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box_coder.cc
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// 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 "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.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 = 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>();
T var_data[4] = {1., 1., 1., 1.};
T *var_ptr = var_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 paddle::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) {
if (target_box.lod().size()) {
PADDLE_ENFORCE_EQ(target_box.lod().size(),
1UL,
phi::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,
phi::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,
phi::errors::InvalidArgument("Size of attribute 'variance' of BoxCoder "
"operator should be 4. But received "
"size = %d",
variance.size()));
}
auto code_type = phi::funcs::GetBoxCodeType(code_type_str);
auto row = target_box.dims()[0];
auto col = prior_box.dims()[0];
if (code_type == phi::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 == phi::funcs::BoxCodeType::kEncodeCenterSize) {
EncodeCenterSize<T>(&target_box,
&prior_box,
prior_box_var.get_ptr(),
normalized,
variance,
output);
} else if (code_type == phi::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) {}