Fixing docs.

Author: datumbox

torchvision/models/quantization/googlenet.py

@@ -153,7 +153,7 @@ def googlenet(
     GPU inference is not yet supported
 
     Args:
-        pretrained (GoogLeNet_QuantizedWeights or GoogLeNet_Weights, optional): The pretrained
+        weights (GoogLeNet_QuantizedWeights or GoogLeNet_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize (bool): If True, return a quantized version of the model

torchvision/models/quantization/inception.py

@@ -223,7 +223,7 @@ def inception_v3(
     GPU inference is not yet supported
 
     Args:
-        pretrained (Inception_V3_QuantizedWeights or Inception_V3_Weights, optional): The pretrained
+        weights (Inception_V3_QuantizedWeights or Inception_V3_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize (bool): If True, return a quantized version of the model

torchvision/models/quantization/mobilenetv2.py

@@ -113,7 +113,7 @@ def mobilenet_v2(
     GPU inference is not yet supported
 
     Args:
-        pretrained (GoogLeNet_QuantizedWeights or GoogLeNet_Weights, optional): The pretrained
+        weights (GoogLeNet_QuantizedWeights or GoogLeNet_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize(bool): If True, returns a quantized model, else returns a float model

torchvision/models/quantization/mobilenetv3.py

@@ -202,7 +202,7 @@ def mobilenet_v3_large(
     GPU inference is not yet supported
 
     Args:
-        pretrained (MobileNet_V3_Large_QuantizedWeights or MobileNet_V3_Large_Weights, optional): The pretrained
+        weights (MobileNet_V3_Large_QuantizedWeights or MobileNet_V3_Large_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize (bool): If True, returns a quantized model, else returns a float model

torchvision/models/quantization/resnet.py

@@ -254,7 +254,7 @@ def resnet18(
     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
 
     Args:
-        pretrained (ResNet18_QuantizedWeights or ResNet18_Weights, optional): The pretrained
+        weights (ResNet18_QuantizedWeights or ResNet18_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize (bool): If True, return a quantized version of the model
@@ -283,7 +283,7 @@ def resnet50(
     `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
 
     Args:
-        pretrained (ResNet50_QuantizedWeights or ResNet50_Weights, optional): The pretrained
+        weights (ResNet50_QuantizedWeights or ResNet50_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize (bool): If True, return a quantized version of the model
@@ -312,7 +312,7 @@ def resnext101_32x8d(
     `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
 
     Args:
-        pretrained (ResNeXt101_32X8D_QuantizedWeights or ResNeXt101_32X8D_Weights, optional): The pretrained
+        weights (ResNeXt101_32X8D_QuantizedWeights or ResNeXt101_32X8D_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize (bool): If True, return a quantized version of the model

torchvision/models/quantization/shufflenetv2.py

@@ -166,7 +166,7 @@ def shufflenet_v2_x0_5(
     <https://arxiv.org/abs/1807.11164>`_.
 
     Args:
-        pretrained (ShuffleNet_V2_X0_5_QuantizedWeights or ShuffleNet_V2_X0_5_Weights, optional): The pretrained
+        weights (ShuffleNet_V2_X0_5_QuantizedWeights or ShuffleNet_V2_X0_5_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize (bool): If True, return a quantized version of the model
@@ -198,7 +198,7 @@ def shufflenet_v2_x1_0(
     <https://arxiv.org/abs/1807.11164>`_.
 
     Args:
-        pretrained (ShuffleNet_V2_X1_0_QuantizedWeights or ShuffleNet_V2_X1_0_Weights, optional): The pretrained
+        weights (ShuffleNet_V2_X1_0_QuantizedWeights or ShuffleNet_V2_X1_0_Weights, optional): The pretrained
             weights for the model
         progress (bool): If True, displays a progress bar of the download to stderr
         quantize (bool): If True, return a quantized version of the model