| /* |
| * Copyright (c) 2019-2022 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" |
| 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); |
| } |
| |
| template <typename T> |
| void bounding_box_transform(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() ? T(bbinfo.scale()) : T(1)); |
| const auto scale_before = T(bbinfo.scale()); |
| ARM_COMPUTE_ERROR_ON(scale_before <= 0); |
| const auto offset = (bbinfo.correct_transform_coords() ? T(1.f) : T(0.f)); |
| |
| auto pred_ptr = reinterpret_cast<T *>(pred_boxes->buffer() + pred_boxes->info()->offset_first_element_in_bytes()); |
| auto delta_ptr = reinterpret_cast<T *>(deltas->buffer() + deltas->info()->offset_first_element_in_bytes()); |
| |
| Iterator box_it(boxes, window); |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const auto ptr = reinterpret_cast<T *>(box_it.ptr()); |
| const auto b0 = *ptr; |
| const auto b1 = *(ptr + 1); |
| const auto b2 = *(ptr + 2); |
| const auto b3 = *(ptr + 3); |
| const T width = (b2 / scale_before) - (b0 / scale_before) + T(1.f); |
| const T height = (b3 / scale_before) - (b1 / scale_before) + T(1.f); |
| const T ctr_x = (b0 / scale_before) + T(0.5f) * width; |
| const T ctr_y = (b1 / scale_before) + T(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 T dx = delta_ptr[delta_id] / T(bbinfo.weights()[0]); |
| const T dy = delta_ptr[delta_id + 1] / T(bbinfo.weights()[1]); |
| T dw = delta_ptr[delta_id + 2] / T(bbinfo.weights()[2]); |
| T dh = delta_ptr[delta_id + 3] / T(bbinfo.weights()[3]); |
| // Clip dw and dh |
| dw = std::min(dw, T(bbinfo.bbox_xform_clip())); |
| dh = std::min(dh, T(bbinfo.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 = std::exp(dw) * width; |
| const T pred_h = std::exp(dh) * height; |
| // Store the prediction into the output tensor |
| pred_ptr[delta_id] = scale_after * utility::clamp<T>(pred_ctr_x - T(0.5f) * pred_w, T(0), T(img_w - 1)); |
| pred_ptr[delta_id + 1] = scale_after * utility::clamp<T>(pred_ctr_y - T(0.5f) * pred_h, T(0), T(img_h - 1)); |
| pred_ptr[delta_id + 2] = scale_after * utility::clamp<T>(pred_ctr_x + T(0.5f) * pred_w - offset, T(0), T(img_w - 1)); |
| pred_ptr[delta_id + 3] = scale_after * utility::clamp<T>(pred_ctr_y + T(0.5f) * pred_h - offset, T(0), T(img_h - 1)); |
| } |
| }, |
| box_it); |
| } |
| |
| template void bounding_box_transform<float>(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window); |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) |
| template void bounding_box_transform<float16_t>(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, BoundingBoxTransformInfo bbinfo, const Window &window); |
| #endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) |
| } // namespace cpu |
| } // namespace arm_compute |