【Classical Network】ResNet-50/101/152

与ResNet18 和 34不一样的是,50,101和152使用了bottleneck结构而不是basic block。在bottleneck中,对channel进行了缩放。

具体的图解可以参考ResNet50网络结构图及结构详解,在这篇文章中,BTNK1对应代码中 bottleneck downsampling = True的情况,BTNK2对应downsampling = False的情况。

import torch
import torch.nn as nn
import torchvision
import numpy as np
from torchsummary import summary
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

__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 = expansion
        self.downsampling = downsampling
        
        self.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 = x
        out = self.bottleneck(x)
        
        if self.downsampling:
            residual = self.downsample(x)
        
        out += residual
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, blocks, num_classes = 1000, expansion = 4):
        super(ResNet, self).__init__()
        self.expansion = expansion
        
        self.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(in_places=places*self.expansion, places=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 x
    
def 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__':
    device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
    model = ResNet50()
    model = model.to(device)
    summary(model, (3,224,224))
    input = torch.randn(1,3,224,224).cuda()
    out = model(input)
    print(out.shape)

使用torchsummary打印网络,对于ResNet-50:

================================================================
Total params: 25,557,032
Trainable params: 25,557,032
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 286.56
Params size (MB): 97.49
Estimated Total Size (MB): 384.62
----------------------------------------------------------------

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