Trjtdtk

I have trained a deep learning model using unet architecture in order to segment the nuclei in python and pytorch. I would like to load this pretrained model and make prediction in C++. For this reason, I obtained trace file(with pt extension). Then, I have run this code:

 #include <iostream>

 #include <torch/script.h> // One-stop header.

 #include <iostream>
 #include <memory>

 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>


 using namespace cv;


 int main(int argc, const char* argv[]) 


 Mat image;
 image = imread("C:/Users/Sercan/PycharmProjects/samplepyTorch/test_2.png", CV_LOAD_IMAGE_COLOR);

 std::shared_ptr<torch::jit::script::Module> module = torch::jit::load("C:/Users/Sercan/PycharmProjects/samplepyTorch/epistroma_unet_best_model_trace.pt");
 module->to(torch::kCUDA);


 std::vector<int64_t> sizes = 1, 3, image.rows, image.cols ;
 torch::TensorOptions options(torch::ScalarType::Byte);
 torch::Tensor tensor_image = torch::from_blob(image.data, torch::IntList(sizes), options);
 tensor_image = tensor_image.toType(torch::kFloat);
 auto result = module->forward( tensor_image.to(at::kCUDA) ).toTensor();


 result = result.squeeze().cpu();
 result = at::sigmoid(result);

 cv::Mat img_out(image.rows, image.cols, CV_32F, result.data<float>());

 cv::imwrite("img_out.png", img_out);

 

Image outputs ( First image: test image, Second image: Python prediction result, Third image: C++ prediction result):

As you see, C++ prediction output is not similar to python prediction output. Could you offer a solution to fix this problem?