Display device_name="MYRIAD" for Intel NCS2 option per user question in https://github.com/ultralytics/yolov5/issues/8154

* 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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* Add PyTorch AMP check

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* Add DWConvTranspose2d() module

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* Uses OpenVINO runtime instead of openvino-dev

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* New TensorFlow `TFCrossConv()` module

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Solution proposed in https://github.com/ultralytics/yolov5/issues/7128 to TRT PyTorch Hub CUDA illegal memory errors.

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* precommit: yapf

* align isort

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# Conflicts:
#	utils/plots.py

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Resolves https://github.com/ultralytics/yolov5/issues/388#issuecomment-1077121821

May resolve https://github.com/ultralytics/yolov5/issues/6947

* Assert engine precision #6777

* Default to FP32 inputs for TensorRT engines

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* Remove wrong line #6777

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* Automatically adjust val.py input precision #6777

* Add missing colon

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* Fix CoreML P6 inference

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* Fix exporting saved_model: pb exceeds 2GB

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* Replace TF v1.x API with TF v2.x API for saved_model export

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* Clean up

* Remove lambda in tf.function()

* Revert "Remove lambda in tf.function()" to be compatible with TF v2.4

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* Add benchmarks.py

* Update

* Add requirements

* Updates

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* Updates

* Updates

* dataset autodownload from root

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* Redirect to /dev/null

* sudo --help

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* Updates

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* Edge TPU TF imports fix

Fix for https://github.com/ultralytics/yolov5/issues/6535#issuecomment-1030631526

* Update common.py

* Edge TPU `tf.lite.experimental.load_delegate` fix

Fix attempt for #6535

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* Add models/tf.py for TensorFlow and TFLite export

* Set auto=False for int8 calibration

* Update requirements.txt for TensorFlow and TFLite export

* Read anchors directly from PyTorch weights

* Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export

* Remove check_anchor_order, check_file, set_logging from import

* Reformat code and optimize imports

* Autodownload model and check cfg

* update --source path, img-size to 320, single output

* Adjust representative_dataset

* Put representative dataset in tfl_int8 block

* detect.py TF inference

* weights to string

* weights to string

* cleanup tf.py

* Add --dynamic-batch-size

* Add xywh normalization to reduce calibration error

* Update requirements.txt

TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error

* Fix imports

Move C3 from models.experimental to models.common

* Add models/tf.py for TensorFlow and TFLite export

* Set auto=False for int8 calibration

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* Read anchors directly from PyTorch weights

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* Remove check_anchor_order, check_file, set_logging from import

* Reformat code and optimize imports

* Autodownload model and check cfg

* update --source path, img-size to 320, single output

* Adjust representative_dataset

* detect.py TF inference

* Put representative dataset in tfl_int8 block

* weights to string

* weights to string

* cleanup tf.py

* Add --dynamic-batch-size

* Add xywh normalization to reduce calibration error

* Update requirements.txt

TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error

* Fix imports

Move C3 from models.experimental to models.common

* implement C3() and SiLU()

* Add TensorFlow and TFLite Detection

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* Fix --img-size list type input

* Update README.md

* Add Android project for TFLite inference

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* Rewrite names init in detect.py

* Change input resolution 640 -> 320 on Android

* Disable NNAPI

* Update README.me --img 640 -> 320

* Update README.me for Edge TPU

* Update README.md

* Fix reshape dim to support dynamic batching

* Fix reshape dim to support dynamic batching

* Add epsilon argument in tf_BN, which is different between TF and PT

* Set stride to None if not using PyTorch, and do not warmup without PyTorch

* Add list support in check_img_size()

* Add list input support in detect.py

* sys.path.append('./') to run from yolov5/

* Add int8 quantization support for TensorFlow 2.5

* Add get_coco128.sh

* Remove --no-tfl-detect in models/tf.py (Use tf-android-tfl-detect branch for EdgeTPU)

* Update requirements.txt

* Replace torch.load() with attempt_load()

* Update requirements.txt

* Add --tf-raw-resize to set half_pixel_centers=False

* Remove android directory

* Update README.md

* Update README.md

* Add multiple OS support for EdgeTPU detection

* Fix export and detect

* Export 3 YOLO heads with Edge TPU models

* Remove xywh denormalization with Edge TPU models in detect.py

* Fix saved_model and pb detect error

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* Fix pre-commit.ci failure

* Add edgetpu in export.py docstring

* Fix Edge TPU model detection exported by TF 2.7

* Add class names for TF/TFLite in DetectMultibackend

* Fix assignment with nl in TFLite Detection

* Add check when getting Edge TPU compiler version

* Add UTF-8 encoding in opening --data file for Windows

* Remove redundant TensorFlow import

* Add Edge TPU in export.py's docstring

* Add the detect layer in Edge TPU model conversion

* Default `dnn=False`

* Cleanup data.yaml loading

* Update detect.py

* Update val.py

* Comments and generalize data.yaml names

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* Check TensorRT>=8.0.0 version

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* Fix tolist() to add the file for each Detection

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* Fix PEP8 requirement for 2 spaces before an inline comment

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* Cleanup

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