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https://github.com/PaddlePaddle/FastDeploy.git
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87bcb5df21
* add style transfer model * add examples for generation model * add unit test * add speed comparison * add speed comparison * add variable for constant * add preprocessor and postprocessor * add preprocessor and postprocessor * fix * fix according to review Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
70 lines
2.2 KiB
C++
70 lines
2.2 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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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_string(device, "cpu",
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"Type of inference device, support 'cpu' or 'gpu'.");
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void PrintUsage() {
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std::cout << "Usage: infer_demo --model model_path --image img_path --device [cpu|gpu]"
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<< std::endl;
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std::cout << "Default value of device: cpu" << std::endl;
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}
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bool CreateRuntimeOption(fastdeploy::RuntimeOption* option) {
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if (FLAGS_device == "gpu") {
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option->UseGpu();
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}
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else if (FLAGS_device == "cpu") {
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option->SetPaddleMKLDNN(false);
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return true;
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} else {
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std::cerr << "Only support device CPU/GPU now, " << FLAGS_device << " is not supported." << std::endl;
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return false;
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}
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return true;
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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 = fastdeploy::RuntimeOption();
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if (!CreateRuntimeOption(&option)) {
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PrintUsage();
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return -1;
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}
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auto model = fastdeploy::vision::generation::AnimeGAN(FLAGS_model+"/model.pdmodel", FLAGS_model+"/model.pdiparams", option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return -1;
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}
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auto im = cv::imread(FLAGS_image);
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cv::Mat 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 -1;
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}
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cv::imwrite("style_transfer_result.png", res);
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std::cout << "Visualized result saved in ./style_transfer_result.png" << std::endl;
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return 0;
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}
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