diff options
Diffstat (limited to 'src/core/NEON/kernels')
-rw-r--r-- | src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp | 140 |
1 files changed, 80 insertions, 60 deletions
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp index 672684d14f..cfd53d7082 100644 --- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp @@ -309,9 +309,9 @@ template class NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>; // Weights transform template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int n_output_channels, int n_input_channels) const +unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const { - const KernelShape shape(n_output_channels, KernelRows, KernelCols, n_input_channels); + const KernelShape shape(num_output_channels, KernelRows, KernelCols, num_input_channels); return static_cast<unsigned int>( // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T WinogradConv::get_kernel_storage_size(shape) / sizeof(T)); @@ -319,7 +319,8 @@ unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTile template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel() - : _transform() + : _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0) + { } @@ -333,15 +334,20 @@ template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, in void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( const ITensor *weights_hwio, T *const output, - const int matrix_stride, /** Stride across matrices in the output. */ - const int n_output_channels, /** Number of filters. */ - const int n_input_channels) /** Number of channels in each filter. */ -{ - const int matrix_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK); - _transform = support::cpp14::make_unique<WeightsTransform>(reinterpret_cast<T *>(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels, - n_input_channels); - Window win; - auto win_last = _transform->get_window(); + const int matrix_stride, /** Stride across matrices in the output. */ + const int num_output_channels, /** Number of filters. */ + const int num_input_channels) /** Number of channels in each filter. */ +{ + _weights_hwio = weights_hwio; + _output = output; + _matrix_stride = matrix_stride; + _num_output_channels = num_output_channels; + _num_input_channels = num_input_channels; + + const int matrix_row_stride = roundup(num_output_channels, WinogradConv::N_BLOCK); + WeightsTransform transform(nullptr, output, matrix_stride, matrix_row_stride, num_output_channels, num_input_channels); + Window win; + auto win_last = transform.get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); INEKernel::configure(win); } @@ -351,9 +357,12 @@ void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, Ke { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - _transform->run(fst, lst); + + const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK); + WeightsTransform transform(reinterpret_cast<T *>(_weights_hwio->buffer()), _output, _matrix_stride, matrix_row_stride, _num_output_channels, _num_input_channels); + const size_t fst = window.x().start(); + const size_t lst = window.x().end(); + transform.run(fst, lst); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -379,16 +388,16 @@ template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>; template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size( - int n_batches, /** Number of batches in the input tensor. */ - int n_channels, /** Number of feature maps in the input tensor. */ - int n_rows, /** Number of rows in each feature map. */ - int n_cols, /** Number of columns in each feature map. */ - bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ + int num_batches, /* Number of batches in the input tensor. */ + int num_channels, /* Number of feature maps in the input tensor. */ + int num_rows, /* Number of rows in each feature map. */ + int num_cols, /* Number of columns in each feature map. */ + bool same_padding /* Use "SAME" padding, otherwise use "VALID". */ ) const { // 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 Tensor4DShape input_shape(num_batches, num_rows, num_cols, num_channels); + const KernelShape kern_shape(1, KernelRows, KernelCols, num_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(T)); @@ -403,25 +412,32 @@ int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kerne template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel() - : _transform() + : _input_nhwc(), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0) { } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const T *const input, /** Input tensor data */ - const int n_batches, /** Number of batches in input tensor. */ - const int n_rows, /** Number of rows in input tensor. */ - const int n_cols, /** Number of columns in input tensor. */ - const int n_channels, /** Number of channels in input tensor. */ - const PaddingType padding, /** Padding type. */ - T *const output, /** Base of output matrices. */ - const int matrix_stride) /** Stride between output matrices. */ -{ - // _input_matrix_row_stride(n_input_channels), - _transform = support::cpp14::make_unique<InputTransform>(input, n_batches, n_rows, n_cols, n_channels, padding, output, matrix_stride, n_channels); - Window win; - auto win_last = _transform->get_window(); + const ITensor *input_nhwc, + const int num_batches, /* Number of batches in input tensor. */ + const int num_rows, /* Number of rows in input tensor. */ + const int num_cols, /* Number of columns in input tensor. */ + const int num_channels, /* Number of channels in input tensor. */ + const PaddingType padding, /* Padding type. */ + T *const output, /* Base of output matrices. */ + const int matrix_stride) /* Stride between output matrices. */ +{ + _input_nhwc = input_nhwc; + _num_batches = num_batches; + _num_rows = num_rows; + _num_cols = num_cols; + _num_channels = num_channels; + _padding = padding; + _output = output; + _matrix_stride = matrix_stride; + InputTransform transform(nullptr, num_batches, num_rows, num_cols, num_channels, padding, output, matrix_stride, num_channels); + Window win; + auto win_last = transform.get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); INEKernel::configure(win); } @@ -431,9 +447,13 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + + InputTransform input_transform(reinterpret_cast<const T *>(_input_nhwc->buffer()), _num_batches, _num_rows, _num_cols, _num_channels, _padding, _output, _matrix_stride, _num_channels); + + // The code below cannot be moved to configure because biases hasn't been allocated at that point const size_t fst = window.x().start(); const size_t lst = window.x().end(); - _transform->run(fst, lst); + input_transform.run(fst, lst); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -453,16 +473,16 @@ template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>; template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size( - int n_batches, /** Number of batches in the output tensor. */ - int n_rows, /** Number of rows in each feature map of the input tensor. */ - int n_cols, /** Number of columns in each feature map of the input tensor. */ - int n_output_channels, /** Number of feature maps in the output tensor. */ - bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ + int num_batches, /* Number of batches in the output tensor. */ + int num_rows, /* Number of rows in each feature map of the input tensor. */ + int num_cols, /* Number of columns in each feature map of the input tensor. */ + int num_output_channels, /* Number of feature maps in the output tensor. */ + bool same_padding /* Use "SAME" padding, otherwise use "VALID". */ ) const { // 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 Tensor4DShape input_shape(num_batches, num_rows, num_cols, 1); + const KernelShape kern_shape(num_output_channels, KernelRows, KernelCols, 1); const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; // Return the size, converted into units of TOut @@ -472,7 +492,7 @@ unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileC template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel() - : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0) + : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0) { } @@ -494,24 +514,24 @@ void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker const ITensor *biases, const T *const output_workingspace, const int matrix_stride, - T *const output, - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels) + ITensor *const output_nhwc, + const int num_batches, + const int num_rows, + const int num_cols, + const int num_channels) { _biases = biases; _output_workspace = output_workingspace; _matrix_stride = matrix_stride; - _matrix_row_stride = roundup(n_channels, WinogradConv::N_BLOCK); - _output = output; - _n_batches = n_batches; - _n_rows = n_rows; - _n_cols = n_cols; - _n_channels = n_channels; + _matrix_row_stride = roundup(num_channels, WinogradConv::N_BLOCK); + _output_nhwc = output_nhwc; + _num_batches = num_batches; + _num_rows = num_rows; + _num_cols = num_cols; + _num_channels = num_channels; // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window - OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, _output, _n_batches, _n_rows, _n_cols, _n_channels); + OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, nullptr, _num_batches, _num_rows, _num_cols, _num_channels); Window win; auto win_last = output_transform.get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); @@ -524,11 +544,11 @@ void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace); - ARM_COMPUTE_ERROR_ON_NULLPTR(_output); + ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc); OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, - (_biases ? reinterpret_cast<T *>(_biases->buffer()) : nullptr), _output, - _n_batches, _n_rows, _n_cols, _n_channels); + (_biases ? reinterpret_cast<T *>(_biases->buffer()) : nullptr), reinterpret_cast<T *>(_output_nhwc->buffer()), + _num_batches, _num_rows, _num_cols, _num_channels); // The code below cannot be moved to configure because biases hasn't been allocated at that point const size_t fst = window.x().start(); |