mirror of
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
83 lines
2.9 KiB
C++
83 lines
2.9 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/mat_batch.h"
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namespace fastdeploy {
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namespace vision {
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#ifdef WITH_GPU
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void FDMatBatch::SetStream(cudaStream_t s) {
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stream = s;
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for (size_t i = 0; i < mats->size(); ++i) {
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(*mats)[i].SetStream(s);
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}
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}
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#endif
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FDTensor* FDMatBatch::Tensor() {
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if (has_batched_tensor) {
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return fd_tensor.get();
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}
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FDASSERT(CheckShapeConsistency(mats), "Mats shapes are not consistent.")
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// Each mat has its own tensor,
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// to get a batched tensor, we need copy these tensors to a batched tensor
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FDTensor* src = (*mats)[0].Tensor();
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auto new_shape = src->Shape();
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new_shape.insert(new_shape.begin(), mats->size());
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input_cache->Resize(new_shape, src->Dtype(), "batch_input_cache", device);
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for (size_t i = 0; i < mats->size(); ++i) {
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FDASSERT(device == (*mats)[i].Tensor()->device,
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"Mats and MatBatch are not on the same device");
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uint8_t* p = reinterpret_cast<uint8_t*>(input_cache->Data());
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int num_bytes = (*mats)[i].Tensor()->Nbytes();
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FDTensor::CopyBuffer(p + i * num_bytes, (*mats)[i].Tensor()->Data(),
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num_bytes, device, false);
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}
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SetTensor(input_cache);
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return fd_tensor.get();
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}
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void FDMatBatch::SetTensor(FDTensor* tensor) {
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fd_tensor->SetExternalData(tensor->Shape(), tensor->Dtype(), tensor->Data(),
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tensor->device, tensor->device_id);
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has_batched_tensor = true;
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}
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FDTensor* CreateCachedGpuInputTensor(FDMatBatch* mat_batch) {
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#ifdef WITH_GPU
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auto mats = mat_batch->mats;
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FDASSERT(CheckShapeConsistency(mats), "Mats shapes are not consistent.")
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FDTensor* src = (*mats)[0].Tensor();
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if (mat_batch->device == Device::GPU) {
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return mat_batch->Tensor();
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} else if (mat_batch->device == Device::CPU) {
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// Mats on CPU, we need copy them to GPU and then get a batched GPU tensor
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for (size_t i = 0; i < mats->size(); ++i) {
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FDTensor* tensor = CreateCachedGpuInputTensor(&(*mats)[i]);
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(*mats)[i].SetTensor(tensor);
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}
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mat_batch->device = Device::GPU;
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return mat_batch->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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} // namespace vision
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} // namespace fastdeploy
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