Always check hash

Author: adamjstewart

torchvision/models/alexnet.py

@@ -114,6 +114,6 @@ def alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True,
     model = AlexNet(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
 
     return model

torchvision/models/convnext.py

@@ -189,7 +189,7 @@ def _convnext(
     model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
 
     return model
 

torchvision/models/densenet.py

@@ -227,7 +227,7 @@ def _load_state_dict(model: nn.Module, weights: WeightsEnum, progress: bool) ->
         r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
     )
 
-    state_dict = weights.get_state_dict(progress=progress)
+    state_dict = weights.get_state_dict(progress=progress, check_hash=True)
     for key in list(state_dict.keys()):
         res = pattern.match(key)
         if res:

torchvision/models/efficientnet.py

@@ -357,7 +357,7 @@ def _efficientnet(
     model = EfficientNet(inverted_residual_setting, dropout, last_channel=last_channel, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
 
     return model
 

torchvision/models/googlenet.py

@@ -332,7 +332,7 @@ def googlenet(*, weights: Optional[GoogLeNet_Weights] = None, progress: bool = T
     model = GoogLeNet(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
         if not original_aux_logits:
             model.aux_logits = False
             model.aux1 = None  # type: ignore[assignment]

torchvision/models/inception.py

@@ -470,7 +470,7 @@ def inception_v3(*, weights: Optional[Inception_V3_Weights] = None, progress: bo
     model = Inception3(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
         if not original_aux_logits:
             model.aux_logits = False
             model.AuxLogits = None

torchvision/models/maxvit.py

@@ -763,7 +763,7 @@ def _maxvit(
     )
 
     if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
 
     return model
 

torchvision/models/mnasnet.py

@@ -317,7 +317,7 @@ def _mnasnet(alpha: float, weights: Optional[WeightsEnum], progress: bool, **kwa
     model = MNASNet(alpha, **kwargs)
 
     if weights:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
 
     return model
 

torchvision/models/mobilenetv2.py

@@ -255,6 +255,6 @@ def mobilenet_v2(
     model = MobileNetV2(**kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
 
     return model

torchvision/models/mobilenetv3.py

@@ -282,7 +282,7 @@ def _mobilenet_v3(
     model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs)
 
     if weights is not None:
-        model.load_state_dict(weights.get_state_dict(progress=progress))
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
 
     return model