TastyPancakes/Flux.jl
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Add dilation kwarg to Conv

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Now that we have dilated convolution support in `NNlib`, this is enables support in Flux's `Conv` layer.
This commit is contained in:
Elliot Saba 2018-05-21 12:20:43 -07:00 committed by staticfloat@gmail.com
parent 5685df1691
commit e6efca4bf4
2 changed files with 18 additions and 16 deletions
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14
src/layers/conv.jl
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@ -10,7 +10,7 @@ Standard convolutional layer. `size` should be a tuple like `(2, 2)`.
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`.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
struct Conv{N,F,A,V}
σ::F
@ -18,17 +18,19 @@ struct Conv{N,F,A,V}
bias::V
stride::NTuple{N,Int}
pad::NTuple{N,Int}
dilation::NTuple{N,Int}
end
Conv(w::AbstractArray{T}, b::AbstractVector{T}, σ = identity;
stride = 1, pad = 0) where T =
Conv(σ, w, b, stride, pad)
stride = 1, pad = 0, dilation=1) where T =
Conv(σ, w, b, stride, pad, dilation)
Conv(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 =
pad::NTuple{N,Integer} = map(_->0,k),
dilation::NTuple{N,Integer} = map(_->0,k)) where N =
Conv(param(init(k..., ch...)), param(zeros(ch[2])), σ,
stride = stride, pad = pad)
stride = stride, pad = pad, dilation = dilation)
Flux.treelike(Conv)
@ -36,7 +38,7 @@ function (c::Conv)(x)
# TODO: breaks gpu broadcast :(
# ndims(x) == ndims(c.weight)-1 && return squeezebatch(c(reshape(x, size(x)..., 1)))
σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
σ.(conv(x, c.weight, stride = c.stride, pad = c.pad) .+ b)
σ.(conv(x, c.weight, stride = c.stride, pad = c.pad, dilation = c.dilation) .+ b)
end
function Base.show(io::IO, l::Conv)

20
src/tracker/array.jl
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@ -314,18 +314,18 @@ logsoftmax(xs::TrackedArray) = track(logsoftmax, xs)
back(::typeof(logsoftmax), Δ, xs) = @back(xs, ∇logsoftmax(Δ, data(xs)))
# TODO: can store kwargs efficiently in namedtuples
_conv(x, w, stride, pad) = conv(x, w, stride = stride, pad = pad)
_conv(x, w, stride, pad, dilation) = conv(x, w, stride = stride, pad = pad, dilation = dilation)
conv(x::TrackedArray{<:Real,N}, w::TrackedArray{<:Real,N}; stride = 1, pad = 0) where N =
track(_conv, x, w, stride, pad)
conv(x::AbstractArray{<:Real,N}, w::TrackedArray{<:Real,N}; stride = 1, pad = 0) where N =
track(_conv, x, w, stride, pad)
conv(x::TrackedArray{<:Real,N}, w::AbstractArray{<:Real,N}; stride = 1, pad = 0) where N =
track(_conv, x, w, stride, pad)
conv(x::TrackedArray{<:Real,N}, w::TrackedArray{<:Real,N}; stride = 1, pad = 0, dilation = 1) where N =
track(_conv, x, w, stride, pad, dilation)
conv(x::AbstractArray{<:Real,N}, w::TrackedArray{<:Real,N}; stride = 1, pad = 0, dilation = 1) where N =
track(_conv, x, w, stride, pad, dilation)
conv(x::TrackedArray{<:Real,N}, w::AbstractArray{<:Real,N}; stride = 1, pad = 0, dilation = 1) where N =
track(_conv, x, w, stride, pad, dilation)
function back(::typeof(_conv), Δ, x, w, stride, pad)
@back(x, NNlib.∇conv_data(Δ, data(x), data(w); stride = stride, pad = pad))
@back(w, NNlib.∇conv_filter(Δ, data(x), data(w); stride = stride, pad = pad))
function back(::typeof(_conv), Δ, x, w, stride, pad, dilation)
@back(x, NNlib.∇conv_data(Δ, data(x), data(w); stride = stride, pad = pad, dilation = dilation))
@back(w, NNlib.∇conv_filter(Δ, data(x), data(w); stride = stride, pad = pad, dilation = dilation))
end
_maxpool(x, k, pad, stride) = maxpool(x, k; pad = pad, stride = stride)