feat: Optimized data loading for PyTorch #362
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fg-mindee
added
type: enhancement
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@@ Coverage Diff @@
## main #362 +/- ##
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+ Coverage 96.11% 96.15% +0.03%
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Files 83 83
Lines 3426 3460 +34
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+ Hits 3293 3327 +34
Misses 133 133
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| torch.backends.cudnn.benchmark = True | ||
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| st = time.time() | ||
| val_set = DetectionDataset( | ||
| img_folder=os.path.join(args.data_path, 'val'), | ||
| label_folder=os.path.join(args.data_path, 'val_labels'), | ||
| sample_transforms=Compose([ | ||
| Lambda(lambda x: x / 255), |
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Why do you remove the normalization here ?
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The to_tensor in the dataset __getitem__ already does that :)
| @@ -137,7 +138,6 @@ def main(args): | |||
| img_folder=os.path.join(args.data_path, 'train'), | |||
| label_folder=os.path.join(args.data_path, 'train_labels'), | |||
| sample_transforms=Compose([ | |||
| Lambda(lambda x: x / 255), | |||
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charlesmindee
approved these changes
Jul 7, 2021
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Following up on #190, this PR introduces the following modifications:
Iterating with the dataloader on FUNSD gets a 25%+ speedup with the same number of workers (3X compared to the original default number of workers)
Closes #190
Any feedback is welcome!