Remove `input_size` argument from ShuffleNetV2 (#886)

* remove 'input_size' parameter from shufflenetv2

* Update shufflenetv2.py

torchvision/models/shufflenetv2.py

@@ -85,7 +85,7 @@ class InvertedResidual(nn.Module):
 
 
 class ShuffleNetV2(nn.Module):
-    def __init__(self, num_classes=1000, input_size=224, width_mult=1):
+    def __init__(self, num_classes=1000, width_mult=1):
         super(ShuffleNetV2, self).__init__()
 
         try:
@@ -145,8 +145,8 @@ class ShuffleNetV2(nn.Module):
         return stages[str(mult)]
 
 
-def shufflenetv2(pretrained=False, num_classes=1000, input_size=224, width_mult=1, **kwargs):
-    model = ShuffleNetV2(num_classes=num_classes, input_size=input_size, width_mult=width_mult)
+def shufflenetv2(pretrained=False, num_classes=1000, width_mult=1, **kwargs):
+    model = ShuffleNetV2(num_classes=num_classes, width_mult=width_mult)
 
     if pretrained:
         # change width_mult to float
@@ -164,17 +164,17 @@ def shufflenetv2(pretrained=False, num_classes=1000, input_size=224, width_mult=
     return model
 
 
-def shufflenetv2_x0_5(pretrained=False, num_classes=1000, input_size=224, **kwargs):
-    return shufflenetv2(pretrained, num_classes, input_size, 0.5)
+def shufflenetv2_x0_5(pretrained=False, num_classes=1000, **kwargs):
+    return shufflenetv2(pretrained, num_classes, 0.5)
 
 
-def shufflenetv2_x1_0(pretrained=False, num_classes=1000, input_size=224, **kwargs):
-    return shufflenetv2(pretrained, num_classes, input_size, 1)
+def shufflenetv2_x1_0(pretrained=False, num_classes=1000, **kwargs):
+    return shufflenetv2(pretrained, num_classes, 1)
 
 
-def shufflenetv2_x1_5(pretrained=False, num_classes=1000, input_size=224, **kwargs):
-    return shufflenetv2(pretrained, num_classes, input_size, 1.5)
+def shufflenetv2_x1_5(pretrained=False, num_classes=1000, **kwargs):
+    return shufflenetv2(pretrained, num_classes, 1.5)
 
 
-def shufflenetv2_x2_0(pretrained=False, num_classes=1000, input_size=224, **kwargs):
-    return shufflenetv2(pretrained, num_classes, input_size, 2)
+def shufflenetv2_x2_0(pretrained=False, num_classes=1000, **kwargs):
+    return shufflenetv2(pretrained, num_classes, 2)