TastyPancakes/Flux.jl
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Adds support for Depthwise Convolutions

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This commit is contained in:
Avik Pal 2018-05-30 15:53:57 +05:30
parent 159ca536ec
commit cd6a0856d5
2 changed files with 60 additions and 2 deletions
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46
src/layers/conv.jl
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@ -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...)

16
src/tracker/array.jl
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@ -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) =