Merge branch 'main' into main

Author: atalman

torchvision/datasets/vision.py

@@ -35,7 +35,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
     ) -> None:
-        _log_api_usage_once(self)
+        _log_api_usage_once("datasets", self.__class__.__name__)
         if isinstance(root, torch._six.string_classes):
             root = os.path.expanduser(root)
         self.root = root

torchvision/models/alexnet.py

@@ -18,7 +18,7 @@
 class AlexNet(nn.Module):
     def __init__(self, num_classes: int = 1000, dropout: float = 0.5) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
         self.features = nn.Sequential(
             nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
             nn.ReLU(inplace=True),

torchvision/models/densenet.py

@@ -163,7 +163,7 @@ def __init__(
     ) -> None:
 
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         # First convolution
         self.features = nn.Sequential(

torchvision/models/detection/generalized_rcnn.py

@@ -27,7 +27,7 @@ class GeneralizedRCNN(nn.Module):
 
     def __init__(self, backbone: nn.Module, rpn: nn.Module, roi_heads: nn.Module, transform: nn.Module) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
         self.transform = transform
         self.backbone = backbone
         self.rpn = rpn

torchvision/models/detection/retinanet.py

@@ -337,7 +337,7 @@ def __init__(
         topk_candidates=1000,
     ):
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         if not hasattr(backbone, "out_channels"):
             raise ValueError(

torchvision/models/detection/ssd.py

@@ -182,7 +182,7 @@ def __init__(
         positive_fraction: float = 0.25,
     ):
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         self.backbone = backbone
 

torchvision/models/detection/ssdlite.py

@@ -120,7 +120,7 @@ def __init__(
         min_depth: int = 16,
     ):
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         assert not backbone[c4_pos].use_res_connect
         self.features = nn.Sequential(

torchvision/models/efficientnet.py

@@ -170,7 +170,7 @@ def __init__(
             norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use
         """
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         if not inverted_residual_setting:
             raise ValueError("The inverted_residual_setting should not be empty")

torchvision/models/googlenet.py

@@ -39,7 +39,7 @@ def __init__(
         dropout_aux: float = 0.7,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
         if blocks is None:
             blocks = [BasicConv2d, Inception, InceptionAux]
         if init_weights is None:

torchvision/models/inception.py

@@ -37,7 +37,7 @@ def __init__(
         dropout: float = 0.5,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
         if inception_blocks is None:
             inception_blocks = [BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE, InceptionAux]
         if init_weights is None: