mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
225 lines
8.1 KiB
C++
225 lines
8.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "fastdeploy/backends/common/multiclass_nms.h"
|
|
#include <algorithm>
|
|
#include "fastdeploy/core/fd_tensor.h"
|
|
#include "fastdeploy/utils/utils.h"
|
|
|
|
namespace fastdeploy {
|
|
namespace backend {
|
|
template <class T>
|
|
bool SortScorePairDescend(const std::pair<float, T>& pair1,
|
|
const std::pair<float, T>& pair2) {
|
|
return pair1.first > pair2.first;
|
|
}
|
|
|
|
void GetMaxScoreIndex(const float* scores, const int& score_size,
|
|
const float& threshold, const int& top_k,
|
|
std::vector<std::pair<float, int>>* sorted_indices) {
|
|
for (size_t i = 0; i < score_size; ++i) {
|
|
if (scores[i] > threshold) {
|
|
sorted_indices->push_back(std::make_pair(scores[i], i));
|
|
}
|
|
}
|
|
// Sort the score pair according to the scores in descending order
|
|
std::stable_sort(sorted_indices->begin(), sorted_indices->end(),
|
|
SortScorePairDescend<int>);
|
|
// Keep top_k scores if needed.
|
|
if (top_k > -1 && top_k < static_cast<int>(sorted_indices->size())) {
|
|
sorted_indices->resize(top_k);
|
|
}
|
|
}
|
|
|
|
float BBoxArea(const float* box, const bool& normalized) {
|
|
if (box[2] < box[0] || box[3] < box[1]) {
|
|
// If coordinate values are is invalid
|
|
// (e.g. xmax < xmin or ymax < ymin), return 0.
|
|
return 0.f;
|
|
} else {
|
|
const float w = box[2] - box[0];
|
|
const float h = box[3] - box[1];
|
|
if (normalized) {
|
|
return w * h;
|
|
} else {
|
|
// If coordinate values are not within range [0, 1].
|
|
return (w + 1) * (h + 1);
|
|
}
|
|
}
|
|
}
|
|
|
|
float JaccardOverlap(const float* box1, const float* box2,
|
|
const bool& normalized) {
|
|
if (box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
|
|
box2[3] < box1[1]) {
|
|
return 0.f;
|
|
} else {
|
|
const float inter_xmin = std::max(box1[0], box2[0]);
|
|
const float inter_ymin = std::max(box1[1], box2[1]);
|
|
const float inter_xmax = std::min(box1[2], box2[2]);
|
|
const float inter_ymax = std::min(box1[3], box2[3]);
|
|
float norm = normalized ? 0.0f : 1.0f;
|
|
float inter_w = inter_xmax - inter_xmin + norm;
|
|
float inter_h = inter_ymax - inter_ymin + norm;
|
|
const float inter_area = inter_w * inter_h;
|
|
const float bbox1_area = BBoxArea(box1, normalized);
|
|
const float bbox2_area = BBoxArea(box2, normalized);
|
|
return inter_area / (bbox1_area + bbox2_area - inter_area);
|
|
}
|
|
}
|
|
|
|
void MultiClassNMS::FastNMS(const float* boxes, const float* scores,
|
|
const int& num_boxes,
|
|
std::vector<int>* keep_indices) {
|
|
std::vector<std::pair<float, int>> sorted_indices;
|
|
GetMaxScoreIndex(scores, num_boxes, score_threshold, nms_top_k,
|
|
&sorted_indices);
|
|
|
|
float adaptive_threshold = nms_threshold;
|
|
while (sorted_indices.size() != 0) {
|
|
const int idx = sorted_indices.front().second;
|
|
bool keep = true;
|
|
for (size_t k = 0; k < keep_indices->size(); ++k) {
|
|
if (!keep) {
|
|
break;
|
|
}
|
|
const int kept_idx = (*keep_indices)[k];
|
|
float overlap =
|
|
JaccardOverlap(boxes + idx * 4, boxes + kept_idx * 4, normalized);
|
|
keep = overlap <= adaptive_threshold;
|
|
}
|
|
if (keep) {
|
|
keep_indices->push_back(idx);
|
|
}
|
|
sorted_indices.erase(sorted_indices.begin());
|
|
if (keep && nms_eta<1.0 & adaptive_threshold> 0.5) {
|
|
adaptive_threshold *= nms_eta;
|
|
}
|
|
}
|
|
}
|
|
|
|
int MultiClassNMS::NMSForEachSample(
|
|
const float* boxes, const float* scores, int num_boxes, int num_classes,
|
|
std::map<int, std::vector<int>>* keep_indices) {
|
|
for (int i = 0; i < num_classes; ++i) {
|
|
if (i == background_label) {
|
|
continue;
|
|
}
|
|
const float* score_for_class_i = scores + i * num_boxes;
|
|
FastNMS(boxes, score_for_class_i, num_boxes, &((*keep_indices)[i]));
|
|
}
|
|
int num_det = 0;
|
|
for (auto iter = keep_indices->begin(); iter != keep_indices->end(); ++iter) {
|
|
num_det += iter->second.size();
|
|
}
|
|
|
|
if (keep_top_k > -1 && num_det > keep_top_k) {
|
|
std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;
|
|
for (const auto& it : *keep_indices) {
|
|
int label = it.first;
|
|
const float* current_score = scores + label * num_boxes;
|
|
auto& label_indices = it.second;
|
|
for (size_t j = 0; j < label_indices.size(); ++j) {
|
|
int idx = label_indices[j];
|
|
score_index_pairs.push_back(
|
|
std::make_pair(current_score[idx], std::make_pair(label, idx)));
|
|
}
|
|
}
|
|
std::stable_sort(score_index_pairs.begin(), score_index_pairs.end(),
|
|
SortScorePairDescend<std::pair<int, int>>);
|
|
score_index_pairs.resize(keep_top_k);
|
|
|
|
std::map<int, std::vector<int>> new_indices;
|
|
for (size_t j = 0; j < score_index_pairs.size(); ++j) {
|
|
int label = score_index_pairs[j].second.first;
|
|
int idx = score_index_pairs[j].second.second;
|
|
new_indices[label].push_back(idx);
|
|
}
|
|
new_indices.swap(*keep_indices);
|
|
num_det = keep_top_k;
|
|
}
|
|
return num_det;
|
|
}
|
|
|
|
void MultiClassNMS::Compute(const float* boxes_data, const float* scores_data,
|
|
const std::vector<int64_t>& boxes_dim,
|
|
const std::vector<int64_t>& scores_dim) {
|
|
int score_size = scores_dim.size();
|
|
|
|
int64_t batch_size = scores_dim[0];
|
|
int64_t box_dim = boxes_dim[2];
|
|
int64_t out_dim = box_dim + 2;
|
|
|
|
int num_nmsed_out = 0;
|
|
FDASSERT(score_size == 3, "Require rank of input scores be 3, but now it's " +
|
|
std::to_string(score_size) + ".");
|
|
FDASSERT(boxes_dim[2] == 4,
|
|
"Require the 3-dimension of input boxes be 4, but now it's " +
|
|
std::to_string(boxes_dim[2]) + ".");
|
|
out_num_rois_data.resize(batch_size);
|
|
|
|
std::vector<std::map<int, std::vector<int>>> all_indices;
|
|
for (size_t i = 0; i < batch_size; ++i) {
|
|
std::map<int, std::vector<int>> indices; // indices kept for each class
|
|
const float* current_boxes_ptr =
|
|
boxes_data + i * boxes_dim[1] * boxes_dim[2];
|
|
const float* current_scores_ptr =
|
|
scores_data + i * scores_dim[1] * scores_dim[2];
|
|
int num = NMSForEachSample(current_boxes_ptr, current_scores_ptr,
|
|
boxes_dim[1], scores_dim[1], &indices);
|
|
num_nmsed_out += num;
|
|
out_num_rois_data[i] = num;
|
|
all_indices.emplace_back(indices);
|
|
}
|
|
std::vector<int64_t> out_box_dims = {num_nmsed_out, 6};
|
|
std::vector<int64_t> out_index_dims = {num_nmsed_out, 1};
|
|
if (num_nmsed_out == 0) {
|
|
for (size_t i = 0; i < batch_size; ++i) {
|
|
out_num_rois_data[i] = 0;
|
|
}
|
|
return;
|
|
}
|
|
out_box_data.resize(num_nmsed_out * 6);
|
|
out_index_data.resize(num_nmsed_out);
|
|
|
|
int count = 0;
|
|
for (size_t i = 0; i < batch_size; ++i) {
|
|
const float* current_boxes_ptr =
|
|
boxes_data + i * boxes_dim[1] * boxes_dim[2];
|
|
const float* current_scores_ptr =
|
|
scores_data + i * scores_dim[1] * scores_dim[2];
|
|
for (const auto& it : all_indices[i]) {
|
|
int label = it.first;
|
|
const auto& indices = it.second;
|
|
const float* current_scores_class_ptr =
|
|
current_scores_ptr + label * scores_dim[2];
|
|
for (size_t j = 0; j < indices.size(); ++j) {
|
|
int start = count * 6;
|
|
out_box_data[start] = label;
|
|
out_box_data[start + 1] = current_scores_class_ptr[indices[j]];
|
|
|
|
out_box_data[start + 2] = current_boxes_ptr[indices[j] * 4];
|
|
out_box_data[start + 3] = current_boxes_ptr[indices[j] * 4 + 1];
|
|
out_box_data[start + 4] = current_boxes_ptr[indices[j] * 4 + 2];
|
|
|
|
out_box_data[start + 5] = current_boxes_ptr[indices[j] * 4 + 3];
|
|
out_index_data[count] = i * boxes_dim[1] + indices[j];
|
|
count += 1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} // namespace backend
|
|
} // namespace fastdeploy
|