PyTorch Hub load directly when possible (#2986)

hubconf.py

 
 import torch
 
-from models.yolo import Model
+from models.yolo import Model, attempt_load
 from utils.general import check_requirements, set_logging
 from utils.google_utils import attempt_download
 from utils.torch_utils import select_device
         pretrained (bool): load pretrained weights into the model
         channels (int): number of input channels
         classes (int): number of model classes
+        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
+        verbose (bool): print all information to screen
 
     Returns:
-        pytorch model
+        YOLOv5 pytorch model
     """
+    set_logging(verbose=verbose)
+    fname = f'{name}.pt'  # checkpoint filename
     try:
-        set_logging(verbose=verbose)
-
-        cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0]  # model.yaml path
-        model = Model(cfg, channels, classes)
-        if pretrained:
-            fname = f'{name}.pt'  # checkpoint filename
-            attempt_download(fname)  # download if not found locally
-            ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
-            msd = model.state_dict()  # model state_dict
-            csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
-            csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
-            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.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
+        if pretrained and channels == 3 and classes == 80:
+            model = attempt_load(fname, map_location=torch.device('cpu'))  # download/load FP32 model
+        else:
+            cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0]  # model.yaml path
+            model = Model(cfg, channels, classes)  # create model
+            if pretrained:
+                attempt_download(fname)  # download if not found locally
+                ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
+                msd = model.state_dict()  # model state_dict
+                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
+                csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
+                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.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
         device = select_device('0' if torch.cuda.is_available() else 'cpu')  # default to GPU if available
         return model.to(device)
 
     except Exception as e:
         help_url = 'https://github.com/ultralytics/yolov5/issues/36'
-        s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url
+        s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
         raise Exception(s) from e