/* 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. */ #include #include #include #include #include "io/paddle_mobile_wrap.h" int main(int argc, char *argv[]) { #ifndef PADDLE_MOBILE_FPGA paddle_mobile::wrap::Net *net = new paddle_mobile::wrap::Net(paddle_mobile::wrap::kGPU_CL); net->SetCLPath("/data/local/tmp/bin"); net->Load("./checked_model/model", "./checked_model/params", false, false, 1, true); int size = 1 * 3 * 416 * 416; std::vector shape{1, 3, 416, 416}; float *data = new float[size]; for (int i = 0; i < size; i++) { data[i] = 0.0; } std::ifstream infile; infile.open("input.txt"); for (int i = 0; i < size; i++) { infile >> data[i]; } infile.close(); // input as vector // std::vector data_as_vector(data, data + size); // auto output = net->Predict(data_as_vector, shape); // for (auto item : output) { // std::cout << item << std::endl; // } // input as float pointer paddle_mobile::wrap::Tensor input(data, paddle_mobile::wrap::make_ddim(shape)); net->Feed("image", input); net->Predict(); auto output = net->Fetch("save_infer_model/scale_0"); int output_size = 1; std::cout << "output shape: "; for (int i = 0; i < output->dims().size(); i++) { std::cout << output->dims()[i] << " "; output_size *= output->dims()[i]; } std::cout << std::endl; std::cout << "output data: "; for (int i = 0; i < output_size; i++) { std::cout << output->data()[i] << std::endl; } #endif return 0; }