VGG16网络结构图及pytorch 代码实现

1.网络结构图及对应输出结果

2.pytorch代码实现

import torch.nn as nn from torchsummary import summary import torch class VGG16(nn.Module): def __init__(self): super(VGG16, self).__init__() self.maxpool1 = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(64, 64, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2) ) self.maxpool2 = nn.Sequential( nn.Conv2d(64, 128, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(128, 128, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2) ) self.maxpool3 = nn.Sequential( nn.Conv2d(128, 256, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2) ) self.maxpool4 = nn.Sequential( nn.Conv2d(256, 512, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2) ) self.maxpool5= nn.Sequential( nn.Conv2d(512, 512, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3,stride=1, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2) ) self.dense = nn.Sequential( nn.Linear(512 * 5 * 5, 4096), nn.ReLU(), nn.Linear(4096, 4096), nn.ReLU(), nn.Linear(4096, 1000) ) def forward(self, x): pool1=self.maxpool1(x) pool2=self.maxpool2(pool1) pool3=self.maxpool3(pool2) pool4=self.maxpool4(pool3) pool5=self.maxpool5(pool4) flat = pool5.view(pool5.size(0), -1) class_ = self.dense(flat) return class_ if __name__ == "__main__": device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') vgg_model=VGG16().to(device) summary(vgg_model, (3,160, 160)) #打印网络结构

3.打印网络结构输出结果