wehub-resource-sync
289 lines
11 KiB
C++
289 lines
11 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/box_coder_kernel.h"
|
|
#include <array>
|
|
|
|
#include "paddle/phi/backends/cpu/cpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/full_kernel.h"
|
|
#include "paddle/phi/kernels/funcs/math_function.h"
|
|
#include "paddle/phi/kernels/impl/box_coder.h"
|
|
namespace phi {
|
|
|
|
template <typename T>
|
|
void EncodeCenterSize(const DenseTensor *target_box,
|
|
const DenseTensor *prior_box,
|
|
const DenseTensor *prior_box_var,
|
|
const bool normalized,
|
|
const std::vector<float> variance,
|
|
T *output) {
|
|
int64_t row = target_box->dims()[0];
|
|
int64_t col = prior_box->dims()[0];
|
|
int64_t len = prior_box->dims()[1];
|
|
|
|
#ifdef PADDLE_WITH_MKLML
|
|
#pragma omp parallel for collapse(2)
|
|
#endif
|
|
for (int64_t i = 0; i < row; ++i) {
|
|
for (int64_t j = 0; j < col; ++j) {
|
|
auto *target_box_data = target_box->data<T>();
|
|
auto *prior_box_data = prior_box->data<T>();
|
|
size_t offset = i * col * len + j * len;
|
|
T prior_box_width = prior_box_data[j * len + 2] -
|
|
prior_box_data[j * len] + (normalized == false);
|
|
T prior_box_height = prior_box_data[j * len + 3] -
|
|
prior_box_data[j * len + 1] + (normalized == false);
|
|
T prior_box_center_x = prior_box_data[j * len] + prior_box_width / 2;
|
|
T prior_box_center_y = prior_box_data[j * len + 1] + prior_box_height / 2;
|
|
|
|
T target_box_center_x =
|
|
(target_box_data[i * len + 2] + target_box_data[i * len]) / 2;
|
|
T target_box_center_y =
|
|
(target_box_data[i * len + 3] + target_box_data[i * len + 1]) / 2;
|
|
T target_box_width = target_box_data[i * len + 2] -
|
|
target_box_data[i * len] + (normalized == false);
|
|
T target_box_height = target_box_data[i * len + 3] -
|
|
target_box_data[i * len + 1] +
|
|
(normalized == false);
|
|
|
|
output[offset] =
|
|
(target_box_center_x - prior_box_center_x) / prior_box_width;
|
|
output[offset + 1] =
|
|
(target_box_center_y - prior_box_center_y) / prior_box_height;
|
|
output[offset + 2] =
|
|
std::log(std::fabs(target_box_width / prior_box_width));
|
|
output[offset + 3] =
|
|
std::log(std::fabs(target_box_height / prior_box_height));
|
|
}
|
|
}
|
|
|
|
if (prior_box_var) {
|
|
const T *prior_box_var_data = prior_box_var->data<T>();
|
|
#ifdef PADDLE_WITH_MKLML
|
|
#pragma omp parallel for collapse(3)
|
|
#endif
|
|
for (int64_t i = 0; i < row; ++i) {
|
|
for (int64_t j = 0; j < col; ++j) {
|
|
for (int k = 0; k < 4; ++k) {
|
|
size_t offset = i * col * len + j * len;
|
|
int prior_var_offset = static_cast<int>(j * len);
|
|
output[offset + k] /= prior_box_var_data[prior_var_offset + k];
|
|
}
|
|
}
|
|
}
|
|
} else if (!(variance.empty())) {
|
|
#ifdef PADDLE_WITH_MKLML
|
|
#pragma omp parallel for collapse(3)
|
|
#endif
|
|
for (int64_t i = 0; i < row; ++i) {
|
|
for (int64_t j = 0; j < col; ++j) {
|
|
for (int k = 0; k < 4; ++k) {
|
|
size_t offset = i * col * len + j * len;
|
|
output[offset + k] /= static_cast<T>(variance[k]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, int axis, int var_size>
|
|
void DecodeCenterSize(const DenseTensor *target_box,
|
|
const DenseTensor *prior_box,
|
|
const DenseTensor *prior_box_var,
|
|
const bool normalized,
|
|
std::vector<float> variance,
|
|
T *output) {
|
|
int64_t row = target_box->dims()[0];
|
|
int64_t col = target_box->dims()[1];
|
|
int64_t len = target_box->dims()[2];
|
|
|
|
#ifdef PADDLE_WITH_MKLML
|
|
#pragma omp parallel for collapse(2)
|
|
#endif
|
|
for (int64_t i = 0; i < row; ++i) {
|
|
for (int64_t j = 0; j < col; ++j) {
|
|
auto *target_box_data = target_box->data<T>();
|
|
auto *prior_box_data = prior_box->data<T>();
|
|
|
|
std::array<T, 4> var_data{1., 1., 1., 1.};
|
|
T *var_ptr = var_data.data();
|
|
size_t offset = i * col * len + j * len;
|
|
int prior_box_offset = axis == 0 ? j * len : i * len;
|
|
|
|
T prior_box_width = prior_box_data[prior_box_offset + 2] -
|
|
prior_box_data[prior_box_offset] +
|
|
(normalized == false);
|
|
T prior_box_height = prior_box_data[prior_box_offset + 3] -
|
|
prior_box_data[prior_box_offset + 1] +
|
|
(normalized == false);
|
|
T prior_box_center_x =
|
|
prior_box_data[prior_box_offset] + prior_box_width / 2;
|
|
T prior_box_center_y =
|
|
prior_box_data[prior_box_offset + 1] + prior_box_height / 2;
|
|
|
|
T target_box_center_x = 0, target_box_center_y = 0;
|
|
T target_box_width = 0, target_box_height = 0;
|
|
int prior_var_offset = axis == 0 ? j * len : i * len;
|
|
if (var_size == 2) {
|
|
std::memcpy(var_ptr,
|
|
prior_box_var->data<T>() + prior_var_offset,
|
|
4 * sizeof(T));
|
|
} else if (var_size == 1) {
|
|
var_ptr = reinterpret_cast<T *>(variance.data());
|
|
}
|
|
T box_var_x = *var_ptr;
|
|
T box_var_y = *(var_ptr + 1);
|
|
T box_var_w = *(var_ptr + 2);
|
|
T box_var_h = *(var_ptr + 3);
|
|
|
|
target_box_center_x =
|
|
box_var_x * target_box_data[offset] * prior_box_width +
|
|
prior_box_center_x;
|
|
target_box_center_y =
|
|
box_var_y * target_box_data[offset + 1] * prior_box_height +
|
|
prior_box_center_y;
|
|
target_box_width =
|
|
std::exp(box_var_w * target_box_data[offset + 2]) * prior_box_width;
|
|
target_box_height =
|
|
std::exp(box_var_h * target_box_data[offset + 3]) * prior_box_height;
|
|
|
|
output[offset] = target_box_center_x - target_box_width / 2;
|
|
output[offset + 1] = target_box_center_y - target_box_height / 2;
|
|
output[offset + 2] =
|
|
target_box_center_x + target_box_width / 2 - (normalized == false);
|
|
output[offset + 3] =
|
|
target_box_center_y + target_box_height / 2 - (normalized == false);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void BoxCoderKernel(const Context &dev_ctx,
|
|
const DenseTensor &prior_box,
|
|
const optional<DenseTensor> &prior_box_var,
|
|
const DenseTensor &target_box,
|
|
const std::string &code_type_str,
|
|
bool normalized,
|
|
int axis,
|
|
const std::vector<float> &variance,
|
|
DenseTensor *output_box) {
|
|
// prior_box and prior_box_var have the same shape, so do not judge
|
|
// prior_box_var
|
|
if (prior_box.numel() == 0 || target_box.numel() == 0) {
|
|
Full<T, Context>(dev_ctx, output_box->dims(), 0, output_box);
|
|
return;
|
|
}
|
|
if (!target_box.lod().empty()) {
|
|
PADDLE_ENFORCE_EQ(target_box.lod().size(),
|
|
1UL,
|
|
common::errors::InvalidArgument(
|
|
"Input(TargetBox) of BoxCoder operator "
|
|
"supports LoD with only one level. But received "
|
|
"level = %d",
|
|
target_box.lod().size()));
|
|
}
|
|
if (prior_box_var) {
|
|
PADDLE_ENFORCE_EQ(variance.empty(),
|
|
true,
|
|
common::errors::InvalidArgument(
|
|
"Input 'PriorBoxVar' and attribute 'variance' "
|
|
"of BoxCoder operator should not be used at the "
|
|
"same time."));
|
|
}
|
|
if (!(variance.empty())) {
|
|
PADDLE_ENFORCE_EQ(static_cast<int>(variance.size()),
|
|
4,
|
|
common::errors::InvalidArgument(
|
|
"Size of attribute 'variance' of BoxCoder "
|
|
"operator should be 4. But received "
|
|
"size = %d",
|
|
variance.size()));
|
|
}
|
|
|
|
auto code_type = funcs::GetBoxCodeType(code_type_str);
|
|
auto row = target_box.dims()[0];
|
|
auto col = prior_box.dims()[0];
|
|
if (code_type == funcs::BoxCodeType::kDecodeCenterSize) {
|
|
col = target_box.dims()[1];
|
|
}
|
|
auto len = prior_box.dims()[1];
|
|
output_box->Resize({row, col, len});
|
|
dev_ctx.template Alloc<T>(output_box);
|
|
T *output = output_box->data<T>();
|
|
if (code_type == funcs::BoxCodeType::kEncodeCenterSize) {
|
|
EncodeCenterSize<T>(&target_box,
|
|
&prior_box,
|
|
prior_box_var.get_ptr(),
|
|
normalized,
|
|
variance,
|
|
output);
|
|
} else if (code_type == funcs::BoxCodeType::kDecodeCenterSize) {
|
|
if (prior_box_var) {
|
|
if (axis == 0) {
|
|
DecodeCenterSize<T, 0, 2>(&target_box,
|
|
&prior_box,
|
|
prior_box_var.get_ptr(),
|
|
normalized,
|
|
variance,
|
|
output);
|
|
} else {
|
|
DecodeCenterSize<T, 1, 2>(&target_box,
|
|
&prior_box,
|
|
prior_box_var.get_ptr(),
|
|
normalized,
|
|
variance,
|
|
output);
|
|
}
|
|
} else if (!(variance.empty())) {
|
|
if (axis == 0) {
|
|
DecodeCenterSize<T, 0, 1>(&target_box,
|
|
&prior_box,
|
|
prior_box_var.get_ptr(),
|
|
normalized,
|
|
variance,
|
|
output);
|
|
} else {
|
|
DecodeCenterSize<T, 1, 1>(&target_box,
|
|
&prior_box,
|
|
prior_box_var.get_ptr(),
|
|
normalized,
|
|
variance,
|
|
output);
|
|
}
|
|
} else {
|
|
if (axis == 0) {
|
|
DecodeCenterSize<T, 0, 0>(&target_box,
|
|
&prior_box,
|
|
prior_box_var.get_ptr(),
|
|
normalized,
|
|
variance,
|
|
output);
|
|
} else {
|
|
DecodeCenterSize<T, 1, 0>(&target_box,
|
|
&prior_box,
|
|
prior_box_var.get_ptr(),
|
|
normalized,
|
|
variance,
|
|
output);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(
|
|
box_coder, CPU, ALL_LAYOUT, phi::BoxCoderKernel, float, double) {}
|