mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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534d5b8c8b
* Add tutorials for intel gpu * fix gflags dependency * Update README_CN.md * Update README.md * Update README.md
101 lines
3.1 KiB
C++
101 lines
3.1 KiB
C++
// Copyright (c) 2022 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 "fastdeploy/vision.h"
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#include "gflags/gflags.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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DEFINE_string(model, "", "Directory of the inference model");
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DEFINE_string(image, "", "Path of the image file.");
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DEFINE_int64(topk, 1, "Topk classify result of the image file");
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DEFINE_string(device, "cpu", "Type of openvino device, 'cpu' or 'intel_gpu'");
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void InitAndInfer(const std::string& model_dir, const std::string& image_file, int topk, const fastdeploy::RuntimeOption& option) {
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auto model_file = model_dir + sep + "inference.pdmodel";
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auto params_file = model_dir + sep + "inference.pdiparams";
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auto config_file = model_dir + sep + "inference_cls.yaml";
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auto model = fastdeploy::vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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model.GetPostprocessor().SetTopk(topk);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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std::cout << "Warmup 20 times..." << std::endl;
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for (int i = 0; i < 20; ++i) {
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fastdeploy::vision::ClassifyResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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}
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std::cout << "Counting time..." << std::endl;
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fastdeploy::TimeCounter tc;
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tc.Start();
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for (int i = 0; i < 50; ++i) {
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fastdeploy::vision::ClassifyResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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}
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tc.End();
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std::cout << "Elapsed time: " << tc.Duration() * 1000 << "ms." << std::endl;
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fastdeploy::vision::ClassifyResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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// print res
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std::cout << res.Str() << std::endl;
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}
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fastdeploy::RuntimeOption BuildOption(const std::string& device) {
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if (device != "cpu" && device != "intel_gpu") {
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std::cerr << "The flag device only can be 'cpu' or 'intel_gpu'" << std::endl;
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std::abort();
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}
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fastdeploy::RuntimeOption option;
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option.UseOpenVINOBackend();
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if (device == "intel_gpu") {
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option.SetOpenVINODevice("GPU");
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std::map<std::string, std::vector<int64_t>> shape_info;
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shape_info["inputs"] = {1, 3, 224, 224};
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option.SetOpenVINOShapeInfo(shape_info);
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}
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return option;
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}
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int main(int argc, char* argv[]) {
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google::ParseCommandLineFlags(&argc, &argv, true);
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auto option = BuildOption(FLAGS_device);
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InitAndInfer(FLAGS_model, FLAGS_image, FLAGS_topk, option);
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return 0;
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}
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