dzhwinter
7 years ago
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <chrono>
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#include <iostream>
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#include <fstream>
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#include "paddle/fluid/inference/api/paddle_inference_api.h"
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namespace paddle {
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std::string DIRNAME = "./LB_icnet_model";
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//std::string DIRNAME = "./infer_models";
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NativeConfig GetConfig() {
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NativeConfig config;
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config.prog_file=DIRNAME + "/__model__";
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config.param_file=DIRNAME + "/__params__";
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config.fraction_of_gpu_memory = 0.8;
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config.use_gpu = true;
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config.device = 0;
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return config;
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}
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using Time = decltype(std::chrono::high_resolution_clock::now());
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Time time() { return std::chrono::high_resolution_clock::now(); };
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double time_diff(Time t1, Time t2) {
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typedef std::chrono::microseconds ms;
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auto diff = t2 - t1;
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ms counter = std::chrono::duration_cast<ms>(diff);
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return counter.count() / 1000.0;
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}
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void test_naive(int batch_size){
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NativeConfig config = GetConfig();
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// config.model_dir = model_path;
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auto predictor = CreatePaddlePredictor<NativeConfig>(config);
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int height = 449;
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int width = 581;
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//int height = 3;
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//int width = 3;
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int num_sum = height * width * 3 * batch_size;
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std::vector<float> data;
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for(int i = 0; i < num_sum; i++) {
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data.push_back(0.0);
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}
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PaddleTensor tensor;
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tensor.shape = std::vector<int>({batch_size, 3, height, width});
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tensor.data.Resize(sizeof(float) * batch_size * 3 * height * width);
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std::copy(data.begin(), data.end(), static_cast<float*>(tensor.data.data()));
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tensor.dtype = PaddleDType::FLOAT32;
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std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
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PaddleTensor tensor_out;
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std::vector<PaddleTensor> outputs(1, tensor_out);
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predictor->Run(paddle_tensor_feeds, &outputs, batch_size);
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std::cout << "start predict123:" << std::endl;
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auto time1 = time();
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for(size_t i = 0; i < 2; i++) {
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predictor->Run(paddle_tensor_feeds, &outputs, batch_size);
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std::cout << "pass " << i;
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}
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auto time2 = time();
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std::ofstream ofresult("naive_test_result.txt", std::ios::app);
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std::cout <<"batch: " << batch_size << " predict cost: " << time_diff(time1, time2) / 100.0 << "ms" << std::endl;
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std::cout << outputs.size() << std::endl;
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/*
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int64_t * data_o = static_cast<int64_t*>(outputs[0].data.data());
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for (size_t j = 0; j < outputs[0].data.length() / sizeof(int64_t); ++j) {
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ofresult << std::to_string(data_o[j]) << " ";
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}
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ofresult << std::endl;
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ofresult.close();
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*/
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}
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} // namespace paddle
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int main(int argc, char** argv) {
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paddle::test_naive(1 << 0);
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return 0;
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}
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