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143 lines
4.2 KiB
143 lines
4.2 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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using contrib::AnalysisConfig;
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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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void SetConfig(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->device = 0;
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cfg->enable_ir_optim = true;
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cfg->specify_input_name = true;
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// TODO(TJ): fix fusion gru
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cfg->ir_passes.push_back("fc_gru_fuse_pass");
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}
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void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
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PADDLE_ENFORCE_EQ(FLAGS_test_all_data, 0, "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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PaddleTensor input;
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input.shape = record.shape;
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input.dtype = PaddleDType::FLOAT32;
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size_t input_size = record.data.size() * sizeof(float);
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input.data.Resize(input_size);
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memcpy(input.data.data(), record.data.data(), input_size);
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std::vector<PaddleTensor> input_slots;
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input_slots.assign({input});
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(*inputs).emplace_back(input_slots);
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}
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// Easy for profiling independently.
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// ocr, mobilenet and se_resnext50
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void profile(bool use_mkldnn = false) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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cfg._use_mkldnn = use_mkldnn;
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std::vector<PaddleTensor> outputs;
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std::vector<std::vector<PaddleTensor>> input_slots_all;
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SetInput(&input_slots_all);
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TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
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if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
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const float ocr_result_data[] = {
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5.273636460856323538e-08, 3.296741795111302054e-07,
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1.873261190610264748e-08, 3.403730275408634043e-08,
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3.383312474625199684e-08};
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PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
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size_t size = GetSize(outputs[0]);
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PADDLE_ENFORCE_GT(size, 0);
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float *result = static_cast<float *>(outputs[0].data.data());
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for (size_t i = 0; i < std::min(5UL, size); i++) {
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EXPECT_NEAR(result[i], ocr_result_data[i], 1e-3);
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}
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}
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}
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TEST(Analyzer_vis, profile) { profile(); }
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#ifdef PADDLE_WITH_MKLDNN
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TEST(Analyzer_vis, profile_mkldnn) { profile(true /* use_mkldnn */); }
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#endif
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// Check the fuse status
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TEST(Analyzer_vis, fuse_statis) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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int num_ops;
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auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
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GetFuseStatis(predictor.get(), &num_ops);
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}
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// Compare result of NativeConfig and AnalysisConfig
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void compare(bool use_mkldnn = false) {
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AnalysisConfig cfg;
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SetConfig(&cfg);
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cfg._use_mkldnn = use_mkldnn;
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std::vector<std::vector<PaddleTensor>> input_slots_all;
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SetInput(&input_slots_all);
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CompareNativeAndAnalysis(cfg, input_slots_all);
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}
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TEST(Analyzer_vis, compare) { compare(); }
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#ifdef PADDLE_WITH_MKLDNN
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TEST(Analyzer_vis, compare_mkldnn) { compare(true /* use_mkldnn */); }
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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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