wehub-resource-sync
379 lines
13 KiB
C++
379 lines
13 KiB
C++
// 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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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/detection/nms_util.h"
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namespace phi {
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template <typename T>
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void SliceOneClass(const phi::DeviceContext& dev_ctx,
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const DenseTensor& items,
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const int class_id,
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DenseTensor* one_class_item) {
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T* item_data = dev_ctx.template Alloc<T>(one_class_item);
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const T* items_data = items.data<T>();
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const int64_t num_item = items.dims()[0];
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const int class_num = static_cast<int>(items.dims()[1]);
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if (items.dims().size() == 3) {
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int item_size = static_cast<int>(items.dims()[2]);
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for (int i = 0; i < num_item; ++i) {
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std::memcpy(item_data + i * item_size,
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items_data + i * class_num * item_size + class_id * item_size,
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sizeof(T) * item_size);
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}
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} else {
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for (int i = 0; i < num_item; ++i) {
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item_data[i] = items_data[i * class_num + class_id];
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}
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}
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}
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template <typename T>
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void NMSFast(const DenseTensor& bbox,
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const DenseTensor& scores,
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const T score_threshold,
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const T nms_threshold,
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const T eta,
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const int64_t top_k,
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std::vector<int>* selected_indices,
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const bool normalized) {
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// The total boxes for each instance.
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int64_t num_boxes = bbox.dims()[0];
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// 4: [xmin ymin xmax ymax]
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// 8: [x1 y1 x2 y2 x3 y3 x4 y4]
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// 16, 24, or 32: [x1 y1 x2 y2 ... xn yn], n = 8, 12 or 16
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int64_t box_size = bbox.dims()[1];
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std::vector<T> scores_data(num_boxes);
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std::copy_n(scores.data<T>(), num_boxes, scores_data.begin());
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std::vector<std::pair<T, int>> sorted_indices;
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funcs::GetMaxScoreIndex(scores_data, score_threshold, top_k, &sorted_indices);
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selected_indices->clear();
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T adaptive_threshold = nms_threshold;
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const T* bbox_data = bbox.data<T>();
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while (!sorted_indices.empty()) {
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const int idx = sorted_indices.front().second;
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bool keep = true;
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for (const auto kept_idx : *selected_indices) {
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if (keep) {
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T overlap = T(0.);
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// 4: [xmin ymin xmax ymax]
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if (box_size == 4) {
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overlap = funcs::JaccardOverlap<T>(bbox_data + idx * box_size,
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bbox_data + kept_idx * box_size,
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normalized);
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}
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// 8: [x1 y1 x2 y2 x3 y3 x4 y4] or 16, 24, 32
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if (box_size == 8 || box_size == 16 || box_size == 24 ||
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box_size == 32) {
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overlap = funcs::PolyIoU<T>(bbox_data + idx * box_size,
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bbox_data + kept_idx * box_size,
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box_size,
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normalized);
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}
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keep = overlap <= adaptive_threshold;
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} else {
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break;
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}
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}
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if (keep) {
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selected_indices->push_back(idx);
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}
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sorted_indices.erase(sorted_indices.begin());
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if (keep && eta < 1 && adaptive_threshold > 0.5) {
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adaptive_threshold *= eta;
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}
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}
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}
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template <typename T, typename Context>
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void MultiClassNMS(const Context& dev_ctx,
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const DenseTensor& scores,
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const DenseTensor& bboxes,
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const int scores_size,
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std::map<int, std::vector<int>>* indices,
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int* num_nmsed_out,
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float score_threshold_in,
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int nms_top_k_in,
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float nms_threshold_in,
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float nms_eta_in,
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int keep_top_k_in,
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bool normalized_in,
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int background_label_in) {
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int64_t background_label = background_label_in;
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int64_t nms_top_k = nms_top_k_in;
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int64_t keep_top_k = keep_top_k_in;
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bool normalized = normalized_in;
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T nms_threshold = static_cast<T>(nms_threshold_in);
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T nms_eta = static_cast<T>(nms_eta_in);
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T score_threshold = static_cast<T>(score_threshold_in);
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int num_det = 0;
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int64_t class_num = scores_size == 3 ? scores.dims()[0] : scores.dims()[1];
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DenseTensor bbox_slice, 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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if (scores_size == 3) {
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score_slice = scores.Slice(c, c + 1);
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bbox_slice = bboxes;
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} else {
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score_slice.Resize({scores.dims()[0], 1});
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bbox_slice.Resize({scores.dims()[0], 4});
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SliceOneClass<T>(dev_ctx, scores, c, &score_slice);
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SliceOneClass<T>(dev_ctx, bboxes, c, &bbox_slice);
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}
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NMSFast<T>(bbox_slice,
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score_slice,
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score_threshold,
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nms_threshold,
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nms_eta,
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nms_top_k,
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&((*indices)[c]), // NOLINT
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normalized);
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if (scores_size == 2) {
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std::stable_sort((*indices)[c].begin(), (*indices)[c].end()); // NOLINT
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}
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num_det += (*indices)[c].size(); // NOLINT
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}
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*num_nmsed_out = num_det;
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const T* scores_data = scores.data<T>();
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if (keep_top_k > -1 && num_det > keep_top_k) {
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const T* sdata = nullptr;
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std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;
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for (const auto& it : *indices) {
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int label = it.first;
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if (scores_size == 3) {
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sdata = scores_data + label * scores.dims()[1];
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} else {
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score_slice.Resize({scores.dims()[0], 1});
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SliceOneClass<T>(dev_ctx, scores, label, &score_slice);
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sdata = score_slice.data<T>();
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}
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const std::vector<int>& label_indices = it.second;
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for (auto idx : label_indices) {
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score_index_pairs.push_back(
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std::make_pair(sdata[idx], std::make_pair(label, idx)));
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}
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}
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// Keep top k results per image.
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std::stable_sort(score_index_pairs.begin(),
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score_index_pairs.end(),
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funcs::SortScorePairDescend<std::pair<int, int>>);
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score_index_pairs.resize(keep_top_k);
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// Store the new indices.
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std::map<int, std::vector<int>> new_indices;
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for (auto& score_index_pair : score_index_pairs) {
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int label = score_index_pair.second.first;
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int idx = score_index_pair.second.second;
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new_indices[label].push_back(idx);
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}
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if (scores_size == 2) {
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for (const auto& it : new_indices) {
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int label = it.first;
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std::stable_sort(new_indices[label].begin(), new_indices[label].end());
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}
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}
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new_indices.swap(*indices);
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*num_nmsed_out = keep_top_k; // NOLINT
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}
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}
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template <typename T, typename Context>
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void MultiClassOutput(const Context& dev_ctx,
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const DenseTensor& scores,
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const DenseTensor& bboxes,
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const std::map<int, std::vector<int>>& selected_indices,
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const int scores_size,
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DenseTensor* outs,
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int* oindices = nullptr,
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const int offset = 0) {
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int64_t class_num = scores.dims()[1];
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int64_t predict_dim = scores.dims()[1];
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int64_t box_size = bboxes.dims()[1];
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if (scores_size == 2) {
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box_size = bboxes.dims()[2];
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}
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int64_t out_dim = box_size + 2;
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auto* scores_data = scores.data<T>();
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auto* bboxes_data = bboxes.data<T>();
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auto* odata = outs->data<T>();
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const T* sdata = nullptr;
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DenseTensor bbox;
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bbox.Resize({scores.dims()[0], box_size});
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int count = 0;
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for (const auto& it : selected_indices) {
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int label = it.first;
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const std::vector<int>& indices = it.second;
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if (scores_size == 2) {
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SliceOneClass<T>(dev_ctx, bboxes, label, &bbox);
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} else {
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sdata = scores_data + label * predict_dim;
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}
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for (auto idx : indices) {
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odata[count * out_dim] = label; // label
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const T* bdata = nullptr;
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if (scores_size == 3) {
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bdata = bboxes_data + idx * box_size;
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odata[count * out_dim + 1] = sdata[idx]; // score
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if (oindices != nullptr) {
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oindices[count] = offset + idx;
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}
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} else {
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bdata = bbox.data<T>() + idx * box_size;
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odata[count * out_dim + 1] = *(scores_data + idx * class_num + label);
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if (oindices != nullptr) {
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oindices[count] = static_cast<int>(offset + idx * class_num + label);
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}
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}
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// xmin, ymin, xmax, ymax or multi-points coordinates
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std::memcpy(odata + count * out_dim + 2, bdata, box_size * sizeof(T));
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count++;
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}
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}
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}
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template <typename T, typename Context>
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void MulticlassNMSv1Kernel(const Context& dev_ctx,
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const DenseTensor& bboxes_in,
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const DenseTensor& scores_in,
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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 nms_threshold,
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float nms_eta,
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bool normalized,
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int background_label,
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DenseTensor* out) {
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auto* boxes = &bboxes_in;
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auto* scores = &scores_in;
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auto* outs = out;
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auto score_dims = vectorize<int>(scores->dims());
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auto score_size = score_dims.size();
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std::vector<std::map<int, std::vector<int>>> all_indices;
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std::vector<size_t> batch_starts = {0};
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int64_t batch_size = score_dims[0];
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int64_t box_dim = boxes->dims()[2];
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int64_t out_dim = box_dim + 2;
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int num_nmsed_out = 0;
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DenseTensor boxes_slice, scores_slice;
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int n = 0;
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n = static_cast<int>(score_size == 3 ? batch_size
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: boxes->lod().back().size() - 1);
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for (int i = 0; i < n; ++i) {
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std::map<int, std::vector<int>> indices;
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if (score_size == 3) {
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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 = boxes->Slice(i, i + 1);
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boxes_slice.Resize({score_dims[2], box_dim});
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} else {
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std::vector<size_t> boxes_lod;
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boxes_lod = boxes->lod().back();
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if (boxes_lod[i] == boxes_lod[i + 1]) {
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all_indices.push_back(indices);
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batch_starts.push_back(batch_starts.back());
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continue;
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}
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scores_slice = scores->Slice(static_cast<int64_t>(boxes_lod[i]),
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static_cast<int64_t>(boxes_lod[i + 1]));
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boxes_slice = boxes->Slice(static_cast<int64_t>(boxes_lod[i]),
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static_cast<int64_t>(boxes_lod[i + 1]));
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}
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MultiClassNMS<T, Context>(dev_ctx,
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scores_slice,
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boxes_slice,
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score_size,
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&indices,
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&num_nmsed_out,
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score_threshold,
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nms_top_k,
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nms_threshold,
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nms_eta,
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keep_top_k,
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normalized,
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background_label);
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all_indices.push_back(indices);
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batch_starts.push_back(batch_starts.back() + num_nmsed_out);
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}
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int num_kept = static_cast<int>(batch_starts.back());
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if (num_kept == 0) {
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outs->Resize({1, 1});
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T* od = dev_ctx.template Alloc<T>(outs);
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od[0] = -1;
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batch_starts = {0, 1};
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} else {
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outs->Resize({num_kept, out_dim});
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dev_ctx.template Alloc<T>(outs);
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int offset = 0;
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int* oindices = nullptr;
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for (int i = 0; i < n; ++i) {
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if (score_size == 3) {
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scores_slice = scores->Slice(i, i + 1);
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boxes_slice = boxes->Slice(i, i + 1);
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scores_slice.Resize({score_dims[1], score_dims[2]});
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boxes_slice.Resize({score_dims[2], box_dim});
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} else {
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std::vector<size_t> boxes_lod;
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boxes_lod = boxes->lod().back();
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if (boxes_lod[i] == boxes_lod[i + 1]) continue;
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scores_slice = scores->Slice(static_cast<int64_t>(boxes_lod[i]),
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static_cast<int64_t>(boxes_lod[i + 1]));
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boxes_slice = boxes->Slice(static_cast<int64_t>(boxes_lod[i]),
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static_cast<int64_t>(boxes_lod[i + 1]));
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}
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int64_t s = static_cast<int64_t>(batch_starts[i]);
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int64_t e = static_cast<int64_t>(batch_starts[i + 1]);
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if (e > s) {
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DenseTensor out = outs->Slice(s, e);
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MultiClassOutput<T>(dev_ctx,
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scores_slice,
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boxes_slice,
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all_indices[i],
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score_dims.size(),
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&out,
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oindices,
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offset);
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}
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}
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}
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LegacyLoD lod;
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lod.emplace_back(batch_starts);
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outs->set_lod(lod);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(multiclass_nms,
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CPU,
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ALL_LAYOUT,
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phi::MulticlassNMSv1Kernel,
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float,
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double) {}
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