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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.
Signed-off-by: davidsvaughn <davidsvaughn@gmail.com>
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* Update dataloaders.py
move extra parameter (rank) to end so won't mess up pre-existing positional args
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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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* Multiply GB displayed by WORLD_SIZE
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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>
Corrected the tensor shape in the doc string.
The incoming masks are stacked in dim=0 therefore the doc is wrong
Signed-off-by: Jonas Heinle <jonasheinle@googlemail.com>
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* process_mask_native() cleanup
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When running segmentation predict on gpu, the conversion from tensor to numpy fails. Calling `.cpu()` solves this problem.
Signed-off-by: Paul Guerrie <97041392+paulguerrie@users.noreply.github.com>
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* Update
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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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* Segmentation `polygons2masks_overlap()` in `np.int32`
May resolve https://github.com/ultralytics/yolov5/issues/9461
WARNING: Masks should be uint8 for fastest speed, change needs profiling results to determine impact.
@AyushExel @Laughing-q
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update dataloaders.py
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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* eliminate permutations for process_mask(upsample=True)
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* Add Annotator.masks()
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* crop always
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