pytorch c++ LSTM

代码

#include <torch/script.h>
#include <ATen/ATen.h>

#include <torch/nn/module.h>
#include <torch/nn/modules/batchnorm.h>
#include <torch/nn/modules/conv.h>
#include <torch/nn/modules/rnn.h>
#include <torch/nn/modules/dropout.h>
#include <torch/nn/modules/embedding.h>
#include <torch/nn/modules/functional.h>
#include <torch/nn/modules/linear.h>
#include <torch/nn/modules/sequential.h>
#include <torch/optim.h>
#include <torch/types.h>
#include <torch/utils.h>

#include <cmath>
#include <cstdlib>
#include <functional>
#include <iostream>
#include <memory>
#include <random>
#include <vector>

using namespace std;
using namespace at;
using namespace torch::nn;
using namespace torch::optim;

void check_lstm_sizes(RNNOutput output) {
  // Expect the LSTM to have 64 outputs and 3 layers, with an input of batch
  // 10 and 16 time steps (10 x 16 x n)

  ASSERT_EQ(output.output.ndimension(), 3);
  ASSERT_EQ(output.output.size(0), 10);
  ASSERT_EQ(output.output.size(1), 16);
  ASSERT_EQ(output.output.size(2), 64);

  ASSERT_EQ(output.state.ndimension(), 4);
  ASSERT_EQ(output.state.size(0), 2); // (hx, cx)
  ASSERT_EQ(output.state.size(1), 3); // layers
  ASSERT_EQ(output.state.size(2), 16); // Batchsize
  ASSERT_EQ(output.state.size(3), 64); // 64 hidden dims
  // Something is in the hiddens
  ASSERT_GT(output.state.norm().item<float>(), 0);
}


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


    LSTM model(LSTMOptions(128, 64).layers(3).dropout(0.2)); // Input size is: sequence length, batch size, input size
    auto x = torch::randn({10, 16, 128}, torch::requires_grad());
    auto output = model->forward(x);
    
    check_lstm_sizes(output);
    
    std::cout << output.output.sizes() << endl;

    std::cout<< "ok\n";
    return 1;
}