Update error messages

Author: datumbox

torchvision/models/convnext.py

@@ -197,7 +197,7 @@ def _convnext(
     model = ConvNeXt(block_setting, stochastic_depth_prob=stochastic_depth_prob, **kwargs)
     if pretrained:
         if arch not in _MODELS_URLS:
-            raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+            raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
         state_dict = load_state_dict_from_url(_MODELS_URLS[arch], progress=progress)
         model.load_state_dict(state_dict)
     return model

torchvision/models/detection/faster_rcnn.py

@@ -436,7 +436,7 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
     )
     if pretrained:
         if model_urls.get(arch, None) is None:
-            raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+            raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
         state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
         model.load_state_dict(state_dict)
     return model

torchvision/models/detection/ssd.py

@@ -624,7 +624,7 @@ def ssd300_vgg16(
     if pretrained:
         arch = "ssd300_vgg16_coco"
         if model_urls.get(arch, None) is None:
-            raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+            raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
         state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
         model.load_state_dict(state_dict)
     return model

torchvision/models/detection/ssdlite.py

@@ -269,7 +269,7 @@ def ssdlite320_mobilenet_v3_large(
     if pretrained:
         arch = "ssdlite320_mobilenet_v3_large_coco"
         if model_urls.get(arch, None) is None:
-            raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+            raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
         state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
         model.load_state_dict(state_dict)
     return model

torchvision/models/efficientnet.py

@@ -373,7 +373,7 @@ def _efficientnet(
     model = EfficientNet(inverted_residual_setting, dropout, last_channel=last_channel, **kwargs)
     if pretrained:
         if model_urls.get(arch, None) is None:
-            raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+            raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
         state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
         model.load_state_dict(state_dict)
     return model

torchvision/models/mnasnet.py

@@ -198,7 +198,7 @@ def _load_from_state_dict(
 
 def _load_pretrained(arch: str, model: nn.Module, progress: bool) -> None:
     if arch not in _MODEL_URLS or _MODEL_URLS[arch] is None:
-        raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+        raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
     checkpoint_url = _MODEL_URLS[arch]
     model.load_state_dict(load_state_dict_from_url(checkpoint_url, progress=progress))
 

torchvision/models/mobilenetv3.py

@@ -294,7 +294,7 @@ def _mobilenet_v3(
     model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs)
     if pretrained:
         if model_urls.get(arch, None) is None:
-            raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+            raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
         state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
         model.load_state_dict(state_dict)
     return model

torchvision/models/quantization/mobilenetv3.py

@@ -114,7 +114,7 @@ def fuse_model(self, is_qat: Optional[bool] = None) -> None:
 
 def _load_weights(arch: str, model: QuantizableMobileNetV3, model_url: Optional[str], progress: bool) -> None:
     if model_url is None:
-        raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+        raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
     state_dict = load_state_dict_from_url(model_url, progress=progress)
     model.load_state_dict(state_dict)
 

torchvision/models/regnet.py

@@ -395,7 +395,7 @@ def _regnet(arch: str, block_params: BlockParams, pretrained: bool, progress: bo
     model = RegNet(block_params, norm_layer=norm_layer, **kwargs)
     if pretrained:
         if arch not in model_urls:
-            raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+            raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
         state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
         model.load_state_dict(state_dict)
     return model

torchvision/models/segmentation/_utils.py

@@ -40,6 +40,6 @@ def forward(self, x: Tensor) -> Dict[str, Tensor]:
 
 def _load_weights(arch: str, model: nn.Module, model_url: Optional[str], progress: bool) -> None:
     if model_url is None:
-        raise NotImplementedError(f"No checkpoint is available for model type {arch}")
+        raise NotImplementedError(f"No pre-trained weights are available for model type {arch}")
     state_dict = load_state_dict_from_url(model_url, progress=progress)
     model.load_state_dict(state_dict)