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* 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
3.1 KiB
C++
80 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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#pragma once
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#include "fastdeploy/fastdeploy_model.h"
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#include "fastdeploy/vision/common/processors/transform.h"
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#include "fastdeploy/vision/generation/contrib/preprocessor.h"
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#include "fastdeploy/vision/generation/contrib/postprocessor.h"
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namespace fastdeploy {
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namespace vision {
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namespace generation {
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/*! @brief AnimeGAN model object is used when load a AnimeGAN model.
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*/
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class FASTDEPLOY_DECL AnimeGAN : public FastDeployModel {
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public:
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/** \brief Set path of model file and the configuration of runtime.
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*
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* \param[in] model_file Path of model file, e.g ./model.pdmodel
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* \param[in] params_file Path of parameter file, e.g ./model.pdiparams, if the model format is ONNX, this parameter will be ignored
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* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
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* \param[in] model_format Model format of the loaded model, default is PADDLE format
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*/
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AnimeGAN(const std::string& model_file, const std::string& params_file = "",
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE);
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std::string ModelName() const { return "styletransfer/animegan"; }
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/** \brief Predict the style transfer result for an input image
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*
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result The output style transfer result will be writen to this structure
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* \return true if the prediction successed, otherwise false
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*/
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bool Predict(cv::Mat& img, cv::Mat* result);
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/** \brief Predict the style transfer result for a batch of input images
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*
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* \param[in] images The list of input images, each element comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] results The list of output style transfer results will be writen to this structure
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* \return true if the batch prediction successed, otherwise false
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*/
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bool BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<cv::Mat>* results);
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// Get preprocessor reference of AnimeGAN
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AnimeGANPreprocessor& GetPreprocessor() {
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return preprocessor_;
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}
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// Get postprocessor reference of AnimeGAN
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AnimeGANPostprocessor& GetPostprocessor() {
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return postprocessor_;
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}
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private:
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bool Initialize();
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AnimeGANPreprocessor preprocessor_;
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AnimeGANPostprocessor postprocessor_;
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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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