import torch.nn as nn
import torch
import torch
import torch.nn as nn
import torch.nn.functional as F

class SE_block(nn.Module):
    def __init__(self, inchannel, ratio = 16):#压缩比默认16
        super(SE_block, self).__init__()
        #全局平均池化
        self.gap = nn.AdaptiveAvgPool2d((1,1))
        #两个全连接层
        self.fc = nn.Sequential(
            nn.Linear(inchannel, inchannel // ratio, bias = False),
            nn.ReLU(),
            nn.Linear(inchannel // ratio, inchannel, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b ,c ,h ,w =x.size()#读取数据图片数量和通道数
        #print(b, c, h, w) (32, 128, 27, 27)
        y = self.gap(x).view(b ,c)#经过池化后输出b*c的矩阵
        y =self.fc(y).view(b ,c, 1, 1)#经过全连接层输出（b，c，1，1）矩阵

        return x * y.expand_as(x)#将得到的权重*原来的特征图x

class ImprovedAlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(ImprovedAlexNet, self).__init__()
        self.features = nn.Sequential(  # 卷积层提取图像特征
            nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224]  output[48, 55, 55]
            nn.ReLU(inplace=True),  # 直接修改覆盖原值，节省运算内存
            nn.MaxPool2d(kernel_size=3, stride=2),  # output[48, 27, 27]
            SE_block(48),
            nn.Conv2d(48, 128, kernel_size=5, padding=2),  # output[128, 27, 27]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),  # output[128, 13, 13]
            nn.Conv2d(128, 192, kernel_size=3, padding=1),  # output[192, 13, 13]
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 192, kernel_size=3, padding=1),  # output[192, 13, 13]
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 128, kernel_size=3, padding=1),  # output[128, 13, 13]
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),  # output[128, 6, 6]
        )
        self.classifier = nn.Sequential(  # 全连接层对图像分类
            nn.Dropout(p=0.5),  # Dropout 随机失活神经元，默认比例为0.5
            nn.Linear(128 * 6 * 6, 2048),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Linear(2048, 2048),
            nn.ReLU(inplace=True),
            nn.Linear(2048, num_classes),
        )

    def forward(self, x):
        x = self.features(x)#进行卷积操作
        x = torch.flatten(x, start_dim=1)#展平
        x = self.classifier(x)#输出
        return x