Changing to AdaptiveAvgPool2d on VGG (#747)

The update allows VGG to process images larger or smaller than prescribed imagenet size using adaptive average pooling. Will be useful while finetuning or testing on different resolution images. Similar to #643 and #672. I did not include adaptive avg pool in features or classifier block so that these predefined blocks can be used as it is.

Author: ekagra-ranjan

torchvision/models/vgg.py

@@ -25,6 +25,7 @@ class VGG(nn.Module):
     def __init__(self, features, num_classes=1000, init_weights=True):
         super(VGG, self).__init__()
         self.features = features
+        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
         self.classifier = nn.Sequential(
             nn.Linear(512 * 7 * 7, 4096),
             nn.ReLU(True),
@@ -39,6 +40,7 @@ def __init__(self, features, num_classes=1000, init_weights=True):
 
     def forward(self, x):
         x = self.features(x)
+        x = self.avgpool(x)
         x = x.view(x.size(0), -1)
         x = self.classifier(x)
         return x