Add pre-activation ResNet

models/resnet.py

@@ -1,4 +1,12 @@
-'''ResNet18/34/50/101/152 in Pytorch.'''
+'''ResNet in Pytorch.
+
+BasicBlock and Bottleneck module is from the original ResNet paper:
+    "Deep Residual Learning for Image Recognition", CVPR2016.
+
+PreActBlock and PreActBottleneck module is from the later paper:
+    "Identity Mappings in Deep Residual Networks", ECCV2016.
+which is considered better than the original structure.
+'''
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -35,6 +43,31 @@ def forward(self, x):
         return out
 
 
+class PreActBlock(nn.Module):
+    '''Pre-activation version of the BasicBlock.'''
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1):
+        super(PreActBlock, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = conv3x3(in_planes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv2 = conv3x3(planes, planes)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion*planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion*planes)
+            )
+
+    def forward(self, x):
+        out = self.conv1(F.relu(self.bn1(x)))
+        out = self.conv2(F.relu(self.bn2(out)))
+        out += self.shortcut(x)
+        return out
+
+
 class Bottleneck(nn.Module):
     expansion = 4
 
@@ -63,6 +96,34 @@ def forward(self, x):
         return out
 
 
+class PreActBottleneck(nn.Module):
+    '''Pre-activation version of the original Bottleneck module.'''
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1):
+        super(PreActBottleneck, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion*planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion*planes)
+            )
+
+    def forward(self, x):
+        out = self.conv1(F.relu(self.bn1(x)))
+        out = self.conv2(F.relu(self.bn2(out)))
+        out = self.conv3(F.relu(self.bn3(out)))
+        out += self.shortcut(x)
+        return out
+
+
 class ResNet(nn.Module):
     def __init__(self, block, num_blocks, num_classes=10):
         super(ResNet, self).__init__()
@@ -97,7 +158,7 @@ def forward(self, x):
 
 
 def ResNet18():
-    return ResNet(BasicBlock, [2,2,2,2])
+    return ResNet(PreActBlock, [2,2,2,2])
 
 def ResNet34():
     return ResNet(BasicBlock, [3,4,6,3])
@@ -111,9 +172,10 @@ def ResNet101():
 def ResNet152():
     return ResNet(Bottleneck, [3,8,36,3])
 
-def test_resnet():
-    net = ResNet50()
+
+def test():
+    net = ResNet18()
     y = net(Variable(torch.randn(1,3,32,32)))
     print(y.size())
 
-# test_resnet()
+# test()