| /* |
| * Copyright (c) 2017 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 "convolution.hpp" |
| #include "winograd_layer.hpp" |
| #include "tensor.hpp" |
| |
| |
| /** Determine how much memory (in units of TIn) to allocate for the transformed |
| * weights. |
| */ |
| template < |
| int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, |
| typename TIn, typename TOut |
| > |
| unsigned int WinogradConvolutionLayer< |
| OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut |
| >::get_weight_storage_size( |
| const int n_output_channels, /** Number of output feature maps. */ |
| const int n_input_channels /** Number of input feature maps. */ |
| ) |
| { |
| const KernelShape shape( |
| n_output_channels, KernelRows, KernelCols, n_input_channels |
| ); |
| return static_cast<unsigned int>( |
| // WinogradConv returns the size in bytes, we divide by `sizeof(TIn)` to |
| // express that in units of TIn. |
| WinogradConv::get_kernel_storage_size(shape) / sizeof(TIn) |
| ); |
| } |
| |
| |
| /** Determine how much memory (in units of TIn) to allocate for the transformed |
| * input. |
| */ |
| template < |
| int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, |
| typename TIn, typename TOut |
| > |
| unsigned int WinogradConvolutionLayer< |
| OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut |
| >::get_input_storage_size( |
| const int n_batches, /** Number of batches in the input tensor. */ |
| const int n_channels, /** Number of feature maps in the input tensor. */ |
| const int n_rows, /** Number of rows in each feature map. */ |
| const int n_cols, /** Number of columns in each feature map. */ |
| const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ |
| ) |
| { |
| // Construct shapes for the input and kernel tensors. |
| const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels); |
| const KernelShape kern_shape(1, KernelRows, KernelCols, n_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(TIn) |
| ); |
| } |
| |
| |
| /** Determine how much memory (in units of TOut) to allocate for the (Winograd |
| * domain) output. |
| */ |
| template < |
| int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, |
| typename TIn, typename TOut |
| > |
| unsigned int WinogradConvolutionLayer< |
| OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut |
| >::get_output_storage_size( |
| const int n_batches, /** Number of batches in the output tensor. */ |
| const int n_rows, /** Number of rows in each feature map of the input tensor. */ |
| const int n_cols, /** Number of columns in each feature map of the input tensor. */ |
| const int n_output_channels, /** Number of feature maps in the output tensor. */ |
| const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ |
| ) |
| { |
| // Construct shapes for the input and kernel tensors. |
| const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1); |
| const KernelShape kern_shape(n_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(TOut) |
| ); |
| } |
| |
| |
| /** Get the shape (rows, cols) of a feature map of the output tensor. */ |
| template < |
| int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, |
| typename TIn, typename TOut |
| > |
| std::pair<int, int> WinogradConvolutionLayer< |
| OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut |
| >::get_output_feature_map_shape( |
| const int n_input_rows, /** Number of rows in the input feature map. */ |
| const int n_input_cols, /** Number of columns in the input feature map. */ |
| const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ |
| ) |
| { |
| // Construct shapes for the input and kernel tensors. |
| const Tensor4DShape input_shape(1, n_input_rows, n_input_cols, 1); |
| const KernelShape kern_shape(1, KernelRows, KernelCols, 1); |
| const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; |
| |
| // Compute the new shape |
| const auto output_shape = WinogradConv::get_output_shape( |
| kern_shape, input_shape, padding |
| ); |
| |
| return std::make_pair(output_shape.n_rows, output_shape.n_cols); |
| } |
| |
| |
| /** Create a new Winograd convolution layer. |
| */ |
| template < |
| int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, |
| typename TIn, typename TOut |
| > |
| WinogradConvolutionLayer<OutputTileRows, OutputTileCols, KernelRows, KernelCols, TIn, TOut>:: |
| WinogradConvolutionLayer( |
| const int n_batches, /** Number of batches in the input and output tensors. */ |
| const int n_input_channels, /** Number of feature maps in a batch of the input tensor. */ |
| const int n_input_rows, /** Number of rows in a feature map of the input tensor. */ |
| const int n_input_cols, /** Number of columns in a feature map of the input tensor. */ |
| const int n_output_channels, /** Number of feature maps in the output tensor. */ |
| const bool same_padding, /** Use "SAME" padding, otherwise use "VALID". */ |
| const TIn* const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */ |
| TIn* const winograd_weights, /** Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size`. */ |
| const TIn* const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */ |
| TIn* const winograd_input, /** Pointer to working space for the input tensor in the Winograd domain. Must be at least the size returned by `get_input_storage_size`. */ |
| TOut* const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */ |
| TOut* const winograd_output /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */ |
| ) : _kernel_shape(n_output_channels, KernelRows, KernelCols, n_input_channels), |
| _input_shape(n_batches, n_input_rows, n_input_cols, n_input_channels), |
| _padding(same_padding ? PADDING_SAME : PADDING_VALID), |
| _output_shape(WinogradConv::get_output_shape(_kernel_shape, _input_shape, _padding)), |
| _n_output_rows(_output_shape.n_rows), |
| _n_output_cols(_output_shape.n_cols), |
| _kernel_matrix_stride(WinogradConv::get_kernel_matrix_stride(_kernel_shape)), |
| _kernel_matrix_row_stride(roundup(n_output_channels, WinogradConv::N_BLOCK)), |
| _input_matrix_stride(WinogradConv::get_input_matrix_stride(_kernel_shape, _input_shape, _padding)), |
| _input_matrix_row_stride(n_input_channels), |
| _output_matrix_stride(WinogradConv::get_output_matrix_stride(_kernel_shape, _input_shape, _padding)), |
| _output_matrix_row_stride(_kernel_matrix_row_stride), |
| _tile_rows(iceildiv(_n_output_rows, OutputTileRows)), |
| _tile_cols(iceildiv(_n_output_cols, OutputTileCols)), |
| _m(n_batches * _tile_rows * _tile_cols), |
| _k(n_input_channels), |
| _n(n_output_channels), |
| weights_transform( |
| weights, winograd_weights, |
| _kernel_matrix_stride, _kernel_matrix_row_stride, |
| n_output_channels, n_input_channels |
| ), |
| input_transform( |
| input, n_batches, n_input_rows, n_input_cols, n_input_channels, _padding, |
| winograd_input, _input_matrix_stride, _input_matrix_row_stride |
| ), |
| gemms( |
| WinogradBase::N_GEMMS, _m, _k, _n, |
| _input_matrix_stride, _input_matrix_row_stride, |
| _kernel_matrix_stride, _kernel_matrix_row_stride, |
| _output_matrix_stride, _output_matrix_row_stride, |
| winograd_input, winograd_weights, winograd_output |
| ), |
| output_transform( |
| winograd_output, _output_matrix_stride, _output_matrix_row_stride, |
| output, n_batches, _n_output_rows, _n_output_cols, n_output_channels |
| ) |
| { |
| } |
| |
| // Instantiate valid implementations. |
| template class WinogradConvolutionLayer<2, 2, 3, 3, float, float>; |
| template class WinogradConvolutionLayer<4, 4, 3, 3, float, float>; |