resnet18简单实现

import torch from torch import nn from torch.nn import functional as F class ResBlk(nn.Module): expansion = 1 def __init__(self, ch_in, ch_out, stride): super(ResBlk, self).__init__() self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1) self.bn1 = nn.BatchNorm2d(ch_out) self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1) self.bn2 = nn.BatchNorm2d(ch_out) self.extra = nn.Sequential() if ch_in != ch_out: self.extra = nn.Sequential( nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride), nn.BatchNorm2d(ch_out) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out = self.extra(x) + out out = F.relu(out) return out class ResNet(nn.Module): def __init__(self, block, num_blocks, num_classes=1000): super(ResNet, self).__init__() self.in_planes = 64 # 初始卷积层 self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) # 四个残差层 self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) # 全连接层 self.linear = nn.Linear(512 * block.expansion, num_classes) # 创建一个残差层 def _make_layer(self, block, planes, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.max_pool2d(out, kernel_size=3, stride=2, padding=1) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) #out = F.avg_pool2d(out, 4) out = F.adaptive_avg_pool2d(out, [1, 1]) out = out.view(out.size(0), -1) out = self.linear(out) return out def ResNet18(): return ResNet(ResBlk, [2, 2, 2, 2], 10) if __name__ == '__main__': model = ResNet18() print(model)