DepthwiseConv corrected again.

src/layers/conv.jl

@@ -138,14 +138,11 @@ end
 """
     DepthwiseConv(size, in=>out)
     DepthwiseConv(size, in=>out, relu)
-
 Depthwise convolutional layer. `size` should be a tuple like `(2, 2)`.
 `in` and `out` specify the number of input and output channels respectively.
 Note that `out` must be an integer multiple of `in`.
-
 Data should be stored in WHCN order. In other words, a 100×100 RGB image would
 be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
-
 Takes the keyword arguments `pad`, `stride` and `dilation`.
 """
 struct DepthwiseConv{N,M,F,A,V}
@@ -165,17 +162,18 @@ function DepthwiseConv(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identit
   return DepthwiseConv(σ, w, b, stride, pad, dilation)
 end
 
-DepthwiseConv(k::NTuple{N,Integer}, ch::Integer, σ = identity; init = glorot_uniform,
+function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
-     stride = 1, pad = 0, dilation = 1) where N =
+     init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N
-  DepthwiseConv(init(k..., 1, ch), zeros(ch), σ,
+  @assert ch[2] % ch[1] == 0 "Output channels must be integer multiple of input channels"
-       stride = stride, pad = pad, dilation=dilation)
+  return DepthwiseConv(
-
+    init(k..., div(ch[2], ch[1]), ch[1]),
-DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = glorot_uniform,
+    zeros(ch[2]),
-     stride::NTuple{N,Integer} = map(_->1,k),
+    σ;
-     pad::NTuple{N,Integer} = map(_->0,2 .* k),
+    stride = stride,
-     dilation::NTuple{N,Integer} = map(_->1,k)) where N =
+    pad = pad,
-  DepthwiseConv(init(k..., ch[2], ch[1]), zeros(ch[2]*ch[1]), σ,
+    dilation = dilation
-       stride = stride, pad = pad)
+  )
+end
 
 @treelike DepthwiseConv
 
@@ -196,7 +194,7 @@ end
   invoke(a, Tuple{AbstractArray}, x)
 
 (a::DepthwiseConv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
-  a(T.(x))
+a(T.(x))
 """
     CrossCor(size, in=>out)
     CrossCor(size, in=>out, relu)