AlexNet-CIFAR10/Classification-noisy/scripts/model.py

'''
AlexNet in Pytorch
'''

import torch
import torch.nn as nn

class AlexNet(nn.Module):
    '''
    AlexNet模型
    '''
    def __init__(self,num_classes=10):
        super(AlexNet,self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=1),   # (32-3+2)/1+1 = 32
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (32-2)/2+1 = 16
        )
        self.conv2 = nn.Sequential(  # 输入 16 * 16 * 6
            nn.Conv2d(in_channels=6, out_channels=16, kernel_size=3, stride=1, padding=1),  # (16-3+2)/1+1 = 16
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (16-2)/2+1 = 8
        )
        self.conv3 = nn.Sequential(  # 输入 8 * 8 * 16
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1), # (8-3+2)/1+1 = 8
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (8-2)/2+1 = 4
        )
        self.conv4 = nn.Sequential(  # 输入 4 * 4 * 64
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1), # (4-3+2)/1+1 = 4
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (4-2)/2+1 = 2
        )
        self.conv5 = nn.Sequential(  # 输入 2 * 2 * 128
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),# (2-3+2)/1+1 = 2
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (2-2)/2+1 = 1
        )                            # 最后一层卷积层，输出 1 * 1 * 128
        # 全连接层
        self.dense = nn.Sequential(
            nn.Linear(128,120),
            nn.ReLU(),
            nn.Linear(120,84),
            nn.ReLU(),
            nn.Linear(84,num_classes)
        )

        # 初始化权重
        self._initialize_weights()

    def forward(self,x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = x.view(x.size()[0],-1)
        x = self.dense(x)
        return x
         
    def feature(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        return x

    def prediction(self, x):
        x = self.conv4(x)
        x = self.conv5(x)
        x = x.view(x.size()[0],-1)
        x = self.dense(x)
        return x
        
    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
    
def test():
    net = AlexNet()
    x = torch.randn(2,3,32,32)
    y = net(x)
    print(y.size())
    from torchinfo import summary
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    net = net.to(device)
    summary(net,(3,32,32))