利用Pytorch复现ResNet34网络

根据残差网络的官方论文可知：
ResNet34的计算图如下：

利用pytorch实现的代码如下：

import numpy as np
import torch
import math
# import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from torch.autograd import Variable


class Block(nn.Module):#残差块
    def __init__(self, in_channels, out_channels, stride):
        super(Block, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=3,stride=stride,padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace = True)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )
        self.relu = nn.ReLU(inplace = True)
        self.downsample = None
        if stride !=1:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_channels = in_channels,out_channels=out_channels,kernel_size=1,stride=stride),
                nn.BatchNorm2d(out_channels)
            )
        
    def forward(self,x):
        identity = x,
        x = self.conv1(x)
        x = self.conv2(x)
        if self.downsample is not None:
            identity = self.downsample(identity)
            print("identity",identity.size())
        x = x + identity
        x = self.relu(x)
        return(x)
        
        
class ResNet34(nn.Module):
    def __init__(self,num_classes):
        super(ResNet34,self).__init__()
        self.head = nn.Conv2d(in_channels=3,out_channels=64,kernel_size=7,padding=3,stride=2)
        self.bn   = nn.BatchNorm2d(num_features=64)
        self.relu = nn.ReLU(inplace=True)
        self.pool = nn.MaxPool2d(kernel_size =3,stride = 2,padding=1)
        self.group1 = self._make_group(block=Block, in_channels=64, out_channels=64, blocks=3, stride=1)
        self.group2 = self._make_group(block=Block, in_channels=64,out_channels=128,blocks=4, stride=1)
        self.group3 = self._make_group(block=Block, in_channels=128,out_channels=256,blocks=6,stride=1)
        self.group4 = self._make_group(block=Block, in_channels=256,out_channels=512,blocks=3,stride=1)
        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.classifier = nn.Linear(in_features=512, out_features=num_classes)
        self._initialize_weights()
        
    def forward(self, x):
        x = self.head(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.group1(x)
        x = self.group2(x)
        x = self.group3(x)
        x = self.group4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x
        
    def _make_group(self,block,in_channels,out_channels,blocks,stride):
        layers = []
        layers.append(block(in_channels=in_channels,out_channels=out_channels,stride=stride))
        stride = 1
        for i in range(blocks):
            layers.append(block(in_channels=out_channels, out_channels=out_channels, stride=stride))
            
        return nn.Sequential(*layers)
    
    def _initialize_weights(self):
        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))
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()