DepthGuide/src/core_v2/ndarray.hpp

#ifndef NDARRAY_H
#define NDARRAY_H

#include "cuda_runtime.h"

#include <cassert>
#include <cstdint>

template<size_t N>
struct ndarray_base {
    using index_type = uint32_t;
    index_type shape[N] = {};
    index_type strides[N] = {};

    template<size_t M = N> requires(M == 1)
    __host__ __device__ [[nodiscard]] index_type size() const {
        return shape[0];
    }

    template<size_t M = N> requires(M >= 2)
    __host__ __device__ [[nodiscard]] index_type width() const {
        return shape[0];
    }

    template<size_t M = N> requires(M >= 2)
    __host__ __device__ [[nodiscard]] index_type height() const {
        return shape[1];
    }

    template<size_t M = N> requires(M >= 3)
    __host__ __device__ [[nodiscard]] index_type depth() const {
        return shape[2];
    }

    template<size_t M = N> requires(M >= 2)
    __host__ __device__ [[nodiscard]] index_type pitch() const {
        return strides[1];
    }

    template<size_t M = N> requires(M >= 2)
    __host__ __device__ [[nodiscard]] size_t byte_width() const {
        return strides[0] * shape[0];
    }
};

template<typename T, size_t N>
struct ndarray : ndarray_base<N> {
    using base_type = ndarray_base<N>;
    using typename base_type::index_type;
    using base_type::shape;
    using base_type::strides;

    void *data = nullptr;

    template<typename... Dims>
        requires(sizeof...(Dims) == N)
    __host__ __device__ T *ptr(Dims... ds) {
        index_type indices[] = {ds...};
        index_type offset = 0;
        for (auto i = 0; i < N; i++) {
            assert(indices[i] < shape[i]);
            offset += indices[i] * strides[i];
        }
        return (T *) ((uint8_t *) data + offset);
    }
};

template<typename U, typename T, size_t N>
ndarray<U, N> type_cast(ndarray<T, N> arr) {
    assert(sizeof(U) <= arr.strides[0]);
    using ret_type = ndarray<U, N>;
    return *(ret_type *) &arr;
}

#endif //NDARRAY_H