| /* |
| * Copyright (c) 2018 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/NEPermuteKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.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 |
| { |
| #include "arm_compute/core/NEON/kernels/convolution/common/shims.hpp" |
| } // namespace |
| |
| #include <cstddef> |
| #include <cstdint> |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8, |
| DataType::U16, DataType::S16, DataType::QS16, |
| DataType::U32, DataType::S32, |
| DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((perm.num_dimensions() == 3 && !(perm[0] == 2 && perm[1] == 0 && perm[2] == 1) && !(perm[0] == 1 && perm[1] == 2 && perm[2] == 0)), |
| "Only [2, 0, 1] and [1, 2, 0] permutation is supported"); |
| |
| const TensorShape output_shape = misc::shape_calculator::compute_permutation_output_shape(*input, perm); |
| |
| // Validate configured output |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| template <typename T> |
| void NEPermuteKernel::run_permute(const Window &window) |
| { |
| // Input window |
| Window window_in = window; |
| window_in.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().end() - window.x().start())); |
| window_in.set(Window::DimY, Window::Dimension(window.y().start(), window.y().end(), window.y().end() - window.y().start())); |
| window_in.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), window.z().end() - window.z().start())); |
| window_in.set(3, Window::Dimension(window[3].start(), window[3].end(), window[3].end() - window[3].start())); |
| |
| // Output window |
| Window window_out(window); |
| const Window::Dimension zero_window = Window::Dimension(0, 0, 0); |
| for(size_t d = 0; d <= _perm.num_dimensions(); ++d) |
| { |
| window_out.set(d, zero_window); |
| } |
| |
| // Create iterators |
| Iterator in(_input, window_in); |
| Iterator out(_output, window_out); |
| |
| // CHW -> HWC |
| if((_perm.num_dimensions() == 3) && (_perm[0] == 2) && (_perm[1] == 0) && (_perm[2] == 1)) |
| { |
| const int in_row_stride = _input->info()->strides_in_bytes().y() / sizeof(T); |
| const int in_channel_stride = _input->info()->strides_in_bytes().z() / sizeof(T); |
| const int in_batch_stride = _input->info()->strides_in_bytes()[3] / sizeof(T); |
| |
| const int out_channel_stride = _output->info()->strides_in_bytes().x() / sizeof(T); |
| const int out_col_stride = _output->info()->strides_in_bytes().y() / sizeof(T); |
| const int out_row_stride = _output->info()->strides_in_bytes().z() / sizeof(T); |
| const int out_batch_stride = _output->info()->strides_in_bytes()[3] / sizeof(T); |
| |
| const int n_cols = _input->info()->tensor_shape().x(); |
| const int n_rows = window_in.y().step(); |
| const int n_channels = _input->info()->tensor_shape().z(); |
| const int n_batches = _input->info()->tensor_shape()[3]; |
| |
| execute_window_loop(window_in, [&](const Coordinates & id) |
| { |
| const int idx = id[0] * out_col_stride + id[1] * out_row_stride + id[2] * out_channel_stride; |
| reorder::nchw_to_nhwc(reinterpret_cast<const T *>(in.ptr()), reinterpret_cast<T *>(out.ptr()) + idx, |
| n_batches, n_channels, n_rows, n_cols, |
| in_batch_stride, in_channel_stride, in_row_stride, |
| out_batch_stride, out_row_stride, out_col_stride); |
| }, |
| in, out); |
| } |
| // HWC -> CHW |
| else if((_perm.num_dimensions() == 3) && (_perm[0] == 1) && (_perm[1] == 2) && (_perm[2] == 0)) |
| { |
| const int in_col_stride = _input->info()->strides_in_bytes().y() / sizeof(T); |
| const int in_row_stride = _input->info()->strides_in_bytes().z() / sizeof(T); |
| const int in_batch_stride = _input->info()->strides_in_bytes()[3] / sizeof(T); |
| |
| const int out_col_stride = _output->info()->strides_in_bytes().x() / sizeof(T); |
| const int out_row_stride = _output->info()->strides_in_bytes().y() / sizeof(T); |
| const int out_channel_stride = _output->info()->strides_in_bytes().z() / sizeof(T); |
| const int out_batch_stride = _output->info()->strides_in_bytes()[3] / sizeof(T); |
| |
| const int n_channels = _input->info()->tensor_shape().x(); |
| const int n_cols = window_in.y().step(); |
| const int n_rows = _input->info()->tensor_shape().z(); |
| const int n_batches = _input->info()->tensor_shape()[3]; |
| |
| execute_window_loop(window_in, [&](const Coordinates & id) |
| { |
| const int idx = id[0] * out_channel_stride + id[1] * out_col_stride + id[2] * out_row_stride; |
| reorder::nhwc_to_nchw(reinterpret_cast<const T *>(in.ptr()), reinterpret_cast<T *>(out.ptr()) + idx, |
| n_batches, n_rows, n_cols, n_channels, |
| in_batch_stride, in_row_stride, in_col_stride, |
| out_batch_stride, out_channel_stride, out_row_stride); |
| }, |
| in, out); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Unsupported permutation vector"); |
| } |
| } |
| |
| NEPermuteKernel::NEPermuteKernel() |
| : _func(), _input(nullptr), _output(nullptr), _perm() |
| { |
| } |
| |
| void NEPermuteKernel::configure(const ITensor *input, ITensor *output, const PermutationVector &perm) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| const TensorShape output_shape = misc::shape_calculator::compute_permutation_output_shape(*input->info(), perm); |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), perm)); |
| |
| _input = input; |
| _output = output; |
| _perm = perm; |
| |
| switch(input->info()->element_size()) |
| { |
| case 1: |
| _func = &NEPermuteKernel::run_permute<uint8_t>; |
| break; |
| case 2: |
| _func = &NEPermuteKernel::run_permute<uint16_t>; |
| break; |
| case 4: |
| _func = &NEPermuteKernel::run_permute<uint32_t>; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Element size not supported"); |
| break; |
| } |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input->info(), Steps()); |
| |
| // The NEPermute 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 NEPermuteKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, perm)); |
| return Status{}; |
| } |
| |
| void NEPermuteKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window); |
| |
| if(_func != nullptr) |
| { |
| (this->*_func)(window); |
| } |
| } |