diff options
Diffstat (limited to 'arm_compute/core/Helpers.h')
| -rw-r--r-- | arm_compute/core/Helpers.h | 74 |
1 files changed, 60 insertions, 14 deletions
diff --git a/arm_compute/core/Helpers.h b/arm_compute/core/Helpers.h index b6635aba6d..960201510a 100644 --- a/arm_compute/core/Helpers.h +++ b/arm_compute/core/Helpers.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2020 Arm Limited. + * Copyright (c) 2016-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -55,6 +55,16 @@ public: */ Iterator(const ITensor *tensor, const Window &window); + /** Create a container iterator for the tensor with the specified number of dimensions, stride, buffer pointer and window. + * + * @param[in] num_dims The number of dimensions. + * @param[in] strides The strides in bytes. + * @param[in] buffer The data buffer. + * @param[in] offset The offset in bytes from the beginning of the buffer to the first element of the tensor. + * @param[in] window The window which will be used to iterate over the tensor. + */ + Iterator(size_t num_dims, const Strides &strides, uint8_t *buffer, size_t offset, const Window &window); + /** Increment the iterator along the specified dimension of the step value associated to the dimension. * * @warning It is the caller's responsibility to call increment(dimension+1) when reaching the end of a dimension, the iterator will not check for overflow. @@ -86,13 +96,22 @@ public: void reset(size_t dimension); private: + /** Initialize a container iterator for the tensor with the specified number of dimensions, stride, buffer pointer and window. + * + * @param[in] num_dims The number of dimensions. + * @param[in] strides The strides in bytes. + * @param[in] buffer The data buffer. + * @param[in] offset The offset in bytes from the beginning of the buffer to the first element of the tensor. + * @param[in] window The window which will be used to iterate over the tensor. + */ + void initialize(size_t num_dims, const Strides &strides, uint8_t *buffer, size_t offset, const Window &window); + uint8_t *_ptr; class Dimension { public: - constexpr Dimension() - : _dim_start(0), _stride(0) + constexpr Dimension() : _dim_start(0), _stride(0) { } @@ -112,7 +131,7 @@ private: * @param[in,out] iterators Tensor iterators which will be updated by this function before calling lambda_function. */ template <typename L, typename... Ts> -inline void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators); +inline void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators); /** Permutes given Dimensions according to a permutation vector * @@ -125,7 +144,7 @@ template <typename T> inline void permute(Dimensions<T> &dimensions, const PermutationVector &perm) { auto dimensions_copy = utility::make_array<Dimensions<T>::num_max_dimensions>(dimensions.begin(), dimensions.end()); - for(unsigned int i = 0; i < perm.num_dimensions(); ++i) + for (unsigned int i = 0; i < perm.num_dimensions(); ++i) { T dimension_val = (perm[i] < dimensions.num_dimensions()) ? dimensions_copy[perm[i]] : 0; dimensions.set(i, dimension_val); @@ -142,7 +161,7 @@ inline void permute(Dimensions<T> &dimensions, const PermutationVector &perm) inline void permute(TensorShape &shape, const PermutationVector &perm) { TensorShape shape_copy = shape; - for(unsigned int i = 0; i < perm.num_dimensions(); ++i) + for (unsigned int i = 0; i < perm.num_dimensions(); ++i) { size_t dimension_val = (perm[i] < shape.num_dimensions()) ? shape_copy[perm[i]] : 1; shape.set(i, dimension_val, false, false); // Avoid changes in _num_dimension @@ -159,8 +178,11 @@ inline void permute(TensorShape &shape, const PermutationVector &perm) * * @return The corresponding valid region */ -ValidRegion calculate_valid_region_scale(const ITensorInfo &src_info, const TensorShape &dst_shape, - InterpolationPolicy interpolate_policy, SamplingPolicy sampling_policy, bool border_undefined); +ValidRegion calculate_valid_region_scale(const ITensorInfo &src_info, + const TensorShape &dst_shape, + InterpolationPolicy interpolate_policy, + SamplingPolicy sampling_policy, + bool border_undefined); /** Convert a linear index into n-dimensional coordinates. * @@ -180,6 +202,22 @@ inline Coordinates index2coords(const TensorShape &shape, int index); */ inline int coords2index(const TensorShape &shape, const Coordinates &coord); +/** Returns a static map used to find an index or dimension based on a data layout + * + * *** Layouts *** + * + * *** 4D *** + * [N C H W] + * [3 2 1 0] + * [N H W C] + * + * * *** 5D *** + * [N C D H W] + * [4 3 2 1 0] + * [N D H W C] + */ +const std::map<DataLayout, std::vector<DataLayoutDimension>> &get_layout_map(); + /** Get the index of the given dimension. * * @param[in] data_layout The data layout. @@ -187,7 +225,8 @@ 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); +inline size_t get_data_layout_dimension_index(const DataLayout &data_layout, + const DataLayoutDimension &data_layout_dimension); /** Get the DataLayoutDimension of a given index and layout. * @@ -196,7 +235,7 @@ inline size_t get_data_layout_dimension_index(const DataLayout data_layout, cons * * @return The dimension which this index is requested for. */ -inline DataLayoutDimension get_index_data_layout_dimension(const DataLayout data_layout, const size_t index); +inline DataLayoutDimension get_index_data_layout_dimension(const DataLayout &data_layout, const size_t index); /** 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 @@ -208,10 +247,17 @@ inline DataLayoutDimension get_index_data_layout_dimension(const DataLayout data * * @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) +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)); + 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)); @@ -240,7 +286,7 @@ inline T wrap_around(T x, T m) */ inline Coordinates &convert_negative_axis(Coordinates &coords, int max_value) { - for(unsigned int i = 0; i < coords.num_dimensions(); ++i) + for (unsigned int i = 0; i < coords.num_dimensions(); ++i) { coords[i] = wrap_around(coords[i], max_value); } |