Hi guys is there any way to make this performant? it looks like a super useful type see Example of usage
Type Definition
struct KernelOp{T,N,S} <: AbstractArray{T,N}
parent::S
op::Function
opdims::CartesianIndex{N}
lowbound::CartesianIndex{N}
upbound::CartesianIndex{N}
@inline function KernelOp{T,N,S}(A::S,operation::Function,dims::CartesianIndex{N}) where {T,N,S <: AbstractArray{T,N}}
l=minimum(CartesianIndices(A))
h=maximum(CartesianIndices(A))
new(A,operation,dims,l,h)
end
end
@inline function KernelOp(A::AbstractArray{T,N},op::Function,dims::NTuple{N,Int}) where {T,N}
KernelOp{eltype(A),ndims(A),typeof(A)}(A,op,CartesianIndex(dims))
end
import Base.size, Base.getindex, Base.IndexStyle
@inline IndexStyle(::KernelOp) = IndexCartesian()
@inline function size_parent(A::KernelOp)
size(A.parent)
end
@inline function size(A::KernelOp)
return size_parent(A)
end
@inline function getindex(A::KernelOp{T,N,S},Index::Vararg{Int,N}) where {T,N,S}
I=CartesianIndex(Index)
@boundscheck checkbounds(A,I)
@inbounds begin
lowI=max(I-A.opdims,A.lowbound)
upI=min(I+A.opdims,A.upbound)
return A.op(A.parent,lowI:upI,I)
end
end
Example
take a julia array, mean filter it (f1),
take maximum filter of the mean filtered array (f2) then compare for equality f1 and f2
@inline function op_mean(A,r,c)
m=zero(eltype(A))
@inbounds begin
@simd for i in r
m+=A[i]
end
end
m/length(r)
end
@inline function op_max(A,r,c)
m=A[r[1]]
@inbounds begin
@simd for i in r
m=max(m,A[i])
end
end
m
end
M=rand(1000,1000)
f1=KernelOp(M,op_mean,(1,1))
f2=KernelOp(f1,op_max,(2,2))
f1.==f2 # here all the computations happen, but it allocates a lot even though it doesnt build explicit copies of the original array
to me this looks super beautiful, but unfortunately it allocates a lot, i would like some advice on making it more performant
