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  • GoogLeNet

    <<Going deeper with convolutions>>
    2014年 Google团队提出

    网络中的亮点:

    1. 引入了Inception结构(融合不同尺度的特征信息)
    2. 使用1x1的卷积核进行降维以及映射处理            减少了参数
    3. 添加两个辅助分类器帮助训练
    4. 丢弃全连接层,使用平均池化层(大大减少模型参数)

    Inception

    image

    整体结构

    imageimage

    参数含义

    image

    InceptionAux 有2个辅助分类器

    image

    代码实现

    class BasicConv2d(nn.Module):
        def __init__(self, in_channels, out_channels, **kwargs):
            super(BasicConv2d, self).__init__()
            self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
            self.relu = nn.ReLU(inplace=True)
    
        def forward(self, x):
            x = self.conv(x)
            x = self.relu(x)
            return x
    
    class Inception(nn.Module):
        def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
            super(Inception, self).__init__()
    
            self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
    
            self.branch2 = nn.Sequential(
                BasicConv2d(in_channels, ch3x3red, kernel_size=1),
                BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)   # 保证输出大小等于输入大小
            )
    
            self.branch3 = nn.Sequential(
                BasicConv2d(in_channels, ch5x5red, kernel_size=1),
                BasicConv2d(ch5x5red, ch5x5, kernel_size=5, padding=2)   # 保证输出大小等于输入大小
            )
    
            self.branch4 = nn.Sequential(
                nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
                BasicConv2d(in_channels, pool_proj, kernel_size=1)
            )
    
        def forward(self, x):
            branch1 = self.branch1(x)
            branch2 = self.branch2(x)
            branch3 = self.branch3(x)
            branch4 = self.branch4(x)
    
            outputs = [branch1, branch2, branch3, branch4]
            return torch.cat(outputs, 1)  #合并的维度  1表示通道   深度拼接
    
    class InceptionAux(nn.Module):
        def __init__(self, in_channels, num_classes):
            super(InceptionAux, self).__init__()
            self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3)
            self.conv = BasicConv2d(in_channels, 128, kernel_size=1)  # output[batch, 128, 4, 4]
    
            self.fc1 = nn.Linear(2048, 1024)
            self.fc2 = nn.Linear(1024, num_classes)
    
        def forward(self, x):
            # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
            x = self.averagePool(x)
            # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
            x = self.conv(x)
            # N x 128 x 4 x 4
            x = torch.flatten(x, 1)
            x = F.dropout(x, 0.5, training=self.training)
    #当我们实例化一个模型model后,可以通过model:train()和model.eval()来控制模型的状态,model.train()模式下self.training=True,在model.eval()模式下self.training=False
         # N x 2048
            x = F.relu(self.fc1(x), inplace=True)
            x = F.dropout(x, 0.5, training=self.training)
            # N x 1024
            x = self.fc2(x)
            # N x num_classes
            return x
    
    class GoogLeNet(nn.Module):
        def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
            super(GoogLeNet, self).__init__()
            self.aux_logits = aux_logits
    
            self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
            self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
    
            self.conv2 = BasicConv2d(64, 64, kernel_size=1)
            self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
            self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
    
            self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
            self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
            self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
    
            self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
            self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
            self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
            self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
            self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
            self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
    
            self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
            self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
    
            if self.aux_logits:
                self.aux1 = InceptionAux(512, num_classes)
                self.aux2 = InceptionAux(528, num_classes)
    
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
            self.dropout = nn.Dropout(0.4)
            self.fc = nn.Linear(1024, num_classes)
            if init_weights:
                self._initialize_weights()
    
        def forward(self, x):
            # N x 3 x 224 x 224
            x = self.conv1(x)
            # N x 64 x 112 x 112
            x = self.maxpool1(x)
            # N x 64 x 56 x 56
            x = self.conv2(x)
            # N x 64 x 56 x 56
            x = self.conv3(x)
            # N x 192 x 56 x 56
            x = self.maxpool2(x)
    
            # N x 192 x 28 x 28
            x = self.inception3a(x)
            # N x 256 x 28 x 28
            x = self.inception3b(x)
            # N x 480 x 28 x 28
            x = self.maxpool3(x)
            # N x 480 x 14 x 14
            x = self.inception4a(x)
            # N x 512 x 14 x 14
            if self.training and self.aux_logits:    # eval model lose this layer
                aux1 = self.aux1(x)
    
            x = self.inception4b(x)
            # N x 512 x 14 x 14
            x = self.inception4c(x)
            # N x 512 x 14 x 14
            x = self.inception4d(x)
            # N x 528 x 14 x 14
            if self.training and self.aux_logits:    # eval model lose this layer
                aux2 = self.aux2(x)
    
            x = self.inception4e(x)
            # N x 832 x 14 x 14
            x = self.maxpool4(x)
            # N x 832 x 7 x 7
            x = self.inception5a(x)
            # N x 832 x 7 x 7
            x = self.inception5b(x)
            # N x 1024 x 7 x 7
    
            x = self.avgpool(x)
            # N x 1024 x 1 x 1
            x = torch.flatten(x, 1)
            # N x 1024
            x = self.dropout(x)
            x = self.fc(x)
            # N x 1000 (num_classes)
            if self.training and self.aux_logits:   # eval model lose this layer
                return x, aux2, aux1
            return x
    
        def _initialize_weights(self):
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Linear):
                    nn.init.normal_(m.weight, 0, 0.01)
                    nn.init.constant_(m.bias, 0)
    
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  • 原文地址:https://www.cnblogs.com/mengting-123/p/14851476.html
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