[C++ Frontend] own dataset example

cpp/own_dataset/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.1 FATAL_ERROR)
+project(own_dataset)
+
+find_package(Torch REQUIRED)
+find_package(OpenCV REQUIRED )
+
+
+add_executable(dataset dataset.cpp)
+target_link_libraries(dataset "${TORCH_LIBRARIES}")
+target_link_libraries(dataset "${OpenCV_LIBS}")
+set_property(TARGET dataset PROPERTY CXX_STANDARD 11)

cpp/own_dataset/README.md

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+# Own Dataset Example with the PyTorch C++ Frontend
+
+This folder contains an example of making an origianl image dataset to training classification model using PyTorch C++ frontend.
+
+The entire dataset code is contained in dataset.cpp
+
+To build the code, run the following commands from your terminal.
+
+```bash
+$ cd own_dataset
+$ mkdir build
+$ cd build
+$ cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
+$ make
+```
+
+where /path/to/libtorch should be the path to the unzipped LibTorch distribution, which you can get from the [PyTorch homepage](https://pytorch.org/get-started/locally/).
+
+Execute the compiled binary to test own dataset:
+
+```bash
+$ ./dataset ../test_data/ ../test_data/labels.txt
+input dim: 4
+target:  1
+[ Variable[CPUByteType]{1} ]
+```
+
+The ```test_data``` directory has the following structure.
+
+```bash
+$ tree ../test_data/
+../test_data/
+|-- images
+|   |-- image1.jpg
+|   |-- image2.jpg
+|   `-- image3.jpg
+`-- labels.txt
+
+1 directory, 4 files
+```
+
+The contents of the ```labels.txt``` have the following format.
+
+```bash
+$ cat ../test_data/labels.txt
+image1.jpg,0
+image2.jpg,0
+image3.jpg,1
+```

cpp/own_dataset/dataset.cpp

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+#include <dirent.h>
+
+#include <iostream>
+#include <map>
+#include <sstream>
+#include <string>
+#include <tuple>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+#include <torch/torch.h>
+#include <torch/data/datasets/base.h>
+#include <torch/data/example.h>
+#include <torch/types.h>
+
+
+// Split label info.
+std::tuple<std::string, std::string> split(std::string &s, char delim) {
+  std::stringstream ss(s);
+  std::vector<std::string> elems;
+
+  std::string item;
+  while (getline(ss, item, delim)) {
+    if (!item.empty()) {
+      elems.push_back(item);
+    }
+  }
+  return std::forward_as_tuple(elems[0], elems[1]);
+}
+
+
+// Define own dataset
+class ImageDataset : public torch::data::datasets::Dataset<ImageDataset> {
+  private:
+    std::string root;
+      std::vector<std::string> files;
+      std::map<std::string, int> labels;
+
+  public:
+    explicit ImageDataset(const std::string root, const std::string labelfile) : root(root) {
+      // get files
+      auto p = opendir(root.c_str());
+      dirent* entry;
+      if(p != nullptr) {
+        do {
+          entry = readdir(p);
+
+          if(entry != nullptr) {
+            if(strcmp(entry->d_name, ".\0") == 0 || strcmp(entry->d_name, "..\0") == 0) continue;
+            files.push_back(entry->d_name);
+          }
+        } while(entry != nullptr);
+      }
+
+      // get labels
+      std::ifstream fs(labelfile);
+
+      std::string buf;
+      std::string fname, label;
+      while(fs >> buf) {
+        std::tie(fname, label) = split(buf, ',');
+        labels[fname] = stoi(label);
+      }
+    }
+
+    torch::data::Example<> get(size_t index) override {
+      std::string fname = this->root + this->files[index];
+      int label = this->labels.at(files[index]);
+
+      cv::Mat image = cv::imread(fname, 1);
+      std::vector<int64_t> sizes = {1, 3, image.rows, image.cols};
+
+      at::Tensor tensor_image = torch::from_blob(image.data, at::IntList(sizes), at::ScalarType::Byte);
+      at::Tensor tensor_label = torch::tensor({label}, torch::dtype(torch::kUInt8));
+
+      tensor_image = tensor_image.toType(at::kFloat);
+
+      return {tensor_image, tensor_label};
+    }
+
+    at::optional<size_t> size() const override {
+      return this->files.size();
+    }
+};
+
+
+int main(int argc, char **argv) {
+  std::string root = argv[1];
+  std::string labelfile = argv[2];
+
+  ImageDataset dataset(root, labelfile);
+
+  auto batch = dataset.get(3);
+
+  std::cout << "input dim: " << batch.data.dim() << std::endl;
+  std::cout << "target: " << batch.target << std::endl;
+
+  return 0;
+}

cpp/own_dataset/test_data/labels.txt

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+image1.jpg,0
+image2.jpg,0
+image3.jpg,1