1.網絡結構圖及對應輸出結果

2.pytorch代碼實現

import torch.nn as nn from torchsummary import summary import torchclass 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)) #打印網絡結構

3.打印網絡結構輸出結果

總結

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