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Add configurable checkpoint output directory #152

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16 changes: 11 additions & 5 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,6 +63,10 @@ def get_argparser():

parser.add_argument("--ckpt", default=None, type=str,
help="restore from checkpoint")
parser.add_argument("--ckpt_dir", default="checkpoints", type=str,
help="checkpoint output directory")
parser.add_argument("--step_ckpt_interval", type=int, default=0,
help="save step_XXXXXX.pth every N iterations; 0 disables it")
parser.add_argument("--continue_training", action='store_true', default=False)

parser.add_argument("--loss_type", type=str, default='cross_entropy',
Expand Down Expand Up @@ -283,7 +287,7 @@ def save_ckpt(path):
}, path)
print("Model saved as %s" % path)

utils.mkdir('checkpoints')
utils.mkdir(opts.ckpt_dir)
# Restore
best_score = 0.0
cur_itrs = 0
Expand Down Expand Up @@ -348,8 +352,10 @@ def save_ckpt(path):
interval_loss = 0.0

if (cur_itrs) % opts.val_interval == 0:
save_ckpt('checkpoints/latest_%s_%s_os%d.pth' %
(opts.model, opts.dataset, opts.output_stride))
save_ckpt(os.path.join(opts.ckpt_dir, 'latest_%s_%s_os%d.pth' %
(opts.model, opts.dataset, opts.output_stride)))
if opts.step_ckpt_interval > 0 and cur_itrs % opts.step_ckpt_interval == 0:
save_ckpt(os.path.join(opts.ckpt_dir, 'step_%06d.pth' % cur_itrs))
print("validation...")
model.eval()
val_score, ret_samples = validate(
Expand All @@ -358,8 +364,8 @@ def save_ckpt(path):
print(metrics.to_str(val_score))
if val_score['Mean IoU'] > best_score: # save best model
best_score = val_score['Mean IoU']
save_ckpt('checkpoints/best_%s_%s_os%d.pth' %
(opts.model, opts.dataset, opts.output_stride))
save_ckpt(os.path.join(opts.ckpt_dir, 'best_%s_%s_os%d.pth' %
(opts.model, opts.dataset, opts.output_stride)))

if vis is not None: # visualize validation score and samples
vis.vis_scalar("[Val] Overall Acc", cur_itrs, val_score['Overall Acc'])
Expand Down