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https://github.com/PaddlePaddle/FastDeploy.git
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2d998223ac
Revert "Revert "[Backend] Add KunlunXin XPU deploy support" (#893)"
This reverts commit 0990ab9b50.
111 lines
3.5 KiB
C++
Executable File
111 lines
3.5 KiB
C++
Executable File
// 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/contrib/rknpu2/utils.h"
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#include <array>
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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 YOLOv5 serials model.
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*/
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class FASTDEPLOY_DECL RKYOLOPostprocessor {
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public:
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/** \brief Create a postprocessor instance for YOLOv5 serials model
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*/
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RKYOLOPostprocessor();
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/** \brief Process the result of runtime and fill to DetectionResult 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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* \param[in] ims_info The shape info list, record input_shape and output_shape
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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>* results);
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/// Set nms_threshold, default 0.45
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void SetNMSThreshold(const float& nms_threshold) {
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nms_threshold_ = nms_threshold;
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}
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/// Set conf_threshold, default 0.25
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void SetConfThreshold(const float& conf_threshold) {
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conf_threshold_ = conf_threshold;
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}
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/// Get conf_threshold, default 0.25
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float GetConfThreshold() const { return conf_threshold_; }
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/// Get nms_threshold, default 0.45
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float GetNMSThreshold() const { return nms_threshold_; }
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// Set height and weight
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void SetHeightAndWeight(int& height, int& width) {
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height_ = height;
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width_ = width;
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}
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// Set pad_hw_values
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void SetPadHWValues(std::vector<std::vector<int>> pad_hw_values) {
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pad_hw_values_ = pad_hw_values;
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}
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// Set scale
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void SetScale(std::vector<float> scale) {
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scale_ = scale;
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}
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// Set Anchor
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void SetAnchor(std::vector<int> anchors, int anchor_per_branch) {
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anchors_ = anchors;
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anchor_per_branch_ = anchor_per_branch;
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}
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private:
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std::vector<int> anchors_ = {10, 13, 16, 30, 33, 23, 30, 61, 62,
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45, 59, 119, 116, 90, 156, 198, 373, 326};
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int strides_[3] = {8, 16, 32};
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int height_ = 0;
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int width_ = 0;
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int anchor_per_branch_ = 0;
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// Process Int8 Model
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int ProcessInt8(int8_t* input, int* anchor, int grid_h, int grid_w,
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int stride, std::vector<float>& boxes,
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std::vector<float>& boxScores, std::vector<int>& classId,
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float threshold, int32_t zp, float scale);
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// Model
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int QuickSortIndiceInverse(std::vector<float>& input, int left, int right,
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std::vector<int>& indices);
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// post_process values
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std::vector<std::vector<int>> pad_hw_values_;
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std::vector<float> scale_;
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float nms_threshold_ = 0.45;
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float conf_threshold_ = 0.25;
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int prob_box_size = 85;
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int obj_class_num = 80;
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int obj_num_bbox_max_size = 200;
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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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