mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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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>
42 lines
1.7 KiB
C++
42 lines
1.7 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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#include "fastdeploy/pybind/main.h"
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namespace fastdeploy {
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void BindProcessorManager(pybind11::module& m) {
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pybind11::class_<vision::ProcessorManager>(m, "ProcessorManager")
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.def("run",
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[](vision::ProcessorManager& self,
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std::vector<pybind11::array>& im_list) {
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std::vector<vision::FDMat> images;
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for (size_t i = 0; i < im_list.size(); ++i) {
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images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
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}
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std::vector<FDTensor> outputs;
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if (!self.Run(&images, &outputs)) {
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throw std::runtime_error("Failed to process the input data");
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}
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if (!self.CudaUsed()) {
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for (size_t i = 0; i < outputs.size(); ++i) {
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outputs[i].StopSharing();
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}
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}
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return outputs;
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})
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.def("use_cuda",
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[](vision::ProcessorManager& self, bool enable_cv_cuda = false,
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int gpu_id = -1) { self.UseCuda(enable_cv_cuda, gpu_id); });
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}
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} // namespace fastdeploy
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