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resnet.py
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resnet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
class ResidualBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = conv3x3(in_channels, out_channels, stride)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(out_channels, out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
self.shortcut = nn.Sequential()
if stride!=1 or in_channels != self.expansion * out_channels:
self.shortcut = nn.Sequential(
conv3x3(in_channels, self.expansion * out_channels, stride),
nn.BatchNorm2d(self.expansion*out_channels))
def forward(self, x):
out = self.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv = conv3x3(3, 64, 1)
self.bn = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 64, layers[0], stride=1)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.fc = nn.Linear(512*block.expansion, num_classes)
def _make_layer(self, block, out_channels, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = self.relu(self.bn(self.conv(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
def resnet():
return ResNet(ResidualBlock, [2, 2, 2, 2])