add data type for zero copy

[NHZlX]

zero copy 使用方式

   std::vector<PaddleTensor> inputs;

    std::vector<std::vector<float>> input(1);
    std::vector<std::vector<float>> output;

    input[0].resize(input_num);
    float sum_i = 0;
      for (int i = 0; i < input_num; i++) {
       //input[0][i] = Random(1, 128.);
        input[0][i] = 1;
       sum_i += input[0][i];
    }

    // set input
    auto input_names = predictor->GetInputNames();
    for (auto& name : input_names) {
      auto input_t = predictor->GetInputTensor(name);
      input_t->Reshape({batch_size, channels, height, width});
      input_t->copy_from_cpu(input[0].data());
    }

    // predict
    CHECK(predictor->ZeroCopyRun());
    // get output
    auto output_names = predictor->GetOutputNames();
    for (auto& name : output_names) {
      std::cout << name << std::endl;
      std::vector<float> temp_data;
      auto output_t = predictor->GetOutputTensor(name);
      PaddleDType output_type = output_t->type();
      std::cout << "Type: " << output_type << std::endl;
      std::vector<int> temp_shape = output_t->shape();
      int num = std::accumulate(temp_shape.begin(), temp_shape.end(), 1, std::multiplies<int>());
      temp_data.resize(num);

      output_t->copy_to_cpu(temp_data.data());
      output.push_back(temp_data);
    }

[luotao1]

We support INT32 as well.

[NHZlX]

Yeah. At present, int32 will not be used of the VIS model and this branch will then merge to develop.