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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>
80 lines
2.5 KiB
C++
80 lines
2.5 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/generation/contrib/animegan.h"
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#include "fastdeploy/function/functions.h"
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namespace fastdeploy {
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namespace vision {
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namespace generation {
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AnimeGAN::AnimeGAN(const std::string& model_file, const std::string& params_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) {
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valid_cpu_backends = {Backend::PDINFER};
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valid_gpu_backends = {Backend::PDINFER};
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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initialized = Initialize();
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}
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bool AnimeGAN::Initialize() {
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << 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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bool AnimeGAN::Predict(cv::Mat& img, cv::Mat* result) {
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std::vector<cv::Mat> results;
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if (!BatchPredict({img}, &results)) {
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return false;
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}
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*result = std::move(results[0]);
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return true;
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}
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bool AnimeGAN::BatchPredict(const std::vector<cv::Mat>& images, std::vector<cv::Mat>* results) {
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std::vector<FDMat> fd_images = WrapMat(images);
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std::vector<FDTensor> processed_data(1);
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if (!preprocessor_.Run(fd_images, &(processed_data))) {
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FDERROR << "Failed to preprocess input data while using model:"
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<< ModelName() << "." << std::endl;
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return false;
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}
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std::vector<FDTensor> infer_result(1);
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processed_data[0].name = InputInfoOfRuntime(0).name;
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if (!Infer(processed_data, &infer_result)) {
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FDERROR << "Failed to inference by runtime." << std::endl;
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return false;
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}
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if (!postprocessor_.Run(infer_result, results)) {
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FDERROR << "Failed to postprocess while using model:" << ModelName() << "."
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<< 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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} // namespace generation
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} // namespace vision
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} // namespace fastdeploy
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