Hey @SantMan,
this should get you going again. First, use python to convert the network into a ScriptModule 25. For example call this convert.py
import torch
import torchvision.models as models

resnet18 = models.resnet18(pretrained=True)

# use an example input to trace the operations of the model
example_input = torch.rand(1, 3, 224, 224) # 224 is the least input size, depends on the dataset you use

script_module = torch.jit.trace(resnet18, example_input)
script_module.save('script_module.pt')

Then, run it. You can now access the ScriptModule from C++ code like so
#include <torch/script.h>

int main()
{
	std::shared_ptr<torch::jit::script::Module> module;
	module = torch::jit::load("../script_module.pt"); 

        // Example forward.
        std::vector<torch::jit::IValue> input;
        input.push_back(torch::zeros({1, 3, 224, 224}));

        torch::Tensor output = module->forward(input).toTensor();

	return 0;
}

Where my folder structure is shown below
../convert.py
   CMakeLists.txt
   main.cpp
   /build

and the CMakeLists.txt looks as follows
cmake_minimum_required(VERSION 3.11 FATAL_ERROR)

project(load_resnet)

find_package(Torch REQUIRED)
include_directories(${TORCH_INCLUDE_DIRS})

add_executable(main main.cpp)
target_link_libraries(main ${TORCH_LIBRARIES})