Add EdgeTPU support (#3630)

* 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

* 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

* 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

* Add --tfl-detect for TFLite Detection

* Add int8 quantized TFLite inference in detect.py

* Add --edgetpu for Edge TPU detection

* Fix --img-size to add rectangle TensorFlow and TFLite input

* Add --no-tf-nms to detect objects using models combined with TensorFlow NMS

* Fix --img-size list type input

* Update README.md

* Add Android project for TFLite inference

* Upgrade TensorFlow v2.3.1 -> v2.4.0

* Disable normalization of xywh

* 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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* 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

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: unknown <fangjiacong@ut.cn>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

detect.py

@@ -38,6 +38,7 @@ from utils.torch_utils import select_device, time_sync
 @torch.no_grad()
 def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
+        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
         imgsz=(640, 640),  # inference size (height, width)
         conf_thres=0.25,  # confidence threshold
         iou_thres=0.45,  # NMS IOU threshold
@@ -76,7 +77,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
 
     # Load model
     device = select_device(device)
-    model = DetectMultiBackend(weights, device=device, dnn=dnn)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data)
     stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
     imgsz = check_img_size(imgsz, s=stride)  # check image size
 
@@ -204,6 +205,7 @@ def parse_opt():
     parser = argparse.ArgumentParser()
     parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
     parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
+    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
     parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
     parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
     parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')