// Copyright (c) 2021 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. #pragma once #include "paddle/fluid/inference/tests/api/tester_helper.h" namespace paddle { namespace inference { using paddle::PaddleTensor; template void GetValueFromStream(std::stringstream *ss, T *t) { (*ss) >> (*t); } template <> void GetValueFromStream(std::stringstream *ss, std::string *t) { *t = ss->str(); } // Split string to vector template void Split(const std::string &line, char sep, std::vector *v) { std::stringstream ss; T t; for (auto c : line) { if (c != sep) { ss << c; } else { GetValueFromStream(&ss, &t); v->push_back(std::move(t)); ss.str({}); ss.clear(); } } if (!ss.str().empty()) { GetValueFromStream(&ss, &t); v->push_back(std::move(t)); ss.str({}); ss.clear(); } } // Parse tensor from string template bool ParseTensor(const std::string &field, paddle::PaddleTensor *tensor) { std::vector data; Split(field, ':', &data); if (data.size() < 2) return false; std::string shape_str = data[0]; std::vector shape; Split(shape_str, ' ', &shape); std::string mat_str = data[1]; std::vector mat; Split(mat_str, ' ', &mat); tensor->shape = shape; auto size = std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()) * sizeof(T); tensor->data.Resize(size); std::copy(mat.begin(), mat.end(), static_cast(tensor->data.data())); tensor->dtype = GetPaddleDType(); return true; } // Parse input tensors from string bool ParseLine(const std::string &line, std::vector *tensors) { std::vector fields; Split(line, ';', &fields); tensors->clear(); tensors->reserve(4); int i = 0; auto input_name = FLAGS_ernie_large ? "eval_placeholder_" : "placeholder_"; for (; i < 3; i++) { paddle::PaddleTensor temp; ParseTensor(fields[i], &temp); temp.name = input_name + std::to_string(i); tensors->push_back(temp); } // input_mask paddle::PaddleTensor input_mask; ParseTensor(fields[i], &input_mask); input_mask.name = input_name + std::to_string(i); tensors->push_back(input_mask); return true; } bool LoadInputData(std::vector> *inputs) { if (FLAGS_infer_data.empty()) { LOG(ERROR) << "please set input data path"; return false; } std::ifstream fin(FLAGS_infer_data); std::string line; int sample = 0; // The unit-test dataset only have 10 samples, each sample have 5 feeds. while (std::getline(fin, line)) { std::vector feed_data; ParseLine(line, &feed_data); inputs->push_back(std::move(feed_data)); sample++; if (!FLAGS_test_all_data && sample == FLAGS_batch_size) break; } LOG(INFO) << "number of samples: " << sample; return true; } void SetConfig(AnalysisConfig *cfg, bool use_mkldnn = false, bool use_gpu = false) { cfg->SetModel(FLAGS_infer_model); if (use_mkldnn) { cfg->EnableMKLDNN(); } if (use_gpu) { cfg->EnableUseGpu(100, 0); } else { cfg->DisableGpu(); } cfg->SwitchSpecifyInputNames(); cfg->SwitchIrOptim(); cfg->SetCpuMathLibraryNumThreads(FLAGS_cpu_num_threads); } void SetIpuConfig(AnalysisConfig *cfg, int batch_size = 1) { cfg->SetModel(FLAGS_infer_model); cfg->EnableIpu(4, batch_size, false, 1); } } // namespace inference } // namespace paddle