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51be3fea78
* first draft * add robx iou * add benchmark for ppyoloe_r * remove trash code * fix bugs * add pybind nms rotated option * add missing head file * fix bug * fix bug2 * fix shape bug --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
117 lines
4.2 KiB
C++
117 lines
4.2 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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#pragma once
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#include "fastdeploy/vision/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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#include "fastdeploy/vision/detection/ppdet/multiclass_nms.h"
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#include "fastdeploy/vision/detection/ppdet/multiclass_nms_rotated.h"
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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/*! @brief Postprocessor object for PaddleDet serials model.
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*/
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class FASTDEPLOY_DECL PaddleDetPostprocessor {
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public:
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PaddleDetPostprocessor() {
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// There may be no NMS config in the yaml file,
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// so we need to give a initial value to multi_class_nms_.
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multi_class_nms_.SetNMSOption(NMSOption());
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multi_class_nms_rotated_.SetNMSRotatedOption(NMSRotatedOption());
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}
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/** \brief Create a preprocessor instance for PaddleDet serials model
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*
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* \param[in] config_file Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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*/
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explicit PaddleDetPostprocessor(const std::string& arch) {
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// Used to differentiate models
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arch_ = arch;
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// There may be no NMS config in the yaml file,
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// so we need to give a initial value to multi_class_nms_.
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multi_class_nms_.SetNMSOption(NMSOption());
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multi_class_nms_rotated_.SetNMSRotatedOption(NMSRotatedOption());
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}
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/** \brief Process the result of runtime and fill to ClassifyResult structure
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*
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* \param[in] tensors The inference result from runtime
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* \param[in] result The output result of detection
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* \return true if the postprocess successed, otherwise false
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*/
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bool Run(const std::vector<FDTensor>& tensors,
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std::vector<DetectionResult>* result);
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/// Apply box decoding and nms step for the outputs for the model.This is
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/// only available for those model exported without box decoding and nms.
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void ApplyNMS() { with_nms_ = false; }
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/// If you do not want to modify the Yaml configuration file,
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/// you can use this function to set rotated NMS parameters.
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void SetNMSRotatedOption(const NMSRotatedOption& option) {
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multi_class_nms_rotated_.SetNMSRotatedOption(option);
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}
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/// If you do not want to modify the Yaml configuration file,
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/// you can use this function to set NMS parameters.
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void SetNMSOption(const NMSOption& option) {
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multi_class_nms_.SetNMSOption(option);
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}
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// Set scale_factor_ value.This is only available for those model exported
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// without nms.
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void SetScaleFactor(const std::vector<float>& scale_factor_value) {
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scale_factor_ = scale_factor_value;
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}
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private:
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std::vector<float> scale_factor_{0.0, 0.0};
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std::vector<float> GetScaleFactor() { return scale_factor_; }
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// for model without nms.
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bool with_nms_ = true;
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// Used to differentiate models
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std::string arch_;
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PaddleMultiClassNMS multi_class_nms_{};
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PaddleMultiClassNMSRotated multi_class_nms_rotated_{};
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// Process for General tensor without nms.
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bool ProcessWithoutNMS(const std::vector<FDTensor>& tensors,
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std::vector<DetectionResult>* results);
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// Process for General tensor with nms.
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bool ProcessWithNMS(const std::vector<FDTensor>& tensors,
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std::vector<DetectionResult>* results);
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// Process SOLOv2
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bool ProcessSolov2(const std::vector<FDTensor>& tensors,
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std::vector<DetectionResult>* results);
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// Process PPYOLOER
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bool ProcessPPYOLOER(const std::vector<FDTensor>& tensors,
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std::vector<DetectionResult>* results);
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// Process mask tensor for MaskRCNN
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bool ProcessMask(const FDTensor& tensor,
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std::vector<DetectionResult>* results);
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};
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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