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	d3d914856d
	
	
	
		
			
			* 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>
		
			
				
	
	
		
			93 lines
		
	
	
		
			2.7 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			93 lines
		
	
	
		
			2.7 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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| 
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| #pragma once
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| 
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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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| 
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| namespace fastdeploy {
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| namespace vision {
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| 
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| class FASTDEPLOY_DECL ProcessorManager {
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|  public:
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|   ~ProcessorManager();
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| 
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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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| 
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|   bool CudaUsed();
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| 
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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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| 
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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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| 
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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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| 
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|   int DeviceId() { return device_id_; }
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| 
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|   /** \brief Process the input image 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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| 
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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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| 
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|  protected:
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|   bool initialized_ = false;
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| 
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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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| 
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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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| 
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| }  // namespace vision
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| }  // namespace fastdeploy
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