Resnet Consistency changes (#656)

* use predefined function

* cleanup resnet

* fix stride

* revert inplanes since it isnt constant

* lint

torchvision/models/resnet.py

@@ -21,6 +21,11 @@ def conv3x3(in_planes, out_planes, stride=1):
                      padding=1, bias=False)
 
 
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
 class BasicBlock(nn.Module):
     expansion = 1
 
@@ -58,12 +63,11 @@ class Bottleneck(nn.Module):
 
     def __init__(self, inplanes, planes, stride=1, downsample=None):
         super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.conv1 = conv1x1(inplanes, planes)
         self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=False)
+        self.conv2 = conv3x3(planes, planes, stride)
         self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
+        self.conv3 = conv1x1(planes, planes * self.expansion)
         self.bn3 = nn.BatchNorm2d(planes * self.expansion)
         self.relu = nn.ReLU(inplace=True)
         self.downsample = downsample
@@ -120,15 +124,14 @@ class ResNet(nn.Module):
         downsample = None
         if stride != 1 or self.inplanes != planes * block.expansion:
             downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
+                conv1x1(self.inplanes, planes * block.expansion, stride),
                 nn.BatchNorm2d(planes * block.expansion),
             )
 
         layers = []
         layers.append(block(self.inplanes, planes, stride, downsample))
         self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
+        for _ in range(1, blocks):
             layers.append(block(self.inplanes, planes))
 
         return nn.Sequential(*layers)