/* * Copyright (c) 2017-2019 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/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 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 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 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 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 (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 (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 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 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 Status INEWinogradLayerTransformWeightsKernel::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; template unsigned int NEWinogradLayerTransformWeightsKernel::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( // 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 NEWinogradLayerTransformWeightsKernel::NEWinogradLayerTransformWeightsKernel() : _transform(nullptr), _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0) { } template int NEWinogradLayerTransformWeightsKernel::get_matrix_stride(const KernelShape &kernel_shape) const { return WinogradConv::get_kernel_matrix_stride(kernel_shape); } #ifndef DOXYGEN_SKIP_THIS template void NEWinogradLayerTransformWeightsKernel::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; _transform = arm_compute::support::cpp14::make_unique(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 void NEWinogradLayerTransformWeightsKernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); const size_t fst = window.x().start(); const size_t lst = window.x().end(); _transform->set_weight_tensor(_weights_hwio->buffer()); const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK); _transform->set_output_matrices(_output->buffer(), _matrix_stride, matrix_row_stride); _transform->set_working_space(_output->buffer()); _transform->run(fst, lst); } template bool NEWinogradLayerTransformWeightsKernel::is_parallelisable() const { return false; } template Status NEWinogradLayerTransformWeightsKernel::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; template class NEWinogradLayerTransformWeightsKernel; template class NEWinogradLayerTransformWeightsKernel; template class NEWinogradLayerTransformWeightsKernel; template class NEWinogradLayerTransformWeightsKernel; template class NEWinogradLayerTransformWeightsKernel; template class NEWinogradLayerTransformWeightsKernel; template class NEWinogradLayerTransformWeightsKernel; template class NEWinogradLayerTransformWeightsKernel; // Input transform template unsigned int NEWinogradLayerTransformInputKernel::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(WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(T)); } template unsigned int NEWinogradLayerTransformInputKernel::get_working_space_size(unsigned int num_threads) const { return _transform->get_working_space_size(num_threads) / sizeof(T); } template int NEWinogradLayerTransformInputKernel::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 NEWinogradLayerTransformInputKernel::NEWinogradLayerTransformInputKernel() : _transform(nullptr), _input_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0), _padding_top(), _padding_left(), _padding_right(), _padding_bottom(), _workspace(nullptr) { } template void NEWinogradLayerTransformInputKernel::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. */ ITensor *workspace) { _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; _workspace = workspace; _padding_top = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0; _padding_left = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0; _padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0; _padding_right = (padding == PADDING_SAME) ? iceildiv(KernelCols - 1, 2) : 0; _transform = arm_compute::support::cpp14::make_unique( KernelRows, KernelCols, num_batches, num_rows, num_cols, num_channels, _padding_top, /**< Padding to apply to the top of the image. */ _padding_left, /**< Padding to apply to the left of the image. */ _padding_bottom, /**< Padding to apply to the bottom of the image. */ _padding_right /**< Padding to apply to the right of the image. */ ); Window win; auto win_last = _transform->get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); INEKernel::configure(win); } template void NEWinogradLayerTransformInputKernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_NULLPTR(_workspace); 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(_input_nhwc->buffer() + _input_nhwc->info()->offset_first_element_in_bytes()); auto output_ptr = reinterpret_cast(_output->buffer() + _output->info()->offset_first_element_in_bytes()); ARM_COMPUTE_ERROR_ON_NULLPTR(output_ptr); _transform->set_input_tensor(input_nhwc_ptr, input_batch_stride, input_row_stride, input_col_stride); _transform->set_output_matrices(output_ptr, _matrix_stride, _num_channels); _transform->set_working_space(_workspace->buffer()); // 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(); _transform->run(fst, lst, info.thread_id); } template Status NEWinogradLayerTransformInputKernel::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; template class NEWinogradLayerTransformInputKernel; template class NEWinogradLayerTransformInputKernel; template class NEWinogradLayerTransformInputKernel; template class NEWinogradLayerTransformInputKernel; template class NEWinogradLayerTransformInputKernel; template class NEWinogradLayerTransformInputKernel; template class NEWinogradLayerTransformInputKernel; template class NEWinogradLayerTransformInputKernel; // Output transform template unsigned int NEWinogradLayerTransformOutputKernel::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( WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(T)); } template NEWinogradLayerTransformOutputKernel::NEWinogradLayerTransformOutputKernel() : _transform(nullptr), _biases(nullptr), _transformed_output(nullptr), _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 unsigned int NEWinogradLayerTransformOutputKernel::get_working_space_size(unsigned int num_threads) const { return _transform->get_working_space_size(num_threads) / sizeof(T); } template int NEWinogradLayerTransformOutputKernel::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 Tensor4DShape NEWinogradLayerTransformOutputKernel::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 void NEWinogradLayerTransformOutputKernel::configure( const ITensor *biases, const ITensor *transformed_output, const int matrix_stride, ITensor *output_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels, ITensor *workspace) { _biases = biases; _workspace = workspace; _transformed_output = transformed_output; _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 _transform = arm_compute::support::cpp14::make_unique(num_batches, num_rows, num_cols, num_channels); Window win; auto win_last = _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 void NEWinogradLayerTransformOutputKernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_NULLPTR(_workspace); ARM_COMPUTE_ERROR_ON_NULLPTR(_transformed_output); ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc); const int out_batch_stride = _output_nhwc->info()->strides_in_bytes()[3] / sizeof(T); 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); _transform->set_input_matrices(_transformed_output->buffer(), _matrix_stride, _matrix_row_stride); _transform->set_bias((_biases ? reinterpret_cast(_biases->buffer() + _biases->info()->offset_first_element_in_bytes()) : nullptr)); _transform->set_output_tensor(_output_nhwc->buffer() + _output_nhwc->info()->offset_first_element_in_bytes(), out_batch_stride, out_row_stride, out_col_stride); _transform->set_working_space(_workspace->buffer()); // 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(); _transform->run(fst, lst, info.thread_id); } template Status NEWinogradLayerTransformOutputKernel::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; template class NEWinogradLayerTransformOutputKernel; template class NEWinogradLayerTransformOutputKernel; template class NEWinogradLayerTransformOutputKernel; template class NEWinogradLayerTransformOutputKernel; template class NEWinogradLayerTransformOutputKernel; template class NEWinogradLayerTransformOutputKernel; template class NEWinogradLayerTransformOutputKernel; template class NEWinogradLayerTransformOutputKernel; } // namespace arm_compute