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* 更新ppdet * 更新ppdet * 更新ppdet * 更新ppdet * 更新ppdet * 新增ppdet_decode * 更新多batch支持 * 更新多batch支持 * 更新多batch支持 * 更新注释内容 * 尝试解决pybind问题 * 尝试解决pybind的问题 * 尝试解决pybind的问题 * 重构代码 * 重构代码 * 重构代码 * 按照要求修改 * 修复部分bug 加入pybind * 修复pybind * 修复pybind错误的问题
77 lines
2.7 KiB
C++
77 lines
2.7 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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#pragma once
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#include <map>
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#include <string>
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#include <vector>
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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/** \brief Config for PaddleMultiClassNMS
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* \param[in] background_label the value of background label
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* \param[in] keep_top_k the value of keep_top_k
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* \param[in] nms_eta the value of nms_eta
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* \param[in] nms_threshold a dict that contains the arguments of nms operations
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* \param[in] nms_top_k if there are more than max_num bboxes after NMS, only top max_num will be kept.
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* \param[in] normalized Determine whether normalized is required
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* \param[in] score_threshold bbox threshold, bboxes with scores lower than it will not be considered.
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*/
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struct NMSOption{
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NMSOption() = default;
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int64_t background_label = -1;
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int64_t keep_top_k = 100;
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float nms_eta = 1.0;
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float nms_threshold = 0.5;
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int64_t nms_top_k = 1000;
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bool normalized = true;
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float score_threshold = 0.3;
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};
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struct PaddleMultiClassNMS {
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int64_t background_label = -1;
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int64_t keep_top_k = -1;
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float nms_eta;
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float nms_threshold = 0.7;
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int64_t nms_top_k;
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bool normalized;
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float score_threshold;
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std::vector<int32_t> out_num_rois_data;
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std::vector<int32_t> out_index_data;
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std::vector<float> out_box_data;
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void FastNMS(const float* boxes, const float* scores, const int& num_boxes,
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std::vector<int>* keep_indices);
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int NMSForEachSample(const float* boxes, const float* scores, int num_boxes,
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int num_classes,
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std::map<int, std::vector<int>>* keep_indices);
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void Compute(const float* boxes, const float* scores,
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const std::vector<int64_t>& boxes_dim,
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const std::vector<int64_t>& scores_dim);
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void SetNMSOption(const struct NMSOption &nms_option){
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background_label = nms_option.background_label;
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keep_top_k = nms_option.keep_top_k;
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nms_eta = nms_option.nms_eta;
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nms_threshold = nms_option.nms_threshold;
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nms_top_k = nms_option.nms_top_k;
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normalized = nms_option.normalized;
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score_threshold = nms_option.score_threshold;
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}
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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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