Default PyTorch Hub to autocast(False) (ultralytics#5926)

models/common.py

@@ -443,6 +443,7 @@ class AutoShape(nn.Module):
     multi_label = False  # NMS multiple labels per box
     classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
     max_det = 1000  # maximum number of detections per image
+    amp = False  # Automatic Mixed Precision (AMP) inference
 
     def __init__(self, model):
         super().__init__()
@@ -476,8 +477,9 @@ def forward(self, imgs, size=640, augment=False, profile=False):
 
         t = [time_sync()]
         p = next(self.model.parameters()) if self.pt else torch.zeros(1)  # for device and type
+        autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
         if isinstance(imgs, torch.Tensor):  # torch
-            with amp.autocast(enabled=p.device.type != 'cpu'):
+            with amp.autocast(enabled=autocast):
                 return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
 
         # Pre-process
@@ -506,7 +508,7 @@ def forward(self, imgs, size=640, augment=False, profile=False):
         x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
         t.append(time_sync())
 
-        with amp.autocast(enabled=p.device.type != 'cpu'):
+        with amp.autocast(enabled=autocast):
             # Inference
             y = self.model(x, augment, profile)  # forward
             t.append(time_sync())