VGGNet.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''
@Project :Awesome-DL-Models 
@File    :VGGNet.py
@Author  :JackHCC
@Date    :2022/3/13 14:06 
@Desc    :

'''
import torch
import torch.nn as nn


class VGG(nn.Module):
    def __init__(self, num_classes=10):
        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(),
            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.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


input1 = torch.rand([5, 3, 224, 224])
model = VGG(10)
print(list(model.parameters()))
output1 = model(input1)
print(output1.shape)