* Reject webcam inference on Colab/Kaggle

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

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Bumps [actions/setup-python](https://github.com/actions/setup-python) from 3 to 4.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v3...v4)

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updated-dependencies:
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  dependency-type: direct:production
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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

* Update autobatch.py

updates:
- [github.com/asottile/pyupgrade: v2.32.0 → v2.32.1](https://github.com/asottile/pyupgrade/compare/v2.32.0...v2.32.1)

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* experimental.py Apple MPS fix

May resolve https://github.com/ultralytics/yolov5/issues/8102

* Update experimental.py

* Update experimental.py

* Improved dataset error introspection

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

* Update general.py

* Update and rename ci-testing.yml to ci.yml

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* Add `psutil` and `ipython` to requirements.txt

Lightweight packages used by YOLOv5 for system utilization (psutil) and interactive notebooks (IPython)

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* sort alphabetically

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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

* Write .yaml file when exporting model to openvino

Write .yaml file automatically when exporting model to openvino to be used during inference

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* Update export.py

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* Load metadata on inference

* Update common.py

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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

* Cleanup

* Cleanup

* Robust for DDP

* Fixes

* Add amp enabled boolean to check_train_batch_size

* Simplify

* space to prefix

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Resolves https://github.com/ultralytics/yolov5/issues/7911#issuecomment-1133671255

* Add DWConvTranspose2d() module

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* Add DWConvTranspose2d() module

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* Fix

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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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* Update val.py

* Remove to device op

* Higher precision int conversion

* Update val.py

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