PyTorch实现的ResNet50、ResNet101和ResNet152

末蓝、 2023-03-12 09:21 48阅读 0赞

PyTorch实现的ResNet50、ResNet101和ResNet152  
PyTorch: [https://github.com/shanglianlm0525/PyTorch-Networks][https_github.com_shanglianlm0525_PyTorch-Networks]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODUyNjc2_size_16_color_FFFFFF_t_70]

import torch
    import torch.nn as nn
    import torchvision
    import numpy as np
    
    print("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 = 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(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 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__':
        #model = torchvision.models.resnet50()
        model = ResNet50()
        print(model)
    
        input = torch.randn(1, 3, 224, 224)
        out = model(input)
        print(out.shape)

[https_github.com_shanglianlm0525_PyTorch-Networks]: https://github.com/shanglianlm0525/PyTorch-Networks
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODUyNjc2_size_16_color_FFFFFF_t_70]: /images/20230312/8fabc183fc4a47cca9be7afbb12ea565.png