Check sha256 of weights (#7219)

Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com>

torchvision/models/quantization/inception.py

@@ -265,7 +265,7 @@ def inception_v3(
         if quantize and not original_aux_logits:
             model.aux_logits = False
             model.AuxLogits = 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 quantize and not original_aux_logits:
             model.aux_logits = False
             model.AuxLogits = None

torchvision/models/quantization/mobilenetv2.py

@@ -149,6 +149,6 @@ def mobilenet_v2(
         quantize_model(model, backend)
 
     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/quantization/mobilenetv3.py

@@ -149,7 +149,7 @@ def _mobilenet_v3_model(
         torch.ao.quantization.prepare_qat(model, inplace=True)
 
     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 quantize:
         torch.ao.quantization.convert(model, inplace=True)

torchvision/models/quantization/resnet.py

@@ -144,7 +144,7 @@ def _resnet(
         quantize_model(model, backend)
 
     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/quantization/shufflenetv2.py

@@ -108,7 +108,7 @@ def _shufflenetv2(
         quantize_model(model, backend)
 
     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/regnet.py

@@ -397,7 +397,7 @@ def _regnet(
     model = RegNet(block_params, norm_layer=norm_layer, **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/resnet.py

@@ -298,7 +298,7 @@ def _resnet(
     model = ResNet(block, layers, **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/segmentation/deeplabv3.py

@@ -275,7 +275,7 @@ def deeplabv3_resnet50(
     model = _deeplabv3_resnet(backbone, num_classes, aux_loss)
 
     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
 
@@ -331,7 +331,7 @@ def deeplabv3_resnet101(
     model = _deeplabv3_resnet(backbone, num_classes, aux_loss)
 
     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
 
@@ -385,6 +385,6 @@ def deeplabv3_mobilenet_v3_large(
     model = _deeplabv3_mobilenetv3(backbone, num_classes, aux_loss)
 
     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/segmentation/fcn.py

@@ -168,7 +168,7 @@ def fcn_resnet50(
     model = _fcn_resnet(backbone, num_classes, aux_loss)
 
     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
 
@@ -227,6 +227,6 @@ def fcn_resnet101(
     model = _fcn_resnet(backbone, num_classes, aux_loss)
 
     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/segmentation/lraspp.py

@@ -173,6 +173,6 @@ def lraspp_mobilenet_v3_large(
     model = _lraspp_mobilenetv3(backbone, num_classes)
 
     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/shufflenetv2.py

@@ -178,7 +178,7 @@ def _shufflenetv2(
     model = ShuffleNetV2(*args, **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/squeezenet.py

@@ -109,7 +109,7 @@ def _squeezenet(
     model = SqueezeNet(version, **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/swin_transformer.py

@@ -639,7 +639,7 @@ def _swin_transformer(
     )
 
     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/vgg.py

@@ -102,7 +102,7 @@ def _vgg(cfg: str, batch_norm: bool, weights: Optional[WeightsEnum], progress: b
             _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
     model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **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/video/mvit.py

@@ -593,7 +593,7 @@ def _mvit(
     )
 
     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/video/resnet.py

@@ -303,7 +303,7 @@ def _video_resnet(
     model = VideoResNet(block, conv_makers, layers, stem, **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/video/s3d.py

@@ -214,6 +214,6 @@ def s3d(*, weights: Optional[S3D_Weights] = None, progress: bool = True, **kwarg
     model = S3D(**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/video/swin_transformer.py

@@ -497,7 +497,7 @@ def _swin_transformer3d(
     )
 
     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/vision_transformer.py

@@ -332,7 +332,7 @@ def _vision_transformer(
     )
 
     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/prototype/models/depth/stereo/crestereo.py

@@ -1052,7 +1052,7 @@ def _crestereo(
     )
 
     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