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/*
* Copyright (c) 2018 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 "PriorBoxLayer.h"
#include "ActivationLayer.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T>
SimpleTensor<T> prior_box_layer(const SimpleTensor<T> &src1, const SimpleTensor<T> &src2, const PriorBoxLayerInfo &info, const TensorShape &output_shape)
{
const auto layer_width = static_cast<int>(src1.shape()[0]);
const auto layer_height = static_cast<int>(src1.shape()[1]);
int img_width = info.img_size().x;
int img_height = info.img_size().y;
if(img_width == 0 || img_height == 0)
{
img_width = static_cast<int>(src2.shape()[0]);
img_height = static_cast<int>(src2.shape()[1]);
}
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_x = static_cast<float>(img_height) / layer_height;
}
// Calculate number of aspect ratios
const int num_priors = info.aspect_ratios().size() * info.min_sizes().size() + info.max_sizes().size();
const int total_elements = layer_width * layer_height * num_priors * 4;
SimpleTensor<T> result(output_shape, src1.data_type());
int idx = 0;
for(int y = 0; y < layer_height; ++y)
{
for(int x = 0; x < layer_width; ++x)
{
const float center_x = (x + info.offset()) * step_x;
const float center_y = (y + info.offset()) * step_y;
float box_width;
float box_height;
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;
// (xmin, ymin, xmax, ymax)
result[idx++] = (center_x - box_width / 2.f) / img_width;
result[idx++] = (center_y - box_height / 2.f) / img_height;
result[idx++] = (center_x + box_width / 2.f) / img_width;
result[idx++] = (center_y + box_height / 2.f) / img_height;
if(!info.max_sizes().empty())
{
const float max_size = info.max_sizes().at(i);
box_width = sqrt(min_size * max_size);
box_height = box_width;
// (xmin, ymin, xmax, ymax)
result[idx++] = (center_x - box_width / 2.f) / img_width;
result[idx++] = (center_y - box_height / 2.f) / img_height;
result[idx++] = (center_x + box_width / 2.f) / img_width;
result[idx++] = (center_y + box_height / 2.f) / img_height;
}
// 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);
// (xmin, ymin, xmax, ymax)
result[idx++] = (center_x - box_width / 2.f) / img_width;
result[idx++] = (center_y - box_height / 2.f) / img_height;
result[idx++] = (center_x + box_width / 2.f) / img_width;
result[idx++] = (center_y + box_height / 2.f) / img_height;
}
}
}
}
// clip the coordinates
if(info.clip())
{
for(int i = 0; i < total_elements; ++i)
{
result[i] = std::min<T>(std::max<T>(result[i], 0.f), 1.f);
}
}
// set the variance.
if(info.variances().size() == 1)
{
std::fill_n(result.data() + idx, total_elements, info.variances().at(0));
}
else
{
for(int h = 0; h < layer_height; ++h)
{
for(int w = 0; w < layer_width; ++w)
{
for(int i = 0; i < num_priors; ++i)
{
for(int j = 0; j < 4; ++j)
{
result[idx++] = info.variances().at(j);
}
}
}
}
}
return result;
}
template SimpleTensor<float> prior_box_layer(const SimpleTensor<float> &src1, const SimpleTensor<float> &src2, const PriorBoxLayerInfo &info, const TensorShape &output_shape);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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