| /* |
| * Copyright (c) 2019 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/NEBoundingBoxTransformKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *boxes, const ITensorInfo *pred_boxes, const ITensorInfo *deltas, const BoundingBoxTransformInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(boxes, pred_boxes, deltas); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(boxes); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(boxes, DataType::QASYMM16, DataType::F32, DataType::F16); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(deltas, DataType::QASYMM8, DataType::F32, DataType::F16); |
| ARM_COMPUTE_RETURN_ERROR_ON(deltas->tensor_shape()[1] != boxes->tensor_shape()[1]); |
| ARM_COMPUTE_RETURN_ERROR_ON(deltas->tensor_shape()[0] % 4 != 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(boxes->tensor_shape()[0] != 4); |
| ARM_COMPUTE_RETURN_ERROR_ON(deltas->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(boxes->num_dimensions() > 2); |
| ARM_COMPUTE_RETURN_ERROR_ON(info.scale() <= 0); |
| |
| if(boxes->data_type() == DataType::QASYMM16) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(deltas, 1, DataType::QASYMM8); |
| const UniformQuantizationInfo deltas_qinfo = deltas->quantization_info().uniform(); |
| ARM_COMPUTE_RETURN_ERROR_ON(deltas_qinfo.scale != 0.125f); |
| ARM_COMPUTE_RETURN_ERROR_ON(deltas_qinfo.offset != 0); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(boxes, deltas); |
| } |
| |
| if(pred_boxes->total_size() > 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(pred_boxes->tensor_shape(), deltas->tensor_shape()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(pred_boxes, deltas); |
| ARM_COMPUTE_RETURN_ERROR_ON(pred_boxes->num_dimensions() > 2); |
| if(pred_boxes->data_type() == DataType::QASYMM16) |
| { |
| const UniformQuantizationInfo pred_qinfo = pred_boxes->quantization_info().uniform(); |
| ARM_COMPUTE_RETURN_ERROR_ON(pred_qinfo.scale != 0.125f); |
| ARM_COMPUTE_RETURN_ERROR_ON(pred_qinfo.offset != 0); |
| } |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *boxes, ITensorInfo *pred_boxes, ITensorInfo *deltas, const BoundingBoxTransformInfo &bb_info) |
| { |
| ARM_COMPUTE_UNUSED(bb_info); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(boxes, pred_boxes); |
| |
| auto_init_if_empty(*pred_boxes, deltas->clone()->set_data_type(boxes->data_type()).set_quantization_info(boxes->quantization_info())); |
| |
| const unsigned int num_boxes = boxes->dimension(1); |
| |
| Window window; |
| window.set(Window::DimX, Window::Dimension(0, 1u)); |
| window.set(Window::DimY, Window::Dimension(0, num_boxes)); |
| |
| AccessWindowHorizontal input_access(boxes, 0, 4u); |
| AccessWindowHorizontal output_access(pred_boxes, 0, 4u); |
| |
| const bool window_changed = update_window_and_padding(window, input_access, output_access); |
| output_access.set_valid_region(window, ValidRegion(Coordinates(), pred_boxes->tensor_shape())); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, window); |
| } |
| |
| } // namespace |
| |
| NEBoundingBoxTransformKernel::NEBoundingBoxTransformKernel() |
| : _boxes(nullptr), _pred_boxes(nullptr), _deltas(nullptr), _bbinfo(0, 0, 0) |
| { |
| } |
| |
| void NEBoundingBoxTransformKernel::configure(const ITensor *boxes, ITensor *pred_boxes, const ITensor *deltas, const BoundingBoxTransformInfo &info) |
| { |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(boxes->info(), pred_boxes->info(), deltas->info(), info)); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(boxes->info(), pred_boxes->info(), deltas->info(), info); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| |
| // Set instance variables |
| _boxes = boxes; |
| _pred_boxes = pred_boxes; |
| _deltas = deltas; |
| _bbinfo = info; |
| |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEBoundingBoxTransformKernel::validate(const ITensorInfo *boxes, const ITensorInfo *pred_boxes, const ITensorInfo *deltas, const BoundingBoxTransformInfo &info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(boxes, pred_boxes, deltas, info)); |
| return Status{}; |
| } |
| |
| template <> |
| void NEBoundingBoxTransformKernel::internal_run<uint16_t>(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 NEBoundingBoxTransformKernel::internal_run(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); |
| } |
| |
| void NEBoundingBoxTransformKernel::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); |
| switch(_boxes->info()->data_type()) |
| { |
| case DataType::F32: |
| { |
| internal_run<float>(window); |
| break; |
| } |
| case DataType::QASYMM16: |
| { |
| internal_run<uint16_t>(window); |
| break; |
| } |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| { |
| internal_run<float16_t>(window); |
| break; |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| default: |
| { |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| } |
| } |
| } |
| } // namespace arm_compute |