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Diffstat (limited to 'arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp')
| -rw-r--r-- | arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp | 129 |
1 files changed, 0 insertions, 129 deletions
diff --git a/arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp b/arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp deleted file mode 100644 index 1db63d750b..0000000000 --- a/arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp +++ /dev/null @@ -1,129 +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. - */ - -#pragma once - -#include <utility> - -#include "batched_blocked_gemm.hpp" -#include "winograd_gemm.hpp" - -/** Example of how to construct an ACL-like interface. - * - * Use `get_weight_storage_size`, `get_input_storage_size` and - * `get_output_storage_size` to allocate memory for the convolution engine. - * Then create a `WinogradConvolutionLayer`. - * - * Initialise the weights using `weights_transform.run(...)`. - * - * For each inference: - * 1. Transform the inputs to the Winograd domain using `input_transform.run(...)` - * 2. Perform a number of GEMMs using `gemms.run(...)` - * 3. Transform the output to the spatial domain using `output_transform.run(...)` - */ -template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, - typename TIn, typename TOut> -class WinogradConvolutionLayer -{ - private: - const KernelShape _kernel_shape; - const Tensor4DShape _input_shape; - const PaddingType _padding; - const Tensor4DShape _output_shape; - const int _n_output_rows, _n_output_cols; - const int _kernel_matrix_stride, _kernel_matrix_row_stride; - const int _input_matrix_stride, _input_matrix_row_stride; - const int _output_matrix_stride, _output_matrix_row_stride; - const int _tile_rows, _tile_cols; - const int _m, _k, _n; - - public: - using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols>; - using WeightsTransform = typename WinogradBase::template WeightsTransform<TIn>; - using InputTransform = typename WinogradBase::template InputTransform<TIn>; - using WinogradConv = typename WinogradBase::template Convolution<TOut, TIn>; - using MultiGEMM = winograd::BatchedBlockedGemm<WinogradConv::M_BLOCK, WinogradConv::N_BLOCK, TIn, TOut>; - using OutputTransform = typename WinogradBase::template OutputTransform<TOut>; - - /* Public member variables. */ - WeightsTransform weights_transform; /** Operator to transform weights to Winograd domain. */ - InputTransform input_transform; /** Operator to transform input to Winograd domain. */ - MultiGEMM gemms; /** Operator to perform multiple GEMMs. */ - OutputTransform output_transform; /** Operator to transform output from Winograd domain. */ - - /** Determine how much memory (in units of TIn) to allocate for the - * transformed weights. - */ - static unsigned int get_weight_storage_size( - const int n_output_channels, /** Number of output feature maps. */ - const int n_input_channels /** Number of input feature maps. */ - ); - - /** Determine how much memory (in units of TIn) to allocate for the - * transformed input. - */ - static unsigned int 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". */ - ); - - /** Determine how much memory (in units of TOut) to allocate for the - * (Winograd domain) output. - */ - static unsigned int 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". */ - ); - - /** Get the shape (rows, cols) of a feature map of the output tensor. */ - static std::pair<int, int> 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". */ - ); - - /** Create a new Winograd convolution layer. - */ - 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 weights_storage, /** 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`. */ - ); -}; |