* 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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* improving evole in train.py
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
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* fix gen_ranges value in mutation part.
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
* fix invalid syntax in line 532
remove on tab from "else"
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* Update train.py
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
* Update train.py
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
* fix range index
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
* Update train.py
fix population size
add crossover min and max rate
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
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* update comments
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* save population for last generation
The latest version incorporates a significant update whereby all hyper parameters are now stored in the population section of "evolve_population.yaml," located in "yolov5\data\hyps," following the transition to the new generation. This development allows for the continuation of a previously abandoned evolution process by utilizing the former population. Additionally, a new argument, "--evolve_population," has been introduced to enable the relocation of the manual "evolve_population.yaml" to any project directory to load for the aforementioned purpose. This enhancement offers greater flexibility and convenience to the users, making it easier for them to resume their evolutionary process.
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
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* Update train.py
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* remove try - except
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* Update train.py
Add resume resume_evolve arg for **resume evolve from last generation**.
Population will load from data/hyp by default and load all yaml file form them.
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
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* Update train.py
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
* Update train.py
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* Update train.py
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* Update train.py
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
* Update train.py
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
* Update README.zh-CN.md
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
* Update train.py
update pop_size
Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com>
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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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* Update val.py
When saving predicted labels, create a folder named labels.
Signed-off-by: Ryan <35791309+Gary55555@users.noreply.github.com>
* Update val.py
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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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
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* Limit tensorflow version and add checks
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* Moving check in export script
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fix: requirements.txt to reduce vulnerabilities
The following vulnerabilities are fixed by pinning transitive dependencies:
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878
Co-authored-by: snyk-bot <snyk-bot@snyk.io>
* 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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fix: requirements.txt to reduce vulnerabilities
The following vulnerabilities are fixed by pinning transitive dependencies:
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878
Co-authored-by: snyk-bot <snyk-bot@snyk.io>
fix: requirements.txt to reduce vulnerabilities
The following vulnerabilities are fixed by pinning transitive dependencies:
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878
Co-authored-by: snyk-bot <snyk-bot@snyk.io>
fix: requirements.txt to reduce vulnerabilities
The following vulnerabilities are fixed by pinning transitive dependencies:
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-6043904
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: snyk-bot <snyk-bot@snyk.io>
fix: requirements.txt to reduce vulnerabilities
The following vulnerabilities are fixed by pinning transitive dependencies:
- https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878
Co-authored-by: snyk-bot <snyk-bot@snyk.io>
fix: utils/google_app_engine/additional_requirements.txt to reduce vulnerabilities
The following vulnerabilities are fixed by pinning transitive dependencies:
- https://snyk.io/vuln/SNYK-PYTHON-WERKZEUG-6035177
Co-authored-by: snyk-bot <snyk-bot@snyk.io>
* Update social media links
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Glenn Jocher
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Adrian Boguszewski
Muhammad Rizwan Munawar
Paula Derrenger
Aarni Koskela
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Victor Sonck
Shayan Mousavinia
Nouran Ali
chaoqin
Yakuho
tiancailin
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