From 877e1ef238414d4e83139c18ddd54207028fa2a5 Mon Sep 17 00:00:00 2001 From: "Ankur Sinha (Ankur Sinha Gmail)" Date: Tue, 19 May 2026 14:41:41 +0100 Subject: [PATCH 1/9] chore: add dependabot --- .github/dependabot.yml | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 00000000..28cb43e9 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,14 @@ +# Source - https://stackoverflow.com/a/78380165 +# Posted by Stefan, modified by community. See post 'Timeline' for change history +# Retrieved 2026-04-14, License - CC BY-SA 4.0 + +# Set update schedule for GitHub Actions + +version: 2 +updates: + + - package-ecosystem: "github-actions" + directory: "/" + schedule: + # Check for updates to GitHub Actions every week + interval: "weekly" From 5895b603fb7da5c8d13a1775d85fb02d37d26a92 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 20 May 2026 15:32:48 +0000 Subject: [PATCH 2/9] Bump pypa/gh-action-pypi-publish from 1.4.1 to 1.14.0 Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.4.1 to 1.14.0. - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/v1.4.1...v1.14.0) --- updated-dependencies: - dependency-name: pypa/gh-action-pypi-publish dependency-version: 1.14.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 393998b5..b6afb53d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -113,7 +113,7 @@ jobs: - uses: actions/upload-artifact@v4 with: path: dist - - uses: pypa/gh-action-pypi-publish@v1.4.1 + - uses: pypa/gh-action-pypi-publish@v1.14.0 if: github.event_name == 'release' && github.event.action == 'published' with: user: __token__ From 13825f5fd31c68d6255dbd91ff8fb56ebfb49178 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 20 May 2026 15:32:51 +0000 Subject: [PATCH 3/9] Bump actions/setup-python from 5 to 6 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5 to 6. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/setup-python dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/ci.yml | 6 +++--- .github/workflows/ci_test_all.yml | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 393998b5..372de33b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -17,7 +17,7 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: '3.10' - uses: pre-commit/action@v3.0.1 @@ -41,7 +41,7 @@ jobs: - uses: actions/checkout@v4 - name: Setup Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} @@ -87,7 +87,7 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: '3.11' - name: Install uv and set the python version diff --git a/.github/workflows/ci_test_all.yml b/.github/workflows/ci_test_all.yml index 7ebb36a9..772b1aed 100644 --- a/.github/workflows/ci_test_all.yml +++ b/.github/workflows/ci_test_all.yml @@ -20,7 +20,7 @@ jobs: steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 + - uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} From 02e66aa1f1001c676037010a4198970e6638c630 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 20 May 2026 15:32:55 +0000 Subject: [PATCH 4/9] Bump astral-sh/setup-uv from 6 to 7 Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 6 to 7. - [Release notes](https://github.com/astral-sh/setup-uv/releases) - [Commits](https://github.com/astral-sh/setup-uv/compare/v6...v7) --- updated-dependencies: - dependency-name: astral-sh/setup-uv dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/ci.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 393998b5..b642e7ca 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -46,7 +46,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install uv and set the python version - uses: astral-sh/setup-uv@v6 + uses: astral-sh/setup-uv@v7 with: python-version: ${{ matrix.python-version }} @@ -91,7 +91,7 @@ jobs: with: python-version: '3.11' - name: Install uv and set the python version - uses: astral-sh/setup-uv@v6 + uses: astral-sh/setup-uv@v7 with: python-version: '3.11' - name: Generate and Build Docs From 5c3702349e067f007388bdcf0383f5918c171ba7 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 20 May 2026 15:32:58 +0000 Subject: [PATCH 5/9] Bump actions/upload-artifact from 4 to 7 Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 4 to 7. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v4...v7) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 393998b5..cb8fef59 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -110,7 +110,7 @@ jobs: - uses: actions/checkout@v4 - name: Build sdist & wheel run: pipx run --spec build pyproject-build - - uses: actions/upload-artifact@v4 + - uses: actions/upload-artifact@v7 with: path: dist - uses: pypa/gh-action-pypi-publish@v1.4.1 From 27f939762fe26022ec48f9381da79d5ee4530a94 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 20 May 2026 15:33:02 +0000 Subject: [PATCH 6/9] Bump actions/checkout from 4 to 6 Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 6. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v4...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/ci.yml | 8 ++++---- .github/workflows/ci_test_all.yml | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 393998b5..9c7ddb5f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -16,7 +16,7 @@ jobs: name: Format runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - uses: actions/setup-python@v5 with: python-version: '3.10' @@ -38,7 +38,7 @@ jobs: test-suite: tensorflow steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - name: Setup Python uses: actions/setup-python@v5 @@ -86,7 +86,7 @@ jobs: needs: tests runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - uses: actions/setup-python@v5 with: python-version: '3.11' @@ -107,7 +107,7 @@ jobs: name: Distribution build runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - name: Build sdist & wheel run: pipx run --spec build pyproject-build - uses: actions/upload-artifact@v4 diff --git a/.github/workflows/ci_test_all.yml b/.github/workflows/ci_test_all.yml index 7ebb36a9..9a03481b 100644 --- a/.github/workflows/ci_test_all.yml +++ b/.github/workflows/ci_test_all.yml @@ -18,7 +18,7 @@ jobs: runs-on: [ubuntu-latest] steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v6 - uses: actions/setup-python@v5 with: From c376b6468ba7493a8cca6f78460609ce94927fe4 Mon Sep 17 00:00:00 2001 From: Padraig Gleeson Date: Wed, 20 May 2026 17:06:13 +0100 Subject: [PATCH 7/9] Regenerated documentation with latest, fixed modelspec --- docs/MDF_function_specifications.json | 8 +- docs/MDF_function_specifications.md | 8 +- docs/MDF_function_specifications.yaml | 9 +- docs/MDF_specification.json | 74 +++--- docs/MDF_specification.yaml | 80 +++--- docs/README.md | 84 +++--- docs/generate.py | 2 +- docs/sphinx/source/api/Contributors.md | 17 +- .../source/api/MDF_function_specifications.md | 8 +- docs/sphinx/source/api/Specification.rst | 248 +++++++++--------- 10 files changed, 276 insertions(+), 262 deletions(-) diff --git a/docs/MDF_function_specifications.json b/docs/MDF_function_specifications.json index 9d0ea36a..cde46305 100644 --- a/docs/MDF_function_specifications.json +++ b/docs/MDF_function_specifications.json @@ -686,7 +686,7 @@ "expression_string": "onnx_ops.lppool(X, auto_pad, kernel_shape, p, pads, strides)" }, "onnx::MatMul": { - "description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\n", + "description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html\n", "arguments": [ "A", "B" @@ -694,7 +694,7 @@ "expression_string": "onnx_ops.matmul(A, B)" }, "onnx::MatMulInteger": { - "description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\nThe production MUST never overflow. The accumulation may overflow if and only if in 32 bits.\n", + "description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.\nThe production MUST never overflow. The accumulation may overflow if and only if in 32 bits.\n", "arguments": [ "A", "B", @@ -711,7 +711,7 @@ "expression_string": "onnx_ops.max(data_0)" }, "onnx::MaxPool": { - "description": "\n MaxPool consumes an input tensor X and applies max pooling across\n the tensor according to kernel sizes, stride sizes, and pad lengths.\n max pooling consisting of computing the max on all values of a\n subset of the input tensor according to the kernel size and downsampling the\n data into the output tensor Y for further processing. The output spatial shape is calculated differently\n depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized.\n With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):\n ```\n output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.\n\n `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])\n ```\n or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):\n ```\n VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1\n ```\n And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:\n ```\n pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]\n ```\n The output of each pooling window is maximum number of elements exclude pad. \n ", + "description": "\n MaxPool consumes an input tensor X and applies max pooling across\n the tensor according to kernel sizes, stride sizes, and pad lengths.\n max pooling consisting of computing the max on all values of a\n subset of the input tensor according to the kernel size and downsampling the\n data into the output tensor Y for further processing. The output spatial shape is calculated differently\n depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized.\n With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):\n ```\n output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.\n\n `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])\n ```\n or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):\n ```\n VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1\n ```\n And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:\n ```\n pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]\n ```\n The output of each pooling window is maximum number of elements exclude pad. \n ", "arguments": [ "X" ], @@ -900,7 +900,7 @@ "expression_string": "onnx_ops.qlinearconv(x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point, B, auto_pad, dilations, group, kernel_shape, pads, strides)" }, "onnx::QLinearMatMul": { - "description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\nIt consumes two quantized input tensors, their scales and zero points, scale and zero point of output,\nand computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).\nFor (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.\nScale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor\n(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row\nor per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be\nan M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]\nfor per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may\nhave shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.\nProduction must never overflow, and accumulation may overflow if and only if in 32 bits.\n", + "description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.\nIt consumes two quantized input tensors, their scales and zero points, scale and zero point of output,\nand computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).\nFor (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.\nScale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor\n(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row\nor per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be\nan M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]\nfor per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may\nhave shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.\nProduction must never overflow, and accumulation may overflow if and only if in 32 bits.\n", "arguments": [ "a", "a_scale", diff --git a/docs/MDF_function_specifications.md b/docs/MDF_function_specifications.md index 32564fa1..e8d6d894 100644 --- a/docs/MDF_function_specifications.md +++ b/docs/MDF_function_specifications.md @@ -1685,7 +1685,7 @@ Python version: `onnx_ops.lppool(X, auto_pad, kernel_shape, p, pads, strides)` ## MatMul

-Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). +Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html

Python version: `onnx_ops.matmul(A, B)` @@ -1695,7 +1695,7 @@ Python version: `onnx_ops.matmul(A, B)` ## MatMulInteger

-Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). +Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.

@@ -1732,7 +1732,7 @@ Python version: `onnx_ops.max(data_0)` ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` - if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. + if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` @@ -2237,7 +2237,7 @@ Python version: `onnx_ops.qlinearconv(x, x_scale, x_zero_point, w, w_scale, w_ze ## QLinearMatMul

-Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). +Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. diff --git a/docs/MDF_function_specifications.yaml b/docs/MDF_function_specifications.yaml index 36129f8f..af73c748 100644 --- a/docs/MDF_function_specifications.yaml +++ b/docs/MDF_function_specifications.yaml @@ -1617,7 +1617,7 @@ onnx::LpPool: onnx::MatMul: description: ' - Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). + Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html ' arguments: @@ -1627,7 +1627,7 @@ onnx::MatMul: onnx::MatMulInteger: description: ' - Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). + Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits. @@ -1668,7 +1668,8 @@ onnx::MaxPool: \ 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i]\ \ + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i]\ \ + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads\ - \ along axis `i`.\n\n `auto_pad` is a DEPRECATED attribute. If you are using\ + \ along axis `i`. Sliding windows that would start in the right padded region\ + \ are ignored.\n\n `auto_pad` is a DEPRECATED attribute. If you are using\ \ them currently, the output spatial shape will be following when ceil_mode\ \ is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i]\ \ - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n\ @@ -2089,7 +2090,7 @@ onnx::QLinearConv: onnx::QLinearMatMul: description: ' - Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). + Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, diff --git a/docs/MDF_specification.json b/docs/MDF_specification.json index 0d15fd8d..eeaca631 100644 --- a/docs/MDF_specification.json +++ b/docs/MDF_specification.json @@ -6,7 +6,7 @@ "definition": "The top level construct in MDF is Model, which may contain multiple :class:`Graph` objects and model attribute(s)", "allowed_parameters": { "metadata": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -22,13 +22,13 @@ "description": "Information on what application generated/saved this file" }, "onnx_opset_version": { - "type": "Optional", + "type": "Union[str, NoneType]", "description": "The ONNX opset used for any ONNX functions in this model." } }, "allowed_children": { "graphs": { - "type": "List", + "type": "Graph", "description": "The collection of graphs that make up the MDF model." } } @@ -37,7 +37,7 @@ "definition": "A directed graph consisting of :class:`~Node`s (with :class:`~Parameter`s and :class:`~Function`s evaluated internally) connected via :class:`~Edge`s.", "allowed_parameters": { "metadata": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -45,21 +45,21 @@ "description": "A unique identifier for this Graph" }, "parameters": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Dictionary of global parameters for the Graph" }, "conditions": { - "type": "Optional", + "type": "Union[ConditionSet, NoneType]", "description": "The ConditionSet stored as dictionary for scheduling of the Graph" } }, "allowed_children": { "nodes": { - "type": "List", + "type": "Node", "description": "One or more :class:`Node`(s) present in the graph" }, "edges": { - "type": "List", + "type": "Edge", "description": "Zero or more :class:`Edge`(s) present in the graph" } } @@ -68,7 +68,7 @@ "definition": "A self contained unit of evaluation receiving input from other nodes on :class:`InputPort`(s). The values from these are processed via a number of :class:`Function`(s) and one or more final values\nare calculated on the :class:`OutputPort`(s)", "allowed_parameters": { "metadata": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -78,19 +78,19 @@ }, "allowed_children": { "input_ports": { - "type": "List", + "type": "InputPort", "description": "Dictionary of the :class:`InputPort` objects in the Node" }, "functions": { - "type": "List", + "type": "Function", "description": "The :class:`Function`(s) for computation the node" }, "parameters": { - "type": "List", + "type": "Parameter", "description": "Dictionary of :class:`Parameter`(s) for the node" }, "output_ports": { - "type": "List", + "type": "OutputPort", "description": "The :class:`OutputPort`(s) containing evaluated quantities from the node" } } @@ -99,7 +99,7 @@ "definition": "The :class:`InputPort` is an attribute of a Node which allows external information to be input to the Node", "allowed_parameters": { "metadata": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -107,19 +107,19 @@ "description": "The unique (for this Node) id of the input port," }, "default_value": { - "type": "Union", + "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", "description": "Value to set at this input port if no edge connected to it." }, "shape": { - "type": "Optional", + "type": "Union[Tuple[int, ...], NoneType]", "description": "The shape of the input port. This uses the same syntax as numpy ndarray shapes\n(e.g., :code:`numpy.zeros(shape)` would produce an array with the correct shape" }, "type": { - "type": "Optional", + "type": "Union[str, NoneType]", "description": "The data type of the input received at a port." }, "reduce": { - "type": "Optional", + "type": "Union[str, NoneType]", "description": "Specifies how to deal with multiple inputs to one port during a single timestep: add: add up all the values; multiply: multiply the values, overwrite: just use the last value supplied (default)" } } @@ -128,7 +128,7 @@ "definition": "A single value which is evaluated as a function of values on :class:`InputPort`(s) and other Functions", "allowed_parameters": { "metadata": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -136,15 +136,15 @@ "description": "The unique (for this Node) id of the function, which will be used in other :class:`~Function`s and\nthe :class:`~OutputPort`s for its value" }, "function": { - "type": "Optional", + "type": "Union[str, NoneType]", "description": "Which of the in-build MDF functions (:code:`linear`, etc.). See supported functions:\nhttps://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html" }, "args": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Dictionary of values for each of the arguments for the Function, e.g. if the in-built function\nis linear(slope),the args here could be {\"slope\":3} or {\"slope\":\"input_port_0 + 2\"}" }, "value": { - "type": "Union", + "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", "description": "If the function is a value expression, this attribute will contain the expression and the function\nand args attributes will be None." } } @@ -153,7 +153,7 @@ "definition": "A parameter of the :class:`Node`, which can be: 1) a specific fixed :code:`value` (a constant (int/float) or an array) 2) a string expression for the :code:`value` referencing other named :class:`Parameter`(s). which may be stateful (i.e. can change value over multiple executions of the :class:`Node`); 3) be evaluated by an\ninbuilt :code:`function` with :code:`args`; 4) or change from a :code:`default_initial_value` with a :code:`time_derivative`.", "allowed_parameters": { "metadata": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -161,29 +161,29 @@ "description": "" }, "value": { - "type": "Union", + "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", "description": "The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values" }, "default_initial_value": { - "type": "Union", + "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", "description": "The initial value of the parameter, only used when parameter is stateful." }, "time_derivative": { - "type": "Optional", + "type": "Union[str, NoneType]", "description": "How the parameter changes with time, i.e. ds/dt. Units of time are seconds." }, "function": { - "type": "Optional", + "type": "Union[str, NoneType]", "description": "Which of the in-build MDF functions (linear etc.) this uses, See\nhttps://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html" }, "args": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Dictionary of values for each of the arguments for the function of the parameter,\ne.g. if the in-build function is :code:`linear(slope)`, the args here could be :code:`{\"slope\": 3}` or\n:code:`{\"slope\": \"input_port_0 + 2\"}`" } }, "allowed_children": { "conditions": { - "type": "List", + "type": "ParameterCondition", "description": "Parameter specific conditions" } } @@ -196,11 +196,11 @@ "description": "A unique identifier for the ParameterCondition" }, "test": { - "type": "Union", + "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", "description": "The boolean expression to evaluate" }, "value": { - "type": "Union", + "type": "Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType]", "description": "The new value of the Parameter if the test is true" } } @@ -209,7 +209,7 @@ "definition": "The :class:`OutputPort` is an attribute of a :class:`Node` which exports information to another :class:`Node` connected by an :class:`Edge`", "allowed_parameters": { "metadata": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -217,15 +217,15 @@ "description": "Unique identifier for the output port." }, "value": { - "type": "Optional", + "type": "Union[str, NoneType]", "description": "The value of the :class:`OutputPort` in terms of the :class:`InputPort`, :class:`Function` values, and\n:class:`Parameter` values." }, "shape": { - "type": "Optional", + "type": "Union[Tuple[int, ...], NoneType]", "description": "The shape of the output port. This uses the same syntax as numpy ndarray shapes\n(e.g., :code:`numpy.zeros(shape)` would produce an array with the correct shape" }, "type": { - "type": "Optional", + "type": "Union[str, NoneType]", "description": "The data type of the output sent by a port." } } @@ -234,7 +234,7 @@ "definition": "An :class:`Edge` is an attribute of a :class:`Graph` that transmits computational results from a sender's :class:`OutputPort` to a receiver's :class:`InputPort`.", "allowed_parameters": { "metadata": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values." }, "id": { @@ -258,7 +258,7 @@ "description": "The id of the InputPort on the receiver :class:`~Node`" }, "parameters": { - "type": "Optional", + "type": "Union[Any, NoneType]", "description": "Dictionary of parameters for the edge." } } diff --git a/docs/MDF_specification.yaml b/docs/MDF_specification.yaml index 26c580f4..c1d4fc2f 100644 --- a/docs/MDF_specification.yaml +++ b/docs/MDF_specification.yaml @@ -7,7 +7,7 @@ specification: :class:`Graph` objects and model attribute(s) allowed_parameters: metadata: - type: Optional + type: Union[Any, NoneType] description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: @@ -21,36 +21,36 @@ specification: description: Information on what application generated/saved this file onnx_opset_version: - type: Optional + type: Union[str, NoneType] description: The ONNX opset used for any ONNX functions in this model. allowed_children: graphs: - type: List + type: Graph description: The collection of graphs that make up the MDF model. Graph: definition: A directed graph consisting of :class:`~Node`s (with :class:`~Parameter`s and :class:`~Function`s evaluated internally) connected via :class:`~Edge`s. allowed_parameters: metadata: - type: Optional + type: Union[Any, NoneType] description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: type: str description: A unique identifier for this Graph parameters: - type: Optional + type: Union[Any, NoneType] description: Dictionary of global parameters for the Graph conditions: - type: Optional + type: Union[ConditionSet, NoneType] description: The ConditionSet stored as dictionary for scheduling of the Graph allowed_children: nodes: - type: List + type: Node description: One or more :class:`Node`(s) present in the graph edges: - type: List + type: Edge description: Zero or more :class:`Edge`(s) present in the graph Node: definition: 'A self contained unit of evaluation receiving input from other @@ -60,7 +60,7 @@ specification: are calculated on the :class:`OutputPort`(s)' allowed_parameters: metadata: - type: Optional + type: Union[Any, NoneType] description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: @@ -68,16 +68,16 @@ specification: description: A unique identifier for the node. allowed_children: input_ports: - type: List + type: InputPort description: Dictionary of the :class:`InputPort` objects in the Node functions: - type: List + type: Function description: The :class:`Function`(s) for computation the node parameters: - type: List + type: Parameter description: Dictionary of :class:`Parameter`(s) for the node output_ports: - type: List + type: OutputPort description: The :class:`OutputPort`(s) containing evaluated quantities from the node InputPort: @@ -85,28 +85,29 @@ specification: external information to be input to the Node allowed_parameters: metadata: - type: Optional + type: Union[Any, NoneType] description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: type: str description: The unique (for this Node) id of the input port, default_value: - type: Union + type: Union[EvaluableExpression, List, Dict, ndarray, int, float, + str, NoneType] description: Value to set at this input port if no edge connected to it. shape: - type: Optional + type: Union[Tuple[int, ...], NoneType] description: 'The shape of the input port. This uses the same syntax as numpy ndarray shapes (e.g., :code:`numpy.zeros(shape)` would produce an array with the correct shape' type: - type: Optional + type: Union[str, NoneType] description: The data type of the input received at a port. reduce: - type: Optional + type: Union[str, NoneType] description: 'Specifies how to deal with multiple inputs to one port during a single timestep: add: add up all the values; multiply: multiply the values, overwrite: just use the last value supplied @@ -116,7 +117,7 @@ specification: and other Functions allowed_parameters: metadata: - type: Optional + type: Union[Any, NoneType] description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: @@ -126,20 +127,21 @@ specification: the :class:`~OutputPort`s for its value' function: - type: Optional + type: Union[str, NoneType] description: 'Which of the in-build MDF functions (:code:`linear`, etc.). See supported functions: https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html' args: - type: Optional + type: Union[Any, NoneType] description: 'Dictionary of values for each of the arguments for the Function, e.g. if the in-built function is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + 2"}' value: - type: Union + type: Union[EvaluableExpression, List, Dict, ndarray, int, float, + str, NoneType] description: 'If the function is a value expression, this attribute will contain the expression and the function @@ -155,32 +157,34 @@ specification: with a :code:`time_derivative`.' allowed_parameters: metadata: - type: Optional + type: Union[Any, NoneType] description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: type: str description: '' value: - type: Union + type: Union[EvaluableExpression, List, Dict, ndarray, int, float, + str, NoneType] description: The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values default_initial_value: - type: Union + type: Union[EvaluableExpression, List, Dict, ndarray, int, float, + str, NoneType] description: The initial value of the parameter, only used when parameter is stateful. time_derivative: - type: Optional + type: Union[str, NoneType] description: How the parameter changes with time, i.e. ds/dt. Units of time are seconds. function: - type: Optional + type: Union[str, NoneType] description: 'Which of the in-build MDF functions (linear etc.) this uses, See https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html' args: - type: Optional + type: Union[Any, NoneType] description: 'Dictionary of values for each of the arguments for the function of the parameter, @@ -190,7 +194,7 @@ specification: :code:`{"slope": "input_port_0 + 2"}`' allowed_children: conditions: - type: List + type: ParameterCondition description: Parameter specific conditions ParameterCondition: definition: A condition to test on a Node's parameters, which if true, sets @@ -200,37 +204,39 @@ specification: type: str description: A unique identifier for the ParameterCondition test: - type: Union + type: Union[EvaluableExpression, List, Dict, ndarray, int, float, + str, NoneType] description: The boolean expression to evaluate value: - type: Union + type: Union[EvaluableExpression, List, Dict, ndarray, int, float, + str, NoneType] description: The new value of the Parameter if the test is true OutputPort: definition: The :class:`OutputPort` is an attribute of a :class:`Node` which exports information to another :class:`Node` connected by an :class:`Edge` allowed_parameters: metadata: - type: Optional + type: Union[Any, NoneType] description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: type: str description: Unique identifier for the output port. value: - type: Optional + type: Union[str, NoneType] description: 'The value of the :class:`OutputPort` in terms of the :class:`InputPort`, :class:`Function` values, and :class:`Parameter` values.' shape: - type: Optional + type: Union[Tuple[int, ...], NoneType] description: 'The shape of the output port. This uses the same syntax as numpy ndarray shapes (e.g., :code:`numpy.zeros(shape)` would produce an array with the correct shape' type: - type: Optional + type: Union[str, NoneType] description: The data type of the output sent by a port. Edge: definition: An :class:`Edge` is an attribute of a :class:`Graph` that transmits @@ -238,7 +244,7 @@ specification: :class:`InputPort`. allowed_parameters: metadata: - type: Optional + type: Union[Any, NoneType] description: Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. id: @@ -262,5 +268,5 @@ specification: type: str description: The id of the InputPort on the receiver :class:`~Node` parameters: - type: Optional + type: Union[Any, NoneType] description: Dictionary of parameters for the edge. diff --git a/docs/README.md b/docs/README.md index 185942c9..8b2365af 100644 --- a/docs/README.md +++ b/docs/README.md @@ -7,7 +7,7 @@ The top level construct in MDF is Model, which may contain multiple onnx_opset_version - Optional + Union[str, NoneType] The ONNX opset used for any ONNX functions in this model. @@ -46,7 +46,7 @@ The top level construct in MDF is Model, which may contain multiple List + Graph The collection of graphs that make up the MDF model. @@ -60,7 +60,7 @@ A directed graph consisting of Nodes (with Nodes (with Nodes (with List + Node One or more Node(s) present in the graph edges - List + Edge Zero or more Edge(s) present in the graph @@ -114,7 +114,7 @@ are calculated on the OutputPort(s) - + @@ -132,28 +132,28 @@ are calculated on the OutputPort(s)
metadataOptionalUnion[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
- + - + - + - + @@ -167,7 +167,7 @@ The InputPort is an attribute of a Node which allows ex
input_portsListInputPort Dictionary of the InputPort objects in the Node
functionsListFunction The Function(s) for computation the node
parametersListParameter Dictionary of Parameter(s) for the node
output_portsListOutputPort The OutputPort(s) containing evaluated quantities from the node
- + @@ -181,14 +181,14 @@ The InputPort is an attribute of a Node which allows ex - + - + @@ -196,14 +196,14 @@ The InputPort is an attribute of a Node which allows ex - + - + @@ -217,7 +217,7 @@ A single value which is evaluated as a function of values on OutputPorts for its value - + @@ -240,7 +240,7 @@ https://mdf.readthedocs.io/en/latest/api/MDFfunctionspec - + @@ -248,7 +248,7 @@ is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + - + @@ -264,7 +264,7 @@ inbuilt function with args; 4) or change from a default_initial
metadataOptionalUnion[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
default_valueUnionUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] Value to set at this input port if no edge connected to it.
shapeOptionalUnion[Tuple[int, ...], NoneType] The shape of the input port. This uses the same syntax as numpy ndarray shapes (e.g., numpy.zeros(shape) would produce an array with the correct shape
typeOptionalUnion[str, NoneType] The data type of the input received at a port.
reduceOptionalUnion[str, NoneType] Specifies how to deal with multiple inputs to one port during a single timestep: add: add up all the values; multiply: multiply the values, overwrite: just use the last value supplied (default)
functionOptionalUnion[str, NoneType] Which of the in-build MDF functions (linear, etc.). See supported functions: https://mdf.readthedocs.io/en/latest/api/MDFfunctionspecifications.html
argsOptionalUnion[Any, NoneType] Dictionary of values for each of the arguments for the Function, e.g. if the in-built function is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + 2"}
valueUnionUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] If the function is a value expression, this attribute will contain the expression and the function and args attributes will be None.
- + @@ -278,28 +278,28 @@ inbuilt function with args; 4) or change from a default_initial - + - + - + - + @@ -307,7 +307,7 @@ https://mdf.readthedocs.io/en/latest/api/MDFfunctionspec - + @@ -320,7 +320,7 @@ e.g. if the in-build function is linear(slope), the args here could be - + @@ -341,14 +341,14 @@ A condition to test on a Node's parameters, which if true, sets the value of thi - + - + @@ -362,7 +362,7 @@ The OutputPort is an attribute of a No
metadataOptionalUnion[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
valueUnionUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values
default_initial_valueUnionUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The initial value of the parameter, only used when parameter is stateful.
time_derivativeOptionalUnion[str, NoneType] How the parameter changes with time, i.e. ds/dt. Units of time are seconds.
functionOptionalUnion[str, NoneType] Which of the in-build MDF functions (linear etc.) this uses, See https://mdf.readthedocs.io/en/latest/api/MDFfunctionspecifications.html
argsOptionalUnion[Any, NoneType] Dictionary of values for each of the arguments for the function of the parameter, e.g. if the in-build function is linear(slope), the args here could be {"slope": 3} or {"slope": "input_port_0 + 2"}
conditionsListParameterCondition Parameter specific conditions
testUnionUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The boolean expression to evaluate
valueUnionUnion[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The new value of the Parameter if the test is true
- + @@ -376,7 +376,7 @@ The OutputPort is an attribute of a No - + @@ -384,7 +384,7 @@ The OutputPort is an attribute of a No - + @@ -392,7 +392,7 @@ The OutputPort is an attribute of a No - + @@ -406,7 +406,7 @@ An Edge is an attribute of a Graph that
metadataOptionalUnion[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
valueOptionalUnion[str, NoneType] The value of the OutputPort in terms of the InputPort, Function values, and Parameter values.
shapeOptionalUnion[Tuple[int, ...], NoneType] The shape of the output port. This uses the same syntax as numpy ndarray shapes (e.g., numpy.zeros(shape) would produce an array with the correct shape
typeOptionalUnion[str, NoneType] The data type of the output sent by a port.
- + @@ -449,7 +449,7 @@ An Edge is an attribute of a Graph that - + @@ -463,7 +463,7 @@ A set of descriptors which specifies conditional execution of Nodes to meet comp
metadataOptionalUnion[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
parametersOptionalUnion[Any, NoneType] Dictionary of parameters for the edge.
- + @@ -477,7 +477,7 @@ A set of descriptors which specifies conditional execution of Nodes to meet comp - + @@ -491,21 +491,21 @@ Specifies the non-default pattern of execution of Nodes
metadataOptionalUnion[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
kwargsOptionalUnion[Any, NoneType] The dictionary of keyword arguments needed to evaluate the Condition
- + - + - + diff --git a/docs/generate.py b/docs/generate.py index 69673bae..9bb6bb0f 100644 --- a/docs/generate.py +++ b/docs/generate.py @@ -27,7 +27,7 @@ import glob for ex in ["ACT-R", "NeuroML", "ONNX", "PyTorch"]: - for suf in ["png", "svg"]: + for suf in ["png", "svg", "py", "yaml", "json"]: for file in glob.glob(f"../examples/{ex}/*.{suf}"): print("Copying: %s" % file) shutil.copy(file, "sphinx/source/api/export_format/%s" % ex) diff --git a/docs/sphinx/source/api/Contributors.md b/docs/sphinx/source/api/Contributors.md index 9b6bedc2..340ade9a 100644 --- a/docs/sphinx/source/api/Contributors.md +++ b/docs/sphinx/source/api/Contributors.md @@ -3,20 +3,24 @@ # ModECI contributors This page list names and Github profiles of contributors to the various ModECI repositories, listed in no particular order. -This page is generated periodically, most recently on 2023-03-06. +This page is generated periodically, most recently on 2026-04-08. - Padraig Gleeson ([@pgleeson](https://github.com/pgleeson)) - David Turner ([@davidt0x](https://github.com/davidt0x)) -- Katherine Mantel ([@kmantel](https://github.com/kmantel)) +- ([@kmantel](https://github.com/kmantel)) +- Marble Kusanele Mpofu ([@kusanele](https://github.com/kusanele)) - Ivy ([@Ivy8127](https://github.com/Ivy8127)) - ([@mraunak](https://github.com/mraunak)) -- Shanka Subhra Mondal ([@Shanka123](https://github.com/Shanka123)) - Onabajo Monsurat ([@Monsurat-Onabajo](https://github.com/Monsurat-Onabajo)) +- Manifest Chakalov ([@mqnifestkelvin](https://github.com/mqnifestkelvin)) +- Peace Ododo ([@Onoyiza](https://github.com/Onoyiza)) +- Shanka Subhra Mondal ([@Shanka123](https://github.com/Shanka123)) - Parikshit Singh Rathore ([@parikshit14](https://github.com/parikshit14)) +- Rimjhim Mittal ([@rimjhimittal](https://github.com/rimjhimittal)) - Patrick Stock ([@patrickstock](https://github.com/patrickstock)) - Jeremy Lee ([@jeremyrl7](https://github.com/jeremyrl7)) - Raghavendra Pradyumna Pothukuchi ([@rpradyumna](https://github.com/rpradyumna)) -- Marble Kusanele Mpofu ([@kusanele](https://github.com/kusanele)) +- Ankur Sinha ([@sanjayankur31](https://github.com/sanjayankur31)) - Somya Agrawal ([@somyagr](https://github.com/somyagr)) - ([@jdcpni](https://github.com/jdcpni)) - Riya Saxena ([@29riyasaxena](https://github.com/29riyasaxena)) @@ -26,10 +30,11 @@ This page is generated periodically, most recently on 2023-03-06. - ([@sakshikaushik717](https://github.com/sakshikaushik717)) - Esraa Abdelmaksoud ([@esraa-abdelmaksoud](https://github.com/esraa-abdelmaksoud)) - Shivani Rana ([@shivani6320](https://github.com/shivani6320)) -- ([@vidhya-metacell](https://github.com/vidhya-metacell)) +- Vidhya Longani ([@vidhyalongani](https://github.com/vidhyalongani)) - ([@nicholwkprinceton](https://github.com/nicholwkprinceton)) - Matteo Cantarelli ([@tarelli](https://github.com/tarelli)) - Brian Broll ([@brollb](https://github.com/brollb)) +- Robert Vickerstaff ([@robertvi](https://github.com/robertvi)) ## Repositories @@ -39,3 +44,5 @@ This page is generated periodically, most recently on 2023-03-06. - [MDFTests](https://github.com/ModECI/MDFTests) - [modelspec](https://github.com/ModECI/modelspec) - [PsyNeuLink](https://github.com/ModECI/PsyNeuLink) +- [MDFpaper](https://github.com/ModECI/MDFpaper) +- [MDF-UI](https://github.com/ModECI/MDF-UI) diff --git a/docs/sphinx/source/api/MDF_function_specifications.md b/docs/sphinx/source/api/MDF_function_specifications.md index 32564fa1..e8d6d894 100644 --- a/docs/sphinx/source/api/MDF_function_specifications.md +++ b/docs/sphinx/source/api/MDF_function_specifications.md @@ -1685,7 +1685,7 @@ Python version: `onnx_ops.lppool(X, auto_pad, kernel_shape, p, pads, strides)` ## MatMul

-Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). +Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html

Python version: `onnx_ops.matmul(A, B)` @@ -1695,7 +1695,7 @@ Python version: `onnx_ops.matmul(A, B)` ## MatMulInteger

-Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). +Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.

@@ -1732,7 +1732,7 @@ Python version: `onnx_ops.max(data_0)` ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` - if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. + if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` @@ -2237,7 +2237,7 @@ Python version: `onnx_ops.qlinearconv(x, x_scale, x_zero_point, w, w_scale, w_ze ## QLinearMatMul

-Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html). +Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. diff --git a/docs/sphinx/source/api/Specification.rst b/docs/sphinx/source/api/Specification.rst index 0b56de98..6e5f1cf0 100644 --- a/docs/sphinx/source/api/Specification.rst +++ b/docs/sphinx/source/api/Specification.rst @@ -11,23 +11,23 @@ The top level construct in MDF is Model, which may contain multiple `Graph <#gra **Allowed parameters** -========================== =========== ============================================================================================= -Allowed field Data Type Description -========================== =========== ============================================================================================= -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str A unique identifier for this Model -**format** str Information on the version of MDF used in this file -**generating_application** str Information on what application generated/saved this file -**onnx_opset_version** Optional The ONNX opset used for any ONNX functions in this model. -========================== =========== ============================================================================================= +========================== ==================== ============================================================================================= +Allowed field Data Type Description +========================== ==================== ============================================================================================= +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str A unique identifier for this Model +**format** str Information on the version of MDF used in this file +**generating_application** str Information on what application generated/saved this file +**onnx_opset_version** Union[str, NoneType] The ONNX opset used for any ONNX functions in this model. +========================== ==================== ============================================================================================= **Allowed children** -=============== ================ ==================================================== -Allowed child Data Type Description -=============== ================ ==================================================== -**graphs** `List <#list>`__ The collection of graphs that make up the MDF model. -=============== ================ ==================================================== +=============== ================== ==================================================== +Allowed child Data Type Description +=============== ================== ==================================================== +**graphs** `Graph <#graph>`__ The collection of graphs that make up the MDF model. +=============== ================== ==================================================== ===== Graph @@ -36,22 +36,22 @@ A directed graph consisting of `Nodes <#node>`__ (with `Parameters <#parameter>` **Allowed parameters** -=============== =========== ============================================================================================= -Allowed field Data Type Description -=============== =========== ============================================================================================= -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str A unique identifier for this Graph -**parameters** Optional Dictionary of global parameters for the Graph -**conditions** Optional The ConditionSet stored as dictionary for scheduling of the Graph -=============== =========== ============================================================================================= +=============== ============================= ============================================================================================= +Allowed field Data Type Description +=============== ============================= ============================================================================================= +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str A unique identifier for this Graph +**parameters** Union[Any, NoneType] Dictionary of global parameters for the Graph +**conditions** Union[ConditionSet, NoneType] The ConditionSet stored as dictionary for scheduling of the Graph +=============== ============================= ============================================================================================= **Allowed children** =============== ================ ===================================================== Allowed child Data Type Description =============== ================ ===================================================== -**nodes** `List <#list>`__ One or more `Node(s) <#node>`__ present in the graph -**edges** `List <#list>`__ Zero or more `Edge(s) <#edge>`__ present in the graph +**nodes** `Node <#node>`__ One or more `Node(s) <#node>`__ present in the graph +**edges** `Edge <#edge>`__ Zero or more `Edge(s) <#edge>`__ present in the graph =============== ================ ===================================================== ==== @@ -62,23 +62,23 @@ are calculated on the `OutputPort(s) <#outputport>`__ **Allowed parameters** -=============== =========== ============================================================================================= -Allowed field Data Type Description -=============== =========== ============================================================================================= -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str A unique identifier for the node. -=============== =========== ============================================================================================= +=============== ==================== ============================================================================================= +Allowed field Data Type Description +=============== ==================== ============================================================================================= +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str A unique identifier for the node. +=============== ==================== ============================================================================================= **Allowed children** -================ ================ ================================================================================= -Allowed child Data Type Description -================ ================ ================================================================================= -**input_ports** `List <#list>`__ Dictionary of the `InputPort <#inputport>`__ objects in the Node -**functions** `List <#list>`__ The `Function(s) <#function>`__ for computation the node -**parameters** `List <#list>`__ Dictionary of `Parameter(s) <#parameter>`__ for the node -**output_ports** `List <#list>`__ The `OutputPort(s) <#outputport>`__ containing evaluated quantities from the node -================ ================ ================================================================================= +================ ============================ ================================================================================= +Allowed child Data Type Description +================ ============================ ================================================================================= +**input_ports** `InputPort <#inputport>`__ Dictionary of the `InputPort <#inputport>`__ objects in the Node +**functions** `Function <#function>`__ The `Function(s) <#function>`__ for computation the node +**parameters** `Parameter <#parameter>`__ Dictionary of `Parameter(s) <#parameter>`__ for the node +**output_ports** `OutputPort <#outputport>`__ The `OutputPort(s) <#outputport>`__ containing evaluated quantities from the node +================ ============================ ================================================================================= ========= InputPort @@ -87,17 +87,17 @@ The `InputPort <#inputport>`__ is an attribute of a Node which allows external i **Allowed parameters** -================= =========== ================================================================================================================================================================================================= -Allowed field Data Type Description -================= =========== ================================================================================================================================================================================================= -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str The unique (for this Node) id of the input port, -**default_value** Union Value to set at this input port if no edge connected to it. -**shape** Optional The shape of the input port. This uses the same syntax as numpy ndarray shapes - (e.g., **numpy.zeros(shape)** would produce an array with the correct shape -**type** Optional The data type of the input received at a port. -**reduce** Optional Specifies how to deal with multiple inputs to one port during a single timestep: add: add up all the values; multiply: multiply the values, overwrite: just use the last value supplied (default) -================= =========== ================================================================================================================================================================================================= +================= ========================================================================== ================================================================================================================================================================================================= +Allowed field Data Type Description +================= ========================================================================== ================================================================================================================================================================================================= +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str The unique (for this Node) id of the input port, +**default_value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] Value to set at this input port if no edge connected to it. +**shape** Union[Tuple[int, ...], NoneType] The shape of the input port. This uses the same syntax as numpy ndarray shapes + (e.g., **numpy.zeros(shape)** would produce an array with the correct shape +**type** Union[str, NoneType] The data type of the input received at a port. +**reduce** Union[str, NoneType] Specifies how to deal with multiple inputs to one port during a single timestep: add: add up all the values; multiply: multiply the values, overwrite: just use the last value supplied (default) +================= ========================================================================== ================================================================================================================================================================================================= ======== Function @@ -106,19 +106,19 @@ A single value which is evaluated as a function of values on `InputPort(s) <#inp **Allowed parameters** -=============== =========== ======================================================================================================== -Allowed field Data Type Description -=============== =========== ======================================================================================================== -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str The unique (for this Node) id of the function, which will be used in other `Functions <#function>`__ and - the `OutputPorts <#outputport>`__ for its value -**function** Optional Which of the in-build MDF functions (**linear**, etc.). See supported functions: - https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html -**args** Optional Dictionary of values for each of the arguments for the Function, e.g. if the in-built function - is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + 2"} -**value** Union If the function is a value expression, this attribute will contain the expression and the function - and args attributes will be None. -=============== =========== ======================================================================================================== +=============== ========================================================================== ======================================================================================================== +Allowed field Data Type Description +=============== ========================================================================== ======================================================================================================== +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str The unique (for this Node) id of the function, which will be used in other `Functions <#function>`__ and + the `OutputPorts <#outputport>`__ for its value +**function** Union[str, NoneType] Which of the in-build MDF functions (**linear**, etc.). See supported functions: + https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html +**args** Union[Any, NoneType] Dictionary of values for each of the arguments for the Function, e.g. if the in-built function + is linear(slope),the args here could be {"slope":3} or {"slope":"input_port_0 + 2"} +**value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] If the function is a value expression, this attribute will contain the expression and the function + and args attributes will be None. +=============== ========================================================================== ======================================================================================================== ========= Parameter @@ -128,28 +128,28 @@ inbuilt **function** with **args**; 4) or change from a **default_initial_value* **Allowed parameters** -========================= =========== ================================================================================================ -Allowed field Data Type Description -========================= =========== ================================================================================================ -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +========================= ========================================================================== ================================================================================================ +Allowed field Data Type Description +========================= ========================================================================== ================================================================================================ +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. **id** str -**value** Union The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values -**default_initial_value** Union The initial value of the parameter, only used when parameter is stateful. -**time_derivative** Optional How the parameter changes with time, i.e. ds/dt. Units of time are seconds. -**function** Optional Which of the in-build MDF functions (linear etc.) this uses, See - https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html -**args** Optional Dictionary of values for each of the arguments for the function of the parameter, - e.g. if the in-build function is **linear(slope)**, the args here could be **{"slope": 3}** or - **{"slope": "input_port_0 + 2"}** -========================= =========== ================================================================================================ +**value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The next value of the parameter, in terms of the inputs, functions and PREVIOUS parameter values +**default_initial_value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The initial value of the parameter, only used when parameter is stateful. +**time_derivative** Union[str, NoneType] How the parameter changes with time, i.e. ds/dt. Units of time are seconds. +**function** Union[str, NoneType] Which of the in-build MDF functions (linear etc.) this uses, See + https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html +**args** Union[Any, NoneType] Dictionary of values for each of the arguments for the function of the parameter, + e.g. if the in-build function is **linear(slope)**, the args here could be **{"slope": 3}** or + **{"slope": "input_port_0 + 2"}** +========================= ========================================================================== ================================================================================================ **Allowed children** -=============== ================ ============================= -Allowed child Data Type Description -=============== ================ ============================= -**conditions** `List <#list>`__ Parameter specific conditions -=============== ================ ============================= +=============== ============================================ ============================= +Allowed child Data Type Description +=============== ============================================ ============================= +**conditions** `ParameterCondition <#parametercondition>`__ Parameter specific conditions +=============== ============================================ ============================= ================== ParameterCondition @@ -158,13 +158,13 @@ A condition to test on a Node's parameters, which if true, sets the value of thi **Allowed parameters** -=============== =========== ================================================== -Allowed field Data Type Description -=============== =========== ================================================== -**id** str A unique identifier for the ParameterCondition -**test** Union The boolean expression to evaluate -**value** Union The new value of the Parameter if the test is true -=============== =========== ================================================== +=============== ========================================================================== ================================================== +Allowed field Data Type Description +=============== ========================================================================== ================================================== +**id** str A unique identifier for the ParameterCondition +**test** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The boolean expression to evaluate +**value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The new value of the Parameter if the test is true +=============== ========================================================================== ================================================== ========== OutputPort @@ -173,17 +173,17 @@ The `OutputPort <#outputport>`__ is an attribute of a `Node <#node>`__ which exp **Allowed parameters** -=============== =========== ============================================================================================================================== -Allowed field Data Type Description -=============== =========== ============================================================================================================================== -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str Unique identifier for the output port. -**value** Optional The value of the `OutputPort <#outputport>`__ in terms of the `InputPort <#inputport>`__, `Function <#function>`__ values, and - `Parameter <#parameter>`__ values. -**shape** Optional The shape of the output port. This uses the same syntax as numpy ndarray shapes - (e.g., **numpy.zeros(shape)** would produce an array with the correct shape -**type** Optional The data type of the output sent by a port. -=============== =========== ============================================================================================================================== +=============== ================================ ============================================================================================================================== +Allowed field Data Type Description +=============== ================================ ============================================================================================================================== +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str Unique identifier for the output port. +**value** Union[str, NoneType] The value of the `OutputPort <#outputport>`__ in terms of the `InputPort <#inputport>`__, `Function <#function>`__ values, and + `Parameter <#parameter>`__ values. +**shape** Union[Tuple[int, ...], NoneType] The shape of the output port. This uses the same syntax as numpy ndarray shapes + (e.g., **numpy.zeros(shape)** would produce an array with the correct shape +**type** Union[str, NoneType] The data type of the output sent by a port. +=============== ================================ ============================================================================================================================== ==== Edge @@ -192,18 +192,18 @@ An `Edge <#edge>`__ is an attribute of a `Graph <#graph>`__ that transmits compu **Allowed parameters** -================= =========== ============================================================================================================ -Allowed field Data Type Description -================= =========== ============================================================================================================ -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**id** str A unique string identifier for this edge. -**sender** str The **id** of the `Node <#node>`__ which is the source of the edge. -**receiver** str The **id** of the `Node <#node>`__ which is the target of the edge. -**sender_port** str The id of the `OutputPort <#outputport>`__ on the sender `Node <#node>`__, whose value should be sent to the - **receiver_port** -**receiver_port** str The id of the InputPort on the receiver `Node <#node>`__ -**parameters** Optional Dictionary of parameters for the edge. -================= =========== ============================================================================================================ +================= ==================== ============================================================================================================ +Allowed field Data Type Description +================= ==================== ============================================================================================================ +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**id** str A unique string identifier for this edge. +**sender** str The **id** of the `Node <#node>`__ which is the source of the edge. +**receiver** str The **id** of the `Node <#node>`__ which is the target of the edge. +**sender_port** str The id of the `OutputPort <#outputport>`__ on the sender `Node <#node>`__, whose value should be sent to the + **receiver_port** +**receiver_port** str The id of the InputPort on the receiver `Node <#node>`__ +**parameters** Union[Any, NoneType] Dictionary of parameters for the edge. +================= ==================== ============================================================================================================ ========= Condition @@ -212,13 +212,13 @@ A set of descriptors which specifies conditional execution of Nodes to meet comp **Allowed parameters** -=============== =========== ============================================================================================= -Allowed field Data Type Description -=============== =========== ============================================================================================= -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**type** str The type of `Condition <#condition>`__ from the library -**kwargs** Optional The dictionary of keyword arguments needed to evaluate the `Condition <#condition>`__ -=============== =========== ============================================================================================= +=============== ==================== ============================================================================================= +Allowed field Data Type Description +=============== ==================== ============================================================================================= +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**type** str The type of `Condition <#condition>`__ from the library +**kwargs** Union[Any, NoneType] The dictionary of keyword arguments needed to evaluate the `Condition <#condition>`__ +=============== ==================== ============================================================================================= ============ ConditionSet @@ -227,10 +227,10 @@ Specifies the non-default pattern of execution of Nodes **Allowed parameters** -================= =========== ============================================================================================= -Allowed field Data Type Description -================= =========== ============================================================================================= -**metadata** Optional Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. -**node_specific** Optional A dictionary mapping nodes to any non-default run conditions -**termination** Optional A dictionary mapping time scales of model execution to conditions indicating when they end -================= =========== ============================================================================================= +================= ========================== ============================================================================================= +Allowed field Data Type Description +================= ========================== ============================================================================================= +**metadata** Union[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values. +**node_specific** Union[Condition, NoneType] A dictionary mapping nodes to any non-default run conditions +**termination** Union[Condition, NoneType] A dictionary mapping time scales of model execution to conditions indicating when they end +================= ========================== ============================================================================================= From 9e3ca026cda2c902be1b458a81dda7c8002e9a94 Mon Sep 17 00:00:00 2001 From: Padraig Gleeson Date: Wed, 20 May 2026 17:07:45 +0100 Subject: [PATCH 8/9] More documentation updated --- .../api/export_format/ACT-R/addition.json | 12 +- .../api/export_format/ACT-R/addition.yaml | 8 +- .../source/api/export_format/ACT-R/count.json | 12 +- .../source/api/export_format/ACT-R/count.py | 1 + .../source/api/export_format/ACT-R/count.yaml | 8 +- .../api/export_format/NeuroML/ABCD.json | 174 + .../api/export_format/NeuroML/ABCD.mdf.json | 522 + .../api/export_format/NeuroML/ABCD.mdf.yaml | 393 + .../source/api/export_format/NeuroML/ABCD.py | 207 + .../source/api/export_format/NeuroML/FN.json | 72 + .../api/export_format/NeuroML/FN.mdf.json | 140 + .../api/export_format/NeuroML/FN.mdf.yaml | 105 + .../source/api/export_format/NeuroML/FN.py | 104 + .../source/api/export_format/NeuroML/FNrun.py | 126 + .../api/export_format/NeuroML/Izh_run.py | 121 + .../api/export_format/NeuroML/Izhikevich.py | 105 + .../NeuroML/IzhikevichTest.mdf.json | 192 + .../NeuroML/IzhikevichTest.mdf.yaml | 137 + .../NeuroML/IzhikevichTest.nmllite.yaml | 51 + .../api/export_format/NeuroML/SimABCD.json | 14 + .../api/export_format/NeuroML/SimFN.json | 23 + .../NeuroML/SimIzhikevichTest.yaml | 10 + .../source/api/export_format/ONNX/ab.json | 76 + .../source/api/export_format/ONNX/ab.yaml | 48 + .../source/api/export_format/ONNX/abc.json | 171 + .../source/api/export_format/ONNX/abc.yaml | 109 + .../api/export_format/ONNX/abc_basic-mdf.json | 98 + .../api/export_format/ONNX/abc_basic-mdf.yaml | 62 + .../api/export_format/ONNX/abc_basic.py | 84 + .../source/api/export_format/ONNX/abcd.json | 274 + .../source/api/export_format/ONNX/abcd.yaml | 170 + .../source/api/export_format/PyTorch/ddm.json | 561 + .../api/export_format/PyTorch/inception.json | 9501 ++++ .../api/export_format/PyTorch/inception.py | 322 + .../export_format/PyTorch/mlp_pure_mdf.json | 44282 ++++++++++++++++ .../api/export_format/PyTorch/mlp_pure_mdf.py | 316 + .../export_format/PyTorch/mlp_pure_mdf.yaml | 43317 +++++++++++++++ .../PyTorch/mod_torch_builtins.py | 383 + .../api/export_format/PyTorch/pytorch_ddm.py | 101 + .../PyTorch/run_translated_mlp_pure_mdf.py | 115 + .../PyTorch/simple_pytorch_to_mdf.json | 421 + .../PyTorch/simple_pytorch_to_mdf.py | 133 + examples/ONNX/abc.json | 26 +- examples/ONNX/abc.yaml | 26 +- 44 files changed, 103071 insertions(+), 62 deletions(-) create mode 100644 docs/sphinx/source/api/export_format/NeuroML/ABCD.json create mode 100644 docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.json create mode 100644 docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.yaml create mode 100644 docs/sphinx/source/api/export_format/NeuroML/ABCD.py create mode 100644 docs/sphinx/source/api/export_format/NeuroML/FN.json create mode 100644 docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json create mode 100644 docs/sphinx/source/api/export_format/NeuroML/FN.mdf.yaml create mode 100644 docs/sphinx/source/api/export_format/NeuroML/FN.py create mode 100644 docs/sphinx/source/api/export_format/NeuroML/FNrun.py create mode 100644 docs/sphinx/source/api/export_format/NeuroML/Izh_run.py create mode 100644 docs/sphinx/source/api/export_format/NeuroML/Izhikevich.py create mode 100644 docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.json create mode 100644 docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.yaml create mode 100644 docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.nmllite.yaml create mode 100644 docs/sphinx/source/api/export_format/NeuroML/SimABCD.json create mode 100644 docs/sphinx/source/api/export_format/NeuroML/SimFN.json create mode 100644 docs/sphinx/source/api/export_format/NeuroML/SimIzhikevichTest.yaml create mode 100644 docs/sphinx/source/api/export_format/ONNX/ab.json create mode 100644 docs/sphinx/source/api/export_format/ONNX/ab.yaml create mode 100644 docs/sphinx/source/api/export_format/ONNX/abc.json create mode 100644 docs/sphinx/source/api/export_format/ONNX/abc.yaml create mode 100644 docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.json create mode 100644 docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.yaml create mode 100644 docs/sphinx/source/api/export_format/ONNX/abc_basic.py create mode 100644 docs/sphinx/source/api/export_format/ONNX/abcd.json create mode 100644 docs/sphinx/source/api/export_format/ONNX/abcd.yaml create mode 100644 docs/sphinx/source/api/export_format/PyTorch/ddm.json create mode 100644 docs/sphinx/source/api/export_format/PyTorch/inception.json create mode 100644 docs/sphinx/source/api/export_format/PyTorch/inception.py create mode 100644 docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.json create mode 100644 docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.py create mode 100644 docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.yaml create mode 100644 docs/sphinx/source/api/export_format/PyTorch/mod_torch_builtins.py create mode 100644 docs/sphinx/source/api/export_format/PyTorch/pytorch_ddm.py create mode 100644 docs/sphinx/source/api/export_format/PyTorch/run_translated_mlp_pure_mdf.py create mode 100644 docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.json create mode 100644 docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.py diff --git a/docs/sphinx/source/api/export_format/ACT-R/addition.json b/docs/sphinx/source/api/export_format/ACT-R/addition.json index 47cfc8ae..a288ed43 100644 --- a/docs/sphinx/source/api/export_format/ACT-R/addition.json +++ b/docs/sphinx/source/api/export_format/ACT-R/addition.json @@ -1,7 +1,7 @@ { "addition": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.1.1", + "generating_application": "Python modeci-mdf v0.4.14", "graphs": { "addition_graph": { "nodes": { @@ -485,16 +485,6 @@ "dependencies": "conflict_resolution" } } - }, - "termination": { - "check_term_true": { - "type": "JustRan", - "kwargs": { - "dependencies": [ - "check_termination" - ] - } - } } } } diff --git a/docs/sphinx/source/api/export_format/ACT-R/addition.yaml b/docs/sphinx/source/api/export_format/ACT-R/addition.yaml index 77fcdafe..7e6df724 100644 --- a/docs/sphinx/source/api/export_format/ACT-R/addition.yaml +++ b/docs/sphinx/source/api/export_format/ACT-R/addition.yaml @@ -1,6 +1,6 @@ addition: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.1.1 + generating_application: Python modeci-mdf v0.4.14 graphs: addition_graph: nodes: @@ -319,9 +319,3 @@ addition: type: JustRan kwargs: dependencies: conflict_resolution - termination: - check_term_true: - type: JustRan - kwargs: - dependencies: - - check_termination diff --git a/docs/sphinx/source/api/export_format/ACT-R/count.json b/docs/sphinx/source/api/export_format/ACT-R/count.json index 1f854915..1f11da00 100644 --- a/docs/sphinx/source/api/export_format/ACT-R/count.json +++ b/docs/sphinx/source/api/export_format/ACT-R/count.json @@ -1,7 +1,7 @@ { "count": { "format": "ModECI MDF v0.4", - "generating_application": "Python modeci-mdf v0.4.1.1", + "generating_application": "Python modeci-mdf v0.4.14", "graphs": { "count_graph": { "nodes": { @@ -409,16 +409,6 @@ "dependencies": "conflict_resolution" } } - }, - "termination": { - "check_term_true": { - "type": "JustRan", - "kwargs": { - "dependencies": [ - "check_termination" - ] - } - } } } } diff --git a/docs/sphinx/source/api/export_format/ACT-R/count.py b/docs/sphinx/source/api/export_format/ACT-R/count.py index 50d05664..be6df249 100644 --- a/docs/sphinx/source/api/export_format/ACT-R/count.py +++ b/docs/sphinx/source/api/export_format/ACT-R/count.py @@ -1,4 +1,5 @@ """Create the MDF files for the count example and run using the scheduler.""" + import os from modeci_mdf.interfaces.actr import actr_to_mdf from modeci_mdf.execution_engine import EvaluableGraph diff --git a/docs/sphinx/source/api/export_format/ACT-R/count.yaml b/docs/sphinx/source/api/export_format/ACT-R/count.yaml index af383429..8123bf7f 100644 --- a/docs/sphinx/source/api/export_format/ACT-R/count.yaml +++ b/docs/sphinx/source/api/export_format/ACT-R/count.yaml @@ -1,6 +1,6 @@ count: format: ModECI MDF v0.4 - generating_application: Python modeci-mdf v0.4.1.1 + generating_application: Python modeci-mdf v0.4.14 graphs: count_graph: nodes: @@ -267,9 +267,3 @@ count: type: JustRan kwargs: dependencies: conflict_resolution - termination: - check_term_true: - type: JustRan - kwargs: - dependencies: - - check_termination diff --git a/docs/sphinx/source/api/export_format/NeuroML/ABCD.json b/docs/sphinx/source/api/export_format/NeuroML/ABCD.json new file mode 100644 index 00000000..107c15e0 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/ABCD.json @@ -0,0 +1,174 @@ +{ + "ABCD": { + "version": "NeuroMLlite v0.6.1", + "notes": "Example of a simplified network", + "parameters": { + "A_initial": 0.1, + "A_slope": 2.2 + }, + "cells": { + "a_input": { + "parameters": { + "variable": "A_initial" + }, + "lems_source_file": "PNL.xml" + }, + "a": { + "lems_source_file": "PNL.xml" + }, + "b": { + "lems_source_file": "PNL.xml" + }, + "c": { + "lems_source_file": "PNL.xml" + }, + "d": { + "lems_source_file": "PNL.xml" + } + }, + "synapses": { + "rsDL": { + "lems_source_file": "PNL.xml" + }, + "silentSyn_proj_input": { + "lems_source_file": "PNL.xml" + }, + "silentSyn_proj0": { + "lems_source_file": "PNL.xml" + }, + "silentSyn_proj1": { + "lems_source_file": "PNL.xml" + }, + "silentSyn_proj2": { + "lems_source_file": "PNL.xml" + }, + "silentSyn_proj3": { + "lems_source_file": "PNL.xml" + } + }, + "regions": { + "region1": { + "x": 0.0, + "y": 0.0, + "z": 0.0, + "width": 1000.0, + "height": 100.0, + "depth": 1000.0 + } + }, + "populations": { + "A_input": { + "size": "1", + "component": "a_input", + "properties": { + "color": "0.2 0.2 0.2", + "radius": 3 + }, + "random_layout": { + "region": "region1" + } + }, + "A": { + "size": "1", + "component": "a", + "properties": { + "color": "0 0.9 0", + "radius": 5 + }, + "random_layout": { + "region": "region1" + } + }, + "B": { + "size": "1", + "component": "b", + "properties": { + "color": ".8 .8 .8", + "radius": 5 + }, + "random_layout": { + "region": "region1" + } + }, + "C": { + "size": "1", + "component": "c", + "properties": { + "color": "0.7 0.7 0.7", + "radius": 5 + }, + "random_layout": { + "region": "region1" + } + }, + "D": { + "size": "1", + "component": "d", + "properties": { + "color": "0.7 0 0", + "radius": 5 + }, + "random_layout": { + "region": "region1" + } + } + }, + "projections": { + "proj_input": { + "presynaptic": "A", + "postsynaptic": "B", + "synapse": "rsDL", + "pre_synapse": "silentSyn_proj_input", + "type": "continuousProjection", + "weight": 1, + "random_connectivity": { + "probability": 1 + } + }, + "proj0": { + "presynaptic": "A_input", + "postsynaptic": "A", + "synapse": "rsDL", + "pre_synapse": "silentSyn_proj0", + "type": "continuousProjection", + "weight": 1, + "random_connectivity": { + "probability": 1 + } + }, + "proj1": { + "presynaptic": "A", + "postsynaptic": "C", + "synapse": "rsDL", + "pre_synapse": "silentSyn_proj1", + "type": "continuousProjection", + "weight": 1, + "random_connectivity": { + "probability": 1 + } + }, + "proj2": { + "presynaptic": "B", + "postsynaptic": "D", + "synapse": "rsDL", + "pre_synapse": "silentSyn_proj2", + "type": "continuousProjection", + "weight": 1, + "random_connectivity": { + "probability": 1 + } + }, + "proj3": { + "presynaptic": "C", + "postsynaptic": "D", + "synapse": "rsDL", + "pre_synapse": "silentSyn_proj3", + "type": "continuousProjection", + "weight": 1, + "random_connectivity": { + "probability": 1 + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.json b/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.json new file mode 100644 index 00000000..90a8da47 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.json @@ -0,0 +1,522 @@ +{ + "ABCD": { + "format": "ModECI MDF v0.4", + "graphs": { + "ABCD": { + "notes": "Example of a simplified network", + "nodes": { + "A_input": { + "metadata": { + "color": "0.2 0.2 0.2", + "radius": 3, + "region": "region1" + }, + "parameters": { + "variable": { + "value": [ + 2.0 + ] + }, + "spike": { + "default_initial_value": [ + 0 + ], + "conditions": { + "condition_0_on": { + "test": "OUTPUT < 0", + "value": 1 + }, + "condition_0_off": { + "test": "spike > 0", + "value": 0 + } + } + }, + "V": { + "value": 0 + }, + "OUTPUT": { + "value": "variable" + } + }, + "input_ports": { + "INPUT": {} + }, + "output_ports": { + "spike": { + "value": "spike" + }, + "V": { + "value": "V" + }, + "OUTPUT": { + "value": "OUTPUT" + } + }, + "notes": "Cell: [Cell(notes=None, id='a_input', parameters={'variable': 'A_initial'}, neuroml2_source_file=None, lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml and in Lems is: Component, id: a_input, type: inputNode,\n parameters: {'variable': '2'}\n parent: None\n" + }, + "A": { + "metadata": { + "color": "0 0.9 0", + "radius": 5, + "region": "region1" + }, + "parameters": { + "slope": { + "value": [ + 2.0 + ] + }, + "intercept": { + "value": [ + 2.0 + ] + }, + "spike": { + "default_initial_value": [ + 0 + ], + "conditions": { + "condition_0_on": { + "test": "OUTPUT < 0", + "value": 1 + }, + "condition_0_off": { + "test": "spike > 0", + "value": 0 + } + } + }, + "V": { + "value": 0 + }, + "OUTPUT": { + "value": "INPUT*slope + intercept" + } + }, + "input_ports": { + "INPUT": {} + }, + "output_ports": { + "spike": { + "value": "spike" + }, + "V": { + "value": "V" + }, + "OUTPUT": { + "value": "OUTPUT" + } + }, + "notes": "Cell: [Cell(notes=None, id='a', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml and in Lems is: Component, id: a, type: pnlLinearFunctionTM,\n parameters: {'slope': '2', 'intercept': '2'}\n parent: None\n" + }, + "B": { + "metadata": { + "color": ".8 .8 .8", + "radius": 5, + "region": "region1" + }, + "parameters": { + "gain": { + "value": [ + 1.0 + ] + }, + "bias": { + "value": [ + 0.0 + ] + }, + "x_0": { + "value": [ + 0.0 + ] + }, + "offset": { + "value": [ + 0.0 + ] + }, + "spike": { + "default_initial_value": [ + 0 + ], + "conditions": { + "condition_0_on": { + "test": "OUTPUT < 0", + "value": 1 + }, + "condition_0_off": { + "test": "spike > 0", + "value": 0 + } + } + }, + "V": { + "value": 0 + }, + "OUTPUT": { + "value": "1/(1+numpy.exp(-1*gain*(INPUT + bias - x_0)+offset))" + } + }, + "input_ports": { + "INPUT": {} + }, + "output_ports": { + "spike": { + "value": "spike" + }, + "V": { + "value": "V" + }, + "OUTPUT": { + "value": "OUTPUT" + } + }, + "notes": "Cell: [Cell(notes=None, id='b', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml and in Lems is: Component, id: b, type: pnlLogisticFunctionTM,\n parameters: {'gain': '1.0', 'bias': '0.0', 'x_0': '0.0', 'offset': '0.0'}\n parent: None\n" + }, + "C": { + "metadata": { + "color": "0.7 0.7 0.7", + "radius": 5, + "region": "region1" + }, + "parameters": { + "rate": { + "value": [ + 1.0 + ] + }, + "bias": { + "value": [ + 0.0 + ] + }, + "scale": { + "value": [ + 1.0 + ] + }, + "offset": { + "value": [ + 0.0 + ] + }, + "spike": { + "default_initial_value": [ + 0 + ], + "conditions": { + "condition_0_on": { + "test": "OUTPUT < 0", + "value": 1 + }, + "condition_0_off": { + "test": "spike > 0", + "value": 0 + } + } + }, + "V": { + "value": 0 + }, + "OUTPUT": { + "value": "scale * numpy.exp((rate * INPUT) + bias) + offset" + } + }, + "input_ports": { + "INPUT": {} + }, + "output_ports": { + "spike": { + "value": "spike" + }, + "V": { + "value": "V" + }, + "OUTPUT": { + "value": "OUTPUT" + } + }, + "notes": "Cell: [Cell(notes=None, id='c', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml and in Lems is: Component, id: c, type: pnlExponentialFunctionTM,\n parameters: {'rate': '1.0', 'bias': '0.0', 'scale': '1.0', 'offset': '0.0'}\n parent: None\n" + }, + "D": { + "metadata": { + "color": "0.7 0 0", + "radius": 5, + "region": "region1" + }, + "parameters": { + "rate": { + "value": [ + 0.05 + ] + }, + "time_step_size": { + "value": [ + 0.1 + ] + }, + "spike": { + "default_initial_value": [ + 0 + ], + "conditions": { + "condition_0_on": { + "test": "OUTPUT < 0", + "value": 1 + }, + "condition_0_off": { + "test": "spike > 0", + "value": 0 + } + } + }, + "OUTPUT": { + "time_derivative": "(rate * INPUT) / time_step_size", + "default_initial_value": [ + 0 + ] + }, + "V": { + "value": 0 + } + }, + "input_ports": { + "INPUT": {} + }, + "output_ports": { + "spike": { + "value": "spike" + }, + "OUTPUT": { + "value": "OUTPUT" + }, + "V": { + "value": "V" + } + }, + "notes": "Cell: [Cell(notes=None, id='d', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml and in Lems is: Component, id: d, type: pnlSimpleIntegratorMechanism,\n parameters: {'rate': '0.05', 'time_step_size': '0.1s'}\n parent: None\n" + }, + "proj_input_rsDL": { + "parameters": { + "weight": { + "value": [ + 1.0 + ] + }, + "SEC": { + "value": [ + 1.0 + ] + }, + "rpeer": { + "value": "peer_OUTPUT" + }, + "I": { + "value": "weight * rpeer" + } + }, + "input_ports": { + "peer_OUTPUT": {} + }, + "output_ports": { + "I": { + "value": "I" + } + }, + "notes": "Cell: [Synapse(notes=None, id='rsDL', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None, pynn_receptor_type=None)] is defined in PNL.xml and in Lems is: Component, id: rsDL, type: synapseDL,\n parameters: {}\n parent: None\n" + }, + "proj0_rsDL": { + "parameters": { + "weight": { + "value": [ + 1.0 + ] + }, + "SEC": { + "value": [ + 1.0 + ] + }, + "rpeer": { + "value": "peer_OUTPUT" + }, + "I": { + "value": "weight * rpeer" + } + }, + "input_ports": { + "peer_OUTPUT": {} + }, + "output_ports": { + "I": { + "value": "I" + } + }, + "notes": "Cell: [Synapse(notes=None, id='rsDL', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None, pynn_receptor_type=None)] is defined in PNL.xml and in Lems is: Component, id: rsDL, type: synapseDL,\n parameters: {}\n parent: None\n" + }, + "proj1_rsDL": { + "parameters": { + "weight": { + "value": [ + 1.0 + ] + }, + "SEC": { + "value": [ + 1.0 + ] + }, + "rpeer": { + "value": "peer_OUTPUT" + }, + "I": { + "value": "weight * rpeer" + } + }, + "input_ports": { + "peer_OUTPUT": {} + }, + "output_ports": { + "I": { + "value": "I" + } + }, + "notes": "Cell: [Synapse(notes=None, id='rsDL', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None, pynn_receptor_type=None)] is defined in PNL.xml and in Lems is: Component, id: rsDL, type: synapseDL,\n parameters: {}\n parent: None\n" + }, + "proj2_rsDL": { + "parameters": { + "weight": { + "value": [ + 1.0 + ] + }, + "SEC": { + "value": [ + 1.0 + ] + }, + "rpeer": { + "value": "peer_OUTPUT" + }, + "I": { + "value": "weight * rpeer" + } + }, + "input_ports": { + "peer_OUTPUT": {} + }, + "output_ports": { + "I": { + "value": "I" + } + }, + "notes": "Cell: [Synapse(notes=None, id='rsDL', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None, pynn_receptor_type=None)] is defined in PNL.xml and in Lems is: Component, id: rsDL, type: synapseDL,\n parameters: {}\n parent: None\n" + }, + "proj3_rsDL": { + "parameters": { + "weight": { + "value": [ + 1.0 + ] + }, + "SEC": { + "value": [ + 1.0 + ] + }, + "rpeer": { + "value": "peer_OUTPUT" + }, + "I": { + "value": "weight * rpeer" + } + }, + "input_ports": { + "peer_OUTPUT": {} + }, + "output_ports": { + "I": { + "value": "I" + } + }, + "notes": "Cell: [Synapse(notes=None, id='rsDL', parameters=None, neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None, pynn_receptor_type=None)] is defined in PNL.xml and in Lems is: Component, id: rsDL, type: synapseDL,\n parameters: {}\n parent: None\n" + } + }, + "edges": { + "A_TO_proj_input_rsDL": { + "name": "A_TO_proj_input_rsDL", + "sender_port": "OUTPUT", + "receiver_port": "peer_OUTPUT", + "sender": "A", + "receiver": "proj_input_rsDL" + }, + "proj_input_rsDL_TO_B": { + "name": "proj_input_rsDL_TO_B", + "sender_port": "I", + "receiver_port": "INPUT", + "sender": "proj_input_rsDL", + "receiver": "B" + }, + "A_input_TO_proj0_rsDL": { + "name": "A_input_TO_proj0_rsDL", + "sender_port": "OUTPUT", + "receiver_port": "peer_OUTPUT", + "sender": "A_input", + "receiver": "proj0_rsDL" + }, + "proj0_rsDL_TO_A": { + "name": "proj0_rsDL_TO_A", + "sender_port": "I", + "receiver_port": "INPUT", + "sender": "proj0_rsDL", + "receiver": "A" + }, + "A_TO_proj1_rsDL": { + "name": "A_TO_proj1_rsDL", + "sender_port": "OUTPUT", + "receiver_port": "peer_OUTPUT", + "sender": "A", + "receiver": "proj1_rsDL" + }, + "proj1_rsDL_TO_C": { + "name": "proj1_rsDL_TO_C", + "sender_port": "I", + "receiver_port": "INPUT", + "sender": "proj1_rsDL", + "receiver": "C" + }, + "B_TO_proj2_rsDL": { + "name": "B_TO_proj2_rsDL", + "sender_port": "OUTPUT", + "receiver_port": "peer_OUTPUT", + "sender": "B", + "receiver": "proj2_rsDL" + }, + "proj2_rsDL_TO_D": { + "name": "proj2_rsDL_TO_D", + "sender_port": "I", + "receiver_port": "INPUT", + "sender": "proj2_rsDL", + "receiver": "D" + }, + "C_TO_proj3_rsDL": { + "name": "C_TO_proj3_rsDL", + "sender_port": "OUTPUT", + "receiver_port": "peer_OUTPUT", + "sender": "C", + "receiver": "proj3_rsDL" + }, + "proj3_rsDL_TO_D": { + "name": "proj3_rsDL_TO_D", + "sender_port": "I", + "receiver_port": "INPUT", + "sender": "proj3_rsDL", + "receiver": "D" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.yaml b/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.yaml new file mode 100644 index 00000000..c3c214ad --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.yaml @@ -0,0 +1,393 @@ +ABCD: + format: ModECI MDF v0.4 + graphs: + ABCD: + notes: Example of a simplified network + nodes: + A_input: + metadata: + color: 0.2 0.2 0.2 + radius: 3 + region: region1 + parameters: + variable: + value: + - 2.0 + spike: + default_initial_value: + - 0 + conditions: + condition_0_on: + test: OUTPUT < 0 + value: 1 + condition_0_off: + test: spike > 0 + value: 0 + V: + value: 0 + OUTPUT: + value: variable + input_ports: + INPUT: {} + output_ports: + spike: + value: spike + V: + value: V + OUTPUT: + value: OUTPUT + notes: "Cell: [Cell(notes=None, id='a_input', parameters={'variable':\ + \ 'A_initial'}, neuroml2_source_file=None, lems_source_file='PNL.xml',\ + \ neuroml2_cell=None, pynn_cell=None, arbor_cell=None, bindsnet_node=None)]\ + \ is defined in PNL.xml and in Lems is: Component, id: a_input,\ + \ type: inputNode,\n parameters: {'variable': '2'}\n parent:\ + \ None\n" + A: + metadata: + color: 0 0.9 0 + radius: 5 + region: region1 + parameters: + slope: + value: + - 2.0 + intercept: + value: + - 2.0 + spike: + default_initial_value: + - 0 + conditions: + condition_0_on: + test: OUTPUT < 0 + value: 1 + condition_0_off: + test: spike > 0 + value: 0 + V: + value: 0 + OUTPUT: + value: INPUT*slope + intercept + input_ports: + INPUT: {} + output_ports: + spike: + value: spike + V: + value: V + OUTPUT: + value: OUTPUT + notes: "Cell: [Cell(notes=None, id='a', parameters=None, neuroml2_source_file=None,\ + \ lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None,\ + \ arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml\ + \ and in Lems is: Component, id: a, type: pnlLinearFunctionTM,\n\ + \ parameters: {'slope': '2', 'intercept': '2'}\n parent:\ + \ None\n" + B: + metadata: + color: .8 .8 .8 + radius: 5 + region: region1 + parameters: + gain: + value: + - 1.0 + bias: + value: + - 0.0 + x_0: + value: + - 0.0 + offset: + value: + - 0.0 + spike: + default_initial_value: + - 0 + conditions: + condition_0_on: + test: OUTPUT < 0 + value: 1 + condition_0_off: + test: spike > 0 + value: 0 + V: + value: 0 + OUTPUT: + value: 1/(1+numpy.exp(-1*gain*(INPUT + bias - x_0)+offset)) + input_ports: + INPUT: {} + output_ports: + spike: + value: spike + V: + value: V + OUTPUT: + value: OUTPUT + notes: "Cell: [Cell(notes=None, id='b', parameters=None, neuroml2_source_file=None,\ + \ lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None,\ + \ arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml\ + \ and in Lems is: Component, id: b, type: pnlLogisticFunctionTM,\n\ + \ parameters: {'gain': '1.0', 'bias': '0.0', 'x_0': '0.0',\ + \ 'offset': '0.0'}\n parent: None\n" + C: + metadata: + color: 0.7 0.7 0.7 + radius: 5 + region: region1 + parameters: + rate: + value: + - 1.0 + bias: + value: + - 0.0 + scale: + value: + - 1.0 + offset: + value: + - 0.0 + spike: + default_initial_value: + - 0 + conditions: + condition_0_on: + test: OUTPUT < 0 + value: 1 + condition_0_off: + test: spike > 0 + value: 0 + V: + value: 0 + OUTPUT: + value: scale * numpy.exp((rate * INPUT) + bias) + offset + input_ports: + INPUT: {} + output_ports: + spike: + value: spike + V: + value: V + OUTPUT: + value: OUTPUT + notes: "Cell: [Cell(notes=None, id='c', parameters=None, neuroml2_source_file=None,\ + \ lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None,\ + \ arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml\ + \ and in Lems is: Component, id: c, type: pnlExponentialFunctionTM,\n\ + \ parameters: {'rate': '1.0', 'bias': '0.0', 'scale': '1.0',\ + \ 'offset': '0.0'}\n parent: None\n" + D: + metadata: + color: 0.7 0 0 + radius: 5 + region: region1 + parameters: + rate: + value: + - 0.05 + time_step_size: + value: + - 0.1 + spike: + default_initial_value: + - 0 + conditions: + condition_0_on: + test: OUTPUT < 0 + value: 1 + condition_0_off: + test: spike > 0 + value: 0 + OUTPUT: + time_derivative: (rate * INPUT) / time_step_size + default_initial_value: + - 0 + V: + value: 0 + input_ports: + INPUT: {} + output_ports: + spike: + value: spike + OUTPUT: + value: OUTPUT + V: + value: V + notes: "Cell: [Cell(notes=None, id='d', parameters=None, neuroml2_source_file=None,\ + \ lems_source_file='PNL.xml', neuroml2_cell=None, pynn_cell=None,\ + \ arbor_cell=None, bindsnet_node=None)] is defined in PNL.xml\ + \ and in Lems is: Component, id: d, type: pnlSimpleIntegratorMechanism,\n\ + \ parameters: {'rate': '0.05', 'time_step_size': '0.1s'}\n\ + \ parent: None\n" + proj_input_rsDL: + parameters: + weight: + value: + - 1.0 + SEC: + value: + - 1.0 + rpeer: + value: peer_OUTPUT + I: + value: weight * rpeer + input_ports: + peer_OUTPUT: {} + output_ports: + I: + value: I + notes: "Cell: [Synapse(notes=None, id='rsDL', parameters=None,\ + \ neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None,\ + \ pynn_receptor_type=None)] is defined in PNL.xml and in Lems\ + \ is: Component, id: rsDL, type: synapseDL,\n parameters:\ + \ {}\n parent: None\n" + proj0_rsDL: + parameters: + weight: + value: + - 1.0 + SEC: + value: + - 1.0 + rpeer: + value: peer_OUTPUT + I: + value: weight * rpeer + input_ports: + peer_OUTPUT: {} + output_ports: + I: + value: I + notes: "Cell: [Synapse(notes=None, id='rsDL', parameters=None,\ + \ neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None,\ + \ pynn_receptor_type=None)] is defined in PNL.xml and in Lems\ + \ is: Component, id: rsDL, type: synapseDL,\n parameters:\ + \ {}\n parent: None\n" + proj1_rsDL: + parameters: + weight: + value: + - 1.0 + SEC: + value: + - 1.0 + rpeer: + value: peer_OUTPUT + I: + value: weight * rpeer + input_ports: + peer_OUTPUT: {} + output_ports: + I: + value: I + notes: "Cell: [Synapse(notes=None, id='rsDL', parameters=None,\ + \ neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None,\ + \ pynn_receptor_type=None)] is defined in PNL.xml and in Lems\ + \ is: Component, id: rsDL, type: synapseDL,\n parameters:\ + \ {}\n parent: None\n" + proj2_rsDL: + parameters: + weight: + value: + - 1.0 + SEC: + value: + - 1.0 + rpeer: + value: peer_OUTPUT + I: + value: weight * rpeer + input_ports: + peer_OUTPUT: {} + output_ports: + I: + value: I + notes: "Cell: [Synapse(notes=None, id='rsDL', parameters=None,\ + \ neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None,\ + \ pynn_receptor_type=None)] is defined in PNL.xml and in Lems\ + \ is: Component, id: rsDL, type: synapseDL,\n parameters:\ + \ {}\n parent: None\n" + proj3_rsDL: + parameters: + weight: + value: + - 1.0 + SEC: + value: + - 1.0 + rpeer: + value: peer_OUTPUT + I: + value: weight * rpeer + input_ports: + peer_OUTPUT: {} + output_ports: + I: + value: I + notes: "Cell: [Synapse(notes=None, id='rsDL', parameters=None,\ + \ neuroml2_source_file=None, lems_source_file='PNL.xml', pynn_synapse_type=None,\ + \ pynn_receptor_type=None)] is defined in PNL.xml and in Lems\ + \ is: Component, id: rsDL, type: synapseDL,\n parameters:\ + \ {}\n parent: None\n" + edges: + A_TO_proj_input_rsDL: + name: A_TO_proj_input_rsDL + sender_port: OUTPUT + receiver_port: peer_OUTPUT + sender: A + receiver: proj_input_rsDL + proj_input_rsDL_TO_B: + name: proj_input_rsDL_TO_B + sender_port: I + receiver_port: INPUT + sender: proj_input_rsDL + receiver: B + A_input_TO_proj0_rsDL: + name: A_input_TO_proj0_rsDL + sender_port: OUTPUT + receiver_port: peer_OUTPUT + sender: A_input + receiver: proj0_rsDL + proj0_rsDL_TO_A: + name: proj0_rsDL_TO_A + sender_port: I + receiver_port: INPUT + sender: proj0_rsDL + receiver: A + A_TO_proj1_rsDL: + name: A_TO_proj1_rsDL + sender_port: OUTPUT + receiver_port: peer_OUTPUT + sender: A + receiver: proj1_rsDL + proj1_rsDL_TO_C: + name: proj1_rsDL_TO_C + sender_port: I + receiver_port: INPUT + sender: proj1_rsDL + receiver: C + B_TO_proj2_rsDL: + name: B_TO_proj2_rsDL + sender_port: OUTPUT + receiver_port: peer_OUTPUT + sender: B + receiver: proj2_rsDL + proj2_rsDL_TO_D: + name: proj2_rsDL_TO_D + sender_port: I + receiver_port: INPUT + sender: proj2_rsDL + receiver: D + C_TO_proj3_rsDL: + name: C_TO_proj3_rsDL + sender_port: OUTPUT + receiver_port: peer_OUTPUT + sender: C + receiver: proj3_rsDL + proj3_rsDL_TO_D: + name: proj3_rsDL_TO_D + sender_port: I + receiver_port: INPUT + sender: proj3_rsDL + receiver: D diff --git a/docs/sphinx/source/api/export_format/NeuroML/ABCD.py b/docs/sphinx/source/api/export_format/NeuroML/ABCD.py new file mode 100644 index 00000000..5bf1f6da --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/ABCD.py @@ -0,0 +1,207 @@ +from neuromllite import ( + Network, + Cell, + Population, + Synapse, + RectangularRegion, + RandomLayout, +) +from neuromllite import ( + Projection, + RandomConnectivity, + OneToOneConnector, + Simulation, + InputSource, + Input, +) + +from neuromllite.NetworkGenerator import check_to_generate_or_run + +import sys + + +def generate(): + + dt = 100 # ms, so 0.1s + simtime = 5000 # ms, so 50s + + ################################################################################ + ### Build new network + + net = Network(id="ABCD") + net.notes = "Example of a simplified network" + + net.parameters = {"A_initial": 0.1, "A_slope": 2.2} + + cellInput = Cell( + id="a_input", lems_source_file="PNL.xml", parameters={"variable": "A_initial"} + ) + + net.cells.append(cellInput) + + cellA = Cell(id="a", lems_source_file="PNL.xml") + net.cells.append(cellA) + cellB = Cell(id="b", lems_source_file="PNL.xml") + net.cells.append(cellB) + cellC = Cell(id="c", lems_source_file="PNL.xml") + net.cells.append(cellC) + cellD = Cell(id="d", lems_source_file="PNL.xml") + net.cells.append(cellD) + + rsDL = Synapse(id="rsDL", lems_source_file="PNL.xml") + net.synapses.append(rsDL) + + r1 = RectangularRegion( + id="region1", x=0, y=0, z=0, width=1000, height=100, depth=1000 + ) + net.regions.append(r1) + + pAin = Population( + id="A_input", + size="1", + component=cellInput.id, + properties={"color": "0.2 0.2 0.2", "radius": 3}, + random_layout=RandomLayout(region=r1.id), + ) + net.populations.append(pAin) + + pA = Population( + id="A", + size="1", + component=cellA.id, + properties={"color": "0 0.9 0", "radius": 5}, + random_layout=RandomLayout(region=r1.id), + ) + net.populations.append(pA) + + pB = Population( + id="B", + size="1", + component=cellB.id, + properties={"color": ".8 .8 .8", "radius": 5}, + random_layout=RandomLayout(region=r1.id), + ) + net.populations.append(pB) + + pC = Population( + id="C", + size="1", + component=cellC.id, + properties={"color": "0.7 0.7 0.7", "radius": 5}, + random_layout=RandomLayout(region=r1.id), + ) + net.populations.append(pC) + + pD = Population( + id="D", + size="1", + component=cellD.id, + properties={"color": "0.7 0 0", "radius": 5}, + random_layout=RandomLayout(region=r1.id), + ) + net.populations.append(pD) + + silentDLin = Synapse(id="silentSyn_proj_input", lems_source_file="PNL.xml") + net.synapses.append(silentDLin) + net.projections.append( + Projection( + id="proj_input", + presynaptic=pA.id, + postsynaptic=pB.id, + synapse=rsDL.id, + pre_synapse=silentDLin.id, + type="continuousProjection", + weight=1, + random_connectivity=RandomConnectivity(probability=1), + ) + ) + + silentDL0 = Synapse(id="silentSyn_proj0", lems_source_file="PNL.xml") + net.synapses.append(silentDL0) + net.projections.append( + Projection( + id="proj0", + presynaptic=pAin.id, + postsynaptic=pA.id, + synapse=rsDL.id, + pre_synapse=silentDL0.id, + type="continuousProjection", + weight=1, + random_connectivity=RandomConnectivity(probability=1), + ) + ) + + silentDL1 = Synapse(id="silentSyn_proj1", lems_source_file="PNL.xml") + net.synapses.append(silentDL1) + net.projections.append( + Projection( + id="proj1", + presynaptic=pA.id, + postsynaptic=pC.id, + synapse=rsDL.id, + pre_synapse=silentDL1.id, + type="continuousProjection", + weight=1, + random_connectivity=RandomConnectivity(probability=1), + ) + ) + + silentDL2 = Synapse(id="silentSyn_proj2", lems_source_file="PNL.xml") + net.synapses.append(silentDL2) + net.projections.append( + Projection( + id="proj2", + presynaptic=pB.id, + postsynaptic=pD.id, + synapse=rsDL.id, + pre_synapse=silentDL2.id, + type="continuousProjection", + weight=1, + random_connectivity=RandomConnectivity(probability=1), + ) + ) + + silentDL3 = Synapse(id="silentSyn_proj3", lems_source_file="PNL.xml") + net.synapses.append(silentDL3) + net.projections.append( + Projection( + id="proj3", + presynaptic=pC.id, + postsynaptic=pD.id, + synapse=rsDL.id, + pre_synapse=silentDL3.id, + type="continuousProjection", + weight=1, + random_connectivity=RandomConnectivity(probability=1), + ) + ) + + new_file = net.to_json_file("%s.json" % net.id) + + ################################################################################ + ### Build Simulation object & save as JSON + + sim = Simulation( + id="Sim%s" % net.id, + network=new_file, + duration=simtime, + dt=dt, + seed=123, + record_variables={"OUTPUT": {"all": "*"}}, + ) # ,'INPUT':{'all':'*'} + + sim.to_json_file() + + return sim, net + + +if __name__ == "__main__": + + sim, net = generate() + + ################################################################################ + ### Run in some simulators + + import sys + + check_to_generate_or_run(sys.argv, sim) diff --git a/docs/sphinx/source/api/export_format/NeuroML/FN.json b/docs/sphinx/source/api/export_format/NeuroML/FN.json new file mode 100644 index 00000000..1c9d2919 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/FN.json @@ -0,0 +1,72 @@ +{ + "FN": { + "version": "NeuroMLlite v0.6.1", + "notes": "FitzHugh Nagumo cell model - originally specified in NeuroML/LEMS", + "parameters": { + "initial_w": 0.0, + "initial_v": -1, + "a_v": -0.3333333333333333, + "b_v": 0.0, + "c_v": 1.0, + "d_v": 1, + "e_v": -1.0, + "f_v": 1.0, + "time_constant_v": 1.0, + "a_w": 1.0, + "b_w": -0.8, + "c_w": 0.7, + "time_constant_w": 12.5, + "threshold": -1.0, + "mode": 1.0, + "uncorrelated_activity": 0.0, + "Iext": 0 + }, + "cells": { + "fn": { + "parameters": { + "initial_w": "initial_w", + "initial_v": "initial_v", + "a_v": "a_v", + "b_v": "b_v", + "c_v": "c_v", + "d_v": "d_v", + "e_v": "e_v", + "f_v": "f_v", + "time_constant_v": "time_constant_v", + "a_w": "a_w", + "b_w": "b_w", + "c_w": "c_w", + "time_constant_w": "time_constant_w", + "threshold": "threshold", + "mode": "mode", + "uncorrelated_activity": "uncorrelated_activity", + "Iext": "Iext" + }, + "lems_source_file": "FN_Definitions.xml" + } + }, + "regions": { + "region1": { + "x": 0.0, + "y": 0.0, + "z": 0.0, + "width": 1000.0, + "height": 100.0, + "depth": 1000.0 + } + }, + "populations": { + "FNpop": { + "size": "1", + "component": "fn", + "properties": { + "color": "0.2 0.2 0.2", + "radius": 3 + }, + "random_layout": { + "region": "region1" + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json b/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json new file mode 100644 index 00000000..5f170961 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json @@ -0,0 +1,140 @@ +{ + "FN": { + "format": "ModECI MDF v0.4", + "graphs": { + "FN": { + "notes": "FitzHugh Nagumo cell model - originally specified in NeuroML/LEMS", + "nodes": { + "FNpop": { + "metadata": { + "color": "0.2 0.2 0.2", + "radius": 3, + "region": "region1" + }, + "parameters": { + "initial_w": { + "value": [ + 0.0 + ] + }, + "initial_v": { + "value": [ + -1.0 + ] + }, + "a_v": { + "value": [ + -0.3333333333333333 + ] + }, + "b_v": { + "value": [ + 0.0 + ] + }, + "c_v": { + "value": [ + 1.0 + ] + }, + "d_v": { + "value": [ + 1.0 + ] + }, + "e_v": { + "value": [ + -1.0 + ] + }, + "f_v": { + "value": [ + 1.0 + ] + }, + "time_constant_v": { + "value": [ + 1.0 + ] + }, + "a_w": { + "value": [ + 1.0 + ] + }, + "b_w": { + "value": [ + -0.8 + ] + }, + "c_w": { + "value": [ + 0.7 + ] + }, + "time_constant_w": { + "value": [ + 12.5 + ] + }, + "threshold_exc": { + "value": [ + -1.0 + ] + }, + "mode": { + "value": [ + 1.0 + ] + }, + "uncorrelated_activity": { + "value": [ + 0.0 + ] + }, + "Iext": { + "value": [ + 0.0 + ] + }, + "MSEC": { + "value": [ + 0.001 + ] + }, + "spike": { + "value": [ + 0 + ] + }, + "V": { + "default_initial_value": "initial_v", + "time_derivative": "(a_v*V*V*V + (1+threshold_exc)*b_v*V*V + (-1*threshold_exc)*c_v*V + d_v + e_v*W + f_v*Iext + INPUT) / (time_constant_v*MSEC)" + }, + "W": { + "default_initial_value": "initial_w", + "time_derivative": "(mode*a_w*V + b_w*W + c_w + (1-mode)*uncorrelated_activity) / (time_constant_w*MSEC)" + } + }, + "input_ports": { + "INPUT": {} + }, + "output_ports": { + "spike": { + "value": "spike" + }, + "V": { + "value": "V" + }, + "W": { + "value": "W" + } + }, + "notes": "Cell: [Cell(notes=None, id='fn', parameters={'initial_w': 'initial_w', 'initial_v': 'initial_v', 'a_v': 'a_v', 'b_v': 'b_v', 'c_v': 'c_v', 'd_v': 'd_v', 'e_v': 'e_v', 'f_v': 'f_v', 'time_constant_v': 'time_constant_v', 'a_w': 'a_w', 'b_w': 'b_w', 'c_w': 'c_w', 'time_constant_w': 'time_constant_w', 'threshold': 'threshold', 'mode': 'mode', 'uncorrelated_activity': 'uncorrelated_activity', 'Iext': 'Iext'}, neuroml2_source_file=None, lems_source_file='FN_Definitions.xml', neuroml2_cell=None, pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is defined in FN_Definitions.xml and in Lems is: Component, id: fn, type: fnCell,\n parameters: {'initial_w': '0.0', 'initial_v': '-1', 'a_v': '-0.3333333333333333', 'b_v': '0.0', 'c_v': '1.0', 'd_v': '1', 'e_v': '-1.0', 'f_v': '1.0', 'time_constant_v': '1.0', 'a_w': '1.0', 'b_w': '-0.8', 'c_w': '0.7', 'time_constant_w': '12.5', 'threshold_exc': '-1.0', 'mode': '1.0', 'uncorrelated_activity': '0.0', 'Iext': '0'}\n parent: None\n" + } + }, + "edges": {} + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.yaml b/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.yaml new file mode 100644 index 00000000..2fe6d035 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.yaml @@ -0,0 +1,105 @@ +FN: + format: ModECI MDF v0.4 + graphs: + FN: + notes: FitzHugh Nagumo cell model - originally specified in NeuroML/LEMS + nodes: + FNpop: + metadata: + color: 0.2 0.2 0.2 + radius: 3 + region: region1 + parameters: + initial_w: + value: + - 0.0 + initial_v: + value: + - -1.0 + a_v: + value: + - -0.3333333333333333 + b_v: + value: + - 0.0 + c_v: + value: + - 1.0 + d_v: + value: + - 1.0 + e_v: + value: + - -1.0 + f_v: + value: + - 1.0 + time_constant_v: + value: + - 1.0 + a_w: + value: + - 1.0 + b_w: + value: + - -0.8 + c_w: + value: + - 0.7 + time_constant_w: + value: + - 12.5 + threshold_exc: + value: + - -1.0 + mode: + value: + - 1.0 + uncorrelated_activity: + value: + - 0.0 + Iext: + value: + - 0.0 + MSEC: + value: + - 0.001 + spike: + value: + - 0 + V: + default_initial_value: initial_v + time_derivative: (a_v*V*V*V + (1+threshold_exc)*b_v*V*V + + (-1*threshold_exc)*c_v*V + d_v + e_v*W + f_v*Iext + + INPUT) / (time_constant_v*MSEC) + W: + default_initial_value: initial_w + time_derivative: (mode*a_w*V + b_w*W + c_w + (1-mode)*uncorrelated_activity) + / (time_constant_w*MSEC) + input_ports: + INPUT: {} + output_ports: + spike: + value: spike + V: + value: V + W: + value: W + notes: "Cell: [Cell(notes=None, id='fn', parameters={'initial_w':\ + \ 'initial_w', 'initial_v': 'initial_v', 'a_v': 'a_v', 'b_v':\ + \ 'b_v', 'c_v': 'c_v', 'd_v': 'd_v', 'e_v': 'e_v', 'f_v':\ + \ 'f_v', 'time_constant_v': 'time_constant_v', 'a_w': 'a_w',\ + \ 'b_w': 'b_w', 'c_w': 'c_w', 'time_constant_w': 'time_constant_w',\ + \ 'threshold': 'threshold', 'mode': 'mode', 'uncorrelated_activity':\ + \ 'uncorrelated_activity', 'Iext': 'Iext'}, neuroml2_source_file=None,\ + \ lems_source_file='FN_Definitions.xml', neuroml2_cell=None,\ + \ pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is\ + \ defined in FN_Definitions.xml and in Lems is: Component,\ + \ id: fn, type: fnCell,\n parameters: {'initial_w': '0.0',\ + \ 'initial_v': '-1', 'a_v': '-0.3333333333333333', 'b_v':\ + \ '0.0', 'c_v': '1.0', 'd_v': '1', 'e_v': '-1.0', 'f_v': '1.0',\ + \ 'time_constant_v': '1.0', 'a_w': '1.0', 'b_w': '-0.8', 'c_w':\ + \ '0.7', 'time_constant_w': '12.5', 'threshold_exc': '-1.0',\ + \ 'mode': '1.0', 'uncorrelated_activity': '0.0', 'Iext': '0'}\n\ + \ parent: None\n" + edges: {} diff --git a/docs/sphinx/source/api/export_format/NeuroML/FN.py b/docs/sphinx/source/api/export_format/NeuroML/FN.py new file mode 100644 index 00000000..6c8a24e7 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/FN.py @@ -0,0 +1,104 @@ +from neuromllite import ( + Network, + Cell, + Population, + Synapse, + RectangularRegion, + RandomLayout, +) +from neuromllite import ( + Projection, + RandomConnectivity, + OneToOneConnector, + Simulation, + InputSource, + Input, +) + +from neuromllite.NetworkGenerator import check_to_generate_or_run + +import sys + + +def generate(): + + dt = 0.05 + simtime = 100 + + ################################################################################ + ### Build new network + + net = Network(id="FN") + net.notes = "FitzHugh Nagumo cell model - originally specified in NeuroML/LEMS" + + net.parameters = { + "initial_w": 0.0, + "initial_v": -1, + "a_v": -0.3333333333333333, + "b_v": 0.0, + "c_v": 1.0, + "d_v": 1, + "e_v": -1.0, + "f_v": 1.0, + "time_constant_v": 1.0, + "a_w": 1.0, + "b_w": -0.8, + "c_w": 0.7, + "time_constant_w": 12.5, + "threshold": -1.0, + "mode": 1.0, + "uncorrelated_activity": 0.0, + "Iext": 0, + } + + cellInput = Cell(id="fn", lems_source_file="FN_Definitions.xml", parameters={}) + for p in net.parameters: + cellInput.parameters[p] = p + net.cells.append(cellInput) + + r1 = RectangularRegion( + id="region1", x=0, y=0, z=0, width=1000, height=100, depth=1000 + ) + net.regions.append(r1) + + pop = Population( + id="FNpop", + size="1", + component=cellInput.id, + properties={"color": "0.2 0.2 0.2", "radius": 3}, + random_layout=RandomLayout(region=r1.id), + ) + net.populations.append(pop) + + new_file = net.to_json_file("%s.json" % net.id) + + ################################################################################ + ### Build Simulation object & save as JSON + + sim = Simulation( + id="Sim%s" % net.id, + network=new_file, + duration=simtime, + dt=dt, + seed=123, + record_variables={"V": {"all": "*"}, "W": {"all": "*"}}, + plots2D={ + "VW": {"x_axis": "%s/0/fn/V" % pop.id, "y_axis": "%s/0/fn/W" % pop.id} + }, + ) + + sim.to_json_file() + + return sim, net + + +if __name__ == "__main__": + + sim, net = generate() + + ################################################################################ + ### Run in some simulators + + import sys + + check_to_generate_or_run(sys.argv, sim) diff --git a/docs/sphinx/source/api/export_format/NeuroML/FNrun.py b/docs/sphinx/source/api/export_format/NeuroML/FNrun.py new file mode 100644 index 00000000..6462660e --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/FNrun.py @@ -0,0 +1,126 @@ +from modeci_mdf.utils import load_mdf, print_summary +from modeci_mdf.mdf import Node, Edge, OutputPort, Parameter + +from modeci_mdf.execution_engine import EvaluableGraph + +from modelspec.utils import FORMAT_NUMPY, FORMAT_TENSORFLOW +import sys +import os +import numpy as np + +verbose = True +verbose = False + + +def execute(multi=False): + + mdf_model = load_mdf("FN.mdf.yaml") + mod_graph = mdf_model.graphs[0] + + dt = 0.00005 + duration = 0.1 + + if not multi: + + fn_node = mod_graph.nodes[0] + fn_node.get_parameter("initial_v").value = [-1.0] + fn_node.get_parameter("initial_w").value = [0.0] + input = np.array([0]) + + else: + + dt = 0.00025 + size = 5 + max_amp = 0.5 + input = np.array([max_amp * (-1 + 2 * i / (size - 1)) for i in range(size)]) + # input = [-0.4,-0.2, 0.,0.2,0.4] + print("Inputs to be applied: %s" % input) + input_node = Node(id="input_node") + + input_node.parameters.append(Parameter(id="input_level", value=input)) + + op1 = OutputPort(id="out_port", value="input_level") + input_node.output_ports.append(op1) + mod_graph.nodes.append(input_node) + + fn_node = mod_graph.nodes[0] + fn_node.get_parameter("initial_v").value = np.array([1.0] * len(input)) + fn_node.get_parameter("initial_w").value = np.array([0.0] * len(input)) + + print(fn_node) + + e1 = Edge( + id="input_edge", + sender=input_node.id, + sender_port=op1.id, + receiver="FNpop", + receiver_port="INPUT", + ) + + mod_graph.edges.append(e1) + + mdf_model.to_graph_image( + engine="dot", + output_format="png", + view_on_render=False, + level=3, + filename_root="FNmulti", + only_warn_on_fail=( + os.name == "nt" + ), # Makes sure test of this doesn't fail on Windows on GitHub Actions + ) + + duration = 0.1 + + eg = EvaluableGraph(mod_graph, verbose) + # duration= 2 + t = 0 + + times = [] + vv = {} + ww = {} + + format = FORMAT_TENSORFLOW if "-tf" in sys.argv else FORMAT_NUMPY + + while t < duration + 0.00005: + times.append(t) + print("====== Evaluating at t = %s ======" % (t)) + if t == 0: + eg.evaluate(array_format=format) # replace with initialize? + else: + eg.evaluate(array_format=format, time_increment=dt) + + for i in range(len(eg.enodes["FNpop"].evaluable_parameters["V"].curr_value)): + if not i in vv: + vv[i] = [] + ww[i] = [] + v = eg.enodes["FNpop"].evaluable_parameters["V"].curr_value[i] + w = eg.enodes["FNpop"].evaluable_parameters["W"].curr_value[i] + print(" Time %s: index: %i, V: %s, W: %s" % (t, i, v, w)) + vv[i].append(v) + ww[i].append(w) + if i == 0: + print(f" Value at {t}: v={v}, w={w}") + t += dt + + import matplotlib.pyplot as plt + + for vi in vv: + print(f"Plotting {vi}, {input}") + plt.plot(times, vv[vi], label="V %.3f" % input[vi]) + plt.plot(times, ww[vi], label="W %.3f" % input[vi]) + + plt.legend() + + if not multi: + plt.savefig("MDFFNrun.png", bbox_inches="tight") + else: + plt.savefig("MDFFNrun.multi.png", bbox_inches="tight") + + if not "-nogui" in sys.argv: + plt.show() + + +if __name__ == "__main__": + + execute("-multi" in sys.argv) diff --git a/docs/sphinx/source/api/export_format/NeuroML/Izh_run.py b/docs/sphinx/source/api/export_format/NeuroML/Izh_run.py new file mode 100644 index 00000000..9e53acd4 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/Izh_run.py @@ -0,0 +1,121 @@ +from modeci_mdf.utils import load_mdf, print_summary +from modeci_mdf.mdf import Node, Edge, OutputPort, Parameter + +from modeci_mdf.execution_engine import EvaluableGraph + +from modelspec.utils import FORMAT_NUMPY, FORMAT_TENSORFLOW +import sys +import os +import numpy as np + + +def execute(): + + mdf_model = load_mdf("IzhikevichTest.mdf.yaml") + mod_graph = mdf_model.graphs[0] + + dt = 0.0005 + duration = 0.7 + + input_node = mod_graph.get_node("InputList_stim") + izh_node = mod_graph.get_node("izhPop") + + num_cells = 1 + + if not "-iaf" in sys.argv: # for testing... + izh_node.get_parameter("v0").value = [-0.08] * num_cells + izh_node.get_parameter("u").default_initial_value = [0.0] * num_cells + izh_node.get_parameter("c").value = [ + izh_node.get_parameter("c").value[0] + ] * num_cells + + input_node.get_parameter("i").conditions[0].value = [0] * num_cells + input_node.get_parameter("amplitude").value = [ + (i + 1) * 1e-10 for i in range(num_cells) + ] + input_node.get_parameter("i").conditions[2].value = [0] * num_cells + else: + izh_node.get_parameter("v").default_initial_value = [-0.08] + izh_node.get_parameter("reset").value = [-0.07] + + print(izh_node.to_dict()) + + mdf_model.to_graph_image( + engine="dot", + output_format="png", + view_on_render=False, + level=3, + filename_root="Izh", + only_warn_on_fail=( + os.name == "nt" + ), # Makes sure test of this doesn't fail on Windows on GitHub Actions + ) + + verbose = "-v" in sys.argv + + eg = EvaluableGraph(mod_graph, verbose) + # duration= 2 + t = 0 + + times = [] + vv = {} + uu = {} + ii = {} + + format = FORMAT_TENSORFLOW if "-tf" in sys.argv else FORMAT_NUMPY + + while t < duration + dt: + times.append(t) + print("====== Evaluating at t = %s ======" % (t)) + if t == 0: + eg.evaluate(array_format=format) # replace with initialize? + else: + eg.evaluate(array_format=format, time_increment=dt) + + for i in range(len(eg.enodes["izhPop"].evaluable_parameters["v"].curr_value)): + if not i in vv: + vv[i] = [] + uu[i] = [] + ii[i] = [] + v = eg.enodes["izhPop"].evaluable_parameters["v"].curr_value[i] + u = eg.enodes["izhPop"].evaluable_parameters["u"].curr_value[i] + vv[i].append(v) + uu[i].append(u) + + ic = eg.enodes[input_node.id].evaluable_parameters["i"].curr_value[i] + ii[i].append(ic) + + print( + f" Value at {t}: v={eg.enodes['izhPop'].evaluable_parameters['v'].curr_value }, \ + u={eg.enodes['izhPop'].evaluable_parameters['u'].curr_value},\ + i={eg.enodes[input_node.id].evaluable_parameters['i'].curr_value}" + ) + t += dt + + import matplotlib.pyplot as plt + + scale = "1e8" + + for vi in vv: + plt.plot(times, vv[vi], label="v cell %i" % vi) + plt.plot( + times, + [x * float(scale) for x in uu[vi]], + label="u cell %i (*%s)" % (vi, scale), + ) + plt.plot( + times, + [x * float(scale) for x in ii[vi]], + label="i cell %i (*%s)" % (vi, scale), + ) + plt.legend() + + plt.savefig("Izh_run.png", bbox_inches="tight") + + if not "-nogui" in sys.argv: + plt.show() + + +if __name__ == "__main__": + + execute() diff --git a/docs/sphinx/source/api/export_format/NeuroML/Izhikevich.py b/docs/sphinx/source/api/export_format/NeuroML/Izhikevich.py new file mode 100644 index 00000000..2bc54a9b --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/Izhikevich.py @@ -0,0 +1,105 @@ +from neuromllite import Network, Cell, InputSource, Population, Synapse +from neuromllite import Projection, RandomConnectivity, Input, Simulation +import sys + +################################################################################ +### Build new network + +net = Network(id="IzhikevichTest") +net.notes = "Example Izhikevich" +net.parameters = {"N": 1} + +if not "-iaf" in sys.argv: # for testing... + + cell = Cell(id="izhCell", neuroml2_cell="izhikevich2007Cell") + cell.parameters = {} + + params = { + "v0": "-80mV", + "C": "100 pF", + "k": "0.7 nS_per_mV", + "vr": "-60 mV", + "vt": "-40 mV", + "vpeak": "35 mV", + "a": "0.03 per_ms", + "b": "-2 nS", + "c": "-50 mV", + "d": "100 pA", + } +else: + + cell = Cell(id="iaf", neuroml2_cell="iafCell") + cell.parameters = {} + + params = { + "leak_reversal": "-50mV", + "leakReversal": "leak_reversal", + "thresh": "-55mV", + "reset": "-70mV", + "C": "0.2nF", + "leak_conductance": "0.01uS", + "leak_conductance": "leakConductance", + } + +for p in params: + cell.parameters[p] = p + net.parameters[p] = params[p] + +net.cells.append(cell) + + +pop = Population( + id="izhPop", size="1", component=cell.id, properties={"color": ".7 0 0"} +) +net.populations.append(pop) + +net.parameters["delay"] = "100ms" +net.parameters["stim_amp"] = "100pA" +net.parameters["duration"] = "500ms" +input_source = InputSource( + id="iclamp_0", + neuroml2_input="pulseGenerator", + parameters={"amplitude": "stim_amp", "delay": "delay", "duration": "duration"}, +) +net.input_sources.append(input_source) + +net.inputs.append( + Input( + id="stim", + input_source=input_source.id, + population=pop.id, + percentage=100, + weight=1, + ) +) + +print(net) +print(net.to_json()) +new_file = net.to_yaml_file("%s.nmllite.yaml" % net.id) + + +################################################################################ +### Build Simulation object & save as JSON + +record_variables = {"v": {"all": "*"}} +if not "-iaf" in sys.argv: + record_variables["u"] = {"all": "*"} + +sim = Simulation( + id="Sim%s" % net.id, + network=new_file, + duration="700", + dt="0.025", + record_variables=record_variables, +) + +sim.to_yaml_file("%s.yaml" % sim.id) + + +################################################################################ +### Run in some simulators + +from neuromllite.NetworkGenerator import check_to_generate_or_run +import sys + +check_to_generate_or_run(sys.argv, sim) diff --git a/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.json b/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.json new file mode 100644 index 00000000..e6588d20 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.json @@ -0,0 +1,192 @@ +{ + "IzhikevichTest": { + "format": "ModECI MDF v0.4", + "graphs": { + "IzhikevichTest": { + "notes": "Example Izhikevich", + "nodes": { + "izhPop": { + "parameters": { + "v0": { + "value": [ + -0.08 + ] + }, + "C": { + "value": [ + 1e-10 + ] + }, + "k": { + "value": [ + 7e-07 + ] + }, + "vr": { + "value": [ + -0.06 + ] + }, + "vt": { + "value": [ + -0.04 + ] + }, + "vpeak": { + "value": [ + 0.035 + ] + }, + "a": { + "value": [ + 30.0 + ] + }, + "b": { + "value": [ + -2e-09 + ] + }, + "c": { + "value": [ + -0.05 + ] + }, + "d": { + "value": [ + 1e-10 + ] + }, + "spike": { + "default_initial_value": [ + 0 + ], + "conditions": { + "condition_0_on": { + "test": "v > vpeak", + "value": 1 + }, + "condition_0_off": { + "test": "spike > 0", + "value": 0 + } + } + }, + "v": { + "default_initial_value": "v0", + "conditions": { + "condition_0": { + "test": "v > vpeak", + "value": "c" + } + }, + "time_derivative": "iMemb / C" + }, + "u": { + "default_initial_value": "0", + "conditions": { + "condition_0": { + "test": "v > vpeak", + "value": "u + d" + } + }, + "time_derivative": "a * (b * (v-vr) - u)" + }, + "iSyn": { + "value": "synapses_i" + }, + "iMemb": { + "value": "k * (v-vr) * (v-vt) + iSyn - u" + } + }, + "input_ports": { + "synapses_i": {} + }, + "output_ports": { + "spike": { + "value": "spike" + }, + "v": { + "value": "v" + }, + "u": { + "value": "u" + }, + "iMemb": { + "value": "iMemb" + } + }, + "notes": "Cell: [Cell(notes=None, id='izhCell', parameters={'v0': 'v0', 'C': 'C', 'k': 'k', 'vr': 'vr', 'vt': 'vt', 'vpeak': 'vpeak', 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd'}, neuroml2_source_file=None, lems_source_file=None, neuroml2_cell='izhikevich2007Cell', pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is defined in None and in Lems is: Component, id: izhCell, type: izhikevich2007Cell,\n parameters: {'v0': '-80mV', 'C': '100 pF', 'k': '0.7 nS_per_mV', 'vr': '-60 mV', 'vt': '-40 mV', 'vpeak': '35 mV', 'a': '0.03 per_ms', 'b': '-2 nS', 'c': '-50 mV', 'd': '100 pA'}\n parent: None\n" + }, + "InputList_stim": { + "parameters": { + "amplitude": { + "value": [ + 1e-10 + ] + }, + "delay": { + "value": [ + 0.1 + ] + }, + "duration": { + "value": [ + 0.5 + ] + }, + "weight": { + "value": [ + 1 + ] + }, + "i": { + "conditions": { + "condition_0": { + "test": "t < delay", + "value": "0" + }, + "condition_1": { + "test": "t >= delay and t < duration + delay", + "value": "weight * amplitude" + }, + "condition_2": { + "test": "t >= duration + delay", + "value": "0" + } + } + }, + "t": { + "default_initial_value": 0, + "time_derivative": "1" + } + }, + "input_ports": { + "spike_input": { + "shape": [ + 1 + ], + "reduce": "add" + } + }, + "output_ports": { + "i": { + "value": "i" + } + }, + "notes": "Cell: [InputSource(notes=None, id='iclamp_0', parameters={'amplitude': 'stim_amp', 'delay': 'delay', 'duration': 'duration'}, neuroml2_source_file=None, neuroml2_input='pulseGenerator', lems_source_file=None, pynn_input=None)] is defined in None and in Lems is: Component, id: iclamp_0, type: pulseGenerator,\n parameters: {'amplitude': '100pA', 'delay': '100ms', 'duration': '500ms'}\n parent: None\n" + } + }, + "edges": { + "Edge InputList_stim to izhPop": { + "name": "Edge InputList_stim to izhPop", + "sender_port": "i", + "receiver_port": "synapses_i", + "sender": "InputList_stim", + "receiver": "izhPop" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.yaml b/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.yaml new file mode 100644 index 00000000..12ef3586 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.yaml @@ -0,0 +1,137 @@ +IzhikevichTest: + format: ModECI MDF v0.4 + graphs: + IzhikevichTest: + notes: Example Izhikevich + nodes: + izhPop: + parameters: + v0: + value: + - -0.08 + C: + value: + - 1.0e-10 + k: + value: + - 7.0e-07 + vr: + value: + - -0.06 + vt: + value: + - -0.04 + vpeak: + value: + - 0.035 + a: + value: + - 30.0 + b: + value: + - -2.0e-09 + c: + value: + - -0.05 + d: + value: + - 1.0e-10 + spike: + default_initial_value: + - 0 + conditions: + condition_0_on: + test: v > vpeak + value: 1 + condition_0_off: + test: spike > 0 + value: 0 + v: + default_initial_value: v0 + conditions: + condition_0: + test: v > vpeak + value: c + time_derivative: iMemb / C + u: + default_initial_value: '0' + conditions: + condition_0: + test: v > vpeak + value: u + d + time_derivative: a * (b * (v-vr) - u) + iSyn: + value: synapses_i + iMemb: + value: k * (v-vr) * (v-vt) + iSyn - u + input_ports: + synapses_i: {} + output_ports: + spike: + value: spike + v: + value: v + u: + value: u + iMemb: + value: iMemb + notes: "Cell: [Cell(notes=None, id='izhCell', parameters={'v0':\ + \ 'v0', 'C': 'C', 'k': 'k', 'vr': 'vr', 'vt': 'vt', 'vpeak':\ + \ 'vpeak', 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd'}, neuroml2_source_file=None,\ + \ lems_source_file=None, neuroml2_cell='izhikevich2007Cell',\ + \ pynn_cell=None, arbor_cell=None, bindsnet_node=None)] is\ + \ defined in None and in Lems is: Component, id: izhCell,\ + \ type: izhikevich2007Cell,\n parameters: {'v0': '-80mV',\ + \ 'C': '100 pF', 'k': '0.7 nS_per_mV', 'vr': '-60 mV', 'vt':\ + \ '-40 mV', 'vpeak': '35 mV', 'a': '0.03 per_ms', 'b': '-2\ + \ nS', 'c': '-50 mV', 'd': '100 pA'}\n parent: None\n" + InputList_stim: + parameters: + amplitude: + value: + - 1.0e-10 + delay: + value: + - 0.1 + duration: + value: + - 0.5 + weight: + value: + - 1 + i: + conditions: + condition_0: + test: t < delay + value: '0' + condition_1: + test: t >= delay and t < duration + delay + value: weight * amplitude + condition_2: + test: t >= duration + delay + value: '0' + t: + default_initial_value: 0 + time_derivative: '1' + input_ports: + spike_input: + shape: + - 1 + reduce: add + output_ports: + i: + value: i + notes: "Cell: [InputSource(notes=None, id='iclamp_0', parameters={'amplitude':\ + \ 'stim_amp', 'delay': 'delay', 'duration': 'duration'}, neuroml2_source_file=None,\ + \ neuroml2_input='pulseGenerator', lems_source_file=None,\ + \ pynn_input=None)] is defined in None and in Lems is: Component,\ + \ id: iclamp_0, type: pulseGenerator,\n parameters: {'amplitude':\ + \ '100pA', 'delay': '100ms', 'duration': '500ms'}\n parent:\ + \ None\n" + edges: + Edge InputList_stim to izhPop: + name: Edge InputList_stim to izhPop + sender_port: i + receiver_port: synapses_i + sender: InputList_stim + receiver: izhPop diff --git a/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.nmllite.yaml b/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.nmllite.yaml new file mode 100644 index 00000000..55800357 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.nmllite.yaml @@ -0,0 +1,51 @@ +IzhikevichTest: + version: NeuroMLlite v0.6.1 + notes: Example Izhikevich + parameters: + N: 1 + v0: -80mV + C: 100 pF + k: 0.7 nS_per_mV + vr: -60 mV + vt: -40 mV + vpeak: 35 mV + a: 0.03 per_ms + b: -2 nS + c: -50 mV + d: 100 pA + delay: 100ms + stim_amp: 100pA + duration: 500ms + cells: + izhCell: + parameters: + v0: v0 + C: C + k: k + vr: vr + vt: vt + vpeak: vpeak + a: a + b: b + c: c + d: d + neuroml2_cell: izhikevich2007Cell + input_sources: + iclamp_0: + parameters: + amplitude: stim_amp + delay: delay + duration: duration + neuroml2_input: pulseGenerator + populations: + izhPop: + size: '1' + component: izhCell + properties: + color: .7 0 0 + inputs: + stim: + input_source: iclamp_0 + population: izhPop + percentage: 100 + weight: 1 diff --git a/docs/sphinx/source/api/export_format/NeuroML/SimABCD.json b/docs/sphinx/source/api/export_format/NeuroML/SimABCD.json new file mode 100644 index 00000000..e5b9cdde --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/SimABCD.json @@ -0,0 +1,14 @@ +{ + "SimABCD": { + "version": "NeuroMLlite v0.6.1", + "network": "ABCD.json", + "duration": 5000.0, + "dt": 100.0, + "seed": 123, + "record_variables": { + "OUTPUT": { + "all": "*" + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/NeuroML/SimFN.json b/docs/sphinx/source/api/export_format/NeuroML/SimFN.json new file mode 100644 index 00000000..449a6594 --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/SimFN.json @@ -0,0 +1,23 @@ +{ + "SimFN": { + "version": "NeuroMLlite v0.6.1", + "network": "FN.json", + "duration": 100.0, + "dt": 0.05, + "seed": 123, + "record_variables": { + "V": { + "all": "*" + }, + "W": { + "all": "*" + } + }, + "plots2D": { + "VW": { + "x_axis": "FNpop/0/fn/V", + "y_axis": "FNpop/0/fn/W" + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/NeuroML/SimIzhikevichTest.yaml b/docs/sphinx/source/api/export_format/NeuroML/SimIzhikevichTest.yaml new file mode 100644 index 00000000..f98bfd7a --- /dev/null +++ b/docs/sphinx/source/api/export_format/NeuroML/SimIzhikevichTest.yaml @@ -0,0 +1,10 @@ +SimIzhikevichTest: + version: NeuroMLlite v0.6.1 + network: IzhikevichTest.nmllite.yaml + duration: 700.0 + dt: 0.025 + record_variables: + v: + all: '*' + u: + all: '*' diff --git a/docs/sphinx/source/api/export_format/ONNX/ab.json b/docs/sphinx/source/api/export_format/ONNX/ab.json new file mode 100644 index 00000000..ce07162a --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/ab.json @@ -0,0 +1,76 @@ +{ + "ONNX Model": { + "format": "ModECI MDF v0.4", + "generating_application": "Python modeci-mdf v0.4.14", + "graphs": { + "main_graph": { + "nodes": { + "/A/Add": { + "input_ports": { + "input": { + "shape": [ + 2, + 3 + ], + "type": "float" + } + }, + "parameters": { + "B": { + "value": 1.0 + }, + "/A/Add": { + "function": "onnx::Add", + "args": { + "A": "input", + "B": "B" + } + } + }, + "output_ports": { + "_A_Add_output_0": { + "value": "/A/Add" + } + } + }, + "/B/Mul": { + "input_ports": { + "_A_Add_output_0": { + "shape": [ + 2, + 3 + ], + "type": "float" + } + }, + "parameters": { + "B": { + "value": 5.0 + }, + "/B/Mul": { + "function": "onnx::Mul", + "args": { + "A": "_A_Add_output_0", + "B": "B" + } + } + }, + "output_ports": { + "_4": { + "value": "/B/Mul" + } + } + } + }, + "edges": { + "/A/Add._A_Add_output_0_/B/Mul._A_Add_output_0": { + "sender": "/A/Add", + "receiver": "/B/Mul", + "sender_port": "_A_Add_output_0", + "receiver_port": "_A_Add_output_0" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/ONNX/ab.yaml b/docs/sphinx/source/api/export_format/ONNX/ab.yaml new file mode 100644 index 00000000..8039d6f2 --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/ab.yaml @@ -0,0 +1,48 @@ +ONNX Model: + format: ModECI MDF v0.4 + generating_application: Python modeci-mdf v0.4.14 + graphs: + main_graph: + nodes: + /A/Add: + input_ports: + input: + shape: + - 2 + - 3 + type: float + parameters: + B: + value: 1.0 + /A/Add: + function: onnx::Add + args: + A: input + B: B + output_ports: + _A_Add_output_0: + value: /A/Add + /B/Mul: + input_ports: + _A_Add_output_0: + shape: + - 2 + - 3 + type: float + parameters: + B: + value: 5.0 + /B/Mul: + function: onnx::Mul + args: + A: _A_Add_output_0 + B: B + output_ports: + _4: + value: /B/Mul + edges: + /A/Add._A_Add_output_0_/B/Mul._A_Add_output_0: + sender: /A/Add + receiver: /B/Mul + sender_port: _A_Add_output_0 + receiver_port: _A_Add_output_0 diff --git a/docs/sphinx/source/api/export_format/ONNX/abc.json b/docs/sphinx/source/api/export_format/ONNX/abc.json new file mode 100644 index 00000000..c87831a0 --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/abc.json @@ -0,0 +1,171 @@ +{ + "ONNX Model": { + "format": "ModECI MDF v0.4", + "generating_application": "Python modeci-mdf v0.4.14", + "graphs": { + "main_graph": { + "nodes": { + "/A/Add": { + "input_ports": { + "input": { + "shape": [ + 2, + 3 + ], + "type": "float" + } + }, + "parameters": { + "B": { + "value": 1.0 + }, + "/A/Add": { + "function": "onnx::Add", + "args": { + "A": "input", + "B": "B" + } + } + }, + "output_ports": { + "_A_Add_output_0": { + "value": "/A/Add" + } + } + }, + "Loop_3": { + "input_ports": { + "B_loop_count": { + "shape": [ + 1 + ], + "type": "float" + }, + "_A_Add_output_0": { + "shape": [ + 2, + 3 + ], + "type": "float" + } + }, + "parameters": { + "body": { + "value": { + "graph_sub_graph": { + "id": "sub_graph", + "nodes": { + "Div_5": { + "input_ports": { + "x.11": { + "shape": [ + 2, + 3 + ], + "type": "float" + } + }, + "parameters": { + "B": { + "value": 10.0 + }, + "Div_5": { + "function": "onnx::Div", + "args": { + "A": "x.11", + "B": "B" + } + } + }, + "output_ports": { + "x": { + "value": "Div_5" + } + } + }, + "Identity_6": { + "parameters": { + "input": { + "value": true + }, + "Identity_6": { + "function": "onnx::Identity", + "args": { + "input": "input" + } + } + }, + "output_ports": { + "_11": { + "value": "Identity_6" + } + } + } + } + } + } + }, + "cond": { + "value": true + }, + "Loop_3": { + "function": "onnx::Loop", + "args": { + "M": "B_loop_count", + "cond": "cond", + "v_initial": "_A_Add_output_0" + } + } + }, + "output_ports": { + "x.3": { + "value": "Loop_3" + } + } + }, + "/C/Mul": { + "input_ports": { + "x.3": { + "shape": [ + 1 + ], + "type": "float" + } + }, + "parameters": { + "B": { + "value": 100.0 + }, + "/C/Mul": { + "function": "onnx::Mul", + "args": { + "A": "x.3", + "B": "B" + } + } + }, + "output_ports": { + "_13": { + "value": "/C/Mul" + } + } + } + }, + "edges": { + "/A/Add._A_Add_output_0_Loop_3._A_Add_output_0": { + "sender": "/A/Add", + "receiver": "Loop_3", + "sender_port": "_A_Add_output_0", + "receiver_port": "_A_Add_output_0" + }, + "Loop_3.x.3_/C/Mul.x.3": { + "sender": "Loop_3", + "receiver": "/C/Mul", + "sender_port": "x.3", + "receiver_port": "x.3" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/ONNX/abc.yaml b/docs/sphinx/source/api/export_format/ONNX/abc.yaml new file mode 100644 index 00000000..714367de --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/abc.yaml @@ -0,0 +1,109 @@ +ONNX Model: + format: ModECI MDF v0.4 + generating_application: Python modeci-mdf v0.4.14 + graphs: + main_graph: + nodes: + /A/Add: + input_ports: + input: + shape: + - 2 + - 3 + type: float + parameters: + B: + value: 1.0 + /A/Add: + function: onnx::Add + args: + A: input + B: B + output_ports: + _A_Add_output_0: + value: /A/Add + Loop_3: + input_ports: + B_loop_count: + shape: + - 1 + type: float + _A_Add_output_0: + shape: + - 2 + - 3 + type: float + parameters: + body: + value: + graph_sub_graph: + id: sub_graph + nodes: + Div_5: + input_ports: + x.11: + shape: + - 2 + - 3 + type: float + parameters: + B: + value: 10.0 + Div_5: + function: onnx::Div + args: + A: x.11 + B: B + output_ports: + x: + value: Div_5 + Identity_6: + parameters: + input: + value: true + Identity_6: + function: onnx::Identity + args: + input: input + output_ports: + _11: + value: Identity_6 + cond: + value: true + Loop_3: + function: onnx::Loop + args: + M: B_loop_count + cond: cond + v_initial: _A_Add_output_0 + output_ports: + x.3: + value: Loop_3 + /C/Mul: + input_ports: + x.3: + shape: + - 1 + type: float + parameters: + B: + value: 100.0 + /C/Mul: + function: onnx::Mul + args: + A: x.3 + B: B + output_ports: + _13: + value: /C/Mul + edges: + /A/Add._A_Add_output_0_Loop_3._A_Add_output_0: + sender: /A/Add + receiver: Loop_3 + sender_port: _A_Add_output_0 + receiver_port: _A_Add_output_0 + Loop_3.x.3_/C/Mul.x.3: + sender: Loop_3 + receiver: /C/Mul + sender_port: x.3 + receiver_port: x.3 diff --git a/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.json b/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.json new file mode 100644 index 00000000..b1e0b431 --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.json @@ -0,0 +1,98 @@ +{ + "ONNX Model": { + "format": "ModECI MDF v0.4", + "generating_application": "Python modeci-mdf v0.4.14", + "graphs": { + "main_graph": { + "nodes": { + "/A/Sin": { + "input_ports": { + "input": { + "shape": [ + 2, + 3 + ], + "type": "float" + } + }, + "parameters": { + "/A/Sin": { + "function": "onnx::Sin", + "args": { + "input": "input" + } + } + }, + "output_ports": { + "_A_Sin_output_0": { + "value": "/A/Sin" + } + } + }, + "/B/Sin": { + "input_ports": { + "_A_Sin_output_0": { + "shape": [ + 2, + 3 + ], + "type": "float" + } + }, + "parameters": { + "/B/Sin": { + "function": "onnx::Sin", + "args": { + "input": "_A_Sin_output_0" + } + } + }, + "output_ports": { + "_B_Sin_output_0": { + "value": "/B/Sin" + } + } + }, + "/C/Cos": { + "input_ports": { + "_B_Sin_output_0": { + "shape": [ + 2, + 3 + ], + "type": "float" + } + }, + "parameters": { + "/C/Cos": { + "function": "onnx::Cos", + "args": { + "input": "_B_Sin_output_0" + } + } + }, + "output_ports": { + "_3": { + "value": "/C/Cos" + } + } + } + }, + "edges": { + "/A/Sin._A_Sin_output_0_/B/Sin._A_Sin_output_0": { + "sender": "/A/Sin", + "receiver": "/B/Sin", + "sender_port": "_A_Sin_output_0", + "receiver_port": "_A_Sin_output_0" + }, + "/B/Sin._B_Sin_output_0_/C/Cos._B_Sin_output_0": { + "sender": "/B/Sin", + "receiver": "/C/Cos", + "sender_port": "_B_Sin_output_0", + "receiver_port": "_B_Sin_output_0" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.yaml b/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.yaml new file mode 100644 index 00000000..ddabc3bc --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.yaml @@ -0,0 +1,62 @@ +ONNX Model: + format: ModECI MDF v0.4 + generating_application: Python modeci-mdf v0.4.14 + graphs: + main_graph: + nodes: + /A/Sin: + input_ports: + input: + shape: + - 2 + - 3 + type: float + parameters: + /A/Sin: + function: onnx::Sin + args: + input: input + output_ports: + _A_Sin_output_0: + value: /A/Sin + /B/Sin: + input_ports: + _A_Sin_output_0: + shape: + - 2 + - 3 + type: float + parameters: + /B/Sin: + function: onnx::Sin + args: + input: _A_Sin_output_0 + output_ports: + _B_Sin_output_0: + value: /B/Sin + /C/Cos: + input_ports: + _B_Sin_output_0: + shape: + - 2 + - 3 + type: float + parameters: + /C/Cos: + function: onnx::Cos + args: + input: _B_Sin_output_0 + output_ports: + _3: + value: /C/Cos + edges: + /A/Sin._A_Sin_output_0_/B/Sin._A_Sin_output_0: + sender: /A/Sin + receiver: /B/Sin + sender_port: _A_Sin_output_0 + receiver_port: _A_Sin_output_0 + /B/Sin._B_Sin_output_0_/C/Cos._B_Sin_output_0: + sender: /B/Sin + receiver: /C/Cos + sender_port: _B_Sin_output_0 + receiver_port: _B_Sin_output_0 diff --git a/docs/sphinx/source/api/export_format/ONNX/abc_basic.py b/docs/sphinx/source/api/export_format/ONNX/abc_basic.py new file mode 100644 index 00000000..303c0141 --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/abc_basic.py @@ -0,0 +1,84 @@ +""" +This file does three things: + - It implements a simple PyTorch model. + - Exports in to ONNX using a combination of tracing and scripting + - Converts it to MDF +""" + +import torch +import onnx + +from onnx import helper + +from modeci_mdf.interfaces.onnx import onnx_to_mdf, convert_file + + +class A(torch.nn.Module): + def forward(self, x): + return torch.sin(x) + + +class B(torch.nn.Module): + def forward(self, x): + return torch.sin(x) + + +class C(torch.nn.Module): + def forward(self, x): + return torch.cos(x) + + +class ABC(torch.nn.Module): + def __init__(self): + super().__init__() + self.A = A() + self.B = B() + self.C = C() + + def forward(self, x): + + # Run A + y = self.A(x) + + # Run B (loop_count times) + y = self.B(y) + + # Run C + y = self.C(y) + + return y + + +def main(): + + model = ABC() + dummy_input = torch.zeros(2, 3) + # loop_count = torch.tensor(5, dtype=torch.long) + torch.onnx.export( + model, (dummy_input), "abc_basic.onnx", verbose=True, input_names=["input"] + ) + + # Load it back in using ONNX package + onnx_model = onnx.load("abc_basic.onnx") + print(onnx_model) + onnx.checker.check_model(onnx_model) + + # Extract the loop or if body as a sub-model, this is just because I want + # to view it in netron and sub-graphs can't be rendered + for node in [ + node for node in onnx_model.graph.node if node.op_type in ["Loop", "If"] + ]: + + # Get the GraphProto of the body + body_graph = node.attribute[0].g + + # Turn it into a model + model_def = helper.make_model(body_graph, producer_name="abc_basic.py") + + onnx.save(model_def, f"examples/{node.name}_body.onnx") + + convert_file("abc_basic.onnx") + + +if __name__ == "__main__": + main() diff --git a/docs/sphinx/source/api/export_format/ONNX/abcd.json b/docs/sphinx/source/api/export_format/ONNX/abcd.json new file mode 100644 index 00000000..0018b14f --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/abcd.json @@ -0,0 +1,274 @@ +{ + "ONNX Model": { + "format": "ModECI MDF v0.4", + "generating_application": "Python modeci-mdf v0.4.14", + "graphs": { + "main_graph": { + "nodes": { + "/A/Mul": { + "input_ports": { + "input": { + "shape": [ + 1, + 1 + ], + "type": "float" + } + }, + "parameters": { + "A": { + "value": [ + [ + 2.0 + ] + ] + }, + "/A/Mul": { + "function": "onnx::Mul", + "args": { + "A": "A", + "B": "input" + } + } + }, + "output_ports": { + "_A_Mul_output_0": { + "value": "/A/Mul" + } + } + }, + "/A/Add": { + "input_ports": { + "_A_Mul_output_0": { + "shape": [ + 1, + 1 + ], + "type": "float" + } + }, + "parameters": { + "B": { + "value": [ + [ + 2.0 + ] + ] + }, + "/A/Add": { + "function": "onnx::Add", + "args": { + "A": "_A_Mul_output_0", + "B": "B" + } + } + }, + "output_ports": { + "_A_Add_output_0": { + "value": "/A/Add" + } + } + }, + "/B/Mul": { + "input_ports": { + "_A_Add_output_0": { + "shape": [ + 1, + 1 + ], + "type": "float" + } + }, + "parameters": { + "A": { + "value": [ + [ + 2.0 + ] + ] + }, + "/B/Mul": { + "function": "onnx::Mul", + "args": { + "A": "A", + "B": "_A_Add_output_0" + } + } + }, + "output_ports": { + "_B_Mul_output_0": { + "value": "/B/Mul" + } + } + }, + "/B/Add": { + "input_ports": { + "_B_Mul_output_0": { + "shape": [ + 1, + 1 + ], + "type": "float" + } + }, + "parameters": { + "B": { + "value": [ + [ + 2.0 + ] + ] + }, + "/B/Add": { + "function": "onnx::Add", + "args": { + "A": "_B_Mul_output_0", + "B": "B" + } + } + }, + "output_ports": { + "_B_Add_output_0": { + "value": "/B/Add" + } + } + }, + "/C/Mul": { + "input_ports": { + "_B_Add_output_0": { + "shape": [ + 1, + 1 + ], + "type": "float" + } + }, + "parameters": { + "A": { + "value": [ + [ + 2.0 + ] + ] + }, + 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"_A_Mul_output_0", + "receiver_port": "_A_Mul_output_0" + }, + "/A/Add._A_Add_output_0_/B/Mul._A_Add_output_0": { + "sender": "/A/Add", + "receiver": "/B/Mul", + "sender_port": "_A_Add_output_0", + "receiver_port": "_A_Add_output_0" + }, + "/B/Mul._B_Mul_output_0_/B/Add._B_Mul_output_0": { + "sender": "/B/Mul", + "receiver": "/B/Add", + "sender_port": "_B_Mul_output_0", + "receiver_port": "_B_Mul_output_0" + }, + "/B/Add._B_Add_output_0_/C/Mul._B_Add_output_0": { + "sender": "/B/Add", + "receiver": "/C/Mul", + "sender_port": "_B_Add_output_0", + "receiver_port": "_B_Add_output_0" + }, + "/C/Mul._C_Mul_output_0_/C/Add._C_Mul_output_0": { + "sender": "/C/Mul", + "receiver": "/C/Add", + "sender_port": "_C_Mul_output_0", + "receiver_port": "_C_Mul_output_0" + }, + "/C/Mul._C_Mul_output_0_/D/Add._C_Mul_output_0": { + "sender": "/C/Mul", + "receiver": "/D/Add", + "sender_port": "_C_Mul_output_0", + "receiver_port": "_C_Mul_output_0" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/ONNX/abcd.yaml b/docs/sphinx/source/api/export_format/ONNX/abcd.yaml new file mode 100644 index 00000000..beab1f98 --- /dev/null +++ b/docs/sphinx/source/api/export_format/ONNX/abcd.yaml @@ -0,0 +1,170 @@ +ONNX Model: + format: ModECI MDF v0.4 + generating_application: Python modeci-mdf v0.4.14 + graphs: + main_graph: + nodes: + /A/Mul: + input_ports: + input: + shape: + - 1 + - 1 + type: float + parameters: + A: + value: + - - 2.0 + /A/Mul: + function: onnx::Mul + args: + A: A + B: input + output_ports: + _A_Mul_output_0: + value: /A/Mul + /A/Add: + input_ports: + _A_Mul_output_0: + shape: + - 1 + - 1 + type: float + parameters: + B: + value: + - - 2.0 + /A/Add: + function: onnx::Add + args: + A: _A_Mul_output_0 + B: B + output_ports: + _A_Add_output_0: + value: /A/Add + /B/Mul: + input_ports: + _A_Add_output_0: + shape: + - 1 + - 1 + type: float + parameters: + A: + value: + - - 2.0 + /B/Mul: + function: onnx::Mul + args: + A: A + B: _A_Add_output_0 + output_ports: + _B_Mul_output_0: + value: /B/Mul + /B/Add: + input_ports: + _B_Mul_output_0: + shape: + - 1 + - 1 + type: float + parameters: + B: + value: + - - 2.0 + /B/Add: + function: onnx::Add + args: + A: _B_Mul_output_0 + B: B + output_ports: + _B_Add_output_0: + value: /B/Add + /C/Mul: + input_ports: + _B_Add_output_0: + shape: + - 1 + - 1 + type: float + parameters: + A: + value: + - - 2.0 + /C/Mul: + function: onnx::Mul + args: + A: A + B: _B_Add_output_0 + output_ports: + _C_Mul_output_0: + value: /C/Mul + /C/Add: + input_ports: + _C_Mul_output_0: + shape: + - 1 + - 1 + type: float + parameters: + B: + value: + - - 2.0 + /C/Add: + function: onnx::Add + args: + A: _C_Mul_output_0 + B: B + output_ports: + c: + value: /C/Add + /D/Add: + input_ports: + _C_Mul_output_0: + shape: + - 1 + - 1 + type: float + parameters: + B: + value: + - - 2.0 + /D/Add: + function: onnx::Add + args: + A: _C_Mul_output_0 + B: B + output_ports: + d: + value: /D/Add + edges: + /A/Mul._A_Mul_output_0_/A/Add._A_Mul_output_0: + sender: /A/Mul + receiver: /A/Add + sender_port: _A_Mul_output_0 + receiver_port: _A_Mul_output_0 + /A/Add._A_Add_output_0_/B/Mul._A_Add_output_0: + sender: /A/Add + receiver: /B/Mul + sender_port: _A_Add_output_0 + receiver_port: _A_Add_output_0 + /B/Mul._B_Mul_output_0_/B/Add._B_Mul_output_0: + sender: /B/Mul + receiver: /B/Add + sender_port: _B_Mul_output_0 + receiver_port: _B_Mul_output_0 + /B/Add._B_Add_output_0_/C/Mul._B_Add_output_0: + sender: /B/Add + receiver: /C/Mul + sender_port: _B_Add_output_0 + receiver_port: _B_Add_output_0 + /C/Mul._C_Mul_output_0_/C/Add._C_Mul_output_0: + sender: /C/Mul + receiver: /C/Add + sender_port: _C_Mul_output_0 + receiver_port: _C_Mul_output_0 + /C/Mul._C_Mul_output_0_/D/Add._C_Mul_output_0: + sender: /C/Mul + receiver: /D/Add + sender_port: _C_Mul_output_0 + receiver_port: _C_Mul_output_0 diff --git a/docs/sphinx/source/api/export_format/PyTorch/ddm.json 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"Sub_224", + "receiver": "ConstantOfShape_226", + "sender_port": "_224", + "receiver_port": "_224" + }, + "Cast_225_Concat_227": { + "sender": "Cast_225", + "receiver": "Concat_227", + "sender_port": "_225", + "receiver_port": "_225" + }, + "ConstantOfShape_226_Concat_227": { + "sender": "ConstantOfShape_226", + "receiver": "Concat_227", + "sender_port": "_226", + "receiver_port": "_226" + }, + "Concat_227_Reshape_229": { + "sender": "Concat_227", + "receiver": "Reshape_229", + "sender_port": "_227", + "receiver_port": "_227" + }, + "Reshape_229_Slice_234": { + "sender": "Reshape_229", + "receiver": "Slice_234", + "sender_port": "_229", + "receiver_port": "_229" + }, + "Slice_234_Transpose_235": { + "sender": "Slice_234", + "receiver": "Transpose_235", + "sender_port": "_234", + "receiver_port": "_234" + }, + "Transpose_235_Reshape_237": { + "sender": "Transpose_235", + "receiver": "Reshape_237", + "sender_port": "_235", + "receiver_port": "_235" + }, + "Reshape_237_Cast_238": { + "sender": "Reshape_237", + "receiver": "Cast_238", + "sender_port": "_237", + "receiver_port": "_237" + }, + "Cast_238_Pad_240": { + "sender": "Cast_238", + "receiver": "Pad_240", + "sender_port": "_238", + "receiver_port": "_238" + }, + "Pad_240_AveragePool_241": { + "sender": "Pad_240", + "receiver": "AveragePool_241", + "sender_port": "_240", + "receiver_port": "_240" + }, + "AveragePool_241_Concat_250": { + "sender": "AveragePool_241", + "receiver": "Concat_250", + "sender_port": "_241", + "receiver_port": "_241" + }, + "Conv_242_PRelu_245": { + "sender": "Conv_242", + "receiver": "PRelu_245", + "sender_port": "_242", + "receiver_port": "_242" + }, + "Unsqueeze_244_PRelu_245": { + "sender": "Unsqueeze_244", + "receiver": "PRelu_245", + "sender_port": "_244", + "receiver_port": "_244" + }, + "PRelu_245_Conv_246": { + "sender": "PRelu_245", + "receiver": "Conv_246", + "sender_port": "_245", + "receiver_port": "_245" + }, + "Conv_246_PRelu_249": { + "sender": "Conv_246", + "receiver": "PRelu_249", + "sender_port": "_246", + "receiver_port": "_246" + }, + "Unsqueeze_248_PRelu_249": { + "sender": "Unsqueeze_248", + "receiver": "PRelu_249", + "sender_port": "_248", + "receiver_port": "_248" + }, + "PRelu_249_Concat_250": { + "sender": "PRelu_249", + "receiver": "Concat_250", + "sender_port": "_249", + "receiver_port": "_249" + }, + "Concat_250_Conv_251": { + "sender": "Concat_250", + "receiver": "Conv_251", + "sender_port": "_250", + "receiver_port": "_250" + }, + "Concat_250_Conv_259": { + "sender": "Concat_250", + "receiver": "Conv_259", + "sender_port": "_250", + "receiver_port": "_250" + }, + "Concat_250_Conv_287": { + "sender": "Concat_250", + "receiver": "Conv_287", + "sender_port": "_250", + "receiver_port": "_250" + }, + "Concat_250_Conv_295": { + "sender": "Concat_250", + "receiver": "Conv_295", + "sender_port": "_250", + "receiver_port": "_250" + }, + "Conv_251_PRelu_254": { + "sender": "Conv_251", + "receiver": "PRelu_254", + "sender_port": "_251", + "receiver_port": "_251" + }, + "Unsqueeze_253_PRelu_254": { + "sender": "Unsqueeze_253", + "receiver": "PRelu_254", + "sender_port": "_253", + "receiver_port": "_253" + }, + "PRelu_254_Conv_255": { + "sender": "PRelu_254", + "receiver": "Conv_255", + "sender_port": "_254", + "receiver_port": "_254" + }, + "Conv_255_PRelu_258": { + "sender": "Conv_255", + "receiver": "PRelu_258", + "sender_port": "_255", + "receiver_port": "_255" + }, + "Unsqueeze_257_PRelu_258": { + "sender": "Unsqueeze_257", + "receiver": "PRelu_258", + "sender_port": "_257", + "receiver_port": "_257" + }, + "PRelu_258_Concat_299": { + "sender": "PRelu_258", + "receiver": "Concat_299", + "sender_port": "_258", + "receiver_port": "_258" + }, + "Conv_259_PRelu_262": { + "sender": "Conv_259", + "receiver": "PRelu_262", + "sender_port": "_259", + "receiver_port": "_259" + }, + "Unsqueeze_261_PRelu_262": { + "sender": "Unsqueeze_261", + "receiver": "PRelu_262", + "sender_port": "_261", + "receiver_port": "_261" + }, + "PRelu_262_Pad_285": { + "sender": "PRelu_262", + "receiver": "Pad_285", + "sender_port": "_262", + "receiver_port": "_262" + }, + "Shape_264_Gather_265": { + "sender": "Shape_264", + "receiver": "Gather_265", + "sender_port": "_264", + "receiver_port": "_264" + }, + "Gather_265_Sub_269": { + "sender": "Gather_265", + "receiver": "Sub_269", + "sender_port": "_265", + "receiver_port": "_265" + }, + "Mul_268_Sub_269": { + "sender": "Mul_268", + "receiver": "Sub_269", + "sender_port": "_268", + "receiver_port": "_268" + }, + "Sub_269_ConstantOfShape_271": { + "sender": "Sub_269", + "receiver": "ConstantOfShape_271", + "sender_port": "_269", + "receiver_port": "_269" + }, + "Cast_270_Concat_272": { + "sender": "Cast_270", + "receiver": "Concat_272", + "sender_port": "_270", + "receiver_port": "_270" + }, + "ConstantOfShape_271_Concat_272": { + "sender": "ConstantOfShape_271", + "receiver": "Concat_272", + "sender_port": "_271", + "receiver_port": "_271" + }, + "Concat_272_Reshape_274": { + "sender": "Concat_272", + "receiver": "Reshape_274", + "sender_port": "_272", + "receiver_port": "_272" + }, + "Reshape_274_Slice_279": { + "sender": "Reshape_274", + "receiver": "Slice_279", + "sender_port": "_274", + "receiver_port": "_274" + }, + "Slice_279_Transpose_280": { + "sender": "Slice_279", + "receiver": "Transpose_280", + "sender_port": "_279", + "receiver_port": "_279" + }, + "Transpose_280_Reshape_282": { + "sender": "Transpose_280", + "receiver": "Reshape_282", + "sender_port": "_280", + "receiver_port": "_280" + }, + "Reshape_282_Cast_283": { + "sender": "Reshape_282", + "receiver": "Cast_283", + "sender_port": "_282", + "receiver_port": "_282" + }, + "Cast_283_Pad_285": { + "sender": "Cast_283", + "receiver": "Pad_285", + "sender_port": "_283", + "receiver_port": "_283" + }, + "Pad_285_AveragePool_286": { + "sender": "Pad_285", + "receiver": "AveragePool_286", + "sender_port": "_285", + "receiver_port": "_285" + }, + "AveragePool_286_Concat_299": { + "sender": "AveragePool_286", + "receiver": "Concat_299", + "sender_port": "_286", + "receiver_port": "_286" + }, + "Conv_287_PRelu_290": { + "sender": "Conv_287", + "receiver": "PRelu_290", + "sender_port": "_287", + "receiver_port": "_287" + }, + "Unsqueeze_289_PRelu_290": { + "sender": "Unsqueeze_289", + "receiver": "PRelu_290", + "sender_port": "_289", + "receiver_port": "_289" + }, + "PRelu_290_Conv_291": { + "sender": "PRelu_290", + "receiver": "Conv_291", + "sender_port": "_290", + "receiver_port": "_290" + }, + "Conv_291_PRelu_294": { + "sender": "Conv_291", + "receiver": "PRelu_294", + "sender_port": "_291", + "receiver_port": "_291" + }, + "Unsqueeze_293_PRelu_294": { + "sender": "Unsqueeze_293", + "receiver": "PRelu_294", + "sender_port": "_293", + "receiver_port": "_293" + }, + "PRelu_294_Concat_299": { + "sender": "PRelu_294", + "receiver": "Concat_299", + "sender_port": "_294", + "receiver_port": "_294" + }, + "Conv_295_PRelu_298": { + "sender": "Conv_295", + "receiver": "PRelu_298", + "sender_port": "_295", + "receiver_port": "_295" + }, + "Unsqueeze_297_PRelu_298": { + "sender": "Unsqueeze_297", + "receiver": "PRelu_298", + "sender_port": "_297", + "receiver_port": "_297" + }, + "PRelu_298_Concat_299": { + "sender": "PRelu_298", + "receiver": "Concat_299", + "sender_port": "_298", + "receiver_port": "_298" + }, + "Concat_299_AveragePool_300": { + "sender": "Concat_299", + "receiver": "AveragePool_300", + "sender_port": "_299", + "receiver_port": "_299" + }, + "AveragePool_300_Conv_301": { + "sender": "AveragePool_300", + "receiver": "Conv_301", + "sender_port": "_300", + "receiver_port": "_300" + }, + "AveragePool_300_Conv_329": { + "sender": "AveragePool_300", + "receiver": "Conv_329", + "sender_port": "_300", + "receiver_port": "_300" + }, + "AveragePool_300_Conv_337": { + "sender": "AveragePool_300", + "receiver": "Conv_337", + "sender_port": "_300", + "receiver_port": "_300" + }, + "Conv_301_PRelu_304": { + "sender": "Conv_301", + "receiver": "PRelu_304", + "sender_port": "_301", + "receiver_port": "_301" + }, + "Unsqueeze_303_PRelu_304": { + "sender": "Unsqueeze_303", + "receiver": "PRelu_304", + "sender_port": "_303", + "receiver_port": "_303" + }, + "PRelu_304_Pad_327": { + "sender": "PRelu_304", + "receiver": "Pad_327", + "sender_port": "_304", + "receiver_port": "_304" + }, + "Shape_306_Gather_307": { + "sender": "Shape_306", + "receiver": "Gather_307", + "sender_port": "_306", + "receiver_port": "_306" + }, + "Gather_307_Sub_311": { + "sender": "Gather_307", + "receiver": "Sub_311", + "sender_port": "_307", + "receiver_port": "_307" + }, + "Mul_310_Sub_311": { + "sender": "Mul_310", + "receiver": "Sub_311", + "sender_port": "_310", + "receiver_port": "_310" + }, + "Sub_311_ConstantOfShape_313": { + "sender": "Sub_311", + "receiver": "ConstantOfShape_313", + "sender_port": "_311", + "receiver_port": "_311" + }, + "Cast_312_Concat_314": { + "sender": "Cast_312", + "receiver": "Concat_314", + "sender_port": "_312", + "receiver_port": "_312" + }, + "ConstantOfShape_313_Concat_314": { + "sender": "ConstantOfShape_313", + "receiver": "Concat_314", + "sender_port": "_313", + "receiver_port": "_313" + }, + "Concat_314_Reshape_316": { + "sender": "Concat_314", + "receiver": "Reshape_316", + "sender_port": "_314", + "receiver_port": "_314" + }, + "Reshape_316_Slice_321": { + "sender": "Reshape_316", + "receiver": "Slice_321", + "sender_port": "_316", + "receiver_port": "_316" + }, + "Slice_321_Transpose_322": { + "sender": "Slice_321", + "receiver": "Transpose_322", + "sender_port": "_321", + "receiver_port": "_321" + }, + "Transpose_322_Reshape_324": { + "sender": "Transpose_322", + "receiver": "Reshape_324", + "sender_port": "_322", + "receiver_port": "_322" + }, + "Reshape_324_Cast_325": { + "sender": "Reshape_324", + "receiver": "Cast_325", + "sender_port": "_324", + "receiver_port": "_324" + }, + "Cast_325_Pad_327": { + "sender": "Cast_325", + "receiver": "Pad_327", + "sender_port": "_325", + "receiver_port": "_325" + }, + "Pad_327_AveragePool_328": { + "sender": "Pad_327", + "receiver": "AveragePool_328", + "sender_port": "_327", + "receiver_port": "_327" + }, + "AveragePool_328_Concat_341": { + "sender": "AveragePool_328", + "receiver": "Concat_341", + "sender_port": "_328", + "receiver_port": "_328" + }, + "Conv_329_PRelu_332": { + "sender": "Conv_329", + "receiver": "PRelu_332", + "sender_port": "_329", + "receiver_port": "_329" + }, + "Unsqueeze_331_PRelu_332": { + "sender": "Unsqueeze_331", + "receiver": "PRelu_332", + "sender_port": "_331", + "receiver_port": "_331" + }, + "PRelu_332_Conv_333": { + "sender": "PRelu_332", + "receiver": "Conv_333", + "sender_port": "_332", + "receiver_port": "_332" + }, + "Conv_333_PRelu_336": { + "sender": "Conv_333", + "receiver": "PRelu_336", + "sender_port": "_333", + "receiver_port": "_333" + }, + "Unsqueeze_335_PRelu_336": { + "sender": "Unsqueeze_335", + "receiver": "PRelu_336", + "sender_port": "_335", + "receiver_port": "_335" + }, + "PRelu_336_Concat_341": { + "sender": "PRelu_336", + "receiver": "Concat_341", + "sender_port": "_336", + "receiver_port": "_336" + }, + "Conv_337_PRelu_340": { + "sender": "Conv_337", + "receiver": "PRelu_340", + "sender_port": "_337", + "receiver_port": "_337" + }, + "Unsqueeze_339_PRelu_340": { + "sender": "Unsqueeze_339", + "receiver": "PRelu_340", + "sender_port": "_339", + "receiver_port": "_339" + }, + "PRelu_340_Concat_341": { + "sender": "PRelu_340", + "receiver": "Concat_341", + "sender_port": "_340", + "receiver_port": "_340" + }, + "Concat_341_Shape_342": { + "sender": "Concat_341", + "receiver": "Shape_342", + "sender_port": "_341", + "receiver_port": "_341" + }, + "Concat_341_Reshape_349": { + "sender": "Concat_341", + "receiver": "Reshape_349", + "sender_port": "_341", + "receiver_port": "_341" + }, + "Shape_342_Slice_346": { + "sender": "Shape_342", + "receiver": "Slice_346", + "sender_port": "_342", + "receiver_port": "_342" + }, + "Slice_346_Concat_348": { + "sender": "Slice_346", + "receiver": "Concat_348", + "sender_port": "_346", + "receiver_port": "_346" + }, + "Concat_348_Reshape_349": { + "sender": "Concat_348", + "receiver": "Reshape_349", + "sender_port": "_348", + "receiver_port": "_348" + }, + "Reshape_349_Concat_350": { + "sender": "Reshape_349", + "receiver": "Concat_350", + "sender_port": "_349", + "receiver_port": "_349" + }, + "Concat_350_MatMul_352": { + "sender": "Concat_350", + "receiver": "MatMul_352", + "sender_port": "_350", + "receiver_port": "_350" + }, + "Transpose_351_MatMul_352": { + "sender": "Transpose_351", + "receiver": "MatMul_352", + "sender_port": "_351", + "receiver_port": "_351" + }, + "MatMul_352_Add_353": { + "sender": "MatMul_352", + "receiver": "Add_353", + "sender_port": "_352", + "receiver_port": "_352" + }, + "Add_353_PRelu_354": { + "sender": "Add_353", + "receiver": "PRelu_354", + "sender_port": "_353", + "receiver_port": "_353" + }, + "PRelu_354_MatMul_356": { + "sender": "PRelu_354", + "receiver": "MatMul_356", + "sender_port": "_354", + "receiver_port": "_354" + }, + "Transpose_355_MatMul_356": { + "sender": "Transpose_355", + "receiver": "MatMul_356", + "sender_port": "_355", + "receiver_port": "_355" + }, + "MatMul_356_Add_357": { + "sender": "MatMul_356", + "receiver": "Add_357", + "sender_port": "_356", + "receiver_port": "_356" + }, + "Add_357_PRelu_358": { + "sender": "Add_357", + "receiver": "PRelu_358", + "sender_port": "_357", + "receiver_port": "_357" + }, + "PRelu_358_MatMul_360": { + "sender": "PRelu_358", + "receiver": "MatMul_360", + "sender_port": "_358", + "receiver_port": "_358" + }, + "Transpose_359_MatMul_360": { + "sender": "Transpose_359", + "receiver": "MatMul_360", + "sender_port": "_359", + "receiver_port": "_359" + }, + "MatMul_360_Add_361": { + "sender": "MatMul_360", + "receiver": "Add_361", + "sender_port": "_360", + "receiver_port": "_360" + } + } + } + }, + "onnx_opset_version": 17 + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/PyTorch/inception.py b/docs/sphinx/source/api/export_format/PyTorch/inception.py new file mode 100644 index 00000000..fd974c3f --- /dev/null +++ b/docs/sphinx/source/api/export_format/PyTorch/inception.py @@ -0,0 +1,322 @@ +import numpy as np +import torch +import os +import torch.nn as nn + +from modeci_mdf.interfaces.pytorch import pytorch_to_mdf + + +class InceptionBlocks(nn.Module): + """An Inception-like model provided as a test case by the WebGME folks""" + + def __init__(self): + super().__init__() + + self.asymmetric_pad = nn.ZeroPad2d((0, 1, 0, 1)) + self.conv2d = nn.Conv2d( + in_channels=5, out_channels=64, kernel_size=(5, 5), padding=2, bias=True + ) + self.prelu = nn.PReLU(init=0.0) + self.averagepooling2d = nn.AvgPool2d((2, 2), stride=2, padding=0) + self.conv2d2 = nn.Conv2d( + in_channels=64, out_channels=48, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu2 = nn.PReLU(init=0.0) + self.conv2d3 = nn.Conv2d( + in_channels=48, out_channels=64, kernel_size=(3, 3), padding=1, bias=True + ) + self.prelu3 = nn.PReLU(init=0.0) + self.conv2d4 = nn.Conv2d( + in_channels=64, out_channels=48, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu4 = nn.PReLU(init=0.0) + self.averagepooling2d2 = nn.AvgPool2d((2, 2), stride=1) + self.conv2d5 = nn.Conv2d( + in_channels=64, out_channels=64, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu5 = nn.PReLU(init=0.0) + self.conv2d6 = nn.Conv2d( + in_channels=64, out_channels=48, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu6 = nn.PReLU(init=0.0) + self.conv2d7 = nn.Conv2d( + in_channels=48, out_channels=64, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu7 = nn.PReLU(init=0.0) + self.conv2d8 = nn.Conv2d( + in_channels=240, out_channels=64, kernel_size=(1, 1), padding=0, bias=True + ) + self.conv2d9 = nn.Conv2d( + in_channels=240, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.conv2d10 = nn.Conv2d( + in_channels=240, out_channels=64, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu8 = nn.PReLU(init=0.0) + self.conv2d11 = nn.Conv2d( + in_channels=64, out_channels=92, kernel_size=(5, 5), padding=2, bias=True + ) + self.prelu9 = nn.PReLU(init=0.0) + self.prelu10 = nn.PReLU(init=0.0) + self.averagepooling2d3 = nn.AvgPool2d((2, 2), stride=1, padding=0) + self.conv2d12 = nn.Conv2d( + in_channels=240, out_channels=64, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu11 = nn.PReLU(init=0.0) + self.conv2d13 = nn.Conv2d( + in_channels=64, out_channels=92, kernel_size=(3, 3), padding=1, bias=True + ) + self.prelu12 = nn.PReLU(init=0.0) + self.prelu13 = nn.PReLU(init=0.0) + self.averagepooling2d4 = nn.AvgPool2d((2, 2), stride=2, padding=0) + self.conv2d14 = nn.Conv2d( + in_channels=340, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu14 = nn.PReLU(init=0.0) + self.conv2d15 = nn.Conv2d( + in_channels=92, out_channels=128, kernel_size=(5, 5), padding=2, bias=True + ) + self.prelu15 = nn.PReLU(init=0.0) + self.conv2d16 = nn.Conv2d( + in_channels=340, out_channels=128, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu16 = nn.PReLU(init=0.0) + self.conv2d17 = nn.Conv2d( + in_channels=340, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu17 = nn.PReLU(init=0.0) + self.averagepooling2d5 = nn.AvgPool2d((2, 2), stride=1, padding=0) + self.conv2d18 = nn.Conv2d( + in_channels=340, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu18 = nn.PReLU(init=0.0) + self.conv2d19 = nn.Conv2d( + in_channels=92, out_channels=128, kernel_size=(3, 3), padding=1, bias=True + ) + self.prelu19 = nn.PReLU(init=0.0) + self.conv2d20 = nn.Conv2d( + in_channels=476, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu20 = nn.PReLU(init=0.0) + self.conv2d21 = nn.Conv2d( + in_channels=92, out_channels=128, kernel_size=(3, 3), padding=1, bias=True + ) + self.prelu21 = nn.PReLU(init=0.0) + self.conv2d22 = nn.Conv2d( + in_channels=476, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu22 = nn.PReLU(init=0.0) + self.averagepooling2d6 = nn.AvgPool2d((2, 2), stride=1, padding=0) + self.conv2d23 = nn.Conv2d( + in_channels=476, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu23 = nn.PReLU(init=0.0) + self.conv2d24 = nn.Conv2d( + in_channels=92, out_channels=128, kernel_size=(5, 5), padding=2, bias=True + ) + self.prelu24 = nn.PReLU(init=0.0) + self.conv2d25 = nn.Conv2d( + in_channels=476, out_channels=128, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu25 = nn.PReLU(init=0.0) + self.averagepooling2d7 = nn.AvgPool2d((2, 2), stride=2, padding=0) + self.conv2d26 = nn.Conv2d( + in_channels=476, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu26 = nn.PReLU(init=0.0) + self.averagepooling2d8 = nn.AvgPool2d((2, 2), stride=1, padding=0) + self.conv2d27 = nn.Conv2d( + in_channels=476, out_channels=92, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu27 = nn.PReLU(init=0.0) + self.conv2d28 = nn.Conv2d( + in_channels=92, out_channels=128, kernel_size=(3, 3), padding=1, bias=True + ) + self.prelu28 = nn.PReLU(init=0.0) + self.conv2d29 = nn.Conv2d( + in_channels=476, out_channels=128, kernel_size=(1, 1), padding=0, bias=True + ) + self.prelu29 = nn.PReLU(init=0.0) + self.dense = nn.Linear(22273, 1096, bias=True) + self.prelu30 = nn.PReLU(init=0.0) + self.dense2 = nn.Linear(1096, 1096, bias=True) + self.prelu31 = nn.PReLU(init=0.0) + self.dense3 = nn.Linear(1096, 180, bias=True) + + def forward(self, galaxy_images_output, ebv_output): + conv2d_output = self.conv2d(galaxy_images_output) + prelu_output = self.prelu(conv2d_output) + averagepooling2d_output = self.averagepooling2d(prelu_output) + conv2d_output2 = self.conv2d2(averagepooling2d_output) + prelu_output2 = self.prelu2(conv2d_output2) + conv2d_output3 = self.conv2d3(prelu_output2) + prelu_output3 = self.prelu3(conv2d_output3) + conv2d_output4 = self.conv2d4(averagepooling2d_output) + prelu_output4 = self.prelu4(conv2d_output4) + prelu_output4 = self.asymmetric_pad(prelu_output4) + averagepooling2d_output2 = self.averagepooling2d2(prelu_output4) + conv2d_output5 = self.conv2d5(averagepooling2d_output) + prelu_output5 = self.prelu5(conv2d_output5) + conv2d_output6 = self.conv2d6(averagepooling2d_output) + prelu_output6 = self.prelu6(conv2d_output6) + conv2d_output7 = self.conv2d7(prelu_output6) + prelu_output7 = self.prelu7(conv2d_output7) + concatenate_output = torch.cat( + (prelu_output5, prelu_output3, prelu_output7, averagepooling2d_output2), + dim=1, + ) + conv2d_output8 = self.conv2d8(concatenate_output) + conv2d_output9 = self.conv2d9(concatenate_output) + conv2d_output10 = self.conv2d10(concatenate_output) + prelu_output8 = self.prelu8(conv2d_output10) + conv2d_output11 = self.conv2d11(prelu_output8) + prelu_output9 = self.prelu9(conv2d_output11) + prelu_output10 = self.prelu10(conv2d_output8) + prelu_output10 = self.asymmetric_pad(prelu_output10) + averagepooling2d_output3 = self.averagepooling2d3(prelu_output10) + conv2d_output12 = self.conv2d12(concatenate_output) + prelu_output11 = self.prelu11(conv2d_output12) + conv2d_output13 = self.conv2d13(prelu_output11) + prelu_output12 = self.prelu12(conv2d_output13) + prelu_output13 = self.prelu13(conv2d_output9) + concatenate_output2 = torch.cat( + (prelu_output13, prelu_output12, prelu_output9, averagepooling2d_output3), + dim=1, + ) + averagepooling2d_output4 = self.averagepooling2d4(concatenate_output2) + conv2d_output14 = self.conv2d14(averagepooling2d_output4) + prelu_output14 = self.prelu14(conv2d_output14) + conv2d_output15 = self.conv2d15(prelu_output14) + prelu_output15 = self.prelu15(conv2d_output15) + conv2d_output16 = self.conv2d16(averagepooling2d_output4) + prelu_output16 = self.prelu16(conv2d_output16) + conv2d_output17 = self.conv2d17(averagepooling2d_output4) + prelu_output17 = self.prelu17(conv2d_output17) + prelu_output17 = self.asymmetric_pad(prelu_output17) + averagepooling2d_output5 = self.averagepooling2d5(prelu_output17) + conv2d_output18 = self.conv2d18(averagepooling2d_output4) + prelu_output18 = self.prelu18(conv2d_output18) + conv2d_output19 = self.conv2d19(prelu_output18) + prelu_output19 = self.prelu19(conv2d_output19) + concatenate_output3 = torch.cat( + (prelu_output16, prelu_output19, prelu_output15, averagepooling2d_output5), + dim=1, + ) + conv2d_output20 = self.conv2d20(concatenate_output3) + prelu_output20 = self.prelu20(conv2d_output20) + conv2d_output21 = self.conv2d21(prelu_output20) + prelu_output21 = self.prelu21(conv2d_output21) + conv2d_output22 = self.conv2d22(concatenate_output3) + prelu_output22 = self.prelu22(conv2d_output22) + prelu_output22 = self.asymmetric_pad(prelu_output22) + averagepooling2d_output6 = self.averagepooling2d6(prelu_output22) + conv2d_output23 = self.conv2d23(concatenate_output3) + prelu_output23 = self.prelu23(conv2d_output23) + conv2d_output24 = self.conv2d24(prelu_output23) + prelu_output24 = self.prelu24(conv2d_output24) + conv2d_output25 = self.conv2d25(concatenate_output3) + prelu_output25 = self.prelu25(conv2d_output25) + concatenate_output4 = torch.cat( + (prelu_output25, prelu_output21, prelu_output24, averagepooling2d_output6), + dim=1, + ) + averagepooling2d_output7 = self.averagepooling2d7(concatenate_output4) + conv2d_output26 = self.conv2d26(averagepooling2d_output7) + prelu_output26 = self.prelu26(conv2d_output26) + prelu_output26 = self.asymmetric_pad(prelu_output26) + averagepooling2d_output8 = self.averagepooling2d8(prelu_output26) + conv2d_output27 = self.conv2d27(averagepooling2d_output7) + prelu_output27 = self.prelu27(conv2d_output27) + conv2d_output28 = self.conv2d28(prelu_output27) + prelu_output28 = self.prelu28(conv2d_output28) + conv2d_output29 = self.conv2d29(averagepooling2d_output7) + prelu_output29 = self.prelu29(conv2d_output29) + concatenate_output5 = torch.cat( + (prelu_output29, prelu_output28, averagepooling2d_output8), dim=1 + ) + flatten_output = torch.flatten(concatenate_output5) + concatenate_output6 = torch.cat((flatten_output, ebv_output), dim=0) + dense_output = self.dense(concatenate_output6) + prelu_output30 = self.prelu30(dense_output) + dense_output2 = self.dense2(prelu_output30) + prelu_output31 = self.prelu31(dense_output2) + dense_output3 = self.dense3(prelu_output31) + + return dense_output3 + + +def main(): + # changed import call + from modeci_mdf.execution_engine import EvaluableGraph + + # Create some test inputs for the model + galaxy_images_output = torch.zeros((1, 5, 64, 64)) + ebv_output = torch.zeros((1,)) + + # Seed the random number generator to get deterministic behavior for weight initialization + torch.manual_seed(0) + + model = InceptionBlocks() + + # Turn on eval mode for model to get rid of any randomization due to things like BatchNorm or Dropout + model.eval() + + # Run the model once to get some ground truth outpot (from PyTorch) + output = model(galaxy_images_output, ebv_output).detach().numpy() + + from modelspec.utils import _val_info + + # print("Inputs: %s, %s"% (_val_info(galaxy_images_output),_val_info(ebv_output))) + print("Evaluated the graph in PyTorch, output: %s" % (_val_info(output))) + + # Convert to MDF + mdf_model, params_dict = pytorch_to_mdf( + model=model, + args=(galaxy_images_output, ebv_output), + trace=True, + ) + + # Get the graph + mdf_graph = mdf_model.graphs[0] + + # Add inputs to the parameters dict so we can feed this to the EvaluableGraph for initialization of all + # graph inputs. + params_dict["input1"] = galaxy_images_output.numpy() + params_dict["input2"] = ebv_output.numpy() + + # Evaluate the model via the MDF scheduler + eg = EvaluableGraph(graph=mdf_graph, verbose=False) + eg.evaluate(initializer=params_dict) + output_mdf = eg.output_enodes[0].get_output() + + print("Evaluated the graph in PyTorch, output: %s" % (_val_info(output_mdf))) + + # Make sure the results are the same between PyTorch and MDF + assert np.allclose( + output, + output_mdf, + ) + print("Passed all comparison tests!") + + # Output the model to JSON + mdf_model.to_json_file("inception.json") + + import sys + + if "-graph" in sys.argv: + mdf_model.to_graph_image( + engine="dot", + output_format="png", + view_on_render=False, + level=1, + filename_root="inception", + only_warn_on_fail=( + os.name == "nt" + ), # Makes sure test of this doesn't fail on Windows on GitHub Actions + is_horizontal=True, + solid_color=True, + ) + + +if __name__ == "__main__": + main() diff --git a/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.json b/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.json new file mode 100644 index 00000000..e3571410 --- /dev/null +++ b/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.json @@ -0,0 +1,44282 @@ +{ + "mlp_pure_mdf": { + "format": "ModECI MDF v0.4", + "generating_application": "Python modeci-mdf v0.4.14", + "graphs": 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"out_port", + "receiver_port": "in_port" + }, + "edge_2": { + "sender": "mlp_relu_1", + "receiver": "mlp_hidden_layer_with_relu", + "sender_port": "out_port", + "receiver_port": "in_port" + }, + "edge_3": { + "sender": "mlp_hidden_layer_with_relu", + "receiver": "mlp_output_layer", + "sender_port": "out_port", + "receiver_port": "in_port" + } + } + } + } + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.py b/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.py new file mode 100644 index 00000000..a216bbeb --- /dev/null +++ b/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.py @@ -0,0 +1,316 @@ +from modeci_mdf.mdf import * + +import numpy as np +import sys +import h5py +import time +import os + + +# Note: the weights for this model were precomputed and saved in the file weights.h5 +def get_weight_info(): + + weights = {} + f = h5py.File("weights.h5", "r") + + for key in list(f.keys()): + weight_mat = f[key][:] + weights[key] = weight_mat + print(f"Loaded {key}: {weight_mat.shape}") + f.close() + + return weights + + +def get_model(): + + mod = Model(id="mlp_pure_mdf") + mod_graph = Graph(id=mod.id) + mod.graphs.append(mod_graph) + + weights = get_weight_info() + + dim0 = 16 + weight = weights[ + "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_input_layer.parameters.weight" + ] + bias = weights[ + "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_input_layer.parameters.bias" + ] + + dummy_input = np.zeros((1, 14 * 14)) + # dummy_input = np.ones((14*14)) + + input_node = Node(id="mlp_input_layer") + input_node.parameters.append(Parameter(id="input", value=dummy_input)) + input_node.parameters.append(Parameter(id="weight", value=weight.T)) + input_node.parameters.append(Parameter(id="bias", value=bias.T)) + + f1 = Parameter(id="mul", function="MatMul", args={"A": "input", "B": "weight"}) + + input_node.parameters.append(f1) + + f2 = Parameter( + id="sum", + function="linear", + args={"variable0": "mul", "slope": 1, "intercept": "bias"}, + ) + input_node.parameters.append(f2) + + input_node.output_ports.append(OutputPort(id="out_port", value="sum")) + mod_graph.nodes.append(input_node) + + relu1_node = Node(id="mlp_relu_1") + relu1_node.input_ports.append(InputPort(id="in_port")) + mod_graph.nodes.append(relu1_node) + + f1 = Parameter(id="relu1", function="Relu", args={"A": "in_port"}) + relu1_node.parameters.append(f1) + + relu1_node.output_ports.append(OutputPort(id="out_port", value=f1.id)) + + e1 = Edge( + id="edge_1", + sender=input_node.id, + sender_port=input_node.output_ports[0].id, + receiver=relu1_node.id, + receiver_port=relu1_node.input_ports[0].id, + ) + mod_graph.edges.append(e1) + + weight = weights[ + "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_hidden_layer_with_relu.parameters.weight" + ] + bias = weights[ + "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_hidden_layer_with_relu.parameters.bias" + ] + + hr_node = Node(id="mlp_hidden_layer_with_relu") + + hr_node.parameters.append(Parameter(id="weight", value=weight.T)) + hr_node.parameters.append(Parameter(id="bias", value=bias.T)) + + mod_graph.nodes.append(hr_node) + hr_node.input_ports.append(InputPort(id="in_port")) + + f1 = Parameter(id="mul", function="MatMul", args={"A": "in_port", "B": "weight"}) + hr_node.parameters.append(f1) + + f2 = Parameter( + id="sum", + function="linear", + args={"variable0": "mul", "slope": 1, "intercept": "bias"}, + ) + hr_node.parameters.append(f2) + + f3 = Parameter(id="relu2", function="Relu", args={"A": "sum"}) + hr_node.parameters.append(f3) + + hr_node.output_ports.append(OutputPort(id="out_port", value="relu2")) + + e2 = Edge( + id="edge_2", + sender=relu1_node.id, + sender_port=relu1_node.output_ports[0].id, + receiver=hr_node.id, + receiver_port=hr_node.input_ports[0].id, + ) + mod_graph.edges.append(e2) + + weight = weights[ + "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_output_layer.parameters.weight" + ] + bias = weights[ + "weights.mlp_classifier.graphs.mlp_classifier.nodes.mlp_output_layer.parameters.bias" + ] + + out_node = Node(id="mlp_output_layer") + + out_node.parameters.append(Parameter(id="weight", value=weight.T)) + out_node.parameters.append(Parameter(id="bias", value=bias.T)) + + mod_graph.nodes.append(out_node) + out_node.input_ports.append(InputPort(id="in_port")) + + f1 = Parameter(id="mul", function="MatMul", args={"A": "in_port", "B": "weight"}) + out_node.parameters.append(f1) + + f2 = Parameter( + id="sum", + function="linear", + args={"variable0": "mul", "slope": 1, "intercept": "bias"}, + ) + out_node.parameters.append(f2) + + out_node.output_ports.append(OutputPort(id="out_port", value="sum")) + + e3 = Edge( + id="edge_3", + sender=hr_node.id, + sender_port=hr_node.output_ports[0].id, + receiver=out_node.id, + receiver_port=out_node.input_ports[0].id, + ) + mod_graph.edges.append(e3) + + return mod + + +def main(): + + test_all = "-test" in sys.argv + + mod = get_model() + + mod_graph = mod.graphs[0] + + yaml_file = mod.to_yaml_file("%s.yaml" % mod.id) + json_file = mod.to_json_file("%s.json" % mod.id) + + if "-mdf_to_pytorch" in sys.argv: + print("Exporting model to pure PyTorch") + from modeci_mdf.interfaces.pytorch import mdf_to_pytorch + + pytorch_model = mdf_to_pytorch( + mod, yaml_file, eval_models=False, version="mdf.s" + ) + + from modelspec.utils import FORMAT_NUMPY, FORMAT_TENSORFLOW + + format = FORMAT_TENSORFLOW if "-tf" in sys.argv else FORMAT_NUMPY + + from modeci_mdf.execution_engine import EvaluableGraph + + eg = EvaluableGraph(mod_graph, verbose=False) + eg.evaluate(array_format=format) + + from modelspec.utils import _val_info + + print("Finished evaluating graph using array format %s" % format) + + for n in [ + "mlp_input_layer", + "mlp_relu_1", + "mlp_hidden_layer_with_relu", + "mlp_output_layer", + ]: + out = _val_info(eg.enodes[n].evaluable_outputs["out_port"].curr_value) + print(f"Final output value of node {n}:\t {out}") + + if "-graph" in sys.argv: + mod.to_graph_image( + engine="dot", + output_format="png", + view_on_render=False, + level=2, + filename_root="mlp_pure_mdf", + only_warn_on_fail=( + os.name == "nt" + ), # Makes sure test of this doesn't fail on Windows on GitHub Actions + ) + + if test_all: + # Iterate on training data, feed forward and log accuracy + imgs = np.load("example_data/imgs.npy") + labels = np.load("example_data/labels.npy") + + import torch.nn + + matches = 0 + imgs_to_test = imgs[:300] + + start = time.time() + all_guess = None + labelled_guess = {} + + for i in range(len(imgs_to_test)): + ii = imgs[i, :, :] + target = labels[i] + img = torch.Tensor(ii).view(-1, 14 * 14).numpy() + # plot_img(img, 'Post_%i (%s)'%(i, img.shape)) + print( + "***********\nTesting image %i (label: %s): %s\n%s" + % (i, target, np.array2string(img, threshold=5, edgeitems=2), img.shape) + ) + # print(mod_graph.nodes[0].parameters['input']) + mod_graph.nodes[0].get_parameter("input").value = img + eg = EvaluableGraph(mod_graph, verbose=False) + eg.evaluate(array_format=format) + for n in ["mlp_output_layer"]: + out = eg.enodes[n].evaluable_outputs["out_port"].curr_value + print( + "Output of evaluated graph: %s %s (%s)" + % (out, out.shape, type(out).__name__) + ) + + """print( + "Guesses: %s, %s" + % (all_guess, all_guess.shape if all_guess is not None else "-") + )""" + if all_guess is None: + all_guess = out + else: + all_guess = np.concatenate((all_guess, out)) + + if target not in labelled_guess: + labelled_guess[target] = out + else: + labelled_guess[target] = np.concatenate( + (labelled_guess[target], out) + ) + + prediction = np.argmax(out) + + match = target == int(prediction) + if match: + matches += 1 + print(f"Target: {target}, prediction: {prediction}, match: {match}") + + t = time.time() - start + print( + "Matches: %i/%i, accuracy: %s%%. Total time: %.4f sec (%.4fs per run)" + % ( + matches, + len(imgs_to_test), + (100.0 * matches) / len(imgs_to_test), + t, + t / len(imgs_to_test), + ) + ) + + print(f"Guesses: {all_guess}, {all_guess.shape}") + + import matplotlib.pyplot as plt + + fig, ax = plt.subplots() + title = "Guesses" + # plt.title(title) + # fig.canvas.set_window_title(title) + import matplotlib + + cm = matplotlib.cm.get_cmap("Blues") + + sorted_guesses = labelled_guess[0] + for i in range(9): + sorted_guesses = np.concatenate((sorted_guesses, labelled_guess[i + 1])) + + im = plt.imshow(sorted_guesses, cmap=cm, aspect="auto") + + cbar = plt.colorbar(im) + + ax = plt.gca() + + ax.set_xticks([i for i in range(10)]) + + ax.set_xlabel("Est. likelihood of each digit") + + ax.set_ylabel("%i images ordered by label" % (len(imgs_to_test))) + + plt.savefig("mlp_pure_mdf.results.png", bbox_inches="tight") + + if "-nogui" not in sys.argv: + plt.show() + + +if __name__ == "__main__": + main() diff --git 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- -0.02381 + - -0.55821 + - 0.24715 + - - 0.02384 + - -0.2582 + - -0.31156 + - 0.39751 + - 0.09358 + - 0.121 + - -0.25243 + - -0.0353 + - 0.11666 + - -0.01319 + - - 0.26324 + - -0.30134 + - 0.07137 + - 0.20997 + - -0.91817 + - 0.20592 + - -0.2902 + - 0.00797 + - 0.26175 + - 0.33194 + - - 0.31819 + - -0.39315 + - 0.34608 + - -0.33852 + - -0.25014 + - -0.01868 + - -0.02253 + - -0.14584 + - 0.11469 + - 0.19406 + - - -0.20672 + - 0.26033 + - 0.47125 + - 0.25134 + - 0.04192 + - -0.18381 + - -0.35067 + - 0.3134 + - -0.18546 + - -0.46055 + bias: + value: + - -0.16918 + - 0.50372 + - -0.19552 + - 0.05669 + - 0.21158 + - 0.04901 + - -0.06641 + - -0.17193 + - -0.08949 + - -0.00062 + mul: + function: MatMul + args: + A: in_port + B: weight + sum: + function: linear + args: + variable0: mul + slope: 1 + intercept: bias + output_ports: + out_port: + value: sum + edges: + edge_1: + sender: mlp_input_layer + receiver: mlp_relu_1 + sender_port: out_port + receiver_port: in_port + edge_2: + sender: mlp_relu_1 + receiver: mlp_hidden_layer_with_relu + sender_port: out_port + receiver_port: in_port + edge_3: + sender: mlp_hidden_layer_with_relu + receiver: mlp_output_layer + sender_port: out_port + receiver_port: in_port diff --git a/docs/sphinx/source/api/export_format/PyTorch/mod_torch_builtins.py b/docs/sphinx/source/api/export_format/PyTorch/mod_torch_builtins.py new file mode 100644 index 00000000..c4932f25 --- /dev/null +++ b/docs/sphinx/source/api/export_format/PyTorch/mod_torch_builtins.py @@ -0,0 +1,383 @@ +""" +Wrap commonly-used torch builtins in nn.Module subclass +for easier automatic construction of script +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class argmax(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + return torch.argmax(A) + + +class argmin(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + return torch.argmin(A) + + +class matmul(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A, B): + return torch.matmul(A, B.T) + + +class add(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A, B): + return torch.add(A, B) + + +class sin(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + return torch.sin(A) + + +class cos(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + return torch.cos(A) + + +class abs(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + return torch.abs(A) + + +class flatten(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + return torch.reshape(A, (1, -1)) + + +class clip(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A, min_val, max_val): + return torch.clamp(A, min_val, max_val) + + +class shape(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + return torch.tensor(A.size()).to(torch.int64) + + +class det(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + return torch.det(A) + + +class And(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A, B): + return torch.logical_and(A > 0, B > 0) + + +class Or(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A, B): + return torch.logical_or(A > 0, B > 0) + + +class Xor(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A, B): + return torch.logical_xor(A > 0, B > 0) + + +class concat(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A, axis=0): + + return torch.cat(A, axis) + + +class ceil(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + + return torch.ceil(A) + + +class floor(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, A): + + return torch.floor(A) + + +class bitshift(torch.nn.Module): + def __init__(self, DIR): + super().__init__() + self.dir = DIR + + def forward(self, A, B): + if self.dir == "RIGHT": + return A.to(torch.int64) >> B.to(torch.int64) + else: + return A.to(torch.int64) << B.to(torch.int64) + + +class conv(torch.nn.Module): + def __init__( + self, + auto_pad="NOTSET", + kernel_shape=None, + group=1, + strides=[1, 1], + dilations=[1, 1], + pads=[0, 0, 0, 0], + ): + super().__init__() + self.group = group + self.auto_pad = auto_pad + self.strides = tuple(strides) + self.dilations = tuple(dilations) + self.kernel_shape = kernel_shape + + def forward(self, A, W, B=None): + if self.auto_pad == "NOTSET": + self.pads = tuple(pads) + + elif self.auto_pad == "VALID": + self.pads = (0, 0, 0, 0) + elif self.auto_pad == "SAME_UPPER": + pad_dim1 = ( + torch.ceil(torch.tensor(A.shape[2]).to(torch.float32) / strides[0]) + .to(torch.int64) + .item() + ) + pad_dim2 = ( + torch.ceil(torch.tensor(A.shape[3]).to(torch.float32) / strides[1]) + .to(torch.int64) + .item() + ) + if pad_dim1 % 2 == 0 and pad_dim2 % 2 == 0: + self.pads = (pad_dim1 // 2, pad_dim1 // 2, pad_dim2 // 2, pad_dim2 // 2) + elif pad_dim1 % 2 == 0 and pad_dim2 % 2 != 0: + self.pads = ( + pad_dim1 // 2, + pad_dim1 // 2, + pad_dim2 // 2, + pad_dim2 // 2 + 1, + ) + elif pad_dim1 % 2 != 0 and pad_dim2 % 2 == 0: + self.pads = ( + pad_dim1 // 2, + pad_dim1 // 2 + 1, + pad_dim2 // 2, + pad_dim2 // 2, + ) + elif pad_dim1 % 2 != 0 and pad_dim2 % 2 != 0: + self.pads = ( + pad_dim1 // 2, + pad_dim1 // 2 + 1, + pad_dim2 // 2, + pad_dim2 // 2 + 1, + ) + + elif self.auto_pad == "SAME_LOWER": + + pad_dim1 = ( + torch.ceil(torch.tensor(A.shape[2]).to(torch.float32) / strides[0]) + .to(torch.int64) + .item() + ) + pad_dim2 = ( + torch.ceil(torch.tensor(A.shape[3]).to(torch.float32) / strides[1]) + .to(torch.int64) + .item() + ) + if pad_dim1 % 2 == 0 and pad_dim2 % 2 == 0: + self.pads = (pad_dim1 // 2, pad_dim1 // 2, pad_dim2 // 2, pad_dim2 // 2) + elif pad_dim1 % 2 == 0 and pad_dim2 % 2 != 0: + self.pads = ( + pad_dim1 // 2, + pad_dim1 // 2, + pad_dim2 // 2 + 1, + pad_dim2 // 2, + ) + elif pad_dim1 % 2 != 0 and pad_dim2 % 2 == 0: + self.pads = ( + pad_dim1 // 2 + 1, + pad_dim1 // 2, + pad_dim2 // 2, + pad_dim2 / 2, + ) + elif pad_dim1 % 2 != 0 and pad_dim2 % 2 != 0: + self.pads = ( + pad_dim1 // 2 + 1, + pad_dim1 // 2, + pad_dim2 // 2 + 1, + pad_dim2 // 2, + ) + + A = F.pad(A, self.pads) + return F.conv2d( + A, + W, + bias=B, + stride=self.strides, + padding=self.pads, + dilation=self.dilations, + groups=self.group, + ) + + +class elu(torch.nn.Module): + def __init__(self, alpha=1.0): + + super().__init__() + self.alpha = alpha + + def forward(self, A): + + return nn.ELU(alpha=self.alpha)(A.to(torch.float32)) + + +class hardsigmoid(torch.nn.Module): + def __init__(self, alpha=0.2, beta=0.5): + + super().__init__() + self.alpha = alpha + self.beta = beta + + def forward(self, A): + + return torch.clamp(self.alpha * (A.to(torch.float32)) + self.beta, 0, 1) + + +class hardswish(torch.nn.Module): + def __init__(self): + + super().__init__() + self.alpha = 1.0 / 6 + self.beta = 0.5 + + def forward(self, A): + + return A * torch.clamp(self.alpha * (A.to(torch.float32)) + self.beta, 0, 1) + + +class hardmax(torch.nn.Module): + def __init__(self, axis=-1): + super().__init__() + + self.axis = axis + + def forward(self, A): + A = A.to(torch.float32) + rank = A.shape + if self.axis < 0: + self.axis += len(rank) + tensor = torch.arange(rank[self.axis]) + repeats = [] + repeats.append(1) + for i, idx in enumerate(reversed(rank[: self.axis])): + + repeats.append(1) + tensor = torch.stack([tensor] * idx) + + for i, idx in enumerate(rank[self.axis + 1 :]): + repeats.append(idx) + + tensor = tensor.unsqueeze(-1).repeat(repeats) + repeats[-1] = 1 + # b = torch.stack([torch.stack([torch.arange(4)] * 3)] *2) + # print(tensor.shape) + max_values, _ = torch.max(A, dim=self.axis) + # print(max_values, max_values.shape) + # tensor = torch.reshape(tensor, tuple(rank)) + tensor[A != torch.unsqueeze(max_values, dim=self.axis)] = rank[self.axis] + # print(b) + first_max, _ = torch.min(tensor, dim=self.axis) + + one_hot = torch.nn.functional.one_hot(first_max, rank[self.axis]) + return one_hot + + +class compress(torch.nn.Module): + def __init__(self, axis=None): + self.axis = axis + super().__init__() + + def forward(self, A, B): + + idx = (B.to(torch.bool) != 0).nonzero().reshape(-1) + if self.axis != None: + return torch.index_select(A, self.axis, idx) + + else: + + return torch.index_select(A.reshape(-1), 0, idx) + + +# TODO: Many more to be implemented + + +__all__ = [ + "argmax", + "argmin", + "matmul", + "add", + "sin", + "cos", + "abs", + "flatten", + "clip", + "shape", + "det", + "And", + "Or", + "Xor", + "concat", + "ceil", + "floor", + "bitshift", + "conv", + "elu", + "hardsigmoid", + "hardswish", + "compress", +] diff --git a/docs/sphinx/source/api/export_format/PyTorch/pytorch_ddm.py b/docs/sphinx/source/api/export_format/PyTorch/pytorch_ddm.py new file mode 100644 index 00000000..f0104ed0 --- /dev/null +++ b/docs/sphinx/source/api/export_format/PyTorch/pytorch_ddm.py @@ -0,0 +1,101 @@ +from modeci_mdf.interfaces.pytorch import pytorch_to_mdf + +import time +import re +import os +import torch +from torch import nn + +from typing import Tuple + +if torch.cuda.is_available(): + dev = "cuda:0" +else: + dev = "cpu" + + +def ddm( + starting_value: torch.Tensor, + drift_rate: torch.Tensor, + non_decision_time: torch.Tensor, + threshold: torch.Tensor, + noise: torch.Tensor, + time_step_size: torch.Tensor, +): + """ + A model that simulates a simple noisy drift diffusion model using Euler-Maruyama integration. This is implemented + without performance in mind. + + Args: + starting_value: The starting value of the particle. + drift_rate: The constant drift rate the particles is under. + non_decision_time: A constant amount of time added to the reaction time that signifies automatic processing. + threshold: The threshold that a particle must reach to stop integration. + noise: The standard deviation of the Gaussian noise added to the particles position at each time step. + time_step_size: The time step size (in seconds) for the integration process. + + Returns: + A two element tuple containing the reaction times and the decisions + """ + + x = starting_value + rt = torch.tensor(0.0) + + # Integrate until the negative or positive threshold is reached + while torch.abs(x) < threshold: + x = x + torch.normal(mean=drift_rate * time_step_size, std=noise) * torch.sqrt( + time_step_size + ) + rt = rt + 1.0 + + # Compute the actual reaction time and the decision (as a bool) + rt = non_decision_time + rt * time_step_size + decision = x >= threshold + + return rt, decision + + +def main(): + + ddm_params = dict( + starting_value=0.0, + drift_rate=0.3, + non_decision_time=0.15, + threshold=0.6, + noise=1.0, + time_step_size=0.001, + ) + + # Move params to device + for key, val in ddm_params.items(): + ddm_params[key] = torch.tensor(val).to(dev) + + # Run a single ddm + rt, decision = ddm(**ddm_params) + + mdf_model, param_dict = pytorch_to_mdf( + model=ddm, + args=tuple(ddm_params.values()), + use_onnx_ops=True, + ) + + # Output the model to JSON + mdf_model.to_json_file("ddm.json") + + import sys + + if "-graph" in sys.argv: + mdf_model.to_graph_image( + engine="dot", + output_format="png", + view_on_render=False, + level=2, + filename_root="ddm", + only_warn_on_fail=( + os.name == "nt" + ), # Makes sure test of this doesn't fail on Windows on GitHub Actions + ) + + +if __name__ == "__main__": + main() diff --git a/docs/sphinx/source/api/export_format/PyTorch/run_translated_mlp_pure_mdf.py b/docs/sphinx/source/api/export_format/PyTorch/run_translated_mlp_pure_mdf.py new file mode 100644 index 00000000..e245a73d --- /dev/null +++ b/docs/sphinx/source/api/export_format/PyTorch/run_translated_mlp_pure_mdf.py @@ -0,0 +1,115 @@ +import json +import ntpath + +from modeci_mdf.functions.standard import mdf_functions, create_python_expression +from typing import List, Tuple, Dict, Optional, Set, Any, Union +from modeci_mdf.utils import load_mdf, print_summary +from modeci_mdf.mdf import * +from modeci_mdf.full_translator import * +from modeci_mdf.execution_engine import EvaluableGraph + +import argparse +import sys +import numpy as np +import os +import h5py +import time + + +def main(): + + verbose = True + dt = 5e-05 + file_path = "mlp_pure_mdf.json" + data = convert_states_to_stateful_parameters(file_path, dt) + # print(data) + with open("Translated_" + file_path, "w") as fp: + json.dump(data, fp, indent=4) + + test_all = "-test" in sys.argv + + mod_graph = load_mdf("Translated_%s" % file_path).graphs[0] + + # mdf_to_graphviz(mod_graph,view_on_render=not test_all, level=3) + + from modelspec.utils import FORMAT_NUMPY, FORMAT_TENSORFLOW + + format = FORMAT_TENSORFLOW if "-tf" in sys.argv else FORMAT_NUMPY + + eg = EvaluableGraph(mod_graph, verbose=False) + eg.evaluate(array_format=format) + + print("Finished evaluating graph using array format %s" % format) + + for n in [ + "mlp_input_layer", + "mlp_relu_1", + "mlp_hidden_layer_with_relu", + "mlp_output_layer", + ]: + out = eg.enodes[n].evaluable_outputs["out_port"].curr_value + print(f"Final output value of node {n}: {out}, shape: {out.shape}") + + if "-graph" in sys.argv: + mod.to_graph_image( + engine="dot", + output_format="png", + view_on_render=False, + level=2, + filename_root="mlp_pure_mdf", + only_warn_on_fail=( + os.name == "nt" + ), # Makes sure test of this doesn't fail on Windows on GitHub Actions + ) + + if test_all: + # Iterate on training data, feed forward and log accuracy + imgs = np.load("example_data/imgs.npy") + labels = np.load("example_data/labels.npy") + + import torch.nn + + matches = 0 + imgs_to_test = imgs[:300] + + start = time.time() + for i in range(len(imgs_to_test)): + ii = imgs[i, :, :] + target = labels[i] + img = torch.Tensor(ii).view(-1, 14 * 14).numpy() + # plot_img(img, 'Post_%i (%s)'%(i, img.shape)) + print( + "***********\nTesting image %i (label: %s): %s\n%s" + % (i, target, np.array2string(img, threshold=5, edgeitems=2), img.shape) + ) + # print(mod_graph.nodes[0].parameters['input']) + mod_graph.nodes[0].get_parameter("input").value = img + eg = EvaluableGraph(mod_graph, verbose=False) + eg.evaluate(array_format=format) + for n in ["mlp_output_layer"]: + out = eg.enodes[n].evaluable_outputs["out_port"].curr_value + print( + "Output of evaluated graph: %s %s (%s)" + % (out, out.shape, type(out).__name__) + ) + prediction = np.argmax(out) + + match = target == int(prediction) + if match: + matches += 1 + print(f"Target: {target}, prediction: {prediction}, match: {match}") + t = time.time() - start + print( + "Matches: %i/%i, accuracy: %s%%. Total time: %.4f sec (%.4fs per run)" + % ( + matches, + len(imgs_to_test), + (100.0 * matches) / len(imgs_to_test), + t, + t / len(imgs_to_test), + ) + ) + + +if __name__ == "__main__": + main() diff --git a/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.json b/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.json new file mode 100644 index 00000000..702bae57 --- /dev/null +++ b/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.json @@ -0,0 +1,421 @@ +{ + "SimpleNet": { + "format": "ModECI MDF v0.4", + "generating_application": "Python modeci-mdf v0.4.14", + "graphs": { + "SimpleNetGraph": { + "nodes": { + "Reshape_10": { + "metadata": { + "color": ".4 .3 .3" + }, + "input_ports": { + "input1": { + "shape": [ + 1, + 3, + 224, + 224 + ], + "type": "float32" + } + }, + "parameters": { + "_9": { + "value": [ + -1, + 150528 + ] + }, + "allowzero": { + "value": 0 + }, + "onnx_Reshape_1": { + "function": "onnx::Reshape", + "args": { + "data": "input1", + "shape": "_9" + } + } + }, + "output_ports": { + "_10": { + "value": "onnx_Reshape_1", + "shape": [ + 1, + 150528 + ], + "type": "float32" + } + } + }, + "Gemm_11": { + "metadata": { + "color": ".2 .3 .5" + }, + "input_ports": { + "_10": { + "shape": [ + 1, + 150528 + ], + "type": "float32" + }, + "fc1_weight": { + "shape": [ + 240, + 150528 + ], + "type": "float32" + }, + "fc1_bias": { + "shape": [ + 240 + ], + "type": "float32" + } + }, + "parameters": { + "alpha": { + "value": 1.0 + }, + "beta": { + "value": 1.0 + }, + "transB": { + "value": 1 + }, + "onnx_Gemm_1": { + "function": "onnx::Gemm", + "args": { + "A": "_10", + "B": "fc1_weight", + "C": "fc1_bias" + } + } + }, + "output_ports": { + "_11": { + "value": "onnx_Gemm_1", + "shape": [ + 1, + 240 + ], + "type": "float32" + } + } + }, + "Relu_12": { + "metadata": { + "color": ".4 .2 .1" + }, + "input_ports": { + "_11": { + "shape": [ + 1, + 240 + ], + "type": "float32" + } + }, + "parameters": { + "onnx_Relu_1": { + "function": "onnx::Relu", + "args": { + "X": "_11" + } + } + }, + "output_ports": { + "_12": { + "value": "onnx_Relu_1", + "shape": [ + 1, + 240 + ], + "type": "float32" + } + } + }, + "Gemm_13": { + "metadata": { + "color": ".2 .3 .5" + }, + "input_ports": { + "_12": { + "shape": [ + 1, + 240 + ], + "type": "float32" + }, + "fc2_weight": { + "shape": [ + 120, + 240 + ], + "type": "float32" + }, + "fc2_bias": { + "shape": [ + 120 + ], + "type": "float32" + } + }, + "parameters": { + "alpha": { + "value": 1.0 + }, + "beta": { + "value": 1.0 + }, + "transB": { + "value": 1 + }, + "onnx_Gemm_1": { + "function": "onnx::Gemm", + "args": { + "A": "_12", + "B": "fc2_weight", + "C": "fc2_bias" + } + } + }, + "output_ports": { + "_13": { + "value": "onnx_Gemm_1", + "shape": [ + 1, + 120 + ], + "type": "float32" + } + } + }, + "Relu_14": { + "metadata": { + "color": ".4 .2 .1" + }, + "input_ports": { + "_13": { + "shape": [ + 1, + 120 + ], + "type": "float32" + } + }, + "parameters": { + "onnx_Relu_1": { + "function": "onnx::Relu", + "args": { + "X": "_13" + } + } + }, + "output_ports": { + "_14": { + "value": "onnx_Relu_1", + "shape": [ + 1, + 120 + ], + "type": "float32" + } + } + }, + "Gemm_15": { + "metadata": { + "color": ".2 .3 .5" + }, + "input_ports": { + "_14": { + "shape": [ + 1, + 120 + ], + "type": "float32" + }, + "fc3_weight": { + "shape": [ + 50, + 120 + ], + "type": "float32" + }, + "fc3_bias": { + "shape": [ + 50 + ], + "type": "float32" + } + }, + "parameters": { + "alpha": { + "value": 1.0 + }, + "beta": { + "value": 1.0 + }, + "transB": { + "value": 1 + }, + "onnx_Gemm_1": { + "function": "onnx::Gemm", + "args": { + "A": "_14", + "B": "fc3_weight", + "C": "fc3_bias" + } + } + }, + "output_ports": { + "_15": { + "value": "onnx_Gemm_1", + "shape": [ + 1, + 50 + ], + "type": "float32" + } + } + }, + "Relu_16": { + "metadata": { + "color": ".4 .2 .1" + }, + "input_ports": { + "_15": { + "shape": [ + 1, + 50 + ], + "type": "float32" + } + }, + "parameters": { + "onnx_Relu_1": { + "function": "onnx::Relu", + "args": { + "X": "_15" + } + } + }, + "output_ports": { + "_16": { + "value": "onnx_Relu_1", + "shape": [ + 1, + 50 + ], + "type": "float32" + } + } + }, + "Gemm_17": { + "metadata": { + "color": ".2 .3 .5" + }, + "input_ports": { + "_16": { + "shape": [ + 1, + 50 + ], + "type": "float32" + }, + "fc4_weight": { + "shape": [ + 2, + 50 + ], + "type": "float32" + }, + "fc4_bias": { + "shape": [ + 2 + ], + "type": "float32" + } + }, + "parameters": { + "alpha": { + "value": 1.0 + }, + "beta": { + "value": 1.0 + }, + "transB": { + "value": 1 + }, + "onnx_Gemm_1": { + "function": "onnx::Gemm", + "args": { + "A": "_16", + "B": "fc4_weight", + "C": "fc4_bias" + } + } + }, + "output_ports": { + "_17": { + "value": "onnx_Gemm_1", + "shape": [ + 1, + 2 + ], + "type": "float32" + } + } + } + }, + "edges": { + "Reshape_10_Gemm_11": { + "sender": "Reshape_10", + "receiver": "Gemm_11", + "sender_port": "_10", + "receiver_port": "_10" + }, + "Gemm_11_Relu_12": { + "sender": "Gemm_11", + "receiver": "Relu_12", + "sender_port": "_11", + "receiver_port": "_11" + }, + "Relu_12_Gemm_13": { + "sender": "Relu_12", + "receiver": "Gemm_13", + "sender_port": "_12", + "receiver_port": "_12" + }, + "Gemm_13_Relu_14": { + "sender": "Gemm_13", + "receiver": "Relu_14", + "sender_port": "_13", + "receiver_port": "_13" + }, + "Relu_14_Gemm_15": { + "sender": "Relu_14", + "receiver": "Gemm_15", + "sender_port": "_14", + "receiver_port": "_14" + }, + "Gemm_15_Relu_16": { + "sender": "Gemm_15", + "receiver": "Relu_16", + "sender_port": "_15", + "receiver_port": "_15" + }, + "Relu_16_Gemm_17": { + "sender": "Relu_16", + "receiver": "Gemm_17", + "sender_port": "_16", + "receiver_port": "_16" + } + } + } + }, + "onnx_opset_version": 17 + } +} \ No newline at end of file diff --git a/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.py b/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.py new file mode 100644 index 00000000..7c59dabf --- /dev/null +++ b/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.py @@ -0,0 +1,133 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import onnx +import onnxruntime as rt +from torchviz import make_dot +import netron +from modeci_mdf.interfaces.pytorch import pytorch_to_mdf +import os + + +class SimpleNet(nn.Module): + def __init__(self): + super().__init__() + self.fc1 = nn.Linear(150528, 240) # Input is calculated as 224*224*3=150528 + self.fc2 = nn.Linear(240, 120) + self.fc3 = nn.Linear(120, 50) + self.fc4 = nn.Linear(50, 2) # Output node with 2 classes + + def forward(self, x): + x = x.view(-1, 150528) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = F.relu(self.fc3(x)) + x = self.fc4(x) + return x + + +def main(): + # changed import call + from modeci_mdf.execution_engine import EvaluableGraph + + # Create some test inputs for the model + input_images = torch.zeros((1, 3, 224, 224), requires_grad=False) + + # Seed the random number generator to get deterministic behavior for weight initialization + torch.manual_seed(0) + + model = SimpleNet() + + model.eval() + # Run the model once to get some ground truth outpot (from PyTorch) + output = model(input_images) + + from modelspec.utils import _val_info + + print("Evaluated the graph in PyTorch, output: %s" % (_val_info(output))) + + # Convert to MDF + mdf_model, params_dict = pytorch_to_mdf( + model=model, + args=(input_images), + trace=True, + ) + + # Get the graph + mdf_graph = mdf_model.graphs[0] + + # Add inputs to the parameters dict so we can feed this to the EvaluableGraph for initialization of graph input. + params_dict["input1"] = input_images.numpy() + + # Evaluate the model via the MDF scheduler + eg = EvaluableGraph(graph=mdf_graph, verbose=False) + eg.evaluate(initializer=params_dict) + output_mdf = eg.output_enodes[0].get_output() + + print("Evaluated the graph in PyTorch, output: %s" % (_val_info(output_mdf))) + + # Make sure the results are the same between PyTorch and MDF + assert np.allclose( + output.detach().numpy(), + output_mdf, + ) + print("Passed all comparison tests!") + + # Output the model to JSON + mdf_model.to_json_file("simple_pytorch_to_mdf.json") + + import sys + + # Exporting as onnx model + torch.onnx.export( + model, + input_images, + "simple_pytorch_to_mdf.onnx", + verbose=True, + input_names=[], + opset_version=9, + ) + onnx_model = onnx.load("simple_pytorch_to_mdf.onnx") + onnx.checker.check_model(onnx_model) + sess = rt.InferenceSession("simple_pytorch_to_mdf.onnx") + res = sess.run(None, {sess.get_inputs()[0].name: input_images.numpy()}) + print("Exported to MDF and ONNX") + + # export to mdf graph + if "-graph" in sys.argv: + mdf_model.to_graph_image( + engine="dot", + output_format="png", + view_on_render=False, + level=1, + filename_root="simple_pytorch_to_mdf.1", + only_warn_on_fail=( + os.name == "nt" + ), # Makes sure test of this doesn't fail on Windows on GitHub Actions + is_horizontal=True, + solid_color=True, + ) + mdf_model.to_graph_image( + engine="dot", + output_format="png", + view_on_render=False, + level=3, + filename_root="simple_pytorch_to_mdf", + only_warn_on_fail=( + os.name == "nt" + ), # Makes sure test of this doesn't fail on Windows on GitHub Actions + solid_color=False, + ) + # export to PyTorch graph + if "-graph-torch" in sys.argv: + make_dot(output, params=dict(list(model.named_parameters()))).render( + "simple_pytorch_to_mdf_torchviz", format="png" + ) + # export to onnx graph + if "-graph-onnx" in sys.argv: + netron.start("simple_pytorch_to_mdf.onnx") + + +if __name__ == "__main__": + main() diff --git a/examples/ONNX/abc.json b/examples/ONNX/abc.json index 97515fee..45b433de 100644 --- a/examples/ONNX/abc.json +++ b/examples/ONNX/abc.json @@ -33,7 +33,7 @@ } } }, - "Loop_6": { + "Loop_3": { "input_ports": { "B_loop_count": { "shape": [ @@ -55,7 +55,7 @@ "graph_sub_graph": { "id": "sub_graph", "nodes": { - "Div_8": { + "Div_5": { "input_ports": { "x.11": { "shape": [ @@ -69,7 +69,7 @@ "B": { "value": 10.0 }, - "Div_8": { + "Div_5": { "function": "onnx::Div", "args": { "A": "x.11", @@ -79,16 +79,16 @@ }, "output_ports": { "x": { - "value": "Div_8" + "value": "Div_5" } } }, - "Identity_9": { + "Identity_6": { "parameters": { "input": { "value": true }, - "Identity_9": { + "Identity_6": { "function": "onnx::Identity", "args": { "input": "input" @@ -97,7 +97,7 @@ }, "output_ports": { "_11": { - "value": "Identity_9" + "value": "Identity_6" } } } @@ -108,7 +108,7 @@ "cond": { "value": true }, - "Loop_6": { + "Loop_3": { "function": "onnx::Loop", "args": { "M": "B_loop_count", @@ -119,7 +119,7 @@ }, "output_ports": { "x.3": { - "value": "Loop_6" + "value": "Loop_3" } } }, @@ -152,14 +152,14 @@ } }, "edges": { - "/A/Add._A_Add_output_0_Loop_6._A_Add_output_0": { + "/A/Add._A_Add_output_0_Loop_3._A_Add_output_0": { "sender": "/A/Add", - "receiver": "Loop_6", + "receiver": "Loop_3", "sender_port": "_A_Add_output_0", "receiver_port": "_A_Add_output_0" }, - "Loop_6.x.3_/C/Mul.x.3": { - "sender": "Loop_6", + "Loop_3.x.3_/C/Mul.x.3": { + "sender": "Loop_3", "receiver": "/C/Mul", "sender_port": "x.3", "receiver_port": "x.3" diff --git a/examples/ONNX/abc.yaml b/examples/ONNX/abc.yaml index 39f52693..714367de 100644 --- a/examples/ONNX/abc.yaml +++ b/examples/ONNX/abc.yaml @@ -22,7 +22,7 @@ ONNX Model: output_ports: _A_Add_output_0: value: /A/Add - Loop_6: + Loop_3: input_ports: B_loop_count: shape: @@ -39,7 +39,7 @@ ONNX Model: graph_sub_graph: id: sub_graph nodes: - Div_8: + Div_5: input_ports: x.11: shape: @@ -49,28 +49,28 @@ ONNX Model: parameters: B: value: 10.0 - Div_8: + Div_5: function: onnx::Div args: A: x.11 B: B output_ports: x: - value: Div_8 - Identity_9: + value: Div_5 + Identity_6: parameters: input: value: true - Identity_9: + Identity_6: function: onnx::Identity args: input: input output_ports: _11: - value: Identity_9 + value: Identity_6 cond: value: true - Loop_6: + Loop_3: function: onnx::Loop args: M: B_loop_count @@ -78,7 +78,7 @@ ONNX Model: v_initial: _A_Add_output_0 output_ports: x.3: - value: Loop_6 + value: Loop_3 /C/Mul: input_ports: x.3: @@ -97,13 +97,13 @@ ONNX Model: _13: value: /C/Mul edges: - /A/Add._A_Add_output_0_Loop_6._A_Add_output_0: + /A/Add._A_Add_output_0_Loop_3._A_Add_output_0: sender: /A/Add - receiver: Loop_6 + receiver: Loop_3 sender_port: _A_Add_output_0 receiver_port: _A_Add_output_0 - Loop_6.x.3_/C/Mul.x.3: - sender: Loop_6 + Loop_3.x.3_/C/Mul.x.3: + sender: Loop_3 receiver: /C/Mul sender_port: x.3 receiver_port: x.3 From 14f9854341c4ec2db216390da19801a3979e42a1 Mon Sep 17 00:00:00 2001 From: Padraig Gleeson Date: Wed, 20 May 2026 18:12:45 +0100 Subject: [PATCH 9/9] Formatting fixes --- docs/sphinx/source/api/export_format/NeuroML/ABCD.json | 2 +- .../source/api/export_format/NeuroML/ABCD.mdf.json | 2 +- docs/sphinx/source/api/export_format/NeuroML/FN.json | 2 +- docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json | 2 +- .../api/export_format/NeuroML/IzhikevichTest.mdf.json | 2 +- .../sphinx/source/api/export_format/NeuroML/SimABCD.json | 2 +- docs/sphinx/source/api/export_format/NeuroML/SimFN.json | 2 +- docs/sphinx/source/api/export_format/ONNX/ab.json | 2 +- docs/sphinx/source/api/export_format/ONNX/abc.json | 2 +- .../source/api/export_format/ONNX/abc_basic-mdf.json | 2 +- docs/sphinx/source/api/export_format/ONNX/abcd.json | 2 +- docs/sphinx/source/api/export_format/ONNX/simple_ab.py | 9 +++++---- docs/sphinx/source/api/export_format/ONNX/simple_abc.py | 9 +++++---- docs/sphinx/source/api/export_format/ONNX/simple_abcd.py | 1 + .../source/api/export_format/PyTorch/inception.json | 2 +- .../source/api/export_format/PyTorch/mlp_pure_mdf.json | 2 +- .../api/export_format/PyTorch/simple_pytorch_to_mdf.json | 2 +- 17 files changed, 25 insertions(+), 22 deletions(-) diff --git a/docs/sphinx/source/api/export_format/NeuroML/ABCD.json b/docs/sphinx/source/api/export_format/NeuroML/ABCD.json index 107c15e0..fa8fbba1 100644 --- a/docs/sphinx/source/api/export_format/NeuroML/ABCD.json +++ b/docs/sphinx/source/api/export_format/NeuroML/ABCD.json @@ -171,4 +171,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.json b/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.json index 90a8da47..fc67c977 100644 --- a/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.json +++ b/docs/sphinx/source/api/export_format/NeuroML/ABCD.mdf.json @@ -519,4 +519,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/NeuroML/FN.json b/docs/sphinx/source/api/export_format/NeuroML/FN.json index 1c9d2919..c34637f8 100644 --- a/docs/sphinx/source/api/export_format/NeuroML/FN.json +++ b/docs/sphinx/source/api/export_format/NeuroML/FN.json @@ -69,4 +69,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json b/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json index 5f170961..5b3b07c7 100644 --- a/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json +++ b/docs/sphinx/source/api/export_format/NeuroML/FN.mdf.json @@ -137,4 +137,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.json b/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.json index e6588d20..b218c396 100644 --- a/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.json +++ b/docs/sphinx/source/api/export_format/NeuroML/IzhikevichTest.mdf.json @@ -189,4 +189,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/NeuroML/SimABCD.json b/docs/sphinx/source/api/export_format/NeuroML/SimABCD.json index e5b9cdde..0eefe726 100644 --- a/docs/sphinx/source/api/export_format/NeuroML/SimABCD.json +++ b/docs/sphinx/source/api/export_format/NeuroML/SimABCD.json @@ -11,4 +11,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/NeuroML/SimFN.json b/docs/sphinx/source/api/export_format/NeuroML/SimFN.json index 449a6594..494efc98 100644 --- a/docs/sphinx/source/api/export_format/NeuroML/SimFN.json +++ b/docs/sphinx/source/api/export_format/NeuroML/SimFN.json @@ -20,4 +20,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/ONNX/ab.json b/docs/sphinx/source/api/export_format/ONNX/ab.json index ce07162a..71dad49f 100644 --- a/docs/sphinx/source/api/export_format/ONNX/ab.json +++ b/docs/sphinx/source/api/export_format/ONNX/ab.json @@ -73,4 +73,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/ONNX/abc.json b/docs/sphinx/source/api/export_format/ONNX/abc.json index c87831a0..45b433de 100644 --- a/docs/sphinx/source/api/export_format/ONNX/abc.json +++ b/docs/sphinx/source/api/export_format/ONNX/abc.json @@ -168,4 +168,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.json b/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.json index b1e0b431..793027f5 100644 --- a/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.json +++ b/docs/sphinx/source/api/export_format/ONNX/abc_basic-mdf.json @@ -95,4 +95,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/ONNX/abcd.json b/docs/sphinx/source/api/export_format/ONNX/abcd.json index 0018b14f..c84579a5 100644 --- a/docs/sphinx/source/api/export_format/ONNX/abcd.json +++ b/docs/sphinx/source/api/export_format/ONNX/abcd.json @@ -271,4 +271,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/ONNX/simple_ab.py b/docs/sphinx/source/api/export_format/ONNX/simple_ab.py index 6d7d5111..0b369e38 100644 --- a/docs/sphinx/source/api/export_format/ONNX/simple_ab.py +++ b/docs/sphinx/source/api/export_format/ONNX/simple_ab.py @@ -4,6 +4,7 @@ - Exports in to ONNX using a combination of tracing and scripting - Converts it to MDF """ + import torch import onnx import sys @@ -86,16 +87,16 @@ def main(): ), # Makes sure test of this doesn't fail on Windows on GitHub Actions ) if "-run" in sys.argv: - verbose = True - verbose = False + + verbose = "-v" in sys.argv from modeci_mdf.execution_engine import EvaluableGraph eg = EvaluableGraph(mdf_model.graphs[0], verbose=verbose) print("Evaluating graph...") - test_values = [0, 1, [1, 2], dummy_input.numpy()] - test_values = [0, 1, [1, 2]] + test_values = [0.0, 1.0, [1.0, 2.0], dummy_input.numpy()] + test_values = [0.0, 1.0, [1.0, 2.0]] for t in test_values: print("===================\nEvaluating MDF model with input: %s" % t) diff --git a/docs/sphinx/source/api/export_format/ONNX/simple_abc.py b/docs/sphinx/source/api/export_format/ONNX/simple_abc.py index ecb57769..62403b85 100644 --- a/docs/sphinx/source/api/export_format/ONNX/simple_abc.py +++ b/docs/sphinx/source/api/export_format/ONNX/simple_abc.py @@ -4,6 +4,7 @@ - Exports in to ONNX using a combination of tracing and scripting - Converts it to MDF """ + import torch import onnx import os @@ -77,16 +78,16 @@ def main(): mdf_model.to_json_file("abc.json") mdf_model.to_yaml_file("abc.yaml") + """ + Can't be exported to graph as Loop not supported... mdf_model.to_graph_image( engine="dot", output_format="png", view_on_render=False, level=3, filename_root="abc", - only_warn_on_fail=( - os.name == "nt" - ), # Makes sure test of this doesn't fail on Windows on GitHub Actions - ) + only_warn_on_fail= (os.name=='nt'), # Makes sure test of this doesn't fail on Windows on GitHub Actions + )""" if __name__ == "__main__": diff --git a/docs/sphinx/source/api/export_format/ONNX/simple_abcd.py b/docs/sphinx/source/api/export_format/ONNX/simple_abcd.py index e584f707..5489c9ee 100644 --- a/docs/sphinx/source/api/export_format/ONNX/simple_abcd.py +++ b/docs/sphinx/source/api/export_format/ONNX/simple_abcd.py @@ -4,6 +4,7 @@ - Exports in to ONNX using a combination of tracing and scripting - Converts it to MDF """ + import torch import onnx diff --git a/docs/sphinx/source/api/export_format/PyTorch/inception.json b/docs/sphinx/source/api/export_format/PyTorch/inception.json index 8d4e4fc8..fc232e28 100644 --- a/docs/sphinx/source/api/export_format/PyTorch/inception.json +++ b/docs/sphinx/source/api/export_format/PyTorch/inception.json @@ -9498,4 +9498,4 @@ }, "onnx_opset_version": 17 } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.json b/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.json index e3571410..22160972 100644 --- a/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.json +++ b/docs/sphinx/source/api/export_format/PyTorch/mlp_pure_mdf.json @@ -44279,4 +44279,4 @@ } } } -} \ No newline at end of file +} diff --git a/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.json b/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.json index 702bae57..e6f03bf2 100644 --- a/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.json +++ b/docs/sphinx/source/api/export_format/PyTorch/simple_pytorch_to_mdf.json @@ -418,4 +418,4 @@ }, "onnx_opset_version": 17 } -} \ No newline at end of file +}

metadataOptionalUnion[Any, NoneType] Optional metadata field, an arbitrary dictionary of string keys and JSON serializable values.
node_specificOptionalUnion[Condition, NoneType] A dictionary mapping nodes to any non-default run conditions
terminationOptionalUnion[Condition, NoneType] A dictionary mapping time scales of model execution to conditions indicating when they end