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https://github.com/PaddlePaddle/FastDeploy.git
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c6480de736
* bind success * bind success fix * FDMat pybind, ResizeByShort pybind * FDMat pybind, ResizeByShort pybind, remove initialized_ * override BindProcessorManager::Run in python is available * PyProcessorManager done * vision_pybind fix * manager.py fix * add tutorials * remove Apply() bind * remove Apply() bind and fix * fix reviewed problem * fix reviewed problem * fix reviewed problem readme * fix reviewed problem readme etc * apply return outputs * nits * update readme * fix FDMatbatch * add op pybind: CenterCrop, Pad * add op overload for pass FDMatBatch --------- Co-authored-by: Wang Xinyu <shaywxy@gmail.com>
102 lines
2.9 KiB
C++
102 lines
2.9 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/utils/utils.h"
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#include "fastdeploy/vision/common/processors/mat.h"
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#include "fastdeploy/vision/common/processors/mat_batch.h"
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#include "fastdeploy/vision/common/processors/base.h"
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namespace fastdeploy {
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namespace vision {
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class FASTDEPLOY_DECL ProcessorManager {
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public:
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~ProcessorManager();
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/** \brief Use CUDA to boost the performance of processors
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*
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* \param[in] enable_cv_cuda ture: use CV-CUDA, false: use CUDA only
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* \param[in] gpu_id GPU device id
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* \return true if the preprocess successed, otherwise false
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*/
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void UseCuda(bool enable_cv_cuda = false, int gpu_id = -1);
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bool CudaUsed();
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#ifdef WITH_GPU
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cudaStream_t Stream() const { return stream_; }
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#endif
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void SetStream(FDMat* mat) {
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#ifdef WITH_GPU
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mat->SetStream(stream_);
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#endif
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}
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void SetStream(FDMatBatch* mat_batch) {
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#ifdef WITH_GPU
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mat_batch->SetStream(stream_);
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#endif
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}
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void SyncStream() {
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#ifdef WITH_GPU
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FDASSERT(cudaStreamSynchronize(stream_) == cudaSuccess,
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"[ERROR] Error occurs while sync cuda stream.");
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#endif
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}
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int DeviceId() { return device_id_; }
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/** \brief Process the input images and prepare input tensors for runtime
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*
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* \param[in] images The input image data list, all the elements are returned by cv::imread()
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* \param[in] outputs The output tensors which will feed in runtime
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* \return true if the preprocess successed, otherwise false
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*/
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bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
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/** \brief Apply() is the body of Run() function, it needs to be implemented by a derived class
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*
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* \param[in] image_batch The input image batch
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* \param[in] outputs The output tensors which will feed in runtime
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* \return true if the preprocess successed, otherwise false
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*/
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virtual bool Apply(FDMatBatch* image_batch,
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std::vector<FDTensor>* outputs) = 0;
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void PreApply(FDMatBatch* image_batch);
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void PostApply();
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protected:
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ProcLib proc_lib_ = ProcLib::DEFAULT;
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private:
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#ifdef WITH_GPU
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cudaStream_t stream_ = nullptr;
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#endif
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int device_id_ = -1;
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std::vector<FDTensor> input_caches_;
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std::vector<FDTensor> output_caches_;
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FDTensor batch_input_cache_;
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FDTensor batch_output_cache_;
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};
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} // namespace vision
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} // namespace fastdeploy
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