| /* |
| * 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/NEWinogradLayerKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "support/ToolchainSupport.h" |
| |
| #include "arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp" |
| |
| namespace |
| { |
| using T = WinogradConvolutionLayer<2, 2, 3, 3, float, float>; |
| } // namespace |
| |
| namespace arm_compute |
| { |
| class Winograd3x3F32::Private |
| { |
| public: |
| Private( |
| 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 float *const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */ |
| float *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 float *const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */ |
| float *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`. */ |
| float *const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */ |
| float *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`. */ |
| ) |
| : convolver(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, output, winograd_output) |
| { |
| } |
| T convolver; |
| }; |
| |
| Winograd3x3F32::~Winograd3x3F32() |
| { |
| } |
| |
| void Winograd3x3F32::transform_output() |
| { |
| auto win = _pimpl->convolver.output_transform.get_window(); |
| _pimpl->convolver.output_transform.run(0, win); |
| } |
| |
| void Winograd3x3F32::transform_input() |
| { |
| auto win = _pimpl->convolver.input_transform.get_window(); |
| _pimpl->convolver.input_transform.run(0, win); |
| } |
| |
| void Winograd3x3F32::transform_weights() |
| { |
| auto win = _pimpl->convolver.weights_transform.get_window(); |
| _pimpl->convolver.weights_transform.run(0, win); |
| } |
| |
| Winograd3x3F32::Winograd3x3F32( |
| 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 float *const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */ |
| float *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 float *const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */ |
| float *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`. */ |
| float *const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */ |
| float *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`. */ |
| ) |
| : _pimpl(support::cpp14::make_unique<Private>(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, output, |
| winograd_output)) |
| { |
| } |
| |
| unsigned int NEWinogradLayerKernel::get_input_storage_size(const int n_batches, const int n_channels, const int n_rows, const int n_cols, const bool same_padding) |
| { |
| return T::get_input_storage_size(n_batches, n_channels, n_rows, n_cols, same_padding); |
| } |
| |
| unsigned int NEWinogradLayerKernel::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". */ |
| ) |
| { |
| return T::get_output_storage_size(n_batches, n_rows, n_cols, n_output_channels, same_padding); |
| } |
| |
| unsigned int NEWinogradLayerKernel::get_weight_storage_size(const int n_output_channels, const int n_input_channels) |
| { |
| return T::get_weight_storage_size(n_output_channels, n_input_channels); |
| } |
| |
| NEWinogradLayerKernel::NEWinogradLayerKernel() |
| : _convolver(nullptr) |
| { |
| } |
| |
| void NEWinogradLayerKernel::configure(Winograd3x3F32 *convolver) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(convolver); |
| _convolver = convolver; |
| Window win; |
| auto win_last = _convolver->_pimpl->convolver.gemms.get_window(); |
| win.set(Window::DimX, Window::Dimension(0, win_last, 1)); |
| INEKernel::configure(win); |
| } |
| |
| void NEWinogradLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| const size_t first_gemm = window.x().start(); |
| const size_t last_gemm = window.x().end(); |
| _convolver->_pimpl->convolver.gemms.run(first_gemm, last_gemm); |
| } |
| } // namespace arm_compute |