class ResNet(nn.Module): ?def __init__(self, block, layers, num_classes=1000): ???self.inplanes = 64 ???super(ResNet, self).__init__() ???self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, ????????????????bias=False) ???self.bn1 = nn.BatchNorm2d(64) ???self.relu = nn.ReLU(inplace=True) ???self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True) # change ??第一次pooling ???self.layer1 = self._make_layer(block, 64, layers[0]) ???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) ???# it is slightly better whereas slower to set stride = 1 ???# self.layer4 = self._make_layer(block, 512, layers[3], stride=1) ???self.avgpool = nn.AvgPool2d(7) ???self.fc = nn.Linear(512 * block.expansion, num_classes) ???for m in self.modules(): ?????if isinstance(m, nn.Conv2d): ???????n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels ???????m.weight.data.normal_(0, math.sqrt(2. / n)) ?????elif isinstance(m, nn.BatchNorm2d): ???????m.weight.data.fill_(1) ???????m.bias.data.zero_() ?def ?_make_layer(self, block, planes, blocks, stride=1): ???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), ???????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): ?????layers.append(block(self.inplanes, planes)) ???return nn.Sequential(*layers) ?def forward(self, x): ???x = self.conv1(x) ???x = self.bn1(x) ???x = self.relu(x) ???x = self.maxpool(x) ????????????????????x = self.layer1(x) ???x = self.layer2(x) ???x = self.layer3(x) ???x = self.layer4(x) ???x = self.avgpool(x) ???x = x.view(x.size(0), -1) ???x = self.fc(x) ???return x
pytroch resnet构建过程理解
原文地址：https://www.cnblogs.com/ya-cpp/p/9648569.html