analyzer_text_classification_tester.cc

// Copyright (c) 2018 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.

#include "paddle/fluid/inference/analysis/analyzer.h"
#include <gflags/gflags.h>
#include <glog/logging.h>  // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/api/timer.h"

DEFINE_string(infer_model, "", "Directory of the inference model.");
DEFINE_string(infer_data, "", "Path of the dataset.");
DEFINE_int32(batch_size, 1, "batch size.");
DEFINE_int32(repeat, 1, "How many times to repeat run.");

namespace paddle {

template <typename T>
std::string to_string(const std::vector<T> &vec) {
  std::stringstream ss;
  for (const auto &c : vec) {
    ss << c << " ";
  }
  return ss.str();
}

void PrintTime(const double latency, const int bs, const int repeat) {
  LOG(INFO) << "===========profile result===========";
  LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
            << ", avg latency: " << latency / repeat << "ms";
  LOG(INFO) << "=====================================";
}

void Main(int batch_size) {
  // Three sequence inputs.
  std::vector<PaddleTensor> input_slots(1);
  // one batch starts
  // data --
  int64_t data0[] = {0, 1, 2};
  for (auto &input : input_slots) {
    input.data.Reset(data0, sizeof(data0));
    input.shape = std::vector<int>({3, 1});
    // dtype --
    input.dtype = PaddleDType::INT64;
    // LoD --
    input.lod = std::vector<std::vector<size_t>>({{0, 3}});
  }

  // shape --
  // Create Predictor --
  AnalysisConfig config;
  config.model_dir = FLAGS_infer_model;
  config.use_gpu = false;
  config.enable_ir_optim = true;
  config.ir_passes.push_back("fc_lstm_fuse_pass");
  auto predictor =
      CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
          config);

  inference::Timer timer;
  double sum = 0;
  std::vector<PaddleTensor> output_slots;
  for (int i = 0; i < FLAGS_repeat; i++) {
    timer.tic();
    CHECK(predictor->Run(input_slots, &output_slots));
    sum += timer.toc();
  }
  PrintTime(sum, batch_size, FLAGS_repeat);

  // Get output
  LOG(INFO) << "get outputs " << output_slots.size();

  for (auto &output : output_slots) {
    LOG(INFO) << "output.shape: " << to_string(output.shape);
    // no lod ?
    CHECK_EQ(output.lod.size(), 0UL);
    LOG(INFO) << "output.dtype: " << output.dtype;
    std::stringstream ss;
    for (int i = 0; i < 5; i++) {
      ss << static_cast<float *>(output.data.data())[i] << " ";
    }
    LOG(INFO) << "output.data summary: " << ss.str();
    // one batch ends
  }
}

TEST(text_classification, basic) { Main(FLAGS_batch_size); }

}  // namespace paddle