| /* |
| * 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/NEPadLayerKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/wrapper/wrapper.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &paddings, const PaddingMode mode) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(mode != PaddingMode::CONSTANT, "Only constant padding mode is supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(paddings.size() > 4, "Padding list bigger than 4 dimensions"); |
| if(output->total_size() != 0) |
| { |
| const TensorShape expected_output_shape = arm_compute::misc::shape_calculator::compute_padded_shape(input->tensor_shape(), paddings); |
| const TensorInfo expected_output_info = input->clone()->set_tensor_shape(expected_output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &expected_output_info); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| return Status{}; |
| } |
| } // namespace |
| |
| template <typename T> |
| void NEPadLayerKernel::run_pad_constant(const Window &window) |
| { |
| Window output_window{ window }; |
| output_window.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| const size_t element_size = _input->info()->element_size(); |
| Iterator output_it(_output, output_window); |
| execute_window_loop(output_window, [&](const Coordinates & id) |
| { |
| Coordinates idin{ id }; |
| for(size_t dim = _padding.size() - 1; dim > 0; --dim) |
| { |
| idin[dim] -= _padding[dim].first; |
| if(idin[dim] < 0 || static_cast<int>(_input->info()->dimension(dim)) - 1 < idin[dim]) |
| { |
| std::fill_n(reinterpret_cast<T *>(output_it.ptr()), _output->info()->dimension(0), _constant_value.get<T>()); |
| return; |
| } |
| } |
| T *input_it_ptr = reinterpret_cast<T *>(_input->ptr_to_element(idin)); |
| T *output_it_ptr = reinterpret_cast<T *>(output_it.ptr()); |
| std::fill_n(output_it_ptr, _padding[0].first, _constant_value.get<T>()); |
| memcpy(output_it_ptr + _padding[0].first, input_it_ptr, _input->info()->dimension(0) * element_size); |
| std::fill_n(output_it_ptr + _padding[0].first + _input->info()->dimension(0), _padding[0].second, _constant_value.get<T>()); |
| }, |
| output_it); |
| } |
| |
| void NEPadLayerKernel::run_pad_constant_uint8_3Dinput_3Dpad(const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(window); |
| |
| const size_t start_plane = window.z().start(); |
| const size_t end_plane = window.z().end(); |
| |
| size_t start_plane_input = start_plane; |
| if(_padding.size() > 2) |
| { |
| start_plane_input = (start_plane < _padding[2].first) ? 0 : start_plane - _padding[2].first; |
| } |
| const int output_plane_size = _output->info()->dimension(0) * _output->info()->dimension(1); |
| const int input_plane_size = _input->info()->dimension(0) * _input->info()->dimension(1); |
| |
| const int pad_y_elems_top = (_padding.size() > 1 ? _padding[1].first : 0) * _output->info()->dimension(0); |
| const int pad_y_elems_bot = (_padding.size() > 1 ? _padding[1].second : 0) * _output->info()->dimension(0); |
| |
| const size_t jump_to_next_row_input = _input->info()->dimension(0); |
| const size_t jump_to_next_row_output = _padding[0].first + _padding[0].second; |
| |
| uint8_t *output_row_ptr = _output->buffer() + _output->info()->offset_first_element_in_bytes() + start_plane * output_plane_size; |
| const uint8_t *input_it_ptr = _input->buffer() + _input->info()->offset_first_element_in_bytes() + start_plane_input * input_plane_size; |
| const auto pad_value = _constant_value.get<uint8_t>(); |
| |
| for(size_t z_i = start_plane; z_i < end_plane; ++z_i) |
| { |
| if(_padding.size() > 2 && z_i < _padding[2].first) |
| { |
| memset(output_row_ptr, pad_value, output_plane_size); |
| output_row_ptr += output_plane_size; |
| } |
| else if(_padding.size() > 2 && z_i > (_input->info()->dimension(2) + _padding[2].first - 1)) |
| { |
| memset(output_row_ptr, pad_value, output_plane_size); |
| output_row_ptr += output_plane_size; |
| } |
| else |
| { |
| memset(output_row_ptr, pad_value, pad_y_elems_top); |
| output_row_ptr += pad_y_elems_top; |
| size_t y_i = _input->info()->dimension(1); |
| // Basic loop unrolling |
| for(; y_i > 3; y_i -= 4) |
| { |
| memset(output_row_ptr, pad_value, _padding[0].first); |
| output_row_ptr += _padding[0].first; |
| |
| memcpy(output_row_ptr, input_it_ptr, _input->info()->dimension(0)); |
| output_row_ptr += _input->info()->dimension(0); |
| input_it_ptr += jump_to_next_row_input; |
| |
| memset(output_row_ptr, pad_value, _padding[0].second + _padding[0].first); |
| output_row_ptr += jump_to_next_row_output; |
| |
| memcpy(output_row_ptr, input_it_ptr, _input->info()->dimension(0)); |
| output_row_ptr += _input->info()->dimension(0); |
| input_it_ptr += jump_to_next_row_input; |
| |
| memset(output_row_ptr, pad_value, _padding[0].second + _padding[0].first); |
| output_row_ptr += jump_to_next_row_output; |
| |
| memcpy(output_row_ptr, input_it_ptr, _input->info()->dimension(0)); |
| output_row_ptr += _input->info()->dimension(0); |
| input_it_ptr += jump_to_next_row_input; |
| |
| memset(output_row_ptr, pad_value, _padding[0].second + _padding[0].first); |
| output_row_ptr += jump_to_next_row_output; |
| |
| memcpy(output_row_ptr, input_it_ptr, _input->info()->dimension(0)); |
| output_row_ptr += _input->info()->dimension(0); |
| input_it_ptr += jump_to_next_row_input; |
| |
| memset(output_row_ptr, pad_value, _padding[0].second); |
| output_row_ptr += _padding[0].second; |
| } |
| for(; y_i > 0; --y_i) |
| { |
| memset(output_row_ptr, pad_value, _padding[0].first); |
| output_row_ptr += _padding[0].first; |
| |
| memcpy(output_row_ptr, input_it_ptr, _input->info()->dimension(0)); |
| output_row_ptr += _input->info()->dimension(0); |
| input_it_ptr += _input->info()->dimension(0); |
| |
| memset(output_row_ptr, pad_value, _padding[0].second); |
| output_row_ptr += _padding[0].second; |
| } |
| memset(output_row_ptr, pad_value, pad_y_elems_bot); |
| output_row_ptr += pad_y_elems_bot; |
| } |
| } |
| } |
| |
| NEPadLayerKernel::NEPadLayerKernel() |
| : _func(), _input(nullptr), _output(nullptr), _padding(), _constant_value(), _mode() |
| { |
| } |
| |
| void NEPadLayerKernel::configure(ITensor *input, ITensor *output, const PaddingList &padding, const PixelValue constant_value, const PaddingMode mode) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| // Auto-init |
| const TensorShape expected_output_shape = arm_compute::misc::shape_calculator::compute_padded_shape(input->info()->tensor_shape(), padding); |
| const TensorInfo expected_output_info = input->info()->clone()->set_tensor_shape(expected_output_shape); |
| auto_init_if_empty(*output->info(), expected_output_info); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), padding, mode)); |
| |
| _input = input; |
| _output = output; |
| _padding = padding; |
| _constant_value = constant_value; |
| _mode = mode; |
| |
| if(_mode == PaddingMode::CONSTANT) |
| { |
| switch(_input->info()->element_size()) |
| { |
| case 1: |
| if(_input->info()->num_dimensions() == 3 && // Is 3D |
| padding.size() <= 3 && // Has 3D padding |
| !_input->info()->has_padding() && !_output->info()->has_padding()) // Input & Output have no padding |
| { |
| _func = &NEPadLayerKernel::run_pad_constant_uint8_3Dinput_3Dpad; |
| } |
| else |
| { |
| _func = &NEPadLayerKernel::run_pad_constant<uint8_t>; |
| } |
| break; |
| case 2: |
| _func = &NEPadLayerKernel::run_pad_constant<uint16_t>; |
| break; |
| case 4: |
| _func = &NEPadLayerKernel::run_pad_constant<uint32_t>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Element size not supported"); |
| break; |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Padding mode not supported"); |
| } |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*output->info(), Steps()); |
| |
| // The NEPad doesn't need padding so update_window_and_padding() can be skipped |
| Coordinates coord; |
| coord.set_num_dimensions(output->info()->num_dimensions()); |
| output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); |
| |
| ICPPKernel::configure(win); |
| } |
| |
| Status NEPadLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding, const PixelValue constant_value, const PaddingMode mode) |
| { |
| ARM_COMPUTE_UNUSED(constant_value); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, padding, mode)); |
| return Status{}; |
| } |
| |
| void NEPadLayerKernel::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); |
| |
| if(_func != nullptr) |
| { |
| (this->*_func)(window); |
| } |
| } |
| } // namespace arm_compute |