标签：教程 features nn int torch libtorch forward out

title: libtorch教程（三）
date: 2021-01-16 11:50:41
tags: libtorch

基本模块搭建

模块化编程的思想非常重要，通过模块化编程可以大幅减少重复的敲代码过程，同时代码可读性也会增加。本章将讲述如何使用libtorch搭建一些MLP和CNN的基本模块。

MLP基本单元

首先是线性层的声明和定义，包括初始化和前向传播函数。代码如下：

class LinearBnReluImpl : public torch::nn::Module{
public:
    LinearBnReluImpl(int intput_features, int output_features);
    torch::Tensor forward(torch::Tensor x);
private:
    //layers
    torch::nn::Linear ln{nullptr};
    torch::nn::BatchNorm1d bn{nullptr};
};
TORCH_MODULE(LinearBnRelu);

LinearBnReluImpl::LinearBnReluImpl(int in_features, int out_features){
    ln = register_module("ln", torch::nn::Linear(torch::nn::LinearOptions(in_features, out_features)));
    bn = register_module("bn", torch::nn::BatchNorm1d(out_features));
}

torch::Tensor LinearBnReluImpl::forward(torch::Tensor x){
    x = torch::relu(ln->forward(x));
    x = bn(x);
    return x;
}

在MLP的构造线性层模块类时，我们继承了torch::nn::Module类，将初始化和前向传播模块作为public，可以给对象使用，而里面的线性层torch::nn::Linear和归一化层torch::nn::BatchNorm1d被隐藏作为私有变量。

定义初始化函数时，需要将原本的指针对象ln和bn进行赋值，同时将两者的名称也确定。前向传播函数就和pytorch中的forward类似。

CNN基本单元

CNN的基本单元构建和MLP的构建类似，但是又稍有不同，首先需要定义的时卷积超参数确定函数。

inline torch::nn::Conv2dOptions conv_options(int64_t in_planes, int64_t out_planes, int64_t kerner_size,
    int64_t stride = 1, int64_t padding = 0, bool with_bias = false) {
    torch::nn::Conv2dOptions conv_options = torch::nn::Conv2dOptions(in_planes, out_planes, kerner_size);
    conv_options.stride(stride);
    conv_options.padding(padding);
    conv_options.bias(with_bias);
    return conv_options;
}

该函数返回torch::nn::Conv2dOptions对象，对象的超参数由函数接口指定，这样可以方便使用。同时指定inline，提高Release模式下代码执行效率。

随后则是和MLP的线性模块类似，CNN的基本模块由卷积层，激活函数和归一化层组成。代码如下：

class ConvReluBnImpl : public torch::nn::Module {
public:
    ConvReluBnImpl(int input_channel=3, int output_channel=64, int kernel_size = 3, int stride = 1);
    torch::Tensor forward(torch::Tensor x);
private:
    // Declare layers
    torch::nn::Conv2d conv{ nullptr };
    torch::nn::BatchNorm2d bn{ nullptr };
};
TORCH_MODULE(ConvReluBn);

ConvReluBnImpl::ConvReluBnImpl(int input_channel, int output_channel, int kernel_size, int stride) {
    conv = register_module("conv", torch::nn::Conv2d(conv_options(input_channel,output_channel,kernel_size,stride,kernel_size/2)));
    bn = register_module("bn", torch::nn::BatchNorm2d(output_channel));

}

torch::Tensor ConvReluBnImpl::forward(torch::Tensor x) {
    x = torch::relu(conv->forward(x));
    x = bn(x);
    return x;
}

简单MLP

在MLP的例子中，我们以搭建一个四层感知机为例，介绍如何使用cpp实现深度学习模型。该感知机接受in_features个特征，输出out_features个编码后的特征。中间特征数定义为32，64和128，其实一般逆序效果更佳，但是只是作为例子也无关紧要。

class MLP: public torch::nn::Module{
public:
    MLP(int in_features, int out_features);
    torch::Tensor forward(torch::Tensor x);
private:
    int mid_features[3] = {32,64,128};
    LinearBnRelu ln1{nullptr};
    LinearBnRelu ln2{nullptr};
    LinearBnRelu ln3{nullptr};
    torch::nn::Linear out_ln{nullptr};
};

MLP::MLP(int in_features, int out_features){
    ln1 = LinearBnRelu(in_features, mid_features[0]);
    ln2 = LinearBnRelu(mid_features[0], mid_features[1]);
    ln3 = LinearBnRelu(mid_features[1], mid_features[2]);
    out_ln = torch::nn::Linear(mid_features[2], out_features);

    ln1 = register_module("ln1", ln1);
    ln2 = register_module("ln2", ln2);
    ln3 = register_module("ln3", ln3);
    out_ln = register_module("out_ln",out_ln);
}

torch::Tensor MLP::forward(torch::Tensor x){
    x = ln1->forward(x);
    x = ln2->forward(x);
    x = ln3->forward(x);
    x = out_ln->forward(x);
    return x;
}

每一层的实现均是通过前面定义的基本模块LinearBnRelu。

简单CNN

前面介绍了构建CNN的基本模块ConvReluBn，接下来尝试用c++搭建CNN模型。该CNN由三个stage组成，每个stage又由一个卷积层一个下采样层组成。这样相当于对原始输入图像进行了8倍下采样。中间层的通道数变化与前面MLP特征数变化相同，均为输入->32->64->128->输出。

class plainCNN : public torch::nn::Module{
public:
    plainCNN(int in_channels, int out_channels);
    torch::Tensor forward(torch::Tensor x);
private:
    int mid_channels[3] = {32,64,128};
    ConvReluBn conv1{nullptr};
    ConvReluBn down1{nullptr};
    ConvReluBn conv2{nullptr};
    ConvReluBn down2{nullptr};
    ConvReluBn conv3{nullptr};
    ConvReluBn down3{nullptr};
    torch::nn::Conv2d out_conv{nullptr};
};

plainCNN::plainCNN(int in_channels, int out_channels){
    conv1 = ConvReluBn(in_channels,mid_channels[0],3);
    down1 = ConvReluBn(mid_channels[0],mid_channels[0],3,2);
    conv2 = ConvReluBn(mid_channels[0],mid_channels[1],3);
    down2 = ConvReluBn(mid_channels[1],mid_channels[1],3,2);
    conv3 = ConvReluBn(mid_channels[1],mid_channels[2],3);
    down3 = ConvReluBn(mid_channels[2],mid_channels[2],3,2);
    out_conv = torch::nn::Conv2d(conv_options(mid_channels[2],out_channels,3));

    conv1 = register_module("conv1",conv1);
    down1 = register_module("down1",down1);
    conv2 = register_module("conv2",conv2);
    down2 = register_module("down2",down2);
    conv3 = register_module("conv3",conv3);
    down3 = register_module("down3",down3);
    out_conv = register_module("out_conv",out_conv);
}

torch::Tensor plainCNN::forward(torch::Tensor x){
    x = conv1->forward(x);
    x = down1->forward(x);
    x = conv2->forward(x);
    x = down2->forward(x);
    x = conv3->forward(x);
    x = down3->forward(x);
    x = out_conv->forward(x);
    return x;
}

假定输入一个三通道图片，输出通道数定义为n，输入表示一个[1,3,224,224]的张量，将得到一个[1,n,28,28]的输出张量。

简单LSTM

最后则是一个简单的LSTM的例子，用以处理时序型特征。在直接使用torch::nn::LSTM类之前，我们先顶一个返回torch::nn::LSTMOptions对象的函数，该函数接受关于LSTM的超参数，返回这些超参数定义的结果。

inline torch::nn::LSTMOptions lstmOption(int in_features, int hidden_layer_size, int num_layers, bool batch_first = false, bool bidirectional = false){
    torch::nn::LSTMOptions lstmOption = torch::nn::LSTMOptions(in_features, hidden_layer_size);
    lstmOption.num_layers(num_layers).batch_first(batch_first).bidirectional(bidirectional);
    return lstmOption;
}

//batch_first: true for io(batch, seq, feature) else io(seq, batch, feature)
class LSTM: public torch::nn::Module{
public:
    LSTM(int in_features, int hidden_layer_size, int out_size, int num_layers, bool batch_first);
    torch::Tensor forward(torch::Tensor x);
private:
    torch::nn::LSTM lstm{nullptr};
    torch::nn::Linear ln{nullptr};
    std::tuple<torch::Tensor, torch::Tensor> hidden_cell;
};

声明好LSTM以后，我们将内部的初始化函数和前向传播函数实现如下：

LSTM::LSTM(int in_features, int hidden_layer_size, int out_size, int num_layers, bool batch_first){
    lstm = torch::nn::LSTM(lstmOption(in_features, hidden_layer_size, num_layers, batch_first));
    ln = torch::nn::Linear(hidden_layer_size, out_size);

    lstm = register_module("lstm",lstm);
    ln = register_module("ln",ln);
}

torch::Tensor LSTM::forward(torch::Tensor x){
    auto lstm_out = lstm->forward(x);
    auto predictions = ln->forward(std::get<0>(lstm_out));
    return predictions.select(1,-1);
}

如果有用请给我一个

标签：教程,features,nn,int,torch,libtorch,forward,out
来源： https://www.cnblogs.com/allentbky/p/14337565.html

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。