| /* |
| * Copyright (c) 2017-2018 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/NEWinogradConvolutionLayerKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "support/ToolchainSupport.h" |
| |
| namespace arm_compute |
| { |
| //Batched Gemms |
| |
| namespace |
| { |
| inline bool is_kernel_size_supported(Size2D size) |
| { |
| const std::array<Size2D, 8> supported_input_sizes = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3), Size2D(1, 5), Size2D(5, 1), Size2D(7, 1), Size2D(1, 7) } }; |
| return std::end(supported_input_sizes) != std::find(std::begin(supported_input_sizes), std::end(supported_input_sizes), size); |
| } |
| |
| Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| |
| const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); |
| const auto input_width = input->dimension(idx_width); |
| const auto input_height = input->dimension(idx_height); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(input_width, input_height)), "Only 1x3, 3x1, 1x5, 5x1, 7x1, 1x7, 3x3 and 5x5 kernels are supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4); |
| const Size2D &output_tile = winograd_info.output_tile_size; |
| const std::array<Size2D, 8> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U), Size2D(4, 1), Size2D(1, 4), Size2D(2, 1), Size2D(1, 2) } }; |
| ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_tile_sizes) == std::find(std::begin(supported_tile_sizes), std::end(supported_tile_sizes), output_tile)); |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info)); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| const Size2D kernel_dims = winograd_info.kernel_size; |
| // Output tensor auto inizialitation if not yet initialized |
| auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info))); |
| |
| unsigned int num_elems_processed_per_iteration_x = kernel_dims.width; |
| unsigned int num_elems_processed_per_iteration_y = kernel_dims.height; |
| |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); |
| bool window_changed = false; |
| |
| AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); |
| AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1)); |
| window_changed = update_window_and_padding(win, input_access, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape())); |
| |
| Window win_collapsed = win.collapse(win, Window::DimZ); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| |
| return std::make_pair(err, win_collapsed); |
| } |
| |
| Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| const Size2D &kernel_dims = winograd_info.kernel_size; |
| const PadStrideInfo &conv_info = winograd_info.convolution_info; |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(kernel_dims.width, kernel_dims.height)), |
| "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); |
| |
| // Validate configured output |
| if(output->total_size() != 0) |
| { |
| const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| const PadStrideInfo conv_info = winograd_info.convolution_info; |
| const Size2D output_tile_size = winograd_info.output_tile_size; |
| const Size2D kernel_dims = winograd_info.kernel_size; |
| const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); |
| |
| unsigned int num_elems_read_per_iteration_x = (output_tile_size.width + kernel_dims.width - 1); |
| unsigned int num_elems_read_per_iteration_y = (output_tile_size.height + kernel_dims.height - 1); |
| |
| Window win = calculate_max_window(*input, Steps(1, 1)); |
| |
| AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y); |
| |
| bool window_changed = update_window_and_padding(win, input_access); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| |
| Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| const PadStrideInfo &conv_info = winograd_info.convolution_info; |
| const Size2D kernel_dims = winograd_info.kernel_size; |
| |
| // Number of tiles along the X and Y direction |
| const unsigned int num_tiles_x = std::ceil((winograd_info.input_dimensions.x() - (kernel_dims.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float> |
| (winograd_info.output_tile_size.width)); |
| const unsigned int num_tiles_y = std::ceil((winograd_info.input_dimensions.y() - (kernel_dims.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float> |
| (winograd_info.output_tile_size.height)); |
| const Size2D num_tiles = Size2D(num_tiles_x, num_tiles_y); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(kernel_dims.width, kernel_dims.height)), |
| "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); |
| |
| const std::array<unsigned int, 3> supported_gemm_sizes = { { 8U, 16U, 36U } }; |
| ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_gemm_sizes) == std::find(std::begin(supported_gemm_sizes), std::end(supported_gemm_sizes), input->dimension(2))); |
| ARM_COMPUTE_UNUSED(kernel_dims); |
| if(bias != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); |
| ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); |
| ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != size_t(1)); |
| } |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info))); |
| |
| constexpr unsigned int num_elems_processed_per_iteration = 1; |
| |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| bool window_changed = false; |
| |
| AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration, num_elems_processed_per_iteration); |
| AccessWindowStatic output_access(output, 0, 0, ceil_to_multiple(output->dimension(0), 2), ceil_to_multiple(output->dimension(1), 2)); |
| |
| if(bias != nullptr) |
| { |
| AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1)); |
| window_changed = update_window_and_padding(win, input_access, bias_access, output_access); |
| } |
| else |
| { |
| window_changed = update_window_and_padding(win, input_access, output_access); |
| } |
| output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| template <typename T> |
| Status INEWinogradLayerTransformWeightsKernel<T>::validate(const ITensorInfo *input, const ITensorInfo *weights) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| const DataLayout data_layout = input->data_layout(); |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(weights->dimension(width_idx), weights->dimension(height_idx))), |
| "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
| return Status{}; |
| } |
| |
| template class INEWinogradLayerTransformWeightsKernel<float>; |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const |
| { |
| const KernelShape shape(num_output_channels, KernelRows, KernelCols, num_input_channels); |
| return static_cast<unsigned int>( |
| // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T |
| WinogradConv::get_kernel_storage_size(shape) / sizeof(T)); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel() |
| : _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0) |
| |
| { |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(const KernelShape &kernel_shape) const |
| { |
| return WinogradConv::get_kernel_matrix_stride(kernel_shape); |
| } |
| |
| #ifndef DOXYGEN_SKIP_THIS |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( |
| const ITensor *weights_hwio, |
| ITensor *output, |
| const int matrix_stride, /** Stride across matrices in the output. */ |
| const int num_output_channels, /** Number of filters. */ |
| const int num_input_channels) /** Number of channels in each filter. */ |
| { |
| _weights_hwio = weights_hwio; |
| _output = output; |
| _matrix_stride = matrix_stride; |
| _num_output_channels = num_output_channels; |
| _num_input_channels = num_input_channels; |
| |
| const int matrix_row_stride = roundup(num_output_channels, WinogradConv::N_BLOCK); |
| WeightsTransform transform(nullptr, nullptr, matrix_stride, matrix_row_stride, num_output_channels, num_input_channels); |
| Window win; |
| auto win_last = transform.get_window(); |
| win.set(Window::DimX, Window::Dimension(0, win_last, 1)); |
| INEKernel::configure(win); |
| } |
| #endif /* DOXYGEN_SKIP_THIS */ |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| |
| const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK); |
| WeightsTransform transform(reinterpret_cast<T *>(_weights_hwio->buffer()), reinterpret_cast<T *>(_output->buffer()), _matrix_stride, matrix_row_stride, _num_output_channels, _num_input_channels); |
| const size_t fst = window.x().start(); |
| const size_t lst = window.x().end(); |
| transform.run(fst, lst); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| bool NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const |
| { |
| return false; |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| Status NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, |
| const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first); |
| return Status{}; |
| } |
| |
| template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>; |
| template class NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>; |
| template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>; |
| template class NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>; |
| template class NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>; |
| |
| template class NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>; |
| template class NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>; |
| template class NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>; |
| template class NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>; |
| // Input transform |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size( |
| int num_batches, /* Number of batches in the input tensor. */ |
| int num_channels, /* Number of feature maps in the input tensor. */ |
| int num_rows, /* Number of rows in each feature map. */ |
| int num_cols, /* Number of columns in each feature map. */ |
| bool same_padding /* Use "SAME" padding, otherwise use "VALID". */ |
| ) const |
| { |
| // Construct shapes for the input and kernel tensors. |
| const Tensor4DShape input_shape(num_batches, num_rows, num_cols, num_channels); |
| const KernelShape kern_shape(1, KernelRows, KernelCols, num_channels); |
| const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; |
| // Return the size, converted into units of TIn |
| return static_cast<unsigned int>(WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(T)); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( |
| const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const |
| { |
| return WinogradConv::get_input_matrix_stride(kernel_shape, input_shape, padding_type); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel() |
| : _input_nhwc(), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0) |
| { |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( |
| const ITensor *input_nhwc, |
| const int num_batches, /* Number of batches in input tensor. */ |
| const int num_rows, /* Number of rows in input tensor. */ |
| const int num_cols, /* Number of columns in input tensor. */ |
| const int num_channels, /* Number of channels in input tensor. */ |
| const PaddingType padding, /* Padding type. */ |
| ITensor *output, /* Base of output matrices. */ |
| const int matrix_stride) /* Stride between output matrices. */ |
| { |
| _input_nhwc = input_nhwc; |
| _num_batches = num_batches; |
| _num_rows = num_rows; |
| _num_cols = num_cols; |
| _num_channels = num_channels; |
| _padding = padding; |
| _output = output; |
| _matrix_stride = matrix_stride; |
| InputTransform transform(nullptr, num_batches, num_rows, num_cols, num_channels, padding, nullptr, matrix_stride, num_channels); |
| Window win; |
| auto win_last = transform.get_window(); |
| win.set(Window::DimX, Window::Dimension(0, win_last, 1)); |
| INEKernel::configure(win); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| |
| const int element_size_in_bytes = _input_nhwc->info()->element_size(); |
| const int input_col_stride = _input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes; |
| const int input_row_stride = _input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes; |
| const int input_batch_stride = _input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes; |
| const auto input_nhwc_ptr = reinterpret_cast<const T *>(_input_nhwc->buffer() + _input_nhwc->info()->offset_first_element_in_bytes()); |
| auto output_ptr = reinterpret_cast<T *>(_output->buffer() + _output->info()->offset_first_element_in_bytes()); |
| InputTransform input_transform(input_nhwc_ptr, |
| _num_batches, _num_rows, _num_cols, _num_channels, _padding, |
| output_ptr, |
| _matrix_stride, _num_channels, input_batch_stride, input_row_stride, input_col_stride); |
| |
| // The code below cannot be moved to configure because biases hasn't been allocated at that point |
| const size_t fst = window.x().start(); |
| const size_t lst = window.x().end(); |
| input_transform.run(fst, lst); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first); |
| |
| return Status{}; |
| } |
| |
| template class NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>; |
| template class NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>; |
| template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>; |
| template class NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>; |
| template class NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>; |
| |
| template class NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>; |
| template class NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>; |
| template class NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>; |
| template class NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>; |
| |
| // Output transform |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size( |
| int num_batches, /* Number of batches in the output tensor. */ |
| int num_rows, /* Number of rows in each feature map of the input tensor. */ |
| int num_cols, /* Number of columns in each feature map of the input tensor. */ |
| int num_output_channels, /* Number of feature maps in the output tensor. */ |
| bool same_padding /* Use "SAME" padding, otherwise use "VALID". */ |
| ) const |
| { |
| // Construct shapes for the input and kernel tensors. |
| const Tensor4DShape input_shape(num_batches, num_rows, num_cols, 1); |
| const KernelShape kern_shape(num_output_channels, KernelRows, KernelCols, 1); |
| const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; |
| |
| // Return the size, converted into units of TOut |
| return static_cast<unsigned int>( |
| WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(T)); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel() |
| : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0) |
| { |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( |
| const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const |
| { |
| return WinogradConv::get_output_matrix_stride(kernel_shape, input_shape, padding_type); |
| } |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| Tensor4DShape NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape( |
| const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const |
| { |
| return WinogradConv::get_output_shape(kernel_shape, in_shape, padding); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( |
| const ITensor *biases, |
| const ITensor *output_workingspace, |
| const int matrix_stride, |
| ITensor *output_nhwc, |
| const int num_batches, |
| const int num_rows, |
| const int num_cols, |
| const int num_channels) |
| { |
| _biases = biases; |
| _output_workspace = output_workingspace; |
| _matrix_stride = matrix_stride; |
| _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK); |
| _output_nhwc = output_nhwc; |
| _num_batches = num_batches; |
| _num_rows = num_rows; |
| _num_cols = num_cols; |
| _num_channels = num_channels; |
| // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window |
| OutputTransform output_transform(nullptr, _matrix_stride, _matrix_row_stride, nullptr, nullptr, _num_batches, _num_rows, _num_cols, _num_channels); |
| |
| Window win; |
| auto win_last = output_transform.get_window(); |
| win.set(Window::DimX, Window::Dimension(0, win_last, 1)); |
| _output_nhwc->info()->set_valid_region(ValidRegion(Coordinates(), _output_nhwc->info()->tensor_shape())); |
| |
| INEKernel::configure(win); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc); |
| |
| const int out_batch_stride = 0; |
| const int out_row_stride = _output_nhwc->info()->strides_in_bytes()[2] / sizeof(T); |
| const int out_col_stride = _output_nhwc->info()->strides_in_bytes()[1] / sizeof(T); |
| |
| OutputTransform output_transform(reinterpret_cast<T *>(_output_workspace->buffer()), _matrix_stride, _matrix_row_stride, |
| (_biases ? reinterpret_cast<T *>(_biases->buffer() + _biases->info()->offset_first_element_in_bytes()) : nullptr), |
| reinterpret_cast<T *>(_output_nhwc->buffer() + _output_nhwc->info()->offset_first_element_in_bytes()), |
| _num_batches, _num_rows, _num_cols, _num_channels, out_batch_stride, out_row_stride, out_col_stride); |
| |
| // The code below cannot be moved to configure because biases hasn't been allocated at that point |
| const size_t fst = window.x().start(); |
| const size_t lst = window.x().end(); |
| output_transform.run(fst, lst); |
| } |
| |
| template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> |
| Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, |
| const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), (bias != nullptr ? bias->clone().get() : nullptr), output->clone().get(), |
| winograd_info) |
| .first); |
| |
| return Status{}; |
| } |
| |
| template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>; |
| template class NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>; |
| template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>; |
| template class NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>; |
| template class NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>; |
| |
| template class NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>; |
| template class NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>; |
| template class NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>; |
| template class NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>; |
| |
| } // namespace arm_compute |