86 lines
2.8 KiB
C++
86 lines
2.8 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.
|
|
|
|
#pragma once
|
|
|
|
#include <algorithm>
|
|
#include <cmath>
|
|
#include <cstring>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
#include "paddle/phi/common/place.h"
|
|
#include "paddle/phi/core/tensor_utils.h"
|
|
#include "paddle/phi/kernels/funcs/math_function.h"
|
|
|
|
namespace phi {
|
|
namespace funcs {
|
|
|
|
const int64_t kBoxDim = 4;
|
|
|
|
template <typename Context>
|
|
inline std::vector<size_t> GetLodFromRoisNum(const Context& dev_ctx,
|
|
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>();
|
|
DenseTensor cpu_tensor;
|
|
if (rois_num->place().GetType() == AllocationType::GPU ||
|
|
rois_num->place().GetType() == AllocationType::XPU) {
|
|
Copy<Context>(dev_ctx, *rois_num, CPUPlace(), true, &cpu_tensor);
|
|
rois_num_data = cpu_tensor.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>();
|
|
DenseTensor cpu_tensor;
|
|
if (rois_num->place().GetType() == AllocationType::GPU ||
|
|
rois_num->place().GetType() == AllocationType::XPU) {
|
|
Copy<Context>(dev_ctx, *rois_num, CPUPlace(), true, &cpu_tensor);
|
|
rois_num_data = cpu_tensor.data<int>();
|
|
}
|
|
rois_lod.push_back(static_cast<size_t>(0));
|
|
for (size_t 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>
|
|
static inline T BBoxArea(const T* box, bool pixel_offset) {
|
|
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 (pixel_offset) {
|
|
// If coordinate values are not within range [0, 1].
|
|
return (w + 1) * (h + 1);
|
|
} else {
|
|
return w * h;
|
|
}
|
|
}
|
|
}
|
|
|
|
} // namespace funcs
|
|
} // namespace phi
|