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

* Update 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

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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

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* Fixes

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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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* Removed shell=True from subprocess commands that require user inputs. Also removed unused arguments

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* Added check=True

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* Create docker.yml

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

Per https://ubuntu.com/blog/we-reduced-our-docker-images-by-60-with-no-install-recommends

* Update Dockerfile-M1

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* Uses OpenVINO runtime instead of openvino-dev

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* export with openvino package

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* Revert export.py

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* New TensorFlow `TFCrossConv()` module

* Move from experimental to common

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* Add C3x

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* Add to C3x to yolo.py

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* TFC3x bug fix

* TFC3x bug fix

* TFC3x bug fix

* Add TFDWConv g==c1==c2 check

* Add comment

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Bumps [cirrus-actions/rebase](https://github.com/cirrus-actions/rebase) from 1.6 to 1.7.
- [Release notes](https://github.com/cirrus-actions/rebase/releases)
- [Commits](https://github.com/cirrus-actions/rebase/compare/1.6...1.7)

---
updated-dependencies:
- dependency-name: cirrus-actions/rebase
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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* New TensorFlow `TFDWConv()` module

Analog to DWConv() module:
8aa2085a7e/models/common.py (L53-L57)

* Fix and new activations() function

* Update tf.py

Resolves bug report #7817