diff options
Diffstat (limited to 'src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp | 69 |
1 files changed, 39 insertions, 30 deletions
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp index 263ded0b84..fda384bc62 100644 --- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp @@ -28,6 +28,7 @@ #include "arm_compute/core/Helpers.h" #include "arm_compute/core/IAccessWindow.h" #include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/kernels/convolution/common/utils.hpp" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" @@ -233,7 +234,7 @@ unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTile 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)); + WinogradConv::get_kernel_storage_size(num_input_channels, num_output_channels) / sizeof(T)); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -243,9 +244,9 @@ NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelR } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(const KernelShape &kernel_shape) const +int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(int num_output_channels, int num_input_channels) const { - return WinogradConv::get_kernel_matrix_stride(kernel_shape); + return WinogradConv::get_kernel_matrix_stride(num_input_channels, num_output_channels); } #ifndef DOXYGEN_SKIP_THIS @@ -325,9 +326,8 @@ unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCo // Construct shapes for the input and kernel tensors. 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)); + return static_cast<unsigned int>(WinogradConv::get_input_storage_size(num_batches, num_rows, num_cols, num_channels, same_padding) / sizeof(T)); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -338,9 +338,13 @@ unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCo template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( - const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const + 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 { - return WinogradConv::get_input_matrix_stride(kernel_shape, input_shape, padding_type); + return WinogradConv::get_input_matrix_stride(num_batches, num_rows, num_cols, num_channels, same_padding); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -446,21 +450,18 @@ template class NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>; template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size( - 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". */ + 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. */ ) const { // Construct shapes for the input and kernel tensors. 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 return static_cast<unsigned int>( - WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(T)); + WinogradConv::get_output_storage_size(num_batches, num_rows, num_cols, num_output_channels) / sizeof(T)); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> @@ -478,28 +479,36 @@ unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileC template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride( - const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const + 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. */ +) const { - return WinogradConv::get_output_matrix_stride(kernel_shape, input_shape, padding_type); + return WinogradConv::get_output_matrix_stride(num_batches, num_rows, num_cols, num_output_channels); } + template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> -Tensor4DShape NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape( - const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const +std::pair<unsigned int, unsigned int> NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape( + 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. */ + bool padding_same) const { - return WinogradConv::get_output_shape(kernel_shape, in_shape, padding); + return WinogradConv::get_output_shape(std::make_pair<unsigned int, unsigned int>(num_rows, num_cols), padding_same); } template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure( - const ITensor *biases, - const ITensor *transformed_output, - const int matrix_stride, - ITensor *output_nhwc, - const int num_batches, - const int num_rows, - const int num_cols, - const int num_channels, - ITensor *workspace) + const ITensor *biases, + const ITensor *transformed_output, + const int matrix_stride, + ITensor *output_nhwc, + const int num_batches, + const int num_rows, + const int num_cols, + const int num_channels, + ITensor *workspace, + const arm_gemm::Activation &activation) { _biases = biases; _workspace = workspace; @@ -512,7 +521,7 @@ void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker _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 - _transform = arm_compute::support::cpp14::make_unique<OutputTransform>(num_batches, num_rows, num_cols, num_channels); + _transform = arm_compute::support::cpp14::make_unique<OutputTransform>(num_batches, num_rows, num_cols, num_channels, activation); Window win; auto win_last = _transform->get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); |