wehub-resource-sync
101 lines
2.8 KiB
C++
101 lines
2.8 KiB
C++
/* Copyright (c) 2022 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 "test/cpp/inference/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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Record() : data(), shape() {}
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};
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Record ProcessALine(const std::string &line) {
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std::vector<std::string> columns;
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split(line, '\t', &columns);
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PADDLE_ENFORCE_EQ(columns.size(),
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2UL,
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common::errors::InvalidArgument(
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"Data format is invalid, 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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return record;
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}
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void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
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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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void SetConfig(AnalysisConfig *cfg, bool use_onednn = false) {
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cfg->SetModel(FLAGS_infer_model + "/inference.pdmodel",
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FLAGS_infer_model + "/inference.pdiparams");
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if (use_onednn) {
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cfg->EnableONEDNN();
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cfg->SwitchIrOptim();
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}
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}
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// Compare results of NativeConfig and AnalysisConfig
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void compare(bool use_onednn = false) {
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AnalysisConfig cfg;
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SetConfig(&cfg, use_onednn);
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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(
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reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
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}
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TEST(Analyzer_vit_ocr, compare) { compare(); }
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#ifdef PADDLE_WITH_DNNL
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TEST(Analyzer_vit_ocr, compare_onednn) { compare(true /* use_onednn */); }
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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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