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129 lines
3.9 KiB
129 lines
3.9 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <fstream>
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#include <iostream>
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#include "paddle/fluid/inference/tests/api/tester_helper.h"
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namespace paddle {
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namespace inference {
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namespace analysis {
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struct Record {
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std::vector<float> data;
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std::vector<int32_t> shape;
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};
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Record ProcessALine(const std::string &line) {
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VLOG(3) << "process a line";
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std::vector<std::string> columns;
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split(line, '\t', &columns);
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CHECK_EQ(columns.size(), 2UL)
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<< "data format error, should be <data>\t<shape>";
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Record record;
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std::vector<std::string> data_strs;
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split(columns[0], ' ', &data_strs);
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for (auto &d : data_strs) {
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record.data.push_back(std::stof(d));
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}
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std::vector<std::string> shape_strs;
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split(columns[1], ' ', &shape_strs);
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for (auto &s : shape_strs) {
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record.shape.push_back(std::stoi(s));
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}
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VLOG(3) << "data size " << record.data.size();
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VLOG(3) << "data shape size " << record.shape.size();
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return record;
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}
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/*
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* Use the native and analysis fluid engine to inference the demo.
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* ocr, mobilenet and se_resnext50
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*/
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void TestVisualPrediction(bool use_mkldnn) {
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std::unique_ptr<PaddlePredictor> predictor;
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AnalysisConfig cfg;
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cfg.param_file = FLAGS_infer_model + "/__params__";
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cfg.prog_file = FLAGS_infer_model + "/__model__";
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cfg.use_gpu = false;
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cfg._use_mkldnn = use_mkldnn;
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cfg.device = 0;
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cfg.enable_ir_optim = true;
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cfg.ir_passes.push_back("fc_gru_fuse_pass");
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predictor =
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CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(cfg);
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// Only have single batch of data.
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std::string line;
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std::ifstream file(FLAGS_infer_data);
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std::getline(file, line);
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auto record = ProcessALine(line);
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file.close();
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// Inference.
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PaddleTensor input;
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input.shape = record.shape;
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input.data =
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PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
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input.dtype = PaddleDType::FLOAT32;
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std::vector<PaddleTensor> outputs_slots;
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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}, &outputs_slots);
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}
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PrintTime(/*batch size*/ 1, FLAGS_repeat, /*num threads*/ 1, /*thread id*/ 0,
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timer.toc() / FLAGS_repeat);
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VLOG(3) << "output.size " << outputs_slots.size();
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// run native as reference
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auto ref_predictor =
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CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(cfg);
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std::vector<PaddleTensor> ref_outputs_slots;
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ref_predictor->Run({input}, &ref_outputs_slots);
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CompareResult(outputs_slots, ref_outputs_slots);
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// print what are fused
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AnalysisPredictor *analysis_predictor =
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dynamic_cast<AnalysisPredictor *>(predictor.get());
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auto &fuse_statis = analysis_predictor->analysis_argument()
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.Get<std::unordered_map<std::string, int>>(
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framework::ir::kFuseStatisAttr);
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for (auto &item : fuse_statis) {
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LOG(INFO) << "fused " << item.first << " " << item.second;
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}
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int num_ops = 0;
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for (auto &node :
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analysis_predictor->analysis_argument().main_dfg->nodes.nodes()) {
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if (node->IsFunction()) {
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++num_ops;
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}
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}
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LOG(INFO) << "has num ops: " << num_ops;
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}
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TEST(Analyzer_vis, analysis) { TestVisualPrediction(/*use_mkldnn*/ false); }
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#ifdef PADDLE_WITH_MKLDNN
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TEST(Analyzer_vis, analysis_mkldnn) {
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TestVisualPrediction(/*use_mkldnn*/ true);
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}
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#endif
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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