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基于pytorch搭建GoogleNet神经网络用于花类识别

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基于pytorch搭建GoogleNet神经网络用于花类识别

 

写在前面

 

前面已经出过 基于pytorch搭建AlexNet神经网络用于花类识别基于pytorch搭建VGGNet神经网络用于花类识别 的文章,建议阅读此文章前先行阅读前两篇。

 

这篇文章用到的网络结构时GoogleNet,因此你需要对GoogleNet的结构有较清晰的了解,不清楚的戳此图标☞☞☞了解详情。

 

和上一篇相同,本篇不会对实现花类识别的每一个步骤进行讲解,只针对GoogleNet的网络搭建细节进行阐述,大家可自行下载 代码 进一步研究。

 

GoogleNet网络模型搭建:sparkles::sparkles::sparkles:

 

GoogleNet的结构乍一看还是挺复杂的,但是其中有大量的重复结构,即Inception结构。我们可以将Inception结构封装成一个类在进行调用,这样会大大提高代码的可读性。Inception类的定义如下:

 

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)

 

这里我不想做过多的解释,大家自己对照着GoogleNet的理论看应该也能很好的理解,但这里我把这个类传入的参数做一个简单的解释,其实就对应着Inception结构的一些参数,如下图所示:

 

 

这里再谈谈 BasicConv2d 这个东东,这个其实也是我们定义的类,定义如下:

 

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

 

这个就更好理解了,其把卷积和后面的Relu激活封装到了一起

 

​ 值得一提的是在GoogleNet网络中,还存在着两个结构相同的辅助分类器,为了简化代码,我们也将其封装成类,如下:

 

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)
        # 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

 

这样一切准备工作即已做好,我们就可以来定义我们的GoogleNet网络了:

 

class GoogLeNet(nn.Module):
    def __init__(self, num_classes=1000, aux_logits=True):
        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)   #ceil_mode=True表示求得的特征为小数时,向上取整
        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))    #自适应的平均池化,将特质图大小变成1x1
        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

 

注意事项

 

这部分谈谈GoogleNet网络模型搭建和使用的注意事项。我们知道在GoogleNet中有两个辅助分类器,但这两个辅助分类器是只在训练时使用的,测试时不使用。【测试时令参数 self.training and self.aux_logits 的值为False】由于训练时使用了两个辅助分类器,因此有三个输出:pineapple::pineapple::pineapple:

 

在预测过程中,我们也不需要我们的辅助分类器,在加载模型参数时需要设置 strict=False :strawberry::strawberry::strawberry:

 

训练结果展示

 

​ 本篇文章不再详细讲解训练步骤,和 基于pytorch搭建AlexNet神经网络用于花类识别 基本一致。这里展示一下训练结果,如下图所示:

 

 

其准确率达到了0.742,我们可以再来看看我们保存的GoogleNet模型,如下图,可以看出GoogleNet的参数相对于VGG可以说是少了许多许多,这和我们的理论部分也是契合的

 

 

小结

 

对于这一部分我强烈建议大家去使用Pycharm的调试功能,一步步的看每次运行的结果,这样你会发现代码结构特别的清晰。

 

参考视频: www.bilibili.com/video/BV1r7… :cherry_blossom::cherry_blossom::cherry_blossom:

 

如若文章对你有所帮助,那就

 

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