use decorator to log API usage

Author: kazhang

torchvision/datasets/vision.py

@@ -28,14 +28,14 @@ class VisionDataset(data.Dataset):
 
     _repr_indent = 4
 
+    @_log_api_usage_once
     def __init__(
         self,
         root: str,
         transforms: Optional[Callable] = None,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
     ) -> None:
-        _log_api_usage_once(self)
         if isinstance(root, torch._six.string_classes):
             root = os.path.expanduser(root)
         self.root = root

torchvision/models/alexnet.py

@@ -16,9 +16,9 @@
 
 
 class AlexNet(nn.Module):
+    @_log_api_usage_once
     def __init__(self, num_classes: int = 1000, dropout: float = 0.5) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         self.features = nn.Sequential(
             nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
             nn.ReLU(inplace=True),

torchvision/models/densenet.py

@@ -151,6 +151,7 @@ class DenseNet(nn.Module):
           but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
     """
 
+    @_log_api_usage_once
     def __init__(
         self,
         growth_rate: int = 32,
@@ -161,9 +162,7 @@ def __init__(
         num_classes: int = 1000,
         memory_efficient: bool = False,
     ) -> None:
-
         super().__init__()
-        _log_api_usage_once(self)
 
         # First convolution
         self.features = nn.Sequential(

torchvision/models/detection/generalized_rcnn.py

@@ -25,9 +25,9 @@ class GeneralizedRCNN(nn.Module):
             the model
     """
 
+    @_log_api_usage_once
     def __init__(self, backbone: nn.Module, rpn: nn.Module, roi_heads: nn.Module, transform: nn.Module) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         self.transform = transform
         self.backbone = backbone
         self.rpn = rpn

torchvision/models/detection/retinanet.py

@@ -316,6 +316,7 @@ class RetinaNet(nn.Module):
         "proposal_matcher": det_utils.Matcher,
     }
 
+    @_log_api_usage_once
     def __init__(
         self,
         backbone,
@@ -337,7 +338,6 @@ def __init__(
         topk_candidates=1000,
     ):
         super().__init__()
-        _log_api_usage_once(self)
 
         if not hasattr(backbone, "out_channels"):
             raise ValueError(

torchvision/models/detection/ssd.py

@@ -165,6 +165,7 @@ class SSD(nn.Module):
         "proposal_matcher": det_utils.Matcher,
     }
 
+    @_log_api_usage_once
     def __init__(
         self,
         backbone: nn.Module,
@@ -182,7 +183,6 @@ def __init__(
         positive_fraction: float = 0.25,
     ):
         super().__init__()
-        _log_api_usage_once(self)
 
         self.backbone = backbone
 

torchvision/models/detection/ssdlite.py

@@ -111,6 +111,7 @@ def __init__(self, in_channels: List[int], num_anchors: List[int], norm_layer: C
 
 
 class SSDLiteFeatureExtractorMobileNet(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         backbone: nn.Module,
@@ -120,7 +121,6 @@ def __init__(
         min_depth: int = 16,
     ):
         super().__init__()
-        _log_api_usage_once(self)
 
         assert not backbone[c4_pos].use_res_connect
         self.features = nn.Sequential(

torchvision/models/efficientnet.py

@@ -148,6 +148,7 @@ def forward(self, input: Tensor) -> Tensor:
 
 
 class EfficientNet(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         inverted_residual_setting: List[MBConvConfig],
@@ -170,7 +171,6 @@ def __init__(
             norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use
         """
         super().__init__()
-        _log_api_usage_once(self)
 
         if not inverted_residual_setting:
             raise ValueError("The inverted_residual_setting should not be empty")

torchvision/models/googlenet.py

@@ -28,6 +28,7 @@
 class GoogLeNet(nn.Module):
     __constants__ = ["aux_logits", "transform_input"]
 
+    @_log_api_usage_once
     def __init__(
         self,
         num_classes: int = 1000,
@@ -39,7 +40,6 @@ def __init__(
         dropout_aux: float = 0.7,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         if blocks is None:
             blocks = [BasicConv2d, Inception, InceptionAux]
         if init_weights is None:

torchvision/models/inception.py

@@ -27,6 +27,7 @@
 
 
 class Inception3(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         num_classes: int = 1000,
@@ -37,7 +38,6 @@ def __init__(
         dropout: float = 0.5,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         if inception_blocks is None:
             inception_blocks = [BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE, InceptionAux]
         if init_weights is None:

torchvision/models/mnasnet.py

@@ -96,9 +96,9 @@ class MNASNet(torch.nn.Module):
     # Version 2 adds depth scaling in the initial stages of the network.
     _version = 2
 
+    @_log_api_usage_once
     def __init__(self, alpha: float, num_classes: int = 1000, dropout: float = 0.2) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         assert alpha > 0.0
         self.alpha = alpha
         self.num_classes = num_classes

torchvision/models/mobilenetv2.py

@@ -86,6 +86,7 @@ def forward(self, x: Tensor) -> Tensor:
 
 
 class MobileNetV2(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         num_classes: int = 1000,
@@ -111,7 +112,6 @@ def __init__(
 
         """
         super().__init__()
-        _log_api_usage_once(self)
 
         if block is None:
             block = InvertedResidual

torchvision/models/mobilenetv3.py

@@ -129,6 +129,7 @@ def forward(self, input: Tensor) -> Tensor:
 
 
 class MobileNetV3(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         inverted_residual_setting: List[InvertedResidualConfig],
@@ -151,7 +152,6 @@ def __init__(
             dropout (float): The droupout probability
         """
         super().__init__()
-        _log_api_usage_once(self)
 
         if not inverted_residual_setting:
             raise ValueError("The inverted_residual_setting should not be empty")

torchvision/models/regnet.py

@@ -299,6 +299,7 @@ def _adjust_widths_groups_compatibilty(
 
 
 class RegNet(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         block_params: BlockParams,
@@ -310,7 +311,6 @@ def __init__(
         activation: Optional[Callable[..., nn.Module]] = None,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
 
         if stem_type is None:
             stem_type = SimpleStemIN

torchvision/models/resnet.py

@@ -162,6 +162,7 @@ def forward(self, x: Tensor) -> Tensor:
 
 
 class ResNet(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         block: Type[Union[BasicBlock, Bottleneck]],
@@ -174,7 +175,6 @@ def __init__(
         norm_layer: Optional[Callable[..., nn.Module]] = None,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         self._norm_layer = norm_layer

torchvision/models/segmentation/_utils.py

@@ -11,9 +11,9 @@
 class _SimpleSegmentationModel(nn.Module):
     __constants__ = ["aux_classifier"]
 
+    @_log_api_usage_once
     def __init__(self, backbone: nn.Module, classifier: nn.Module, aux_classifier: Optional[nn.Module] = None) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         self.backbone = backbone
         self.classifier = classifier
         self.aux_classifier = aux_classifier

torchvision/models/segmentation/lraspp.py

@@ -34,11 +34,11 @@ class LRASPP(nn.Module):
         inter_channels (int, optional): the number of channels for intermediate computations.
     """
 
+    @_log_api_usage_once
     def __init__(
         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)
         self.backbone = backbone
         self.classifier = LRASPPHead(low_channels, high_channels, num_classes, inter_channels)
 

torchvision/models/shufflenetv2.py

@@ -92,6 +92,7 @@ def forward(self, x: Tensor) -> Tensor:
 
 
 class ShuffleNetV2(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         stages_repeats: List[int],
@@ -100,7 +101,6 @@ def __init__(
         inverted_residual: Callable[..., nn.Module] = InvertedResidual,
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
 
         if len(stages_repeats) != 3:
             raise ValueError("expected stages_repeats as list of 3 positive ints")

torchvision/models/squeezenet.py

@@ -34,9 +34,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class SqueezeNet(nn.Module):
+    @_log_api_usage_once
     def __init__(self, version: str = "1_0", num_classes: int = 1000, dropout: float = 0.5) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         self.num_classes = num_classes
         if version == "1_0":
             self.features = nn.Sequential(

torchvision/models/vgg.py

@@ -33,11 +33,11 @@
 
 
 class VGG(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self, features: nn.Module, num_classes: int = 1000, init_weights: bool = True, dropout: float = 0.5
     ) -> None:
         super().__init__()
-        _log_api_usage_once(self)
         self.features = features
         self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
         self.classifier = nn.Sequential(

torchvision/models/video/resnet.py

@@ -188,6 +188,7 @@ def __init__(self) -> None:
 
 
 class VideoResNet(nn.Module):
+    @_log_api_usage_once
     def __init__(
         self,
         block: Type[Union[BasicBlock, Bottleneck]],
@@ -209,7 +210,6 @@ def __init__(
             zero_init_residual (bool, optional): Zero init bottleneck residual BN. Defaults to False.
         """
         super().__init__()
-        _log_api_usage_once(self)
         self.inplanes = 64
 
         self.stem = stem()