AutoShape() models as DetectMultiBackend() instances (#5845)

* Update AutoShape()

* autodownload ONNX

* Cleanup

* Finish updates

* Add Usage

* Update

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* fix device

* Update hubconf.py

* Update common.py

* smart param selection

* autodownload all formats

* autopad only pytorch models

* new_shape edits

* stride tensor fix

* Cleanup

hubconf.py

@@ -5,6 +5,7 @@ PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
 Usage:
     import torch
     model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+    model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx')  # file from branch
 """
 
 import torch
@@ -27,26 +28,25 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     """
     from pathlib import Path
 
-    from models.common import AutoShape
-    from models.experimental import attempt_load
+    from models.common import AutoShape, DetectMultiBackend
     from models.yolo import Model
     from utils.downloads import attempt_download
     from utils.general import check_requirements, intersect_dicts, set_logging
     from utils.torch_utils import select_device
 
-    file = Path(__file__).resolve()
     check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
     set_logging(verbose=verbose)
 
-    save_dir = Path('') if str(name).endswith('.pt') else file.parent
-    path = (save_dir / name).with_suffix('.pt')  # checkpoint path
+    name = Path(name)
+    path = name.with_suffix('.pt') if name.suffix == '' else name  # checkpoint path
     try:
         device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
 
         if pretrained and channels == 3 and classes == 80:
-            model = attempt_load(path, map_location=device)  # download/load FP32 model
+            model = DetectMultiBackend(path, device=device)  # download/load FP32 model
+            # model = models.experimental.attempt_load(path, map_location=device)  # download/load FP32 model
         else:
-            cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0]  # model.yaml path
+            cfg = list((Path(__file__).parent / 'models').rglob(f'{path.name}.yaml'))[0]  # model.yaml path
             model = Model(cfg, channels, classes)  # create model
             if pretrained:
                 ckpt = torch.load(attempt_download(path), map_location=device)  # load