I am running a Python3 trained, torch_script exported CNN with C++.
However, I can not reproduce results on testing data.
The class labels the classifier returns in respective language are opposite for the identical data patch. Not sure if some memory is corrupted in the C++ implementation, but I was able to validate at least the Python implementation gives correct results. How else shall I debug? Right now I am trying to compare the weight matrices of the conv kernels to validate the model is consistent among both implementations. Note - patch stats are identical between both languages, could be that the memory is misaligned in C++?
Anything else:
Python export script:
    cnn = CNN1()

    cnn.load_state_dict(torch.load(args.cnn))

    torch_script_nn = torch.jit.script(cnn)

    script_file_name = args.cnn.replace('.torch', '.pytorch_script')
    torch_script_nn.save(script_file_name)

    print('{} CONVERTED TO {} '.format(args.cnn, script_file_name))

Python test script
    cnn = CNN1()
    cnn.load_state_dict(torch.load(cnn_file))

    i = 100
    j = 100
    patch_size = 14
    img = np.load(f)['arr_0']
    # plt.imshow(img, cmap='gray');plt.show()

    patch = img[i: i+ patch_size, j:j+patch_size]
    print('Patch max : {}, min {}'.format(np.max(patch), np.min(patch)))

    plt.imshow(patch, cmap='gray');plt.show()
    patch = np.expand_dims(patch, axis=2)
    normalized_patch = (patch - np.min(patch)) / np.max(patch - np.min(patch))
    print('Normalized Patch max : {}, min {}'.format(np.max(normalized_patch), np.min(normalized_patch)))
    torch_stack = torch.stack([torch.Tensor(normalized_patch)])
    print(torch_stack.shape)
    res = cnn(torch_stack.permute(0, 3, 1, 2))

    print('Class label {}'.format(res))

Prints
Patch max : 16.035221099853516, min 11.432785987854004
Normalized Patch max : 1.0, min 0.0
torch.Size([1, 14, 14, 1])
tensor([[9.9995e-01, 5.4011e-05]], grad_fn=<SoftmaxBackward>)

C++ test
std::cout << "TESTING CNN" << std::endl;
    std::size_t i = 100;
    std::size_t j = 100;

    const cv::Rect roi(i, j, patchSize, patchSize);
    cv::Mat amplitudePatch = amplitude(roi);
    double min;
    double max;
    cv::minMaxLoc(amplitudePatch, &min, &max);
    std::cout << "Patch Max : " << max << std::endl;
    std::cout << "Patch Min " << min << std::endl;

    cv::imshow("Patch", amplitudePatch);
    cv::Mat normalizedPatch = normalizeAmplitudePatch(amplitudePatch);
    cv::imshow("NPatch", normalizedPatch);
    cv::minMaxLoc(normalizedPatch, &min, &max);
    std::cout << "Normalized Patch Max : " << max << std::endl;
    std::cout << "Normalized Patch Min " << min << std::endl;

    cv::waitKey(0); 
    auto options = torch::TensorOptions().dtype(torch::kF32);

    std::cout << 1 << " " << amplitudePatch.size().height<< " " <<
        amplitudePatch.size().width<< " " << amplitudePatch.channels() << std::endl;

    torch::Tensor patchTensor = torch::from_blob(
      amplitudePatch.data, 
      {1, amplitudePatch.size().height,
        amplitudePatch.size().width, amplitudePatch.channels()},
      options);

    std::vector<c10::IValue> inputs;
    inputs.push_back(patchTensor.permute({0, 3, 1, 2}));
    torch::Tensor result = cnn.forward(inputs).toTensor();

    std::cout << result << std::endl;

Prints
Patch Max : 16.0352
Patch Min : 11.4328
Could not initialize OpenGL for RasterGLSurface, reverting to RasterSurface.
Could not initialize OpenGL for RasterGLSurface, reverting to RasterSurface.
Normalized Patch Max : 1
Normalized Patch Min 0
1 14 14 1
 0  1
[ Variable[CPUFloatType]{1,2} ]