TensorShape.h

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/*
 * Copyright (c) 2016-2021, 2023 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
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#ifndef ARM_COMPUTE_TENSORSHAPE_H
#define ARM_COMPUTE_TENSORSHAPE_H
 
#include "arm_compute/core/Dimensions.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/utils/misc/Utility.h"
 
#include <algorithm>
#include <array>
#include <functional>
#include <numeric>
 
namespace arm_compute
{
/** Shape of a tensor */
class TensorShape : public Dimensions<size_t>
{
public:
    /** Constructor to initialize the tensor shape.
     *
     * @param[in] dims Values to initialize the dimensions.
     */
    template <typename... Ts>
    TensorShape(Ts... dims)
        : Dimensions{ dims... }
    {
        // Initialize unspecified dimensions to 1
        if(_num_dimensions > 0)
        {
            std::fill(_id.begin() + _num_dimensions, _id.end(), 1);
        }
 
        // Correct number dimensions to ignore trailing dimensions of size 1
        apply_dimension_correction();
    }
    /** Allow instances of this class to be copy constructed */
    TensorShape(const TensorShape &) = default;
    /** Allow instances of this class to be copied */
    TensorShape &operator=(const TensorShape &) = default;
    /** Allow instances of this class to be move constructed */
    TensorShape(TensorShape &&) = default;
    /** Allow instances of this class to be moved */
    TensorShape &operator=(TensorShape &&) = default;
    /** Default destructor */
    ~TensorShape() = default;
 
    /** Accessor to set the value of one of the dimensions.
     *
     * @param[in] dimension            Dimension for which the value is set.
     * @param[in] value                Value to be set for the dimension.
     * @param[in] apply_dim_correction (Optional) Flag to state whether apply dimension correction after setting one dimension. E.g. when permuting NCHW -> NHWC, 1x1x2 would become 2x1x1, but _num_dimensions should be 3 rather than 1.
     * @param[in] increase_dim_unit    (Optional) Set to true if new unit dimensions increase the number of dimensions of the shape.
     *
     * @return *this.
     */
    TensorShape &set(size_t dimension, size_t value, bool apply_dim_correction = true, bool increase_dim_unit = true)
    {
        // Clear entire shape if one dimension is zero
        if(value == 0)
        {
            _num_dimensions = 0;
            std::fill(_id.begin(), _id.end(), 0);
        }
        else
        {
            // Make sure all empty dimensions are filled with 1
            std::fill(_id.begin() + _num_dimensions, _id.end(), 1);
 
            // Set the specified dimension and increase the number of dimensions if
            // necessary
            Dimensions::set(dimension, value, increase_dim_unit);
 
            // Correct number dimensions to ignore trailing dimensions of size 1
            if(apply_dim_correction)
            {
                apply_dimension_correction();
            }
        }
        return *this;
    }
 
    /** Accessor to remove the dimension n from the tensor shape.
     *
     * @note The upper dimensions of the tensor shape will be shifted down by 1
     *
     * @param[in] n                    Dimension to remove
     * @param[in] apply_dim_correction (Optional) Flag to state whether apply dimension correction (removing trailing dimensions with size of 1) after removing a dimension.
     */
    void remove_dimension(size_t n, bool apply_dim_correction = true)
    {
        ARM_COMPUTE_ERROR_ON(_num_dimensions < 1);
        ARM_COMPUTE_ERROR_ON(n >= _num_dimensions);
 
        std::copy(_id.begin() + n + 1, _id.end(), _id.begin() + n);
 
        // Reduce number of dimensions
        _num_dimensions--;
 
        // Make sure all empty dimensions are filled with 1
        std::fill(_id.begin() + _num_dimensions, _id.end(), 1);
 
        // Correct number dimensions to ignore trailing dimensions of size 1
        if(apply_dim_correction)
        {
            apply_dimension_correction();
        }
    }
 
    /** Collapse the first n dimensions.
     *
     * @param[in] n     Number of dimensions to collapse into @p first
     * @param[in] first Dimensions into which the following @p n are collapsed.
     */
    void collapse(size_t n, size_t first = 0)
    {
        Dimensions::collapse(n, first);
 
        // Make sure all empty dimensions are filled with 1
        std::fill(_id.begin() + _num_dimensions, _id.end(), 1);
    }
    /** Shifts right the tensor shape increasing its dimensions
     *
     * @param[in] step Rotation step
     */
    void shift_right(size_t step)
    {
        ARM_COMPUTE_ERROR_ON(step > TensorShape::num_max_dimensions - num_dimensions());
 
        std::rotate(begin(), begin() + TensorShape::num_max_dimensions - step, end());
        _num_dimensions += step;
 
        // Correct number dimensions to ignore trailing dimensions of size 1
        apply_dimension_correction();
    }
 
    /** Return a copy with collapsed dimensions starting from a given point.
     *
     * @param[in] start Starting point of collapsing dimensions.
     *
     * @return A copy with collapse dimensions starting from start.
     */
    TensorShape collapsed_from(size_t start) const
    {
        TensorShape copy(*this);
        copy.collapse(num_dimensions() - start, start);
        return copy;
    }
 
    /** Collapses all dimensions to a single linear total size.
     *
     * @return The total tensor size in terms of elements.
     */
    size_t total_size() const
    {
        return std::accumulate(_id.begin(), _id.end(), 1, std::multiplies<size_t>());
    }
    /** Collapses given dimension and above.
     *
     * @param[in] dimension Size of the wanted dimension
     *
     * @return The linear size of the collapsed dimensions
     */
    size_t total_size_upper(size_t dimension) const
    {
        ARM_COMPUTE_ERROR_ON(dimension >= TensorShape::num_max_dimensions);
        return std::accumulate(_id.begin() + dimension, _id.end(), 1, std::multiplies<size_t>());
    }
 
    /** Compute size of dimensions lower than the given one.
     *
     * @param[in] dimension Upper boundary.
     *
     * @return The linear size of the collapsed dimensions.
     */
    size_t total_size_lower(size_t dimension) const
    {
        ARM_COMPUTE_ERROR_ON(dimension > TensorShape::num_max_dimensions);
        return std::accumulate(_id.begin(), _id.begin() + dimension, 1, std::multiplies<size_t>());
    }
 
    /** If shapes are broadcast compatible, return the broadcasted shape.
     *
     * Two tensor shapes are broadcast compatible if for each dimension, they're equal or one of them is 1.
     *
     * If two shapes are compatible, each dimension in the broadcasted shape is the max of the original dimensions.
     *
     * @param[in] shapes Tensor shapes.
     *
     * @return The broadcasted shape or an empty shape if the shapes are not broadcast compatible.
     */
    template <typename... Shapes>
    static TensorShape broadcast_shape(const Shapes &... shapes)
    {
        TensorShape bc_shape;
 
        auto broadcast = [&bc_shape](const TensorShape & other)
        {
            if(bc_shape.num_dimensions() == 0)
            {
                bc_shape = other;
            }
            else if(other.num_dimensions() != 0)
            {
                for(size_t d = 0; d < TensorShape::num_max_dimensions; ++d)
                {
                    const size_t dim_min = std::min(bc_shape[d], other[d]);
                    const size_t dim_max = std::max(bc_shape[d], other[d]);
 
                    if((dim_min != 1) && (dim_min != dim_max))
                    {
                        bc_shape = TensorShape{ 0U };
                        break;
                    }
 
                    bc_shape.set(d, dim_max);
                }
            }
        };
 
        utility::for_each(broadcast, shapes...);
 
        return bc_shape;
    }
 
private:
    /** Remove trailing dimensions of size 1 from the reported number of dimensions. */
    void apply_dimension_correction()
    {
        for(int i = static_cast<int>(_num_dimensions) - 1; i > 0; --i)
        {
            if(_id[i] == 1)
            {
                --_num_dimensions;
            }
            else
            {
                break;
            }
        }
    }
};
}
#endif /*ARM_COMPUTE_TENSORSHAPE_H*/