* Refactor code for speed and clarity
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* Update dataloaders.py
This is to address (and hopefully fix) this issue: Multi-GPU DDP RAM multiple-cache bug #3818 (https://github.com/ultralytics/yolov5/issues/3818). This was a very serious and "blocking" issue until I could figure out what was going on. The problem was especially bad when running Multi-GPU jobs with 8 GPUs, RAM usage was 8x higher than expected (!), causing repeated OOM failures. Hopefully this fix will help others.
DDP causes each RANK to launch it's own process (one for each GPU) with it's own trainloader, and its own RAM image cache. The DistributedSampler used by DDP (https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py) will feed only a subset of images (1/WORLD_SIZE) to each available GPU on each epoch, but since the images are shuffled between epochs, each GPU process must still cache all images. So I created a subclass of DistributedSampler called SmartDistributedSampler that forces each GPU process to always sample the same subset (using modulo arithmetic with RANK and WORLD_SIZE) while still allowing random shuffling between epochs. I don't believe this disrupts the overall "randomness" of the sampling, and I haven't noticed any performance degradation.
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* Update dataloaders.py
move extra parameter (rank) to end so won't mess up pre-existing positional args
* Update dataloaders.py
removing extra '#'
* Update dataloaders.py
sample from DDP index array (self.idx) in mixup mosaic
* Merging self.indices and self.idx (DDP indices) into single attribute (self.indices).
Also adding SmartDistributedSampler to segmentation dataloader
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* Update links
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* Update LICENSE to AGPL-3.0
This pull request updates the license of the YOLOv5 project from GNU General Public License v3.0 (GPL-3.0) to GNU Affero General Public License v3.0 (AGPL-3.0).
We at Ultralytics have decided to make this change in order to better protect our intellectual property and ensure that any modifications made to the YOLOv5 source code will be shared back with the community when used over a network.
AGPL-3.0 is very similar to GPL-3.0, but with an additional clause to address the use of software over a network. This change ensures that if someone modifies YOLOv5 and provides it as a service over a network (e.g., through a web application or API), they must also make the source code of their modified version available to users of the service.
This update includes the following changes:
- Replace the `LICENSE` file with the AGPL-3.0 license text
- Update the license reference in the `README.md` file
- Update the license headers in source code files
We believe that this change will promote a more collaborative environment and help drive further innovation within the YOLOv5 community.
Please review the changes and let us know if you have any questions or concerns.
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update headers to AGPL-3.0
---------
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* dataloaders: fix class filtering for segmentation
self.segments[i] and segment[j] are lists so they cannot be indexed with booleans
self.segments is a tuple so it has to be converted into a list first
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* Security improvements
* Security improvements
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* fix bug "resize to 0"
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* Use math.ceil() for resize to enforce min floor of 1 pixel
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* set seed with parameter
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* make seed to be a large number
* set seed with a parameter
* set a seed of dataloader with opt for more randomness
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* Update streams.txt default
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* Change streams list extension to .streams
* Read txt as media per line
* Missed one
* Missed another one
* Update dataloaders.py
* Update detect.py
* Update dataloaders.py
* Update detect.py
* Update predict.py
* Update predict.py
* Update README.md
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* New global TQDM_BAR_FORMAT
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* Simplify dataloader tqdm descriptions
@AyushExel this should help our tqdm dataloader messages fit better within a single line in our Colab notebooks and also help avoid confusion about missing/empty labels, now combined into 'backgrounds'.
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update dataloaders.py
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* Fix dataloader filepath modification to perform only once and not for all occurences of string
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* cleanup
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* AutoCache
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* AutoCache
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* AutoCache
* AutoCache
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* Cleanup
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* Cleanup
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* Added cutout import from utils/augmentations.py to use Cutout Aug in data loader by un-commenting line 679, 680, 681
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* Try to fix DDP mAP drop by setting generator's seed to RANK
* Fix default activation bug
* Update dataloaders.py
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* Update dataloaders.py
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* Add `RandomResizedCrop(ratio)`
* Update ratio
* Update ratio
* Update ratio
* Update ratio
* Update ratio
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Create augmentations.py
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* Update augmentations.py
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* Standardize warnings
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* Fix duplicate plots.py
* Fix check_font()
* # torch.use_deterministic_algorithms(True)
* update doc detect->predict
* Resolve precommit for segment/train and segment/val
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* Resolve precommit utils/segment/general
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* DetectMultiBackend compatibility
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* update plot colors
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* enable overlap by default
* Merge detect/segment output_to_target() function
* Start segmentation CI
* fix plots
* Update ci-testing.yml
* fix training whitespace
* optimize process mask functions (can we merge both?)
* Update predict/detect
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Per
```
/content/yolov5/utils/dataloaders.py:458: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
```
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Long Tran
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