diff --git a/.gitignore b/.gitignore
index 534fed2..4bdf96e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -109,3 +109,6 @@ venv.bak/
 *.jpeg
 *.pt
 .DS_Store
+src/libtorch/
+*.sh
+.vscode/settings.json
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
new file mode 100644
index 0000000..dba2550
--- /dev/null
+++ b/src/CMakeLists.txt
@@ -0,0 +1,10 @@
+cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
+project(custom_ops)
+# set(OpenCV_DIR /home/zjs/opt/OpenCV-3.4.0-cuda-9.0/share/OpenCV)
+# include (CTest)
+find_package(OpenCV REQUIRED)
+include_directories(${OpenCV_INCLUDE_DIRS})
+find_package(Torch REQUIRED)
+add_executable(main main.cpp)
+target_link_libraries(main ${TORCH_LIBRARIES} ${OpenCV_LIBS})
+set_property(TARGET main PROPERTY CXX_STANDARD 14)
\ No newline at end of file
diff --git a/src/main.cpp b/src/main.cpp
new file mode 100644
index 0000000..555ead8
--- /dev/null
+++ b/src/main.cpp
@@ -0,0 +1,117 @@
+#include <torch/script.h> // One-stop header.
+#include <torch/nn/functional/upsampling.h>
+
+#include <iostream>
+#include <memory>
+#include <string> 
+#include <opencv2/core/cuda.hpp>
+#include <opencv2/opencv.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+#define GPU 1 
+
+template<class T>
+T remove_extension(T const & filename)
+{
+  typename T::size_type const p(filename.find_last_of('.'));
+  return p > 0 && p != T::npos ? filename.substr(0, p) : filename;
+}
+std::string base_name(std::string const & path)
+{
+  return path.substr(path.find_last_of("/\\") + 1);
+}
+int main(int argc, const char* argv[]) {
+    if (argc != 3) {
+        std::cerr << "usage: main <path-to-exported-script-module> <path to input image folder>\n";
+        return -1;
+    }
+
+    torch::jit::script::Module module;
+    try {
+        // Deserialize the ScriptModule from a file using torch::jit::load().
+        module = torch::jit::load(argv[1]);
+        std::cout<<"Module loaded successfuly"<<std::endl; 
+    }
+    catch (const c10::Error& e) {
+        std::cerr << "error loading the model\n";
+        return -1;
+    }
+
+    module.to(at::kCUDA);
+  
+    std::string extention = ".png";
+    std::vector<cv::String> fn;
+    glob(argv[2], fn, false);
+        
+    std::vector<cv::Mat> images;
+    size_t count = fn.size(); //number of png files in images folder
+    for (size_t i=0; i<count; i++){
+        cv::Mat data;
+        data = cv::imread(fn[i], 1);
+        std::cout<<"Processing image: "<<remove_extension(base_name(fn[i]))<<std::endl; 
+        int col = data.cols; 
+        int row = data.rows; 
+        cv::cvtColor(data, data, CV_BGR2RGB);
+        int64 start = cv::getTickCount();
+        cv::resize(data, data, cv::Size(672, 384), cv::INTER_CUBIC);
+
+        data.convertTo(data, CV_32FC3);
+
+        cv::Mat ch_first = data.clone();
+        if (data.type() != CV_32FC3) std::cout << "wrong type" << std::endl;
+
+        float* feed_data = (float*)data.data;
+        float* ch_first_data = (float*)ch_first.data;
+
+        for (int p = 0; p < (int)data.total(); ++p)
+        {
+            // R
+            ch_first_data[p] = feed_data[p * 3];
+            // G
+            ch_first_data[p + (int)data.total()] = feed_data[p * 3 + 1];
+            // B
+            ch_first_data[p + 2 * (int)data.total()] = feed_data[p * 3 + 2];
+        }
+
+        torch::Tensor image_input = torch::from_blob((float*)ch_first.data, { 1, 3, data.rows, data.cols});
+        image_input = image_input.to(at::kCUDA);
+        image_input = image_input.div(255);
+        image_input = (image_input.sub(0.5f)).div(0.5f);
+        image_input = image_input.toType(torch::kFloat32);
+        auto net_out = module.forward({ image_input }).toTensor();
+        namespace F = torch::nn::functional;
+        net_out = F::interpolate(
+            net_out.unsqueeze(1),
+            F::InterpolateFuncOptions()
+                    .mode(torch::kBicubic)
+                    .size(std::vector<int64_t>({row, col})).align_corners(false)); 
+
+        net_out = net_out.squeeze();
+        net_out = (net_out.min(net_out.max()) / (net_out.max() - net_out.min())).mul(65535.0f); 
+        net_out = net_out.to(torch::kCPU);
+        int height = net_out.sizes()[0];
+        int width = net_out.sizes()[1]; 
+        int x = net_out.sizes()[2];
+        
+        try
+        {
+            cv::Mat output_mat(cv::Size{width, height}, CV_32FC1, net_out.data_ptr());
+            output_mat.convertTo(output_mat, CV_16UC1); 
+            double fps = cv::getTickFrequency() / (cv::getTickCount() - start);
+            std::cout << "FPS : " << fps << std::endl;
+            std::string output_address = "./output/"; 
+
+            output_address.append(remove_extension(base_name(fn[i])).append(extention)); 
+            cv::imwrite(output_address, output_mat);
+            
+        }
+        catch (const c10::Error& e)
+        {
+            std::cout << "an error has occured : " << e.msg() << std::endl;
+        }
+    }
+
+}
+