mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 17:17:14 +08:00
62e051e21d
* cvcuda resize * cvcuda center crop * cvcuda resize * add a fdtensor in fdmat * get cv mat and get tensor support gpu * paddleclas cvcuda preprocessor * fix compile err * fix windows compile error * rename reused to cached * address comment * remove debug code * add comment * add manager run * use cuda and cuda used * use cv cuda doc * address comment --------- Co-authored-by: Jason <jiangjiajun@baidu.com>
117 lines
3.8 KiB
C++
117 lines
3.8 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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#include "fastdeploy/vision/common/processors/base.h"
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#include "fastdeploy/utils/utils.h"
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#include "fastdeploy/vision/common/processors/proc_lib.h"
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namespace fastdeploy {
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namespace vision {
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bool Processor::operator()(Mat* mat, ProcLib lib) {
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ProcLib target = lib;
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if (lib == ProcLib::DEFAULT) {
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target = DefaultProcLib::default_lib;
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}
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if (target == ProcLib::FLYCV) {
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#ifdef ENABLE_FLYCV
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return ImplByFlyCV(mat);
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#else
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FDASSERT(false, "FastDeploy didn't compile with FlyCV.");
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#endif
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} else if (target == ProcLib::CUDA) {
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#ifdef WITH_GPU
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FDASSERT(mat->Stream() != nullptr,
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"CUDA processor requires cuda stream, please set stream for Mat");
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return ImplByCuda(mat);
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#else
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FDASSERT(false, "FastDeploy didn't compile with WITH_GPU.");
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#endif
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} else if (target == ProcLib::CVCUDA) {
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#ifdef ENABLE_CVCUDA
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FDASSERT(mat->Stream() != nullptr,
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"CV-CUDA requires cuda stream, please set stream for Mat");
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return ImplByCvCuda(mat);
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#else
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FDASSERT(false, "FastDeploy didn't compile with CV-CUDA.");
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#endif
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}
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// DEFAULT & OPENCV
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return ImplByOpenCV(mat);
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}
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FDTensor* Processor::UpdateAndGetCachedTensor(
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const std::vector<int64_t>& new_shape, const FDDataType& data_type,
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const std::string& tensor_name, const Device& new_device,
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const bool& use_pinned_memory) {
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if (cached_tensors_.count(tensor_name) == 0) {
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cached_tensors_[tensor_name] = FDTensor();
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}
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cached_tensors_[tensor_name].is_pinned_memory = use_pinned_memory;
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cached_tensors_[tensor_name].Resize(new_shape, data_type, tensor_name,
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new_device);
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return &cached_tensors_[tensor_name];
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}
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FDTensor* Processor::CreateCachedGpuInputTensor(
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Mat* mat, const std::string& tensor_name) {
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#ifdef WITH_GPU
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FDTensor* src = mat->Tensor();
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if (src->device == Device::GPU) {
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return src;
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} else if (src->device == Device::CPU) {
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FDTensor* tensor = UpdateAndGetCachedTensor(src->Shape(), src->Dtype(),
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tensor_name, Device::GPU);
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FDASSERT(cudaMemcpyAsync(tensor->Data(), src->Data(), tensor->Nbytes(),
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cudaMemcpyHostToDevice, mat->Stream()) == 0,
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"[ERROR] Error occurs while copy memory from CPU to GPU.");
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return tensor;
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} else {
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FDASSERT(false, "FDMat is on unsupported device: %d", src->device);
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}
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#else
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FDASSERT(false, "FastDeploy didn't compile with WITH_GPU.");
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#endif
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return nullptr;
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}
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void EnableFlyCV() {
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#ifdef ENABLE_FLYCV
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DefaultProcLib::default_lib = ProcLib::FLYCV;
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FDINFO << "Will change to use image processing library "
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<< DefaultProcLib::default_lib << std::endl;
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#else
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FDWARNING << "FastDeploy didn't compile with FlyCV, "
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"will fallback to use OpenCV instead."
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<< std::endl;
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#endif
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}
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void DisableFlyCV() {
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DefaultProcLib::default_lib = ProcLib::OPENCV;
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FDINFO << "Will change to use image processing library "
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<< DefaultProcLib::default_lib << std::endl;
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}
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void SetProcLibCpuNumThreads(int threads) {
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cv::setNumThreads(threads);
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#ifdef ENABLE_FLYCV
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fcv::set_thread_num(threads);
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#endif
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}
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} // namespace vision
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} // namespace fastdeploy
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