Update hubconf.py (#1210)

hubconf.py

 import torch
 
 from models.yolo import Model
+from utils.general import set_logging
 from utils.google_utils import attempt_download
 
+set_logging()
+
 
 def create(name, pretrained, channels, classes):
     """Creates a specified YOLOv5 model
     Returns:
         pytorch model
     """
-    config = os.path.join(os.path.dirname(__file__), 'models', '%s.yaml' % name)  # model.yaml path
+    config = os.path.join(os.path.dirname(__file__), 'models', f'{name}.yaml')  # model.yaml path
     try:
         model = Model(config, channels, classes)
         if pretrained:
-            ckpt = '%s.pt' % name  # checkpoint filename
-            attempt_download(ckpt)  # download if not found locally
-            state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict()  # to FP32
+            fname = f'{name}.pt'  # checkpoint filename
+            attempt_download(fname)  # download if not found locally
+            ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
+            state_dict = ckpt['model'].float().state_dict()  # to FP32
             state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape}  # filter
             model.load_state_dict(state_dict, strict=False)  # load
-            # model = model.autoshape()  # cv2/PIL/np/torch inference:  predictions = model(Image.open('image.jpg'))
+            if len(ckpt['model'].names) == classes:
+                model.names = ckpt['model'].names  # set class names attribute
+            # model = model.autoshape()  # for autoshaping of PIL/cv2/np inputs and NMS
         return model
 
     except Exception as e:
         pytorch model
     """
     return create('yolov5x', pretrained, channels, classes)
+
+
+if __name__ == '__main__':
+    model = create(name='yolov5s', pretrained=True, channels=3, classes=80)  # example