diff options
Diffstat (limited to 'src/core/NEON/kernels/NEWinogradLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEWinogradLayerKernel.cpp | 87 |
1 files changed, 58 insertions, 29 deletions
diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp index d17630a92e..24d72eddd8 100644 --- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017, 2018 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -29,11 +29,11 @@ #include "arm_compute/core/TensorInfo.h" #include "support/ToolchainSupport.h" -#include "src/core/NEON/kernels/winograd/winograd_gemm.hpp" +#include "arm_compute/core/NEON/kernels/winograd/winograd_layer.hpp" namespace { -using T = winograd::Winograd2x2_3x3GEMM<float, float>; +using T = WinogradConvolutionLayer<2, 2, 3, 3, float, float>; } // namespace namespace arm_compute @@ -41,11 +41,23 @@ namespace arm_compute class Winograd3x3F32::Private { public: - Private(const KernelShape &kernel_shape, const Tensor4DShape input_shape, const PaddingType padding_type, void *kernel_storage) - : convolver(kernel_shape, input_shape, padding_type, kernel_storage) + 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; }; @@ -53,46 +65,62 @@ Winograd3x3F32::~Winograd3x3F32() { } -void Winograd3x3F32::transform_weights(const void *const kernel, void *transform_working_space) +void Winograd3x3F32::transform_output() { - _pimpl->convolver.transform_weights(reinterpret_cast<const float *>(kernel), transform_working_space); + auto win = _pimpl->convolver.output_transform.get_window(); + _pimpl->convolver.output_transform.run(0, win); } -void Winograd3x3F32::reshape_input(const Tensor4DShape &input_shape, const PaddingType padding_type, const void *const input, void *working_space) +void Winograd3x3F32::transform_input() { - _pimpl->convolver.reshape_input(input_shape, padding_type, reinterpret_cast<const float *>(input), working_space); + auto win = _pimpl->convolver.input_transform.get_window(); + _pimpl->convolver.input_transform.run(0, win); } -void Winograd3x3F32::reshape_output(const Tensor4DShape &input_shape, const PaddingType padding_type, void *const output) +void Winograd3x3F32::transform_weights() { -#if defined(__aarch64__) - _pimpl->convolver.reshape_output(input_shape, padding_type, reinterpret_cast<float *const>(output)); -#else /* __aarch64__ */ - ARM_COMPUTE_UNUSED(input_shape); - ARM_COMPUTE_UNUSED(padding_type); - ARM_COMPUTE_UNUSED(output); - ARM_COMPUTE_ERROR("Not implemented"); -#endif /* __aarch64__ */ + auto win = _pimpl->convolver.weights_transform.get_window(); + _pimpl->convolver.weights_transform.run(0, win); } -Winograd3x3F32::Winograd3x3F32(const KernelShape &kernel_shape, const Tensor4DShape input_shape, const PaddingType padding_type, void *kernel_storage) - : _pimpl(support::cpp14::make_unique<Private>(kernel_shape, input_shape, padding_type, kernel_storage)) +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)) { } -size_t NEWinogradLayerKernel::get_kernel_storage_size(const KernelShape &shape) +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_kernel_storage_size(shape); + return T::get_input_storage_size(n_batches, n_channels, n_rows, n_cols, same_padding); } -size_t NEWinogradLayerKernel::get_working_space_size(const Tensor4DShape &input_shape, const KernelShape &k_shape, const PaddingType 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_working_space_size(input_shape, k_shape, padding); + return T::get_output_storage_size(n_batches, n_rows, n_cols, n_output_channels, same_padding); } -size_t NEWinogradLayerKernel::get_kernel_transform_working_size(const KernelShape &shape) +size_t NEWinogradLayerKernel::get_weight_storage_size(const int n_output_channels, const int n_input_channels) { - return T::get_kernel_transform_working_size(shape); + return T::get_weight_storage_size(n_output_channels, n_input_channels); } NEWinogradLayerKernel::NEWinogradLayerKernel() @@ -105,7 +133,8 @@ void NEWinogradLayerKernel::configure(Winograd3x3F32 *convolver) ARM_COMPUTE_ERROR_ON_NULLPTR(convolver); _convolver = convolver; Window win; - win.set(Window::DimX, Window::Dimension(0, 15, 1)); + auto win_last = _convolver->_pimpl->convolver.gemms.get_window(); + win.set(Window::DimX, Window::Dimension(0, win_last, 1)); INEKernel::configure(win); } @@ -115,6 +144,6 @@ void NEWinogradLayerKernel::run(const Window &window, const ThreadInfo &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.execute(first_gemm, last_gemm); + _convolver->_pimpl->convolver.gemms.run(first_gemm, last_gemm); } } // namespace arm_compute |