fix: concrete type Number => parameterization

Author: skyleaworlder

src/layers/conv.jl

@@ -626,7 +626,7 @@ function (g::GlobalMeanPool)(x)
 end
 
 """
-    GlobalLPNormPool(p::Float64)
+    GlobalLPNormPool(p::T)
 
 Global lp norm pooling layer.
 
@@ -636,16 +636,16 @@ by performing lp norm pooling on the complete (w,h)-shaped feature maps.
 See also [`LPNormPool`](@ref).
 
 ```jldoctest
-julia> xs = rand(Float32, 100, 100, 3, 50)
+julia> xs = rand(Float32, 100, 100, 3, 50);
 
-julia> m = Chain(Conv((3,3), 3 => 7), GlobalLPNormPool(2.0))
+julia> m = Chain(Conv((3,3), 3 => 7), GlobalLPNormPool(2.0));
 
 julia> m(xs) |> size
 (1, 1, 7, 50)
 ```
 """
-struct GlobalLPNormPool
-  p::Float64
+struct GlobalLPNormPool{T<:Number}
+  p::T
 end
 
 function (g::GlobalLPNormPool)(x)
@@ -778,7 +778,7 @@ function Base.show(io::IO, m::MeanPool)
 end
 
 """
-    LPNormPool(window::NTuple, p::Float64; pad=0, stride=window)
+    LPNormPool(window::NTuple, p::T; pad=0, stride=window)
 
 Lp norm pooling layer, calculating p-norm distance for each window,
 also known as LPPool in pytorch.
@@ -801,7 +801,7 @@ julia> xs = rand(Float32, 100, 100, 3, 50);
 julia> m = Chain(Conv((5,5), 3 => 7), LPNormPool((5,5), 2.0; pad=SamePad()))
 Chain(
   Conv((5, 5), 3 => 7),                 # 532 parameters
-  LPNormPool((5, 5), p=2, pad=2),
+  LPNormPool((5, 5), 2.0, pad=2),
 )
 
 julia> m[1](xs) |> size
@@ -811,20 +811,20 @@ julia> m(xs) |> size
 (20, 20, 7, 50)
 
 julia> layer = LPNormPool((5,), 2.0, pad=2, stride=(3,))  # one-dimensional window
-LPNormPool((5,), p=2, pad=2, stride=3)
+LPNormPool((5,), 2.0, pad=2, stride=3)
 
 julia> layer(rand(Float32, 100, 7, 50)) |> size
 (34, 7, 50)
 ```
 """
-struct LPNormPool{N,M}
+struct LPNormPool{N,M,T<:Number}
   k::NTuple{N,Int}
-  p::Float64
+  p::T
   pad::NTuple{M,Int}
   stride::NTuple{N,Int}
 end
 
-function LPNormPool(k::NTuple{N,Integer}, p::Float64; pad = 0, stride = k) where N
+function LPNormPool(k::NTuple{N,Integer}, p::T; pad = 0, stride = k) where {N,T}
   stride = expand(Val(N), stride)
   pad = calc_padding(LPNormPool, pad, k, 1, stride)
   return LPNormPool(k, p, pad, stride)