libtorch学习第六

本文主要是介绍libtorch学习第六，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

构建卷积网络

#include<torch/torch.h>
#include<torch/script.h>
#include<iostream>using std::cout; using std::endl;class LinearBnReluImpl : public torch::nn::Module
{
private:torch::nn::Linear ln{ nullptr };torch::nn::BatchNorm1d bn{ nullptr };public:LinearBnReluImpl(int input_features, int out_features);torch::Tensor forward(torch::Tensor x);
};
TORCH_MODULE(LinearBnRelu);inline torch::nn::Conv2dOptions conv_options(int64_t in_planes, int64_t out_planes, int64_t kernel_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, kernel_size);conv_options.stride(stride);conv_options.padding(padding);conv_options.bias(with_bias);return conv_options;
}class ConvReluBnImpl : public torch::nn::Module
{
private:torch::nn::Conv2d conv{ nullptr };torch::nn::BatchNorm2d bn{ nullptr };public:ConvReluBnImpl(int input_channel, int output_channel, int kernel_size, int stride, int padding=1);torch::Tensor forward(torch::Tensor x);
};
TORCH_MODULE(ConvReluBn);class MLP : public torch::nn::Module
{
private:int mid_features[3] = { 32, 64, 128 };LinearBnRelu ln1{ nullptr };LinearBnRelu ln2{ nullptr };LinearBnRelu ln3{ nullptr };torch::nn::Linear out_ln{ nullptr };public:MLP(int in_features, int out_features);torch::Tensor forward(torch::Tensor x);
};class plainCNN : public torch::nn::Module
{
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 };public:plainCNN(int in_channels, int out_channels);torch::Tensor forward(torch::Tensor x);
};int main()
{plainCNN c(3, 2);auto x = torch::rand({ 1,3,224,224 }, torch::kFloat);//cout << x.sizes() << endl;auto a = c.forward(x);cout <<"[in Main]: "<< a.sizes() << endl;return 0;
}LinearBnReluImpl::LinearBnReluImpl(int input_features, int out_features)
{ln = register_module("ln", torch::nn::Linear(torch::nn::LinearOptions(input_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;
}ConvReluBnImpl::ConvReluBnImpl(int input_channel, int output_channel, int kernel_size, int stride, int padding)
{conv = register_module("conv", torch::nn::Conv2d(conv_options(input_channel, output_channel, kernel_size, stride, padding)));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;
}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;
}plainCNN::plainCNN(int in_channels, int out_channels)
{conv1 = ConvReluBn(in_channels, mid_channels[0], 3, 1);down1 = ConvReluBn(mid_channels[0], mid_channels[0], 3, 2);conv2 = ConvReluBn(mid_channels[0], mid_channels[1], 3,1);down2 = ConvReluBn(mid_channels[1], mid_channels[1], 3, 2);conv3 = ConvReluBn(mid_channels[1], mid_channels[2], 3,1);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);cout << x.sizes() << endl;x = down1->forward(x);cout << x.sizes() << endl;x = conv2->forward(x);cout << x.sizes() << endl;x = down2->forward(x);cout << x.sizes() << endl;x = conv3->forward(x);cout << x.sizes() << endl;x = down3->forward(x);cout << x.sizes() << endl;x = out_conv->forward(x);cout << x.sizes() << endl;return x;
}

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