Adds support for Depthwise Convolutions

src/layers/conv.jl

@@ -1,4 +1,4 @@
-using NNlib: conv
+using NNlib: conv, depthwiseconv
 
 """
     Conv(size, in=>out)
@@ -46,5 +46,49 @@ function Base.show(io::IO, l::Conv)
   print(io, ")")
 end
 
+"""
+    DepthwiseConv(size, in=>mul)
+    DepthwiseConv(size, in=>mul, relu)
+
+Depthwise convolutional layer. `size` should be a tuple like `(2, 2)`.
+`in` and `mul` specify the number of input channels and channel multiplier respectively.
+
+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` and `stride`.
+"""
+struct DepthwiseConv{N,F,A,V}
+  σ::F
+  weight::A
+  bias::V
+  stride::NTuple{N,Int}
+  pad::NTuple{N,Int}
+end
+
+DepthwiseConv(w::AbstractArray{T}, b::AbstractVector{T}, σ = identity;
+       stride = 1, pad = 0) where T =
+  DepthwiseConv(σ, w, b, stride, pad)
+
+DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = initn,
+     stride::NTuple{N,Integer} = map(_->1,k),
+     pad::NTuple{N,Integer} = map(_->0,k)) where N =
+  DepthwiseConv(param(init(k..., ch[2], ch[1])), param(zeros(ch[2]*ch[1])), σ,
+       stride = stride, pad = pad)
+
+Flux.treelike(DepthwiseConv)
+
+function (c::DepthwiseConv)(x)
+  σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
+  σ.(depthwiseconv(x, c.weight, stride = c.stride, pad = c.pad) .+ b)
+end
+
+function Base.show(io::IO, l::Conv)
+  print(io, "DepthwiseConv(", size(l.weight)[1:ndims(l.weight)-2])
+  print(io, ", ", size(l.weight, ndims(l.weight)), "=>", size(l.weight, ndims(l.weight)-1))
+  l.σ == identity || print(io, ", ", l.σ)
+  print(io, ")")
+end
+
 # v0.5
 @deprecate Conv2D(args...; kw...) Conv(args...; kw...)

src/tracker/array.jl

@@ -234,7 +234,7 @@ end
 # NNlib
 
 using NNlib
-import NNlib: softmax, ∇softmax, logsoftmax, ∇logsoftmax, conv, maxpool, meanpool
+import NNlib: softmax, ∇softmax, logsoftmax, ∇logsoftmax, conv, depthwiseconv, maxpool, meanpool
 
 softmax(xs::TrackedArray) = track(softmax, xs)
 
@@ -259,6 +259,20 @@ function back(::typeof(_conv), Δ, x, w, stride, pad)
   @back(w, NNlib.∇conv_filter(Δ, data(x), data(w); stride = stride, pad = pad))
 end
 
+_depthwiseconv(x, w, stride, pad) = depthwiseconv(x, w, stride = stride, pad = pad)
+
+depthwiseconv(x::TrackedArray{<:Real,N}, w::TrackedArray{<:Real,N}; stride = 1, pad = 0) where N =
+  track(_depthwiseconv, x, w, stride, pad)
+depthwiseconv(x::AbstractArray{<:Real,N}, w::TrackedArray{<:Real,N}; stride = 1, pad = 0) where N =
+  track(_depthwiseconv, x, w, stride, pad)
+depthwiseconv(x::TrackedArray{<:Real,N}, w::AbstractArray{<:Real,N}; stride = 1, pad = 0) where N =
+  track(_depthwiseconv, x, w, stride, pad)
+
+function back(::typeof(_depthwiseconv), Δ, x, w, stride, pad)
+  @back(x, NNlib.∇depthwiseconv_data(Δ, data(x), data(w), stride = stride, pad = pad))
+  @back(x, NNlib.∇depthwiseconv_filter(Δ, data(x), data(w), stride = stride, pad = pad))
+end
+
 _maxpool(x, k, pad, stride) = maxpool(x, k; pad = pad, stride = stride)
 
 maxpool(x::TrackedArray, k; pad = map(_->0,k), stride = k) =