// Copyright (c) 2018 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 #include #include #include #include #include "paddle/fluid/inference/api/paddle_inference_api.h" namespace paddle { namespace inference { // Timer for timer class Timer { public: double start; double startu; void tic() { struct timeval tp; gettimeofday(&tp, NULL); start = tp.tv_sec; startu = tp.tv_usec; } double toc() { struct timeval tp; gettimeofday(&tp, NULL); double used_time_ms = (tp.tv_sec - start) * 1000.0 + (tp.tv_usec - startu) / 1000.0; return used_time_ms; } }; static void split(const std::string &str, char sep, std::vector *pieces) { pieces->clear(); if (str.empty()) { return; } size_t pos = 0; size_t next = str.find(sep, pos); while (next != std::string::npos) { pieces->push_back(str.substr(pos, next - pos)); pos = next + 1; next = str.find(sep, pos); } if (!str.substr(pos).empty()) { pieces->push_back(str.substr(pos)); } } static void split_to_float(const std::string &str, char sep, std::vector *fs) { std::vector pieces; split(str, sep, &pieces); std::transform(pieces.begin(), pieces.end(), std::back_inserter(*fs), [](const std::string &v) { return std::stof(v); }); } template std::string to_string(const std::vector &vec) { std::stringstream ss; for (const auto &c : vec) { ss << c << " "; } return ss.str(); } template <> std::string to_string>( const std::vector> &vec); template <> std::string to_string>>( const std::vector>> &vec); // clang-format off static void TensorAssignData(PaddleTensor *tensor, const std::vector> &data) { // Assign buffer int dim = std::accumulate(tensor->shape.begin(), tensor->shape.end(), 1, [](int a, int b) { return a * b; }); tensor->data.Resize(sizeof(float) * dim); int c = 0; for (const auto &f : data) { for (float v : f) { static_cast(tensor->data.data())[c++] = v; } } } } // namespace inference } // namespace paddle