PyTorch实现的ResNet50、ResNet101和ResNet152
PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks

import torch
import torch.nn as nn
import torchvision
import numpy as npprint("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)__all__ = ['ResNet50', 'ResNet101','ResNet152']def Conv1(in_planes, places, stride=2):return nn.Sequential(nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False),nn.BatchNorm2d(places),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=3, stride=2, padding=1))class Bottleneck(nn.Module):def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 4):super(Bottleneck,self).__init__()self.expansion = expansionself.downsampling = downsamplingself.bottleneck = nn.Sequential(nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),nn.BatchNorm2d(places),nn.ReLU(inplace=True),nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),nn.BatchNorm2d(places),nn.ReLU(inplace=True),nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),nn.BatchNorm2d(places*self.expansion),)if self.downsampling:self.downsample = nn.Sequential(nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(places*self.expansion))self.relu = nn.ReLU(inplace=True)def forward(self, x):residual = xout = self.bottleneck(x)if self.downsampling:residual = self.downsample(x)out += residualout = self.relu(out)return outclass ResNet(nn.Module):def __init__(self,blocks, num_classes=1000, expansion = 4):super(ResNet,self).__init__()self.expansion = expansionself.conv1 = Conv1(in_planes = 3, places= 64)self.layer1 = self.make_layer(in_places = 64, places= 64, block=blocks[0], stride=1)self.layer2 = self.make_layer(in_places = 256,places=128, block=blocks[1], stride=2)self.layer3 = self.make_layer(in_places=512,places=256, block=blocks[2], stride=2)self.layer4 = self.make_layer(in_places=1024,places=512, block=blocks[3], stride=2)self.avgpool = nn.AvgPool2d(7, stride=1)self.fc = nn.Linear(2048,num_classes)for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)def make_layer(self, in_places, places, block, stride):layers = []layers.append(Bottleneck(in_places, places,stride, downsampling =True))for i in range(1, block):layers.append(Bottleneck(places*self.expansion, places))return nn.Sequential(*layers)def forward(self, x):x = self.conv1(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 xdef ResNet50():return ResNet([3, 4, 6, 3])def ResNet101():return ResNet([3, 4, 23, 3])def ResNet152():return ResNet([3, 8, 36, 3])if __name__=='__main__':#model = torchvision.models.resnet50()model = ResNet50()print(model)input = torch.randn(1, 3, 224, 224)out = model(input)print(out.shape)