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#coding:utf8
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# Copyright 2019 longpeng2008. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# If you find any problem,please contact us
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#
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# longpeng2008to2012@gmail.com
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#
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# or create issues
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# =============================================================================
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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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import numpy as np
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## 3层卷积神经网络simpleconv3定义
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## 包括3个卷积层，3个BN层，3个ReLU激活层，3个全连接层
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class simpleconv3(nn.Module):
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## 初始化函数
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def __init__(self,nclass):
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super(simpleconv3,self).__init__()
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self.conv1 = nn.Conv2d(3, 12, 3, 2, 1) #输入图片大小为3*48*48，输出特征图大小为12*24*24，卷积核大小为3*3，步长为2
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self.bn1 = nn.BatchNorm2d(12)
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self.conv2 = nn.Conv2d(12, 24, 3, 2, 1) #输入图片大小为12*24*24，输出特征图大小为24*12*12，卷积核大小为3*3，步长为2
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self.bn2 = nn.BatchNorm2d(24)
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self.conv3 = nn.Conv2d(24, 48, 3, 2, 1) #输入图片大小为24*12*12，输出特征图大小为48*6*6，卷积核大小为3*3，步长为2
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self.bn3 = nn.BatchNorm2d(48)
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self.fc1 = nn.Linear(48 * 6 * 6 , 512) #输入向量长为48*6*6=1728，输出向量长为512
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self.fc2 = nn.Linear(512 , 128) #输入向量长为512，输出向量长为128
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self.fc3 = nn.Linear(128 , nclass) #输入向量长为128，输出向量长为nclass，等于类别数
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## 前向函数
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def forward(self, x):
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## relu函数，不需要进行实例化，直接进行调用
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## conv，fc层需要调用nn.Module进行实例化
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x = F.relu(self.bn1(self.conv1(x)))
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.relu(self.bn3(self.conv3(x)))
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x = x.view(-1 , 48 * 6 * 6)
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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if __name__ == '__main__':
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import torch
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x = torch.randn(1,3,48,48).cuda()
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model = simpleconv3(4).cuda()
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y = model(x)
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## 可视化
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from visualize import make_dot
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g = make_dot(y)
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g.view()
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## 统计参数信息
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from torchsummary import summary
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summary(model,input_size=(3,48,48))
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print(model) |
