revamp log api usage method (#5072)

* revamp log api usage method

torchvision/datasets/vision.py

@@ -35,7 +35,7 @@ class VisionDataset(data.Dataset):
         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 @@ model_urls = {
 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 @@ class DenseNet(nn.Module):
     ) -> 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 @@ class RetinaNet(nn.Module):
         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 @@ class SSD(nn.Module):
         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 @@ class SSDLiteFeatureExtractorMobileNet(nn.Module):
         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 @@ class EfficientNet(nn.Module):
             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 @@ class GoogLeNet(nn.Module):
         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 @@ class Inception3(nn.Module):
         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:

torchvision/models/mnasnet.py

@@ -98,7 +98,7 @@ class MNASNet(torch.nn.Module):
 
     def __init__(self, alpha: float, num_classes: int = 1000, dropout: float = 0.2) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
         assert alpha > 0.0
         self.alpha = alpha
         self.num_classes = num_classes

torchvision/models/mobilenetv2.py

@@ -111,7 +111,7 @@ class MobileNetV2(nn.Module):
 
         """
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         if block is None:
             block = InvertedResidual

torchvision/models/mobilenetv3.py

@@ -151,7 +151,7 @@ class MobileNetV3(nn.Module):
             dropout (float): The droupout probability
         """
         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/optical_flow/raft.py

@@ -440,7 +440,7 @@ class RAFT(nn.Module):
                 If ``None`` (default), the flow is upsampled using interpolation.
         """
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         self.feature_encoder = feature_encoder
         self.context_encoder = context_encoder

torchvision/models/regnet.py

@@ -310,7 +310,7 @@ class RegNet(nn.Module):
         activation: Optional[Callable[..., nn.Module]] = None,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         if stem_type is None:
             stem_type = SimpleStemIN

torchvision/models/resnet.py

@@ -174,7 +174,7 @@ class ResNet(nn.Module):
         norm_layer: Optional[Callable[..., nn.Module]] = None,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         self._norm_layer = norm_layer

torchvision/models/segmentation/_utils.py

@@ -13,7 +13,7 @@ class _SimpleSegmentationModel(nn.Module):
 
     def __init__(self, backbone: nn.Module, classifier: nn.Module, aux_classifier: Optional[nn.Module] = None) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
         self.backbone = backbone
         self.classifier = classifier
         self.aux_classifier = aux_classifier

torchvision/models/segmentation/lraspp.py

@@ -38,7 +38,7 @@ class LRASPP(nn.Module):
         self, backbone: nn.Module, low_channels: int, high_channels: int, num_classes: int, inter_channels: int = 128
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
         self.backbone = backbone
         self.classifier = LRASPPHead(low_channels, high_channels, num_classes, inter_channels)
 

torchvision/models/shufflenetv2.py

@@ -100,7 +100,7 @@ class ShuffleNetV2(nn.Module):
         inverted_residual: Callable[..., nn.Module] = InvertedResidual,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
+        _log_api_usage_once("models", self.__class__.__name__)
 
         if len(stages_repeats) != 3:
             raise ValueError("expected stages_repeats as list of 3 positive ints")

torchvision/models/squeezenet.py

@@ -36,7 +36,7 @@ class Fire(nn.Module):
 class SqueezeNet(nn.Module):
     def __init__(self, version: str = "1_0", 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.num_classes = num_classes
         if version == "1_0":
             self.features = nn.Sequential(