Fix the error message of _ovewrite_value_param

torchvision/models/_utils.py

@@ -240,11 +240,11 @@ def _ovewrite_named_param(kwargs: Dict[str, Any], param: str, new_value: V) -> N
         kwargs[param] = new_value
 
 
-def _ovewrite_value_param(param: Optional[V], new_value: V) -> V:
-    if param is not None:
-        if param != new_value:
-            raise ValueError(f"The parameter '{param}' expected value {new_value} but got {param} instead.")
-    return new_value
+def _ovewrite_value_param(param: str, actual: Optional[V], expected: V) -> V:
+    if actual is not None:
+        if actual != expected:
+            raise ValueError(f"The parameter '{param}' expected value {expected} but got {actual} instead.")
+    return expected
 
 
 class _ModelURLs(dict):

torchvision/models/detection/faster_rcnn.py

@@ -550,7 +550,7 @@ def fasterrcnn_resnet50_fpn(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 
@@ -621,7 +621,7 @@ def fasterrcnn_resnet50_fpn_v2(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 
@@ -661,7 +661,7 @@ def _fasterrcnn_mobilenet_v3_large_fpn(
 ) -> FasterRCNN:
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 

torchvision/models/detection/fcos.py

@@ -749,7 +749,7 @@ def fcos_resnet50_fpn(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 

torchvision/models/detection/keypoint_rcnn.py

@@ -444,8 +444,8 @@ def keypointrcnn_resnet50_fpn(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
-        num_keypoints = _ovewrite_value_param(num_keypoints, len(weights.meta["keypoint_names"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
+        num_keypoints = _ovewrite_value_param("num_keypoints", num_keypoints, len(weights.meta["keypoint_names"]))
     else:
         if num_classes is None:
             num_classes = 2

torchvision/models/detection/mask_rcnn.py

@@ -484,7 +484,7 @@ def maskrcnn_resnet50_fpn(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 
@@ -552,7 +552,7 @@ def maskrcnn_resnet50_fpn_v2(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 

torchvision/models/detection/retinanet.py

@@ -795,7 +795,7 @@ def retinanet_resnet50_fpn(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 
@@ -868,7 +868,7 @@ def retinanet_resnet50_fpn_v2(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 

torchvision/models/detection/ssd.py

@@ -649,7 +649,7 @@ def ssd300_vgg16(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 

torchvision/models/detection/ssdlite.py

@@ -268,7 +268,7 @@ def ssdlite320_mobilenet_v3_large(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 91
 

torchvision/models/segmentation/deeplabv3.py

@@ -260,8 +260,8 @@ def deeplabv3_resnet50(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
-        aux_loss = _ovewrite_value_param(aux_loss, True)
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
+        aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True)
     elif num_classes is None:
         num_classes = 21
 
@@ -316,8 +316,8 @@ def deeplabv3_resnet101(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
-        aux_loss = _ovewrite_value_param(aux_loss, True)
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
+        aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True)
     elif num_classes is None:
         num_classes = 21
 
@@ -370,8 +370,8 @@ def deeplabv3_mobilenet_v3_large(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
-        aux_loss = _ovewrite_value_param(aux_loss, True)
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
+        aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True)
     elif num_classes is None:
         num_classes = 21
 

torchvision/models/segmentation/fcn.py

@@ -155,8 +155,8 @@ def fcn_resnet50(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
-        aux_loss = _ovewrite_value_param(aux_loss, True)
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
+        aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True)
     elif num_classes is None:
         num_classes = 21
 
@@ -214,8 +214,8 @@ def fcn_resnet101(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
-        aux_loss = _ovewrite_value_param(aux_loss, True)
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
+        aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True)
     elif num_classes is None:
         num_classes = 21
 

torchvision/models/segmentation/lraspp.py

@@ -163,7 +163,7 @@ def lraspp_mobilenet_v3_large(
 
     if weights is not None:
         weights_backbone = None
-        num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"]))
+        num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
     elif num_classes is None:
         num_classes = 21