mirror of
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
3.1 KiB
C++
102 lines
3.1 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/manager.h"
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namespace fastdeploy {
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namespace vision {
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ProcessorManager::~ProcessorManager() {
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#ifdef WITH_GPU
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if (stream_) cudaStreamDestroy(stream_);
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#endif
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}
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void ProcessorManager::UseCuda(bool enable_cv_cuda, int gpu_id) {
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#ifdef WITH_GPU
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if (gpu_id >= 0) {
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device_id_ = gpu_id;
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FDASSERT(cudaSetDevice(device_id_) == cudaSuccess,
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"[ERROR] Error occurs while setting cuda device.");
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}
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FDASSERT(cudaStreamCreate(&stream_) == cudaSuccess,
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"[ERROR] Error occurs while creating cuda stream.");
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proc_lib_ = ProcLib::CUDA;
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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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if (enable_cv_cuda) {
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#ifdef ENABLE_CVCUDA
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proc_lib_ = ProcLib::CVCUDA;
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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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}
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bool ProcessorManager::CudaUsed() {
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return (proc_lib_ == ProcLib::CUDA || proc_lib_ == ProcLib::CVCUDA);
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}
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void ProcessorManager::PreApply(FDMatBatch* image_batch) {
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FDASSERT(image_batch->mats != nullptr, "The mats is empty.");
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FDASSERT(image_batch->mats->size() > 0,
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"The size of input images should be greater than 0.");
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if (image_batch->mats->size() > input_caches_.size()) {
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input_caches_.resize(image_batch->mats->size());
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output_caches_.resize(image_batch->mats->size());
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}
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image_batch->input_cache = &batch_input_cache_;
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image_batch->output_cache = &batch_output_cache_;
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if (CudaUsed()) {
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SetStream(image_batch);
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}
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for (size_t i = 0; i < image_batch->mats->size(); ++i) {
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FDMat* mat = &(image_batch->mats->at(i));
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mat->input_cache = &input_caches_[i];
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mat->output_cache = &output_caches_[i];
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mat->proc_lib = proc_lib_;
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if (mat->mat_type == ProcLib::CUDA) {
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// Make a copy of the input data ptr, so that the original data ptr of
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// FDMat won't be modified.
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auto fd_tensor = std::make_shared<FDTensor>();
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fd_tensor->SetExternalData(mat->Tensor()->shape, mat->Tensor()->Dtype(),
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mat->Tensor()->Data(), mat->Tensor()->device,
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mat->Tensor()->device_id);
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mat->SetTensor(fd_tensor);
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}
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}
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}
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void ProcessorManager::PostApply() {
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if (CudaUsed()) {
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SyncStream();
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}
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}
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bool ProcessorManager::Run(std::vector<FDMat>* images,
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std::vector<FDTensor>* outputs) {
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FDMatBatch image_batch(images);
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PreApply(&image_batch);
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bool ret = Apply(&image_batch, outputs);
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PostApply();
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return ret;
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}
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} // namespace vision
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} // namespace fastdeploy
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