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/*
* Copyright (c) 2018-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/core/NEON/kernels/NEPriorBoxLayerKernel.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include <arm_neon.h>
namespace arm_compute
{
namespace
{
Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const PriorBoxLayerInfo &info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input1, input2);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
// Check variances
const int var_size = info.variances().size();
if(var_size > 1)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(var_size != 4, "Must provide 4 variance values");
for(int i = 0; i < var_size; ++i)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(var_size <= 0, "Must be greater than 0");
}
}
ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.steps()[0] < 0.f, "Step x should be greater or equal to 0");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.steps()[1] < 0.f, "Step y should be greater or equal to 0");
if(!info.max_sizes().empty())
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.max_sizes().size() != info.min_sizes().size(), "Max and min sizes dimensions should match");
}
for(unsigned int i = 0; i < info.max_sizes().size(); ++i)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.max_sizes()[i] < info.min_sizes()[i], "Max size should be greater than min size");
}
if(output != nullptr && output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != 2);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output);
}
return Status{};
}
} // namespace
NEPriorBoxLayerKernel::NEPriorBoxLayerKernel()
: _input1(nullptr), _input2(nullptr), _output(nullptr), _info()
{
}
void NEPriorBoxLayerKernel::store_coordinates(float *out, const int offset, const float center_x, const float center_y, const float box_width, const float box_height, const int width,
const int height)
{
float xmin = (center_x - box_width / 2.f) / width;
float ymin = (center_y - box_height / 2.f) / height;
float xmax = (center_x + box_width / 2.f) / width;
float ymax = (center_y + box_height / 2.f) / height;
float32x4_t vec_elements = { xmin, ymin, xmax, ymax };
if(_info.clip())
{
static const float32x4_t CONST_0 = vdupq_n_f32(0.f);
static const float32x4_t CONST_1 = vdupq_n_f32(1.f);
vec_elements = vmaxq_f32(vminq_f32(vec_elements, CONST_1), CONST_0);
}
vst1q_f32(out + offset, vec_elements);
}
void NEPriorBoxLayerKernel::calculate_prior_boxes(const Window &window)
{
const int num_priors = _info.aspect_ratios().size() * _info.min_sizes().size() + _info.max_sizes().size();
const DataLayout data_layout = _input1->info()->data_layout();
const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
const int layer_width = _input1->info()->dimension(width_idx);
const int layer_height = _input1->info()->dimension(height_idx);
int img_width = _info.img_size().x;
int img_height = _info.img_size().y;
if(img_width == 0 || img_height == 0)
{
img_width = _input2->info()->dimension(width_idx);
img_height = _input2->info()->dimension(height_idx);
}
float step_x = _info.steps()[0];
float step_y = _info.steps()[1];
if(step_x == 0.f || step_y == 0.f)
{
step_x = static_cast<float>(img_width) / layer_width;
step_y = static_cast<float>(img_height) / layer_height;
}
Window slice = window.first_slice_window_2D();
slice.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 2));
Iterator output(_output, slice);
execute_window_loop(slice, [&](const Coordinates & id)
{
float center_x = 0;
float center_y = 0;
int idx = id.x() / (4 * num_priors);
center_x = (static_cast<float>(idx % layer_width) + _info.offset()) * step_x;
center_y = (static_cast<float>(idx / layer_width) + _info.offset()) * step_y;
float box_width;
float box_height;
int offset = 0;
auto out = reinterpret_cast<float *>(output.ptr());
for(unsigned int i = 0; i < _info.min_sizes().size(); ++i)
{
const float min_size = _info.min_sizes().at(i);
box_width = min_size;
box_height = min_size;
store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height);
offset += 4;
if(!_info.max_sizes().empty())
{
const float max_size = _info.max_sizes().at(i);
box_width = std::sqrt(min_size * max_size);
box_height = box_width;
store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height);
offset += 4;
}
// rest of priors
for(auto ar : _info.aspect_ratios())
{
if(fabs(ar - 1.) < 1e-6)
{
continue;
}
box_width = min_size * sqrt(ar);
box_height = min_size / sqrt(ar);
store_coordinates(out, offset, center_x, center_y, box_width, box_height, img_width, img_height);
offset += 4;
}
}
// set the variance
out = reinterpret_cast<float *>(_output->ptr_to_element(Coordinates(id.x(), 1)));
float32x4_t var;
if(_info.variances().size() == 1)
{
var = vdupq_n_f32(_info.variances().at(0));
}
else
{
const float32x4_t vars = { _info.variances().at(0), _info.variances().at(1), _info.variances().at(2), _info.variances().at(3) };
var = vars;
}
for(int i = 0; i < num_priors; ++i)
{
vst1q_f32(out + 4 * i, var);
}
},
output);
}
void NEPriorBoxLayerKernel::configure(const ITensor *input1, const ITensor *input2, ITensor *output, const PriorBoxLayerInfo &info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), info));
_input1 = input1;
_input2 = input2;
_info = info;
_output = output;
// Configure kernel window
const int num_priors = info.aspect_ratios().size() * info.min_sizes().size() + info.max_sizes().size();
Window win = calculate_max_window(*output->info(), Steps(num_priors * 4));
Coordinates coord;
coord.set_num_dimensions(output->info()->num_dimensions());
output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));
INEKernel::configure(win);
}
Status NEPriorBoxLayerKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const PriorBoxLayerInfo &info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, info));
return Status{};
}
void NEPriorBoxLayerKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
// Run function
calculate_prior_boxes(window);
}
} // namespace arm_compute
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