自从 PyTorch C 接口发布以来，很少有教程专门针对这方面讲解。我们 PyTorch 中文网今天开始整理一套 PyTorch C API 系列教程，供大家参考。内容均源于网络。
完整的 PyTorch C++ 系列教程目录如下（或者点击这里查看）：
- 《PyTorch C++ API 系列 1：用 VGG-16 识别 MNIST》
- 《PyTorch C++ API 系列 2：使用自定义数据集》
- 《PyTorch C++ API 系列 3：训练网络》
- 《PyTorch C++ API 系列 4：实现猫狗分类器（一）》
- 《PyTorch C++ API 系列 5：实现猫狗分类器（二）》
本文讲解如何用 PyTorch C 实现 VGG-16 来识别 MNIST 数据集。
安装
首先下载 libtorch：
CPU 版本：
1 | wget https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-latest.zip -O libtorch.zip |
GPU (CUDA 9.0) 版本：
1 | wget https://download.pytorch.org/libtorch/cu90/libtorch-shared-with-deps-latest.zip -O libtorch.zip |
GPU (CUDA 10.0) 版本：
1 | wget https://download.pytorch.org/libtorch/cu100/libtorch-shared-with-deps-latest.zip |
然后将下载的压缩包解压缩。后面我们将使用解压后后的文件夹的绝对路径。
实现
VGG-16 的网络结构如下：
首先引入头文件：
1 | #include <torch/torch.h> |
然后实现网络定义：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | /* Sample code for training a FCN on MNIST dataset using PyTorch C++ API */ /* This code uses VGG-16 Layer Network */ struct Net: torch::nn::Module { // VGG-16 Layer // conv1_1 - conv1_2 - pool 1 - conv2_1 - conv2_2 - pool 2 - conv3_1 - conv3_2 - conv3_3 - pool 3 - // conv4_1 - conv4_2 - conv4_3 - pool 4 - conv5_1 - conv5_2 - conv5_3 - pool 5 - fc6 - fc7 - fc8 Net() { // Initialize VGG-16 // On how to pass strides and padding: https://github.com/pytorch/pytorch/issues/12649#issuecomment-430156160 conv1_1 = register_module("conv1_1", torch::nn::Conv2d(torch::nn::Conv2dOptions(1, 10, 3).padding(1))); conv1_2 = register_module("conv1_2", torch::nn::Conv2d(torch::nn::Conv2dOptions(10, 20, 3).padding(1))); // Insert pool layer conv2_1 = register_module("conv2_1", torch::nn::Conv2d(torch::nn::Conv2dOptions(20, 30, 3).padding(1))); conv2_2 = register_module("conv2_2", torch::nn::Conv2d(torch::nn::Conv2dOptions(30, 40, 3).padding(1))); // Insert pool layer conv3_1 = register_module("conv3_1", torch::nn::Conv2d(torch::nn::Conv2dOptions(40, 50, 3).padding(1))); conv3_2 = register_module("conv3_2", torch::nn::Conv2d(torch::nn::Conv2dOptions(50, 60, 3).padding(1))); conv3_3 = register_module("conv3_3", torch::nn::Conv2d(torch::nn::Conv2dOptions(60, 70, 3).padding(1))); // Insert pool layer conv4_1 = register_module("conv4_1", torch::nn::Conv2d(torch::nn::Conv2dOptions(70, 80, 3).padding(1))); conv4_2 = register_module("conv4_2", torch::nn::Conv2d(torch::nn::Conv2dOptions(80, 90, 3).padding(1))); conv4_3 = register_module("conv4_3", torch::nn::Conv2d(torch::nn::Conv2dOptions(90, 100, 3).padding(1))); // Insert pool layer conv5_1 = register_module("conv5_1", torch::nn::Conv2d(torch::nn::Conv2dOptions(100, 110, 3).padding(1))); conv5_2 = register_module("conv5_2", torch::nn::Conv2d(torch::nn::Conv2dOptions(110, 120, 3).padding(1))); conv5_3 = register_module("conv5_3", torch::nn::Conv2d(torch::nn::Conv2dOptions(120, 130, 3).padding(1))); // Insert pool layer fc1 = register_module("fc1", torch::nn::Linear(130, 50)); fc2 = register_module("fc2", torch::nn::Linear(50, 20)); fc3 = register_module("fc3", torch::nn::Linear(20, 10)); } // Implement Algorithm torch::Tensor forward(torch::Tensor x) { x = torch::relu(conv1_1->forward(x)); x = torch::relu(conv1_2->forward(x)); x = torch::max_pool2d(x, 2); x = torch::relu(conv2_1->forward(x)); x = torch::relu(conv2_2->forward(x)); x = torch::max_pool2d(x, 2); x = torch::relu(conv3_1->forward(x)); x = torch::relu(conv3_2->forward(x)); x = torch::relu(conv3_3->forward(x)); x = torch::max_pool2d(x, 2); x = torch::relu(conv4_1->forward(x)); x = torch::relu(conv4_2->forward(x)); x = torch::relu(conv4_3->forward(x)); x = torch::max_pool2d(x, 2); x = torch::relu(conv5_1->forward(x)); x = torch::relu(conv5_2->forward(x)); x = torch::relu(conv5_3->forward(x)); x = torch::max_pool2d(x, 2); x = x.view({-1, 130}); x = torch::relu(fc1->forward(x)); x = torch::relu(fc2->forward(x)); x = fc3->forward(x); return torch::log_softmax(x, 1); } // Declare layers torch::nn::Conv2d conv1_1{nullptr}; torch::nn::Conv2d conv1_2{nullptr}; torch::nn::Conv2d conv2_1{nullptr}; torch::nn::Conv2d conv2_2{nullptr}; torch::nn::Conv2d conv3_1{nullptr}; torch::nn::Conv2d conv3_2{nullptr}; torch::nn::Conv2d conv3_3{nullptr}; torch::nn::Conv2d conv4_1{nullptr}; torch::nn::Conv2d conv4_2{nullptr}; torch::nn::Conv2d conv4_3{nullptr}; torch::nn::Conv2d conv5_1{nullptr}; torch::nn::Conv2d conv5_2{nullptr}; torch::nn::Conv2d conv5_3{nullptr}; torch::nn::Linear fc1{nullptr}, fc2{nullptr}, fc3{nullptr}; }; |
训练
接下来我们测试训练网络，我们训练 10 个 epoch，学习率 0.01，使用 nll_loss 损失函数：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | int main() { // Create multi-threaded data loader for MNIST data auto data_loader = torch::data::make_data_loader<torch::data::samplers::SequentialSampler>( std::move(torch::data::datasets::MNIST("../../data").map(torch::data::transforms::Normalize<>(0.13707, 0.3081)).map( torch::data::transforms::Stack<>())), 64); // Build VGG-16 Network auto net = std::make_shared<Net>(); torch::optim::SGD optimizer(net->parameters(), 0.01); // Learning Rate 0.01 // net.train(); for(size_t epoch=1; epoch<=10; ++epoch) { size_t batch_index = 0; // Iterate data loader to yield batches from the dataset for (auto& batch: *data_loader) { // Reset gradients optimizer.zero_grad(); // Execute the model torch::Tensor prediction = net->forward(batch.data); // Compute loss value torch::Tensor loss = torch::nll_loss(prediction, batch.target); // Compute gradients loss.backward(); // Update the parameters optimizer.step(); // Output the loss and checkpoint every 100 batches if (++batch_index % 2 == 0) { std::cout << "Epoch: " << epoch << " | Batch: " << batch_index << " | Loss: " << loss.item<float>() << std::endl; torch::save(net, "net.pt"); } } } } |
完整代码请参考：https://github.com/krshrimali/Digit-Recognition-MNIST-SVHN-PyTorch-CPP
参考资料
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