pytorch | Resnet

import torch.nn as nn
import torch


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
        """
        18、34层基础残差模块
        :param in_channel: 输入通道
        :param out_channel: 输出通道
        :param stride:  步长
        :param downsample:  下采样函数，如果输入和输出维度不一致，则进行降维、改变通道数等操作，如原论文中conv3_1、conv4_1、conv5_1处，
                            应用于原论文网络结构中虚线残差模块
        :param kwargs:
        """

        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
                               kernel_size=3, stride=stride, padding=1, bias=False)

        self.bn1 = nn.BatchNorm2d(out_channel)  # 对上一次卷积后结果做batch归一化，参数为输入的channel
        self.relu = nn.ReLU()

        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channel)
        self.downsample = downsample

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


class BottleNeck(nn.Module):
    """
        50、101、152 BottleNeck式残差模块
    """
    expansion = 4

    def __init__(self, in_channel, out_channel, stride=1, downsample=None, groups=1, width_per_group=64, **kwargs):
        super(BottleNeck, self).__init__()

        width = int(out_channel * (width_per_group / 64.)) * groups

        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, kernel_size=1, stride=1, bias=False)

        self.bn1 = nn.BatchNorm2d(width)

        self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups, kernel_size=3, stride=stride,
                               bias=False)

        self.bn2 = nn.BatchNorm2d(width)

        self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel * self.expansion, kernel_size=1, stride=1,
                               bias=False)
        self.bn3 = nn.BatchNorm2d(out_channel * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

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

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

            return out


class Resnet(nn.Module):
    def __init__(self, block, blocks_num, num_classes=1000, include_top=True,
                 groups=1, width_per_group=64):
        super(Resnet, self).__init__()
        self.include_top = include_top
        self.in_channel = 64

        self.groups = groups
        self.width_per_group = width_per_group

        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
                               padding=3, bias=False)

        self.bn1 = nn.BatchNorm2d(self.in_channel)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 64, blocks_num[0])
        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)

        if self.include_top:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
            self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')

    def _make_layer(self, block, channel, block_num, stride=1):
        downsample = None
        if stride != 1 or self.in_channel != channel * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(channel * block.expansion))
        layers = []
        layers.append(block(self.in_channel,
                            channel,
                            downsample=downsample,
                            stride=stride,
                            groups=self.groups,
                            width_per_group=self.width_per_group))

        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)

        if self.include_top:
            x = self.avgpool(x)  # 平均池化层  [N,C,1,1]
            x = torch.flatten(x, 1)  # (input,1)  它把第 1 个维度到最后一个维度全部推平合并 得到 [N, C]
            x = self.fc(x)

        return x

def resnet34(num_classes=1000, include_top=True):
    return Resnet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)

def resnet50(num_classes=1000, include_top=True):
    return Resnet(BottleNeck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)


def resnet101(num_classes=1000, include_top=True):
    return Resnet(BottleNeck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)