diff options
Diffstat (limited to 'src/core/NEON/kernels/winograd/winograd_layer.cpp')
| -rw-r--r-- | src/core/NEON/kernels/winograd/winograd_layer.cpp | 206 |
1 files changed, 0 insertions, 206 deletions
diff --git a/src/core/NEON/kernels/winograd/winograd_layer.cpp b/src/core/NEON/kernels/winograd/winograd_layer.cpp deleted file mode 100644 index f16d62c0ef..0000000000 --- a/src/core/NEON/kernels/winograd/winograd_layer.cpp +++ /dev/null @@ -1,206 +0,0 @@ -/* - * 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`. */ - const TOut* const biases, /** Pointer to biases vector. */ - 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, biases, - 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>; -template class WinogradConvolutionLayer<2, 2, 5, 5, float, float>; |