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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 "BoundingBoxTransform.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/misc/Utility.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T, typename TDeltas>
SimpleTensor<T> bounding_box_transform(const SimpleTensor<T> &boxes, const SimpleTensor<TDeltas> &deltas, const BoundingBoxTransformInfo &info)
{
const DataType boxes_data_type = boxes.data_type();
SimpleTensor<T> pred_boxes(deltas.shape(), boxes_data_type);
const size_t num_classes = deltas.shape()[0] / 4;
const size_t num_boxes = deltas.shape()[1];
const TDeltas *deltas_ptr = deltas.data();
T *pred_boxes_ptr = pred_boxes.data();
const int img_h = floor(info.img_height() / info.scale() + 0.5f);
const int img_w = floor(info.img_width() / info.scale() + 0.5f);
const auto scale_after = (info.apply_scale() ? T(info.scale()) : T(1));
const auto scale_before = T(info.scale());
ARM_COMPUTE_ERROR_ON(scale_before <= 0);
const auto offset = (info.correct_transform_coords() ? T(1.f) : T(0.f));
const size_t box_fields = 4;
const size_t class_fields = 4;
#if defined(_OPENMP)
#pragma omp parallel for
#endif /* _OPENMP */
for(size_t i = 0; i < num_boxes; ++i)
{
// Extract ROI information
const size_t start_box = box_fields * i;
const T width = (boxes[start_box + 2] / scale_before) - (boxes[start_box] / scale_before) + T(1.f);
const T height = (boxes[start_box + 3] / scale_before) - (boxes[start_box + 1] / scale_before) + T(1.f);
const T ctr_x = (boxes[start_box] / scale_before) + T(0.5f) * width;
const T ctr_y = (boxes[start_box + 1] / scale_before) + T(0.5f) * height;
for(size_t j = 0; j < num_classes; ++j)
{
// Extract deltas
const size_t start_delta = i * num_classes * class_fields + class_fields * j;
const TDeltas dx = deltas_ptr[start_delta] / TDeltas(info.weights()[0]);
const TDeltas dy = deltas_ptr[start_delta + 1] / TDeltas(info.weights()[1]);
TDeltas dw = deltas_ptr[start_delta + 2] / TDeltas(info.weights()[2]);
TDeltas dh = deltas_ptr[start_delta + 3] / TDeltas(info.weights()[3]);
// Clip dw and dh
dw = std::min(dw, TDeltas(info.bbox_xform_clip()));
dh = std::min(dh, TDeltas(info.bbox_xform_clip()));
// Determine the predictions
const T pred_ctr_x = dx * width + ctr_x;
const T pred_ctr_y = dy * height + ctr_y;
const T pred_w = T(std::exp(dw)) * width;
const T pred_h = T(std::exp(dh)) * height;
// Store the prediction into the output tensor
pred_boxes_ptr[start_delta] = scale_after * utility::clamp<T>(pred_ctr_x - T(0.5f) * pred_w, T(0), T(img_w - 1));
pred_boxes_ptr[start_delta + 1] = scale_after * utility::clamp<T>(pred_ctr_y - T(0.5f) * pred_h, T(0), T(img_h - 1));
pred_boxes_ptr[start_delta + 2] = scale_after * utility::clamp<T>(pred_ctr_x + T(0.5f) * pred_w - offset, T(0), T(img_w - 1));
pred_boxes_ptr[start_delta + 3] = scale_after * utility::clamp<T>(pred_ctr_y + T(0.5f) * pred_h - offset, T(0), T(img_h - 1));
}
}
return pred_boxes;
}
template SimpleTensor<float> bounding_box_transform(const SimpleTensor<float> &boxes, const SimpleTensor<float> &deltas, const BoundingBoxTransformInfo &info);
template SimpleTensor<half> bounding_box_transform(const SimpleTensor<half> &boxes, const SimpleTensor<half> &deltas, const BoundingBoxTransformInfo &info);
template <>
SimpleTensor<uint16_t> bounding_box_transform(const SimpleTensor<uint16_t> &boxes, const SimpleTensor<uint8_t> &deltas, const BoundingBoxTransformInfo &info)
{
SimpleTensor<float> boxes_tmp = convert_from_asymmetric(boxes);
SimpleTensor<float> deltas_tmp = convert_from_asymmetric(deltas);
SimpleTensor<float> pred_boxes_tmp = bounding_box_transform<float, float>(boxes_tmp, deltas_tmp, info);
SimpleTensor<uint16_t> pred_boxes = convert_to_asymmetric<uint16_t>(pred_boxes_tmp, boxes.quantization_info());
return pred_boxes;
}
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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