PaddleSpeech/speechx/examples/ds2_ol/nnet/ds2-model-ol-test.cc

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

// deepspeech2 online model info

#include <algorithm>
#include <fstream>
#include <functional>
#include <iostream>
#include <iterator>
#include <numeric>
#include <thread>
#include "base/flags.h"
#include "base/log.h"
#include "paddle_inference_api.h"

using std::cout;
using std::endl;


DEFINE_string(model_path, "", "xxx.pdmodel");
DEFINE_string(param_path, "", "xxx.pdiparams");
DEFINE_int32(chunk_size, 35, "feature chunk size, unit:frame");
DEFINE_int32(feat_dim, 161, "feature dim");


void produce_data(std::vector<std::vector<float>>* data);
void model_forward_test();

void produce_data(std::vector<std::vector<float>>* data) {
    int chunk_size = FLAGS_chunk_size;  // chunk_size in frame
    int col_size = FLAGS_feat_dim;      // feat dim
    cout << "chunk size: " << chunk_size << endl;
    cout << "feat dim: " << col_size << endl;

    data->reserve(chunk_size);
    data->back().reserve(col_size);
    for (int row = 0; row < chunk_size; ++row) {
        data->push_back(std::vector<float>());
        for (int col_idx = 0; col_idx < col_size; ++col_idx) {
            data->back().push_back(0.201);
        }
    }
}

void model_forward_test() {
    std::cout << "1. read the data" << std::endl;
    std::vector<std::vector<float>> feats;
    produce_data(&feats);

    std::cout << "2. load the model" << std::endl;
    ;
    std::string model_graph = FLAGS_model_path;
    std::string model_params = FLAGS_param_path;
    CHECK(model_graph != "");
    CHECK(model_params != "");
    cout << "model path: " << model_graph << endl;
    cout << "model param path : " << model_params << endl;

    paddle_infer::Config config;
    config.SetModel(model_graph, model_params);
    config.SwitchIrOptim(false);
    cout << "SwitchIrOptim: " << false << endl;
    config.DisableFCPadding();
    cout << "DisableFCPadding: " << endl;
    auto predictor = paddle_infer::CreatePredictor(config);

    std::cout << "3. feat shape, row=" << feats.size()
              << ",col=" << feats[0].size() << std::endl;
    std::vector<float> pp_input_mat;
    for (const auto& item : feats) {
        pp_input_mat.insert(pp_input_mat.end(), item.begin(), item.end());
    }

    std::cout << "4. fead the data to model" << std::endl;
    int row = feats.size();
    int col = feats[0].size();
    std::vector<std::string> input_names = predictor->GetInputNames();
    std::vector<std::string> output_names = predictor->GetOutputNames();
    for (auto name : input_names) {
        cout << "model input names: " << name << endl;
    }
    for (auto name : output_names) {
        cout << "model output names: " << name << endl;
    }

    // input
    std::unique_ptr<paddle_infer::Tensor> input_tensor =
        predictor->GetInputHandle(input_names[0]);
    std::vector<int> INPUT_SHAPE = {1, row, col};
    input_tensor->Reshape(INPUT_SHAPE);
    input_tensor->CopyFromCpu(pp_input_mat.data());

    // input length
    std::unique_ptr<paddle_infer::Tensor> input_len =
        predictor->GetInputHandle(input_names[1]);
    std::vector<int> input_len_size = {1};
    input_len->Reshape(input_len_size);
    std::vector<int64_t> audio_len;
    audio_len.push_back(row);
    input_len->CopyFromCpu(audio_len.data());

    // state_h
    std::unique_ptr<paddle_infer::Tensor> chunk_state_h_box =
        predictor->GetInputHandle(input_names[2]);
    std::vector<int> chunk_state_h_box_shape = {5, 1, 1024};
    chunk_state_h_box->Reshape(chunk_state_h_box_shape);
    int chunk_state_h_box_size =
        std::accumulate(chunk_state_h_box_shape.begin(),
                        chunk_state_h_box_shape.end(),
                        1,
                        std::multiplies<int>());
    std::vector<float> chunk_state_h_box_data(chunk_state_h_box_size, 0.0f);
    chunk_state_h_box->CopyFromCpu(chunk_state_h_box_data.data());

    // state_c
    std::unique_ptr<paddle_infer::Tensor> chunk_state_c_box =
        predictor->GetInputHandle(input_names[3]);
    std::vector<int> chunk_state_c_box_shape = {5, 1, 1024};
    chunk_state_c_box->Reshape(chunk_state_c_box_shape);
    int chunk_state_c_box_size =
        std::accumulate(chunk_state_c_box_shape.begin(),
                        chunk_state_c_box_shape.end(),
                        1,
                        std::multiplies<int>());
    std::vector<float> chunk_state_c_box_data(chunk_state_c_box_size, 0.0f);
    chunk_state_c_box->CopyFromCpu(chunk_state_c_box_data.data());

    // run
    bool success = predictor->Run();

    // state_h out
    std::unique_ptr<paddle_infer::Tensor> h_out =
        predictor->GetOutputHandle(output_names[2]);
    std::vector<int> h_out_shape = h_out->shape();
    int h_out_size = std::accumulate(
        h_out_shape.begin(), h_out_shape.end(), 1, std::multiplies<int>());
    std::vector<float> h_out_data(h_out_size);
    h_out->CopyToCpu(h_out_data.data());

    // stage_c out
    std::unique_ptr<paddle_infer::Tensor> c_out =
        predictor->GetOutputHandle(output_names[3]);
    std::vector<int> c_out_shape = c_out->shape();
    int c_out_size = std::accumulate(
        c_out_shape.begin(), c_out_shape.end(), 1, std::multiplies<int>());
    std::vector<float> c_out_data(c_out_size);
    c_out->CopyToCpu(c_out_data.data());

    // output tensor
    std::unique_ptr<paddle_infer::Tensor> output_tensor =
        predictor->GetOutputHandle(output_names[0]);
    std::vector<int> output_shape = output_tensor->shape();
    std::vector<float> output_probs;
    int output_size = std::accumulate(
        output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
    output_probs.resize(output_size);
    output_tensor->CopyToCpu(output_probs.data());
    row = output_shape[1];
    col = output_shape[2];

    // probs
    std::vector<std::vector<float>> probs;
    probs.reserve(row);
    for (int i = 0; i < row; i++) {
        probs.push_back(std::vector<float>());
        probs.back().reserve(col);

        for (int j = 0; j < col; j++) {
            probs.back().push_back(output_probs[i * col + j]);
        }
    }

    std::vector<std::vector<float>> log_feat = probs;
    std::cout << "probs, row: " << log_feat.size()
              << " col: " << log_feat[0].size() << std::endl;
    for (size_t row_idx = 0; row_idx < log_feat.size(); ++row_idx) {
        for (size_t col_idx = 0; col_idx < log_feat[row_idx].size();
             ++col_idx) {
            std::cout << log_feat[row_idx][col_idx] << " ";
        }
        std::cout << std::endl;
    }
}

int main(int argc, char* argv[]) {
    gflags::ParseCommandLineFlags(&argc, &argv, false);
    google::InitGoogleLogging(argv[0]);

    model_forward_test();
    return 0;
}