegPost @@ -130,6 +132,15 @@ def test_semantic_postprocess_rejects_nonfinite_logits(invalid_value: float) -> post(logits) +@pytest.mark.parametrize("invalid_target", [1.5, -1.0, float("nan"), float("inf")]) +def test_semantic_metrics_reject_invalid_target_ids(invalid_target: float) -> None: + """Do not treat corrupted semantic ground truth as ignored pixels.""" + + target = torch.tensor([[invalid_target]], dtype=torch.float32).numpy() + with pytest.raises(ValueError, match="Semantic targets must be finite class IDs"): + SemanticMetricAccumulator(nc=2).update(np.zeros((1, 1)), target) + + def test_semantic_postprocess_restores_letterbox_padding() -> None: """Crop padding before nearest-restoring a semantic map.""" From 49deb5084f7cecc04636711be2cab9a255fe2fdc Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 14:33:43 +0900 Subject: [PATCH 32/48] fix: harden vision validation boundaries --- mblt_vision/utils/datasets/dataloader.py | 24 +++++++++++---- mblt_vision/utils/datasets/organizer.py | 6 ++++ mblt_vision/utils/datasets/readiness.py | 23 ++++++++++++--- .../utils/evaluation/eval_nyu_depth.py | 2 ++ mblt_vision/utils/postprocess/base.py | 9 ++++++ mblt_vision/utils/postprocess/cls_post.py | 13 ++++++++- mblt_vision/wrapper.py | 5 ++++ tests/test_eval_nyu_depth.py | 9 ++++++ tests/test_postprocess_hierarchy.py | 29 +++++++++++++++++++ tests/test_wrapper.py | 12 ++++++++ 10 files changed, 122 insertions(+), 10 deletions(-) diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index 774e825..16f8574 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -172,7 +172,7 @@ def __init__(self, root: str) -> None: ] image_stems = [os.path.splitext(name)[0] for name in image_names] if len(image_stems) != len(set(image_stems)): - raise ValueError("ADE20K images contain duplicate filename stems.") + raise ValueError("NYU Depth images contain duplicate filename stems.") images = { stem: os.path.join(image_root, name) for stem, name in zip(image_stems, image_names, strict=True) @@ -307,10 +307,17 @@ def __init__(self, root: str) -> None: raise FileNotFoundError( f"ADE20K requires images/ and annotations/ directories under: {root}" ) - images = { - os.path.splitext(name)[0]: os.path.join(image_root, name) + image_names = [ + name for name in os.listdir(image_root) if name.lower().endswith(self.IMG_EXTENSIONS) + ] + image_stems = [os.path.splitext(name)[0] for name in image_names] + if len(image_stems) != len(set(image_stems)): + raise ValueError("ADE20K images contain duplicate filename stems.") + images = { + stem: os.path.join(image_root, name) + for stem, name in zip(image_stems, image_names, strict=True) } annotations = { os.path.splitext(name)[0]: os.path.join(annotation_root, name) @@ -458,10 +465,17 @@ def __init__(self, root: str) -> None: raise FileNotFoundError( f"Cityscapes requires images/ and annotations/ directories under: {root}" ) - images = { - os.path.splitext(name)[0]: os.path.join(image_root, name) + image_names = [ + name for name in os.listdir(image_root) if name.lower().endswith(self.IMG_EXTENSIONS) + ] + image_stems = [os.path.splitext(name)[0] for name in image_names] + if len(image_stems) != len(set(image_stems)): + raise ValueError("Cityscapes images contain duplicate filename stems.") + images = { + stem: os.path.join(image_root, name) + for stem, name in zip(image_stems, image_names, strict=True) } annotations = { os.path.splitext(name)[0]: os.path.join(annotation_root, name) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index b083b82..0ba905a 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -6,6 +6,7 @@ import concurrent.futures import hashlib +import math import os import re import shutil @@ -1493,6 +1494,11 @@ def _write_dotav1_yolo_labels( f"Unsupported DOTAv1 class in {original_label_path}: {class_name}" ) coordinates = [float(value) for value in fields[:8]] + if not all(math.isfinite(coordinate) for coordinate in coordinates): + raise ValueError( + f"DOTAv1 coordinates must be finite in {original_label_path} " + f"at line {line_number}." + ) if fields[9] not in {"0", "1", "2"}: raise ValueError( f"Unsupported DOTAv1 difficulty flag {fields[9]!r} in " diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 998907f..cfc74b0 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -33,6 +33,7 @@ CITYSCAPES_SAMPLE_ID_PATTERN = re.compile( r"^(?P[A-Za-z][A-Za-z0-9-]*)_\d{6}_\d{6}$" ) +CITYSCAPES_VALIDATION_CITY_COUNTS = {"frankfurt": 267, "lindau": 59, "munster": 174} IMAGENET_SYNSETS = frozenset( files("mblt_vision.datasets") .joinpath("imagenet_synsets.txt") @@ -341,10 +342,14 @@ def _widerface_difficulty_metadata_ready( keep_indices = np.asarray(event_indices[image_index][0]) except (IndexError, TypeError): return False - if ( - not np.isfinite(keep_indices).all() - or not np.equal(keep_indices, np.trunc(keep_indices)).all() - ): + try: + valid_indices = ( + np.isfinite(keep_indices).all() + and np.equal(keep_indices, np.trunc(keep_indices)).all() + ) + except (TypeError, ValueError): + return False + if not valid_indices: return False if keep_indices.size and ( int(keep_indices.min()) < 1 or int(keep_indices.max()) > face_count @@ -446,6 +451,12 @@ def dense_dataset_ready(data_path: str | Path, dataset: str) -> bool: ) ) if normalized == "cityscapes": + city_counts: dict[str, int] = {} + for stem in images: + match = CITYSCAPES_SAMPLE_ID_PATTERN.fullmatch(stem) + if match is not None: + city = match.group("city") + city_counts[city] = city_counts.get(city, 0) + 1 return ( len(images) == CITYSCAPES_VALIDATION_SAMPLE_COUNT and all( @@ -453,6 +464,10 @@ def dense_dataset_ready(data_path: str | Path, dataset: str) -> bool: for stem in images ) and all(path.suffix.lower() == ".png" for path in images.values()) + and ( + CITYSCAPES_VALIDATION_SAMPLE_COUNT != 500 + or city_counts == CITYSCAPES_VALIDATION_CITY_COUNTS + ) ) return False diff --git a/mblt_vision/utils/evaluation/eval_nyu_depth.py b/mblt_vision/utils/evaluation/eval_nyu_depth.py index 84358b4..1a5432c 100644 --- a/mblt_vision/utils/evaluation/eval_nyu_depth.py +++ b/mblt_vision/utils/evaluation/eval_nyu_depth.py @@ -59,6 +59,8 @@ def update(self, prediction: np.ndarray, target: np.ndarray) -> None: raise ValueError( f"NYU Depth prediction and target shapes must match, got {prediction.shape} and {target.shape}." ) + if not np.isfinite(target).all(): + raise ValueError("NYU Depth target contains non-finite values.") valid = ( np.isfinite(target) & (target > self.MIN_DEPTH) & (target < self.MAX_DEPTH) ) diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index 246ff9a..35193b0 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -384,6 +384,15 @@ def _final_detection_batches( f"invalid rows: {invalid_rows}." ) labels = batch[:, 5] + scores = batch[:, 4] + if not bool(((scores >= 0) & (scores <= 1)).all()): + invalid_scores = ( + scores[(scores < 0) | (scores > 1)].detach().cpu().tolist() + ) + raise ValueError( + "Decoded detection confidence values must be in [0, 1]; " + f"got {invalid_scores}." + ) valid_labels = ( torch.isfinite(labels) & (labels == labels.round()) diff --git a/mblt_vision/utils/postprocess/cls_post.py b/mblt_vision/utils/postprocess/cls_post.py index 7fcb2a4..f07c5f6 100644 --- a/mblt_vision/utils/postprocess/cls_post.py +++ b/mblt_vision/utils/postprocess/cls_post.py @@ -73,7 +73,18 @@ def __call__(self, x: TensorLike | ListTensorLike) -> torch.Tensor: if x.ndim == 2: x = x.unsqueeze(-1).unsqueeze(-1) elif x.ndim == 3: - x = x.unsqueeze(0) + if self.num_classes is None: + raise ValueError( + "Classification 3D outputs require a configured class count to disambiguate layout." + ) + if x.shape[1] == self.num_classes and x.shape[-1] == 1: + x = x.unsqueeze(-1) + elif x.shape[0] == self.num_classes: + x = x.unsqueeze(0) + else: + raise ValueError( + f"Unsupported 3D classification output shape {tuple(x.shape)} for {self.num_classes} classes." + ) if x.ndim != 4: raise ValueError( f"Classification output must be convertible to NCHW, got shape {tuple(x.shape)}." diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index f17bb1b..93b98a2 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -457,6 +457,11 @@ def __init__( cast(Sequence[str] | None, model_path or None), ) model_config_part = resolve_model_config(model_cls, model_type) + for section in ("file_cfg", "pre_cfg", "post_cfg"): + if not isinstance(model_config_part.get(section), dict): + raise ValueError( + f"Model configuration section '{section}' must be a mapping." + ) if mxq_path and Path(mxq_path).suffix.lower() != ".mxq": raise ValueError(f"Explicit mxq_path must end in '.mxq', got {mxq_path!r}.") diff --git a/tests/test_eval_nyu_depth.py b/tests/test_eval_nyu_depth.py index a0f9271..4fea03d 100644 --- a/tests/test_eval_nyu_depth.py +++ b/tests/test_eval_nyu_depth.py @@ -46,3 +46,12 @@ def postprocess(self, output: torch.Tensor) -> SimpleNamespace: ValueError, match=r"output batch length mismatch: maps=2, targets=1" ): eval_nyu_depth_module.eval_nyu_depth(_Model(), "/dataset", batch_size=1) + + +@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) +def test_nyu_depth_metrics_reject_nonfinite_targets(invalid_value: float) -> None: + """Keep direct metric callers from silently excluding corrupt depth targets.""" + + target = np.array([[1.0, invalid_value]], dtype=np.float32) + with pytest.raises(ValueError, match="target contains non-finite"): + eval_nyu_depth_module.calculate_nyu_depth_metrics(np.ones((1, 2)), target) diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 7923925..c24a449 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -68,6 +68,15 @@ def test_classification_postprocessor_rejects_wrong_taxonomy_width() -> None: postprocessor(torch.zeros((1, 999), dtype=torch.float32)) +def test_classification_postprocessor_keeps_batched_singleton_outputs() -> None: + """Preserve batch size for local logits shaped [B, C, 1].""" + + postprocessor = ClsPost({}, {"task": "image_classification", "dataset": "imagenet"}) + output = postprocessor(torch.zeros((2, 1000, 1), dtype=torch.float32)) + + assert output.shape == (2, 1000) + + @pytest.mark.parametrize("invalid_score", [float("nan"), float("inf")]) def test_classification_postprocessor_rejects_nonfinite_scores( invalid_score: float, @@ -123,6 +132,26 @@ def test_already_decoded_detections_reject_nonfinite_rows( postprocessor._final_detection_batches(detections) +@pytest.mark.parametrize("invalid_score", [-0.1, 1.1]) +def test_already_decoded_detections_reject_invalid_confidence( + invalid_score: float, +) -> None: + """Reject finite but impossible confidence values before thresholding.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + postprocessor.conf_thres = 0.25 + postprocessor.nc = 2 + detections = torch.tensor( + [[[0.0, 0.0, 1.0, 1.0, invalid_score, 1.0]]], dtype=torch.float32 + ) + + with pytest.raises(ValueError, match=r"confidence values must be in \[0, 1\]"): + postprocessor._final_detection_batches(detections) + + @pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) def test_detection_postprocessor_rejects_nonfinite_raw_heads( invalid_value: float, diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 2436b8c..76d0f17 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -337,6 +337,18 @@ def test_engine_init_rejects_wrong_suffix_for_mxq_path(tmp_path: Path) -> None: ) +@pytest.mark.parametrize("section", ["file_cfg", "pre_cfg", "post_cfg"]) +def test_engine_init_rejects_nonmapping_direct_configuration_sections( + section: str, +) -> None: + """Report malformed direct model mappings before backend construction.""" + + config: dict[str, Any] = {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}} + config[section] = [] + with pytest.raises(ValueError, match=rf"section '{section}' must be a mapping"): + MBLT_Engine(config) + + def test_engine_init_disposes_mxq_backend_after_postprocess_setup_failure( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: From fe2ecf7af2d14604fe1fc813566d3085afea3445 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 14:35:20 +0900 Subject: [PATCH 33/48] fix: import RatioPad from letterbox --- mblt_vision/utils/postprocess/base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index 35193b0..3fac5c7 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -8,9 +8,10 @@ import torch from ..._tasks import normalize_vision_task +from ..letterbox import RatioPad from ..preprocess._validation import normalize_image_size from ..types import ListTensorLike, TensorLike -from .common import RatioPad, nmsout2eval, process_mask_upsample +from .common import nmsout2eval, process_mask_upsample class PostBase(ABC): From bf92255f663384aceff980d7f2add44a60aced27 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 15:01:36 +0900 Subject: [PATCH 34/48] fix: validate vision artifacts --- mblt_vision/compile/vision.py | 59 +++++++++++++++++++++-- mblt_vision/utils/datasets/dataloader.py | 36 ++++++++++++-- mblt_vision/utils/datasets/organizer.py | 16 +++--- mblt_vision/utils/datasets/readiness.py | 10 ++++ mblt_vision/utils/postprocess/base.py | 6 +++ mblt_vision/utils/postprocess/cls_post.py | 13 +++++ mblt_vision/wrapper.py | 20 +++++++- tests/test_compile_vision.py | 12 +++++ tests/test_dataloader.py | 31 ++++++++++++ tests/test_dataset_readiness.py | 28 +++++++++++ tests/test_depth_estimation.py | 19 ++++++++ tests/test_postprocess_hierarchy.py | 38 +++++++++++++++ tests/test_wrapper.py | 35 ++++++++++++++ 13 files changed, 306 insertions(+), 17 deletions(-) diff --git a/mblt_vision/compile/vision.py b/mblt_vision/compile/vision.py index 8559bd1..ef781fb 100644 --- a/mblt_vision/compile/vision.py +++ b/mblt_vision/compile/vision.py @@ -7,7 +7,7 @@ import json import shutil import warnings -from collections.abc import Callable, Sequence +from collections.abc import Callable, Mapping, Sequence from pathlib import Path from tempfile import TemporaryDirectory, mkdtemp from typing import Any @@ -544,7 +544,41 @@ def get_subset_images(subset_path: str | Path) -> list[Path]: return images -def validate_calibration_dataset(calib_data_path: str | Path) -> Path: +def _calibration_image_shape(pre_cfg: Mapping[str, Any]) -> tuple[int, int]: + """Derive the final HWC spatial shape produced by a model preprocessor.""" + + image_shape: tuple[int, int] | None = None + for operation, config in pre_cfg.items(): + if operation not in {"LetterBox", "Resize", "CenterCrop"} or not isinstance( + config, Mapping + ): + continue + size = config.get("img_size", config.get("size")) + if isinstance(size, int) and not isinstance(size, bool) and size > 0: + image_shape = (size, size) + elif ( + isinstance(size, Sequence) + and not isinstance(size, (str, bytes)) + and len(size) == 2 + and all( + isinstance(value, int) and not isinstance(value, bool) and value > 0 + for value in size + ) + ): + image_shape = (size[0], size[1]) + if image_shape is None: + raise ValueError( + "Unable to derive calibration image shape from pre_cfg; expected a " + "positive LetterBox.img_size, Resize.size, or CenterCrop.size." + ) + return image_shape + + +def validate_calibration_dataset( + calib_data_path: str | Path, + *, + image_shape: tuple[int, int] | None = None, +) -> Path: """Validate a ready directory of preprocessed calibration arrays. Args: @@ -572,6 +606,11 @@ def validate_calibration_dataset(calib_data_path: str | Path) -> Path: raise ValueError( f"Calibration tensor {array_path} must be HWC with three channels; got {array.shape}." ) + if image_shape is not None and array.shape[:2] != image_shape: + raise ValueError( + f"Calibration tensor {array_path} must match the model pre_cfg " + f"image shape {image_shape}, got {array.shape[:2]}." + ) if array.dtype != np.float32: raise ValueError( f"Calibration tensor {array_path} must use float32; got {array.dtype}." @@ -927,10 +966,15 @@ def compile_vision_model( mxq_compile, calibration_config_class = _load_qbcompiler() model_config = resolve_model_config(model_cls, model_type) file_cfg = model_config.get("file_cfg") + pre_cfg = model_config.get("pre_cfg") post_cfg = model_config.get("post_cfg") - if not isinstance(file_cfg, dict) or not isinstance(post_cfg, dict): + if ( + not isinstance(file_cfg, dict) + or not isinstance(pre_cfg, dict) + or not isinstance(post_cfg, dict) + ): raise ValueError( - "Resolved vision model configuration requires `file_cfg` and `post_cfg` objects." + "Resolved vision model configuration requires `file_cfg`, `pre_cfg`, and `post_cfg` objects." ) file_cfg["target_device"] = target_device @@ -959,7 +1003,12 @@ def compile_vision_model( ) mxq_compile( model=str(resolved_onnx), - calib_data_path=str(validate_calibration_dataset(calib_data_path)), + calib_data_path=str( + validate_calibration_dataset( + calib_data_path, + image_shape=_calibration_image_shape(pre_cfg), + ) + ), save_path=str(output_path), image_channels=3, backend="onnx", diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index 16f8574..a609cef 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -5,6 +5,7 @@ from __future__ import annotations import os +from pathlib import Path from typing import Any, Callable import cv2 @@ -58,10 +59,23 @@ def __init__( def _load_image(self, image_id: int) -> np.ndarray: """Load image by ID""" - image_path = os.path.join( - self.root, self.coco.loadImgs(image_id)[0]["file_name"] - ) - image = cv2.imread(image_path) # Load image (BGR format) + file_name = self.coco.loadImgs(image_id)[0]["file_name"] + if not isinstance(file_name, str) or not file_name: + raise ValueError(f"COCO image ID {image_id} has an invalid file_name.") + relative_path = Path(file_name) + if relative_path.is_absolute() or ".." in relative_path.parts: + raise ValueError( + f"COCO image ID {image_id} has an unsafe file_name: {file_name!r}." + ) + image_root = Path(self.root).resolve() + image_path = (image_root / relative_path).resolve() + try: + image_path.relative_to(image_root) + except ValueError as exc: + raise ValueError( + f"COCO image ID {image_id} resolves outside the image root: {file_name!r}." + ) from exc + image = cv2.imread(str(image_path)) # Load image (BGR format) if image is None: raise FileNotFoundError(f"Image not found: {image_path}") @@ -208,7 +222,15 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: image = cv2.imread(image_path) if image is None: raise FileNotFoundError(f"NYU Depth image not found: {image_path}") - depth = np.asarray(np.load(depth_path), dtype=np.float32) + raw_depth = np.load(depth_path, allow_pickle=False) + if not np.issubdtype(raw_depth.dtype, np.number) or np.issubdtype( + raw_depth.dtype, np.complexfloating + ): + raise ValueError( + "NYU Depth target must use a real numeric dtype, " + f"got {raw_depth.dtype}: {depth_path}" + ) + depth = np.asarray(raw_depth, dtype=np.float32) if depth.ndim != 2: raise ValueError( f"NYU Depth target must be two-dimensional, got {depth.shape}: {depth_path}" @@ -654,6 +676,10 @@ def __init__(self, root: str) -> None: self.ids = [ os.path.splitext(os.path.basename(path))[0] for path in self.image_paths ] + if len(self.ids) != len(set(self.ids)): + raise ValueError( + "DOTAv1 validation images contain duplicate filename stems." + ) def _find_image_paths(self, image_root: str) -> list[str]: """Return supported image files directly under a DOTAv1 image directory.""" diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 0ba905a..4233caf 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -1542,19 +1542,23 @@ def construct_dotav1_from_archives( _safe_unpack_archive(image_archive, image_dir) _safe_unpack_archive(label_archive, label_dir) + label_paths = list(_iter_files(label_dir, [".txt"])) labels = { - os.path.splitext(os.path.basename(path))[0]: path - for path in _iter_files(label_dir, [".txt"]) + os.path.splitext(os.path.basename(path))[0]: path for path in label_paths } + if len(labels) != len(label_paths): + raise ValueError("DOTAv1 archive contains duplicate label stems.") if not labels: raise ValueError(f"No DOTAv1 label files found in {label_archive}.") + image_paths = list( + _iter_files(image_dir, [".bmp", ".jpg", ".jpeg", ".png", ".tif", ".tiff"]) + ) images = { - os.path.splitext(os.path.basename(path))[0]: path - for path in _iter_files( - image_dir, [".bmp", ".jpg", ".jpeg", ".png", ".tif", ".tiff"] - ) + os.path.splitext(os.path.basename(path))[0]: path for path in image_paths } + if len(images) != len(image_paths): + raise ValueError("DOTAv1 archive contains duplicate image stems.") image_ids = set(images) label_ids = set(labels) missing_labels = sorted(image_ids - label_ids) diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index cfc74b0..559932c 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -14,6 +14,7 @@ from scipy.io.matlab import MatReadError from ..._tasks import normalize_vision_task +from ...datasets import get_dataset_category_ids IMAGE_SUFFIXES = {".bmp", ".jpeg", ".jpg", ".png", ".tif", ".tiff", ".webp"} IMAGENET_CLASS_COUNT = 1000 @@ -48,6 +49,8 @@ "instances_val2017.json": 80, "person_keypoints_val2017.json": 1, } +COCO_CATEGORY_IDS = frozenset(get_dataset_category_ids("coco")) +COCO_PERSON_KEYPOINT_CATEGORY_IDS = frozenset({1}) def _path_has_symlink_component(path: Path) -> bool: @@ -183,6 +186,13 @@ def _load_coco_image_names(annotation_path: Path) -> set[str] | None: or len(category_ids) != len(set(category_ids)) ): return None + expected_category_ids = ( + COCO_PERSON_KEYPOINT_CATEGORY_IDS + if annotation_path.name == "person_keypoints_val2017.json" + else COCO_CATEGORY_IDS + ) + if set(category_ids) != expected_category_ids: + return None annotation_ids: list[int] = [] for record in annotation_records: if not isinstance(record, dict): diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index 3fac5c7..bd8eeb0 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -394,6 +394,12 @@ def _final_detection_batches( "Decoded detection confidence values must be in [0, 1]; " f"got {invalid_scores}." ) + if not bool( + ((batch[:, 2] > batch[:, 0]) & (batch[:, 3] > batch[:, 1])).all() + ): + raise ValueError( + "Decoded detection boxes must have positive xyxy area." + ) valid_labels = ( torch.isfinite(labels) & (labels == labels.round()) diff --git a/mblt_vision/utils/postprocess/cls_post.py b/mblt_vision/utils/postprocess/cls_post.py index f07c5f6..48bd5bf 100644 --- a/mblt_vision/utils/postprocess/cls_post.py +++ b/mblt_vision/utils/postprocess/cls_post.py @@ -100,5 +100,18 @@ def __call__(self, x: TensorLike | ListTensorLike) -> torch.Tensor: if not torch.isfinite(x).all(): raise ValueError("Classification output scores must all be finite.") if self.softmax: + if not bool(((x >= 0) & (x <= 1)).all()): + raise ValueError( + "Classification probability outputs must be in [0, 1] when post_cfg.softmax is true." + ) + if not torch.allclose( + x.sum(dim=-1), + torch.ones(x.shape[0], dtype=x.dtype, device=x.device), + rtol=1e-4, + atol=1e-4, + ): + raise ValueError( + "Classification probability outputs must sum to 1 per sample when post_cfg.softmax is true." + ) return x return x.softmax(dim=-1) diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index 93b98a2..ae576fe 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -127,7 +127,15 @@ def _default_cache_dir() -> str: preferred = Path(os.path.expanduser("~/.mblt_model_zoo")) try: - preferred.mkdir(parents=True, exist_ok=True) + preferred.mkdir(parents=True, mode=0o700, exist_ok=True) + preferred_stat = os.lstat(preferred) + if ( + not stat.S_ISDIR(preferred_stat.st_mode) + or stat.S_ISLNK(preferred_stat.st_mode) + or preferred_stat.st_uid != os.getuid() + or preferred_stat.st_mode & 0o077 + ): + return _fallback_cache_dir() with tempfile.NamedTemporaryFile(prefix=".write_test-", dir=preferred): pass return str(preferred) @@ -469,6 +477,16 @@ def __init__( raise ValueError( f"Explicit onnx_path must end in '.onnx', got {onnx_path!r}." ) + for key, suffix in (("mxq_path", ".mxq"), ("onnx_path", ".onnx")): + configured_path = model_config_part["file_cfg"].get(key) + if configured_path and ( + not isinstance(configured_path, str) + or Path(configured_path).suffix.lower() != suffix + ): + raise ValueError( + f"Configured file_cfg.{key} must end in {suffix!r}, " + f"got {configured_path!r}." + ) file_cfg_model_path = str(model_config_part["file_cfg"].get("model_path", "")) file_cfg_onnx_path = str(model_config_part["file_cfg"].get("onnx_path", "")) diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index 17f3f13..db7a597 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -499,6 +499,17 @@ def test_validate_calibration_dataset_rejects_nonfinite_values( validate_calibration_dataset(tmp_path) +def test_validate_calibration_dataset_rejects_unexpected_model_image_shape( + tmp_path: Path, +) -> None: + """Ready calibration arrays must retain the configured preprocessor geometry.""" + + np.save(tmp_path / "selected.npy", np.ones((2, 2, 3), dtype=np.float32)) + + with pytest.raises(ValueError, match=r"pre_cfg image shape \(3, 2\)"): + validate_calibration_dataset(tmp_path, image_shape=(3, 2)) + + def test_quantization_explicit_values_do_not_fetch( monkeypatch: pytest.MonkeyPatch, ) -> None: @@ -698,6 +709,7 @@ def _compile(**kwargs: Any) -> None: "filename": "hosted-model.mxq", "onnx_path": str(hosted_onnx), }, + "pre_cfg": {"LetterBox": {"img_size": [2, 2]}}, "post_cfg": {"task": task, "dataset": dataset}, }, ) diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index eb6f228..80b8162 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -15,6 +15,7 @@ CustomCOCODataset, CustomADE20K, CustomCityscapes, + CustomDOTAv1, CustomImageFolder, CustomWiderFaceDataset, ) @@ -70,6 +71,36 @@ def test_coco_dataset_rejects_annotation_geometry_mismatching_image() -> None: CustomCOCODataset.__getitem__(dataset, 0) +@pytest.mark.parametrize("file_name", ["/tmp/outside.jpg", "../outside.jpg"]) +def test_coco_dataset_rejects_unsafe_annotation_file_names(file_name: str) -> None: + """Do not let COCO metadata access images outside the configured root.""" + + dataset = cast( + CustomCOCODataset, + SimpleNamespace( + root="/dataset", + coco=SimpleNamespace( + loadImgs=lambda _: [{"file_name": file_name}], + ), + ), + ) + + with pytest.raises(ValueError, match="unsafe file_name"): + CustomCOCODataset._load_image(dataset, 1) + + +def test_dota_dataset_rejects_duplicate_image_stems(tmp_path: Path) -> None: + """Direct DOTAv1 loading must not silently collapse same-stem images.""" + + image_dir = tmp_path / "images" + image_dir.mkdir() + (image_dir / "sample.jpg").write_bytes(b"jpg") + (image_dir / "sample.png").write_bytes(b"png") + + with pytest.raises(ValueError, match="duplicate filename stems"): + CustomDOTAv1(str(tmp_path)) + + def test_cityscapes_dataset_rejects_unknown_source_ids(tmp_path: Path) -> None: """Reject corrupted Cityscapes labels instead of remapping them to ignore.""" diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index 2f999c9..07c8ee1 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -76,6 +76,7 @@ def test_coco_readiness_matches_images_to_task_annotations( monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 2) monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, annotation_name, 2) monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, annotation_name, 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) image_names = ["000000000001.jpg", "000000000002.jpg"] for image_name in image_names: _write_file(tmp_path / "val2017" / image_name) @@ -107,6 +108,31 @@ def test_coco_readiness_matches_images_to_task_annotations( assert not readiness.dataset_ready(tmp_path, task, "imagenet") +def test_coco_readiness_rejects_noncanonical_category_ids( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Require the canonical COCO category-ID set, not merely its count.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 1) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) + image_name = "000000000001.jpg" + _write_file(tmp_path / "val2017" / image_name) + (tmp_path / "instances_val2017.json").write_text( + json.dumps( + { + "images": [{"id": 1, "file_name": image_name}], + "categories": [{"id": 2}], + "annotations": [{"id": 1, "image_id": 1, "category_id": 2}], + } + ), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") + + def test_coco_readiness_rejects_duplicate_image_ids( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: @@ -115,6 +141,7 @@ def test_coco_readiness_rejects_duplicate_image_ids( monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 2) monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 2) monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) image_names = ["000000000001.jpg", "000000000002.jpg"] for image_name in image_names: _write_file(tmp_path / "val2017" / image_name) @@ -146,6 +173,7 @@ def test_coco_readiness_rejects_truncated_annotation_table( monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 2) monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 2) monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) image_names = ["000000000001.jpg", "000000000002.jpg"] for image_name in image_names: _write_file(tmp_path / "val2017" / image_name) diff --git a/tests/test_depth_estimation.py b/tests/test_depth_estimation.py index 79c9f90..5bd9087 100644 --- a/tests/test_depth_estimation.py +++ b/tests/test_depth_estimation.py @@ -161,3 +161,22 @@ def test_nyu_depth_dataset_rejects_nonfinite_targets( with pytest.raises(ValueError, match=r"finite values: .*sample\.npy"): CustomNYUDepth(str(tmp_path))[0] + + +@pytest.mark.parametrize("dtype", [np.complex64, np.dtype("U4")]) +def test_nyu_depth_dataset_rejects_nonreal_or_nonnumeric_targets( + tmp_path, dtype: np.dtype +) -> None: + """Reject target arrays before lossy conversion to float32.""" + + image_dir = tmp_path / "images" + depth_dir = tmp_path / "depth" + image_dir.mkdir() + depth_dir.mkdir() + assert cv2.imwrite( + str(image_dir / "sample.png"), np.zeros((4, 5, 3), dtype=np.uint8) + ) + np.save(depth_dir / "sample.npy", np.ones((4, 5), dtype=dtype)) + + with pytest.raises(ValueError, match="real numeric dtype"): + CustomNYUDepth(str(tmp_path))[0] diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index c24a449..d9b3a26 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -77,6 +77,26 @@ def test_classification_postprocessor_keeps_batched_singleton_outputs() -> None: assert output.shape == (2, 1000) +@pytest.mark.parametrize( + "scores", + [ + torch.tensor([[1.1] + [0.0] * 999]), + torch.tensor([[0.5] + [0.0] * 999]), + ], +) +def test_classification_probability_postprocessor_rejects_invalid_probabilities( + scores: torch.Tensor, +) -> None: + """Validate local artifacts that declare already-softmaxed outputs.""" + + postprocessor = ClsPost( + {}, {"task": "image_classification", "dataset": "imagenet", "softmax": True} + ) + + with pytest.raises(ValueError, match="probability outputs"): + postprocessor(scores) + + @pytest.mark.parametrize("invalid_score", [float("nan"), float("inf")]) def test_classification_postprocessor_rejects_nonfinite_scores( invalid_score: float, @@ -152,6 +172,24 @@ def test_already_decoded_detections_reject_invalid_confidence( postprocessor._final_detection_batches(detections) +@pytest.mark.parametrize("coordinates", [(1.0, 0.0, 1.0, 2.0), (0.0, 2.0, 1.0, 2.0)]) +def test_already_decoded_detections_reject_nonpositive_box_area( + coordinates: tuple[float, float, float, float], +) -> None: + """Reject degenerate decoded XYXY boxes before they reach evaluation.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + postprocessor.conf_thres = 0.25 + postprocessor.nc = 2 + detections = torch.tensor([[[*coordinates, 0.9, 1.0]]], dtype=torch.float32) + + with pytest.raises(ValueError, match="positive xyxy area"): + postprocessor._final_detection_batches(detections) + + @pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) def test_detection_postprocessor_rejects_nonfinite_raw_heads( invalid_value: float, diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 76d0f17..4f202e5 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -152,6 +152,7 @@ def test_default_cache_dir_preserves_existing_probe_named_file( preferred = tmp_path / "mblt_model_zoo" preferred.mkdir() + preferred.chmod(0o700) marker = preferred / ".write_test" marker.write_bytes(b"caller-owned") monkeypatch.setattr( @@ -162,6 +163,40 @@ def test_default_cache_dir_preserves_existing_probe_named_file( assert marker.read_bytes() == b"caller-owned" +def test_default_cache_dir_rejects_unsafe_preferred_directory( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Do not use a preferred cache other users can write to.""" + + preferred = tmp_path / "mblt_model_zoo" + preferred.mkdir(mode=0o700) + preferred.chmod(0o777) + fallback = tmp_path / "fallback" + monkeypatch.setattr( + "mblt_vision.wrapper.os.path.expanduser", lambda _: str(preferred) + ) + monkeypatch.setattr(wrapper, "_fallback_cache_dir", lambda: str(fallback)) + + assert wrapper._default_cache_dir() == str(fallback) + + +@pytest.mark.parametrize( + ("key", "value", "expected_suffix"), + [("mxq_path", "artifact.onnx", ".mxq"), ("onnx_path", "artifact.mxq", ".onnx")], +) +def test_engine_rejects_wrong_suffix_in_direct_file_config( + key: str, value: str, expected_suffix: str +) -> None: + """Validate compatibility path aliases provided through a direct config mapping.""" + + with pytest.raises( + ValueError, match=rf"file_cfg\.{key} must end in '{expected_suffix}'" + ): + MBLT_Engine( + {"file_cfg": {key: value}, "pre_cfg": {}, "post_cfg": {}}, + ) + + def test_cache_directory_is_resolved_lazily(monkeypatch: pytest.MonkeyPatch) -> None: """Do not create or probe the artifact cache until an operation needs it.""" From a7f87edf82b4683b532b289ad0b299e448eeaa39 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 15:26:26 +0900 Subject: [PATCH 35/48] fix: harden vision evaluation inputs --- mblt_vision/datasets/imagenet_synsets.txt | 2 +- mblt_vision/utils/datasets/organizer.py | 5 ++ mblt_vision/utils/datasets/readiness.py | 29 ++++++++ mblt_vision/utils/evaluation/eval_coco.py | 64 +++++++++++++++-- mblt_vision/utils/evaluation/eval_dota.py | 18 +++++ .../utils/evaluation/eval_nyu_depth.py | 17 ++++- mblt_vision/utils/postprocess/base.py | 28 ++++++-- tests/test_dataset_organizer.py | 28 ++++++++ tests/test_dataset_readiness.py | 68 ++++++++++++++++++- tests/test_eval_coco.py | 31 ++++++++- tests/test_eval_dota.py | 28 ++++++++ tests/test_eval_nyu_depth.py | 10 +++ tests/test_postprocess_hierarchy.py | 53 +++++++++++++++ 13 files changed, 363 insertions(+), 18 deletions(-) diff --git a/mblt_vision/datasets/imagenet_synsets.txt b/mblt_vision/datasets/imagenet_synsets.txt index 1fab3e9..88aa58f 100644 --- a/mblt_vision/datasets/imagenet_synsets.txt +++ b/mblt_vision/datasets/imagenet_synsets.txt @@ -1,4 +1,4 @@ -+n01440764 +n01440764 n01443537 n01484850 n01491361 diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 4233caf..59cfe95 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -1262,6 +1262,11 @@ def organize_cityscapes( os.path.join(staged_annotation_dir, f"{sample_id}.png"), ) + if not dataset_ready(staging_dir, "semantic_segmentation", "cityscapes"): + raise ValueError( + "Staged Cityscapes validation data failed identity and completeness checks." + ) + replacements = ( (staged_image_dir, os.path.join(output_dir, "images")), (staged_annotation_dir, os.path.join(output_dir, "annotations")), diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 559932c..96f98b8 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -2,6 +2,7 @@ from __future__ import annotations +import hashlib import json import os import re @@ -51,6 +52,9 @@ } COCO_CATEGORY_IDS = frozenset(get_dataset_category_ids("coco")) COCO_PERSON_KEYPOINT_CATEGORY_IDS = frozenset({1}) +COCO_VALIDATION_IMAGE_IDENTITIES_SHA256 = ( + "f57f71ba25171a0fd99be8c425a91d4a6fdd43d25aadc7e5be51dd37a73281a7" +) def _path_has_symlink_component(path: Path) -> bool: @@ -163,6 +167,16 @@ def _load_coco_image_names(annotation_path: Path) -> set[str] | None: or len(image_ids) != len(set(image_ids)) ): return None + if len(image_records) == 5000: + identity_payload = "".join( + f"{image_id}:{file_name}\n" + for image_id, file_name in sorted(zip(image_ids, names, strict=True)) + ).encode() + if ( + hashlib.sha256(identity_payload).hexdigest() + != COCO_VALIDATION_IMAGE_IDENTITIES_SHA256 + ): + return None annotation_records = annotation.get("annotations") categories = annotation.get("categories") expected_annotations = COCO_ANNOTATION_COUNTS.get(annotation_path.name) @@ -209,6 +223,21 @@ def _load_coco_image_names(annotation_path: Path) -> set[str] | None: or category_id not in category_ids ): return None + if annotation_path.name == "instances_val2017.json": + bbox = record.get("bbox") + if ( + not isinstance(bbox, list) + or len(bbox) != 4 + or any( + not isinstance(value, (int, float)) + or isinstance(value, bool) + or not np.isfinite(value) + for value in bbox + ) + or bbox[2] <= 0 + or bbox[3] <= 0 + ): + return None annotation_ids.append(annotation_id) if len(annotation_ids) != len(set(annotation_ids)): return None diff --git a/mblt_vision/utils/evaluation/eval_coco.py b/mblt_vision/utils/evaluation/eval_coco.py index 15f84a4..8ff9b75 100644 --- a/mblt_vision/utils/evaluation/eval_coco.py +++ b/mblt_vision/utils/evaluation/eval_coco.py @@ -40,6 +40,20 @@ def secondary_score(self) -> float: return self.map50 +def _require_batch_cardinality(expected: int, **values: Any) -> None: + """Reject postprocessing metadata that cannot represent every input image.""" + + invalid = { + name: len(value) for name, value in values.items() if len(value) != expected + } + if invalid: + details = ", ".join(f"{name}={length}" for name, length in invalid.items()) + raise ValueError( + "COCO evaluation batch cardinality mismatch: " + f"expected {expected}, got {details}." + ) + + def format_coco_results( task: str, nms_outs: Results, @@ -71,7 +85,17 @@ def format_coco_results( org_shape, ratio_pad=ratio_pad, ) - for i, labels, boxes, scores in zip(idx, labels_list, boxes_list, scores_list): + _require_batch_cardinality( + len(idx), + org_shape=org_shape, + ratio_pad=ratio_pad, + labels=labels_list, + boxes=boxes_list, + scores=scores_list, + ) + for i, labels, boxes, scores in zip( + idx, labels_list, boxes_list, scores_list, strict=True + ): results.extend( [ { @@ -80,7 +104,7 @@ def format_coco_results( "bbox": box, "score": score, } - for box, score, label in zip(boxes, scores, labels) + for box, score, label in zip(boxes, scores, labels, strict=True) ] ) elif task == "instance_segmentation": @@ -90,8 +114,17 @@ def format_coco_results( org_shape, ratio_pad=ratio_pad, ) + _require_batch_cardinality( + len(idx), + org_shape=org_shape, + ratio_pad=ratio_pad, + labels=labels_list, + boxes=boxes_list, + scores=scores_list, + extra=extra_list, + ) for i, labels, boxes, scores, extra in zip( - idx, labels_list, boxes_list, scores_list, extra_list + idx, labels_list, boxes_list, scores_list, extra_list, strict=True ): results.extend( [ @@ -102,7 +135,9 @@ def format_coco_results( "score": score, "segmentation": extra, } - for box, score, label, extra in zip(boxes, scores, labels, extra) + for box, score, label, extra in zip( + boxes, scores, labels, extra, strict=True + ) ] ) elif task == "pose_estimation": @@ -112,8 +147,17 @@ def format_coco_results( org_shape, ratio_pad=ratio_pad, ) + _require_batch_cardinality( + len(idx), + org_shape=org_shape, + ratio_pad=ratio_pad, + labels=labels_list, + boxes=boxes_list, + scores=scores_list, + extra=extra_list, + ) for i, labels, boxes, scores, extra in zip( - idx, labels_list, boxes_list, scores_list, extra_list + idx, labels_list, boxes_list, scores_list, extra_list, strict=True ): results.extend( [ @@ -124,7 +168,9 @@ def format_coco_results( "score": score, "keypoints": extra, } - for box, score, label, extra in zip(boxes, scores, labels, extra) + for box, score, label, extra in zip( + boxes, scores, labels, extra, strict=True + ) ] ) else: @@ -171,6 +217,12 @@ def eval_coco_metrics( model.post_cfg["task"], supported=("object_detection", "instance_segmentation", "pose_estimation"), ) + dataset_name = model.post_cfg.get("dataset") + if not isinstance(dataset_name, str) or dataset_name.lower() != "coco": + raise ValueError( + "COCO evaluation requires model post_cfg.dataset to be 'coco', " + f"got {dataset_name!r}." + ) if task in {"object_detection", "instance_segmentation"}: dataset = CustomCOCODataset( os.path.join(data_path, "val2017"), diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index cc49a92..41ab221 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -62,6 +62,22 @@ def _label_to_index(label: str) -> int: return DOTAV1_CLASS_TO_IDX[label] +def _validate_polygon_area( + coords: torch.Tensor, annotation_path: Path, line_number: int +) -> None: + """Require a non-degenerate quadrilateral before converting it to an OBB.""" + + shifted = torch.roll(coords, shifts=-1, dims=0) + area = 0.5 * torch.abs( + torch.sum(coords[:, 0] * shifted[:, 1] - coords[:, 1] * shifted[:, 0]) + ) + if not bool(area > 0): + raise ValueError( + "DOTAv1 annotation polygon must have positive area at " + f"{annotation_path}:{line_number}." + ) + + def _load_ground_truths( data_path: str, dataset: CustomDOTAv1 ) -> dict[str, dict[str, torch.Tensor]]: @@ -120,6 +136,7 @@ def _load_ground_truths( if coords.numel() and float(coords.max()) <= 1.5: coords[:, 0] *= width coords[:, 1] *= height + _validate_polygon_area(coords, label_path, line_number) if len(parts) >= 10 and parts[9] not in {"0", "1", "2"}: raise ValueError( f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " @@ -159,6 +176,7 @@ def _load_ground_truths( "DOTAv1 annotation coordinates must be finite at " f"{original_label_path}:{line_number}." ) + _validate_polygon_area(coords, original_label_path, line_number) if parts[9] not in {"0", "1", "2"}: raise ValueError( f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " diff --git a/mblt_vision/utils/evaluation/eval_nyu_depth.py b/mblt_vision/utils/evaluation/eval_nyu_depth.py index 1a5432c..1e5ed83 100644 --- a/mblt_vision/utils/evaluation/eval_nyu_depth.py +++ b/mblt_vision/utils/evaluation/eval_nyu_depth.py @@ -53,8 +53,8 @@ def __init__(self) -> None: def update(self, prediction: np.ndarray, target: np.ndarray) -> None: """Median-align one prediction and add its valid-pixel statistics.""" - prediction = np.asarray(prediction, dtype=np.float32) - target = np.asarray(target, dtype=np.float32) + prediction = _as_real_float32(prediction, "prediction") + target = _as_real_float32(target, "target") if prediction.shape != target.shape: raise ValueError( f"NYU Depth prediction and target shapes must match, got {prediction.shape} and {target.shape}." @@ -98,6 +98,19 @@ def result(self) -> NYUDepthResult: ) +def _as_real_float32(values: np.ndarray, name: str) -> np.ndarray: + """Validate metric input dtype before converting it to float32.""" + + array = np.asarray(values) + if not np.issubdtype(array.dtype, np.number) or np.issubdtype( + array.dtype, np.complexfloating + ): + raise ValueError( + f"NYU Depth {name} must use a real numeric dtype, got {array.dtype}." + ) + return np.asarray(array, dtype=np.float32) + + def calculate_nyu_depth_metrics( prediction: np.ndarray, target: np.ndarray ) -> NYUDepthResult: diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index bd8eeb0..cf6fdf3 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -323,6 +323,12 @@ def extract_final_outputs( try: normalized_proto = self._normalize_proto_batch(output) except ValueError: + if ( + normalized_detections is not None + and self.task == "instance_segmentation" + and output.ndim == 4 + ): + raise continue if normalized_detections is not None: @@ -394,12 +400,6 @@ def _final_detection_batches( "Decoded detection confidence values must be in [0, 1]; " f"got {invalid_scores}." ) - if not bool( - ((batch[:, 2] > batch[:, 0]) & (batch[:, 3] > batch[:, 1])).all() - ): - raise ValueError( - "Decoded detection boxes must have positive xyxy area." - ) valid_labels = ( torch.isfinite(labels) & (labels == labels.round()) @@ -412,7 +412,21 @@ def _final_detection_batches( "Decoded detection class IDs must be finite integral values in " f"[0, {self.nc}); got {invalid_labels}." ) - batches.append(batch[batch[:, 4] > self.conf_thres]) + retained = batch[batch[:, 4] > self.conf_thres] + if getattr(self, "task", "object_detection") == "obb": + valid_geometry = (retained[:, 2] > 0) & (retained[:, 3] > 0) + geometry_description = "positive width and height" + else: + valid_geometry = (retained[:, 2] > retained[:, 0]) & ( + retained[:, 3] > retained[:, 1] + ) + geometry_description = "positive xyxy area" + if not bool(valid_geometry.all()): + raise ValueError( + "Decoded detection boxes must have " + f"{geometry_description} after confidence filtering." + ) + batches.append(retained) return batches def _normalize_proto_batch( diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index ff6d073..efe0827 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -1186,6 +1186,7 @@ def test_organize_cityscapes_materializes_lossless_validation_pairs( """Copy only exactly paired validation images and label-ID masks without transcoding.""" monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 2) + monkeypatch.setattr(organizer, "dataset_ready", lambda *_: True) sample_ids = ["frankfurt_000000_000294", "munster_000001_000019"] image_archive, annotation_archive = _write_cityscapes_archives(tmp_path, sample_ids) output_dir = tmp_path / "cityscapes" @@ -1321,6 +1322,7 @@ def test_organize_cityscapes_rolls_back_failed_atomic_replacement( """Restore both previous directories if the staged installation fails.""" monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(organizer, "dataset_ready", lambda *_: True) image_archive, annotation_archive = _write_cityscapes_archives( tmp_path, ["lindau_000000_000019"] ) @@ -1356,6 +1358,32 @@ def _fail_annotation_install(source: str, destination: str) -> None: assert (output_dir / "annotations" / "keep.png").read_bytes() == b"old annotation" +def test_organize_cityscapes_preserves_cache_when_staging_fails_readiness( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Validate the staged tree before it can replace a usable Cityscapes cache.""" + + monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(organizer, "dataset_ready", lambda *_: False) + image_archive, annotation_archive = _write_cityscapes_archives( + tmp_path, ["lindau_000000_000019"] + ) + output_dir = tmp_path / "cityscapes" + (output_dir / "images").mkdir(parents=True) + (output_dir / "annotations").mkdir() + (output_dir / "images" / "keep.png").write_bytes(b"old image") + (output_dir / "annotations" / "keep.png").write_bytes(b"old annotation") + + with pytest.raises(ValueError, match="failed identity and completeness checks"): + organizer.organize_cityscapes( + str(image_archive), str(annotation_archive), str(output_dir) + ) + + assert (output_dir / "images" / "keep.png").read_bytes() == b"old image" + assert (output_dir / "annotations" / "keep.png").read_bytes() == b"old annotation" + + def test_dense_install_preserves_backups_when_rollback_fails( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index 07c8ee1..a0b4e9a 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -2,6 +2,7 @@ from __future__ import annotations +import hashlib import json from pathlib import Path @@ -96,7 +97,12 @@ def test_coco_readiness_matches_images_to_task_annotations( ], "categories": [{"id": 1}], "annotations": [ - {"id": index, "image_id": index, "category_id": 1} + { + "id": index, + "image_id": index, + "category_id": 1, + "bbox": [0, 0, 1, 1], + } for index in range(1, 3) ], } @@ -133,6 +139,66 @@ def test_coco_readiness_rejects_noncanonical_category_ids( assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") +@pytest.mark.parametrize("bbox", [[0, 0, 0, 1], [0, 0, 1, float("nan")]]) +def test_coco_readiness_rejects_invalid_instance_box_geometry( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, bbox: list[float] +) -> None: + """Reject invalid instance boxes before a malformed cache reaches COCOeval.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 1) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) + _write_file(tmp_path / "val2017" / "000000000001.jpg") + (tmp_path / "instances_val2017.json").write_text( + json.dumps( + { + "images": [{"id": 1, "file_name": "000000000001.jpg"}], + "categories": [{"id": 1}], + "annotations": [ + {"id": 1, "image_id": 1, "category_id": 1, "bbox": bbox} + ], + } + ), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") + + +def test_coco_annotation_identity_digest_rejects_wrong_validation_split( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Bind a complete COCO annotation table to its canonical image identities.""" + + images = [ + {"id": index, "file_name": f"{index:012d}.jpg"} for index in range(1, 5001) + ] + payload = "".join(f"{item['id']}:{item['file_name']}\n" for item in images).encode() + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 5000) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 0) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) + monkeypatch.setattr( + readiness, + "COCO_VALIDATION_IMAGE_IDENTITIES_SHA256", + hashlib.sha256(payload).hexdigest(), + ) + annotation_path = tmp_path / "instances_val2017.json" + annotation_path.write_text( + json.dumps({"images": images, "categories": [{"id": 1}], "annotations": []}), + encoding="utf-8", + ) + + assert readiness._load_coco_image_names(annotation_path) is not None + images[0]["file_name"] = "999999999999.jpg" + annotation_path.write_text( + json.dumps({"images": images, "categories": [{"id": 1}], "annotations": []}), + encoding="utf-8", + ) + assert readiness._load_coco_image_names(annotation_path) is None + + def test_coco_readiness_rejects_duplicate_image_ids( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index 3c13eba..db25e74 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -31,7 +31,7 @@ def __len__(self) -> int: return len(self.ids) class _Model: - post_cfg = {"task": "pose_estimation"} + post_cfg = {"task": "pose_estimation", "dataset": "coco"} def set_postprocess_thresholds( self, *, conf_thres: float | None, iou_thres: float | None @@ -65,3 +65,32 @@ def _evaluate( ) assert evaluated_image_ids == [1, 2] assert result.map5095 == result.map50 == 0.1 + + +def test_coco_evaluation_rejects_non_coco_model_taxonomy() -> None: + """Do not evaluate another taxonomy using the hard-coded COCO ID mapping.""" + + model = SimpleNamespace(post_cfg={"task": "object_detection", "dataset": "dotav1"}) + + with pytest.raises(ValueError, match="post_cfg.dataset to be 'coco'"): + eval_coco_module.eval_coco_metrics(model, "/dataset", batch_size=1) + + +def test_coco_result_formatter_rejects_truncated_postprocess_batch() -> None: + """Require one decoded result for every submitted COCO image.""" + + postprocess = SimpleNamespace( + nmsout2eval=lambda *_args, **_kwargs: ([[1]], [[[0, 0, 1, 1]]], [[0.9]]) + ) + + with pytest.raises(ValueError, match="batch cardinality mismatch"): + eval_coco_module.format_coco_results( + "object_detection", + SimpleNamespace(output=[]), + (640, 640), + [(640, 640), (640, 640)], + [None, None], + [0, 1], + [101, 102], + postprocess, + ) diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 9cc2bda..459d475 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -146,6 +146,34 @@ def test_original_truncated_annotation_raises_with_file_and_line(tmp_path) -> No _load_ground_truths(str(tmp_path), dataset) +@pytest.mark.parametrize( + ("label_path", "annotation"), + [ + ("labels/val/image.txt", "0 0 0 1 0 2 0 3 0"), + ("labels/val_original/image.txt", "0 0 1 0 2 0 3 0 plane 0"), + ], +) +def test_dota_annotations_reject_degenerate_polygons( + tmp_path, label_path: str, annotation: str +) -> None: + """Reject line-like quadrilaterals before deriving rotated boxes.""" + + path = tmp_path / label_path + path.parent.mkdir(parents=True) + path.write_text(annotation, encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises(ValueError, match="polygon must have positive area"): + _load_ground_truths(str(tmp_path), dataset) + + @pytest.mark.parametrize( ("label_path", "annotation"), [ diff --git a/tests/test_eval_nyu_depth.py b/tests/test_eval_nyu_depth.py index 4fea03d..4dddf0b 100644 --- a/tests/test_eval_nyu_depth.py +++ b/tests/test_eval_nyu_depth.py @@ -55,3 +55,13 @@ def test_nyu_depth_metrics_reject_nonfinite_targets(invalid_value: float) -> Non target = np.array([[1.0, invalid_value]], dtype=np.float32) with pytest.raises(ValueError, match="target contains non-finite"): eval_nyu_depth_module.calculate_nyu_depth_metrics(np.ones((1, 2)), target) + + +@pytest.mark.parametrize("dtype", [np.complex64, np.dtype("U4")]) +def test_nyu_depth_metrics_reject_nonreal_or_nonnumeric_inputs(dtype: np.dtype) -> None: + """Direct metric callers must not lose source-dtype corruption during casting.""" + + values = np.ones((1, 2), dtype=dtype) + + with pytest.raises(ValueError, match="real numeric dtype"): + eval_nyu_depth_module.calculate_nyu_depth_metrics(values, values) diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index d9b3a26..3f422f4 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -190,6 +190,59 @@ def test_already_decoded_detections_reject_nonpositive_box_area( postprocessor._final_detection_batches(detections) +def test_already_decoded_detections_ignore_degenerate_padding_rows() -> None: + """Validate geometry only after low-confidence fixed-size padding is removed.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + postprocessor.conf_thres = 0.25 + postprocessor.nc = 2 + detections = torch.tensor([[[0.0, 0.0, 0.0, 0.0, 0.1, 1.0]]], dtype=torch.float32) + + assert postprocessor._final_detection_batches(detections)[0].shape == (0, 6) + + +def test_already_decoded_obb_uses_width_height_geometry() -> None: + """Do not interpret OBB center/size fields as XYXY corners.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + postprocessor.conf_thres = 0.25 + postprocessor.nc = 2 + postprocessor.task = "obb" + detections = torch.tensor( + [[[500.0, 20.0, 100.0, 10.0, 0.9, 1.0]]], dtype=torch.float32 + ) + + assert postprocessor._final_detection_batches(detections)[0].shape == (1, 6) + + +def test_decoded_segmentation_propagates_invalid_mask_prototypes() -> None: + """Do not discard malformed required prototype tensors as unrelated outputs.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + postprocessor.conf_thres = 0.25 + postprocessor.nc = 2 + postprocessor.n_extra = 2 + postprocessor.task = "instance_segmentation" + detections = torch.tensor( + [[[0.0, 0.0, 1.0, 1.0, 0.9, 1.0, 0.0, 0.0]]], dtype=torch.float32 + ) + invalid_proto = torch.full((1, 2, 2, 2), float("nan")) + + with pytest.raises( + ValueError, match="Mask prototype tensor must contain only finite" + ): + postprocessor.extract_final_outputs([detections, invalid_proto]) + + @pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) def test_detection_postprocessor_rejects_nonfinite_raw_heads( invalid_value: float, From e1846a164028febb5ace92d07c367d307a8330fe Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 15:44:38 +0900 Subject: [PATCH 36/48] fix: validate vision dataset evaluators --- mblt_vision/utils/datasets/organizer.py | 41 +++++++++ mblt_vision/utils/evaluation/eval_dota.py | 12 +++ mblt_vision/utils/evaluation/eval_imagenet.py | 6 ++ .../utils/evaluation/eval_nyu_depth.py | 6 ++ .../utils/evaluation/eval_widerface.py | 22 ++++- tests/test_dataset_organizer.py | 23 +++++ tests/test_eval_dota.py | 31 ++++++- tests/test_eval_imagenet.py | 11 +++ tests/test_eval_nyu_depth.py | 10 +++ tests/test_eval_widerface.py | 84 +++++++++++++++++++ 10 files changed, 244 insertions(+), 2 deletions(-) create mode 100644 tests/test_eval_widerface.py diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 59cfe95..ad784d6 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -5,6 +5,7 @@ from __future__ import annotations import concurrent.futures +import cv2 import hashlib import math import os @@ -22,6 +23,7 @@ from urllib.parse import urlparse import requests +import numpy as np from gdown.download import download from gdown.download_folder import download_folder from PIL import Image @@ -926,6 +928,8 @@ def construct_nyu_depth(dataset_dir: str, output_dir: str) -> None: os.path.join(staged_depth_dir, os.path.basename(depths[sample_id])), ) + _validate_staged_nyu_depth(staging_dir) + replacements = ( (staged_image_dir, os.path.join(output_dir, "images")), (staged_depth_dir, os.path.join(output_dir, "depth")), @@ -938,6 +942,43 @@ def construct_nyu_depth(dataset_dir: str, output_dir: str) -> None: ) +def _validate_staged_nyu_depth(staging_dir: str) -> None: + """Decode staged NYU pairs before they can replace an existing cache.""" + + image_dir = Path(staging_dir) / "images" + depth_dir = Path(staging_dir) / "depth" + for image_path in sorted(image_dir.iterdir()): + if image_path.suffix.lower() not in {".jpg", ".jpeg", ".png"}: + continue + depth_path = depth_dir / f"{image_path.stem}.npy" + image = cv2.imread(str(image_path)) + if image is None: + raise ValueError(f"Staged NYU Depth image is unreadable: {image_path}.") + try: + raw_depth = np.load(depth_path, allow_pickle=False) + except (OSError, ValueError) as exc: + raise ValueError( + f"Unable to load staged NYU Depth target {depth_path}: {exc}." + ) from exc + if not np.issubdtype(raw_depth.dtype, np.number) or np.issubdtype( + raw_depth.dtype, np.complexfloating + ): + raise ValueError( + "Staged NYU Depth target must use a real numeric dtype, " + f"got {raw_depth.dtype}: {depth_path}." + ) + depth = np.asarray(raw_depth, dtype=np.float32) + if depth.ndim != 2 or depth.shape != image.shape[:2]: + raise ValueError( + "Staged NYU Depth image and target shapes must match: " + f"image {image.shape[:2]}, depth {depth.shape}: {image_path}." + ) + if not bool(np.isfinite(depth).all()): + raise ValueError( + f"Staged NYU Depth target must contain only finite values: {depth_path}." + ) + + def organize_nyu_depth( dataset_path: str = NYU_DEPTH_URL, output_dir: str | None = None, diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 41ab221..90b2356 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -189,6 +189,12 @@ def _load_ground_truths( classes.append(cls) polygons.append(coords) + else: + raise FileNotFoundError( + "DOTAv1 annotation not found for image " + f"{image_id!r}; expected {label_path} or {original_label_path}." + ) + polygon_tensor = ( torch.stack(polygons).to(torch.float32) if polygons @@ -693,6 +699,12 @@ def eval_dota( raise NotImplementedError( f"Task {model.post_cfg['task']} is not supported for DOTAv1 evaluation." ) + dataset_name = model.post_cfg.get("dataset") + if not isinstance(dataset_name, str) or dataset_name.lower() != "dotav1": + raise ValueError( + "DOTAv1 evaluation requires model post_cfg.dataset to be 'dotav1', " + f"got {dataset_name!r}." + ) dataset = CustomDOTAv1(data_path) dataloader = get_dota_loader(dataset, batch_size, model.preprocess_with_metadata) diff --git a/mblt_vision/utils/evaluation/eval_imagenet.py b/mblt_vision/utils/evaluation/eval_imagenet.py index fe46a6c..292c68c 100644 --- a/mblt_vision/utils/evaluation/eval_imagenet.py +++ b/mblt_vision/utils/evaluation/eval_imagenet.py @@ -50,6 +50,12 @@ def eval_imagenet_metrics( Returns: ImageNetResult: Top-1 primary accuracy and Top-5 secondary accuracy. """ + dataset_name = model.post_cfg.get("dataset") + if not isinstance(dataset_name, str) or dataset_name.lower() != "imagenet": + raise ValueError( + "ImageNet evaluation requires model post_cfg.dataset to be 'imagenet', " + f"got {dataset_name!r}." + ) dataset = CustomImageFolder(data_path) dataloader = get_imagenet_loader(dataset, batch_size, model.preprocess) num_data = len(dataset) diff --git a/mblt_vision/utils/evaluation/eval_nyu_depth.py b/mblt_vision/utils/evaluation/eval_nyu_depth.py index 1e5ed83..6e8caf0 100644 --- a/mblt_vision/utils/evaluation/eval_nyu_depth.py +++ b/mblt_vision/utils/evaluation/eval_nyu_depth.py @@ -126,6 +126,12 @@ def eval_nyu_depth( ) -> NYUDepthResult: """Evaluate a depth model on paired NYU validation images and depth maps.""" + dataset_name = model.post_cfg.get("dataset") + if not isinstance(dataset_name, str) or dataset_name.lower() != "nyu-depth": + raise ValueError( + "NYU Depth evaluation requires model post_cfg.dataset to be 'nyu-depth', " + f"got {dataset_name!r}." + ) dataset = CustomNYUDepth(data_path) letterbox_cfg = model.pre_cfg.get("LetterBox") if not isinstance(letterbox_cfg, dict) or "img_size" not in letterbox_cfg: diff --git a/mblt_vision/utils/evaluation/eval_widerface.py b/mblt_vision/utils/evaluation/eval_widerface.py index ac7879b..c6b33ed 100644 --- a/mblt_vision/utils/evaluation/eval_widerface.py +++ b/mblt_vision/utils/evaluation/eval_widerface.py @@ -103,6 +103,12 @@ def eval_widerface( raise NotImplementedError( f"Task {model.post_cfg['task']} is not supported for WiderFace evaluation." ) + dataset_name = model.post_cfg.get("dataset") + if not isinstance(dataset_name, str) or dataset_name.lower() != "widerface": + raise ValueError( + "WiderFace evaluation requires model post_cfg.dataset to be 'widerface', " + f"got {dataset_name!r}." + ) dataset = CustomWiderface(os.path.join(data_path, "images")) dataloader = get_widerface_loader( @@ -132,9 +138,23 @@ def eval_widerface( img0_shapes, ratio_pad=ratio_pad, ) + batch_lengths = { + "input batch": int(input_npu.shape[0]), + "original shapes": len(org_shape), + "ratio pads": len(ratio_pad), + "target classes": len(target_classes), + "file names": len(fnames), + "boxes": len(boxes_list), + "scores": len(scores_list), + } + if len(set(batch_lengths.values())) != 1: + details = ", ".join( + f"{name}={length}" for name, length in batch_lengths.items() + ) + raise ValueError(f"WiderFace evaluation batch length mismatch: {details}.") for event_name, file_name, boxes, scores in zip( - target_classes, fnames, boxes_list, scores_list + target_classes, fnames, boxes_list, scores_list, strict=True ): predictions[event_name][os.path.splitext(file_name)[0]] = ( _boxes_scores_to_prediction(boxes, scores) diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index efe0827..8a72e05 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -16,6 +16,7 @@ import pytest import requests +import numpy as np from PIL import Image import mblt_vision.utils.datasets.organizer as organizer @@ -755,6 +756,7 @@ def test_organize_nyu_depth_extracts_only_validation_layout( """Install only NYU Depth validation image/depth pairs from an archive.""" monkeypatch.setattr(organizer, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(organizer, "_validate_staged_nyu_depth", lambda _: None) archive_path = tmp_path / "nyu-depth.zip" with ZipFile(archive_path, "w") as archive: archive.writestr("nyu-depth/images/train/nyu_train.jpg", b"training image") @@ -831,6 +833,7 @@ def test_nyu_depth_install_preserves_backups_when_rollback_fails( """Leave recoverable NYU backups outside staging when rollback fails.""" monkeypatch.setattr(organizer, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(organizer, "_validate_staged_nyu_depth", lambda _: None) dataset_dir = tmp_path / "source" (dataset_dir / "images").mkdir(parents=True) (dataset_dir / "depth").mkdir() @@ -873,6 +876,26 @@ def _fail_install_and_rollback(source: str, destination: str) -> None: assert (backup_dirs[0] / "depth" / "keep.npy").read_bytes() == b"old depth" +@pytest.mark.parametrize( + "depth", + [np.array([[np.nan]], dtype=np.float32), np.array([[1]], dtype=np.complex64)], +) +def test_staged_nyu_depth_validation_rejects_malformed_payloads( + tmp_path: Path, depth: np.ndarray +) -> None: + """Reject corrupt staged NYU targets before cache replacement can begin.""" + + image_dir = tmp_path / "images" + depth_dir = tmp_path / "depth" + image_dir.mkdir() + depth_dir.mkdir() + Image.new("RGB", (1, 1)).save(image_dir / "sample.png") + np.save(depth_dir / "sample.npy", depth) + + with pytest.raises(ValueError, match="real numeric dtype|finite values"): + organizer._validate_staged_nyu_depth(str(tmp_path)) + + def _create_ade20k_source(tmp_path: Path) -> Path: """Create a compact extracted ADE20K validation source.""" diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 459d475..9d5e83d 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -39,7 +39,7 @@ def close(self) -> None: return None class _FakeModel: - post_cfg = {"task": "obb"} + post_cfg = {"task": "obb", "dataset": "dotav1"} preprocess_with_metadata = object() def set_postprocess_thresholds(self, **kwargs) -> None: @@ -74,6 +74,35 @@ def __len__(self) -> int: eval_dota_module.eval_dota(_FakeModel(), str(tmp_path), batch_size=2) +def test_eval_dota_rejects_wrong_model_taxonomy(tmp_path) -> None: + """Do not score another OBB taxonomy against DOTAv1 ground truth.""" + + with pytest.raises(ValueError, match="post_cfg.dataset to be 'dotav1'"): + eval_dota_module.eval_dota( + SimpleNamespace(post_cfg={"task": "obb", "dataset": "coco"}), + str(tmp_path), + batch_size=1, + ) + + +def test_dota_ground_truth_requires_annotation_for_every_image(tmp_path) -> None: + """Do not silently manufacture empty ground truth for unlabeled DOTAv1 samples.""" + + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises( + FileNotFoundError, match="annotation not found for image 'image'" + ): + _load_ground_truths(str(tmp_path), dataset) + + def test_normalized_difficult_flag_loads_as_an_ignored_region(tmp_path) -> None: """Read organizer-produced difficult metadata before selecting evaluation targets.""" diff --git a/tests/test_eval_imagenet.py b/tests/test_eval_imagenet.py index f478d14..ed5d3b3 100644 --- a/tests/test_eval_imagenet.py +++ b/tests/test_eval_imagenet.py @@ -37,6 +37,8 @@ def close(self) -> None: return None class _FakeModel: + post_cfg = {"dataset": "imagenet"} + def preprocess(self, value: object) -> object: return value @@ -63,3 +65,12 @@ def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: eval_imagenet_module.eval_imagenet_metrics( _FakeModel(), "/dataset", batch_size=2 ) + + +def test_imagenet_evaluation_rejects_wrong_model_taxonomy() -> None: + """Do not score another classification taxonomy against ImageNet labels.""" + + with pytest.raises(ValueError, match="post_cfg.dataset to be 'imagenet'"): + eval_imagenet_module.eval_imagenet_metrics( + SimpleNamespace(post_cfg={"dataset": "coco"}), "/dataset", batch_size=1 + ) diff --git a/tests/test_eval_nyu_depth.py b/tests/test_eval_nyu_depth.py index 4dddf0b..58e7fb0 100644 --- a/tests/test_eval_nyu_depth.py +++ b/tests/test_eval_nyu_depth.py @@ -20,6 +20,7 @@ def test_nyu_depth_evaluation_rejects_surplus_output_batch( """Do not silently discard depth maps from a malformed backend batch.""" class _Model: + post_cfg = {"dataset": "nyu-depth"} pre_cfg = {"LetterBox": {"img_size": [4, 4]}} def __call__(self, inputs: torch.Tensor) -> torch.Tensor: @@ -65,3 +66,12 @@ def test_nyu_depth_metrics_reject_nonreal_or_nonnumeric_inputs(dtype: np.dtype) with pytest.raises(ValueError, match="real numeric dtype"): eval_nyu_depth_module.calculate_nyu_depth_metrics(values, values) + + +def test_nyu_depth_evaluation_rejects_wrong_model_taxonomy() -> None: + """Require the model's declared depth taxonomy before loading a dataset.""" + + with pytest.raises(ValueError, match="post_cfg.dataset to be 'nyu-depth'"): + eval_nyu_depth_module.eval_nyu_depth( + SimpleNamespace(post_cfg={"dataset": "ade20k"}), "/dataset", batch_size=1 + ) diff --git a/tests/test_eval_widerface.py b/tests/test_eval_widerface.py new file mode 100644 index 0000000..1c897cb --- /dev/null +++ b/tests/test_eval_widerface.py @@ -0,0 +1,84 @@ +"""Focused WiderFace evaluator validation tests.""" + +from __future__ import annotations + +import importlib +from types import SimpleNamespace + +import numpy as np +import pytest +import torch + +eval_widerface_module = importlib.import_module( + "mblt_vision.utils.evaluation.eval_widerface" +) + + +def test_widerface_evaluation_rejects_wrong_model_taxonomy() -> None: + """Do not evaluate another detector taxonomy using WiderFace metadata.""" + + with pytest.raises(ValueError, match="post_cfg.dataset to be 'widerface'"): + eval_widerface_module.eval_widerface( + SimpleNamespace(post_cfg={"task": "face_detection", "dataset": "coco"}), + "/dataset", + batch_size=1, + ) + + +def test_widerface_evaluation_rejects_truncated_postprocess_batch( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Require one decoded face prediction list for every submitted image.""" + + class _Dataset: + samples = [("unused", "event", "first.jpg"), ("unused", "event", "second.jpg")] + + def __len__(self) -> int: + return 2 + + class _Progress: + def __init__(self, iterable, **kwargs) -> None: + del kwargs + self.iterable = iterable + + def __iter__(self): + return iter(self.iterable) + + def set_postfix_str(self, value: str) -> None: + del value + + def close(self) -> None: + return None + + class _Model: + post_cfg = {"task": "face_detection", "dataset": "widerface"} + postprocessor = SimpleNamespace( + nmsout2eval=lambda *_args, **_kwargs: ([], [[]], [[]]) + ) + preprocess_with_metadata = object() + + def set_postprocess_thresholds(self, **kwargs) -> None: + del kwargs + + def __call__(self, inputs: torch.Tensor) -> torch.Tensor: + return inputs + + def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: + del outputs + return SimpleNamespace(output=[]) + + batch = ( + torch.zeros((2, 8, 8, 3)), + np.array([[8, 8], [8, 8]]), + [None, None], + ("event", "event"), + ("first.jpg", "second.jpg"), + ) + monkeypatch.setattr(eval_widerface_module, "CustomWiderface", lambda _: _Dataset()) + monkeypatch.setattr( + eval_widerface_module, "get_widerface_loader", lambda *_: [batch] + ) + monkeypatch.setattr(eval_widerface_module, "tqdm", _Progress) + + with pytest.raises(ValueError, match="WiderFace evaluation batch length mismatch"): + eval_widerface_module.eval_widerface(_Model(), "/dataset", batch_size=2) From 7c53025ed99998751cc079092b5b6f8667d88766 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 16:15:43 +0900 Subject: [PATCH 37/48] fix: validate vision evaluation inputs --- mblt_vision/utils/datasets/dataloader.py | 80 ++++++++-------------- mblt_vision/utils/datasets/readiness.py | 77 ++++++++++++++++++++- mblt_vision/utils/postprocess/base.py | 8 +++ mblt_vision/wrapper.py | 9 ++- tests/test_dataloader.py | 27 ++++++++ tests/test_dataset_readiness.py | 86 ++++++++++++++++++++++++ tests/test_wrapper.py | 73 ++++++++++++++++++++ 7 files changed, 305 insertions(+), 55 deletions(-) diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index a609cef..27c7df4 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -19,6 +19,21 @@ from .readiness import IMAGE_SUFFIXES +def _unique_paths_by_stem( + directory: str, suffixes: tuple[str, ...], description: str +) -> dict[str, str]: + """Return supported direct-child files keyed by unique case-preserving stems.""" + + names = [name for name in os.listdir(directory) if name.lower().endswith(suffixes)] + stems = [os.path.splitext(name)[0] for name in names] + if len(stems) != len(set(stems)): + raise ValueError(f"{description} contain duplicate filename stems.") + return { + stem: os.path.join(directory, name) + for stem, name in zip(stems, names, strict=True) + } + + class CustomCOCODataset(torch.utils.data.Dataset[tuple[np.ndarray, int, int, int]]): """Custom COCO dataset class for loading images and metadata. @@ -179,23 +194,10 @@ def __init__(self, root: str) -> None: raise FileNotFoundError( f"NYU Depth requires images/ and depth/ directories under: {root}" ) - image_names = [ - name - for name in os.listdir(image_root) - if name.lower().endswith(self.IMG_EXTENSIONS) - ] - image_stems = [os.path.splitext(name)[0] for name in image_names] - if len(image_stems) != len(set(image_stems)): - raise ValueError("NYU Depth images contain duplicate filename stems.") - images = { - stem: os.path.join(image_root, name) - for stem, name in zip(image_stems, image_names, strict=True) - } - depths = { - os.path.splitext(name)[0]: os.path.join(depth_root, name) - for name in os.listdir(depth_root) - if name.lower().endswith(".npy") - } + images = _unique_paths_by_stem( + image_root, self.IMG_EXTENSIONS, "NYU Depth images" + ) + depths = _unique_paths_by_stem(depth_root, (".npy",), "NYU Depth depth maps") missing_depths, missing_images = ( sorted(set(images) - set(depths)), sorted(set(depths) - set(images)), @@ -329,23 +331,10 @@ def __init__(self, root: str) -> None: raise FileNotFoundError( f"ADE20K requires images/ and annotations/ directories under: {root}" ) - image_names = [ - name - for name in os.listdir(image_root) - if name.lower().endswith(self.IMG_EXTENSIONS) - ] - image_stems = [os.path.splitext(name)[0] for name in image_names] - if len(image_stems) != len(set(image_stems)): - raise ValueError("ADE20K images contain duplicate filename stems.") - images = { - stem: os.path.join(image_root, name) - for stem, name in zip(image_stems, image_names, strict=True) - } - annotations = { - os.path.splitext(name)[0]: os.path.join(annotation_root, name) - for name in os.listdir(annotation_root) - if name.lower().endswith(".png") - } + images = _unique_paths_by_stem(image_root, self.IMG_EXTENSIONS, "ADE20K images") + annotations = _unique_paths_by_stem( + annotation_root, (".png",), "ADE20K annotations" + ) missing_annotations = sorted(set(images) - set(annotations)) missing_images = sorted(set(annotations) - set(images)) if missing_annotations or missing_images: @@ -487,23 +476,12 @@ def __init__(self, root: str) -> None: raise FileNotFoundError( f"Cityscapes requires images/ and annotations/ directories under: {root}" ) - image_names = [ - name - for name in os.listdir(image_root) - if name.lower().endswith(self.IMG_EXTENSIONS) - ] - image_stems = [os.path.splitext(name)[0] for name in image_names] - if len(image_stems) != len(set(image_stems)): - raise ValueError("Cityscapes images contain duplicate filename stems.") - images = { - stem: os.path.join(image_root, name) - for stem, name in zip(image_stems, image_names, strict=True) - } - annotations = { - os.path.splitext(name)[0]: os.path.join(annotation_root, name) - for name in os.listdir(annotation_root) - if name.lower().endswith(".png") - } + images = _unique_paths_by_stem( + image_root, self.IMG_EXTENSIONS, "Cityscapes images" + ) + annotations = _unique_paths_by_stem( + annotation_root, (".png",), "Cityscapes annotations" + ) missing_annotations = sorted(set(images) - set(annotations)) missing_images = sorted(set(annotations) - set(images)) if missing_annotations or missing_images: diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 96f98b8..43752b9 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -132,7 +132,9 @@ def _imagenet_ready(root: Path) -> bool: return len(image_names) == IMAGENET_CLASS_COUNT * IMAGENET_IMAGES_PER_CLASS -def _load_coco_image_names(annotation_path: Path) -> set[str] | None: +def _load_coco_image_names( + annotation_path: Path, task: str = "object_detection" +) -> set[str] | None: """Load unique validation image filenames from a COCO annotation file.""" try: @@ -223,7 +225,10 @@ def _load_coco_image_names(annotation_path: Path) -> set[str] | None: or category_id not in category_ids ): return None - if annotation_path.name == "instances_val2017.json": + if annotation_path.name in { + "instances_val2017.json", + "person_keypoints_val2017.json", + }: bbox = record.get("bbox") if ( not isinstance(bbox, list) @@ -238,6 +243,71 @@ def _load_coco_image_names(annotation_path: Path) -> set[str] | None: or bbox[3] <= 0 ): return None + if task == "instance_segmentation": + segmentation = record.get("segmentation") + if isinstance(segmentation, list): + if not segmentation or any( + not isinstance(polygon, list) + or len(polygon) < 6 + or len(polygon) % 2 + or any( + not isinstance(value, (int, float)) + or isinstance(value, bool) + or not np.isfinite(value) + for value in polygon + ) + for polygon in segmentation + ): + return None + elif isinstance(segmentation, dict): + counts = segmentation.get("counts") + size = segmentation.get("size") + if ( + not isinstance(size, list) + or len(size) != 2 + or any( + not isinstance(value, int) + or isinstance(value, bool) + or value <= 0 + for value in size + ) + or not isinstance(counts, (str, list)) + or ( + isinstance(counts, list) + and any( + not isinstance(value, int) + or isinstance(value, bool) + or value < 0 + for value in counts + ) + ) + ): + return None + else: + return None + if task == "pose_estimation": + keypoints = record.get("keypoints") + num_keypoints = record.get("num_keypoints") + if ( + not isinstance(keypoints, list) + or len(keypoints) != 51 + or any( + not isinstance(value, (int, float)) + or isinstance(value, bool) + or not np.isfinite(value) + for value in keypoints + ) + or not isinstance(num_keypoints, int) + or isinstance(num_keypoints, bool) + or not 0 <= num_keypoints <= 17 + or any( + keypoints[index] not in {0, 1, 2} + for index in range(2, len(keypoints), 3) + ) + or num_keypoints + != sum(keypoints[index] > 0 for index in range(2, len(keypoints), 3)) + ): + return None annotation_ids.append(annotation_id) if len(annotation_ids) != len(set(annotation_ids)): return None @@ -252,7 +322,7 @@ def _coco_ready(root: Path, task: str) -> bool: if task == "pose_estimation" else "instances_val2017.json" ) - annotation_names = _load_coco_image_names(root / annotation_name) + annotation_names = _load_coco_image_names(root / annotation_name, task) if annotation_names is None: return False image_dir = root / "val2017" @@ -373,6 +443,7 @@ def _widerface_difficulty_metadata_ready( if ( face_array.ndim != 2 or face_array.shape[1] != 4 + or len(face_array) == 0 or not np.isfinite(face_array).all() or (face_array[:, 2:] <= 0).any() ): diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index cf6fdf3..84ae50b 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -426,6 +426,14 @@ def _final_detection_batches( "Decoded detection boxes must have " f"{geometry_description} after confidence filtering." ) + if getattr(self, "task", "object_detection") == "pose_estimation": + keypoint_confidences = retained[:, 8::3] + if not bool( + ((keypoint_confidences >= 0) & (keypoint_confidences <= 1)).all() + ): + raise ValueError( + "Decoded pose keypoint confidence values must be in [0, 1]." + ) batches.append(retained) return batches diff --git a/mblt_vision/wrapper.py b/mblt_vision/wrapper.py index ae576fe..73552a3 100644 --- a/mblt_vision/wrapper.py +++ b/mblt_vision/wrapper.py @@ -601,7 +601,14 @@ def __init__( onnx_model.create() self._onnx_session = onnx_model.session self.model = self._onnx_session - self.input_name = self._onnx_session.get_inputs()[0].name + onnx_inputs = self._onnx_session.get_inputs() + if len(onnx_inputs) != 1: + raise ValueError( + "ONNX models must declare exactly one input because " + "MBLT_Engine accepts one preprocessed tensor; got " + f"{len(onnx_inputs)} inputs." + ) + self.input_name = onnx_inputs[0].name self.output_names = [o.name for o in self._onnx_session.get_outputs()] else: mxq_model = MobilintNPUBackend(**self._mxq_backend_kwargs()) diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index 80b8162..cb25ac1 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -17,6 +17,7 @@ CustomCityscapes, CustomDOTAv1, CustomImageFolder, + CustomNYUDepth, CustomWiderFaceDataset, ) @@ -101,6 +102,32 @@ def test_dota_dataset_rejects_duplicate_image_stems(tmp_path: Path) -> None: CustomDOTAv1(str(tmp_path)) +@pytest.mark.parametrize( + ("dataset_class", "target_dir", "target_names"), + [ + (CustomNYUDepth, "depth", ("sample.npy", "sample.NPY")), + (CustomADE20K, "annotations", ("sample.png", "sample.PNG")), + (CustomCityscapes, "annotations", ("sample.png", "sample.PNG")), + ], +) +def test_dense_datasets_reject_duplicate_target_stems( + tmp_path: Path, + dataset_class: type[CustomNYUDepth] | type[CustomADE20K] | type[CustomCityscapes], + target_dir: str, + target_names: tuple[str, str], +) -> None: + """Do not let case variants silently overwrite a dense target mapping.""" + + (tmp_path / "images").mkdir() + (tmp_path / target_dir).mkdir() + (tmp_path / "images" / "sample.jpg").write_bytes(b"image") + for target_name in target_names: + (tmp_path / target_dir / target_name).write_bytes(b"target") + + with pytest.raises(ValueError, match="duplicate filename stems"): + dataset_class(str(tmp_path)) + + def test_cityscapes_dataset_rejects_unknown_source_ids(tmp_path: Path) -> None: """Reject corrupted Cityscapes labels instead of remapping them to ignore.""" diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index a0b4e9a..48069f8 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -102,6 +102,16 @@ def test_coco_readiness_matches_images_to_task_annotations( "image_id": index, "category_id": 1, "bbox": [0, 0, 1, 1], + **( + {"segmentation": [[0, 0, 1, 0, 1, 1]]} + if task == "instance_segmentation" + else {} + ), + **( + {"keypoints": [0, 0, 0] * 17, "num_keypoints": 0} + if task == "pose_estimation" + else {} + ), } for index in range(1, 3) ], @@ -114,6 +124,56 @@ def test_coco_readiness_matches_images_to_task_annotations( assert not readiness.dataset_ready(tmp_path, task, "imagenet") +@pytest.mark.parametrize( + ("task", "invalid_field"), + [ + ("instance_segmentation", "segmentation"), + ("pose_estimation", "keypoints"), + ("pose_estimation", "num_keypoints"), + ], +) +def test_coco_readiness_rejects_missing_task_specific_ground_truth( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + task: str, + invalid_field: str, +) -> None: + """Reject COCO metadata missing the payload required by its evaluation task.""" + + annotation_name = ( + "person_keypoints_val2017.json" + if task == "pose_estimation" + else "instances_val2017.json" + ) + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, annotation_name, 1) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, annotation_name, 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) + _write_file(tmp_path / "val2017" / "000000000001.jpg") + annotation = { + "id": 1, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "segmentation": [[0, 0, 1, 0, 1, 1]], + "keypoints": [0, 0, 0] * 17, + "num_keypoints": 0, + } + del annotation[invalid_field] + (tmp_path / annotation_name).write_text( + json.dumps( + { + "images": [{"id": 1, "file_name": "000000000001.jpg"}], + "categories": [{"id": 1}], + "annotations": [annotation], + } + ), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, task, "coco") + + def test_coco_readiness_rejects_noncanonical_category_ids( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: @@ -281,6 +341,32 @@ def test_widerface_readiness_rejects_invalid_difficulty_metadata( assert not readiness.dataset_ready(tmp_path, "face_detection", "widerface") +def test_widerface_readiness_rejects_empty_face_ground_truth( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Do not accept empty face arrays that evaluation would silently skip.""" + + face_boxes = np.empty((1, 1), dtype=object) + event_faces = np.empty((1, 1), dtype=object) + event_faces[0, 0] = np.empty((0, 4), dtype=np.float64) + face_boxes[0, 0] = event_faces + difficulty = np.empty((1, 1), dtype=object) + event_indices = np.empty((1, 1), dtype=object) + event_indices[0, 0] = np.empty((0,), dtype=np.int64) + difficulty[0, 0] = event_indices + + def _loadmat(path: Path) -> dict[str, np.ndarray]: + if path.name == "wider_face_val.mat": + return {"face_bbx_list": face_boxes} + return {"gt_list": difficulty} + + monkeypatch.setattr(readiness, "loadmat", _loadmat) + + assert not readiness._widerface_difficulty_metadata_ready( + tmp_path, {"0--Parade": {"sample.jpg"}} + ) + + @pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) def test_dotav1_readiness_requires_complete_image_label_pairs( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 4f202e5..2b90362 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -1193,6 +1193,52 @@ def test_engine_init_rejects_framework_conflicting_with_onnx_path_alias( ) +@pytest.mark.parametrize("input_count", [0, 2]) +def test_engine_init_rejects_onnx_models_without_exactly_one_input( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, input_count: int +) -> None: + """The public tensor-only engine API cannot satisfy multi-input ONNX graphs.""" + + onnx_path = tmp_path / "model.onnx" + onnx_path.write_bytes(b"onnx") + + class _Input: + def __init__(self, index: int) -> None: + self.name = f"input-{index}" + + class _Session: + def get_inputs(self) -> list[_Input]: + return [_Input(index) for index in range(input_count)] + + def get_outputs(self) -> list[object]: + return [] + + class _Backend: + def __init__(self, *args: Any, **kwargs: Any) -> None: + del args, kwargs + self.session = _Session() + + def create(self) -> None: + return None + + def dispose(self) -> None: + return None + + class _Ort: + @staticmethod + def get_available_providers() -> list[str]: + return ["CPUExecutionProvider"] + + monkeypatch.setattr(wrapper, "ONNXBackend", _Backend) + monkeypatch.setattr(wrapper, "_load_onnxruntime", _Ort) + + with pytest.raises(ValueError, match=rf"got {input_count} inputs"): + MBLT_Engine( + {"file_cfg": {}, "pre_cfg": {}, "post_cfg": {}}, + onnx_path=str(onnx_path), + ) + + def test_engine_init_rejects_conflicting_framework_and_model_path( tmp_path: Path, ) -> None: @@ -1753,6 +1799,33 @@ def test_final_onnx_pose_normalizes_detections() -> None: assert torch.equal(result[0], final_output[0, :1]) +def test_final_onnx_pose_rejects_invalid_keypoint_confidence() -> None: + """Reject decoded pose confidences that normal decoding would sigmoid.""" + + postprocessor = cast( + YOLODetectionPostBase, + build_postprocess( + {"LetterBox": {"img_size": [640, 640]}}, + { + "task": "pose_estimation", + "nl": 3, + "n_extra": 51, + "reg_max": 16, + "conf_thres": 0.5, + "iou_thres": 0.7, + }, + ), + ) + final_output = torch.zeros((1, 1, 57), dtype=torch.float32) + final_output[0, 0, :6] = torch.tensor([10.0, 20.0, 30.0, 40.0, 0.9, 0.0]) + final_output[0, 0, 8] = 1.1 + + with pytest.raises( + ValueError, match="keypoint confidence values must be in \\[0, 1\\]" + ): + postprocessor(final_output) + + @pytest.mark.parametrize( ("task", "post_cfg_extra", "converted_dim"), [ From 7bf6b25d3189ab157002cdee697547237073d769 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 16:38:49 +0900 Subject: [PATCH 38/48] fix: validate vision dataset payloads --- mblt_vision/utils/datasets/dataloader.py | 4 + mblt_vision/utils/datasets/organizer.py | 174 +++++++++++++++++- mblt_vision/utils/datasets/readiness.py | 28 ++- mblt_vision/utils/evaluation/eval_imagenet.py | 13 ++ .../utils/evaluation/eval_nyu_depth.py | 2 + .../utils/postprocess/yolo_anchor_post.py | 54 ++++-- tests/test_dataloader.py | 14 ++ tests/test_dataset_organizer.py | 44 ++++- tests/test_dataset_readiness.py | 75 ++++++++ tests/test_eval_imagenet.py | 51 +++++ tests/test_eval_nyu_depth.py | 6 +- tests/test_wrapper.py | 28 +++ 12 files changed, 469 insertions(+), 24 deletions(-) diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index 27c7df4..034b06a 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -246,6 +246,10 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: raise ValueError( f"NYU Depth target must contain only finite values: {depth_path}" ) + if bool((depth < 0).any()): + raise ValueError( + f"NYU Depth target must not contain negative values: {depth_path}" + ) return ( cv2.cvtColor(image, cv2.COLOR_BGR2RGB), depth, diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index ad784d6..d6735db 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -150,12 +150,169 @@ def _validate_staged_dataset( ValueError: If the staged dataset is incomplete or has mismatched identity. """ - if all(dataset_ready(staged_output_dir, task, dataset) for task in tasks): - return - raise ValueError( - f"Staged {dataset} validation dataset is incomplete or has mismatched metadata; " - "the existing dataset cache was not replaced." - ) + if not all(dataset_ready(staged_output_dir, task, dataset) for task in tasks): + raise ValueError( + f"Staged {dataset} validation dataset is incomplete or has mismatched metadata; " + "the existing dataset cache was not replaced." + ) + _validate_staged_payloads(Path(staged_output_dir), dataset) + + +def _validate_staged_payloads(staged_root: Path, dataset: str) -> None: + """Decode staged data files before a structurally valid cache is replaced.""" + + image_roots = { + "imagenet": (staged_root,), + "coco": (staged_root / "val2017",), + "widerface": (staged_root / "images",), + "dotav1": (staged_root / "images",), + "ade20k": (staged_root / "images",), + "cityscapes": (staged_root / "images",), + } + for image_root in image_roots.get(dataset, ()): + for image_path in image_root.rglob("*"): + if image_path.is_file() and image_path.suffix.lower() in { + ".bmp", + ".jpeg", + ".jpg", + ".png", + ".tif", + ".tiff", + ".webp", + }: + if cv2.imread(str(image_path), cv2.IMREAD_COLOR) is None: + raise ValueError( + f"Staged {dataset} image is unreadable: {image_path}." + ) + + if dataset in {"ade20k", "cityscapes"}: + _validate_staged_semantic_masks(staged_root, dataset) + elif dataset == "dotav1": + _validate_staged_dotav1_labels(staged_root) + + +def _validate_staged_semantic_masks(staged_root: Path, dataset: str) -> None: + """Validate decoded semantic targets against their paired staged images.""" + + image_dir = staged_root / "images" + annotation_dir = staged_root / "annotations" + for annotation_path in sorted(annotation_dir.glob("*.png")): + image_path = next( + ( + candidate + for candidate in image_dir.glob(f"{annotation_path.stem}.*") + if candidate.suffix.lower() in {".jpg", ".jpeg", ".png"} + ), + None, + ) + if image_path is None: + raise ValueError( + f"Staged {dataset} target has no paired image: {annotation_path}." + ) + image = cv2.imread(str(image_path), cv2.IMREAD_COLOR) + try: + with Image.open(annotation_path) as annotation_image: + annotation = np.asarray(annotation_image) + except OSError as exc: + raise ValueError( + f"Staged {dataset} annotation is unreadable: {annotation_path}." + ) from exc + if dataset == "cityscapes" and annotation.ndim == 3: + if ( + annotation.shape[2] not in {3, 4} + or not np.array_equal(annotation[..., 0], annotation[..., 1]) + or not np.array_equal(annotation[..., 0], annotation[..., 2]) + ): + raise ValueError( + f"Staged Cityscapes annotation must be grayscale or RGB-grayscale: {annotation_path}." + ) + annotation = annotation[..., 0] + if image is None or annotation.ndim != 2 or annotation.shape != image.shape[:2]: + raise ValueError( + f"Staged {dataset} image and annotation geometry is invalid: {annotation_path}." + ) + if dataset == "ade20k" and ( + annotation.dtype != np.uint8 + or (annotation.size and int(annotation.max()) > 150) + ): + raise ValueError( + f"Staged ADE20K annotation must be an 8-bit mask with values in [0, 150]: {annotation_path}." + ) + + +def _validate_staged_dotav1_labels(staged_root: Path) -> None: + """Validate both DOTAv1 label representations before cache replacement.""" + + label_dirs = { + "normalized": staged_root / "labels" / "val", + "original": staged_root / "labels" / "val_original", + } + valid_indices = set(DOTAV1_CLASS_TO_IDX.values()) + for kind, label_dir in label_dirs.items(): + for label_path in sorted(label_dir.glob("*.txt")): + for line_number, line in enumerate( + label_path.read_text(encoding="utf-8").splitlines(), start=1 + ): + fields = line.split() + if not fields or ( + kind == "original" + and ( + fields[0].startswith("imagesource:") + or fields[0].startswith("gsd:") + ) + ): + continue + min_fields = 9 if kind == "normalized" else 10 + if len(fields) < min_fields: + raise ValueError( + f"Malformed staged DOTAv1 {kind} annotation at " + f"{label_path}:{line_number}: expected at least {min_fields} fields." + ) + try: + coordinates = [ + float(value) + for value in ( + fields[1:9] if kind == "normalized" else fields[:8] + ) + ] + except ValueError as exc: + raise ValueError( + f"Malformed staged DOTAv1 coordinates at {label_path}:{line_number}." + ) from exc + if not all(math.isfinite(value) for value in coordinates): + raise ValueError( + f"Staged DOTAv1 coordinates must be finite at {label_path}:{line_number}." + ) + points = np.asarray(coordinates, dtype=np.float64).reshape(4, 2) + signed_double_area = np.dot( + points[:, 0], np.roll(points[:, 1], -1) + ) - np.dot(points[:, 1], np.roll(points[:, 0], -1)) + if abs(signed_double_area) <= 0: + raise ValueError( + f"Staged DOTAv1 polygon must have positive area at {label_path}:{line_number}." + ) + if kind == "normalized": + try: + class_index = int(fields[0]) + except ValueError as exc: + raise ValueError( + f"Malformed staged DOTAv1 class index at {label_path}:{line_number}." + ) from exc + difficulty = fields[9] if len(fields) >= 10 else "0" + if class_index not in valid_indices: + raise ValueError( + f"Unsupported staged DOTAv1 class index at {label_path}:{line_number}." + ) + else: + difficulty = fields[9] + if fields[8] not in DOTAV1_CLASS_TO_IDX: + raise ValueError( + f"Unsupported staged DOTAv1 class at {label_path}:{line_number}." + ) + if difficulty not in {"0", "1", "2"}: + raise ValueError( + f"Unsupported staged DOTAv1 difficulty flag at {label_path}:{line_number}." + ) class _GoogleDriveDownloadEntry(Protocol): @@ -977,6 +1134,10 @@ def _validate_staged_nyu_depth(staging_dir: str) -> None: raise ValueError( f"Staged NYU Depth target must contain only finite values: {depth_path}." ) + if bool((depth < 0).any()): + raise ValueError( + f"Staged NYU Depth target must not contain negative values: {depth_path}." + ) def organize_nyu_depth( @@ -1307,6 +1468,7 @@ def organize_cityscapes( raise ValueError( "Staged Cityscapes validation data failed identity and completeness checks." ) + _validate_staged_payloads(Path(staging_dir), "cityscapes") replacements = ( (staged_image_dir, os.path.join(output_dir, "images")), diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 43752b9..c47b48d 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -36,12 +36,13 @@ r"^(?P[A-Za-z][A-Za-z0-9-]*)_\d{6}_\d{6}$" ) CITYSCAPES_VALIDATION_CITY_COUNTS = {"frankfurt": 267, "lindau": 59, "munster": 174} -IMAGENET_SYNSETS = frozenset( +IMAGENET_SYNSET_ORDER = tuple( files("mblt_vision.datasets") .joinpath("imagenet_synsets.txt") .read_text(encoding="utf-8") .splitlines() ) +IMAGENET_SYNSETS = frozenset(IMAGENET_SYNSET_ORDER) COCO_ANNOTATION_COUNTS = { "instances_val2017.json": 36781, "person_keypoints_val2017.json": 11004, @@ -105,6 +106,16 @@ def _files_by_stem( return files if len(files) == len(paths) else None +def _has_positive_polygon_area(polygon: list[int | float]) -> bool: + """Return whether a finite flat polygon encloses non-zero signed area.""" + + points = np.asarray(polygon, dtype=np.float64).reshape(-1, 2) + signed_double_area = np.dot(points[:, 0], np.roll(points[:, 1], -1)) - np.dot( + points[:, 1], np.roll(points[:, 0], -1) + ) + return bool(abs(signed_double_area) > 0) + + def _imagenet_ready(root: Path) -> bool: """Check the organizer's complete ImageNet-1k validation class tree.""" @@ -243,6 +254,18 @@ def _load_coco_image_names( or bbox[3] <= 0 ): return None + area = record.get("area") + iscrowd = record.get("iscrowd") + if ( + not isinstance(area, (int, float)) + or isinstance(area, bool) + or not np.isfinite(area) + or area <= 0 + or not isinstance(iscrowd, int) + or isinstance(iscrowd, bool) + or iscrowd not in {0, 1} + ): + return None if task == "instance_segmentation": segmentation = record.get("segmentation") if isinstance(segmentation, list): @@ -256,6 +279,7 @@ def _load_coco_image_names( or not np.isfinite(value) for value in polygon ) + or not _has_positive_polygon_area(polygon) for polygon in segmentation ): return None @@ -465,6 +489,8 @@ def _widerface_difficulty_metadata_ready( int(keep_indices.min()) < 1 or int(keep_indices.max()) > face_count ): return False + if len(np.unique(keep_indices)) != keep_indices.size: + return False return True diff --git a/mblt_vision/utils/evaluation/eval_imagenet.py b/mblt_vision/utils/evaluation/eval_imagenet.py index 292c68c..ed23ddf 100644 --- a/mblt_vision/utils/evaluation/eval_imagenet.py +++ b/mblt_vision/utils/evaluation/eval_imagenet.py @@ -13,6 +13,7 @@ from tqdm import tqdm from ..datasets import CustomImageFolder, get_imagenet_loader +from ..datasets.readiness import IMAGENET_SYNSET_ORDER, IMAGENET_SYNSETS if TYPE_CHECKING: from ...wrapper import MBLT_Engine @@ -57,6 +58,18 @@ def eval_imagenet_metrics( f"got {dataset_name!r}." ) dataset = CustomImageFolder(data_path) + unknown_synsets = set(dataset.classes) - IMAGENET_SYNSETS + if unknown_synsets: + raise ValueError( + "ImageNet evaluation found non-canonical synset directories: " + f"{', '.join(sorted(unknown_synsets)[:5])}." + ) + dataset.class_to_idx = { + synset: index + for index, synset in enumerate(IMAGENET_SYNSET_ORDER) + if synset in dataset.class_to_idx + } + dataset.make_dataset() dataloader = get_imagenet_loader(dataset, batch_size, model.preprocess) num_data = len(dataset) total_iter = math.ceil(num_data / batch_size) diff --git a/mblt_vision/utils/evaluation/eval_nyu_depth.py b/mblt_vision/utils/evaluation/eval_nyu_depth.py index 6e8caf0..6d0099a 100644 --- a/mblt_vision/utils/evaluation/eval_nyu_depth.py +++ b/mblt_vision/utils/evaluation/eval_nyu_depth.py @@ -61,6 +61,8 @@ def update(self, prediction: np.ndarray, target: np.ndarray) -> None: ) if not np.isfinite(target).all(): raise ValueError("NYU Depth target contains non-finite values.") + if (target < 0).any(): + raise ValueError("NYU Depth target contains negative values.") valid = ( np.isfinite(target) & (target > self.MIN_DEPTH) & (target < self.MAX_DEPTH) ) diff --git a/mblt_vision/utils/postprocess/yolo_anchor_post.py b/mblt_vision/utils/postprocess/yolo_anchor_post.py index 4aac9e7..3848cd4 100644 --- a/mblt_vision/utils/postprocess/yolo_anchor_post.py +++ b/mblt_vision/utils/postprocess/yolo_anchor_post.py @@ -193,7 +193,13 @@ def process_conversion(x: torch.Tensor) -> torch.Tensor: return [process_conversion(xi) for xi in x_list] def _nms_single( - self, xi: torch.Tensor, max_det: int, max_nms: int, max_wh: int + self, + xi: torch.Tensor, + max_det: int, + max_nms: int, + max_wh: int, + *, + multi_label: bool, ) -> torch.Tensor: """Apply anchor-based NMS to a single decoded image tensor.""" mi = 5 + self.nc # mask index @@ -201,13 +207,19 @@ def _nms_single( return xi.new_zeros((0, 6 + self.n_extra)) scores = xi[:, 5:mi] * xi[:, 4:5] - match_index = (scores > self.conf_thres).nonzero(as_tuple=False) - if match_index.numel() == 0: - return xi.new_zeros((0, 6 + self.n_extra)) - - i = match_index[:, 0] - j = match_index[:, 1] - rows = xi[i] + if multi_label: + match_index = (scores > self.conf_thres).nonzero(as_tuple=False) + if match_index.numel() == 0: + return xi.new_zeros((0, 6 + self.n_extra)) + i, j = match_index[:, 0], match_index[:, 1] + rows = xi[i] + row_scores = scores[i, j] + else: + row_scores, j = scores.max(dim=1) + keep = row_scores > self.conf_thres + if not bool(keep.any()): + return xi.new_zeros((0, 6 + self.n_extra)) + rows, row_scores, j = xi[keep], row_scores[keep], j[keep] boxes_xywh = rows[:, :4] out = torch.empty( (rows.shape[0], 6 + self.n_extra), dtype=rows.dtype, device=rows.device @@ -216,7 +228,7 @@ def _nms_single( out[:, 1] = boxes_xywh[:, 1] - boxes_xywh[:, 3] / 2 out[:, 2] = boxes_xywh[:, 0] + boxes_xywh[:, 2] / 2 out[:, 3] = boxes_xywh[:, 1] + boxes_xywh[:, 3] / 2 - out[:, 4] = scores[i, j] + out[:, 4] = row_scores out[:, 5] = j.to(rows.dtype) if self.n_extra > 0: out[:, 6:] = rows[:, mi:] @@ -232,6 +244,7 @@ def nms( max_det: int = 300, max_nms: int = 30000, max_wh: int = 7680, + multi_label: bool = False, ) -> list[torch.Tensor]: """ Perform Non-Maximum Suppression (NMS) on the decoded detections. @@ -247,14 +260,33 @@ def nms( """ if isinstance(x, list): return [ - self._nms_single(xi, max_det=max_det, max_nms=max_nms, max_wh=max_wh) + self._nms_single( + xi, + max_det=max_det, + max_nms=max_nms, + max_wh=max_wh, + multi_label=multi_label, + ) for xi in x ] return [ - self._nms_single(xi, max_det=max_det, max_nms=max_nms, max_wh=max_wh) + self._nms_single( + xi, + max_det=max_det, + max_nms=max_nms, + max_wh=max_wh, + multi_label=multi_label, + ) for xi in x ] + def nms_multilabel( + self, x: torch.Tensor | list[torch.Tensor] + ) -> list[torch.Tensor]: + """Perform Ultralytics-compatible multi-label NMS for validation.""" + + return self.nms(x, multi_label=True) + def make_anchor_grid(self) -> None: """ Pre-calculate the anchor grid for decoding. diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index cb25ac1..edb6371 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -128,6 +128,20 @@ def test_dense_datasets_reject_duplicate_target_stems( dataset_class(str(tmp_path)) +def test_nyu_dataset_rejects_negative_depth_targets(tmp_path: Path) -> None: + """Do not silently exclude corrupted negative depths during evaluation.""" + + image_dir = tmp_path / "images" + depth_dir = tmp_path / "depth" + image_dir.mkdir() + depth_dir.mkdir() + assert cv2.imwrite(str(image_dir / "sample.jpg"), np.zeros((1, 1, 3), np.uint8)) + np.save(depth_dir / "sample.npy", np.array([[-1]], dtype=np.float32)) + + with pytest.raises(ValueError, match="must not contain negative values"): + CustomNYUDepth(str(tmp_path))[0] + + def test_cityscapes_dataset_rejects_unknown_source_ids(tmp_path: Path) -> None: """Reject corrupted Cityscapes labels instead of remapping them to ignore.""" diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index 8a72e05..5fba268 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -72,7 +72,7 @@ def test_dotav1_organizer_rejects_truncated_raw_annotation_rows( Image.new("RGB", (100, 50)).save(image_path) source_path = tmp_path / "source.txt" source_path.write_text( - "imagesource:GoogleEarth\n" "gsd:0.5\n" "0 0 20 0 20 20 0 plane 0\n", + "imagesource:GoogleEarth\ngsd:0.5\n0 0 20 0 20 20 0 plane 0\n", encoding="utf-8", ) @@ -530,6 +530,7 @@ def _dataset_ready(_: str, task: str, dataset: str) -> bool: return True monkeypatch.setattr(organizer, "dataset_ready", _dataset_ready) + monkeypatch.setattr(organizer, "_validate_staged_payloads", lambda *_: None) output_dir = tmp_path / "imagenet" (output_dir / "stale-class").mkdir(parents=True) @@ -577,6 +578,7 @@ def _dataset_ready(_: str, task: str, dataset: str) -> bool: return True monkeypatch.setattr(organizer, "dataset_ready", _dataset_ready) + monkeypatch.setattr(organizer, "_validate_staged_payloads", lambda *_: None) output_dir = tmp_path / "coco" (output_dir / "val2017").mkdir(parents=True) @@ -612,6 +614,7 @@ def _dataset_ready(_: str, task: str, dataset: str) -> bool: return True monkeypatch.setattr(organizer, "dataset_ready", _dataset_ready) + monkeypatch.setattr(organizer, "_validate_staged_payloads", lambda *_: None) output_dir = tmp_path / "widerface" (output_dir / "images" / "stale-event").mkdir(parents=True) @@ -878,7 +881,11 @@ def _fail_install_and_rollback(source: str, destination: str) -> None: @pytest.mark.parametrize( "depth", - [np.array([[np.nan]], dtype=np.float32), np.array([[1]], dtype=np.complex64)], + [ + np.array([[np.nan]], dtype=np.float32), + np.array([[1]], dtype=np.complex64), + np.array([[-1]], dtype=np.float32), + ], ) def test_staged_nyu_depth_validation_rejects_malformed_payloads( tmp_path: Path, depth: np.ndarray @@ -892,10 +899,38 @@ def test_staged_nyu_depth_validation_rejects_malformed_payloads( Image.new("RGB", (1, 1)).save(image_dir / "sample.png") np.save(depth_dir / "sample.npy", depth) - with pytest.raises(ValueError, match="real numeric dtype|finite values"): + with pytest.raises( + ValueError, match="real numeric dtype|finite values|negative values" + ): organizer._validate_staged_nyu_depth(str(tmp_path)) +@pytest.mark.parametrize("dataset", ["ade20k", "dotav1"]) +def test_staged_payload_validation_rejects_corrupt_files( + tmp_path: Path, dataset: str +) -> None: + """Do not replace a valid cache with structurally complete corrupt data.""" + + image_dir = tmp_path / "images" + image_dir.mkdir() + if dataset == "ade20k": + annotation_dir = tmp_path / "annotations" + annotation_dir.mkdir() + (image_dir / "ADE_val_00000001.jpg").write_bytes(b"not an image") + (annotation_dir / "ADE_val_00000001.png").write_bytes(b"not a PNG") + else: + Image.new("RGB", (1, 1)).save(image_dir / "P0001.png") + label_dir = tmp_path / "labels" / "val" + original_label_dir = tmp_path / "labels" / "val_original" + label_dir.mkdir(parents=True) + original_label_dir.mkdir() + (label_dir / "P0001.txt").write_text("bad label", encoding="utf-8") + (original_label_dir / "P0001.txt").write_text("bad label", encoding="utf-8") + + with pytest.raises(ValueError, match="unreadable|Malformed staged DOTAv1"): + organizer._validate_staged_payloads(tmp_path, dataset) + + def _create_ade20k_source(tmp_path: Path) -> Path: """Create a compact extracted ADE20K validation source.""" @@ -916,6 +951,7 @@ def test_organize_ade20k_extracts_flat_validation_layout( monkeypatch.setattr(organizer, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) monkeypatch.setattr(readiness_module, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setattr(organizer, "_validate_staged_payloads", lambda *_: None) archive_path = tmp_path / "ADEChallengeData2016.zip" with ZipFile(archive_path, "w") as archive: archive.writestr( @@ -1210,6 +1246,7 @@ def test_organize_cityscapes_materializes_lossless_validation_pairs( monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 2) monkeypatch.setattr(organizer, "dataset_ready", lambda *_: True) + monkeypatch.setattr(organizer, "_validate_staged_payloads", lambda *_: None) sample_ids = ["frankfurt_000000_000294", "munster_000001_000019"] image_archive, annotation_archive = _write_cityscapes_archives(tmp_path, sample_ids) output_dir = tmp_path / "cityscapes" @@ -1346,6 +1383,7 @@ def test_organize_cityscapes_rolls_back_failed_atomic_replacement( monkeypatch.setattr(organizer, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) monkeypatch.setattr(organizer, "dataset_ready", lambda *_: True) + monkeypatch.setattr(organizer, "_validate_staged_payloads", lambda *_: None) image_archive, annotation_archive = _write_cityscapes_archives( tmp_path, ["lindau_000000_000019"] ) diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index 48069f8..d4d03c3 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -102,6 +102,8 @@ def test_coco_readiness_matches_images_to_task_annotations( "image_id": index, "category_id": 1, "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, **( {"segmentation": [[0, 0, 1, 0, 1, 1]]} if task == "instance_segmentation" @@ -155,6 +157,8 @@ def test_coco_readiness_rejects_missing_task_specific_ground_truth( "image_id": 1, "category_id": 1, "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, "segmentation": [[0, 0, 1, 0, 1, 1]], "keypoints": [0, 0, 0] * 17, "num_keypoints": 0, @@ -199,6 +203,51 @@ def test_coco_readiness_rejects_noncanonical_category_ids( assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") +@pytest.mark.parametrize( + ("field", "value"), + [ + ("segmentation", [[0, 0, 1, 1, 2, 2]]), + ("area", 0), + ("iscrowd", 2), + ], +) +def test_coco_readiness_rejects_invalid_evaluation_metadata( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + field: str, + value: object, +) -> None: + """Reject metadata that can alter COCO matching or area-range evaluation.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 1) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) + _write_file(tmp_path / "val2017" / "000000000001.jpg") + annotation = { + "id": 1, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, + "segmentation": [[0, 0, 1, 0, 1, 1]], + } + annotation[field] = value + (tmp_path / "instances_val2017.json").write_text( + json.dumps( + { + "images": [{"id": 1, "file_name": "000000000001.jpg"}], + "categories": [{"id": 1}], + "annotations": [annotation], + } + ), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, "instance_segmentation", "coco") + + @pytest.mark.parametrize("bbox", [[0, 0, 0, 1], [0, 0, 1, float("nan")]]) def test_coco_readiness_rejects_invalid_instance_box_geometry( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, bbox: list[float] @@ -367,6 +416,32 @@ def _loadmat(path: Path) -> dict[str, np.ndarray]: ) +def test_widerface_readiness_rejects_duplicate_difficulty_indices( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Difficulty masks must identify each eligible face at most once.""" + + face_boxes = np.empty((1, 1), dtype=object) + event_faces = np.empty((1, 1), dtype=object) + event_faces[0, 0] = np.array([[0, 0, 1, 1]], dtype=np.float64) + face_boxes[0, 0] = event_faces + difficulty = np.empty((1, 1), dtype=object) + event_indices = np.empty((1, 1), dtype=object) + event_indices[0, 0] = np.array([1, 1], dtype=np.int64) + difficulty[0, 0] = event_indices + + def _loadmat(path: Path) -> dict[str, np.ndarray]: + if path.name == "wider_face_val.mat": + return {"face_bbx_list": face_boxes} + return {"gt_list": difficulty} + + monkeypatch.setattr(readiness, "loadmat", _loadmat) + + assert not readiness._widerface_difficulty_metadata_ready( + tmp_path, {"0--Parade": {"sample.jpg"}} + ) + + @pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) def test_dotav1_readiness_requires_complete_image_label_pairs( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/test_eval_imagenet.py b/tests/test_eval_imagenet.py index ed5d3b3..3d532bb 100644 --- a/tests/test_eval_imagenet.py +++ b/tests/test_eval_imagenet.py @@ -19,6 +19,12 @@ def test_imagenet_evaluation_rejects_truncated_classification_batches( """Do not broadcast one classification row across a multi-image label batch.""" class _FakeDataset: + classes = ["n00000000", "n00000001"] + class_to_idx = {"n00000000": 0, "n00000001": 1} + + def make_dataset(self) -> None: + return None + def __len__(self) -> int: return 2 @@ -57,6 +63,12 @@ def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: eval_imagenet_module, "get_imagenet_loader", lambda *args: [batch] ) monkeypatch.setattr(eval_imagenet_module, "tqdm", _FakeProgress) + monkeypatch.setattr( + eval_imagenet_module, "IMAGENET_SYNSET_ORDER", ("n00000000", "n00000001") + ) + monkeypatch.setattr( + eval_imagenet_module, "IMAGENET_SYNSETS", frozenset({"n00000000", "n00000001"}) + ) with pytest.raises( ValueError, @@ -67,6 +79,45 @@ def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: ) +def test_imagenet_evaluation_preserves_canonical_synset_indices( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Do not renumber a partial ImageNet directory tree by local sort order.""" + + dataset = SimpleNamespace( + classes=["n00000001"], + class_to_idx={"n00000001": 0}, + make_dataset=lambda: None, + ) + monkeypatch.setattr(eval_imagenet_module, "CustomImageFolder", lambda _: dataset) + monkeypatch.setattr( + eval_imagenet_module, "IMAGENET_SYNSET_ORDER", ("n00000000", "n00000001") + ) + monkeypatch.setattr( + eval_imagenet_module, "IMAGENET_SYNSETS", frozenset({"n00000000", "n00000001"}) + ) + + class _StopAfterClassMapping(Exception): + pass + + def _stop_after_mapping(*args: object) -> object: + del args + raise _StopAfterClassMapping + + monkeypatch.setattr( + eval_imagenet_module, "get_imagenet_loader", _stop_after_mapping + ) + + with pytest.raises(_StopAfterClassMapping): + eval_imagenet_module.eval_imagenet_metrics( + SimpleNamespace(post_cfg={"dataset": "imagenet"}, preprocess=object()), + "/dataset", + batch_size=1, + ) + + assert dataset.class_to_idx == {"n00000001": 1} + + def test_imagenet_evaluation_rejects_wrong_model_taxonomy() -> None: """Do not score another classification taxonomy against ImageNet labels.""" diff --git a/tests/test_eval_nyu_depth.py b/tests/test_eval_nyu_depth.py index 58e7fb0..e635938 100644 --- a/tests/test_eval_nyu_depth.py +++ b/tests/test_eval_nyu_depth.py @@ -49,12 +49,12 @@ def postprocess(self, output: torch.Tensor) -> SimpleNamespace: eval_nyu_depth_module.eval_nyu_depth(_Model(), "/dataset", batch_size=1) -@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) -def test_nyu_depth_metrics_reject_nonfinite_targets(invalid_value: float) -> None: +@pytest.mark.parametrize("invalid_value", [float("nan"), float("inf"), -1.0]) +def test_nyu_depth_metrics_reject_invalid_targets(invalid_value: float) -> None: """Keep direct metric callers from silently excluding corrupt depth targets.""" target = np.array([[1.0, invalid_value]], dtype=np.float32) - with pytest.raises(ValueError, match="target contains non-finite"): + with pytest.raises(ValueError, match="target contains non-finite|negative values"): eval_nyu_depth_module.calculate_nyu_depth_metrics(np.ones((1, 2)), target) diff --git a/tests/test_wrapper.py b/tests/test_wrapper.py index 2b90362..5e671e9 100644 --- a/tests/test_wrapper.py +++ b/tests/test_wrapper.py @@ -2646,6 +2646,34 @@ def test_anchorless_prediction_nms_keeps_best_class_per_box() -> None: assert torch.equal(result[0][:, 5], torch.tensor([0.0, 1.0])) +def test_anchor_prediction_nms_keeps_best_class_per_box() -> None: + """Keep ordinary anchor-model prediction output single-label per box.""" + + postprocessor = cast( + YOLODetectionPostBase, + build_postprocess( + {"LetterBox": {"img_size": [640, 640]}}, + { + "task": "object_detection", + "anchors": [[10, 13, 16, 30, 33, 23]], + "nl": 1, + "nc": 2, + "conf_thres": 0.25, + "iou_thres": 0.7, + }, + ), + ) + decoded = torch.tensor( + [[50.0, 50.0, 20.0, 20.0, 1.0, 0.9, 0.8]], dtype=torch.float32 + ) + + prediction = postprocessor.nms([decoded]) + validation = postprocessor.nms_multilabel([decoded]) + + assert prediction[0].shape == (1, 6) + assert validation[0].shape == (2, 6) + + @pytest.mark.parametrize( ("layout", "candidate_count"), [ From 14c872459f55174f05125db0f19b172ec766547d Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 17:19:14 +0900 Subject: [PATCH 39/48] fix: harden vision review validation --- mblt_vision/utils/datasets/organizer.py | 92 ++++++++++++++++------- mblt_vision/utils/evaluation/eval_dota.py | 4 +- mblt_vision/utils/postprocess/base.py | 18 +++-- tests/test_dataset_organizer.py | 61 +++++++++++++++ tests/test_eval_dota.py | 9 +++ tests/test_postprocess_hierarchy.py | 10 ++- 6 files changed, 154 insertions(+), 40 deletions(-) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index d6735db..8cbd604 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -238,6 +238,14 @@ def _validate_staged_semantic_masks(staged_root: Path, dataset: str) -> None: raise ValueError( f"Staged ADE20K annotation must be an 8-bit mask with values in [0, 150]: {annotation_path}." ) + if dataset == "cityscapes": + valid_ids = (annotation <= 33) | (annotation == 255) + if not np.all(valid_ids): + invalid_ids = np.unique(annotation[~valid_ids]) + raise ValueError( + "Staged Cityscapes annotation contains unsupported source IDs " + f"{invalid_ids.tolist()}; expected IDs in [0, 33] or 255: {annotation_path}." + ) def _validate_staged_dotav1_labels(staged_root: Path) -> None: @@ -784,7 +792,7 @@ def construct_coco(image_dir: str, annotation_dir: str, output_dir: str) -> None _validate_staged_dataset( staged_output_dir, "coco", - ("object_detection", "pose_estimation"), + ("object_detection", "instance_segmentation", "pose_estimation"), ) _replace_staged_directories( ((staged_output_dir, output_dir),), @@ -960,6 +968,32 @@ def _validate_dense_source_file(source_path: str, dataset_root: Path) -> str: return str(resolved_source) +def _collect_unique_dense_sources( + source_paths: Iterable[str], + dataset_root: Path, + source_description: str, +) -> dict[str, str]: + """Return validated dense sources keyed by unique filename stem. + + Dense organizers flatten source files into a single output directory. Reject + repeated stems up front instead of silently retaining whichever recursive + traversal entry happened to be processed last. + """ + + sources: dict[str, str] = {} + for source_path in sorted(source_paths): + sample_id = Path(source_path).stem + validated_path = _validate_dense_source_file(source_path, dataset_root) + previous_path = sources.get(sample_id) + if previous_path is not None: + raise ValueError( + f"{source_description} contain duplicate filename stem {sample_id!r}: " + f"{previous_path} and {validated_path}." + ) + sources[sample_id] = validated_path + return sources + + def _validate_dense_output_root( output_dir: str, dataset_name: str, @@ -1004,18 +1038,16 @@ def _collect_nyu_depth_validation_files( ) -> tuple[dict[str, str], dict[str, str]]: """Validates and returns matching NYU Depth validation image/depth pairs.""" - images = { - os.path.splitext(os.path.basename(path))[0]: _validate_dense_source_file( - path, dataset_root - ) - for path in _iter_files(image_dir, [".jpg", ".jpeg", ".png"]) - } - depths = { - os.path.splitext(os.path.basename(path))[0]: _validate_dense_source_file( - path, dataset_root - ) - for path in _iter_files(depth_dir, [".npy"]) - } + images = _collect_unique_dense_sources( + _iter_files(image_dir, [".jpg", ".jpeg", ".png"]), + dataset_root, + "NYU Depth images", + ) + depths = _collect_unique_dense_sources( + _iter_files(depth_dir, [".npy"]), + dataset_root, + "NYU Depth depth maps", + ) missing_depths = sorted(set(images) - set(depths)) missing_images = sorted(set(depths) - set(images)) if missing_depths or missing_images: @@ -1209,22 +1241,24 @@ def construct_ade20k(dataset_dir: str, output_dir: str) -> None: raise ValueError( f"Unable to resolve ADE20K dataset root {dataset_root}: {exc}." ) from exc - images = { - os.path.splitext(file_name)[0]: _validate_dense_source_file( - os.path.join(image_dir, file_name), - resolved_dataset_root, - ) - for file_name in os.listdir(image_dir) - if file_name.startswith("ADE_val_") and file_name.lower().endswith(".jpg") - } - annotations = { - os.path.splitext(file_name)[0]: _validate_dense_source_file( - os.path.join(annotation_dir, file_name), - resolved_dataset_root, - ) - for file_name in os.listdir(annotation_dir) - if file_name.startswith("ADE_val_") and file_name.lower().endswith(".png") - } + images = _collect_unique_dense_sources( + ( + os.path.join(image_dir, file_name) + for file_name in os.listdir(image_dir) + if file_name.startswith("ADE_val_") and file_name.lower().endswith(".jpg") + ), + resolved_dataset_root, + "ADE20K images", + ) + annotations = _collect_unique_dense_sources( + ( + os.path.join(annotation_dir, file_name) + for file_name in os.listdir(annotation_dir) + if file_name.startswith("ADE_val_") and file_name.lower().endswith(".png") + ), + resolved_dataset_root, + "ADE20K annotations", + ) if set(images) != set(annotations): raise ValueError( "ADE20K validation images and annotations must have matching file stems." diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 90b2356..2d70b73 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -278,8 +278,8 @@ def _compute_ap( recall: np.ndarray, precision: np.ndarray ) -> tuple[float, np.ndarray, np.ndarray]: """Compute AP from recall and precision curves with Ultralytics interpolation.""" - mrec = np.concatenate(([0.0], recall, [recall[-1] if len(recall) else 1.0], [1.0])) - mpre = np.concatenate(([1.0], precision, [0.0], [0.0])) + mrec = np.concatenate(([0.0], recall, [1.0])) + mpre = np.concatenate(([1.0], precision, [0.0])) mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) grid = np.linspace(0, 1, 101) integrate = getattr(np, "trapezoid", None) diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index 84ae50b..6b07792 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -310,6 +310,7 @@ def extract_final_outputs( normalized_detections: np.ndarray | torch.Tensor | None = None normalized_proto: torch.Tensor | None = None + invalid_proto_error: ValueError | None = None for output in x: if not isinstance(output, (np.ndarray, torch.Tensor)): continue @@ -322,16 +323,19 @@ def extract_final_outputs( if normalized_proto is None: try: normalized_proto = self._normalize_proto_batch(output) - except ValueError: - if ( - normalized_detections is not None - and self.task == "instance_segmentation" - and output.ndim == 4 - ): - raise + except ValueError as exc: + if self.task == "instance_segmentation" and output.ndim == 4: + # Defer this until detections are found so unrelated + # four-dimensional outputs do not prevent raw-head + # decoding. Once this is a decoded segmentation output, + # every candidate prototype must be valid regardless of + # its position in the backend output sequence. + invalid_proto_error = exc continue if normalized_detections is not None: + if invalid_proto_error is not None: + raise invalid_proto_error return self._final_detection_batches( normalized_detections ), normalized_proto diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index 5fba268..d33b23a 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -596,6 +596,7 @@ def _dataset_ready(_: str, task: str, dataset: str) -> bool: } assert readiness_calls == [ ("object_detection", "coco"), + ("instance_segmentation", "coco"), ("pose_estimation", "coco"), ] @@ -829,6 +830,31 @@ def test_construct_nyu_depth_rejects_source_outside_resolved_root( organizer.construct_nyu_depth(str(dataset_dir), str(tmp_path / "organized")) +def test_construct_nyu_depth_rejects_duplicate_nested_source_stems( + tmp_path: Path, +) -> None: + """Do not let recursive NYU source traversal overwrite a flattened sample.""" + + dataset_dir = tmp_path / "source" + for relative_path in ( + "images/first/sample.jpg", + "images/second/sample.png", + "depth/sample.npy", + ): + path = dataset_dir / relative_path + path.parent.mkdir(parents=True, exist_ok=True) + path.write_bytes(b"source") + + with pytest.raises( + ValueError, match="NYU Depth images contain duplicate filename stem" + ): + organizer._collect_nyu_depth_validation_files( + str(dataset_dir / "images"), + str(dataset_dir / "depth"), + dataset_dir.resolve(), + ) + + def test_nyu_depth_install_preserves_backups_when_rollback_fails( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, @@ -931,6 +957,24 @@ def test_staged_payload_validation_rejects_corrupt_files( organizer._validate_staged_payloads(tmp_path, dataset) +def test_staged_cityscapes_payload_validation_rejects_unknown_source_ids( + tmp_path: Path, +) -> None: + """Reject source IDs the Cityscapes loader cannot classify consistently.""" + + image_dir = tmp_path / "images" + annotation_dir = tmp_path / "annotations" + image_dir.mkdir() + annotation_dir.mkdir() + Image.new("RGB", (2, 2)).save(image_dir / "sample.png") + Image.fromarray(np.array([[0, 34], [255, 7]], dtype=np.uint8)).save( + annotation_dir / "sample.png" + ) + + with pytest.raises(ValueError, match="unsupported source IDs.*34"): + organizer._validate_staged_payloads(tmp_path, "cityscapes") + + def _create_ade20k_source(tmp_path: Path) -> Path: """Create a compact extracted ADE20K validation source.""" @@ -1004,6 +1048,23 @@ def test_construct_ade20k_requires_metadata_before_replacing_cache( assert (output_dir / "valid-cache-marker").read_bytes() == b"existing" +def test_construct_ade20k_rejects_duplicate_source_stems( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + """Reject flat ADE20K source names that would overwrite during staging.""" + + monkeypatch.setattr(organizer, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) + dataset_dir = _create_ade20k_source(tmp_path) + image_path = dataset_dir / "images" / "ADE_val_00000001.jpg" + shutil.copy2(image_path, image_path.with_suffix(".JPG")) + + with pytest.raises( + ValueError, match="ADE20K images contain duplicate filename stem" + ): + organizer.construct_ade20k(str(dataset_dir), str(tmp_path / "organized")) + + @pytest.mark.parametrize( "relative_path", [ diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 9d5e83d..ca1427e 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -317,3 +317,12 @@ def test_difficult_regions_do_not_count_as_positive_or_false_positive() -> None: baseline = evaluate_dota_predictions(ground_truths, predictions[1:]) assert result == baseline + + +def test_dota_ap_interpolation_uses_terminal_recall_sentinel() -> None: + """Preserve the reference AP curve after the final observed recall point.""" + + ap, _, recall_curve = eval_dota_module._compute_ap(np.array([0.5]), np.array([1.0])) + + assert recall_curve.tolist() == [0.0, 0.5, 1.0] + assert ap == pytest.approx(0.75) diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 3f422f4..10c2e57 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -221,7 +221,10 @@ def test_already_decoded_obb_uses_width_height_geometry() -> None: assert postprocessor._final_detection_batches(detections)[0].shape == (1, 6) -def test_decoded_segmentation_propagates_invalid_mask_prototypes() -> None: +@pytest.mark.parametrize("prototype_first", [False, True]) +def test_decoded_segmentation_propagates_invalid_mask_prototypes( + prototype_first: bool, +) -> None: """Do not discard malformed required prototype tensors as unrelated outputs.""" postprocessor = cast( @@ -236,11 +239,14 @@ def test_decoded_segmentation_propagates_invalid_mask_prototypes() -> None: [[[0.0, 0.0, 1.0, 1.0, 0.9, 1.0, 0.0, 0.0]]], dtype=torch.float32 ) invalid_proto = torch.full((1, 2, 2, 2), float("nan")) + outputs = ( + [invalid_proto, detections] if prototype_first else [detections, invalid_proto] + ) with pytest.raises( ValueError, match="Mask prototype tensor must contain only finite" ): - postprocessor.extract_final_outputs([detections, invalid_proto]) + postprocessor.extract_final_outputs(outputs) @pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) From 89977abdc499a34998e028d0253ebe2d46473f19 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 17:41:29 +0900 Subject: [PATCH 40/48] fix: validate vision evaluation payloads --- mblt_vision/utils/datasets/readiness.py | 113 ++++++++++++++---- mblt_vision/utils/evaluation/eval_imagenet.py | 6 +- mblt_vision/utils/postprocess/base.py | 8 ++ tests/test_dataset_readiness.py | 51 ++++++++ tests/test_eval_imagenet.py | 42 ++++++- tests/test_postprocess_hierarchy.py | 19 +++ 6 files changed, 215 insertions(+), 24 deletions(-) diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index c47b48d..93e6fbf 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -4,6 +4,7 @@ import hashlib import json +import math import os import re from importlib.resources import files @@ -11,6 +12,7 @@ from typing import Any import numpy as np +from faster_coco_eval import mask as coco_mask from scipy.io import loadmat from scipy.io.matlab import MatReadError @@ -162,6 +164,7 @@ def _load_coco_image_names( return None names: list[str] = [] image_ids: list[int] = [] + image_shapes: dict[int, tuple[int, int] | None] = {} for record in image_records: if not isinstance(record, dict): continue @@ -174,6 +177,18 @@ def _load_coco_image_names( ): names.append(file_name) image_ids.append(image_id) + height, width = record.get("height"), record.get("width") + if ( + isinstance(height, int) + and not isinstance(height, bool) + and height > 0 + and isinstance(width, int) + and not isinstance(width, bool) + and width > 0 + ): + image_shapes[image_id] = (height, width) + else: + image_shapes[image_id] = None if ( len(names) != len(image_records) or len(names) != len(set(names)) @@ -284,28 +299,7 @@ def _load_coco_image_names( ): return None elif isinstance(segmentation, dict): - counts = segmentation.get("counts") - size = segmentation.get("size") - if ( - not isinstance(size, list) - or len(size) != 2 - or any( - not isinstance(value, int) - or isinstance(value, bool) - or value <= 0 - for value in size - ) - or not isinstance(counts, (str, list)) - or ( - isinstance(counts, list) - and any( - not isinstance(value, int) - or isinstance(value, bool) - or value < 0 - for value in counts - ) - ) - ): + if not _valid_coco_rle(segmentation, image_shapes.get(image_id)): return None else: return None @@ -338,6 +332,81 @@ def _load_coco_image_names( return set(names) +def _decode_coco_rle_counts(counts: str) -> list[int] | None: + """Decode COCO's compact RLE run-length string without trusting its payload.""" + + run_counts: list[int] = [] + position = 0 + while position < len(counts): + value = 0 + shift = 0 + more = True + while more: + if position >= len(counts): + return None + code = ord(counts[position]) - 48 + position += 1 + if not 0 <= code <= 0x3F: + return None + value |= (code & 0x1F) << shift + more = bool(code & 0x20) + shift += 5 + if shift > 60: + return None + if not more and code & 0x10: + value |= -1 << shift + if len(run_counts) > 2: + value += run_counts[-2] + if value < 0: + return None + run_counts.append(value) + return run_counts + + +def _valid_coco_rle( + segmentation: dict[str, Any], image_shape: tuple[int, int] | None +) -> bool: + """Validate and decode an RLE mask against its referenced COCO image shape.""" + + counts = segmentation.get("counts") + size = segmentation.get("size") + if ( + image_shape is None + or not isinstance(size, list) + or len(size) != 2 + or any( + not isinstance(value, int) or isinstance(value, bool) or value <= 0 + for value in size + ) + or tuple(size) != image_shape + or not isinstance(counts, (str, list)) + ): + return False + if isinstance(counts, list): + if any( + not isinstance(value, int) or isinstance(value, bool) or value < 0 + for value in counts + ): + return False + run_counts = counts + else: + run_counts = _decode_coco_rle_counts(counts) + if run_counts is None: + return False + if sum(run_counts) != math.prod(size): + return False + try: + encoded = ( + coco_mask.frPyObjects(segmentation, size[0], size[1]) + if isinstance(counts, list) + else segmentation + ) + decoded = np.asarray(coco_mask.decode(encoded)) + except (RuntimeError, TypeError, ValueError): + return False + return decoded.shape == tuple(size) and bool(np.any(decoded)) + + def _coco_ready(root: Path, task: str) -> bool: """Check the complete COCO 2017 image split and task annotation metadata.""" diff --git a/mblt_vision/utils/evaluation/eval_imagenet.py b/mblt_vision/utils/evaluation/eval_imagenet.py index ed23ddf..c0eb716 100644 --- a/mblt_vision/utils/evaluation/eval_imagenet.py +++ b/mblt_vision/utils/evaluation/eval_imagenet.py @@ -70,8 +70,12 @@ def eval_imagenet_metrics( if synset in dataset.class_to_idx } dataset.make_dataset() - dataloader = get_imagenet_loader(dataset, batch_size, model.preprocess) num_data = len(dataset) + if num_data == 0: + raise ValueError( + f"ImageNet evaluation dataset contains no supported images: {data_path}." + ) + dataloader = get_imagenet_loader(dataset, batch_size, model.preprocess) total_iter = math.ceil(num_data / batch_size) pbar = tqdm(dataloader, total=total_iter, desc="Evaluating ImageNet") inference_time = 0.0 diff --git a/mblt_vision/utils/postprocess/base.py b/mblt_vision/utils/postprocess/base.py index 6b07792..4671e15 100644 --- a/mblt_vision/utils/postprocess/base.py +++ b/mblt_vision/utils/postprocess/base.py @@ -336,6 +336,10 @@ def extract_final_outputs( if normalized_detections is not None: if invalid_proto_error is not None: raise invalid_proto_error + if self.task == "instance_segmentation" and normalized_proto is None: + raise ValueError( + "Decoded instance-segmentation outputs require a mask prototype tensor." + ) return self._final_detection_batches( normalized_detections ), normalized_proto @@ -343,6 +347,10 @@ def extract_final_outputs( normalized_x = self._normalize_final_detection_tensor(x, final_det_dim) if normalized_x is not None: + if self.task == "instance_segmentation": + raise ValueError( + "Decoded instance-segmentation outputs require a mask prototype tensor." + ) return self._final_detection_batches(normalized_x), None return None, None diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index d4d03c3..c81a0a7 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -275,6 +275,57 @@ def test_coco_readiness_rejects_invalid_instance_box_geometry( assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") +@pytest.mark.parametrize( + ("counts", "expected_ready"), + [([3, 1], True), ("31", True), ([4], False), ([3], False), ("4", False)], +) +def test_coco_readiness_decodes_and_validates_rle_segmentations( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + counts: list[int] | str, + expected_ready: bool, +) -> None: + """Require nonempty, complete RLE masks with the referenced image geometry.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 1) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) + _write_file(tmp_path / "val2017" / "000000000001.jpg") + (tmp_path / "instances_val2017.json").write_text( + json.dumps( + { + "images": [ + { + "id": 1, + "file_name": "000000000001.jpg", + "height": 2, + "width": 2, + } + ], + "categories": [{"id": 1}], + "annotations": [ + { + "id": 1, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, + "segmentation": {"size": [2, 2], "counts": counts}, + } + ], + } + ), + encoding="utf-8", + ) + + assert ( + readiness.dataset_ready(tmp_path, "instance_segmentation", "coco") + is expected_ready + ) + + def test_coco_annotation_identity_digest_rejects_wrong_validation_split( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: diff --git a/tests/test_eval_imagenet.py b/tests/test_eval_imagenet.py index 3d532bb..4520575 100644 --- a/tests/test_eval_imagenet.py +++ b/tests/test_eval_imagenet.py @@ -84,7 +84,11 @@ def test_imagenet_evaluation_preserves_canonical_synset_indices( ) -> None: """Do not renumber a partial ImageNet directory tree by local sort order.""" - dataset = SimpleNamespace( + class _Dataset(SimpleNamespace): + def __len__(self) -> int: + return 1 + + dataset = _Dataset( classes=["n00000001"], class_to_idx={"n00000001": 0}, make_dataset=lambda: None, @@ -118,6 +122,42 @@ def _stop_after_mapping(*args: object) -> object: assert dataset.class_to_idx == {"n00000001": 1} +def test_imagenet_evaluation_rejects_empty_dataset( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Fail before loader construction when no supported samples are available.""" + + class _EmptyDataset: + classes = ["n00000000"] + class_to_idx = {"n00000000": 0} + + def make_dataset(self) -> None: + return None + + def __len__(self) -> int: + return 0 + + monkeypatch.setattr( + eval_imagenet_module, "CustomImageFolder", lambda _: _EmptyDataset() + ) + monkeypatch.setattr(eval_imagenet_module, "IMAGENET_SYNSET_ORDER", ("n00000000",)) + monkeypatch.setattr( + eval_imagenet_module, "IMAGENET_SYNSETS", frozenset({"n00000000"}) + ) + monkeypatch.setattr( + eval_imagenet_module, + "get_imagenet_loader", + lambda *_: pytest.fail("empty datasets must not create an ImageNet loader"), + ) + + with pytest.raises(ValueError, match="contains no supported images"): + eval_imagenet_module.eval_imagenet_metrics( + SimpleNamespace(post_cfg={"dataset": "imagenet"}, preprocess=object()), + "/empty", + batch_size=1, + ) + + def test_imagenet_evaluation_rejects_wrong_model_taxonomy() -> None: """Do not score another classification taxonomy against ImageNet labels.""" diff --git a/tests/test_postprocess_hierarchy.py b/tests/test_postprocess_hierarchy.py index 10c2e57..a03d490 100644 --- a/tests/test_postprocess_hierarchy.py +++ b/tests/test_postprocess_hierarchy.py @@ -249,6 +249,25 @@ def test_decoded_segmentation_propagates_invalid_mask_prototypes( postprocessor.extract_final_outputs(outputs) +def test_decoded_segmentation_requires_mask_prototypes() -> None: + """Reject decoded segmentation artifacts that cannot produce instance masks.""" + + postprocessor = cast( + Any, YOLOAnchorlessDetectionPost.__new__(YOLOAnchorlessDetectionPost) + ) + postprocessor.device = torch.device("cpu") + postprocessor.conf_thres = 0.25 + postprocessor.nc = 2 + postprocessor.n_extra = 2 + postprocessor.task = "instance_segmentation" + detections = torch.tensor( + [[[0.0, 0.0, 1.0, 1.0, 0.9, 1.0, 0.0, 0.0]]], dtype=torch.float32 + ) + + with pytest.raises(ValueError, match="require a mask prototype tensor"): + postprocessor.extract_final_outputs([detections]) + + @pytest.mark.parametrize("invalid_value", [float("nan"), float("inf")]) def test_detection_postprocessor_rejects_nonfinite_raw_heads( invalid_value: float, From c150f2f28145e89b0c4c5cf917abece38da169a7 Mon Sep 17 00:00:00 2001 From: jinman Date: Fri, 14 Aug 2026 18:25:07 +0900 Subject: [PATCH 41/48] fix: harden vision dataset evaluation --- mblt_vision/utils/datasets/dataloader.py | 8 ++ mblt_vision/utils/datasets/organizer.py | 78 ++++++++++++++++++- mblt_vision/utils/evaluation/eval_coco.py | 25 ++++++ .../utils/evaluation/eval_widerface.py | 14 ++++ tests/test_dataloader.py | 25 ++++++ tests/test_dataset_organizer.py | 53 ++++++++++++- tests/test_eval_coco.py | 18 ++++- tests/test_eval_widerface.py | 34 ++++++++ 8 files changed, 250 insertions(+), 5 deletions(-) diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index 034b06a..3bdfa1b 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -390,6 +390,10 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: target = np.full(annotation.shape, 255, dtype=np.uint8) valid = annotation > 0 target[valid] = annotation[valid] - 1 + if not valid.any(): + raise ValueError( + f"ADE20K annotation contains no evaluable class IDs: {annotation_path}" + ) return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), target, stem def __len__(self) -> int: @@ -552,6 +556,10 @@ def __getitem__(self, index: int) -> tuple[np.ndarray, np.ndarray, str]: f"{unknown_ids.tolist()}: {annotation_path}" ) target = CITYSCAPES_SOURCE_TO_TRAIN_ID[annotation.astype(np.uint8)] + if not (target != 255).any(): + raise ValueError( + f"Cityscapes annotation contains no evaluable class IDs: {annotation_path}" + ) return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), target, stem def __len__(self) -> int: diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 8cbd604..9eac44d 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -7,6 +7,7 @@ import concurrent.futures import cv2 import hashlib +import json import math import os import re @@ -30,6 +31,7 @@ from tqdm import tqdm from ...datasets import get_dataset_config +from .cityscapes import CITYSCAPES_SOURCE_TO_TRAIN_ID from .readiness import ( ADE20K_METADATA_FILES, ADE20K_VALIDATION_SAMPLE_COUNT, @@ -163,7 +165,6 @@ def _validate_staged_payloads(staged_root: Path, dataset: str) -> None: image_roots = { "imagenet": (staged_root,), - "coco": (staged_root / "val2017",), "widerface": (staged_root / "images",), "dotav1": (staged_root / "images",), "ade20k": (staged_root / "images",), @@ -185,12 +186,72 @@ def _validate_staged_payloads(staged_root: Path, dataset: str) -> None: f"Staged {dataset} image is unreadable: {image_path}." ) - if dataset in {"ade20k", "cityscapes"}: + if dataset == "coco": + _validate_staged_coco_image_geometry(staged_root) + elif dataset in {"ade20k", "cityscapes"}: _validate_staged_semantic_masks(staged_root, dataset) elif dataset == "dotav1": _validate_staged_dotav1_labels(staged_root) +def _validate_staged_coco_image_geometry(staged_root: Path) -> None: + """Compare every staged COCO image with its JSON-declared geometry.""" + + image_root = staged_root / "val2017" + decoded_shapes: dict[str, tuple[int, int]] = {} + for annotation_path in sorted(staged_root.glob("*_val2017.json")): + try: + annotation = json.loads(annotation_path.read_text(encoding="utf-8")) + image_records = annotation["images"] + except (json.JSONDecodeError, KeyError, OSError, TypeError) as exc: + raise ValueError( + f"Staged COCO annotation is unreadable: {annotation_path}." + ) from exc + if not isinstance(image_records, list): + raise ValueError( + f"Staged COCO annotation has an invalid images table: {annotation_path}." + ) + for record in image_records: + if not isinstance(record, dict): + raise ValueError( + f"Staged COCO annotation has an invalid image record: {annotation_path}." + ) + file_name, height, width = ( + record.get("file_name"), + record.get("height"), + record.get("width"), + ) + if ( + not isinstance(file_name, str) + or not file_name + or Path(file_name).is_absolute() + or ".." in Path(file_name).parts + or not isinstance(height, int) + or isinstance(height, bool) + or not isinstance(width, int) + or isinstance(width, bool) + or height <= 0 + or width <= 0 + ): + raise ValueError( + f"Staged COCO image metadata is invalid: {annotation_path}." + ) + image_shape = decoded_shapes.get(file_name) + if image_shape is None: + image = cv2.imread(str(image_root / file_name), cv2.IMREAD_COLOR) + if image is None: + raise ValueError( + f"Staged COCO image is unreadable: {image_root / file_name}." + ) + image_shape = (int(image.shape[0]), int(image.shape[1])) + decoded_shapes[file_name] = image_shape + if image_shape != (height, width): + raise ValueError( + "Staged COCO image geometry does not match annotation metadata for " + f"{file_name}: image {image_shape}, annotation {(height, width)}." + ) + + def _validate_staged_semantic_masks(staged_root: Path, dataset: str) -> None: """Validate decoded semantic targets against their paired staged images.""" @@ -246,6 +307,17 @@ def _validate_staged_semantic_masks(staged_root: Path, dataset: str) -> None: "Staged Cityscapes annotation contains unsupported source IDs " f"{invalid_ids.tolist()}; expected IDs in [0, 33] or 255: {annotation_path}." ) + has_evaluable_class = bool( + ( + CITYSCAPES_SOURCE_TO_TRAIN_ID[annotation.astype(np.uint8)] != 255 + ).any() + ) + else: + has_evaluable_class = bool((annotation > 0).any()) + if not has_evaluable_class: + raise ValueError( + f"Staged {dataset} annotation contains no evaluable class IDs: {annotation_path}." + ) def _validate_staged_dotav1_labels(staged_root: Path) -> None: @@ -1322,7 +1394,7 @@ def organize_ade20k( ) -> None: """Organizes ADE20K validation data, downloading and unpacking when necessary.""" - output_dir = _resolve_organizer_output_dir(output_dir, "ADEChallengeData2016") + output_dir = _resolve_organizer_output_dir(output_dir, "ade20k") output_dir = _validate_dense_output_root( output_dir, "ADE20K", ("images", "annotations") ) diff --git a/mblt_vision/utils/evaluation/eval_coco.py b/mblt_vision/utils/evaluation/eval_coco.py index 8ff9b75..84e3067 100644 --- a/mblt_vision/utils/evaluation/eval_coco.py +++ b/mblt_vision/utils/evaluation/eval_coco.py @@ -12,6 +12,7 @@ from tqdm import tqdm from ..._tasks import normalize_vision_task +from ...datasets import get_dataset_category_ids from ..datasets import CustomCOCODataset, get_coco_loader if TYPE_CHECKING: @@ -54,6 +55,29 @@ def _require_batch_cardinality(expected: int, **values: Any) -> None: ) +def _validate_coco_dataset_taxonomy(dataset: CustomCOCODataset, task: str) -> None: + """Ensure direct COCO evaluation uses only task-compatible category IDs.""" + + categories = getattr(dataset.coco, "cats", None) + if not isinstance(categories, dict) or not categories: + raise ValueError("COCO evaluation dataset must define at least one category.") + category_ids = set(categories) + if any( + not isinstance(category_id, int) or isinstance(category_id, bool) + for category_id in category_ids + ): + raise ValueError("COCO evaluation dataset contains invalid category IDs.") + expected_ids = ( + {1} if task == "pose_estimation" else set(get_dataset_category_ids("coco")) + ) + unsupported_ids = category_ids - expected_ids + if unsupported_ids: + raise ValueError( + "COCO evaluation dataset contains unsupported category IDs: " + f"{sorted(unsupported_ids)}." + ) + + def format_coco_results( task: str, nms_outs: Results, @@ -233,6 +257,7 @@ def eval_coco_metrics( os.path.join(data_path, "val2017"), os.path.join(data_path, "person_keypoints_val2017.json"), ) + _validate_coco_dataset_taxonomy(dataset, task) dataloader = get_coco_loader(dataset, batch_size, model.preprocess_with_metadata) model.set_postprocess_thresholds(conf_thres=conf_thres, iou_thres=iou_thres) diff --git a/mblt_vision/utils/evaluation/eval_widerface.py b/mblt_vision/utils/evaluation/eval_widerface.py index c6b33ed..646d07e 100644 --- a/mblt_vision/utils/evaluation/eval_widerface.py +++ b/mblt_vision/utils/evaluation/eval_widerface.py @@ -4,6 +4,7 @@ import math import os +from pathlib import Path from time import time from typing import TYPE_CHECKING, Any, NamedTuple, cast @@ -13,6 +14,10 @@ from tqdm import tqdm from ..datasets import CustomWiderFaceDataset, get_widerface_loader +from ..datasets.readiness import ( + _load_widerface_image_names, + _widerface_difficulty_metadata_ready, +) if TYPE_CHECKING: from ...wrapper import MBLT_Engine @@ -110,6 +115,15 @@ def eval_widerface( f"got {dataset_name!r}." ) + dataset_root = Path(data_path) + expected_images = _load_widerface_image_names(dataset_root / "wider_face_val.mat") + if expected_images is None or not _widerface_difficulty_metadata_ready( + dataset_root, expected_images + ): + raise ValueError( + "WiderFace evaluation metadata is malformed or has inconsistent difficulty indices." + ) + dataset = CustomWiderface(os.path.join(data_path, "images")) dataloader = get_widerface_loader( dataset, batch_size, model.preprocess_with_metadata diff --git a/tests/test_dataloader.py b/tests/test_dataloader.py index edb6371..6ca39c8 100644 --- a/tests/test_dataloader.py +++ b/tests/test_dataloader.py @@ -102,6 +102,31 @@ def test_dota_dataset_rejects_duplicate_image_stems(tmp_path: Path) -> None: CustomDOTAv1(str(tmp_path)) +@pytest.mark.parametrize( + ("dataset_class", "source_id"), + [(CustomADE20K, 0), (CustomCityscapes, 255)], +) +def test_dense_semantic_datasets_reject_all_ignored_targets( + tmp_path: Path, + dataset_class: type[CustomADE20K] | type[CustomCityscapes], + source_id: int, +) -> None: + """Reject direct semantic evaluation samples that contain no valid labels.""" + + image_dir = tmp_path / "images" + annotation_dir = tmp_path / "annotations" + image_dir.mkdir() + annotation_dir.mkdir() + Image.new("RGB", (2, 2)).save(image_dir / "sample.png") + Image.fromarray(np.full((2, 2), source_id, dtype=np.uint8)).save( + annotation_dir / "sample.png" + ) + dataset = dataset_class(str(tmp_path)) + + with pytest.raises(ValueError, match="contains no evaluable class IDs"): + dataset[0] + + @pytest.mark.parametrize( ("dataset_class", "target_dir", "target_names"), [ diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index d33b23a..f856c3b 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -5,6 +5,7 @@ import hashlib import io import inspect +import json import os import shutil import tarfile @@ -92,7 +93,7 @@ def test_dotav1_organizer_rejects_truncated_raw_annotation_rows( (organizer.organize_coco, "coco"), (organizer.organize_widerface, "widerface"), (organizer.organize_nyu_depth, "nyu-depth"), - (organizer.organize_ade20k, "ADEChallengeData2016"), + (organizer.organize_ade20k, "ade20k"), (organizer.organize_cityscapes, "cityscapes"), (organizer.organize_dotav1, "dotav1"), ], @@ -975,6 +976,56 @@ def test_staged_cityscapes_payload_validation_rejects_unknown_source_ids( organizer._validate_staged_payloads(tmp_path, "cityscapes") +@pytest.mark.parametrize( + ("dataset", "source_id"), + [("ade20k", 0), ("cityscapes", 255)], +) +def test_staged_semantic_payload_validation_rejects_all_ignored_targets( + tmp_path: Path, dataset: str, source_id: int +) -> None: + """Do not install semantic masks that cannot contribute to validation metrics.""" + + image_dir = tmp_path / "images" + annotation_dir = tmp_path / "annotations" + image_dir.mkdir() + annotation_dir.mkdir() + Image.new("RGB", (2, 2)).save(image_dir / "sample.png") + Image.fromarray(np.full((2, 2), source_id, dtype=np.uint8)).save( + annotation_dir / "sample.png" + ) + + with pytest.raises(ValueError, match="contains no evaluable class IDs"): + organizer._validate_staged_payloads(tmp_path, dataset) + + +def test_staged_coco_payload_validation_rejects_annotation_geometry_mismatch( + tmp_path: Path, +) -> None: + """Preserve a usable cache when JSON dimensions differ from staged image bytes.""" + + image_dir = tmp_path / "val2017" + image_dir.mkdir() + Image.new("RGB", (2, 1)).save(image_dir / "000000000001.jpg") + (tmp_path / "instances_val2017.json").write_text( + json.dumps( + { + "images": [ + { + "id": 1, + "file_name": "000000000001.jpg", + "height": 2, + "width": 1, + } + ] + } + ), + encoding="utf-8", + ) + + with pytest.raises(ValueError, match="geometry does not match"): + organizer._validate_staged_payloads(tmp_path, "coco") + + def _create_ade20k_source(tmp_path: Path) -> Path: """Create a compact extracted ADE20K validation source.""" diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index db25e74..9e39909 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -25,7 +25,7 @@ def __init__(self, root: str, annotation_path: str, **kwargs: Any) -> None: root=root, annotation_path=annotation_path, **kwargs ) self.ids = [1, 2] - self.coco = object() + self.coco = SimpleNamespace(cats={1: {}}) def __len__(self) -> int: return len(self.ids) @@ -76,6 +76,22 @@ def test_coco_evaluation_rejects_non_coco_model_taxonomy() -> None: eval_coco_module.eval_coco_metrics(model, "/dataset", batch_size=1) +def test_coco_evaluation_rejects_noncanonical_artifact_categories( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Do not score direct COCO artifacts with an incompatible category taxonomy.""" + + dataset = SimpleNamespace(coco=SimpleNamespace(cats={999: {}})) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match=r"unsupported category IDs: \[999\]"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace(post_cfg={"task": "object_detection", "dataset": "coco"}), + "/dataset", + batch_size=1, + ) + + def test_coco_result_formatter_rejects_truncated_postprocess_batch() -> None: """Require one decoded result for every submitted COCO image.""" diff --git a/tests/test_eval_widerface.py b/tests/test_eval_widerface.py index 1c897cb..4cda20c 100644 --- a/tests/test_eval_widerface.py +++ b/tests/test_eval_widerface.py @@ -75,6 +75,14 @@ def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: ("first.jpg", "second.jpg"), ) monkeypatch.setattr(eval_widerface_module, "CustomWiderface", lambda _: _Dataset()) + monkeypatch.setattr( + eval_widerface_module, + "_load_widerface_image_names", + lambda _: {"event": {"first.jpg", "second.jpg"}}, + ) + monkeypatch.setattr( + eval_widerface_module, "_widerface_difficulty_metadata_ready", lambda *_: True + ) monkeypatch.setattr( eval_widerface_module, "get_widerface_loader", lambda *_: [batch] ) @@ -82,3 +90,29 @@ def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: with pytest.raises(ValueError, match="WiderFace evaluation batch length mismatch"): eval_widerface_module.eval_widerface(_Model(), "/dataset", batch_size=2) + + +def test_widerface_evaluation_rejects_malformed_difficulty_metadata( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Validate metadata before direct evaluation constructs a dataset or loader.""" + + model = SimpleNamespace(post_cfg={"task": "face_detection", "dataset": "widerface"}) + monkeypatch.setattr( + eval_widerface_module, + "_load_widerface_image_names", + lambda _: {"0--Parade": {"sample.jpg"}}, + ) + monkeypatch.setattr( + eval_widerface_module, "_widerface_difficulty_metadata_ready", lambda *_: False + ) + monkeypatch.setattr( + eval_widerface_module, + "CustomWiderface", + lambda _: pytest.fail( + "invalid metadata must not construct a WiderFace dataset" + ), + ) + + with pytest.raises(ValueError, match="metadata is malformed"): + eval_widerface_module.eval_widerface(model, "/dataset", batch_size=1) From a58723d2d047b53e9953b83f2af85d72570369c0 Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 18 Aug 2026 09:25:16 +0900 Subject: [PATCH 42/48] fix: validate vision evaluation boundaries --- mblt_vision/compile/vision.py | 20 ++++++++-- mblt_vision/utils/datasets/organizer.py | 5 +++ mblt_vision/utils/datasets/readiness.py | 6 +++ mblt_vision/utils/evaluation/eval_coco.py | 18 +++++++++ mblt_vision/utils/evaluation/eval_dota.py | 31 +++++++++++++++- .../utils/evaluation/eval_widerface.py | 9 +++++ tests/test_compile_vision.py | 22 +++++++++++ tests/test_dataset_organizer.py | 5 ++- tests/test_dataset_readiness.py | 26 +++++++++++++ tests/test_eval_coco.py | 22 ++++++++++- tests/test_eval_dota.py | 37 +++++++++++++++++++ tests/test_eval_widerface.py | 28 ++++++++++++++ 12 files changed, 221 insertions(+), 8 deletions(-) diff --git a/mblt_vision/compile/vision.py b/mblt_vision/compile/vision.py index ef781fb..0b9c019 100644 --- a/mblt_vision/compile/vision.py +++ b/mblt_vision/compile/vision.py @@ -208,9 +208,11 @@ def ensure_calibration_dataset( normalized_task = _normalize_task(task) config = get_dataset_config_for_task(normalized_task, dataset) dataset_name = str(config["name"]) - resolved_path = Path( - data_path if data_path is not None else config["path"] - ).expanduser() + resolved_path = ( + Path(data_path).expanduser() + if data_path is not None + else _resolve_default_dataset_path(config["path"]) + ) if not _dataset_ready(normalized_task, resolved_path, dataset_name): _organize_dataset(normalized_task, resolved_path, dataset) if not _dataset_ready(normalized_task, resolved_path, dataset_name): @@ -221,6 +223,18 @@ def ensure_calibration_dataset( return resolved_path +def _resolve_default_dataset_path(configured_path: str | Path) -> Path: + """Translate registry cache paths to the active writable artifact cache.""" + + path = Path(configured_path).expanduser() + default_cache_root = Path.home() / ".mblt_model_zoo" + try: + relative_path = path.relative_to(default_cache_root) + except ValueError: + return path + return Path(get_mobilint_cache_dir()) / relative_path + + def _validate_subset_size(subset_size: int) -> None: """Validate a requested calibration subset size. diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 9eac44d..26556be 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -1242,6 +1242,11 @@ def _validate_staged_nyu_depth(staging_dir: str) -> None: raise ValueError( f"Staged NYU Depth target must not contain negative values: {depth_path}." ) + if not bool(((depth > 0.001) & (depth < 100.0)).any()): + raise ValueError( + "Staged NYU Depth target must contain at least one valid metric depth " + f"in the (0.001, 100.0) range: {depth_path}." + ) def organize_nyu_depth( diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 93e6fbf..0185942 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -545,6 +545,12 @@ def _widerface_difficulty_metadata_ready( keep_indices = np.asarray(event_indices[image_index][0]) except (IndexError, TypeError): return False + if keep_indices.ndim == 0: + keep_indices = keep_indices.reshape(1) + elif keep_indices.ndim == 2 and keep_indices.shape[1] == 1: + keep_indices = keep_indices[:, 0] + elif keep_indices.ndim != 1: + return False try: valid_indices = ( np.isfinite(keep_indices).all() diff --git a/mblt_vision/utils/evaluation/eval_coco.py b/mblt_vision/utils/evaluation/eval_coco.py index 84e3067..8726f67 100644 --- a/mblt_vision/utils/evaluation/eval_coco.py +++ b/mblt_vision/utils/evaluation/eval_coco.py @@ -76,6 +76,24 @@ def _validate_coco_dataset_taxonomy(dataset: CustomCOCODataset, task: str) -> No "COCO evaluation dataset contains unsupported category IDs: " f"{sorted(unsupported_ids)}." ) + annotations = getattr(dataset.coco, "anns", None) + if not isinstance(annotations, dict): + raise ValueError("COCO evaluation dataset must define an annotation table.") + for annotation_id, annotation in annotations.items(): + if not isinstance(annotation, dict): + raise ValueError( + f"COCO evaluation dataset has an invalid annotation {annotation_id!r}." + ) + category_id = annotation.get("category_id") + if ( + not isinstance(category_id, int) + or isinstance(category_id, bool) + or category_id not in category_ids + ): + raise ValueError( + "COCO evaluation annotation references an undeclared or unsupported " + f"category ID {category_id!r}: annotation {annotation_id!r}." + ) def format_coco_results( diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 2d70b73..019351e 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -256,11 +256,38 @@ def format_dota_results( ratio_pad=ratio_pad, include_xywhr=True, ) + outer_lengths = { + "image IDs": len(image_ids), + "labels": len(labels_list), + "polygons": len(polygons_list), + "scores": len(scores_list), + "rotated boxes": len(xywhr_list), + } + if len(set(outer_lengths.values())) != 1: + details = ", ".join( + f"{name}={length}" for name, length in outer_lengths.items() + ) + raise ValueError(f"DOTAv1 export batch length mismatch: {details}.") results = [] for image_id, labels, polygons, scores, xywhrs in zip( - image_ids, labels_list, polygons_list, scores_list, xywhr_list + image_ids, labels_list, polygons_list, scores_list, xywhr_list, strict=True ): - for label, polygon, score, xywhr in zip(labels, polygons, scores, xywhrs): + detection_lengths = { + "labels": len(labels), + "polygons": len(polygons), + "scores": len(scores), + "rotated boxes": len(xywhrs), + } + if len(set(detection_lengths.values())) != 1: + details = ", ".join( + f"{name}={length}" for name, length in detection_lengths.items() + ) + raise ValueError( + f"DOTAv1 export detection length mismatch for {image_id}: {details}." + ) + for label, polygon, score, xywhr in zip( + labels, polygons, scores, xywhrs, strict=True + ): results.append( { "image_id": image_id, diff --git a/mblt_vision/utils/evaluation/eval_widerface.py b/mblt_vision/utils/evaluation/eval_widerface.py index 646d07e..757dce1 100644 --- a/mblt_vision/utils/evaluation/eval_widerface.py +++ b/mblt_vision/utils/evaluation/eval_widerface.py @@ -125,6 +125,15 @@ def eval_widerface( ) dataset = CustomWiderface(os.path.join(data_path, "images")) + actual_images: dict[str, set[str]] = {} + for _, event_name, file_name in dataset.samples: + actual_images.setdefault(event_name, set()).add(file_name) + if actual_images != expected_images or sum( + len(file_names) for file_names in actual_images.values() + ) != len(dataset.samples): + raise ValueError( + "WiderFace image tree does not match the validation metadata identities." + ) dataloader = get_widerface_loader( dataset, batch_size, model.preprocess_with_metadata ) diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index db7a597..4c0c7a2 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -102,6 +102,28 @@ def test_obb_selects_calibration_images(tmp_path: Path) -> None: assert set(select_calibration_images("obb", tmp_path, subset_size=2)) == set(images) +def test_calibration_default_dataset_path_uses_resolved_cache_root( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Keep implicit compilation datasets alongside the active artifact cache.""" + + configured_path = Path.home() / ".mblt_model_zoo" / "datasets" / "coco" + fallback_cache = tmp_path / "cache" + monkeypatch.setattr( + compile_module, + "get_dataset_config_for_task", + lambda *_: {"name": "coco", "path": str(configured_path)}, + ) + monkeypatch.setattr( + compile_module, "get_mobilint_cache_dir", lambda: str(fallback_cache) + ) + monkeypatch.setattr(compile_module, "_dataset_ready", lambda *_: True) + + assert compile_module.ensure_calibration_dataset("object_detection") == ( + fallback_cache / "datasets" / "coco" + ) + + @pytest.mark.parametrize( ("task", "relative_dir"), [ diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index f856c3b..06f5c53 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -912,6 +912,8 @@ def _fail_install_and_rollback(source: str, destination: str) -> None: np.array([[np.nan]], dtype=np.float32), np.array([[1]], dtype=np.complex64), np.array([[-1]], dtype=np.float32), + np.array([[0]], dtype=np.float32), + np.array([[100]], dtype=np.float32), ], ) def test_staged_nyu_depth_validation_rejects_malformed_payloads( @@ -927,7 +929,8 @@ def test_staged_nyu_depth_validation_rejects_malformed_payloads( np.save(depth_dir / "sample.npy", depth) with pytest.raises( - ValueError, match="real numeric dtype|finite values|negative values" + ValueError, + match="real numeric dtype|finite values|negative values|valid metric depth", ): organizer._validate_staged_nyu_depth(str(tmp_path)) diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index c81a0a7..f6db87f 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -493,6 +493,32 @@ def _loadmat(path: Path) -> dict[str, np.ndarray]: ) +def test_widerface_readiness_rejects_row_vector_difficulty_indices( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Reject row vectors that the evaluator would count as one eligible face.""" + + face_boxes = np.empty((1, 1), dtype=object) + event_faces = np.empty((1, 1), dtype=object) + event_faces[0, 0] = np.array([[0, 0, 1, 1], [1, 1, 1, 1]], dtype=np.float64) + face_boxes[0, 0] = event_faces + difficulty = np.empty((1, 1), dtype=object) + event_indices = np.empty((1, 1), dtype=object) + event_indices[0, 0] = np.array([[1, 2]], dtype=np.int64) + difficulty[0, 0] = event_indices + + def _loadmat(path: Path) -> dict[str, np.ndarray]: + if path.name == "wider_face_val.mat": + return {"face_bbx_list": face_boxes} + return {"gt_list": difficulty} + + monkeypatch.setattr(readiness, "loadmat", _loadmat) + + assert not readiness._widerface_difficulty_metadata_ready( + tmp_path, {"0--Parade": {"sample.jpg"}} + ) + + @pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) def test_dotav1_readiness_requires_complete_image_label_pairs( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index 9e39909..ec3aa12 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -25,7 +25,7 @@ def __init__(self, root: str, annotation_path: str, **kwargs: Any) -> None: root=root, annotation_path=annotation_path, **kwargs ) self.ids = [1, 2] - self.coco = SimpleNamespace(cats={1: {}}) + self.coco = SimpleNamespace(cats={1: {}}, anns={}) def __len__(self) -> int: return len(self.ids) @@ -81,7 +81,7 @@ def test_coco_evaluation_rejects_noncanonical_artifact_categories( ) -> None: """Do not score direct COCO artifacts with an incompatible category taxonomy.""" - dataset = SimpleNamespace(coco=SimpleNamespace(cats={999: {}})) + dataset = SimpleNamespace(coco=SimpleNamespace(cats={999: {}}, anns={})) monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) with pytest.raises(ValueError, match=r"unsupported category IDs: \[999\]"): @@ -92,6 +92,24 @@ def test_coco_evaluation_rejects_noncanonical_artifact_categories( ) +def test_coco_evaluation_rejects_undeclared_annotation_categories( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Do not let malformed annotation categories be omitted from direct AP.""" + + dataset = SimpleNamespace( + coco=SimpleNamespace(cats={1: {}}, anns={7: {"category_id": 999}}) + ) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match="undeclared or unsupported"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace(post_cfg={"task": "object_detection", "dataset": "coco"}), + "/dataset", + batch_size=1, + ) + + def test_coco_result_formatter_rejects_truncated_postprocess_batch() -> None: """Require one decoded result for every submitted COCO image.""" diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index ca1427e..c0725ff 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -14,6 +14,7 @@ from mblt_vision.utils.evaluation.eval_dota import ( _load_ground_truths, evaluate_dota_predictions, + format_dota_results, ) eval_dota_module = importlib.import_module("mblt_vision.utils.evaluation.eval_dota") @@ -326,3 +327,39 @@ def test_dota_ap_interpolation_uses_terminal_recall_sentinel() -> None: assert recall_curve.tolist() == [0.0, 0.5, 1.0] assert ap == pytest.approx(0.75) + + +def test_dota_export_rejects_truncated_converted_batches() -> None: + """Do not silently omit Task1 files when converted output batches are short.""" + + postprocess = SimpleNamespace( + nmsout2eval=lambda *_args, **_kwargs: ([[]], [[]], [[]], [[]]) + ) + + with pytest.raises(ValueError, match="export batch length mismatch"): + format_dota_results( + SimpleNamespace(output=[]), + (640, 640), + [(640, 640), (640, 640)], + [None, None], + ("first", "second"), + postprocess, + ) + + +def test_dota_export_rejects_mismatched_detection_fields() -> None: + """Require a polygon, score, and rotated box for every exported label.""" + + postprocess = SimpleNamespace( + nmsout2eval=lambda *_args, **_kwargs: ([["plane"]], [[]], [[0.9]], [[]]) + ) + + with pytest.raises(ValueError, match="export detection length mismatch"): + format_dota_results( + SimpleNamespace(output=[]), + (640, 640), + [(640, 640)], + [None], + ("first",), + postprocess, + ) diff --git a/tests/test_eval_widerface.py b/tests/test_eval_widerface.py index 4cda20c..1425671 100644 --- a/tests/test_eval_widerface.py +++ b/tests/test_eval_widerface.py @@ -116,3 +116,31 @@ def test_widerface_evaluation_rejects_malformed_difficulty_metadata( with pytest.raises(ValueError, match="metadata is malformed"): eval_widerface_module.eval_widerface(model, "/dataset", batch_size=1) + + +def test_widerface_evaluation_rejects_image_tree_mismatching_metadata( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Require exact event and filename identities before direct inference.""" + + class _Dataset: + samples = [("unused", "0--Parade", "sample.png")] + + model = SimpleNamespace(post_cfg={"task": "face_detection", "dataset": "widerface"}) + monkeypatch.setattr( + eval_widerface_module, + "_load_widerface_image_names", + lambda _: {"0--Parade": {"sample.jpg"}}, + ) + monkeypatch.setattr( + eval_widerface_module, "_widerface_difficulty_metadata_ready", lambda *_: True + ) + monkeypatch.setattr(eval_widerface_module, "CustomWiderface", lambda _: _Dataset()) + monkeypatch.setattr( + eval_widerface_module, + "get_widerface_loader", + lambda *_: pytest.fail("mismatched image trees must not create a loader"), + ) + + with pytest.raises(ValueError, match="does not match the validation metadata"): + eval_widerface_module.eval_widerface(model, "/dataset", batch_size=1) From 2ce00fb25c7c0b3bf37fe8516c2460ee8eb2df3c Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 18 Aug 2026 09:39:56 +0900 Subject: [PATCH 43/48] fix: validate direct vision evaluation --- mblt_vision/utils/datasets/readiness.py | 102 +++++++++++++--------- mblt_vision/utils/evaluation/eval_coco.py | 44 +++++++--- tests/test_dataset_readiness.py | 26 ++++++ tests/test_eval_coco.py | 48 +++++++++- 4 files changed, 163 insertions(+), 57 deletions(-) diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 0185942..823e2b4 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -216,17 +216,15 @@ def _load_coco_image_names( or len(categories) != expected_categories ): return None - category_ids = [ - category.get("id") for category in categories if isinstance(category, dict) - ] - if ( - len(category_ids) != len(categories) - or any( - not isinstance(category_id, int) or isinstance(category_id, bool) - for category_id in category_ids - ) - or len(category_ids) != len(set(category_ids)) - ): + category_ids: list[int] = [] + for category in categories: + if not isinstance(category, dict): + return None + category_id = category.get("id") + if not isinstance(category_id, int) or isinstance(category_id, bool): + return None + category_ids.append(category_id) + if len(category_ids) != len(set(category_ids)): return None expected_category_ids = ( COCO_PERSON_KEYPOINT_CATEGORY_IDS @@ -235,10 +233,31 @@ def _load_coco_image_names( ) if set(category_ids) != expected_category_ids: return None + if not _coco_task_annotations_valid( + annotation_records, + image_ids=set(image_ids), + category_ids=set(category_ids), + image_shapes=image_shapes, + task=task, + ): + return None + return set(names) + + +def _coco_task_annotations_valid( + annotation_records: list[Any], + *, + image_ids: set[int], + category_ids: set[int], + image_shapes: dict[int, tuple[int, int] | None], + task: str, +) -> bool: + """Validate task-specific COCO annotation payloads for readiness and APIs.""" + annotation_ids: list[int] = [] for record in annotation_records: if not isinstance(record, dict): - return None + return False annotation_id = record.get("id") image_id = record.get("image_id") category_id = record.get("category_id") @@ -248,27 +267,25 @@ def _load_coco_image_names( or not isinstance(image_id, int) or isinstance(image_id, bool) or image_id not in image_ids + or not isinstance(category_id, int) + or isinstance(category_id, bool) or category_id not in category_ids ): - return None - if annotation_path.name in { - "instances_val2017.json", - "person_keypoints_val2017.json", - }: - bbox = record.get("bbox") - if ( - not isinstance(bbox, list) - or len(bbox) != 4 - or any( - not isinstance(value, (int, float)) - or isinstance(value, bool) - or not np.isfinite(value) - for value in bbox - ) - or bbox[2] <= 0 - or bbox[3] <= 0 - ): - return None + return False + bbox = record.get("bbox") + if ( + not isinstance(bbox, list) + or len(bbox) != 4 + or any( + not isinstance(value, (int, float)) + or isinstance(value, bool) + or not np.isfinite(value) + for value in bbox + ) + or bbox[2] <= 0 + or bbox[3] <= 0 + ): + return False area = record.get("area") iscrowd = record.get("iscrowd") if ( @@ -280,7 +297,7 @@ def _load_coco_image_names( or isinstance(iscrowd, bool) or iscrowd not in {0, 1} ): - return None + return False if task == "instance_segmentation": segmentation = record.get("segmentation") if isinstance(segmentation, list): @@ -297,12 +314,12 @@ def _load_coco_image_names( or not _has_positive_polygon_area(polygon) for polygon in segmentation ): - return None + return False elif isinstance(segmentation, dict): if not _valid_coco_rle(segmentation, image_shapes.get(image_id)): - return None + return False else: - return None + return False if task == "pose_estimation": keypoints = record.get("keypoints") num_keypoints = record.get("num_keypoints") @@ -325,11 +342,9 @@ def _load_coco_image_names( or num_keypoints != sum(keypoints[index] > 0 for index in range(2, len(keypoints), 3)) ): - return None + return False annotation_ids.append(annotation_id) - if len(annotation_ids) != len(set(annotation_ids)): - return None - return set(names) + return len(annotation_ids) == len(set(annotation_ids)) def _decode_coco_rle_counts(counts: str) -> list[int] | None: @@ -547,8 +562,13 @@ def _widerface_difficulty_metadata_ready( return False if keep_indices.ndim == 0: keep_indices = keep_indices.reshape(1) - elif keep_indices.ndim == 2 and keep_indices.shape[1] == 1: - keep_indices = keep_indices[:, 0] + elif keep_indices.ndim == 2: + if keep_indices.size == 0: + keep_indices = keep_indices.reshape(0) + elif keep_indices.shape[1] == 1: + keep_indices = keep_indices[:, 0] + else: + return False elif keep_indices.ndim != 1: return False try: diff --git a/mblt_vision/utils/evaluation/eval_coco.py b/mblt_vision/utils/evaluation/eval_coco.py index 8726f67..92175f3 100644 --- a/mblt_vision/utils/evaluation/eval_coco.py +++ b/mblt_vision/utils/evaluation/eval_coco.py @@ -14,6 +14,7 @@ from ..._tasks import normalize_vision_task from ...datasets import get_dataset_category_ids from ..datasets import CustomCOCODataset, get_coco_loader +from ..datasets.readiness import _coco_task_annotations_valid if TYPE_CHECKING: from ...wrapper import MBLT_Engine @@ -79,21 +80,38 @@ def _validate_coco_dataset_taxonomy(dataset: CustomCOCODataset, task: str) -> No annotations = getattr(dataset.coco, "anns", None) if not isinstance(annotations, dict): raise ValueError("COCO evaluation dataset must define an annotation table.") - for annotation_id, annotation in annotations.items(): - if not isinstance(annotation, dict): - raise ValueError( - f"COCO evaluation dataset has an invalid annotation {annotation_id!r}." - ) - category_id = annotation.get("category_id") + images = getattr(dataset.coco, "imgs", None) + if not isinstance(images, dict): + raise ValueError("COCO evaluation dataset must define an image table.") + image_shapes: dict[int, tuple[int, int] | None] = {} + for image_id, image in images.items(): if ( - not isinstance(category_id, int) - or isinstance(category_id, bool) - or category_id not in category_ids + not isinstance(image_id, int) + or isinstance(image_id, bool) + or not isinstance(image, dict) ): - raise ValueError( - "COCO evaluation annotation references an undeclared or unsupported " - f"category ID {category_id!r}: annotation {annotation_id!r}." - ) + raise ValueError("COCO evaluation dataset contains invalid image metadata.") + height, width = image.get("height"), image.get("width") + image_shapes[image_id] = ( + (height, width) + if isinstance(height, int) + and not isinstance(height, bool) + and height > 0 + and isinstance(width, int) + and not isinstance(width, bool) + and width > 0 + else None + ) + if not _coco_task_annotations_valid( + list(annotations.values()), + image_ids=set(images), + category_ids=category_ids, + image_shapes=image_shapes, + task=task, + ): + raise ValueError( + "COCO evaluation dataset contains invalid task-specific annotations." + ) def format_coco_results( diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index f6db87f..8dcdcc8 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -519,6 +519,32 @@ def _loadmat(path: Path) -> dict[str, np.ndarray]: ) +def test_widerface_readiness_accepts_empty_difficulty_indices( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Keep valid images with no eligible faces in a difficulty split.""" + + face_boxes = np.empty((1, 1), dtype=object) + event_faces = np.empty((1, 1), dtype=object) + event_faces[0, 0] = np.array([[0, 0, 1, 1]], dtype=np.float64) + face_boxes[0, 0] = event_faces + difficulty = np.empty((1, 1), dtype=object) + event_indices = np.empty((1, 1), dtype=object) + event_indices[0, 0] = np.empty((0, 0), dtype=np.int64) + difficulty[0, 0] = event_indices + + def _loadmat(path: Path) -> dict[str, np.ndarray]: + if path.name == "wider_face_val.mat": + return {"face_bbx_list": face_boxes} + return {"gt_list": difficulty} + + monkeypatch.setattr(readiness, "loadmat", _loadmat) + + assert readiness._widerface_difficulty_metadata_ready( + tmp_path, {"0--Parade": {"sample.jpg"}} + ) + + @pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) def test_dotav1_readiness_requires_complete_image_label_pairs( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index ec3aa12..4b4366d 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -25,7 +25,11 @@ def __init__(self, root: str, annotation_path: str, **kwargs: Any) -> None: root=root, annotation_path=annotation_path, **kwargs ) self.ids = [1, 2] - self.coco = SimpleNamespace(cats={1: {}}, anns={}) + self.coco = SimpleNamespace( + cats={1: {}}, + anns={}, + imgs={1: {"height": 1, "width": 1}, 2: {"height": 1, "width": 1}}, + ) def __len__(self) -> int: return len(self.ids) @@ -98,11 +102,15 @@ def test_coco_evaluation_rejects_undeclared_annotation_categories( """Do not let malformed annotation categories be omitted from direct AP.""" dataset = SimpleNamespace( - coco=SimpleNamespace(cats={1: {}}, anns={7: {"category_id": 999}}) + coco=SimpleNamespace( + cats={1: {}}, + anns={7: {"category_id": 999}}, + imgs={1: {"height": 1, "width": 1}}, + ) ) monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) - with pytest.raises(ValueError, match="undeclared or unsupported"): + with pytest.raises(ValueError, match="invalid task-specific annotations"): eval_coco_module.eval_coco_metrics( SimpleNamespace(post_cfg={"task": "object_detection", "dataset": "coco"}), "/dataset", @@ -110,6 +118,40 @@ def test_coco_evaluation_rejects_undeclared_annotation_categories( ) +def test_coco_evaluation_rejects_invalid_task_payloads( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Use readiness-equivalent checks before direct COCO evaluation starts.""" + + dataset = SimpleNamespace( + coco=SimpleNamespace( + cats={1: {}}, + imgs={1: {"height": 2, "width": 2}}, + anns={ + 7: { + "id": 7, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": -1, + "iscrowd": 0, + "segmentation": [[0, 0, 1, 0, 1, 1]], + } + }, + ) + ) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match="invalid task-specific annotations"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace( + post_cfg={"task": "instance_segmentation", "dataset": "coco"} + ), + "/dataset", + batch_size=1, + ) + + def test_coco_result_formatter_rejects_truncated_postprocess_batch() -> None: """Require one decoded result for every submitted COCO image.""" From 065bc47daea3b4491164acff6a7692db411f5979 Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 18 Aug 2026 10:25:35 +0900 Subject: [PATCH 44/48] fix: validate vision ground truth geometry --- mblt_vision/utils/datasets/organizer.py | 34 ++++ mblt_vision/utils/datasets/readiness.py | 183 ++++++++++++++++-- mblt_vision/utils/evaluation/eval_dota.py | 35 +++- .../utils/evaluation/eval_widerface.py | 19 +- tests/test_dataset_readiness.py | 33 +++- tests/test_eval_coco.py | 34 ++++ tests/test_eval_dota.py | 50 +++++ tests/test_eval_widerface.py | 27 ++- 8 files changed, 393 insertions(+), 22 deletions(-) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 26556be..33111f3 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -38,8 +38,10 @@ CITYSCAPES_SAMPLE_ID_PATTERN, CITYSCAPES_VALIDATION_SAMPLE_COUNT, DOTAV1_VALIDATION_SAMPLE_COUNT, + IMAGE_SUFFIXES, NYU_DEPTH_VALIDATION_SAMPLE_COUNT, _path_has_symlink_component, + _polygon_has_positive_image_overlap, dataset_ready, ) @@ -327,9 +329,23 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: "normalized": staged_root / "labels" / "val", "original": staged_root / "labels" / "val_original", } + image_paths = { + path.stem: path + for path in (staged_root / "images").iterdir() + if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES + } valid_indices = set(DOTAV1_CLASS_TO_IDX.values()) for kind, label_dir in label_dirs.items(): for label_path in sorted(label_dir.glob("*.txt")): + image_path = image_paths.get(label_path.stem) + if image_path is None: + raise ValueError( + f"Staged DOTAv1 label has no matching image: {label_path}." + ) + image = cv2.imread(str(image_path), cv2.IMREAD_COLOR) + if image is None: + raise ValueError(f"Unable to decode staged DOTAv1 image: {image_path}.") + height, width = image.shape[:2] for line_number, line in enumerate( label_path.read_text(encoding="utf-8").splitlines(), start=1 ): @@ -371,6 +387,19 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: raise ValueError( f"Staged DOTAv1 polygon must have positive area at {label_path}:{line_number}." ) + image_coordinates = coordinates.copy() + if kind == "normalized": + image_coordinates = [ + coordinate * (width if index % 2 == 0 else height) + for index, coordinate in enumerate(coordinates) + ] + if not _polygon_has_positive_image_overlap( + image_coordinates, (height, width) + ): + raise ValueError( + "Staged DOTAv1 polygon must overlap its source image at " + f"{label_path}:{line_number}." + ) if kind == "normalized": try: class_index = int(fields[0]) @@ -1818,6 +1847,11 @@ def _write_dotav1_yolo_labels( f"DOTAv1 coordinates must be finite in {original_label_path} " f"at line {line_number}." ) + if not _polygon_has_positive_image_overlap(coordinates, (height, width)): + raise ValueError( + "DOTAv1 polygon must overlap its source image in " + f"{original_label_path} at line {line_number}." + ) if fields[9] not in {"0", "1", "2"}: raise ValueError( f"Unsupported DOTAv1 difficulty flag {fields[9]!r} in " diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 823e2b4..bec35e6 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -13,6 +13,7 @@ import numpy as np from faster_coco_eval import mask as coco_mask +from PIL import Image from scipy.io import loadmat from scipy.io.matlab import MatReadError @@ -118,6 +119,84 @@ def _has_positive_polygon_area(polygon: list[int | float]) -> bool: return bool(abs(signed_double_area) > 0) +def _polygon_has_positive_image_overlap( + polygon: list[int | float], image_shape: tuple[int, int] | None +) -> bool: + """Return whether a polygon covers non-zero area within an image rectangle.""" + + if image_shape is None: + return True + height, width = image_shape + if height <= 0 or width <= 0: + return False + points = [ + tuple(point) for point in np.asarray(polygon, dtype=np.float64).reshape(-1, 2) + ] + + def _clip( + vertices: list[tuple[float, float]], + inside: Any, + intersect: Any, + ) -> list[tuple[float, float]]: + clipped: list[tuple[float, float]] = [] + if not vertices: + return clipped + previous = vertices[-1] + previous_inside = bool(inside(previous)) + for current in vertices: + current_inside = bool(inside(current)) + if current_inside != previous_inside: + clipped.append(intersect(previous, current)) + if current_inside: + clipped.append(current) + previous = current + previous_inside = current_inside + return clipped + + def _vertical_intersection( + x: float, start: tuple[float, float], end: tuple[float, float] + ) -> tuple[float, float]: + delta_x = end[0] - start[0] + if delta_x == 0: + return x, start[1] + ratio = (x - start[0]) / delta_x + return x, start[1] + ratio * (end[1] - start[1]) + + def _horizontal_intersection( + y: float, start: tuple[float, float], end: tuple[float, float] + ) -> tuple[float, float]: + delta_y = end[1] - start[1] + if delta_y == 0: + return start[0], y + ratio = (y - start[1]) / delta_y + return start[0] + ratio * (end[0] - start[0]), y + + points = _clip( + points, + lambda point: point[0] >= 0, + lambda a, b: _vertical_intersection(0, a, b), + ) + points = _clip( + points, + lambda point: point[0] <= width, + lambda a, b: _vertical_intersection(width, a, b), + ) + points = _clip( + points, + lambda point: point[1] >= 0, + lambda a, b: _horizontal_intersection(0, a, b), + ) + points = _clip( + points, + lambda point: point[1] <= height, + lambda a, b: _horizontal_intersection(height, a, b), + ) + if len(points) < 3: + return False + clipped = [coordinate for point in points for coordinate in point] + return _has_positive_polygon_area(clipped) + + def _imagenet_ready(root: Path) -> bool: """Check the organizer's complete ImageNet-1k validation class tree.""" @@ -312,6 +391,9 @@ def _coco_task_annotations_valid( for value in polygon ) or not _has_positive_polygon_area(polygon) + or not _polygon_has_positive_image_overlap( + polygon, image_shapes.get(image_id) + ) for polygon in segmentation ): return False @@ -501,18 +583,25 @@ def _widerface_ready(root: Path) -> bool: } for event_dir in event_dirs } + image_shapes = _widerface_image_shapes(root, expected_images) return ( actual_images == expected_images and sum(len(image_names) for image_names in actual_images.values()) == WIDERFACE_VALIDATION_SAMPLE_COUNT - and _widerface_difficulty_metadata_ready(root, expected_images) + and image_shapes is not None + and _widerface_difficulty_metadata_ready( + root, expected_images, image_shapes=image_shapes + ) ) def _widerface_difficulty_metadata_ready( - root: Path, expected_images: dict[str, set[str]] + root: Path, + expected_images: dict[str, set[str]], + *, + image_shapes: list[list[tuple[int, int]]] | None = None, ) -> bool: - """Validate WiderFace difficulty table dimensions and one-based face indices.""" + """Validate WiderFace difficulty metadata and its decoded-image geometry.""" try: main = loadmat(root / "wider_face_val.mat") @@ -531,6 +620,8 @@ def _widerface_difficulty_metadata_ready( len(table) != len(expected_images) for table in difficulties ): return False + if image_shapes is not None and len(image_shapes) != len(expected_images): + return False for event_index, image_names in enumerate(expected_images.values()): try: event_faces = face_boxes[event_index][0] @@ -538,6 +629,38 @@ def _widerface_difficulty_metadata_ready( return False if len(event_faces) != len(image_names): return False + if image_shapes is not None and len(image_shapes[event_index]) != len( + image_names + ): + return False + face_counts: list[int] = [] + for image_index, face_entry in enumerate(event_faces): + try: + face_array = np.asarray(face_entry[0]) + except (IndexError, TypeError): + return False + if ( + face_array.ndim != 2 + or face_array.shape[1] != 4 + or len(face_array) == 0 + or not np.isfinite(face_array).all() + or (face_array[:, 2:] <= 0).any() + ): + return False + if image_shapes is not None: + height, width = image_shapes[event_index][image_index] + if ( + height <= 0 + or width <= 0 + or not ( + (face_array[:, 0] < width) + & (face_array[:, 0] + face_array[:, 2] > 0) + & (face_array[:, 1] < height) + & (face_array[:, 1] + face_array[:, 3] > 0) + ).all() + ): + return False + face_counts.append(len(face_array)) for table in difficulties: try: event_indices = table[event_index][0] @@ -545,18 +668,8 @@ def _widerface_difficulty_metadata_ready( return False if len(event_indices) != len(image_names): return False - for image_index, face_entry in enumerate(event_faces): + for image_index in range(len(event_faces)): try: - face_array = np.asarray(face_entry[0]) - if ( - face_array.ndim != 2 - or face_array.shape[1] != 4 - or len(face_array) == 0 - or not np.isfinite(face_array).all() - or (face_array[:, 2:] <= 0).any() - ): - return False - face_count = len(face_array) keep_indices = np.asarray(event_indices[image_index][0]) except (IndexError, TypeError): return False @@ -581,7 +694,8 @@ def _widerface_difficulty_metadata_ready( if not valid_indices: return False if keep_indices.size and ( - int(keep_indices.min()) < 1 or int(keep_indices.max()) > face_count + int(keep_indices.min()) < 1 + or int(keep_indices.max()) > face_counts[image_index] ): return False if len(np.unique(keep_indices)) != keep_indices.size: @@ -633,6 +747,45 @@ def _load_widerface_image_names(annotation_path: Path) -> dict[str, set[str]] | return expected +def _widerface_image_shapes( + root: Path, expected_images: dict[str, set[str]] +) -> list[list[tuple[int, int]]] | None: + """Load WiderFace image shapes in the annotation's event and file order.""" + + try: + annotation = loadmat(root / "wider_face_val.mat") + event_list = annotation["event_list"] + file_list = annotation["file_list"] + except (IndexError, KeyError, MatReadError, OSError, TypeError, ValueError): + return None + if len(event_list) != len(file_list) or len(event_list) != len(expected_images): + return None + + all_shapes: list[list[tuple[int, int]]] = [] + for event_cell, file_cell in zip(event_list, file_list, strict=True): + event_names = _flatten_matlab_strings(event_cell) + image_stems = _flatten_matlab_strings(file_cell) + if ( + len(event_names) != 1 + or not image_stems + or expected_images.get(event_names[0]) + != {f"{stem}.jpg" for stem in image_stems} + ): + return None + event_shapes: list[tuple[int, int]] = [] + for stem in image_stems: + try: + with Image.open( + root / "images" / event_names[0] / f"{stem}.jpg" + ) as image: + width, height = image.size + except OSError: + return None + event_shapes.append((height, width)) + all_shapes.append(event_shapes) + return all_shapes + + def dense_dataset_ready(data_path: str | Path, dataset: str) -> bool: """Return whether a dense dataset matches its taxonomy and full validation split. diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 019351e..98ec17e 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -26,6 +26,7 @@ get_dotav1_class_num, get_dotav1_label, ) +from ..datasets.readiness import _polygon_has_positive_image_overlap from ..letterbox import RatioPad, resolve_ratio_pad if TYPE_CHECKING: @@ -78,6 +79,22 @@ def _validate_polygon_area( ) +def _validate_polygon_image_overlap( + coords: torch.Tensor, + image_shape: tuple[int, int], + annotation_path: Path, + line_number: int, +) -> None: + """Require a quadrilateral to cover non-zero area inside its source image.""" + + polygon = [coordinate for point in coords.tolist() for coordinate in point] + if not _polygon_has_positive_image_overlap(polygon, image_shape): + raise ValueError( + "DOTAv1 annotation polygon must overlap its source image at " + f"{annotation_path}:{line_number}." + ) + + def _load_ground_truths( data_path: str, dataset: CustomDOTAv1 ) -> dict[str, dict[str, torch.Tensor]]: @@ -93,6 +110,16 @@ def _load_ground_truths( """ label_dir = Path(data_path) / "labels" / "val" original_label_dir = Path(data_path) / "labels" / "val_original" + image_ids = set(dataset.ids) + label_ids = {path.stem for path in label_dir.glob("*.txt")} | { + path.stem for path in original_label_dir.glob("*.txt") + } + orphan_label_ids = sorted(label_ids - image_ids) + if orphan_label_ids: + raise ValueError( + "DOTAv1 annotations have no corresponding validation image: " + f"{', '.join(orphan_label_ids[:5])}." + ) ground_truths: dict[str, dict[str, torch.Tensor]] = {} for image_id, image_path in zip(dataset.ids, dataset.image_paths): image = dataset._load_image(image_path) @@ -137,6 +164,9 @@ def _load_ground_truths( coords[:, 0] *= width coords[:, 1] *= height _validate_polygon_area(coords, label_path, line_number) + _validate_polygon_image_overlap( + coords, (height, width), label_path, line_number + ) if len(parts) >= 10 and parts[9] not in {"0", "1", "2"}: raise ValueError( f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " @@ -177,6 +207,9 @@ def _load_ground_truths( f"{original_label_path}:{line_number}." ) _validate_polygon_area(coords, original_label_path, line_number) + _validate_polygon_image_overlap( + coords, (height, width), original_label_path, line_number + ) if parts[9] not in {"0", "1", "2"}: raise ValueError( f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " @@ -503,7 +536,7 @@ def _evaluate_stats(stats: dict[str, list[np.ndarray]], niou: int = 10) -> DOTAR else np.zeros(0, dtype=np.float64) ) if target_cls.size == 0: - return DOTAResult(map5095=0.0, map50=0.0) + raise ValueError("DOTAv1 evaluation requires at least one non-ignored target.") tp = ( np.concatenate(stats["tp"], 0) diff --git a/mblt_vision/utils/evaluation/eval_widerface.py b/mblt_vision/utils/evaluation/eval_widerface.py index 757dce1..148e332 100644 --- a/mblt_vision/utils/evaluation/eval_widerface.py +++ b/mblt_vision/utils/evaluation/eval_widerface.py @@ -16,6 +16,7 @@ from ..datasets import CustomWiderFaceDataset, get_widerface_loader from ..datasets.readiness import ( _load_widerface_image_names, + _widerface_image_shapes, _widerface_difficulty_metadata_ready, ) @@ -75,6 +76,11 @@ def _boxes_scores_to_prediction( ) -> np.ndarray: """Convert xywh boxes and scores to a WiderFace prediction array.""" + if len(boxes) != len(scores): + raise ValueError( + "WiderFace postprocess returned unequal box and score counts: " + f"boxes={len(boxes)}, scores={len(scores)}." + ) if not boxes: return _empty_prediction() prediction = np.zeros((len(boxes), 5), dtype=np.float32) @@ -117,8 +123,17 @@ def eval_widerface( dataset_root = Path(data_path) expected_images = _load_widerface_image_names(dataset_root / "wider_face_val.mat") - if expected_images is None or not _widerface_difficulty_metadata_ready( - dataset_root, expected_images + image_shapes = ( + _widerface_image_shapes(dataset_root, expected_images) + if expected_images is not None + else None + ) + if ( + expected_images is None + or image_shapes is None + or not _widerface_difficulty_metadata_ready( + dataset_root, expected_images, image_shapes=image_shapes + ) ): raise ValueError( "WiderFace evaluation metadata is malformed or has inconsistent difficulty indices." diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index 8dcdcc8..94d3f5c 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -545,6 +545,34 @@ def _loadmat(path: Path) -> dict[str, np.ndarray]: ) +def test_widerface_readiness_rejects_face_boxes_outside_images( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Face ground truth must retain foreground in its decoded source image.""" + + face_boxes = np.empty((1, 1), dtype=object) + event_faces = np.empty((1, 1), dtype=object) + event_faces[0, 0] = np.array([[11, 0, 1, 1]], dtype=np.float64) + face_boxes[0, 0] = event_faces + difficulty = np.empty((1, 1), dtype=object) + event_indices = np.empty((1, 1), dtype=object) + event_indices[0, 0] = np.array([1], dtype=np.int64) + difficulty[0, 0] = event_indices + + def _loadmat(path: Path) -> dict[str, np.ndarray]: + if path.name == "wider_face_val.mat": + return {"face_bbx_list": face_boxes} + return {"gt_list": difficulty} + + monkeypatch.setattr(readiness, "loadmat", _loadmat) + + assert not readiness._widerface_difficulty_metadata_ready( + tmp_path, + {"0--Parade": {"sample.jpg"}}, + image_shapes=[[(10, 10)]], + ) + + @pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) def test_dotav1_readiness_requires_complete_image_label_pairs( monkeypatch: pytest.MonkeyPatch, @@ -587,7 +615,10 @@ def test_widerface_readiness_requires_complete_event_tree_and_metadata( monkeypatch.setattr(readiness, "WIDERFACE_EVENT_COUNT", 2) monkeypatch.setattr(readiness, "WIDERFACE_VALIDATION_SAMPLE_COUNT", 2) monkeypatch.setattr( - readiness, "_widerface_difficulty_metadata_ready", lambda *_: True + readiness, "_widerface_difficulty_metadata_ready", lambda *_, **__: True + ) + monkeypatch.setattr( + readiness, "_widerface_image_shapes", lambda *_: [[(1, 1)], [(1, 1)]] ) _write_widerface_metadata( tmp_path / "wider_face_val.mat", diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index 4b4366d..88af5ef 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -152,6 +152,40 @@ def test_coco_evaluation_rejects_invalid_task_payloads( ) +def test_coco_evaluation_rejects_polygons_outside_image_bounds( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Do not let a nonempty off-image polygon become an empty COCO mask.""" + + dataset = SimpleNamespace( + coco=SimpleNamespace( + cats={1: {}}, + imgs={1: {"height": 10, "width": 10}}, + anns={ + 7: { + "id": 7, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, + "segmentation": [[11, 0, 12, 0, 12, 1, 11, 1]], + } + }, + ) + ) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match="invalid task-specific annotations"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace( + post_cfg={"task": "instance_segmentation", "dataset": "coco"} + ), + "/dataset", + batch_size=1, + ) + + def test_coco_result_formatter_rejects_truncated_postprocess_batch() -> None: """Require one decoded result for every submitted COCO image.""" diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index c0725ff..c48c55e 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -204,6 +204,49 @@ def test_dota_annotations_reject_degenerate_polygons( _load_ground_truths(str(tmp_path), dataset) +@pytest.mark.parametrize( + ("label_path", "annotation"), + [ + ("labels/val/image.txt", "0 1.1 0 1.2 0 1.2 0.1 1.1 0.1"), + ("labels/val_original/image.txt", "110 0 120 0 120 10 110 10 plane 0"), + ], +) +def test_dota_annotations_reject_polygons_outside_images( + tmp_path, label_path: str, annotation: str +) -> None: + """Ground-truth polygons must retain foreground within their source images.""" + + path = tmp_path / label_path + path.parent.mkdir(parents=True) + path.write_text(annotation, encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises(ValueError, match="must overlap its source image"): + _load_ground_truths(str(tmp_path), dataset) + + +def test_dota_ground_truth_rejects_orphan_label_files(tmp_path) -> None: + """Direct evaluation must not ignore labels without a selected image.""" + + label_path = tmp_path / "labels" / "val" / "orphan.txt" + label_path.parent.mkdir(parents=True) + label_path.write_text("0 0 0 0.2 0 0.2 0.2 0 0.2", encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace(ids=[], image_paths=[], _load_image=lambda _: None), + ) + + with pytest.raises(ValueError, match="no corresponding validation image"): + _load_ground_truths(str(tmp_path), dataset) + + @pytest.mark.parametrize( ("label_path", "annotation"), [ @@ -329,6 +372,13 @@ def test_dota_ap_interpolation_uses_terminal_recall_sentinel() -> None: assert ap == pytest.approx(0.75) +def test_dota_metrics_require_non_ignored_ground_truth() -> None: + """Empty or difficult-only labels must not yield a normal AP result.""" + + with pytest.raises(ValueError, match="at least one non-ignored target"): + eval_dota_module._evaluate_stats(eval_dota_module._empty_stats()) + + def test_dota_export_rejects_truncated_converted_batches() -> None: """Do not silently omit Task1 files when converted output batches are short.""" diff --git a/tests/test_eval_widerface.py b/tests/test_eval_widerface.py index 1425671..d86f651 100644 --- a/tests/test_eval_widerface.py +++ b/tests/test_eval_widerface.py @@ -81,7 +81,14 @@ def postprocess(self, outputs: torch.Tensor) -> SimpleNamespace: lambda _: {"event": {"first.jpg", "second.jpg"}}, ) monkeypatch.setattr( - eval_widerface_module, "_widerface_difficulty_metadata_ready", lambda *_: True + eval_widerface_module, + "_widerface_difficulty_metadata_ready", + lambda *_, **__: True, + ) + monkeypatch.setattr( + eval_widerface_module, + "_widerface_image_shapes", + lambda *_: [[(8, 8), (8, 8)]], ) monkeypatch.setattr( eval_widerface_module, "get_widerface_loader", lambda *_: [batch] @@ -104,7 +111,9 @@ def test_widerface_evaluation_rejects_malformed_difficulty_metadata( lambda _: {"0--Parade": {"sample.jpg"}}, ) monkeypatch.setattr( - eval_widerface_module, "_widerface_difficulty_metadata_ready", lambda *_: False + eval_widerface_module, + "_widerface_difficulty_metadata_ready", + lambda *_, **__: False, ) monkeypatch.setattr( eval_widerface_module, @@ -133,7 +142,12 @@ class _Dataset: lambda _: {"0--Parade": {"sample.jpg"}}, ) monkeypatch.setattr( - eval_widerface_module, "_widerface_difficulty_metadata_ready", lambda *_: True + eval_widerface_module, + "_widerface_difficulty_metadata_ready", + lambda *_, **__: True, + ) + monkeypatch.setattr( + eval_widerface_module, "_widerface_image_shapes", lambda *_: [[(8, 8)]] ) monkeypatch.setattr(eval_widerface_module, "CustomWiderface", lambda _: _Dataset()) monkeypatch.setattr( @@ -144,3 +158,10 @@ class _Dataset: with pytest.raises(ValueError, match="does not match the validation metadata"): eval_widerface_module.eval_widerface(model, "/dataset", batch_size=1) + + +def test_widerface_evaluation_rejects_unequal_box_and_score_counts() -> None: + """Do not fabricate or drop face detections during result conversion.""" + + with pytest.raises(ValueError, match="unequal box and score counts"): + eval_widerface_module._boxes_scores_to_prediction([[0, 0, 1, 1]], []) From ee9150b1c1c386691fe9ac0b2c3a12fbc04bcaea Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 18 Aug 2026 10:38:30 +0900 Subject: [PATCH 45/48] fix: validate vision evaluation geometry --- mblt_vision/utils/datasets/organizer.py | 14 +++++ mblt_vision/utils/datasets/readiness.py | 34 +++++++++++ mblt_vision/utils/evaluation/eval_dota.py | 5 +- tests/test_dataset_organizer.py | 14 +++++ tests/test_dataset_readiness.py | 6 +- tests/test_eval_coco.py | 69 +++++++++++++++++++++++ tests/test_eval_dota.py | 22 ++++++++ 7 files changed, 158 insertions(+), 6 deletions(-) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 33111f3..b3545b4 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -335,8 +335,11 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES } valid_indices = set(DOTAV1_CLASS_TO_IDX.values()) + label_stems = {kind: set() for kind in label_dirs} + positive_stems = {kind: set() for kind in label_dirs} for kind, label_dir in label_dirs.items(): for label_path in sorted(label_dir.glob("*.txt")): + label_stems[kind].add(label_path.stem) image_path = image_paths.get(label_path.stem) if image_path is None: raise ValueError( @@ -346,6 +349,7 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: if image is None: raise ValueError(f"Unable to decode staged DOTAv1 image: {image_path}.") height, width = image.shape[:2] + has_positive_target = False for line_number, line in enumerate( label_path.read_text(encoding="utf-8").splitlines(), start=1 ): @@ -422,6 +426,16 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: raise ValueError( f"Unsupported staged DOTAv1 difficulty flag at {label_path}:{line_number}." ) + has_positive_target |= difficulty == "0" + if has_positive_target: + positive_stems[kind].add(label_path.stem) + authoritative_positive_stems = positive_stems["original"] | ( + positive_stems["normalized"] - label_stems["original"] + ) + if not authoritative_positive_stems: + raise ValueError( + "Staged DOTAv1 dataset must contain at least one non-difficult target." + ) class _GoogleDriveDownloadEntry(Protocol): diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index bec35e6..e42aae7 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -394,6 +394,7 @@ def _coco_task_annotations_valid( or not _polygon_has_positive_image_overlap( polygon, image_shapes.get(image_id) ) + or not _valid_coco_polygon(polygon, image_shapes.get(image_id)) for polygon in segmentation ): return False @@ -425,6 +426,18 @@ def _coco_task_annotations_valid( != sum(keypoints[index] > 0 for index in range(2, len(keypoints), 3)) ): return False + image_shape = image_shapes.get(image_id) + if image_shape is not None: + height, width = image_shape + if any( + keypoints[index + 2] == 2 + and not ( + 0 <= keypoints[index] < width + and 0 <= keypoints[index + 1] < height + ) + for index in range(0, len(keypoints), 3) + ): + return False annotation_ids.append(annotation_id) return len(annotation_ids) == len(set(annotation_ids)) @@ -504,6 +517,22 @@ def _valid_coco_rle( return decoded.shape == tuple(size) and bool(np.any(decoded)) +def _valid_coco_polygon( + polygon: list[int | float], image_shape: tuple[int, int] | None +) -> bool: + """Require a COCO polygon to rasterize to foreground in its image.""" + + if image_shape is None: + return True + height, width = image_shape + try: + encoded = coco_mask.frPyObjects([polygon], height, width) + decoded = np.asarray(coco_mask.decode(encoded)) + except (RuntimeError, TypeError, ValueError): + return False + return bool(np.any(decoded)) + + def _coco_ready(root: Path, task: str) -> bool: """Check the complete COCO 2017 image split and task annotation metadata.""" @@ -668,6 +697,7 @@ def _widerface_difficulty_metadata_ready( return False if len(event_indices) != len(image_names): return False + table_has_eligible_face = False for image_index in range(len(event_faces)): try: keep_indices = np.asarray(event_indices[image_index][0]) @@ -700,6 +730,9 @@ def _widerface_difficulty_metadata_ready( return False if len(np.unique(keep_indices)) != keep_indices.size: return False + table_has_eligible_face |= bool(keep_indices.size) + if not table_has_eligible_face: + return False return True @@ -778,6 +811,7 @@ def _widerface_image_shapes( with Image.open( root / "images" / event_names[0] / f"{stem}.jpg" ) as image: + image.load() width, height = image.size except OSError: return None diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 98ec17e..828b2fa 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -160,9 +160,8 @@ def _load_ground_truths( "DOTAv1 annotation coordinates must be finite at " f"{label_path}:{line_number}." ) - if coords.numel() and float(coords.max()) <= 1.5: - coords[:, 0] *= width - coords[:, 1] *= height + coords[:, 0] *= width + coords[:, 1] *= height _validate_polygon_area(coords, label_path, line_number) _validate_polygon_image_overlap( coords, (height, width), label_path, line_number diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index 06f5c53..dcb9964 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -86,6 +86,20 @@ def test_dotav1_organizer_rejects_truncated_raw_annotation_rows( ) +def test_dotav1_stage_requires_a_non_difficult_target(tmp_path: Path) -> None: + """Do not replace a usable DOTAv1 cache with an unscorable staged split.""" + + image_path = tmp_path / "images" / "P0001.png" + image_path.parent.mkdir() + Image.new("RGB", (100, 50)).save(image_path) + label_path = tmp_path / "labels" / "val_original" / "P0001.txt" + label_path.parent.mkdir(parents=True) + label_path.write_text("0 0 20 0 20 20 0 20 plane 1\n", encoding="utf-8") + + with pytest.raises(ValueError, match="at least one non-difficult target"): + organizer._validate_staged_dotav1_labels(tmp_path) + + @pytest.mark.parametrize( ("organize_dataset", "dataset_name"), [ diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index 94d3f5c..f9d574d 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -519,10 +519,10 @@ def _loadmat(path: Path) -> dict[str, np.ndarray]: ) -def test_widerface_readiness_accepts_empty_difficulty_indices( +def test_widerface_readiness_rejects_all_empty_difficulty_indices( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: - """Keep valid images with no eligible faces in a difficulty split.""" + """Every complete WiderFace difficulty split needs an eligible face.""" face_boxes = np.empty((1, 1), dtype=object) event_faces = np.empty((1, 1), dtype=object) @@ -540,7 +540,7 @@ def _loadmat(path: Path) -> dict[str, np.ndarray]: monkeypatch.setattr(readiness, "loadmat", _loadmat) - assert readiness._widerface_difficulty_metadata_ready( + assert not readiness._widerface_difficulty_metadata_ready( tmp_path, {"0--Parade": {"sample.jpg"}} ) diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index 88af5ef..51f25ae 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -186,6 +186,75 @@ def test_coco_evaluation_rejects_polygons_outside_image_bounds( ) +def test_coco_evaluation_rejects_polygons_without_rasterized_foreground( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Continuous subpixel overlap must not admit an empty raster mask.""" + + dataset = SimpleNamespace( + coco=SimpleNamespace( + cats={1: {}}, + imgs={1: {"height": 10, "width": 10}}, + anns={ + 7: { + "id": 7, + "image_id": 1, + "category_id": 1, + "bbox": [9, 9, 1, 1], + "area": 1, + "iscrowd": 0, + "segmentation": [[9.9, 9.9, 9.99, 9.9, 9.99, 9.99]], + } + }, + ) + ) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match="invalid task-specific annotations"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace( + post_cfg={"task": "instance_segmentation", "dataset": "coco"} + ), + "/dataset", + batch_size=1, + ) + + +def test_coco_evaluation_rejects_visible_keypoints_outside_images( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Visible pose coordinates must refer to pixels in the source image.""" + + keypoints = [0.0, 0.0, 0.0] * 17 + keypoints[:3] = [11.0, 1.0, 2.0] + dataset = SimpleNamespace( + coco=SimpleNamespace( + cats={1: {}}, + imgs={1: {"height": 10, "width": 10}}, + anns={ + 7: { + "id": 7, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, + "keypoints": keypoints, + "num_keypoints": 1, + } + }, + ) + ) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match="invalid task-specific annotations"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace(post_cfg={"task": "pose_estimation", "dataset": "coco"}), + "/dataset", + batch_size=1, + ) + + def test_coco_result_formatter_rejects_truncated_postprocess_batch() -> None: """Require one decoded result for every submitted COCO image.""" diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index c48c55e..8963ead 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -232,6 +232,28 @@ def test_dota_annotations_reject_polygons_outside_images( _load_ground_truths(str(tmp_path), dataset) +def test_dota_normalized_coordinates_are_scaled_even_when_partially_clipped( + tmp_path, +) -> None: + """The normalized-label directory always uses normalized image coordinates.""" + + label_path = tmp_path / "labels" / "val" / "image.txt" + label_path.parent.mkdir(parents=True) + label_path.write_text("0 0.9 0 1.6 0 1.6 0.1 0.9 0.1", encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + ground_truth = _load_ground_truths(str(tmp_path), dataset)["image"] + + assert ground_truth["polygons"][0, :, 0].tolist() == [90.0, 160.0, 160.0, 90.0] + + def test_dota_ground_truth_rejects_orphan_label_files(tmp_path) -> None: """Direct evaluation must not ignore labels without a selected image.""" From a91d56dabe5f184da8f3ad9b3f062f83cd1f29c5 Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 18 Aug 2026 10:54:35 +0900 Subject: [PATCH 46/48] fix: validate vision dataset geometry --- mblt_vision/utils/datasets/organizer.py | 25 +++++ mblt_vision/utils/datasets/readiness.py | 47 +++++++- mblt_vision/utils/evaluation/eval_dota.py | 22 ++++ tests/test_dataset_organizer.py | 14 +++ tests/test_dataset_readiness.py | 129 ++++++++++++++++++++-- tests/test_eval_coco.py | 33 ++++++ tests/test_eval_dota.py | 28 +++++ 7 files changed, 285 insertions(+), 13 deletions(-) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index b3545b4..ade7dfb 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -391,6 +391,7 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: raise ValueError( f"Staged DOTAv1 polygon must have positive area at {label_path}:{line_number}." ) + _validate_dotav1_polygon_vertices(coordinates, label_path, line_number) image_coordinates = coordinates.copy() if kind == "normalized": image_coordinates = [ @@ -438,6 +439,27 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: ) +def _validate_dotav1_polygon_vertices( + coordinates: list[float], annotation_path: str | Path, line_number: int +) -> None: + """Require four distinct, consistently ordered DOTAv1 quadrilateral vertices.""" + + points = np.asarray(coordinates, dtype=np.float64).reshape(4, 2) + if len(np.unique(points, axis=0)) != 4: + raise ValueError( + "DOTAv1 polygon must contain four distinct vertices in " + f"{annotation_path} at line {line_number}." + ) + edges = np.roll(points, -1, axis=0) - points + next_edges = np.roll(edges, -1, axis=0) + turns = edges[:, 0] * next_edges[:, 1] - edges[:, 1] * next_edges[:, 0] + if not (np.all(turns > 0) or np.all(turns < 0)): + raise ValueError( + "DOTAv1 polygon vertices must be consistently ordered in " + f"{annotation_path} at line {line_number}." + ) + + class _GoogleDriveDownloadEntry(Protocol): """The public attributes needed from a gdown folder-listing entry.""" @@ -1866,6 +1888,9 @@ def _write_dotav1_yolo_labels( "DOTAv1 polygon must overlap its source image in " f"{original_label_path} at line {line_number}." ) + _validate_dotav1_polygon_vertices( + coordinates, original_label_path, line_number + ) if fields[9] not in {"0", "1", "2"}: raise ValueError( f"Unsupported DOTAv1 difficulty flag {fields[9]!r} in " diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index e42aae7..4fb982b 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -377,6 +377,8 @@ def _coco_task_annotations_valid( or iscrowd not in {0, 1} ): return False + if task == "pose_estimation" and area > bbox[2] * bbox[3]: + return False if task == "instance_segmentation": segmentation = record.get("segmentation") if isinstance(segmentation, list): @@ -557,7 +559,37 @@ def _coco_ready(root: Path, task: str) -> bool: for path in image_paths ): return False - return {path.name for path in image_paths} == annotation_names + if {path.name for path in image_paths} != annotation_names: + return False + try: + payload = json.loads((root / annotation_name).read_text(encoding="utf-8")) + image_records = payload["images"] + image_shapes = { + record["file_name"]: (record["height"], record["width"]) + for record in image_records + } + except (KeyError, TypeError, ValueError, json.JSONDecodeError, OSError): + return False + for image_path in image_paths: + expected_shape = image_shapes.get(image_path.name) + if ( + not isinstance(expected_shape, tuple) + or len(expected_shape) != 2 + or any( + not isinstance(value, int) or isinstance(value, bool) or value <= 0 + for value in expected_shape + ) + ): + return False + try: + with Image.open(image_path) as image: + image.load() + width, height = image.size + except OSError: + return False + if (height, width) != expected_shape: + return False + return True def _dotav1_ready(root: Path) -> bool: @@ -651,6 +683,7 @@ def _widerface_difficulty_metadata_ready( return False if image_shapes is not None and len(image_shapes) != len(expected_images): return False + difficulty_has_eligible_face = [False] * len(difficulties) for event_index, image_names in enumerate(expected_images.values()): try: event_faces = face_boxes[event_index][0] @@ -674,6 +707,7 @@ def _widerface_difficulty_metadata_ready( or len(face_array) == 0 or not np.isfinite(face_array).all() or (face_array[:, 2:] <= 0).any() + or len(np.unique(face_array, axis=0)) != len(face_array) ): return False if image_shapes is not None: @@ -690,14 +724,13 @@ def _widerface_difficulty_metadata_ready( ): return False face_counts.append(len(face_array)) - for table in difficulties: + for difficulty_index, table in enumerate(difficulties): try: event_indices = table[event_index][0] except (IndexError, TypeError): return False if len(event_indices) != len(image_names): return False - table_has_eligible_face = False for image_index in range(len(event_faces)): try: keep_indices = np.asarray(event_indices[image_index][0]) @@ -730,9 +763,11 @@ def _widerface_difficulty_metadata_ready( return False if len(np.unique(keep_indices)) != keep_indices.size: return False - table_has_eligible_face |= bool(keep_indices.size) - if not table_has_eligible_face: - return False + difficulty_has_eligible_face[difficulty_index] |= bool( + keep_indices.size + ) + if not all(difficulty_has_eligible_face): + return False return True diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 828b2fa..535c898 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -79,6 +79,26 @@ def _validate_polygon_area( ) +def _validate_polygon_vertices( + coords: torch.Tensor, annotation_path: Path, line_number: int +) -> None: + """Require four distinct, consistently ordered DOTAv1 quadrilateral vertices.""" + + if torch.unique(coords, dim=0).shape[0] != 4: + raise ValueError( + "DOTAv1 annotation polygon must contain four distinct vertices at " + f"{annotation_path}:{line_number}." + ) + edges = torch.roll(coords, shifts=-1, dims=0) - coords + next_edges = torch.roll(edges, shifts=-1, dims=0) + turns = edges[:, 0] * next_edges[:, 1] - edges[:, 1] * next_edges[:, 0] + if not bool(torch.all(turns > 0) or torch.all(turns < 0)): + raise ValueError( + "DOTAv1 annotation polygon vertices must be consistently ordered at " + f"{annotation_path}:{line_number}." + ) + + def _validate_polygon_image_overlap( coords: torch.Tensor, image_shape: tuple[int, int], @@ -163,6 +183,7 @@ def _load_ground_truths( coords[:, 0] *= width coords[:, 1] *= height _validate_polygon_area(coords, label_path, line_number) + _validate_polygon_vertices(coords, label_path, line_number) _validate_polygon_image_overlap( coords, (height, width), label_path, line_number ) @@ -206,6 +227,7 @@ def _load_ground_truths( f"{original_label_path}:{line_number}." ) _validate_polygon_area(coords, original_label_path, line_number) + _validate_polygon_vertices(coords, original_label_path, line_number) _validate_polygon_image_overlap( coords, (height, width), original_label_path, line_number ) diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index dcb9964..fb7da89 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -86,6 +86,20 @@ def test_dotav1_organizer_rejects_truncated_raw_annotation_rows( ) +def test_dotav1_organizer_rejects_repeated_polygon_vertices(tmp_path: Path) -> None: + """Keep malformed triangle-like rows out of the staged normalized labels.""" + + image_path = tmp_path / "image.png" + Image.new("RGB", (100, 50)).save(image_path) + source_path = tmp_path / "source.txt" + source_path.write_text("0 0 20 0 20 20 0 0 plane 0\n", encoding="utf-8") + + with pytest.raises(ValueError, match="four distinct vertices"): + organizer._write_dotav1_yolo_labels( + str(image_path), str(source_path), str(tmp_path / "normalized.txt") + ) + + def test_dotav1_stage_requires_a_non_difficult_target(tmp_path: Path) -> None: """Do not replace a usable DOTAv1 cache with an unscorable staged split.""" diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index f9d574d..3fd176d 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -8,6 +8,7 @@ import numpy as np import pytest +from PIL import Image from scipy.io import savemat from mblt_vision.utils.datasets import readiness @@ -20,6 +21,13 @@ def _write_file(path: Path) -> None: path.write_bytes(b"data") +def _write_image(path: Path, size: tuple[int, int]) -> None: + """Create a decodable RGB image with the requested ``(width, height)``.""" + + path.parent.mkdir(parents=True, exist_ok=True) + Image.new("RGB", size).save(path) + + def _write_widerface_metadata(path: Path, event_images: dict[str, list[str]]) -> None: """Write the WiderFace event and image-name cell arrays used by readiness.""" @@ -80,7 +88,7 @@ def test_coco_readiness_matches_images_to_task_annotations( monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) image_names = ["000000000001.jpg", "000000000002.jpg"] for image_name in image_names: - _write_file(tmp_path / "val2017" / image_name) + _write_image(tmp_path / "val2017" / image_name, (2, 2)) (tmp_path / annotation_name).write_text( json.dumps({"images": [{"id": 1, "file_name": image_names[0]}]}), encoding="utf-8", @@ -92,7 +100,12 @@ def test_coco_readiness_matches_images_to_task_annotations( json.dumps( { "images": [ - {"id": index, "file_name": image_name} + { + "id": index, + "file_name": image_name, + "height": 2, + "width": 2, + } for index, image_name in enumerate(image_names, start=1) ], "categories": [{"id": 1}], @@ -101,11 +114,11 @@ def test_coco_readiness_matches_images_to_task_annotations( "id": index, "image_id": index, "category_id": 1, - "bbox": [0, 0, 1, 1], - "area": 1, + "bbox": [0, 0, 2, 2], + "area": 4, "iscrowd": 0, **( - {"segmentation": [[0, 0, 1, 0, 1, 1]]} + {"segmentation": [[0, 0, 2, 0, 2, 2]]} if task == "instance_segmentation" else {} ), @@ -151,7 +164,7 @@ def test_coco_readiness_rejects_missing_task_specific_ground_truth( monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, annotation_name, 1) monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, annotation_name, 1) monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) - _write_file(tmp_path / "val2017" / "000000000001.jpg") + _write_image(tmp_path / "val2017" / "000000000001.jpg", (2, 2)) annotation = { "id": 1, "image_id": 1, @@ -291,7 +304,7 @@ def test_coco_readiness_decodes_and_validates_rle_segmentations( monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 1) monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) - _write_file(tmp_path / "val2017" / "000000000001.jpg") + _write_image(tmp_path / "val2017" / "000000000001.jpg", (2, 2)) (tmp_path / "instances_val2017.json").write_text( json.dumps( { @@ -326,6 +339,50 @@ def test_coco_readiness_decodes_and_validates_rle_segmentations( ) +def test_coco_readiness_rejects_corrupt_or_mismatched_images( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Cache readiness must decode COCO images and match annotation geometry.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 1) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) + _write_image(tmp_path / "val2017" / "000000000001.jpg", (1, 1)) + annotation_path = tmp_path / "instances_val2017.json" + annotation_path.write_text( + json.dumps( + { + "images": [ + { + "id": 1, + "file_name": "000000000001.jpg", + "height": 2, + "width": 2, + } + ], + "categories": [{"id": 1}], + "annotations": [ + { + "id": 1, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, + } + ], + } + ), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") + + _write_file(tmp_path / "val2017" / "000000000001.jpg") + assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") + + def test_coco_annotation_identity_digest_rejects_wrong_validation_split( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: @@ -573,6 +630,64 @@ def _loadmat(path: Path) -> dict[str, np.ndarray]: ) +def test_widerface_readiness_allows_empty_event_difficulty_contributions( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """A difficulty split may have no eligible faces in one event but not all.""" + + face_boxes = np.empty((2, 1), dtype=object) + difficulty = np.empty((2, 1), dtype=object) + for event_index in range(2): + event_faces = np.empty((1, 1), dtype=object) + event_faces[0, 0] = np.array([[0, 0, 1, 1]], dtype=np.float64) + face_boxes[event_index, 0] = event_faces + event_indices = np.empty((1, 1), dtype=object) + event_indices[0, 0] = ( + np.empty((0, 0), dtype=np.int64) + if event_index == 0 + else np.array([1], dtype=np.int64) + ) + difficulty[event_index, 0] = event_indices + + def _loadmat(path: Path) -> dict[str, np.ndarray]: + if path.name == "wider_face_val.mat": + return {"face_bbx_list": face_boxes} + return {"gt_list": difficulty} + + monkeypatch.setattr(readiness, "loadmat", _loadmat) + + assert readiness._widerface_difficulty_metadata_ready( + tmp_path, + {"0--Parade": {"first.jpg"}, "1--Handshaking": {"second.jpg"}}, + ) + + +def test_widerface_readiness_rejects_duplicate_face_boxes( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Duplicate ground-truth rows cannot alter the WiderFace recall denominator.""" + + face_boxes = np.empty((1, 1), dtype=object) + event_faces = np.empty((1, 1), dtype=object) + event_faces[0, 0] = np.array([[0, 0, 1, 1], [0, 0, 1, 1]], dtype=np.float64) + face_boxes[0, 0] = event_faces + difficulty = np.empty((1, 1), dtype=object) + event_indices = np.empty((1, 1), dtype=object) + event_indices[0, 0] = np.array([1, 2], dtype=np.int64) + difficulty[0, 0] = event_indices + + def _loadmat(path: Path) -> dict[str, np.ndarray]: + if path.name == "wider_face_val.mat": + return {"face_bbx_list": face_boxes} + return {"gt_list": difficulty} + + monkeypatch.setattr(readiness, "loadmat", _loadmat) + + assert not readiness._widerface_difficulty_metadata_ready( + tmp_path, {"0--Parade": {"sample.jpg"}} + ) + + @pytest.mark.parametrize("relative_image_dir", ["images", "images/val"]) def test_dotav1_readiness_requires_complete_image_label_pairs( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index 51f25ae..bca0248 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -255,6 +255,39 @@ def test_coco_evaluation_rejects_visible_keypoints_outside_images( ) +def test_coco_evaluation_rejects_pose_area_larger_than_its_box( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Do not let inflated pose areas weaken OKS matching thresholds.""" + + dataset = SimpleNamespace( + coco=SimpleNamespace( + cats={1: {}}, + imgs={1: {"height": 10, "width": 10}}, + anns={ + 7: { + "id": 7, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": 100, + "iscrowd": 0, + "keypoints": [0, 0, 0] * 17, + "num_keypoints": 0, + } + }, + ) + ) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match="invalid task-specific annotations"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace(post_cfg={"task": "pose_estimation", "dataset": "coco"}), + "/dataset", + batch_size=1, + ) + + def test_coco_result_formatter_rejects_truncated_postprocess_batch() -> None: """Require one decoded result for every submitted COCO image.""" diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 8963ead..a6c209e 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -204,6 +204,34 @@ def test_dota_annotations_reject_degenerate_polygons( _load_ground_truths(str(tmp_path), dataset) +@pytest.mark.parametrize( + ("label_path", "annotation"), + [ + ("labels/val/image.txt", "0 0 0 0.2 0 0.2 0.2 0 0"), + ("labels/val_original/image.txt", "0 0 20 0 20 20 0 0 plane 0"), + ], +) +def test_dota_annotations_reject_repeated_vertices( + tmp_path, label_path: str, annotation: str +) -> None: + """DOTAv1 quadrilaterals cannot silently degrade into triangles.""" + + path = tmp_path / label_path + path.parent.mkdir(parents=True) + path.write_text(annotation, encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises(ValueError, match="four distinct vertices"): + _load_ground_truths(str(tmp_path), dataset) + + @pytest.mark.parametrize( ("label_path", "annotation"), [ From 0fd5226975335f8c89f2720ca3d99289931f4ac3 Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 18 Aug 2026 11:09:46 +0900 Subject: [PATCH 47/48] fix: validate cached vision datasets --- mblt_vision/utils/datasets/organizer.py | 18 ++++++ mblt_vision/utils/datasets/readiness.py | 69 +++++++++++++++++++++-- mblt_vision/utils/evaluation/eval_dota.py | 15 +++++ tests/test_compile_vision.py | 21 +++++-- tests/test_dataset_organizer.py | 28 +++++++-- tests/test_dataset_readiness.py | 6 +- tests/test_eval_coco.py | 9 +-- tests/test_eval_dota.py | 28 +++++++++ 8 files changed, 174 insertions(+), 20 deletions(-) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index ade7dfb..3c20afe 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -350,6 +350,7 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: raise ValueError(f"Unable to decode staged DOTAv1 image: {image_path}.") height, width = image.shape[:2] has_positive_target = False + seen_targets: set[tuple[int | str, tuple[float, ...]]] = set() for line_number, line in enumerate( label_path.read_text(encoding="utf-8").splitlines(), start=1 ): @@ -417,16 +418,25 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: raise ValueError( f"Unsupported staged DOTAv1 class index at {label_path}:{line_number}." ) + target_class: int | str = class_index else: difficulty = fields[9] if fields[8] not in DOTAV1_CLASS_TO_IDX: raise ValueError( f"Unsupported staged DOTAv1 class at {label_path}:{line_number}." ) + target_class = fields[8] if difficulty not in {"0", "1", "2"}: raise ValueError( f"Unsupported staged DOTAv1 difficulty flag at {label_path}:{line_number}." ) + target_key = (target_class, tuple(image_coordinates)) + if target_key in seen_targets: + raise ValueError( + "Duplicate staged DOTAv1 annotation target at " + f"{label_path}:{line_number}." + ) + seen_targets.add(target_key) has_positive_target |= difficulty == "0" if has_positive_target: positive_stems[kind].add(label_path.stem) @@ -1859,6 +1869,7 @@ def _write_dotav1_yolo_labels( with Image.open(image_path) as image: width, height = image.size converted_lines: list[str] = [] + seen_targets: set[tuple[str, tuple[float, ...]]] = set() with open(original_label_path, encoding="utf-8") as label_file: for line_number, line in enumerate(label_file, start=1): fields = line.split() @@ -1896,6 +1907,13 @@ def _write_dotav1_yolo_labels( f"Unsupported DOTAv1 difficulty flag {fields[9]!r} in " f"{original_label_path} at line {line_number}." ) + target_key = (class_name, tuple(coordinates)) + if target_key in seen_targets: + raise ValueError( + "Duplicate DOTAv1 annotation target in " + f"{original_label_path} at line {line_number}." + ) + seen_targets.add(target_key) normalized = [ coordinate / (width if index % 2 == 0 else height) for index, coordinate in enumerate(coordinates) diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index 4fb982b..fa62e2a 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -19,6 +19,7 @@ from ..._tasks import normalize_vision_task from ...datasets import get_dataset_category_ids +from .cityscapes import CITYSCAPES_SOURCE_TO_TRAIN_ID IMAGE_SUFFIXES = {".bmp", ".jpeg", ".jpg", ".png", ".tif", ".tiff", ".webp"} IMAGENET_CLASS_COUNT = 1000 @@ -432,7 +433,7 @@ def _coco_task_annotations_valid( if image_shape is not None: height, width = image_shape if any( - keypoints[index + 2] == 2 + keypoints[index + 2] > 0 and not ( 0 <= keypoints[index] < width and 0 <= keypoints[index + 1] < height @@ -842,10 +843,11 @@ def _widerface_image_shapes( return None event_shapes: list[tuple[int, int]] = [] for stem in image_stems: + image_path = root / "images" / event_names[0] / f"{stem}.jpg" + if image_path.is_symlink(): + return None try: - with Image.open( - root / "images" / event_names[0] / f"{stem}.jpg" - ) as image: + with Image.open(image_path) as image: image.load() width, height = image.size except OSError: @@ -878,11 +880,31 @@ def dense_dataset_ready(data_path: str | Path, dataset: str) -> bool: depths = _files_by_stem(root / "depth", {".npy"}, reject_symlinks=True) if images is None or depths is None: return False - return ( + if not ( len(images) == NYU_DEPTH_VALIDATION_SAMPLE_COUNT and len(depths) == NYU_DEPTH_VALIDATION_SAMPLE_COUNT and images.keys() == depths.keys() - ) + ): + return False + for stem, image_path in images.items(): + try: + with Image.open(image_path) as image: + image.load() + image_shape = (image.height, image.width) + depth = np.load(depths[stem], allow_pickle=False) + except (OSError, ValueError): + return False + if ( + not np.issubdtype(depth.dtype, np.number) + or np.issubdtype(depth.dtype, np.complexfloating) + or depth.ndim != 2 + or depth.shape != image_shape + or not np.isfinite(depth).all() + or bool((depth < 0).any()) + or not bool(((depth >= 0.001) & (depth <= 100.0)).any()) + ): + return False + return True images = _files_by_stem( root / "images", {".jpg", ".jpeg", ".png"}, reject_symlinks=True @@ -891,6 +913,41 @@ def dense_dataset_ready(data_path: str | Path, dataset: str) -> bool: if images is None or annotations is None or images.keys() != annotations.keys(): return False + for stem, image_path in images.items(): + try: + with Image.open(image_path) as image: + image.load() + image_shape = (image.height, image.width) + with Image.open(annotations[stem]) as annotation_image: + annotation = np.asarray(annotation_image) + except OSError: + return False + if normalized == "cityscapes" and annotation.ndim == 3: + if ( + annotation.shape[2] not in {3, 4} + or not np.array_equal(annotation[..., 0], annotation[..., 1]) + or not np.array_equal(annotation[..., 0], annotation[..., 2]) + ): + return False + annotation = annotation[..., 0] + if annotation.ndim != 2 or annotation.shape != image_shape: + return False + if normalized == "ade20k": + if annotation.dtype != np.uint8 or ( + annotation.size and int(annotation.max()) > 150 + ): + return False + if not bool((annotation > 0).any()): + return False + elif normalized == "cityscapes": + if annotation.size and ( + int(annotation.min()) < 0 or int(annotation.max()) > 255 + ): + return False + train_ids = CITYSCAPES_SOURCE_TO_TRAIN_ID[annotation.astype(np.uint8)] + if not bool((train_ids != 255).any()): + return False + if normalized == "ade20k": return ( len(images) == ADE20K_VALIDATION_SAMPLE_COUNT diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 535c898..11e7797 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -150,6 +150,7 @@ def _load_ground_truths( polygons = [] ignore_classes = [] ignore_polygons = [] + seen_targets: set[tuple[int, tuple[float, ...]]] = set() # Organizer output keeps the official original annotation alongside the # normalized convenience label. Prefer the authoritative original when @@ -192,6 +193,13 @@ def _load_ground_truths( f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " f"{label_path}:{line_number}." ) + target_key = (cls, tuple(coords.flatten().tolist())) + if target_key in seen_targets: + raise ValueError( + "Duplicate DOTAv1 annotation target at " + f"{label_path}:{line_number}." + ) + seen_targets.add(target_key) if len(parts) >= 10 and parts[9] in {"1", "2"}: ignore_classes.append(cls) ignore_polygons.append(coords) @@ -236,6 +244,13 @@ def _load_ground_truths( f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " f"{original_label_path}:{line_number}." ) + target_key = (cls, tuple(coords.flatten().tolist())) + if target_key in seen_targets: + raise ValueError( + "Duplicate DOTAv1 annotation target at " + f"{original_label_path}:{line_number}." + ) + seen_targets.add(target_key) if parts[9] in {"1", "2"}: ignore_classes.append(cls) ignore_polygons.append(coords) diff --git a/tests/test_compile_vision.py b/tests/test_compile_vision.py index 4c0c7a2..467f55b 100644 --- a/tests/test_compile_vision.py +++ b/tests/test_compile_vision.py @@ -8,6 +8,7 @@ import numpy as np import pytest +from PIL import Image from mblt_vision.compile import vision as compile_module from mblt_vision.compile.vision import ( @@ -42,6 +43,13 @@ def _write_images(directory: Path, names: list[str]) -> list[Path]: return created +def _write_image(path: Path, size: tuple[int, int] = (2, 2)) -> None: + """Write a minimal valid RGB image for dataset-readiness tests.""" + + path.parent.mkdir(parents=True, exist_ok=True) + Image.new("RGB", size).save(path) + + def test_resolve_model_config_derives_onnx_filename() -> None: """Derive ONNX artifact names consistently from MXQ artifact names.""" @@ -324,11 +332,12 @@ def test_depth_dataset_readiness_requires_complete_pairs( """Require all paired NYU samples before sampling calibration images.""" monkeypatch.setattr(readiness_module, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) - _write_images(tmp_path / "images", ["image.png"]) + image_path = tmp_path / "images" / "image.png" + _write_image(image_path) (tmp_path / "depth").mkdir() assert not compile_module._dataset_ready("depth_estimation", tmp_path, "nyu-depth") - (tmp_path / "depth" / "image.npy").write_bytes(b"depth") + np.save(tmp_path / "depth" / "image.npy", np.ones((2, 2), dtype=np.float32)) assert compile_module._dataset_ready("depth_estimation", tmp_path, "nyu-depth") @@ -350,11 +359,15 @@ def test_semantic_dataset_readiness_checks_taxonomy( monkeypatch.setattr(readiness_module, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) monkeypatch.setattr(readiness_module, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) - image_path = _write_images(tmp_path / "images", [image_name])[0] + image_path = tmp_path / "images" / image_name + _write_image(image_path) (tmp_path / "annotations").mkdir() assert not compile_module._dataset_ready("semantic_segmentation", tmp_path, dataset) - (tmp_path / "annotations" / f"{image_path.stem}.png").write_bytes(b"annotation") + annotation_value = 1 if dataset == "ade20k" else 7 + Image.new("L", (2, 2), color=annotation_value).save( + tmp_path / "annotations" / f"{image_path.stem}.png" + ) if dataset == "ade20k": (tmp_path / "objectInfo150.txt").write_bytes(b"labels") (tmp_path / "sceneCategories.txt").write_bytes(b"scenes") diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index fb7da89..ef45532 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -100,6 +100,21 @@ def test_dotav1_organizer_rejects_repeated_polygon_vertices(tmp_path: Path) -> N ) +def test_dotav1_organizer_rejects_duplicate_targets(tmp_path: Path) -> None: + """Reject duplicate raw targets before creating normalized labels.""" + + image_path = tmp_path / "image.png" + Image.new("RGB", (100, 50)).save(image_path) + source_path = tmp_path / "source.txt" + target = "0 0 20 0 20 20 0 20 plane 0\n" + source_path.write_text(target * 2, encoding="utf-8") + + with pytest.raises(ValueError, match="Duplicate DOTAv1 annotation target"): + organizer._write_dotav1_yolo_labels( + str(image_path), str(source_path), str(tmp_path / "normalized.txt") + ) + + def test_dotav1_stage_requires_a_non_difficult_target(tmp_path: Path) -> None: """Do not replace a usable DOTAv1 cache with an unscorable staged split.""" @@ -1079,6 +1094,10 @@ def test_organize_ade20k_extracts_flat_validation_layout( monkeypatch.setattr(readiness_module, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) monkeypatch.setattr(organizer, "_validate_staged_payloads", lambda *_: None) archive_path = tmp_path / "ADEChallengeData2016.zip" + image_buffer = io.BytesIO() + Image.new("RGB", (1, 1)).save(image_buffer, format="JPEG") + annotation_buffer = io.BytesIO() + Image.new("L", (1, 1), color=1).save(annotation_buffer, format="PNG") with ZipFile(archive_path, "w") as archive: archive.writestr( "ADEChallengeData2016/images/training/ADE_train_00000001.jpg", b"training" @@ -1088,11 +1107,12 @@ def test_organize_ade20k_extracts_flat_validation_layout( b"training", ) archive.writestr( - "ADEChallengeData2016/images/validation/ADE_val_00000001.jpg", b"validation" + "ADEChallengeData2016/images/validation/ADE_val_00000001.jpg", + image_buffer.getvalue(), ) archive.writestr( "ADEChallengeData2016/annotations/validation/ADE_val_00000001.png", - b"validation", + annotation_buffer.getvalue(), ) archive.writestr("ADEChallengeData2016/objectInfo150.txt", b"labels") archive.writestr("ADEChallengeData2016/sceneCategories.txt", b"scenes") @@ -1102,10 +1122,10 @@ def test_organize_ade20k_extracts_flat_validation_layout( assert ( output_dir / "images" / "ADE_val_00000001.jpg" - ).read_bytes() == b"validation" + ).read_bytes() == image_buffer.getvalue() assert ( output_dir / "annotations" / "ADE_val_00000001.png" - ).read_bytes() == b"validation" + ).read_bytes() == annotation_buffer.getvalue() assert (output_dir / "objectInfo150.txt").read_bytes() == b"labels" assert (output_dir / "sceneCategories.txt").read_bytes() == b"scenes" assert not (output_dir / "images" / "training").exists() diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index 3fd176d..a913830 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -794,8 +794,10 @@ def test_ade20k_readiness_requires_source_metadata( """Require both ADE20K metadata files before reusing an organized cache.""" monkeypatch.setattr(readiness, "ADE20K_VALIDATION_SAMPLE_COUNT", 1) - _write_file(tmp_path / "images" / "ADE_val_00000001.jpg") - _write_file(tmp_path / "annotations" / "ADE_val_00000001.png") + _write_image(tmp_path / "images" / "ADE_val_00000001.jpg", (1, 1)) + annotation_path = tmp_path / "annotations" / "ADE_val_00000001.png" + annotation_path.parent.mkdir(parents=True, exist_ok=True) + Image.new("L", (1, 1), color=1).save(annotation_path) assert not readiness.dataset_ready(tmp_path, "semantic_segmentation", "ade20k") diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index bca0248..7265625 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -220,13 +220,14 @@ def test_coco_evaluation_rejects_polygons_without_rasterized_foreground( ) -def test_coco_evaluation_rejects_visible_keypoints_outside_images( - monkeypatch: pytest.MonkeyPatch, +@pytest.mark.parametrize("visibility", [1, 2]) +def test_coco_evaluation_rejects_labeled_keypoints_outside_images( + monkeypatch: pytest.MonkeyPatch, visibility: int ) -> None: - """Visible pose coordinates must refer to pixels in the source image.""" + """Both occluded and visible pose coordinates must be in the source image.""" keypoints = [0.0, 0.0, 0.0] * 17 - keypoints[:3] = [11.0, 1.0, 2.0] + keypoints[:3] = [11.0, 1.0, visibility] dataset = SimpleNamespace( coco=SimpleNamespace( cats={1: {}}, diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index a6c209e..5d66604 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -151,6 +151,34 @@ def test_normalized_truncated_annotation_raises_with_file_and_line(tmp_path) -> _load_ground_truths(str(tmp_path), dataset) +@pytest.mark.parametrize( + ("label_path", "annotation"), + [ + ("labels/val/image.txt", "0 0 0 0.2 0 0.2 0.2 0 0.2"), + ("labels/val_original/image.txt", "0 0 20 0 20 20 0 20 plane 0"), + ], +) +def test_dota_annotations_reject_duplicate_targets( + tmp_path, label_path: str, annotation: str +) -> None: + """A target may be represented only once within one source image.""" + + path = tmp_path / label_path + path.parent.mkdir(parents=True) + path.write_text(f"{annotation}\n{annotation}\n", encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises(ValueError, match="Duplicate DOTAv1 annotation target"): + _load_ground_truths(str(tmp_path), dataset) + + def test_original_truncated_annotation_raises_with_file_and_line(tmp_path) -> None: """Require a difficulty flag when loading original DOTAv1 labels directly.""" From c1ff88935e14eafcafd03960bafa038730d2f813 Mon Sep 17 00:00:00 2001 From: jinman Date: Tue, 18 Aug 2026 11:35:09 +0900 Subject: [PATCH 48/48] fix: harden vision dataset validation --- mblt_vision/utils/datasets/dataloader.py | 12 +++ mblt_vision/utils/datasets/organizer.py | 8 +- mblt_vision/utils/datasets/readiness.py | 71 +++++++++++++----- mblt_vision/utils/evaluation/eval_coco.py | 89 +++++++++++++++++++++-- mblt_vision/utils/evaluation/eval_dota.py | 17 ++++- tests/test_dataset_organizer.py | 19 +++++ tests/test_dataset_readiness.py | 85 +++++++++++++++++++++- tests/test_eval_coco.py | 66 ++++++++++++++++- tests/test_eval_dota.py | 39 +++++++++- 9 files changed, 372 insertions(+), 34 deletions(-) diff --git a/mblt_vision/utils/datasets/dataloader.py b/mblt_vision/utils/datasets/dataloader.py index 3bdfa1b..d23d719 100644 --- a/mblt_vision/utils/datasets/dataloader.py +++ b/mblt_vision/utils/datasets/dataloader.py @@ -4,6 +4,7 @@ from __future__ import annotations +import json import os from pathlib import Path from typing import Any, Callable @@ -58,6 +59,17 @@ def __init__( annotation whose ``num_keypoints`` is greater than this value. """ self.root = root + try: + raw_annotation = json.loads(Path(annFile).read_text(encoding="utf-8")) + except (OSError, UnicodeError, json.JSONDecodeError) as exc: + raise ValueError( + f"Unable to read COCO annotation file {annFile}: {exc}." + ) from exc + if not isinstance(raw_annotation, dict): + raise ValueError( + f"COCO annotation file {annFile} must contain a JSON object." + ) + self.raw_annotation = raw_annotation self.coco = COCO(annFile) if min_keypoints is None: self.ids = list(sorted(self.coco.imgs.keys())) diff --git a/mblt_vision/utils/datasets/organizer.py b/mblt_vision/utils/datasets/organizer.py index 3c20afe..f8c3484 100644 --- a/mblt_vision/utils/datasets/organizer.py +++ b/mblt_vision/utils/datasets/organizer.py @@ -40,6 +40,7 @@ DOTAV1_VALIDATION_SAMPLE_COUNT, IMAGE_SUFFIXES, NYU_DEPTH_VALIDATION_SAMPLE_COUNT, + _canonicalize_quadrilateral, _path_has_symlink_component, _polygon_has_positive_image_overlap, dataset_ready, @@ -430,7 +431,10 @@ def _validate_staged_dotav1_labels(staged_root: Path) -> None: raise ValueError( f"Unsupported staged DOTAv1 difficulty flag at {label_path}:{line_number}." ) - target_key = (target_class, tuple(image_coordinates)) + target_key = ( + target_class, + _canonicalize_quadrilateral(image_coordinates), + ) if target_key in seen_targets: raise ValueError( "Duplicate staged DOTAv1 annotation target at " @@ -1907,7 +1911,7 @@ def _write_dotav1_yolo_labels( f"Unsupported DOTAv1 difficulty flag {fields[9]!r} in " f"{original_label_path} at line {line_number}." ) - target_key = (class_name, tuple(coordinates)) + target_key = (class_name, _canonicalize_quadrilateral(coordinates)) if target_key in seen_targets: raise ValueError( "Duplicate DOTAv1 annotation target in " diff --git a/mblt_vision/utils/datasets/readiness.py b/mblt_vision/utils/datasets/readiness.py index fa62e2a..3aeaa5d 100644 --- a/mblt_vision/utils/datasets/readiness.py +++ b/mblt_vision/utils/datasets/readiness.py @@ -120,6 +120,25 @@ def _has_positive_polygon_area(polygon: list[int | float]) -> bool: return bool(abs(signed_double_area) > 0) +def _canonicalize_quadrilateral( + coordinates: list[int | float] | tuple[int | float, ...], +) -> tuple[float, ...]: + """Return a quadrilateral key independent of its start vertex and winding.""" + + if len(coordinates) != 8: + raise ValueError( + "A quadrilateral must contain exactly four two-dimensional vertices." + ) + points = tuple( + (float(coordinates[index]), float(coordinates[index + 1])) + for index in range(0, len(coordinates), 2) + ) + candidates = [] + for winding in (points, tuple(reversed(points))): + candidates.extend(winding[index:] + winding[:index] for index in range(4)) + return tuple(coordinate for point in min(candidates) for coordinate in point) + + def _polygon_has_positive_image_overlap( polygon: list[int | float], image_shape: tuple[int, int] | None ) -> bool: @@ -268,7 +287,7 @@ def _load_coco_image_names( ): image_shapes[image_id] = (height, width) else: - image_shapes[image_id] = None + return None if ( len(names) != len(image_records) or len(names) != len(set(names)) @@ -352,6 +371,10 @@ def _coco_task_annotations_valid( or category_id not in category_ids ): return False + image_shape = image_shapes.get(image_id) + if image_shape is None: + return False + image_height, image_width = image_shape bbox = record.get("bbox") if ( not isinstance(bbox, list) @@ -364,6 +387,10 @@ def _coco_task_annotations_valid( ) or bbox[2] <= 0 or bbox[3] <= 0 + or bbox[0] >= image_width + or bbox[0] + bbox[2] <= 0 + or bbox[1] >= image_height + or bbox[1] + bbox[3] <= 0 ): return False area = record.get("area") @@ -373,6 +400,7 @@ def _coco_task_annotations_valid( or isinstance(area, bool) or not np.isfinite(area) or area <= 0 + or area > image_height * image_width or not isinstance(iscrowd, int) or isinstance(iscrowd, bool) or iscrowd not in {0, 1} @@ -429,18 +457,15 @@ def _coco_task_annotations_valid( != sum(keypoints[index] > 0 for index in range(2, len(keypoints), 3)) ): return False - image_shape = image_shapes.get(image_id) - if image_shape is not None: - height, width = image_shape - if any( - keypoints[index + 2] > 0 - and not ( - 0 <= keypoints[index] < width - and 0 <= keypoints[index + 1] < height - ) - for index in range(0, len(keypoints), 3) - ): - return False + height, width = image_shape + if any( + keypoints[index + 2] > 0 + and not ( + 0 <= keypoints[index] < width and 0 <= keypoints[index + 1] < height + ) + for index in range(0, len(keypoints), 3) + ): + return False annotation_ids.append(annotation_id) return len(annotation_ids) == len(set(annotation_ids)) @@ -706,6 +731,8 @@ def _widerface_difficulty_metadata_ready( face_array.ndim != 2 or face_array.shape[1] != 4 or len(face_array) == 0 + or not np.issubdtype(face_array.dtype, np.number) + or np.issubdtype(face_array.dtype, np.complexfloating) or not np.isfinite(face_array).all() or (face_array[:, 2:] <= 0).any() or len(np.unique(face_array, axis=0)) != len(face_array) @@ -741,6 +768,8 @@ def _widerface_difficulty_metadata_ready( keep_indices = keep_indices.reshape(1) elif keep_indices.ndim == 2: if keep_indices.size == 0: + if keep_indices.shape[0] != 0: + return False keep_indices = keep_indices.reshape(0) elif keep_indices.shape[1] == 1: keep_indices = keep_indices[:, 0] @@ -891,17 +920,21 @@ def dense_dataset_ready(data_path: str | Path, dataset: str) -> bool: with Image.open(image_path) as image: image.load() image_shape = (image.height, image.width) - depth = np.load(depths[stem], allow_pickle=False) + raw_depth = np.load(depths[stem], allow_pickle=False) except (OSError, ValueError): return False + if not np.issubdtype(raw_depth.dtype, np.number) or np.issubdtype( + raw_depth.dtype, np.complexfloating + ): + return False + with np.errstate(over="ignore", invalid="ignore"): + depth = np.asarray(raw_depth, dtype=np.float32) if ( - not np.issubdtype(depth.dtype, np.number) - or np.issubdtype(depth.dtype, np.complexfloating) - or depth.ndim != 2 + depth.ndim != 2 or depth.shape != image_shape or not np.isfinite(depth).all() or bool((depth < 0).any()) - or not bool(((depth >= 0.001) & (depth <= 100.0)).any()) + or not bool(((depth > 0.001) & (depth < 100.0)).any()) ): return False return True @@ -944,6 +977,8 @@ def dense_dataset_ready(data_path: str | Path, dataset: str) -> bool: int(annotation.min()) < 0 or int(annotation.max()) > 255 ): return False + if not bool(np.all((annotation <= 33) | (annotation == 255))): + return False train_ids = CITYSCAPES_SOURCE_TO_TRAIN_ID[annotation.astype(np.uint8)] if not bool((train_ids != 255).any()): return False diff --git a/mblt_vision/utils/evaluation/eval_coco.py b/mblt_vision/utils/evaluation/eval_coco.py index 92175f3..b1e00c7 100644 --- a/mblt_vision/utils/evaluation/eval_coco.py +++ b/mblt_vision/utils/evaluation/eval_coco.py @@ -59,6 +59,36 @@ def _require_batch_cardinality(expected: int, **values: Any) -> None: def _validate_coco_dataset_taxonomy(dataset: CustomCOCODataset, task: str) -> None: """Ensure direct COCO evaluation uses only task-compatible category IDs.""" + raw_annotation = getattr(dataset, "raw_annotation", None) + if raw_annotation is not None and not isinstance(raw_annotation, dict): + raise ValueError("COCO evaluation dataset has invalid raw annotation data.") + raw_images = ( + raw_annotation.get("images") if isinstance(raw_annotation, dict) else None + ) + raw_categories = ( + raw_annotation.get("categories") if isinstance(raw_annotation, dict) else None + ) + raw_annotations = ( + raw_annotation.get("annotations") if isinstance(raw_annotation, dict) else None + ) + raw_image_records: list[Any] = [] + raw_category_records: list[Any] = [] + raw_annotation_records: list[Any] = [] + if raw_annotation is not None: + if not isinstance(raw_images, list): + raise ValueError("COCO evaluation dataset has malformed raw image table.") + if not isinstance(raw_categories, list): + raise ValueError( + "COCO evaluation dataset has malformed raw category table." + ) + if not isinstance(raw_annotations, list): + raise ValueError( + "COCO evaluation dataset has malformed raw annotation table." + ) + raw_image_records = raw_images + raw_category_records = raw_categories + raw_annotation_records = raw_annotations + categories = getattr(dataset.coco, "cats", None) if not isinstance(categories, dict) or not categories: raise ValueError("COCO evaluation dataset must define at least one category.") @@ -92,18 +122,65 @@ def _validate_coco_dataset_taxonomy(dataset: CustomCOCODataset, task: str) -> No ): raise ValueError("COCO evaluation dataset contains invalid image metadata.") height, width = image.get("height"), image.get("width") - image_shapes[image_id] = ( - (height, width) - if isinstance(height, int) + if not ( + isinstance(height, int) and not isinstance(height, bool) and height > 0 and isinstance(width, int) and not isinstance(width, bool) and width > 0 - else None - ) + ): + raise ValueError( + "COCO evaluation dataset contains invalid image dimensions." + ) + image_shapes[image_id] = (height, width) + + annotation_records = ( + raw_annotation_records + if raw_annotation is not None + else list(annotations.values()) + ) + if not annotation_records: + raise ValueError("COCO evaluation dataset must define at least one annotation.") + if raw_annotation is not None: + raw_image_ids = [ + record.get("id") if isinstance(record, dict) else None + for record in raw_image_records + ] + raw_category_ids = [ + record.get("id") if isinstance(record, dict) else None + for record in raw_category_records + ] + raw_annotation_ids = [ + record.get("id") if isinstance(record, dict) else None + for record in raw_annotation_records + ] + if ( + any( + not isinstance(record_id, int) or isinstance(record_id, bool) + for record_id in ( + *raw_image_ids, + *raw_category_ids, + *raw_annotation_ids, + ) + ) + or len(raw_image_ids) != len(set(raw_image_ids)) + or len(raw_category_ids) != len(set(raw_category_ids)) + or len(raw_annotation_ids) != len(set(raw_annotation_ids)) + ): + raise ValueError( + "COCO evaluation dataset has duplicate or invalid raw IDs." + ) + if ( + set(raw_image_ids) != set(images) + or set(raw_category_ids) != category_ids + or set(raw_annotation_ids) != set(annotations) + ): + raise ValueError( + "COCO evaluation dataset raw and indexed records disagree." + ) if not _coco_task_annotations_valid( - list(annotations.values()), + annotation_records, image_ids=set(images), category_ids=category_ids, image_shapes=image_shapes, diff --git a/mblt_vision/utils/evaluation/eval_dota.py b/mblt_vision/utils/evaluation/eval_dota.py index 11e7797..8db6392 100644 --- a/mblt_vision/utils/evaluation/eval_dota.py +++ b/mblt_vision/utils/evaluation/eval_dota.py @@ -26,7 +26,10 @@ get_dotav1_class_num, get_dotav1_label, ) -from ..datasets.readiness import _polygon_has_positive_image_overlap +from ..datasets.readiness import ( + _canonicalize_quadrilateral, + _polygon_has_positive_image_overlap, +) from ..letterbox import RatioPad, resolve_ratio_pad if TYPE_CHECKING: @@ -193,7 +196,10 @@ def _load_ground_truths( f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " f"{label_path}:{line_number}." ) - target_key = (cls, tuple(coords.flatten().tolist())) + target_key = ( + cls, + _canonicalize_quadrilateral(coords.flatten().tolist()), + ) if target_key in seen_targets: raise ValueError( "Duplicate DOTAv1 annotation target at " @@ -244,7 +250,10 @@ def _load_ground_truths( f"Unsupported DOTAv1 difficulty flag {parts[9]!r} at " f"{original_label_path}:{line_number}." ) - target_key = (cls, tuple(coords.flatten().tolist())) + target_key = ( + cls, + _canonicalize_quadrilateral(coords.flatten().tolist()), + ) if target_key in seen_targets: raise ValueError( "Duplicate DOTAv1 annotation target at " @@ -300,7 +309,7 @@ def _load_ground_truths( def format_dota_results( nms_outs: Results, input_shape: tuple[int, ...], - org_shape: tuple[int, ...], + org_shape: list[tuple[int, int]], ratio_pad: list[Any], image_ids: tuple[str, ...], postprocess: Any, diff --git a/tests/test_dataset_organizer.py b/tests/test_dataset_organizer.py index ef45532..94a4098 100644 --- a/tests/test_dataset_organizer.py +++ b/tests/test_dataset_organizer.py @@ -115,6 +115,25 @@ def test_dotav1_organizer_rejects_duplicate_targets(tmp_path: Path) -> None: ) +def test_dotav1_organizer_rejects_reordered_duplicate_targets( + tmp_path: Path, +) -> None: + """Raw DOTAv1 labels cannot duplicate a target with another start vertex.""" + + image_path = tmp_path / "image.png" + Image.new("RGB", (100, 50)).save(image_path) + source_path = tmp_path / "source.txt" + source_path.write_text( + "0 0 20 0 20 20 0 20 plane 0\n20 0 20 20 0 20 0 0 plane 0\n", + encoding="utf-8", + ) + + with pytest.raises(ValueError, match="Duplicate DOTAv1 annotation target"): + organizer._write_dotav1_yolo_labels( + str(image_path), str(source_path), str(tmp_path / "normalized.txt") + ) + + def test_dotav1_stage_requires_a_non_difficult_target(tmp_path: Path) -> None: """Do not replace a usable DOTAv1 cache with an unscorable staged split.""" diff --git a/tests/test_dataset_readiness.py b/tests/test_dataset_readiness.py index a913830..bf2737f 100644 --- a/tests/test_dataset_readiness.py +++ b/tests/test_dataset_readiness.py @@ -288,6 +288,47 @@ def test_coco_readiness_rejects_invalid_instance_box_geometry( assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") +@pytest.mark.parametrize( + ("bbox", "area"), + [([3, 0, 1, 1], 1), ([0, 0, 1, 1], 5)], +) +def test_coco_readiness_rejects_unrealizable_box_or_area( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + bbox: list[int], + area: int, +) -> None: + """Boxes and areas must describe foreground that fits the source image.""" + + monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 1) + monkeypatch.setitem(readiness.COCO_ANNOTATION_COUNTS, "instances_val2017.json", 1) + monkeypatch.setitem(readiness.COCO_CATEGORY_COUNTS, "instances_val2017.json", 1) + monkeypatch.setattr(readiness, "COCO_CATEGORY_IDS", frozenset({1})) + image_name = "000000000001.jpg" + _write_image(tmp_path / "val2017" / image_name, (2, 2)) + (tmp_path / "instances_val2017.json").write_text( + json.dumps( + { + "images": [{"id": 1, "file_name": image_name, "height": 2, "width": 2}], + "categories": [{"id": 1}], + "annotations": [ + { + "id": 1, + "image_id": 1, + "category_id": 1, + "bbox": bbox, + "area": area, + "iscrowd": 0, + } + ], + } + ), + encoding="utf-8", + ) + + assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") + + @pytest.mark.parametrize( ("counts", "expected_ready"), [([3, 1], True), ("31", True), ([4], False), ([3], False), ("4", False)], @@ -383,13 +424,55 @@ def test_coco_readiness_rejects_corrupt_or_mismatched_images( assert not readiness.dataset_ready(tmp_path, "object_detection", "coco") +def test_nyu_readiness_matches_the_evaluator_depth_domain( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Cache reuse must reject depth targets that evaluation cannot score.""" + + monkeypatch.setattr(readiness, "NYU_DEPTH_VALIDATION_SAMPLE_COUNT", 1) + _write_image(tmp_path / "images" / "sample.png", (2, 2)) + depth_path = tmp_path / "depth" / "sample.npy" + depth_path.parent.mkdir() + + np.save(depth_path, np.full((2, 2), 0.001, dtype=np.float64)) + assert not readiness.dense_dataset_ready(tmp_path, "nyu-depth") + + np.save( + depth_path, + np.array([[1.0, np.finfo(np.float64).max], [1.0, 1.0]], dtype=np.float64), + ) + assert not readiness.dense_dataset_ready(tmp_path, "nyu-depth") + + np.save(depth_path, np.ones((2, 2), dtype=np.float32)) + assert readiness.dense_dataset_ready(tmp_path, "nyu-depth") + + +def test_cityscapes_readiness_rejects_unsupported_source_ids( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + """Source IDs outside the Cityscapes label taxonomy cannot hide as ignore.""" + + monkeypatch.setattr(readiness, "CITYSCAPES_VALIDATION_SAMPLE_COUNT", 1) + stem = "frankfurt_000000_000000" + _write_image(tmp_path / "images" / f"{stem}.png", (2, 2)) + annotation_path = tmp_path / "annotations" / f"{stem}.png" + annotation_path.parent.mkdir() + Image.new("L", (2, 2), color=34).save(annotation_path) + + assert not readiness.dense_dataset_ready(tmp_path, "cityscapes") + + Image.new("L", (2, 2), color=7).save(annotation_path) + assert readiness.dense_dataset_ready(tmp_path, "cityscapes") + + def test_coco_annotation_identity_digest_rejects_wrong_validation_split( monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: """Bind a complete COCO annotation table to its canonical image identities.""" images = [ - {"id": index, "file_name": f"{index:012d}.jpg"} for index in range(1, 5001) + {"id": index, "file_name": f"{index:012d}.jpg", "height": 1, "width": 1} + for index in range(1, 5001) ] payload = "".join(f"{item['id']}:{item['file_name']}\n" for item in images).encode() monkeypatch.setattr(readiness, "COCO_VALIDATION_SAMPLE_COUNT", 5000) diff --git a/tests/test_eval_coco.py b/tests/test_eval_coco.py index 7265625..98b0b68 100644 --- a/tests/test_eval_coco.py +++ b/tests/test_eval_coco.py @@ -27,7 +27,18 @@ def __init__(self, root: str, annotation_path: str, **kwargs: Any) -> None: self.ids = [1, 2] self.coco = SimpleNamespace( cats={1: {}}, - anns={}, + anns={ + 1: { + "id": 1, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, + "keypoints": [0, 0, 0] * 17, + "num_keypoints": 0, + } + }, imgs={1: {"height": 1, "width": 1}, 2: {"height": 1, "width": 1}}, ) @@ -96,6 +107,59 @@ def test_coco_evaluation_rejects_noncanonical_artifact_categories( ) +def test_coco_evaluation_rejects_duplicate_raw_annotation_ids( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Validate raw COCO IDs before the backend's index can overwrite one row.""" + + annotation = { + "id": 1, + "image_id": 1, + "category_id": 1, + "bbox": [0, 0, 1, 1], + "area": 1, + "iscrowd": 0, + } + dataset = SimpleNamespace( + raw_annotation={ + "images": [{"id": 1, "height": 1, "width": 1}], + "categories": [{"id": 1}], + "annotations": [annotation, annotation.copy()], + }, + coco=SimpleNamespace( + cats={1: {}}, + imgs={1: {"height": 1, "width": 1}}, + anns={1: annotation}, + ), + ) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match="duplicate or invalid raw IDs"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace(post_cfg={"task": "object_detection", "dataset": "coco"}), + "/dataset", + batch_size=1, + ) + + +def test_coco_evaluation_rejects_empty_ground_truth( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """A direct COCO run must not present undefined AP as a normal metric.""" + + dataset = SimpleNamespace( + coco=SimpleNamespace(cats={1: {}}, imgs={1: {"height": 1, "width": 1}}, anns={}) + ) + monkeypatch.setattr(eval_coco_module, "CustomCOCODataset", lambda *_: dataset) + + with pytest.raises(ValueError, match="at least one annotation"): + eval_coco_module.eval_coco_metrics( + SimpleNamespace(post_cfg={"task": "object_detection", "dataset": "coco"}), + "/dataset", + batch_size=1, + ) + + def test_coco_evaluation_rejects_undeclared_annotation_categories( monkeypatch: pytest.MonkeyPatch, ) -> None: diff --git a/tests/test_eval_dota.py b/tests/test_eval_dota.py index 5d66604..759054c 100644 --- a/tests/test_eval_dota.py +++ b/tests/test_eval_dota.py @@ -16,6 +16,7 @@ evaluate_dota_predictions, format_dota_results, ) +from mblt_vision.utils.results import Results eval_dota_module = importlib.import_module("mblt_vision.utils.evaluation.eval_dota") @@ -179,6 +180,40 @@ def test_dota_annotations_reject_duplicate_targets( _load_ground_truths(str(tmp_path), dataset) +@pytest.mark.parametrize( + ("label_path", "annotations"), + [ + ( + "labels/val/image.txt", + "0 0 0 0.2 0 0.2 0.2 0 0.2\n0 0.2 0 0.2 0.2 0 0.2 0 0", + ), + ( + "labels/val_original/image.txt", + "0 0 20 0 20 20 0 20 plane 0\n20 0 20 20 0 20 0 0 plane 0", + ), + ], +) +def test_dota_annotations_reject_equivalent_reordered_targets( + tmp_path, label_path: str, annotations: str +) -> None: + """Duplicate quadrilaterals remain duplicates after cyclic reordering.""" + + path = tmp_path / label_path + path.parent.mkdir(parents=True) + path.write_text(annotations, encoding="utf-8") + dataset = cast( + CustomDOTAv1, + SimpleNamespace( + ids=["image"], + image_paths=["unused"], + _load_image=lambda _: np.zeros((100, 100, 3), dtype=np.uint8), + ), + ) + + with pytest.raises(ValueError, match="Duplicate DOTAv1 annotation target"): + _load_ground_truths(str(tmp_path), dataset) + + def test_original_truncated_annotation_raises_with_file_and_line(tmp_path) -> None: """Require a difficulty flag when loading original DOTAv1 labels directly.""" @@ -466,7 +501,7 @@ def test_dota_export_rejects_truncated_converted_batches() -> None: with pytest.raises(ValueError, match="export batch length mismatch"): format_dota_results( - SimpleNamespace(output=[]), + cast(Results, SimpleNamespace(output=[])), (640, 640), [(640, 640), (640, 640)], [None, None], @@ -484,7 +519,7 @@ def test_dota_export_rejects_mismatched_detection_fields() -> None: with pytest.raises(ValueError, match="export detection length mismatch"): format_dota_results( - SimpleNamespace(output=[]), + cast(Results, SimpleNamespace(output=[])), (640, 640), [(640, 640)], [None],

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