chore: import upstream snapshot with attribution
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wehub-resource-sync
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// Copyright (c) 2024 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 <algorithm>
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#include <cmath>
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#include <cstring>
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#include <numeric>
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#include <string>
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#include <vector>
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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const int kBoxDim = 4;
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template <typename T>
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struct ScoreWithID {
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T score;
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int batch_id;
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int index;
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int level;
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ScoreWithID() {
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batch_id = -1;
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index = -1;
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level = -1;
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}
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ScoreWithID(T score_, int batch_id_, int index_, int level_) {
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score = score_;
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batch_id = batch_id_;
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index = index_;
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level = level_;
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}
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};
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template <typename T>
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static inline bool CompareByScore(ScoreWithID<T> a, ScoreWithID<T> b) {
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return a.score >= b.score;
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}
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template <typename T>
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static inline bool CompareByBatchid(ScoreWithID<T> a, ScoreWithID<T> b) {
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return a.batch_id < b.batch_id;
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}
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template <typename T, typename Context>
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void CollectFpnProposalsOpKernel(
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const Context& dev_ctx,
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const std::vector<const DenseTensor*>& multi_level_rois,
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const std::vector<const DenseTensor*>& multi_level_scores,
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const optional<std::vector<const DenseTensor*>>& multi_level_rois_num,
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int post_nms_topn,
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DenseTensor* fpn_rois_out,
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DenseTensor* rois_num_out) {
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auto multi_layer_rois = multi_level_rois;
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auto multi_layer_scores = multi_level_scores;
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auto multi_rois_num = multi_level_rois_num
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? multi_level_rois_num.get()
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: std::vector<const DenseTensor*>();
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int num_size = multi_rois_num.size();
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auto* fpn_rois = fpn_rois_out;
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PADDLE_ENFORCE_GE(post_nms_topn,
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0UL,
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common::errors::InvalidArgument(
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"The parameter post_nms_topn must be "
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"a positive integer. But received post_nms_topn = %d",
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post_nms_topn));
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// assert that the length of Rois and scores are same
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PADDLE_ENFORCE_EQ(
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multi_layer_rois.size(),
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multi_layer_scores.size(),
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common::errors::InvalidArgument(
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"The number of RoIs and Scores should"
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" be the same. But received number of RoIs is %d, number of Scores "
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"is %d",
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multi_layer_rois.size(),
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multi_layer_scores.size()));
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// Check if the lod information of two DenseTensor is same
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const int num_fpn_level = multi_layer_rois.size();
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std::vector<int> integral_of_all_rois(num_fpn_level + 1, 0);
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for (int i = 0; i < num_fpn_level; ++i) {
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int all_rois = 0;
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if (num_size == 0) {
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auto cur_rois_lod = multi_layer_rois[i]->lod().back();
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all_rois = cur_rois_lod[cur_rois_lod.size() - 1];
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} else {
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const int* cur_rois_num = multi_rois_num[i]->data<int>();
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all_rois = std::accumulate(
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cur_rois_num, cur_rois_num + multi_rois_num[i]->numel(), 0);
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}
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integral_of_all_rois[i + 1] = integral_of_all_rois[i] + all_rois;
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}
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const int batch_size = (num_size == 0)
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? multi_layer_rois[0]->lod().back().size() - 1
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: multi_rois_num[0]->numel();
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// concatenate all fpn rois scores into a list
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// create a vector to store all scores
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std::vector<ScoreWithID<T>> scores_of_all_rois(
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integral_of_all_rois[num_fpn_level], ScoreWithID<T>());
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for (int i = 0; i < num_fpn_level; ++i) {
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const T* cur_level_scores = multi_layer_scores[i]->data<T>();
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int cur_level_num = integral_of_all_rois[i + 1] - integral_of_all_rois[i];
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int cur_batch_id = 0;
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int pre_num = 0;
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for (int j = 0; j < cur_level_num; ++j) {
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if (num_size == 0) {
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auto cur_scores_lod = multi_layer_scores[i]->lod().back();
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if (static_cast<size_t>(j) >= cur_scores_lod[cur_batch_id + 1]) {
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cur_batch_id++;
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}
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} else {
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const int* rois_num_data = multi_rois_num[i]->data<int>();
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if (j >= pre_num + rois_num_data[cur_batch_id]) {
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pre_num += rois_num_data[cur_batch_id];
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cur_batch_id++;
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}
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}
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int cur_index = j + integral_of_all_rois[i];
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scores_of_all_rois[cur_index].score = cur_level_scores[j];
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scores_of_all_rois[cur_index].index = j;
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scores_of_all_rois[cur_index].level = i;
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scores_of_all_rois[cur_index].batch_id = cur_batch_id;
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}
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}
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// keep top post_nms_topn rois
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// sort the rois by the score
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if (post_nms_topn > integral_of_all_rois[num_fpn_level]) {
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post_nms_topn = integral_of_all_rois[num_fpn_level];
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}
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std::stable_sort(
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scores_of_all_rois.begin(), scores_of_all_rois.end(), CompareByScore<T>);
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scores_of_all_rois.resize(post_nms_topn);
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// sort by batch id
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std::stable_sort(scores_of_all_rois.begin(),
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scores_of_all_rois.end(),
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CompareByBatchid<T>);
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// create a pointer array
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std::vector<const T*> multi_fpn_rois_data(num_fpn_level);
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for (int i = 0; i < num_fpn_level; ++i) {
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multi_fpn_rois_data[i] = multi_layer_rois[i]->data<T>();
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}
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// initialize the outputs
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fpn_rois->Resize({post_nms_topn, kBoxDim});
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dev_ctx.template Alloc<T>(fpn_rois);
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T* fpn_rois_data = fpn_rois->data<T>();
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std::vector<size_t> lod0(1, 0);
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int cur_batch_id = 0;
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std::vector<int64_t> num_per_batch;
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int pre_idx = 0;
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int cur_num = 0;
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for (int i = 0; i < post_nms_topn; ++i) {
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int cur_fpn_level = scores_of_all_rois[i].level;
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int cur_level_index = scores_of_all_rois[i].index;
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memcpy(fpn_rois_data,
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multi_fpn_rois_data[cur_fpn_level] + cur_level_index * kBoxDim,
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kBoxDim * sizeof(T));
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fpn_rois_data += kBoxDim;
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if (scores_of_all_rois[i].batch_id != cur_batch_id) {
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cur_batch_id = scores_of_all_rois[i].batch_id;
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lod0.emplace_back(i);
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cur_num = i - pre_idx;
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pre_idx = i;
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num_per_batch.emplace_back(cur_num);
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}
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}
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num_per_batch.emplace_back(post_nms_topn - pre_idx);
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if (rois_num_out != nullptr) {
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auto* rois_num = rois_num_out;
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rois_num->Resize({batch_size});
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int* rois_num_data = dev_ctx.template Alloc<int>(rois_num);
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for (int i = 0; i < batch_size; i++) {
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rois_num_data[i] = num_per_batch[i];
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}
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}
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lod0.emplace_back(post_nms_topn);
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LegacyLoD lod;
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lod.emplace_back(lod0);
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fpn_rois->set_lod(lod);
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}
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} // namespace phi
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