wehub-resource-sync
327 lines
11 KiB
C++
327 lines
11 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 "paddle/phi/kernels/matrix_nms_kernel.h"
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#include "paddle/common/ddim.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <class T>
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static inline T BBoxArea(const T* box, const bool normalized) {
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if (box[2] < box[0] || box[3] < box[1]) {
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// If coordinate values are invalid
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// (e.g. xmax < xmin or ymax < ymin), return 0.
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return static_cast<T>(0.);
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} else {
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const T w = box[2] - box[0];
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const T h = box[3] - box[1];
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if (normalized) {
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return w * h;
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} else {
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// If coordinate values are not within range [0, 1].
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return (w + 1) * (h + 1);
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}
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}
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}
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template <class T>
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static inline T JaccardOverlap(const T* box1,
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const T* box2,
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const bool normalized) {
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if (box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
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box2[3] < box1[1]) {
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return static_cast<T>(0.);
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} else {
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const T inter_xmin = std::max(box1[0], box2[0]);
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const T inter_ymin = std::max(box1[1], box2[1]);
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const T inter_xmax = std::min(box1[2], box2[2]);
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const T inter_ymax = std::min(box1[3], box2[3]);
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T norm = normalized ? static_cast<T>(0.) : static_cast<T>(1.);
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T inter_w = inter_xmax - inter_xmin + norm;
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T inter_h = inter_ymax - inter_ymin + norm;
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const T inter_area = inter_w * inter_h;
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const T bbox1_area = BBoxArea<T>(box1, normalized);
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const T bbox2_area = BBoxArea<T>(box2, normalized);
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return inter_area / (bbox1_area + bbox2_area - inter_area);
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}
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}
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template <typename T, bool gaussian>
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struct decay_score;
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template <typename T>
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struct decay_score<T, true> {
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T operator()(T iou, T max_iou, T sigma) {
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return std::exp((max_iou * max_iou - iou * iou) * sigma);
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}
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};
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template <typename T>
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struct decay_score<T, false> {
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T operator()(T iou, T max_iou, T sigma UNUSED) {
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return (1. - iou) / (1. - max_iou);
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}
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};
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template <typename T, bool gaussian>
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void NMSMatrix(const DenseTensor& bbox,
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const DenseTensor& scores,
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const T score_threshold,
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const T post_threshold,
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const float sigma,
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const int64_t top_k,
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const bool normalized,
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std::vector<int>* selected_indices,
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std::vector<T>* decayed_scores) {
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int64_t num_boxes = bbox.dims()[0];
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int64_t box_size = bbox.dims()[1];
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auto score_ptr = scores.data<T>();
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auto bbox_ptr = bbox.data<T>();
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std::vector<int32_t> perm(num_boxes);
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std::iota(perm.begin(), perm.end(), 0);
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auto end = std::remove_if(
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perm.begin(), perm.end(), [&score_ptr, score_threshold](int32_t idx) {
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return score_ptr[idx] <= score_threshold;
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});
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auto sort_fn = [&score_ptr](int32_t lhs, int32_t rhs) {
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return score_ptr[lhs] > score_ptr[rhs];
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};
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int64_t num_pre = std::distance(perm.begin(), end);
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if (num_pre <= 0) {
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return;
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}
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if (top_k > -1 && num_pre > top_k) {
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num_pre = top_k;
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}
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std::partial_sort(perm.begin(), perm.begin() + num_pre, end, sort_fn);
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std::vector<T> iou_matrix((num_pre * (num_pre - 1)) >> 1);
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std::vector<T> iou_max(num_pre);
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iou_max[0] = 0.;
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for (int64_t i = 1; i < num_pre; i++) {
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T max_iou = 0.;
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auto idx_a = perm[i];
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for (int64_t j = 0; j < i; j++) {
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auto idx_b = perm[j];
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auto iou = JaccardOverlap<T>(
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bbox_ptr + idx_a * box_size, bbox_ptr + idx_b * box_size, normalized);
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max_iou = std::max(max_iou, iou);
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iou_matrix[i * (i - 1) / 2 + j] = iou;
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}
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iou_max[i] = max_iou;
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}
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if (score_ptr[perm[0]] > post_threshold) {
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selected_indices->push_back(perm[0]);
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decayed_scores->push_back(score_ptr[perm[0]]);
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}
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decay_score<T, gaussian> decay_fn;
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for (int64_t i = 1; i < num_pre; i++) {
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T min_decay = 1.;
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for (int64_t j = 0; j < i; j++) {
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auto max_iou = iou_max[j];
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auto iou = iou_matrix[i * (i - 1) / 2 + j];
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auto decay = decay_fn(iou, max_iou, sigma);
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min_decay = std::min(min_decay, decay);
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}
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auto ds = min_decay * score_ptr[perm[i]];
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if (ds <= post_threshold) continue;
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selected_indices->push_back(perm[i]);
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decayed_scores->push_back(ds);
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}
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}
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template <typename T>
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size_t MultiClassMatrixNMS(const DenseTensor& scores,
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const DenseTensor& bboxes,
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std::vector<T>* out,
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std::vector<int>* indices,
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int start,
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int64_t background_label,
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int64_t nms_top_k,
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int64_t keep_top_k,
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bool normalized,
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T score_threshold,
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T post_threshold,
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bool use_gaussian,
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float gaussian_sigma) {
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std::vector<int> all_indices;
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std::vector<T> all_scores;
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std::vector<T> all_classes;
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all_indices.reserve(scores.numel());
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all_scores.reserve(scores.numel());
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all_classes.reserve(scores.numel());
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size_t num_det = 0;
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auto class_num = scores.dims()[0];
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DenseTensor score_slice;
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for (int64_t c = 0; c < class_num; ++c) {
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if (c == background_label) continue;
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score_slice = scores.Slice(c, c + 1);
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if (use_gaussian) {
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NMSMatrix<T, true>(bboxes,
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score_slice,
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score_threshold,
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post_threshold,
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gaussian_sigma,
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nms_top_k,
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normalized,
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&all_indices,
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&all_scores);
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} else {
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NMSMatrix<T, false>(bboxes,
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score_slice,
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score_threshold,
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post_threshold,
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gaussian_sigma,
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nms_top_k,
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normalized,
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&all_indices,
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&all_scores);
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}
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for (size_t i = 0; i < all_indices.size() - num_det; i++) {
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all_classes.push_back(static_cast<T>(c));
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}
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num_det = all_indices.size();
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}
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if (num_det <= 0) {
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return num_det;
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}
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if (keep_top_k > -1) {
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auto k = static_cast<size_t>(keep_top_k);
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if (num_det > k) num_det = k;
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}
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std::vector<int32_t> perm(all_indices.size());
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std::iota(perm.begin(), perm.end(), 0);
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std::partial_sort(perm.begin(),
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perm.begin() + num_det, // NOLINT
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perm.end(),
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[&all_scores](int lhs, int rhs) {
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return all_scores[lhs] > all_scores[rhs];
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});
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for (size_t i = 0; i < num_det; i++) {
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auto p = perm[i];
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auto idx = all_indices[p];
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auto cls = all_classes[p];
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auto score = all_scores[p];
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auto bbox = bboxes.data<T>() + idx * bboxes.dims()[1];
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(*indices).push_back(start + idx);
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(*out).push_back(cls);
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(*out).push_back(score);
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for (int j = 0; j < bboxes.dims()[1]; j++) {
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(*out).push_back(bbox[j]);
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}
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}
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return num_det;
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}
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template <typename T, typename Context>
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void MatrixNMSKernel(const Context& dev_ctx,
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const DenseTensor& bboxes,
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const DenseTensor& scores,
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float score_threshold,
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int nms_top_k,
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int keep_top_k,
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float post_threshold,
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bool use_gaussian,
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float gaussian_sigma,
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int background_label,
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bool normalized,
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DenseTensor* out,
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DenseTensor* index,
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DenseTensor* roisnum) {
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auto score_dims = vectorize<int>(scores.dims());
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auto batch_size = score_dims[0];
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auto num_boxes = score_dims[2];
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auto box_dim = bboxes.dims()[2];
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auto out_dim = box_dim + 2;
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DenseTensor boxes_slice, scores_slice;
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size_t num_out = 0;
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std::vector<size_t> offsets = {0};
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std::vector<T> detections;
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std::vector<int> indices;
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std::vector<int> num_per_batch;
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detections.reserve(out_dim * num_boxes * batch_size);
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indices.reserve(num_boxes * batch_size);
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num_per_batch.reserve(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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scores_slice = scores.Slice(i, i + 1);
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scores_slice.Resize({score_dims[1], score_dims[2]});
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boxes_slice = bboxes.Slice(i, i + 1);
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boxes_slice.Resize({score_dims[2], box_dim});
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int start = i * score_dims[2];
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num_out = MultiClassMatrixNMS(scores_slice,
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boxes_slice,
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&detections,
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&indices,
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start,
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background_label,
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nms_top_k,
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keep_top_k,
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normalized,
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static_cast<T>(score_threshold),
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static_cast<T>(post_threshold),
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use_gaussian,
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gaussian_sigma);
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offsets.push_back(offsets.back() + num_out);
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num_per_batch.emplace_back(num_out);
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}
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int64_t num_kept = static_cast<int64_t>(offsets.back());
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if (num_kept == 0) {
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out->Resize({0, out_dim});
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dev_ctx.template Alloc<T>(out);
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index->Resize({0, 1});
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dev_ctx.template Alloc<int>(index);
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} else {
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out->Resize({num_kept, out_dim});
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dev_ctx.template Alloc<T>(out);
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index->Resize({num_kept, 1});
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dev_ctx.template Alloc<int>(index);
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std::copy(detections.begin(), detections.end(), out->data<T>());
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std::copy(indices.begin(), indices.end(), index->data<int>());
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}
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if (roisnum != nullptr) {
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roisnum->Resize({batch_size});
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dev_ctx.template Alloc<int>(roisnum);
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std::copy(num_per_batch.begin(), num_per_batch.end(), roisnum->data<int>());
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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matrix_nms, CPU, ALL_LAYOUT, phi::MatrixNMSKernel, float, double) {
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kernel->OutputAt(1).SetDataType(phi::DataType::INT32);
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kernel->OutputAt(2).SetDataType(phi::DataType::INT32);
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}
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