Check sha256 of weights

torchvision/models/_api.py

@@ -85,8 +85,8 @@ def verify(cls, obj: Any) -> Any:
                 )
         return obj
 
-    def get_state_dict(self, progress: bool) -> Mapping[str, Any]:
-        return load_state_dict_from_url(self.url, progress=progress)
+    def get_state_dict(self, *args: Any, **kwargs: Any) -> Mapping[str, Any]:
+        return load_state_dict_from_url(self.url, *args, **kwargs)
 
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}.{self._name_}"

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/detection/faster_rcnn.py

@@ -571,7 +571,7 @@ def fasterrcnn_resnet50_fpn(
     model = FasterRCNN(backbone, num_classes=num_classes, **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 weights == FasterRCNN_ResNet50_FPN_Weights.COCO_V1:
             overwrite_eps(model, 0.0)
 
@@ -653,7 +653,7 @@ def fasterrcnn_resnet50_fpn_v2(
     )
 
     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
 
@@ -694,7 +694,7 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
     )
 
     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/detection/fcos.py

@@ -766,6 +766,6 @@ def fcos_resnet50_fpn(
     model = FCOS(backbone, num_classes, **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/detection/keypoint_rcnn.py

@@ -465,7 +465,7 @@ def keypointrcnn_resnet50_fpn(
     model = KeypointRCNN(backbone, num_classes, num_keypoints=num_keypoints, **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 weights == KeypointRCNN_ResNet50_FPN_Weights.COCO_V1:
             overwrite_eps(model, 0.0)
 

torchvision/models/detection/mask_rcnn.py

@@ -501,7 +501,7 @@ def maskrcnn_resnet50_fpn(
     model = MaskRCNN(backbone, num_classes=num_classes, **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 weights == MaskRCNN_ResNet50_FPN_Weights.COCO_V1:
             overwrite_eps(model, 0.0)
 
@@ -582,6 +582,6 @@ def maskrcnn_resnet50_fpn_v2(
     )
 
     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/detection/retinanet.py

@@ -815,7 +815,7 @@ def retinanet_resnet50_fpn(
     model = RetinaNet(backbone, num_classes, **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 weights == RetinaNet_ResNet50_FPN_Weights.COCO_V1:
             overwrite_eps(model, 0.0)
 
@@ -894,6 +894,6 @@ def retinanet_resnet50_fpn_v2(
     model = RetinaNet(backbone, num_classes, anchor_generator=anchor_generator, head=head, **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/detection/ssd.py

@@ -677,6 +677,6 @@ def ssd300_vgg16(
     model = SSD(backbone, anchor_generator, (300, 300), num_classes, **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/detection/ssdlite.py

@@ -326,6 +326,6 @@ def ssdlite320_mobilenet_v3_large(
     )
 
     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/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
 

torchvision/models/optical_flow/raft.py

@@ -818,7 +818,7 @@ def _raft(
     )
 
     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/googlenet.py

@@ -197,7 +197,7 @@ def googlenet(
         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))
         if not original_aux_logits:
             model.aux_logits = False
             model.aux1 = None  # type: ignore[assignment]

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