卷积神经网络-ResNet

你的名字 2023-02-11 12:28 30阅读 0赞

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#加强版：三个卷积，分别是1x1,3x3,1x1,分别用来压缩维度，卷积处理，恢复维度，expansion是对输出通道数的倍乘
    
    class BasicBlock2(nn.Module):
        expansion = 4
        
        def __init__(self, in_num, num, stride=1, downsample=None):
            super(BasicBlock2, self).__init__()
            self.conv1 = nn.Conv2d(ni_num, num, kernel_size=1, baid=False)
            self.bn1 = nn.BatchNorm2d(num)
            
            self.conv2 = nn.Conv2d(num, num, kernel_size=3, stride=stride,padding=1, baid=False)
            self.bn2 = nn.BatchNorm2d(num)
            
            self.conv3 = nn.Conv2d(num, num*self.expansion, kernel_size=1,baid=False)
            self.bn3 = nn.BatchNorm2d(num*self.expansion)
            
            self.relu = self.ReLU(True)
            self.downsample = downsample
            self.stride = stride
            
        def forward(self, x):
            residual = x
            
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)
            
            out = self.conv2(out)
            out = self.bn2(out)
            out = self.relu(out)
            
            out = self.conv3(out)
            out = self.bn3(out)
            
            if self.downsample is not None:
                residual = self.downsample(x)
                
            out += residual
            out = self.relu(out)
            return out

class ResNet(nn.Module):    
        def __init__(self, block, layers, num_classes=1000):
            self.inplanes = 64
            
            super(ResNet, self).__init__()
            self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
            self.bn1 = nn.BatchNorm2d(64)
            self.relu = nn.ReLU(True)
            self.maxpool = nn.MaxPool2d(3, 2, 1)
            
            self.layer1 = self._make_layer(block, 64, layers[0])
            self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
            self.avgpool = nn.AvgPool2d(7,stride=1)
            self.fc = nn.Linear(512*block.expansion, num_classes)
            
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.kaiming_normal(m.weight, mode='fan_out', nonlinearity='relu')
                elif isinstance(m, nn.BatchNorm2d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
                    
        def _make_layer(self, block, planes, blocks, stride=1):
            downsample = None
            if stride != 1 or self.inplanes != planes * block.expansion:
                downsample = nn.Sequential(
                    nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(planes * block.expansion),
                    
                )
            layers = []
            layers.append(block(self.inplanes, planes, stride, downsample))
            self.inplanes = planes * block.expansion
            for i in range(1, blocks):
                layers.append(block(self.inplanes, planes))
            return nn.Sequential(*layers)
                
            
            def forward(self, x):
                x = self.conv1(x)
                x = self.bn1(x)
                x = self.relu(x)
                x = self.maxpool(x)
                
                x = self.layer1(x)
                x = self.layer2(x)
                x = self.layer3(x)
                x = self.layer4(x)
                x = self.avgpool(x)
                x = x.view(x.size(0), -1)
                out = self.fc(x)
                return out

def resnet18(pretrained=False, **kwargs):
        model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
        if pretrained:
            model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
        return model
    
    model = resnet18()
    print(model)

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