luotao1
7 years ago
parent
823c4f87be
commit
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <google/protobuf/text_format.h>
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#include <gtest/gtest.h>
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#include "paddle/fluid/framework/ir/pass.h"
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#include "paddle/fluid/inference/analysis/analyzer.h"
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#include "paddle/fluid/inference/analysis/ut_helper.h"
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#include "paddle/fluid/inference/api/helper.h"
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#include "paddle/fluid/inference/api/paddle_inference_api.h"
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#include "paddle/fluid/platform/profiler.h"
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DEFINE_string(infer_model, "", "model path");
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DEFINE_string(infer_data, "", "data path");
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DEFINE_int32(batch_size, 10, "batch size.");
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DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
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namespace paddle {
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namespace inference {
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struct DataRecord {
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std::vector<std::vector<int64_t>> word_data_all, mention_data_all;
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std::vector<std::vector<int64_t>> rnn_word_datas, rnn_mention_datas;
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std::vector<size_t> lod; // two inputs have the same lod info.
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size_t batch_iter{0};
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size_t batch_size{1};
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DataRecord() = default;
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explicit DataRecord(const std::string &path, int batch_size = 1)
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: batch_size(batch_size) {
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Load(path);
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}
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DataRecord NextBatch() {
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DataRecord data;
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size_t batch_end = batch_iter + batch_size;
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// NOTE skip the final batch, if no enough data is provided.
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if (batch_end <= word_data_all.size()) {
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data.word_data_all.assign(word_data_all.begin() + batch_iter,
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word_data_all.begin() + batch_end);
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data.mention_data_all.assign(mention_data_all.begin() + batch_iter,
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mention_data_all.begin() + batch_end);
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// Prepare LoDs
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data.lod.push_back(0);
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CHECK(!data.word_data_all.empty());
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CHECK(!data.mention_data_all.empty());
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CHECK_EQ(data.word_data_all.size(), data.mention_data_all.size());
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for (size_t j = 0; j < data.word_data_all.size(); j++) {
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data.rnn_word_datas.push_back(data.word_data_all[j]);
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data.rnn_mention_datas.push_back(data.mention_data_all[j]);
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// calculate lod
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data.lod.push_back(data.lod.back() + data.word_data_all[j].size());
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}
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}
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batch_iter += batch_size;
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return data;
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}
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void Load(const std::string &path) {
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std::ifstream file(path);
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std::string line;
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int num_lines = 0;
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while (std::getline(file, line)) {
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num_lines++;
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std::vector<std::string> data;
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split(line, ';', &data);
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// load word data
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std::vector<int64_t> word_data;
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split_to_int64(data[1], ' ', &word_data);
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// load mention data
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std::vector<int64_t> mention_data;
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split_to_int64(data[3], ' ', &mention_data);
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word_data_all.push_back(std::move(word_data));
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mention_data_all.push_back(std::move(mention_data));
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}
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}
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};
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void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
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int batch_size) {
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PaddleTensor lod_word_tensor, lod_mention_tensor;
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lod_word_tensor.name = "word";
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lod_mention_tensor.name = "mention";
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auto one_batch = data->NextBatch();
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int size = one_batch.lod[one_batch.lod.size() - 1]; // token batch size
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lod_word_tensor.shape.assign({size, 1});
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lod_word_tensor.lod.assign({one_batch.lod});
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lod_mention_tensor.shape.assign({size, 1});
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lod_mention_tensor.lod.assign({one_batch.lod});
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// assign data
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TensorAssignData<int64_t>(&lod_word_tensor, one_batch.rnn_word_datas);
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TensorAssignData<int64_t>(&lod_mention_tensor, one_batch.rnn_mention_datas);
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// Set inputs.
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input_slots->assign({lod_word_tensor, lod_mention_tensor});
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for (auto &tensor : *input_slots) {
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tensor.dtype = PaddleDType::INT64;
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}
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}
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// the first inference result
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const int chinese_ner_result_data[] = {30, 45, 41, 48, 17, 26,
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48, 39, 38, 16, 25};
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void TestChineseNERPrediction() {
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NativeConfig config;
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config.prog_file = FLAGS_infer_model + "/__model__";
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config.param_file = FLAGS_infer_model + "/param";
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config.use_gpu = false;
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config.device = 0;
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config.specify_input_name = true;
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auto predictor =
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CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
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std::vector<PaddleTensor> input_slots;
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DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
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// Prepare inputs.
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PrepareInputs(&input_slots, &data, FLAGS_batch_size);
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std::vector<PaddleTensor> outputs;
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Timer timer;
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timer.tic();
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for (int i = 0; i < FLAGS_repeat; i++) {
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predictor->Run(input_slots, &outputs);
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}
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LOG(INFO) << "===========profile result===========";
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LOG(INFO) << "batch_size: " << FLAGS_batch_size
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<< ", repeat: " << FLAGS_repeat
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<< ", latency: " << timer.toc() / FLAGS_repeat << "ms";
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LOG(INFO) << "=====================================";
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PADDLE_ENFORCE(outputs.size(), 1UL);
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auto &out = outputs[0];
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size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
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[](int a, int b) { return a * b; });
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PADDLE_ENFORCE_GT(size, 0);
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int64_t *result = static_cast<int64_t *>(out.data.data());
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for (size_t i = 0; i < std::min(11UL, size); i++) {
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PADDLE_ENFORCE(result[i], chinese_ner_result_data[i]);
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}
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}
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// Directly infer with the original model.
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TEST(Analyzer, Chinese_ner) { TestChineseNERPrediction(); }
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} // namespace inference
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} // namespace paddle
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