t-bing_ads_model.cc

#include <assert.h>
#include <onnxruntime_cxx_api.h>
#include <cmath>
#include <stdlib.h>
#include <stdio.h>
#include <vector>
#include <iostream>

using namespace Ort;

int main(int argc, char* argv[]) {
  std::cout << "Start\n";
  Env env(ORT_LOGGING_LEVEL_VERBOSE, "test");
  SessionOptions session_options;
  session_options.DisableCpuMemArena();
  session_options.DisableMemPattern();
  Session session(env, "/home/pranav/bing_ads_model/classifier.onnx", session_options);
  size_t input_tensor_size = 1*12;
  std::vector<int64_t> input_tensor_values(input_tensor_size);
  int j = 0;
  for (int i = 0; i < input_tensor_size; ++i) {
    input_tensor_values[i] = j++;
  }

  std::vector<const char*> output_node_names{ "dummy_score", "qvec"};
  std::vector<int64_t> input_node_dims{ 1, 12 };

  MemoryInfo memory_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
  auto input_tensor1 = Value::CreateTensor(memory_info, input_tensor_values.data(),
                                           input_tensor_size * sizeof(int64_t), input_node_dims.data(), 2, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64);
  auto input_tensor2 = Value::CreateTensor(memory_info, input_tensor_values.data(),
                                           input_tensor_size * sizeof(int64_t), input_node_dims.data(), 2, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64);

  std::vector<Value> input_tensor_list;
  input_tensor_list.push_back(std::move(input_tensor1));
  input_tensor_list.push_back(std::move(input_tensor2));
  std::vector<const char*> input_node_names{"qseq", "qmask"};
  auto ret = session.Run(RunOptions{nullptr}, input_node_names.data(), input_tensor_list.data(),
                         input_tensor_list.size(), output_node_names.data(), output_node_names.size());
  std::cout << "Done\n";
  return 0;
}