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import torch.nn as nn
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import torch
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class SE_block(nn.Module):
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def __init__(self, inchannel, ratio = 16):#压缩比默认16
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super(SE_block, self).__init__()
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#全局平均池化
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self.gap = nn.AdaptiveAvgPool2d((1,1))
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#两个全连接层
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self.fc = nn.Sequential(
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nn.Linear(inchannel, inchannel // ratio, bias = False),
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nn.ReLU(),
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nn.Linear(inchannel // ratio, inchannel, bias=False),
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nn.Sigmoid()
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)
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def forward(self, x):
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b ,c ,h ,w =x.size()#读取数据图片数量和通道数
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#print(b, c, h, w) (32, 128, 27, 27)
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y = self.gap(x).view(b ,c)#经过池化后输出b*c的矩阵
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y =self.fc(y).view(b ,c, 1, 1)#经过全连接层输出（b，c，1，1）矩阵
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return x * y.expand_as(x)#将得到的权重*原来的特征图x
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class ImprovedAlexNet(nn.Module):
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def __init__(self, num_classes=1000):
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super(ImprovedAlexNet, self).__init__()
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self.features = nn.Sequential( # 卷积层提取图像特征
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nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2), # input[3, 224, 224] output[48, 55, 55]
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nn.ReLU(inplace=True), # 直接修改覆盖原值，节省运算内存
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nn.MaxPool2d(kernel_size=3, stride=2), # output[48, 27, 27]
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SE_block(48),
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nn.Conv2d(48, 128, kernel_size=5, padding=2), # output[128, 27, 27]
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2), # output[128, 13, 13]
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nn.Conv2d(128, 192, kernel_size=3, padding=1), # output[192, 13, 13]
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nn.ReLU(inplace=True),
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nn.Conv2d(192, 192, kernel_size=3, padding=1), # output[192, 13, 13]
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nn.ReLU(inplace=True),
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nn.Conv2d(192, 128, kernel_size=3, padding=1), # output[128, 13, 13]
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2), # output[128, 6, 6]
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)
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self.classifier = nn.Sequential( # 全连接层对图像分类
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nn.Dropout(p=0.5), # Dropout 随机失活神经元，默认比例为0.5
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nn.Linear(128 * 6 * 6, 2048),
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nn.ReLU(inplace=True),
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nn.Dropout(p=0.5),
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nn.Linear(2048, 2048),
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nn.ReLU(inplace=True),
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nn.Linear(2048, num_classes),
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)
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def forward(self, x):
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x = self.features(x)#进行卷积操作
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x = torch.flatten(x, start_dim=1)#展平
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x = self.classifier(x)#输出
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return x
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