深度学习之目标检测backone篇——ResNet网络（50,101,152）_resnet50、resnet101

• 关于ResNet的实现

• 通用框架的实现

import torch
from torch import Tensor
import torch.nn.functional as F
from torch import nn


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channel, out_channel, stride=1, downsample=None, norm_layer=None):
        super(Bottleneck, self).__init__()

        if norm_layer is None:
            norm_layer = nn.BatchNorm2d

        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
                               kernel_size=(1,1), stride=(1,1), bias=False)  # squeeze channels
        self.bn1 = norm_layer(out_channel)

        # -----------------------------------------
        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
                               kernel_size=(3,3), stride=(stride,stride), bias=False, padding=(1,1))
        self.bn2 = norm_layer(out_channel)
        # -----------------------------------------
        self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel * self.expansion,
                               kernel_size=(1,1), stride=(1,1), bias=False)  # unsqueeze channels
        self.bn3 = norm_layer(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, norm_layer=None):
        '''
        :param block:块
        :param blocks_num:块数
        :param num_classes: 分类数
        :param include_top:
        :param norm_layer: BN
        '''
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.include_top = include_top
        self.in_channel = 64

        self.conv1 = nn.Conv2d(in_channels=3, out_channels=self.in_channel, kernel_size=(7,7), stride=(2,2),
                               padding=(3,3), bias=False)
        self.bn1 = norm_layer(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)

        '''
        初始化
        '''
        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):
        norm_layer = self._norm_layer
        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=(1,1), stride=(stride,stride), bias=False),
                norm_layer(channel * block.expansion))

        layers = []
        layers.append(block(self.in_channel, channel, downsample=downsample,
                            stride=stride, norm_layer=norm_layer))
        self.in_channel = channel * block.expansion

        for _ in range(1, block_num):
            layers.append(block(self.in_channel, channel, norm_layer=norm_layer))

        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)
            x = torch.flatten(x, 1)
            x = self.fc(x)

        return x

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• 通过传入超参数的不同实现不同的ResNet结构
  

• 不同的传入参数结构

• resnet50 [3,4,6,3]

• resnet101 [3,4,23,3]

• resnet152 [3,8,36,3]