用c++ libtorch实现pytorch的ImageFolder方法

以下是使用C++和libtorch实现类似于pytorch的ImageFolder方法的示例代码：

#include <torch/torch.h>
#include <opencv2/opencv.hpp>

class ImageFolderDataset : public torch::data::datasets::Dataset<ImageFolderDataset> {
public:
    ImageFolderDataset(const std::string& root_dir, const std::string& extensions = ".jpg") 
        : root_dir_(root_dir), extensions_(extensions) {
        // find all image files in the root directory
        for (auto& dir_entry : std::filesystem::directory_iterator(root_dir)) {
            auto path = dir_entry.path();
            if (std::filesystem::is_regular_file(path) && is_image_file(path)) {
                image_paths_.push_back(path);
            }
        }
    }

    // get the i-th example in the dataset
    torch::data::Example<> get(size_t index) override {
        // load the image and convert to tensor
        auto image = cv::imread(image_paths_[index].string());
        cv::cvtColor(image, image, cv::COLOR_BGR2RGB); // convert from BGR to RGB
        torch::Tensor tensor_image = torch::from_blob(image.data, { image.rows, image.cols, 3 }, torch::kByte).permute({ 2, 0, 1 }).toType(torch::kFloat) / 255.0;
        
        // get the label from the directory name
        auto label_path = image_paths_[index].parent_path();
        int label = std::distance(std::filesystem::directory_iterator(root_dir_), std::find_if(std::filesystem::directory_iterator(root_dir_), std::filesystem::directory_iterator(), [&label_path](const auto& dir_entry) { return dir_entry.path() == label_path; }));

        return { tensor_image.clone(), torch::tensor(label) };
    }

    // return the number of examples in the dataset
    torch::optional<size_t> size() const override {
        return image_paths_.size();
    }

private:
    std::vector<std::filesystem::path> image_paths_;
    std::string root_dir_;
    std::string extensions_;

    bool is_image_file(const std::filesystem::path& path) const {
        auto extension = path.extension().string();
        return extensions_.empty() || std::find(extensions_.begin(), extensions_.end(), extension) != extensions_.end();
    }
};

int main() {
    // create the dataset and dataloader
    std::string root_dir = "/path/to/dataset";
    ImageFolderDataset dataset(root_dir);
    auto data_loader = torch::data::make_data_loader<torch::data::samplers::SequentialSampler>(dataset, torch::data::DataLoaderOptions().batch_size(32));

    // train the model using the dataset
    // ...
}

在上面的代码中，`ImageFolderDataset`类继承了`torch::data::datasets::Dataset`，并实现了`get()`和`size()`方法，以便能够使用`torch::data::make_data_loader()`函数创建一个数据加载器。在`get()`方法中，我们加载了图像并将其转换为张量，并从目录名称中获取标签。在`size()`方法中，我们返回数据集中的示例数。

注意，这里我们使用了OpenCV库来加载和处理图像。如果您想使用其他库，可以相应地修改代码。