// Copyright (c) 2019 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. #include #include #include "paddle_api.h" // NOLINT using namespace paddle::lite_api; // NOLINT int64_t ShapeProduction(const shape_t& shape) { int64_t res = 1; for (auto i : shape) res *= i; return res; } void RunModel(std::string model_dir) { // 1. Set MobileConfig MobileConfig config; config.set_model_dir(model_dir); // 2. Create PaddlePredictor by MobileConfig std::shared_ptr predictor = CreatePaddlePredictor(config); // 3. Prepare input data std::unique_ptr input_tensor(std::move(predictor->GetInput(0))); input_tensor->Resize({1, 3, 224, 224}); auto* data = input_tensor->mutable_data(); for (int i = 0; i < ShapeProduction(input_tensor->shape()); ++i) { data[i] = 1; } // 4. Run predictor predictor->Run(); // 5. Get output std::unique_ptr output_tensor( std::move(predictor->GetOutput(0))); std::cout << "Output shape " << output_tensor->shape()[1] << std::endl; for (int i = 0; i < ShapeProduction(output_tensor->shape()); i += 100) { std::cout << "Output[" << i << "]: " << output_tensor->data()[i] << std::endl; } } int main(int argc, char** argv) { if (argc < 2) { std::cerr << "[ERROR] usage: ./" << argv[0] << " naive_buffer_model_dir\n"; exit(1); } std::string model_dir = argv[1]; RunModel(model_dir); return 0; }