diff options
Diffstat (limited to 'arm_compute/core/Helpers.h')
| -rw-r--r-- | arm_compute/core/Helpers.h | 60 |
1 files changed, 38 insertions, 22 deletions
diff --git a/arm_compute/core/Helpers.h b/arm_compute/core/Helpers.h index 374e36442b..ef59323073 100644 --- a/arm_compute/core/Helpers.h +++ b/arm_compute/core/Helpers.h @@ -111,28 +111,6 @@ struct is_contained<T, std::tuple<U, Ts...>> : is_contained<T, std::tuple<Ts...> }; } -/** Calculate the number of output tiles required by Winograd Convolution layer. This utility function can be used by the Winograd input transform - * to know the number of tiles on the x and y direction - * - * @param[in] in_dims Spatial dimensions of the input tensor of convolution layer - * @param[in] kernel_size Kernel size - * @param[in] output_tile_size Size of a single output tile - * @param[in] conv_info Convolution info (i.e. pad, stride,...) - * - * @return the number of output tiles along the x and y directions of size "output_tile_size" - */ -inline Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, const Size2D &kernel_size, const Size2D &output_tile_size, const PadStrideInfo &conv_info) -{ - int num_tiles_x = std::ceil((in_dims.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width)); - int num_tiles_y = std::ceil((in_dims.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height)); - - // Clamp in case we provide paddings but we have 1D convolution - num_tiles_x = std::min(num_tiles_x, static_cast<int>(in_dims.width)); - num_tiles_y = std::min(num_tiles_y, static_cast<int>(in_dims.height)); - - return Size2D(num_tiles_x, num_tiles_y); -} - /** Computes bilinear interpolation using the pointer to the top-left pixel and the pixel's distance between * the real coordinates and the smallest following integer coordinates. Input must be in single channel format. * @@ -694,6 +672,44 @@ inline int coords2index(const TensorShape &shape, const Coordinates &coord); * @return The int conversion of the requested data layout index. */ inline size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension); + +/** Calculate the normalization dimension index for a given normalization type + * + * @param[in] layout Data layout of the input and output tensor + * @param[in] info Normalization info + * + * @return Normalization dimension index + */ +inline unsigned int get_normalization_dimension_index(DataLayout layout, const NormalizationLayerInfo &info) +{ + const unsigned int width_idx = get_data_layout_dimension_index(layout, DataLayoutDimension::WIDTH); + const unsigned int channel_idx = get_data_layout_dimension_index(layout, DataLayoutDimension::CHANNEL); + + return info.is_in_map() ? width_idx : channel_idx; +} + +/** Calculate the number of output tiles required by Winograd Convolution layer. This utility function can be used by the Winograd input transform + * to know the number of tiles on the x and y direction + * + * @param[in] in_dims Spatial dimensions of the input tensor of convolution layer + * @param[in] kernel_size Kernel size + * @param[in] output_tile_size Size of a single output tile + * @param[in] conv_info Convolution info (i.e. pad, stride,...) + * + * @return the number of output tiles along the x and y directions of size "output_tile_size" + */ +inline Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, const Size2D &kernel_size, const Size2D &output_tile_size, const PadStrideInfo &conv_info) +{ + int num_tiles_x = std::ceil((in_dims.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width)); + int num_tiles_y = std::ceil((in_dims.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height)); + + // Clamp in case we provide paddings but we have 1D convolution + num_tiles_x = std::min(num_tiles_x, static_cast<int>(in_dims.width)); + num_tiles_y = std::min(num_tiles_y, static_cast<int>(in_dims.height)); + + return Size2D(num_tiles_x, num_tiles_y); +} + } // namespace arm_compute #include "arm_compute/core/Helpers.inl" |