* Add filesize to benchmarks.py

* Add filesize to benchmarks.py

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Improve DDP console response in https://github.com/ultralytics/yolov5/issues/8283

* HUB dataset_stats() error reporting

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Negatively impacts YouTube inference but removes any lag on webcams/RTSP/RTMP etc.

Resolves https://github.com/ultralytics/yolov5/issues/4465

* Reject webcam inference on Colab/Kaggle

Improve user error understanding for https://github.com/ultralytics/yolov5/issues/8180

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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

* Update autobatch.py

* Improved dataset error introspection

Intended to help https://github.com/ultralytics/yolov5/issues/8090

* Update general.py

* 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

Resolves https://github.com/ultralytics/yolov5/discussions/7931

* Code refactor for general.py

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* Add PyTorch AMP check

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* Cleanup

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* Fixes

* Add amp enabled boolean to check_train_batch_size

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Fix for https://github.com/ultralytics/yolov5/discussions/7901

* 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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* Update Dockerfile-cpu `--no-install-recommends`

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* Update Dockerfile-M1

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* add random_interpolation option to make model robust to interpolation methods

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* Fix precommit error

* Update augmentations.py

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@sergiossm

* Pin downloads to release version

Fixes a release version to avoid forward-compatibility issues in future releases.

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* Ability to dowlnoad older assets

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* FROM nvcr.io/nvidia/pytorch:22.04-py3

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fix bug in issue #7650

Improved robustness to filename edge cases like `data/images/zidane.23.jpg` that currently fail. May resolve https://github.com/ultralytics/yolov5/issues/7432