* 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
* 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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* Multiply GB displayed by WORLD_SIZE
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* Added ClearML instance segmentation and classification support
* Cleaned up ClearML plot output
* typos
* Log results as plots instead of debug samples
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* added imagenet small versions 10,100 and 1000
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* Fix the bug that multi webcam detection failed with OpenVINO
It would failed with the following error when detect multi webcam.
"Input blob size is not equal network input size (2457600!=1228800)"
Signed-off-by: Chao Qin <chaoqin_cmkj@163.com>
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* Update selectable device Profile
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* Fix bug in #12457.
When run 'python.exe segment/predict.py --visualize' will throw AttributeError: 'tuple' object has no attribute 'shape'
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* Add ndjson logging for training
This adds support for NDJSON (newline-delimited JSON) metrics logging,
for both console (stdout) output and a file (like the current CSV file).
NDJSON can be easily grepped from the output and/or parsed with e.g. `jq`.
The feature is enabled with the `--ndjson-console` and `--ndjson-file`
switches to `train.py`.
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* Add option to quantize per-tensor
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* Parametrize multiple of number of channels in Conv
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* Fix issue when exporting
Signed-off-by: Angelo Delli Santi <dellisanti.angelo@gmail.com>
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* Limit tensorflow version and add checks
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* Moving check in export script
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* Increase NCCL timeout to 3 hours
When training on a large dataset using DDP, the scanning process will be very long, and it will raise NCCL timeout error. Change the default timeout 30min to 3 hours, same as ultralytics yolov8 (https://github.com/ultralytics/ultralytics/pull/3343)
Signed-off-by: Troy <wudashuo@vip.qq.com>
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* Created using Colaboratory
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* Update social media links
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* Update export.py
Signed-off-by: Luis Filipe Araujo de Souza <58831491+Doquey@users.noreply.github.com>
* Update export.py
Signed-off-by: Luis Filipe Araujo de Souza <58831491+Doquey@users.noreply.github.com>
* Update export.py
Transformed the f variable into a string on the export onnx. This bug was making it impossible to export any models in .onnx, since it was making the typehint not accept the users input as it is specified in the functions documentation
Signed-off-by: Luis Filipe Araujo de Souza <58831491+Doquey@users.noreply.github.com>
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Glenn Jocher