Files
FastDeploy/fastdeploy/vision/common/processors/manager.h
Wang Xinyu d3d914856d [CVCUDA] Utilize CV-CUDA batch processing function (#1223)
* norm and permute batch processing

* move cache to mat, batch processors

* get batched tensor logic, resize on cpu logic

* fix cpu compile error

* remove vector mat api

* nits

* add comments

* nits

* fix batch size

* move initial resize on cpu option to use_cuda api

* fix pybind

* processor manager pybind

* rename mat and matbatch

* move initial resize on cpu to ppcls preprocessor

---------

Co-authored-by: Jason <jiangjiajun@baidu.com>
2023-02-07 13:44:30 +08:00

93 lines
2.7 KiB
C++

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "fastdeploy/utils/utils.h"
#include "fastdeploy/vision/common/processors/mat.h"
#include "fastdeploy/vision/common/processors/mat_batch.h"
namespace fastdeploy {
namespace vision {
class FASTDEPLOY_DECL ProcessorManager {
public:
~ProcessorManager();
/** \brief Use CUDA to boost the performance of processors
*
* \param[in] enable_cv_cuda ture: use CV-CUDA, false: use CUDA only
* \param[in] gpu_id GPU device id
* \return true if the preprocess successed, otherwise false
*/
void UseCuda(bool enable_cv_cuda = false, int gpu_id = -1);
bool CudaUsed();
void SetStream(FDMat* mat) {
#ifdef WITH_GPU
mat->SetStream(stream_);
#endif
}
void SetStream(FDMatBatch* mat_batch) {
#ifdef WITH_GPU
mat_batch->SetStream(stream_);
#endif
}
void SyncStream() {
#ifdef WITH_GPU
FDASSERT(cudaStreamSynchronize(stream_) == cudaSuccess,
"[ERROR] Error occurs while sync cuda stream.");
#endif
}
int DeviceId() { return device_id_; }
/** \brief Process the input image and prepare input tensors for runtime
*
* \param[in] images The input image data list, all the elements are returned by cv::imread()
* \param[in] outputs The output tensors which will feed in runtime
* \return true if the preprocess successed, otherwise false
*/
bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
/** \brief Apply() is the body of Run() function, it needs to be implemented by a derived class
*
* \param[in] image_batch The input image batch
* \param[in] outputs The output tensors which will feed in runtime
* \return true if the preprocess successed, otherwise false
*/
virtual bool Apply(FDMatBatch* image_batch,
std::vector<FDTensor>* outputs) = 0;
protected:
bool initialized_ = false;
private:
#ifdef WITH_GPU
cudaStream_t stream_ = nullptr;
#endif
int device_id_ = -1;
std::vector<FDTensor> input_caches_;
std::vector<FDTensor> output_caches_;
FDTensor batch_input_cache_;
FDTensor batch_output_cache_;
};
} // namespace vision
} // namespace fastdeploy