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/*
* Copyright (c) 2019-2023 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 "src/cpu/kernels/boundingboxtransform/generic/neon/impl.h"
#include "src/cpu/CpuTypes.h"
namespace arm_compute
{
namespace cpu
{
void bounding_box_transform_qsymm16(const ITensor *boxes,
ITensor *pred_boxes,
const ITensor *deltas,
BoundingBoxTransformInfo bbinfo,
const Window &window)
{
const size_t num_classes = deltas->info()->tensor_shape()[0] >> 2;
const size_t deltas_width = deltas->info()->tensor_shape()[0];
const int img_h = std::floor(bbinfo.img_height() / bbinfo.scale() + 0.5f);
const int img_w = std::floor(bbinfo.img_width() / bbinfo.scale() + 0.5f);
const auto scale_after = (bbinfo.apply_scale() ? bbinfo.scale() : 1.f);
const auto scale_before = bbinfo.scale();
const auto offset = (bbinfo.correct_transform_coords() ? 1.f : 0.f);
auto pred_ptr =
reinterpret_cast<uint16_t *>(pred_boxes->buffer() + pred_boxes->info()->offset_first_element_in_bytes());
auto delta_ptr = reinterpret_cast<uint8_t *>(deltas->buffer() + deltas->info()->offset_first_element_in_bytes());
const auto boxes_qinfo = boxes->info()->quantization_info().uniform();
const auto deltas_qinfo = deltas->info()->quantization_info().uniform();
const auto pred_qinfo = pred_boxes->info()->quantization_info().uniform();
Iterator box_it(boxes, window);
execute_window_loop(
window,
[&](const Coordinates &id)
{
const auto ptr = reinterpret_cast<uint16_t *>(box_it.ptr());
const auto b0 = dequantize_qasymm16(*ptr, boxes_qinfo);
const auto b1 = dequantize_qasymm16(*(ptr + 1), boxes_qinfo);
const auto b2 = dequantize_qasymm16(*(ptr + 2), boxes_qinfo);
const auto b3 = dequantize_qasymm16(*(ptr + 3), boxes_qinfo);
const float width = (b2 / scale_before) - (b0 / scale_before) + 1.f;
const float height = (b3 / scale_before) - (b1 / scale_before) + 1.f;
const float ctr_x = (b0 / scale_before) + 0.5f * width;
const float ctr_y = (b1 / scale_before) + 0.5f * height;
for (size_t j = 0; j < num_classes; ++j)
{
// Extract deltas
const size_t delta_id = id.y() * deltas_width + 4u * j;
const float dx = dequantize_qasymm8(delta_ptr[delta_id], deltas_qinfo) / bbinfo.weights()[0];
const float dy = dequantize_qasymm8(delta_ptr[delta_id + 1], deltas_qinfo) / bbinfo.weights()[1];
float dw = dequantize_qasymm8(delta_ptr[delta_id + 2], deltas_qinfo) / bbinfo.weights()[2];
float dh = dequantize_qasymm8(delta_ptr[delta_id + 3], deltas_qinfo) / bbinfo.weights()[3];
// Clip dw and dh
dw = std::min(dw, bbinfo.bbox_xform_clip());
dh = std::min(dh, bbinfo.bbox_xform_clip());
// Determine the predictions
const float pred_ctr_x = dx * width + ctr_x;
const float pred_ctr_y = dy * height + ctr_y;
const float pred_w = std::exp(dw) * width;
const float pred_h = std::exp(dh) * height;
// Store the prediction into the output tensor
pred_ptr[delta_id] = quantize_qasymm16(
scale_after * utility::clamp<float>(pred_ctr_x - 0.5f * pred_w, 0.f, img_w - 1.f), pred_qinfo);
pred_ptr[delta_id + 1] = quantize_qasymm16(
scale_after * utility::clamp<float>(pred_ctr_y - 0.5f * pred_h, 0.f, img_h - 1.f), pred_qinfo);
pred_ptr[delta_id + 2] = quantize_qasymm16(
scale_after * utility::clamp<float>(pred_ctr_x + 0.5f * pred_w - offset, 0.f, img_w - 1.f),
pred_qinfo);
pred_ptr[delta_id + 3] = quantize_qasymm16(
scale_after * utility::clamp<float>(pred_ctr_y + 0.5f * pred_h - offset, 0.f, img_h - 1.f),
pred_qinfo);
}
},
box_it);
}
} // namespace cpu
} // namespace arm_compute
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