Fix missing attr model.model when loading custom yolov model (#8830)

* Update hubconf.py

Loading a custom yolov model causes this line to fail. Adding a test to check if the model actually has a model.model field. With this check, I'm able to load the model no prob.

Loading model via

```py
    model = torch.hub.load(
        'ultralytics/yolov5', 'custom', 'models/frozen_backbone_coco_unlabeled_best.onnx',
        autoshape=True, force_reload=False
    )
```

Causes traceback:

```
Traceback (most recent call last):
  File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/flask/app.py", line 2077, in wsgi_app
    response = self.full_dispatch_request()
  File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/flask/app.py", line 1525, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/flask/app.py", line 1523, in full_dispatch_request
    rv = self.dispatch_request()
  File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/flask/app.py", line 1509, in dispatch_request
    return self.ensure_sync(self.view_functions[rule.endpoint])(**req.view_args)
  File "/Users/jackson/Documents/GitHub/w210-capstone/api/endpoints/predictions.py", line 26, in post_predictions
    yolov_predictions = predict_bounding_boxes_for_collection(collection_id)
  File "/Users/jackson/Documents/GitHub/w210-capstone/api/predictions/predict_bounding_boxes.py", line 43, in predict_bounding_boxes_for_collection
    model = torch.hub.load(
  File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/torch/hub.py", line 404, in load
    model = _load_local(repo_or_dir, model, *args, **kwargs)
  File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/torch/hub.py", line 433, in _load_local
    model = entry(*args, **kwargs)
  File "/Users/jackson/.cache/torch/hub/ultralytics_yolov5_master/hubconf.py", line 72, in custom
    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
  File "/Users/jackson/.cache/torch/hub/ultralytics_yolov5_master/hubconf.py", line 67, in _create
    raise Exception(s) from e
Exception: 'DetectMultiBackend' object has no attribute 'model'. Cache may be out of date, try `force_reload=True` or see #36 for help.
Exception on /api/v1/predictions [POST]
Traceback (most recent call last):
  File "/Users/jackson/.cache/torch/hub/ultralytics_yolov5_master/hubconf.py", line 58, in _create
    model.model.model[-1].inplace = False  # Detect.inplace=False for safe multithread inference
  File "/Users/jackson/Documents/GitHub/w210-capstone/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1185, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'DetectMultiBackend' object has no attribute 'model'
```

* Update hubconf.py

* Update common.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

hubconf.py

@@ -29,6 +29,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     from pathlib import Path
 
     from models.common import AutoShape, DetectMultiBackend
+    from models.experimental import attempt_load
     from models.yolo import Model
     from utils.downloads import attempt_download
     from utils.general import LOGGER, check_requirements, intersect_dicts, logging
@@ -42,8 +43,12 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     try:
         device = select_device(device)
         if pretrained and channels == 3 and classes == 80:
-            model = DetectMultiBackend(path, device=device, fuse=autoshape)  # download/load FP32 model
-            # model = models.experimental.attempt_load(path, map_location=device)  # download/load FP32 model
+            try:
+                model = DetectMultiBackend(path, device=device, fuse=autoshape)  # detection model
+                if autoshape:
+                    model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
+            except Exception:
+                model = attempt_load(path, device=device, fuse=False)  # arbitrary model
         else:
             cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0]  # model.yaml path
             model = Model(cfg, channels, classes)  # create model
@@ -54,9 +59,6 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
                 model.load_state_dict(csd, strict=False)  # load
                 if len(ckpt['model'].names) == classes:
                     model.names = ckpt['model'].names  # set class names attribute
-        if autoshape:
-            model.model.model[-1].inplace = False  # Detect.inplace=False for safe multithread inference
-            model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
         if not verbose:
             LOGGER.setLevel(logging.INFO)  # reset to default
         return model.to(device)

models/common.py

@@ -562,6 +562,9 @@ def __init__(self, model, verbose=True):
         self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
         self.pt = not self.dmb or model.pt  # PyTorch model
         self.model = model.eval()
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.inplace = False  # Detect.inplace=False for safe multithread inference
 
     def _apply(self, fn):
         # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers