pytorch-VGG网络

VGG网络结构 

第一层: 3x3x3x64, 步长为1, padding=1 

第二层: 3x3x64x64, 步长为1, padding=1 

第三层: 3x3x64x128, 步长为1, padding=1

第四层: 3x3x128x128, 步长为1, padding=1

第五层: 3x3x128x256, 步长为1, padding=1

第六层: 3x3x256x256, 步长为1, padding=1

第七层: 3x3x256x256, 步长为1, padding=1

第八层: 3x3x256x512, 步长为1, padding=1 

第九层: 3x3x512x512, 步长为1, padding=1 

第十层:3x3x512x512, 步长为1, padding=1 

第十一层: 3x3x512x512, 步长为1, padding=1 

第十二层: 3x3x512x512, 步长为1, padding=1 

第十三层:3x3x512x512, 步长为1, padding=1 

第十四层: 512*7*7, 4096的全连接操作

第十五层: 4096, 4096的全连接操作

第十六层: 4096, num_classes 的 全连接操作

import torch
from torch import nn

class VGG(nn.Module):
    def __init__(self, num_classes):
        super(VGG, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),)
        self.classifier = nn.Sequential(
            nn.Linear(512*7*7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)

        return x