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
102 lines
3.4 KiB
C++
102 lines
3.4 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/ocr/ppocr/dbdetector.h"
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#include "fastdeploy/utils/perf.h"
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#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace ocr {
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DBDetector::DBDetector() {}
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DBDetector::DBDetector(const std::string& model_file,
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const std::string& params_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) {
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if (model_format == ModelFormat::ONNX) {
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valid_cpu_backends = {Backend::ORT, Backend::OPENVINO};
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valid_gpu_backends = {Backend::ORT, Backend::TRT};
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} else {
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::OPENVINO,
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Backend::LITE};
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valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
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valid_kunlunxin_backends = {Backend::LITE};
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valid_ascend_backends = {Backend::LITE};
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valid_sophgonpu_backends = {Backend::SOPHGOTPU};
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}
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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initialized = Initialize();
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}
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// Init
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bool DBDetector::Initialize() {
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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return true;
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}
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std::unique_ptr<DBDetector> DBDetector::Clone() const {
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std::unique_ptr<DBDetector> clone_model =
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utils::make_unique<DBDetector>(DBDetector(*this));
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clone_model->SetRuntime(clone_model->CloneRuntime());
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return clone_model;
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}
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bool DBDetector::Predict(const cv::Mat& img,
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std::vector<std::array<int, 8>>* boxes_result) {
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std::vector<std::vector<std::array<int, 8>>> det_results;
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if (!BatchPredict({img}, &det_results)) {
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return false;
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}
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*boxes_result = std::move(det_results[0]);
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return true;
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}
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bool DBDetector::BatchPredict(
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const std::vector<cv::Mat>& images,
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std::vector<std::vector<std::array<int, 8>>>* det_results) {
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std::vector<FDMat> fd_images = WrapMat(images);
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if (!preprocessor_.Run(&fd_images, &reused_input_tensors_)) {
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FDERROR << "Failed to preprocess input image." << std::endl;
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return false;
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}
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auto batch_det_img_info = preprocessor_.GetBatchImgInfo();
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reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
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if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
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FDERROR << "Failed to inference by runtime." << std::endl;
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return false;
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}
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if (!postprocessor_.Run(reused_output_tensors_, det_results,
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*batch_det_img_info)) {
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FDERROR << "Failed to postprocess the inference cls_results by runtime."
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<< std::endl;
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return false;
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}
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return true;
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}
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} // namespace ocr
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} // namespace vision
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} // namespace fastdeploy
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