dzhwinter
6 years ago
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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/*
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* This file contains a simple demo for how to take a model for inference.
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*/
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#include <cassert>
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#include <cctype>
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#include <algorithm>
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#include <iostream>
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#include <iterator>
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#include <memory>
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#include <sstream>
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#include <string>
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#include <thread> //NOLINT
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#include "paddle/fluid/inference/paddle_inference_api.h"
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std::string DIRNAME = ""; /* "Directory of the inference model." */ // NOLINT
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bool USE_GPU = false; /*"Whether use gpu."*/
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auto message_err = []() {
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std::cout << "Copyright (c) 2018 PaddlePaddle Authors." << std::endl;
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std::cout << "Demo Case for windows inference. "
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<< "\n"
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<< "Usage: Input your model path and use_gpu as the guide requires,"
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<< "then run the demo inference, and will get a result."
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<< std::endl;
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std::cout << std::endl;
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};
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void ParseArgs() {
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message_err();
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std::cout << "DIRNAME:[D:/Paddle/xxx/path_to_model_dir]" << std::endl;
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std::cin >> DIRNAME;
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std::cout << "USE_GPU:[yes|no]";
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std::string value;
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std::cin >> value;
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std::transform(value.begin(), value.end(), value.begin(), ::toupper);
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USE_GPU = (value == "YES") ? true : false;
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}
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namespace paddle {
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namespace demo {
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std::string ToString(const NativeConfig& config) {
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std::stringstream ss;
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ss << "Use GPU : " << (config.use_gpu ? "True" : "False") << "\n"
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<< "Device : " << config.device << "\n"
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<< "fraction_of_gpu_memory : " << config.fraction_of_gpu_memory << "\n"
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<< "specify_input_name : "
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<< (config.specify_input_name ? "True" : "False") << "\n"
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<< "Program File : " << config.prog_file << "\n"
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<< "Param File : " << config.param_file;
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return ss.str();
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}
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void Main(bool use_gpu) {
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//# 1. Create PaddlePredictor with a config.
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NativeConfig config;
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config.model_dir = DIRNAME;
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config.use_gpu = USE_GPU;
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config.fraction_of_gpu_memory = 0.15;
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config.device = 0;
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std::cout << ToString(config) << std::endl;
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auto predictor =
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CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
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for (int batch_id = 0; batch_id < 3; batch_id++) {
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//# 2. Prepare input.
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int64_t data[4] = {1, 2, 3, 4};
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PaddleTensor tensor;
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tensor.shape = std::vector<int>({4, 1});
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tensor.data = PaddleBuf(data, sizeof(data));
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tensor.dtype = PaddleDType::INT64;
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// For simplicity, we set all the slots with the same data.
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std::vector<PaddleTensor> slots(4, tensor);
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//# 3. Run
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std::vector<PaddleTensor> outputs;
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assert(predictor->Run(slots, &outputs) == true &&
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"Predict run expect true");
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//# 4. Get output.
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assert(outputs.size() == 1UL);
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// Check the output buffer size and result of each tid.
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assert(outputs.front().data.length() == 33168UL);
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float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
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0.000932706};
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const size_t num_elements = outputs.front().data.length() / sizeof(float);
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// The outputs' buffers are in CPU memory.
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for (size_t i = 0; i < std::min(static_cast<size_t>(5), num_elements);
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i++) {
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assert(static_cast<float*>(outputs.front().data.data())[i] == result[i]);
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std::cout << "expect the output "
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<< static_cast<float*>(outputs.front().data.data())[i]
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<< std::endl;
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}
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}
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}
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void MainThreads(int num_threads, bool USE_GPU) {
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// Multi-threads only support on CPU
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// 0. Create PaddlePredictor with a config.
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NativeConfig config;
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config.model_dir = DIRNAME;
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config.use_gpu = USE_GPU;
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config.fraction_of_gpu_memory = 0.15;
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config.device = 0;
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std::cout << ToString(config) << std::endl;
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auto main_predictor =
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CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
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std::vector<std::thread> threads;
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for (int tid = 0; tid < num_threads; ++tid) {
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threads.emplace_back([&, tid]() {
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// 1. clone a predictor which shares the same parameters
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auto predictor = main_predictor->Clone();
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constexpr int num_batches = 3;
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for (int batch_id = 0; batch_id < num_batches; ++batch_id) {
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// 2. Dummy Input Data
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int64_t data[4] = {1, 2, 3, 4};
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PaddleTensor tensor;
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tensor.shape = std::vector<int>({4, 1});
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tensor.data = PaddleBuf(data, sizeof(data));
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tensor.dtype = PaddleDType::INT64;
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std::vector<PaddleTensor> inputs(4, tensor);
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std::vector<PaddleTensor> outputs;
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// 3. Run
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assert(predictor->Run(inputs, &outputs) == true);
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// 4. Get output.
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assert(outputs.size() == 1UL);
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// Check the output buffer size and result of each tid.
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assert(outputs.front().data.length() == 33168UL);
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float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
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0.000932706};
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const size_t num_elements =
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outputs.front().data.length() / sizeof(float);
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// The outputs' buffers are in CPU memory.
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for (size_t i = 0; i < std::min(static_cast<size_t>(5), num_elements);
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i++) {
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assert(static_cast<float*>(outputs.front().data.data())[i] ==
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result[i]);
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}
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}
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});
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}
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for (int i = 0; i < num_threads; ++i) {
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threads[i].join();
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}
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}
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} // namespace demo
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} // namespace paddle
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int main(int argc, char** argv) {
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// ParseArgs();
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DIRNAME = "./icnet";
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USE_GPU = true;
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paddle::demo::Main(false /* USE_GPU*/);
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paddle::demo::MainThreads(1, false /* USE_GPU*/);
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paddle::demo::MainThreads(4, false /* USE_GPU*/);
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if (USE_GPU) {
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paddle::demo::Main(true /*USE_GPU*/);
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paddle::demo::MainThreads(1, true /*USE_GPU*/);
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paddle::demo::MainThreads(4, true /*USE_GPU*/);
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}
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system("pause");
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return 0;
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}
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