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import torch.nn as nn | |
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): | |
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 | |
def forward(self, x): | |
residual = x | |
out = self.conv1(x) | |
out = self.bn1(out) | |
out = self.relu(out) | |
out = self.conv2(out) | |
out = self.bn2(out) | |
if self.downsample: | |
residual = self.downsample(x) | |
out += residual | |
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 = 16 | |
self.conv = conv3x3(3, 16) | |
self.bn = nn.BatchNorm2d(16) | |
self.relu = nn.ReLU(inplace=True) | |
self.layer1 = self.make_layer(block, 16, layers[0]) | |
self.layer2 = self.make_layer(block, 32, layers[1], 2) | |
self.layer3 = self.make_layer(block, 64, layers[2], 2) | |
self.avg_pool = nn.AvgPool2d(8) | |
self.fc = nn.Linear(64, num_classes) | |
def make_layer(self, block, out_channels, blocks, stride=1): | |
downsample = None | |
if (stride != 1) or (self.in_channels != out_channels): | |
downsample = nn.Sequential( | |
conv3x3(self.in_channels, out_channels, stride=stride), | |
nn.BatchNorm2d(out_channels), | |
) | |
layers = [] | |
layers.append( | |
block(self.in_channels, out_channels, stride, downsample) | |
) | |
self.in_channels = out_channels | |
for i in range(1, blocks): | |
layers.append(block(out_channels, out_channels)) | |
return nn.Sequential(*layers) | |
def forward(self, x): | |
out = self.conv(x) | |
out = self.bn(out) | |
out = self.relu(out) | |
out = self.layer1(out) | |
out = self.layer2(out) | |
out = self.layer3(out) | |
out = self.avg_pool(out) | |
out = out.view(out.size(0), -1) | |
out = self.fc(out) | |
return out | |