* Add `--hard-fail` list argument to benchmarks for CI
Will cause CI to fail on a benchmark failure for given indices.
* Update ci-testing.yml
* Attempt Failure (CI should fail)
* Update benchmarks.py
* Update export.py
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* attempt at reproducibility
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* use deterministic algs
* fix everything :)
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* revert dataloader changes
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* process_batch as np
* remove newline
* Remove dataloader init fcn
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* revert additional changes
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* Update train.py
* Add --seed arg
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* Add deterministic argument to init_seeds()
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* Add filesize to benchmarks.py
* Add filesize to benchmarks.py
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* HUB dataset_stats() error reporting
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* Reject webcam inference on Colab/Kaggle
Improve user error understanding for https://github.com/ultralytics/yolov5/issues/8180
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* cleanup
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* Update dataloaders.py
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* AutoBatch checks against failed solutions
@kalenmike this is a simple improvement to AutoBatch to verify that returned solutions have not already failed, i.e. return batch-size 8 when 8 already produced CUDA out of memory.
This is a halfway fix until I can implement a 'final solution' that will actively verify the solved-for batch size rather than passively assume it works.
* Update autobatch.py
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* Apple Metal Performance Shader (MPS) device support
Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```
* Update device strategy to fix MPS issue
* Code refactor for general.py
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* Update restapi.py
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* Add PyTorch AMP check
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* Cleanup
* Cleanup
* Cleanup
* Robust for DDP
* Fixes
* Add amp enabled boolean to check_train_batch_size
* Simplify
* space to prefix
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* Improve mAP0.5-0.95
Two changes provided
1. Added limit on the maximum number of detections for each image likewise pycocotools
2. Rework process_batch function
Changes #2 solved issue #4251
I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function.
These changes solve this problem.
Currently during validation yolov5x.pt model the following results were obtained:
from yolov5 validation
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00, 2.33it/s]
all 5000 36335 0.743 0.626 0.682 0.506
from pycocotools
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.685
These results are very close, although not completely pass the competition issue #2258.
I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution.
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* Remove line to retain pycocotools results
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* Create docker.yml
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* Cleanup
* Cleanup2
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* Add `@threaded` decorator
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