Add comments regarding downsampling layers of resnet (#794)

In reference to #729 added comments to clarify the naming and action of the layers performing downsampling in resnets.

torchvision/models/resnet.py

@@ -31,6 +31,7 @@ class BasicBlock(nn.Module):
 
     def __init__(self, inplanes, planes, stride=1, downsample=None):
         super(BasicBlock, self).__init__()
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv3x3(inplanes, planes, stride)
         self.bn1 = nn.BatchNorm2d(planes)
         self.relu = nn.ReLU(inplace=True)
@@ -63,6 +64,7 @@ class Bottleneck(nn.Module):
 
     def __init__(self, inplanes, planes, stride=1, downsample=None):
         super(Bottleneck, self).__init__()
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv1x1(inplanes, planes)
         self.bn1 = nn.BatchNorm2d(planes)
         self.conv2 = conv3x3(planes, planes, stride)