[FIXED] How can I copy the parameters of one model to another in LibTorch?

Issue

How can I copy the parameters of one model to another in LibTorch? I know how to do it in Torch (Python).

net2.load_state_dict(net.state_dict())

I have tried with the code below in C++ with quite a bit of work. It didn't copy one to another.
I don't see an option to copy the parameters of one like model into another like model.

#include <torch/torch.h>
using namespace torch::indexing;

torch::Device device(torch::kCUDA);

void loadstatedict(torch::nn::Module& model, torch::nn::Module& target_model) {
torch::autograd::GradMode::set_enabled(false);  // make parameters copying possible
auto new_params = target_model.named_parameters(); // implement this
auto params = model.named_parameters(true /*recurse*/);
auto buffers = model.named_buffers(true /*recurse*/);
for (auto& val : new_params) {
    auto name = val.key();
    auto* t = params.find(name);
    if (t != nullptr) {
        t->copy_(val.value());
    } else {
        t = buffers.find(name);
        if (t != nullptr) {
            t->copy_(val.value());
            }
        }
    }
}

struct Critic_Net : torch::nn::Module {
    torch::Tensor next_state_batch__sampled_action;
    public:
    Critic_Net() {
        lin1 = torch::nn::Linear(3, 3);
        lin2 = torch::nn::Linear(3, 1);
        lin1->to(device);
        lin2->to(device);
    }
    torch::Tensor forward(torch::Tensor next_state_batch__sampled_action) {
        auto h = next_state_batch__sampled_action;
        h = torch::relu(lin1->forward(h));
        h = lin2->forward(h);
        return h;
    }
    torch::nn::Linear lin1{nullptr}, lin2{nullptr};
};

auto net = Critic_Net();
auto net2 = Critic_Net();
auto the_ones = torch::ones({3, 3}).to(device);

int main() {
    std::cout << net.forward(the_ones);
    std::cout << net2.forward(the_ones);
    loadstatedict(net, net2);
    std::cout << net.forward(the_ones);
    std::cout << net2.forward(the_ones);

}

Solution

Your solution with load_state_dict should work if I understand correctly. The problem here is the same as in your previous question : nothing is registered as either parameters or buffers or submodules. Add the register_module calls and it should work fine.

Link to the question

How this class should look like :

struct Critic_Net : torch::nn::Module {
public:
    Critic_Net() {
        lin1 = register_module("lin1", torch::nn::Linear(427, 42));
        lin2 = register_module("lin1", torch::nn::Linear(42, 286));
        lin3 = register_module("lin1", torch::nn::Linear(286, 1));
    }
     torch::Tensor forward(torch::Tensor next_state_batch__sampled_action) {
     // unchanged 
    }
    torch::nn::Linear lin1{nullptr}, lin2{nullptr}, lin3{nullptr};
};

Answered By - trialNerror