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Adapt to changes in NNlib to use mode externally #308

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Adapt to changes in NNlib to use mode externally #308

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13 changes: 7 additions & 6 deletions src/layers/conv.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ 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`, `stride` and `dilation`.
Takes the keyword arguments `pad`, `stride`,`dilation` and `flipkernel`.
"""
struct Conv{N,F,A,V}
σ::F
Expand All @@ -24,24 +24,25 @@ struct Conv{N,F,A,V}
stride::NTuple{N,Int}
pad::NTuple{N,Int}
dilation::NTuple{N,Int}
flipkernel::Bool
end

Conv(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N} =
Conv(σ, w, b, expand.(sub2(Val{N}), (stride, pad, dilation))...)
stride = 1, pad = 0, dilation = 1, flipkernel = true) where {T,N} =
Conv(σ, w, b, expand.(sub2(Val{N}), (stride, pad, dilation))... , flipkernel)

Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = initn,
stride = 1, pad = 0, dilation = 1) where N =
stride = 1, pad = 0, dilation = 1, flipkernel = true) where N =
Conv(param(init(k..., ch...)), param(zeros(ch[2])), σ,
stride = stride, pad = pad, dilation = dilation)
stride = stride, pad = pad, dilation = dilation, flipkernel = flipkernel)

Flux.treelike(Conv)

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, dilation = c.dilation) .+ b)
σ.(conv(x, c.weight, stride = c.stride, pad = c.pad, dilation = c.dilation, flipkernel = c.flipkernel) .+ b)
end

function Base.show(io::IO, l::Conv)
Expand Down