# 构建一个AlexNet模型

# 1.导入依赖包
import torch.nn as nn


# 2.  1个继承  2个方法
# 2.1 定义类，继承nn.Module
class AlexNet_Model(nn.Module):
    # 2.2 定义构造方法__init__
    def __init__(self, num_classes):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(3, 96, 11, 4, 0),
            nn.ReLU(inplace=True),  # inplace=True 内存复用
            nn.MaxPool2d(3, 2),

            nn.Conv2d(96, 256, 5, 1, 2),
            nn.ReLU(inplace=True),  # inplace=True 内存复用
            nn.MaxPool2d(3, 2),

            nn.Conv2d(256, 384, 3, 1, 1),
            nn.ReLU(inplace=True),  # inplace=True 内存复用
            nn.Conv2d(384, 384, 3, 1, 1),
            nn.ReLU(inplace=True),  # inplace=True 内存复用
            nn.Conv2d(384, 256, 3, 1, 1),
            nn.ReLU(inplace=True),  # inplace=True 内存复用
            nn.MaxPool2d(3, 2),
        )
        self.fc = nn.Sequential(
            nn.Linear(9216, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),

            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),

            nn.Linear(4096, num_classes)
        )

    # 2.3 定义前向传播方法forward
    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


if __name__ == '__main__':
    # 查看模型结构 torchsummary--summary
    from torchsummary import summary

    # 1.实例化模型
    model = AlexNet_Model(num_classes=1000)

    # 2.查看模型结构
    summary(model, input_size=(3, 224, 224), device='cpu')   # 如果输入224*224    第一个线性层输入6400