resent代码详解

resnet中最中重要的就是代码残差块，

def forward(self,x)identity = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)if self.downsample is not None:identity = self.downsampleout += identityout = self.relu(out)return out

残差块结构:

左边的就叫做BasicBlock,右边就叫bottleneck

BasicBlock

class BasicBlock(nn.Module):expansion =1def __init__(self, inplanes, planes, stride=1, downsample=None):super(BasicBlock,self).__init__()self.conv1 = conv3x3(inplanes,planes,stride)self.bn1 = nn.BatchNorm2d(planes)self.relu = nn.ReLU(inplace=True)self.conv2 = conv3x3(planes,planes)self.bn2 = nn.BatchNorm2d(planes)self.downsample = downsampleself.stride =stride
def forward(self,x)identity = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)if self.downsample is not None:identity = self.downsampleout += identityout = self.relu(out)return out

bottleneck

注意Res18、Res34用的是BasicBlock，其余用的是Bottleneck

resnet18： ResNet(BasicBlock, [2, 2, 2, 2])

resnet34： ResNet(BasicBlock, [3, 4, 6, 3])

resnet50：ResNet(Bottleneck, [3, 4, 6, 3])

resnet101:ResNet(Bottleneck, [3, 4, 23, 3])

resnet152:ResNet(Bottleneck, [3, 8, 36, 3])

expansion = 4，因为Bottleneck中每个残差结构输出维度都是输入维度的4倍

class Bottleneck(nn.Module):
expansion = 4

def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

def forward(self, x):
identity = x

out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)

out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)

out = self.conv3(out)
out = self.bn3(out)

if self.downsample is not None:
identity = self.downsample(x)

out += identity
out = self.relu(out)

return out

ResNet类

几个关键点：

1.在残差结构之前，先对原始224 x 224的图片处理，在经过7 x 7的大卷积核、BN、ReLU、最大池化之后得到56 x 56 x 64的feature map
2.从layer1、layer2、layer3、layer4的定义可以看出，第一个stage不会减小feature map，其余都会在stage的第一层用步长2的3 x 3卷积进行feature map长和宽减半
3._make_layer函数中downsample对残差结构的输入进行升维，直接1 x 1卷积再加上BN即可，后面BasicBlock类和Bottleneck类用得到
4.最后的池化层使用的是自适应平均池化，而非论文中的全局平均池化

class ResNet(nn.Module):

def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
        super(ResNet, self).__init__()
        self.inplanes = 64
        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=1)
        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)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion),
            )

layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ 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