VGG16

import torch
import torch.nn as nn
import torchvision

def Conv3x3BNReLU(in_channels,out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=3,stride=1,padding=1),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )

class VGG(nn.Module):
    def __init__(self, block_nums,num_classes=1000):
        super(VGG, self).__init__()

        self.stage1 = self._make_layers(in_channels=3, out_channels=64, block_num=block_nums[0])
        self.stage2 = self._make_layers(in_channels=64, out_channels=128, block_num=block_nums[1])
        self.stage3 = self._make_layers(in_channels=128, out_channels=256, block_num=block_nums[2])
        self.stage4 = self._make_layers(in_channels=256, out_channels=512, block_num=block_nums[3])
        self.stage5 = self._make_layers(in_channels=512, out_channels=512, block_num=block_nums[4])

        self.classifier = nn.Sequential(
            nn.Linear(in_features=512*7*7,out_features=4096),
            nn.Dropout(p=0.2),
            nn.Linear(in_features=4096, out_features=4096),
            nn.Dropout(p=0.2),
            nn.Linear(in_features=4096, out_features=num_classes)
        )

        self._init_params()

    def _make_layers(self, in_channels, out_channels, block_num):
        layers = []
        layers.append(Conv3x3BNReLU(in_channels,out_channels))
        for i in range(1,block_num):
            layers.append(Conv3x3BNReLU(out_channels,out_channels))
        layers.append(nn.MaxPool2d(kernel_size=2,stride=2, ceil_mode=False))
        return nn.Sequential(*layers)

    def _init_params(self):
        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 forward(self, x):
        x = self.stage1(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.stage5(x)
        x = x.view(x.size(0),-1)
        out = self.classifier(x)
        return out

def VGG16():
    block_nums = [2, 2, 3, 3, 3]
    model = VGG(block_nums)
    return model

def VGG19():
    block_nums = [2, 2, 4, 4, 4]
    model = VGG(block_nums)
    return model

if __name__ == '__main__':
    model = VGG16()
    print(model)
    torchvision.models.vgg16_bn()

    input = torch.randn(1,3,224,224)
    out = model(input)
    print(out.shape)

标签：layers,nn,VGG16,self,channels,block,out
来源： https://blog.csdn.net/hhhhhhhhhhwwwwwwwwww/article/details/121573973