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https://github.com/PaddlePaddle/FastDeploy.git
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91a1c72f98
* move manager initialized_ flag to ppcls * update dbdetector preprocess api * declare processor op * ppocr detector preprocessor support cvcuda * move cvcuda op to class member * ppcls use manager register api * refactor det preprocessor init api * add set preprocessor api * add create processor macro * new processor call api * ppcls preprocessor init resize on cpu * ppocr detector preprocessor set normalize api * revert ppcls pybind * remove dbdetector set preprocessor * refine dbdetector preprocessor includes * remove mean std in py constructor * add comments * update comment * Update __init__.py
78 lines
2.3 KiB
C++
78 lines
2.3 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/vision/common/processors/mat.h"
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#ifdef WITH_GPU
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#include <cuda_runtime_api.h>
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#endif
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namespace fastdeploy {
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namespace vision {
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enum FDMatBatchLayout { NHWC, NCHW };
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struct FASTDEPLOY_DECL FDMatBatch {
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FDMatBatch() = default;
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// MatBatch is intialized with a list of mats,
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// the data is stored in the mats separately.
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// Call Tensor() function to get a batched 4-dimension tensor.
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explicit FDMatBatch(std::vector<FDMat>* _mats) {
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mats = _mats;
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layout = FDMatBatchLayout::NHWC;
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mat_type = ProcLib::OPENCV;
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}
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// Get the batched 4-dimension tensor.
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FDTensor* Tensor();
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void SetTensor(FDTensor* tensor);
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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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std::shared_ptr<FDTensor> fd_tensor = std::make_shared<FDTensor>();
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public:
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// When using CV-CUDA/CUDA, please set input/output cache,
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// refer to manager.cc
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FDTensor* input_cache;
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FDTensor* output_cache;
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#ifdef WITH_GPU
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cudaStream_t Stream() const { return stream; }
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void SetStream(cudaStream_t s);
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#endif
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std::vector<FDMat>* mats;
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ProcLib mat_type = ProcLib::OPENCV;
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FDMatBatchLayout layout = FDMatBatchLayout::NHWC;
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Device device = Device::CPU;
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ProcLib proc_lib = ProcLib::DEFAULT;
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// False: the data is stored in the mats separately
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// True: the data is stored in the fd_tensor continuously in 4 dimensions
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bool has_batched_tensor = false;
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};
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// Create a batched input tensor on GPU and save into input_cache.
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// If the MatBatch is on GPU, return the Tensor() directly.
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// If the MatBatch is on CPU, then copy the CPU tensors to GPU and get a GPU
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// batched input tensor.
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FDTensor* CreateCachedGpuInputTensor(FDMatBatch* mat_batch);
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} // namespace vision
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} // namespace fastdeploy
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