* 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

* Bug Fixed: OpenVINO metadata

* Bug Fixed: OpenVINO metadata

* Update export.py

* Update export.py
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* Rename OpenVINO meta.yaml to model name

* Rename OpenVINO meta.yaml to model name

* Rename OpenVINO meta.yaml to model name

* fix

* 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 Ultralytics HUB CI errors.

Adjust to 5%, fixes failing Colab AMP check with V100 (1.5% different) with 200% safety margin.

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

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

Locate file in subdirectories.

* Add DWConvTranspose2d() module

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* Add DWConvTranspose2d() module

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* Fix

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* Fix

* 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

* Update val.py

* Update val.py

* Remove to device op

* Higher precision int conversion

* Update val.py
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* Added Windows cmd to count GPU devices

* Cleanup
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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

* Revert line add
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To appear on Docker Hub in top down order:

- latest
- latest-cpu
- latest-arm64

* Update Dockerfile-arm64

* Update docker.yml

Enabled grouped non c1 == c2 convolutions in TF YOLOv5 models.

* Update Dockerfile-cpu install `libpython3.8-dev`

Fix OpenVINO export

* Update Dockerfile-arm64

* Create docker.yml

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* Cleanup

* Cleanup2

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* Update README.md

* Update greetings.yml

* 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

* Update Dockerfile

* Update Dockerfile-M1

* Update Dockerfile-cpu

* Uses OpenVINO runtime instead of openvino-dev

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* export with openvino package

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* Revert export.py

* Update common.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

* Add to C3x to tf.py

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* TFC3x bug fix

* TFC3x bug fix

* TFC3x bug fix

* Add TFDWConv g==c1==c2 check

* Add comment

* Update tf.py

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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:
https://github.com/ultralytics/yolov5/blob/8aa2085a7e7ae20a17a7548edefbdb2960f2b29b/models/common.py#L53-L57

* Fix and new activations() function

* Update tf.py

Resolves bug report #7817

* Update torch_utils.py

* Additional code refactoring

* tuples to sets

* Cleanup

* Add `@threaded` decorator

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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

* Update augmentations.py

* Update augmentations.py

* Update datasets.py

* Update datasets.py
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* Add Dockerfile-M1

* Update

* Update

* Update Dockerfile-M1

@sergiossm

* Implement DDP `static_graph=True`

Experimental implementation of new PyTorch 1.11.0 DDP feature.

* Add 1.11.0 check

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