wehub-resource-sync
638 lines
22 KiB
C++
638 lines
22 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 "paddle/phi/kernels/multiclass_nms3_kernel.h"
|
|
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/core/tensor_utils.h"
|
|
#include "paddle/phi/kernels/funcs/gpc.h"
|
|
|
|
namespace phi {
|
|
|
|
template <class T>
|
|
class Point_ {
|
|
public:
|
|
// default constructor
|
|
Point_() = default;
|
|
Point_(T _x, T _y) {}
|
|
Point_(const Point_& pt UNUSED) {}
|
|
|
|
Point_& operator=(const Point_& pt);
|
|
// conversion to another data type
|
|
// template<typename _T> operator Point_<_T>() const;
|
|
// conversion to the old-style C structures
|
|
// operator Vec<T, 2>() const;
|
|
|
|
// checks whether the point is inside the specified rectangle
|
|
// bool inside(const Rect_<T>& r) const;
|
|
T x; //!< x coordinate of the point
|
|
T y; //!< y coordinate of the point
|
|
};
|
|
|
|
template <class T>
|
|
void Array2PointVec(const T* box,
|
|
const size_t box_size,
|
|
std::vector<Point_<T>>* vec) {
|
|
size_t pts_num = box_size / 2;
|
|
(*vec).resize(pts_num);
|
|
for (size_t i = 0; i < pts_num; i++) {
|
|
(*vec).at(i).x = box[2 * i];
|
|
(*vec).at(i).y = box[2 * i + 1];
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
void Array2Poly(const T* box, const size_t box_size, funcs::gpc_polygon* poly) {
|
|
size_t pts_num = box_size / 2;
|
|
(*poly).num_contours = 1;
|
|
(*poly).hole = reinterpret_cast<int*>(malloc(sizeof(int))); // NOLINT
|
|
(*poly).hole[0] = 0;
|
|
(*poly).contour = (funcs::gpc_vertex_list*)malloc( // NOLINT
|
|
sizeof(funcs::gpc_vertex_list));
|
|
(*poly).contour->num_vertices = static_cast<int>(pts_num);
|
|
(*poly).contour->vertex = (funcs::gpc_vertex*)malloc( // NOLINT
|
|
sizeof(funcs::gpc_vertex) * pts_num);
|
|
for (size_t i = 0; i < pts_num; ++i) {
|
|
(*poly).contour->vertex[i].x = box[2 * i];
|
|
(*poly).contour->vertex[i].y = box[2 * i + 1];
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
void PointVec2Poly(const std::vector<Point_<T>>& vec,
|
|
funcs::gpc_polygon* poly) {
|
|
size_t pts_num = vec.size();
|
|
(*poly).num_contours = 1;
|
|
(*poly).hole = reinterpret_cast<int*>(malloc(sizeof(int))); // NOLINT
|
|
(*poly).hole[0] = 0;
|
|
(*poly).contour = (funcs::gpc_vertex_list*)malloc( // NOLINT
|
|
sizeof(funcs::gpc_vertex_list));
|
|
(*poly).contour->num_vertices = pts_num;
|
|
(*poly).contour->vertex = (funcs::gpc_vertex*)malloc( // NOLINT
|
|
sizeof(funcs::gpc_vertex) * pts_num);
|
|
for (size_t i = 0; i < pts_num; ++i) {
|
|
(*poly).contour->vertex[i].x = vec[i].x;
|
|
(*poly).contour->vertex[i].y = vec[i].y;
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
void Poly2PointVec(const funcs::gpc_vertex_list& contour,
|
|
std::vector<Point_<T>>* vec) {
|
|
int pts_num = contour.num_vertices;
|
|
(*vec).resize(pts_num);
|
|
for (int i = 0; i < pts_num; i++) {
|
|
(*vec).at(i).x = contour.vertex[i].x;
|
|
(*vec).at(i).y = contour.vertex[i].y;
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
T GetContourArea(const std::vector<Point_<T>>& vec) {
|
|
size_t pts_num = vec.size();
|
|
if (pts_num < 3) return T(0.);
|
|
T area = T(0.);
|
|
for (size_t i = 0; i < pts_num; ++i) {
|
|
area += vec[i].x * vec[(i + 1) % pts_num].y -
|
|
vec[i].y * vec[(i + 1) % pts_num].x;
|
|
}
|
|
return std::fabs(area / 2.0);
|
|
}
|
|
|
|
template <class T>
|
|
T PolyArea(const T* box, const size_t box_size, const bool normalized UNUSED) {
|
|
// If coordinate values are is invalid
|
|
// if area size <= 0, return 0.
|
|
std::vector<Point_<T>> vec;
|
|
Array2PointVec<T>(box, box_size, &vec);
|
|
return GetContourArea<T>(vec);
|
|
}
|
|
|
|
template <class T>
|
|
T PolyOverlapArea(const T* box1,
|
|
const T* box2,
|
|
const size_t box_size,
|
|
const bool normalized UNUSED) {
|
|
funcs::gpc_polygon poly1;
|
|
funcs::gpc_polygon poly2;
|
|
Array2Poly<T>(box1, box_size, &poly1);
|
|
Array2Poly<T>(box2, box_size, &poly2);
|
|
funcs::gpc_polygon respoly;
|
|
funcs::gpc_op op = funcs::GPC_INT;
|
|
funcs::gpc_polygon_clip(op, &poly2, &poly1, &respoly);
|
|
|
|
T inter_area = T(0.);
|
|
int contour_num = respoly.num_contours;
|
|
for (int i = 0; i < contour_num; ++i) {
|
|
std::vector<Point_<T>> resvec;
|
|
Poly2PointVec<T>(respoly.contour[i], &resvec);
|
|
// inter_area += std::fabs(cv::contourArea(resvec)) + 0.5f *
|
|
// (cv::arcLength(resvec, true));
|
|
inter_area += GetContourArea<T>(resvec);
|
|
}
|
|
|
|
funcs::gpc_free_polygon(&poly1);
|
|
funcs::gpc_free_polygon(&poly2);
|
|
funcs::gpc_free_polygon(&respoly);
|
|
return inter_area;
|
|
}
|
|
|
|
template <class T>
|
|
bool SortScorePairDescend(const std::pair<float, T>& pair1,
|
|
const std::pair<float, T>& pair2) {
|
|
return pair1.first > pair2.first;
|
|
}
|
|
|
|
template <class T>
|
|
static inline void GetMaxScoreIndex(
|
|
const std::vector<T>& scores,
|
|
const T threshold,
|
|
int top_k,
|
|
std::vector<std::pair<T, int>>* sorted_indices) {
|
|
for (size_t i = 0; i < scores.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);
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
static inline T BBoxArea(const T* 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 static_cast<T>(0.);
|
|
} else {
|
|
const T w = box[2] - box[0];
|
|
const T 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);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
static inline T JaccardOverlap(const T* box1,
|
|
const T* box2,
|
|
const bool normalized) {
|
|
if (box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
|
|
box2[3] < box1[1]) {
|
|
return static_cast<T>(0.);
|
|
} else {
|
|
const T inter_xmin = std::max(box1[0], box2[0]);
|
|
const T inter_ymin = std::max(box1[1], box2[1]);
|
|
const T inter_xmax = std::min(box1[2], box2[2]);
|
|
const T inter_ymax = std::min(box1[3], box2[3]);
|
|
T norm = normalized ? static_cast<T>(0.) : static_cast<T>(1.);
|
|
T inter_w = inter_xmax - inter_xmin + norm;
|
|
T inter_h = inter_ymax - inter_ymin + norm;
|
|
const T inter_area = inter_w * inter_h;
|
|
const T bbox1_area = BBoxArea<T>(box1, normalized);
|
|
const T bbox2_area = BBoxArea<T>(box2, normalized);
|
|
return inter_area / (bbox1_area + bbox2_area - inter_area);
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
T PolyIoU(const T* box1,
|
|
const T* box2,
|
|
const size_t box_size,
|
|
const bool normalized) {
|
|
T bbox1_area = PolyArea<T>(box1, box_size, normalized);
|
|
T bbox2_area = PolyArea<T>(box2, box_size, normalized);
|
|
T inter_area = PolyOverlapArea<T>(box1, box2, box_size, normalized);
|
|
if (bbox1_area == 0 || bbox2_area == 0 || inter_area == 0) {
|
|
// If coordinate values are invalid
|
|
// if area size <= 0, return 0.
|
|
return T(0.);
|
|
} else {
|
|
return inter_area / (bbox1_area + bbox2_area - inter_area);
|
|
}
|
|
}
|
|
|
|
inline std::vector<size_t> GetNmsLodFromRoisNum(const DenseTensor* rois_num) {
|
|
std::vector<size_t> rois_lod;
|
|
if (rois_num->dtype() == DataType::INT64) {
|
|
auto* rois_num_data = rois_num->data<int64_t>();
|
|
rois_lod.push_back(static_cast<size_t>(0));
|
|
for (int64_t i = 0; i < rois_num->numel(); ++i) {
|
|
rois_lod.push_back(rois_lod.back() +
|
|
static_cast<size_t>(rois_num_data[i]));
|
|
}
|
|
} else if (rois_num->dtype() == DataType::INT32) {
|
|
auto* rois_num_data = rois_num->data<int>();
|
|
rois_lod.push_back(static_cast<size_t>(0));
|
|
for (int i = 0; i < rois_num->numel(); ++i) {
|
|
rois_lod.push_back(rois_lod.back() +
|
|
static_cast<size_t>(rois_num_data[i]));
|
|
}
|
|
}
|
|
return rois_lod;
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void SliceOneClass(const Context& dev_ctx,
|
|
const DenseTensor& items,
|
|
const int class_id,
|
|
DenseTensor* one_class_item) {
|
|
T* item_data = dev_ctx.template Alloc<T>(one_class_item);
|
|
const T* items_data = items.data<T>();
|
|
const int64_t num_item = items.dims()[0];
|
|
const int class_num = static_cast<int>(items.dims()[1]);
|
|
if (items.dims().size() == 3) {
|
|
int item_size = static_cast<int>(items.dims()[2]);
|
|
for (int i = 0; i < num_item; ++i) {
|
|
std::memcpy(item_data + i * item_size,
|
|
items_data + i * class_num * item_size + class_id * item_size,
|
|
sizeof(T) * item_size);
|
|
}
|
|
} else {
|
|
for (int i = 0; i < num_item; ++i) {
|
|
item_data[i] = items_data[i * class_num + class_id];
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
void NMSFast(const DenseTensor& bbox,
|
|
const DenseTensor& scores,
|
|
const T score_threshold,
|
|
const T nms_threshold,
|
|
const T eta,
|
|
const int64_t top_k,
|
|
std::vector<int>* selected_indices,
|
|
const bool normalized) {
|
|
// The total boxes for each instance.
|
|
int64_t num_boxes = bbox.dims()[0];
|
|
// 4: [xmin ymin xmax ymax]
|
|
// 8: [x1 y1 x2 y2 x3 y3 x4 y4]
|
|
// 16, 24, or 32: [x1 y1 x2 y2 ... xn yn], n = 8, 12 or 16
|
|
int64_t box_size = bbox.dims()[1];
|
|
|
|
std::vector<T> scores_data(num_boxes);
|
|
std::copy_n(scores.data<T>(), num_boxes, scores_data.begin());
|
|
std::vector<std::pair<T, int>> sorted_indices;
|
|
GetMaxScoreIndex<T>(scores_data, score_threshold, top_k, &sorted_indices);
|
|
|
|
selected_indices->clear();
|
|
T adaptive_threshold = nms_threshold;
|
|
const T* bbox_data = bbox.data<T>();
|
|
|
|
size_t num_indices = sorted_indices.size();
|
|
selected_indices->reserve(num_indices);
|
|
for (size_t i = 0; i < num_indices; ++i) {
|
|
const int idx = sorted_indices[i].second;
|
|
bool keep = true;
|
|
const T* current_bbox = bbox_data + idx * box_size;
|
|
size_t selected_size = selected_indices->size();
|
|
|
|
for (size_t j = 0; j < selected_size; ++j) {
|
|
const auto kept_idx = (*selected_indices)[j];
|
|
T overlap = T(0.);
|
|
const T* kept_bbox = bbox_data + kept_idx * box_size;
|
|
// 4: [xmin ymin xmax ymax]
|
|
if (box_size == 4) {
|
|
overlap = JaccardOverlap<T>(current_bbox, kept_bbox, normalized);
|
|
} else if (box_size == 8 || box_size == 16 || box_size == 24 ||
|
|
box_size ==
|
|
32) { // 8: [x1 y1 x2 y2 x3 y3 x4 y4] or 16, 24, 32
|
|
overlap = PolyIoU<T>(current_bbox, kept_bbox, box_size, normalized);
|
|
}
|
|
keep = overlap <= adaptive_threshold;
|
|
if (!keep) {
|
|
break;
|
|
}
|
|
}
|
|
if (keep) {
|
|
selected_indices->push_back(idx);
|
|
if (eta < 1 && adaptive_threshold > 0.5) {
|
|
adaptive_threshold *= eta;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void MultiClassNMS(const Context& dev_ctx,
|
|
const DenseTensor& scores,
|
|
const DenseTensor& bboxes,
|
|
const int scores_size,
|
|
float scorethreshold,
|
|
int nms_top_k,
|
|
int keep_top_k,
|
|
float nmsthreshold,
|
|
bool normalized,
|
|
float nmseta,
|
|
int background_label,
|
|
std::map<int, std::vector<int>>* indices,
|
|
int* num_nmsed_out) {
|
|
T nms_threshold = static_cast<T>(nmsthreshold);
|
|
T nms_eta = static_cast<T>(nmseta);
|
|
T score_threshold = static_cast<T>(scorethreshold);
|
|
|
|
int num_det = 0;
|
|
|
|
int class_num =
|
|
static_cast<int>(scores_size == 3 ? scores.dims()[0] : scores.dims()[1]);
|
|
DenseTensor bbox_slice, score_slice;
|
|
for (int c = 0; c < class_num; ++c) {
|
|
if (c == background_label) continue;
|
|
if (scores_size == 3) {
|
|
score_slice = scores.Slice(c, c + 1);
|
|
bbox_slice = bboxes;
|
|
} else {
|
|
score_slice.Resize({scores.dims()[0], 1});
|
|
bbox_slice.Resize({scores.dims()[0], 4});
|
|
SliceOneClass<T, Context>(dev_ctx, scores, c, &score_slice);
|
|
SliceOneClass<T, Context>(dev_ctx, bboxes, c, &bbox_slice);
|
|
}
|
|
NMSFast<T>(bbox_slice,
|
|
score_slice,
|
|
score_threshold,
|
|
nms_threshold,
|
|
nms_eta,
|
|
nms_top_k,
|
|
&((*indices)[c]),
|
|
normalized);
|
|
if (scores_size == 2) {
|
|
std::stable_sort((*indices)[c].begin(), (*indices)[c].end());
|
|
}
|
|
num_det += static_cast<int>((*indices)[c].size());
|
|
}
|
|
|
|
*num_nmsed_out = num_det;
|
|
const T* scores_data = scores.data<T>();
|
|
if (keep_top_k > -1 && num_det > keep_top_k) {
|
|
const T* sdata = nullptr;
|
|
std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;
|
|
for (const auto& it : *indices) {
|
|
int label = it.first;
|
|
if (scores_size == 3) {
|
|
sdata = scores_data + label * scores.dims()[1];
|
|
} else {
|
|
score_slice.Resize({scores.dims()[0], 1});
|
|
SliceOneClass<T, Context>(dev_ctx, scores, label, &score_slice);
|
|
sdata = score_slice.data<T>();
|
|
}
|
|
const std::vector<int>& label_indices = it.second;
|
|
for (auto idx : label_indices) {
|
|
score_index_pairs.push_back(
|
|
std::make_pair(sdata[idx], std::make_pair(label, idx)));
|
|
}
|
|
}
|
|
// Keep top k results per image.
|
|
std::stable_sort(score_index_pairs.begin(),
|
|
score_index_pairs.end(),
|
|
SortScorePairDescend<std::pair<int, int>>);
|
|
score_index_pairs.resize(keep_top_k);
|
|
|
|
// Store the new indices.
|
|
std::map<int, std::vector<int>> new_indices;
|
|
for (auto& score_index_pair : score_index_pairs) {
|
|
int label = score_index_pair.second.first;
|
|
int idx = score_index_pair.second.second;
|
|
new_indices[label].push_back(idx);
|
|
}
|
|
if (scores_size == 2) {
|
|
for (const auto& it : new_indices) {
|
|
int label = it.first;
|
|
std::stable_sort(new_indices[label].begin(), new_indices[label].end());
|
|
}
|
|
}
|
|
new_indices.swap(*indices);
|
|
*num_nmsed_out = keep_top_k;
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void MultiClassOutput(const Context& dev_ctx,
|
|
const DenseTensor& scores,
|
|
const DenseTensor& bboxes,
|
|
const std::map<int, std::vector<int>>& selected_indices,
|
|
const int scores_size,
|
|
DenseTensor* out,
|
|
int* oindices = nullptr,
|
|
const int offset = 0) {
|
|
int64_t class_num = scores.dims()[1];
|
|
int64_t predict_dim = scores.dims()[1];
|
|
int64_t box_size = bboxes.dims()[1];
|
|
if (scores_size == 2) {
|
|
box_size = bboxes.dims()[2];
|
|
}
|
|
int64_t out_dim = box_size + 2;
|
|
auto* scores_data = scores.data<T>();
|
|
auto* bboxes_data = bboxes.data<T>();
|
|
auto* odata = out->data<T>();
|
|
const T* sdata = nullptr;
|
|
DenseTensor bbox;
|
|
bbox.Resize({scores.dims()[0], box_size});
|
|
int count = 0;
|
|
for (const auto& it : selected_indices) {
|
|
int label = it.first;
|
|
const std::vector<int>& indices = it.second;
|
|
if (scores_size == 2) {
|
|
SliceOneClass<T, Context>(dev_ctx, bboxes, label, &bbox);
|
|
} else {
|
|
sdata = scores_data + label * predict_dim;
|
|
}
|
|
|
|
for (auto idx : indices) {
|
|
odata[count * out_dim] = label; // label
|
|
const T* bdata = nullptr;
|
|
if (scores_size == 3) {
|
|
bdata = bboxes_data + idx * box_size;
|
|
odata[count * out_dim + 1] = sdata[idx]; // score
|
|
if (oindices != nullptr) {
|
|
oindices[count] = offset + idx;
|
|
}
|
|
} else {
|
|
bdata = bbox.data<T>() + idx * box_size;
|
|
odata[count * out_dim + 1] = *(scores_data + idx * class_num + label);
|
|
if (oindices != nullptr) {
|
|
oindices[count] = offset + idx * class_num + label; // NOLINT
|
|
}
|
|
}
|
|
// xmin, ymin, xmax, ymax or multi-points coordinates
|
|
std::memcpy(odata + count * out_dim + 2, bdata, box_size * sizeof(T));
|
|
count++;
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void MultiClassNMSKernel(const Context& dev_ctx,
|
|
const DenseTensor& bboxes,
|
|
const DenseTensor& scores,
|
|
const optional<DenseTensor>& rois_num,
|
|
float score_threshold,
|
|
int nms_top_k,
|
|
int keep_top_k,
|
|
float nms_threshold,
|
|
bool normalized,
|
|
float nms_eta,
|
|
int background_label,
|
|
DenseTensor* out,
|
|
DenseTensor* index,
|
|
DenseTensor* nms_rois_num) {
|
|
bool return_index = index != nullptr;
|
|
bool has_roisnum = rois_num.get_ptr() != nullptr;
|
|
auto score_dims = vectorize<int>(scores.dims());
|
|
auto score_size = score_dims.size();
|
|
|
|
std::vector<std::map<int, std::vector<int>>> all_indices;
|
|
std::vector<size_t> batch_starts = {0};
|
|
int64_t batch_size = score_dims[0];
|
|
int64_t box_dim = bboxes.dims()[2];
|
|
int64_t out_dim = box_dim + 2;
|
|
int num_nmsed_out = 0;
|
|
DenseTensor boxes_slice, scores_slice;
|
|
int n = 0;
|
|
if (has_roisnum) {
|
|
n = static_cast<int>(score_size == 3 ? batch_size
|
|
: rois_num.get_ptr()->numel());
|
|
} else {
|
|
n = static_cast<int>(score_size == 3 ? batch_size
|
|
: bboxes.lod().back().size() - 1);
|
|
}
|
|
for (int i = 0; i < n; ++i) {
|
|
std::map<int, std::vector<int>> indices;
|
|
if (score_size == 3) {
|
|
scores_slice = scores.Slice(i, i + 1);
|
|
scores_slice.Resize({score_dims[1], score_dims[2]});
|
|
boxes_slice = bboxes.Slice(i, i + 1);
|
|
boxes_slice.Resize({score_dims[2], box_dim});
|
|
} else {
|
|
std::vector<size_t> boxes_lod;
|
|
if (has_roisnum) {
|
|
boxes_lod = GetNmsLodFromRoisNum(rois_num.get_ptr());
|
|
} else {
|
|
boxes_lod = bboxes.lod().back();
|
|
}
|
|
if (boxes_lod[i] == boxes_lod[i + 1]) {
|
|
all_indices.push_back(indices);
|
|
batch_starts.push_back(batch_starts.back());
|
|
continue;
|
|
}
|
|
scores_slice = scores.Slice(boxes_lod[i], boxes_lod[i + 1]); // NOLINT
|
|
boxes_slice = bboxes.Slice(boxes_lod[i], boxes_lod[i + 1]); // NOLINT
|
|
}
|
|
MultiClassNMS<T, Context>(dev_ctx,
|
|
scores_slice,
|
|
boxes_slice,
|
|
score_size,
|
|
score_threshold,
|
|
nms_top_k,
|
|
keep_top_k,
|
|
nms_threshold,
|
|
normalized,
|
|
nms_eta,
|
|
background_label,
|
|
&indices,
|
|
&num_nmsed_out);
|
|
all_indices.push_back(indices);
|
|
batch_starts.push_back(batch_starts.back() + num_nmsed_out);
|
|
}
|
|
|
|
int num_kept = static_cast<int>(batch_starts.back());
|
|
if (num_kept == 0) {
|
|
if (return_index) {
|
|
out->Resize({0, out_dim});
|
|
dev_ctx.template Alloc<T>(out);
|
|
index->Resize({0, 1});
|
|
dev_ctx.template Alloc<int>(index);
|
|
} else {
|
|
out->Resize({1, 1});
|
|
T* od = dev_ctx.template Alloc<T>(out);
|
|
od[0] = -1;
|
|
batch_starts = {0, 1};
|
|
}
|
|
} else {
|
|
out->Resize({num_kept, out_dim});
|
|
dev_ctx.template Alloc<T>(out);
|
|
int offset = 0;
|
|
int* oindices = nullptr;
|
|
for (int i = 0; i < n; ++i) {
|
|
if (score_size == 3) {
|
|
scores_slice = scores.Slice(i, i + 1);
|
|
boxes_slice = bboxes.Slice(i, i + 1);
|
|
scores_slice.Resize({score_dims[1], score_dims[2]});
|
|
boxes_slice.Resize({score_dims[2], box_dim});
|
|
if (return_index) {
|
|
offset = i * score_dims[2];
|
|
}
|
|
} else {
|
|
std::vector<size_t> boxes_lod;
|
|
if (has_roisnum) {
|
|
boxes_lod = GetNmsLodFromRoisNum(rois_num.get_ptr());
|
|
} else {
|
|
boxes_lod = bboxes.lod().back();
|
|
}
|
|
if (boxes_lod[i] == boxes_lod[i + 1]) continue;
|
|
scores_slice = scores.Slice(boxes_lod[i], boxes_lod[i + 1]); // NOLINT
|
|
boxes_slice = bboxes.Slice(boxes_lod[i], boxes_lod[i + 1]); // NOLINT
|
|
if (return_index) {
|
|
offset = static_cast<int>(boxes_lod[i] * score_dims[1]);
|
|
}
|
|
}
|
|
|
|
int64_t s = static_cast<int64_t>(batch_starts[i]);
|
|
int64_t e = static_cast<int64_t>(batch_starts[i + 1]);
|
|
if (e > s) {
|
|
DenseTensor nout = out->Slice(s, e);
|
|
if (return_index) {
|
|
index->Resize({num_kept, 1});
|
|
int* output_idx = dev_ctx.template Alloc<int>(index);
|
|
oindices = output_idx + s;
|
|
}
|
|
MultiClassOutput<T, Context>(dev_ctx,
|
|
scores_slice,
|
|
boxes_slice,
|
|
all_indices[i],
|
|
score_dims.size(),
|
|
&nout,
|
|
oindices,
|
|
offset);
|
|
}
|
|
}
|
|
}
|
|
if (nms_rois_num != nullptr) {
|
|
nms_rois_num->Resize({n});
|
|
dev_ctx.template Alloc<int>(nms_rois_num);
|
|
int* num_data = nms_rois_num->data<int>();
|
|
for (int i = 1; i <= n; i++) {
|
|
num_data[i - 1] = batch_starts[i] - batch_starts[i - 1]; // NOLINT
|
|
}
|
|
nms_rois_num->Resize({n});
|
|
}
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(
|
|
multiclass_nms3, CPU, ALL_LAYOUT, phi::MultiClassNMSKernel, float, double) {
|
|
kernel->OutputAt(1).SetDataType(phi::DataType::INT32);
|
|
kernel->OutputAt(2).SetDataType(phi::DataType::INT32);
|
|
}
|