TastyPancakes/Flux.jl
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change IN to in

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This commit is contained in:
David Pollack 2019-03-07 09:44:55 +01:00
parent 83b4b3a714
commit 7b9b64f1cb
1 changed files with 18 additions and 18 deletions
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36
src/layers/normalise.jl
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@ -203,9 +203,9 @@ InstanceNorm(chs::Integer, λ = identity;
InstanceNorm(λ, param(initβ(chs)), param(initγ(chs)), InstanceNorm(λ, param(initβ(chs)), param(initγ(chs)),
zeros(chs), ones(chs), ϵ, momentum, true) zeros(chs), ones(chs), ϵ, momentum, true)
function (IN::InstanceNorm)(x) function (in::InstanceNorm)(x)
size(x, ndims(x)-1) == length(IN.β) || size(x, ndims(x)-1) == length(in.β) ||
error("InstanceNorm expected $(length(IN.β)) channels, got $(size(x, ndims(x)-1))") error("InstanceNorm expected $(length(in.β)) channels, got $(size(x, ndims(x)-1))")
ndims(x) > 2 || ndims(x) > 2 ||
error("InstanceNorm requires at least 3 dimensions. With 2 dimensions an array of zeros would be returned") error("InstanceNorm requires at least 3 dimensions. With 2 dimensions an array of zeros would be returned")
# these are repeated later on depending on the batch size # these are repeated later on depending on the batch size
@ -216,39 +216,39 @@ function (IN::InstanceNorm)(x)
affine_shape[end-1] = c affine_shape[end-1] = c
affine_shape[end] = bs affine_shape[end] = bs
m = prod(size(x)[1:end-2]) m = prod(size(x)[1:end-2])
γ, β = expand_inst(IN.γ, affine_shape), expand_inst(IN.β, affine_shape) γ, β = expand_inst(in.γ, affine_shape), expand_inst(in.β, affine_shape)
if !IN.active if !in.active
μ = expand_inst(IN.μ, affine_shape) μ = expand_inst(in.μ, affine_shape)
σ² = expand_inst(IN.σ², affine_shape) σ² = expand_inst(in.σ², affine_shape)
ϵ = IN.ϵ ϵ = in.ϵ
else else
T = eltype(x) T = eltype(x)
ϵ = data(convert(T, IN.ϵ)) ϵ = data(convert(T, in.ϵ))
axes = 1:dims-2 # axes to reduce along (all but channels and batch size axes) axes = 1:dims-2 # axes to reduce along (all but channels and batch size axes)
μ = mean(x, dims = axes) μ = mean(x, dims = axes)
σ² = mean((x .- μ) .^ 2, dims = axes) σ² = mean((x .- μ) .^ 2, dims = axes)
# update moving mean/std # update moving mean/std
mtm = data(convert(T, IN.momentum)) mtm = data(convert(T, in.momentum))
IN.μ = dropdims(mean(repeat((1 - mtm) .* IN.μ, outer=[1, bs]) .+ mtm .* reshape(data(μ), (c, bs)), dims = 2), dims=2) in.μ = dropdims(mean(repeat((1 - mtm) .* in.μ, outer=[1, bs]) .+ mtm .* reshape(data(μ), (c, bs)), dims = 2), dims=2)
IN.σ² = dropdims(mean((repeat((1 - mtm) .* IN.σ², outer=[1, bs]) .+ (mtm * m / (m - 1)) .* reshape(data(σ²), (c, bs))), dims = 2), dims=2) in.σ² = dropdims(mean((repeat((1 - mtm) .* in.σ², outer=[1, bs]) .+ (mtm * m / (m - 1)) .* reshape(data(σ²), (c, bs))), dims = 2), dims=2)
end end
let λ = IN.λ let λ = in.λ
x̂ = (x .- μ) ./ sqrt.(σ² .+ ϵ) x̂ = (x .- μ) ./ sqrt.(σ² .+ ϵ)
λ.(γ .* x̂ .+ β) λ.(γ .* x̂ .+ β)
end end
end end
children(IN::InstanceNorm) = children(in::InstanceNorm) =
(IN.λ, IN.β, IN.γ, IN.μ, IN.σ², IN.ϵ, IN.momentum, IN.active) (in.λ, in.β, in.γ, in.μ, in.σ², in.ϵ, in.momentum, in.active)
mapchildren(f, IN::InstanceNorm) = # e.g. mapchildren(cu, IN) mapchildren(f, in::InstanceNorm) = # e.g. mapchildren(cu, in)
InstanceNorm(IN.λ, f(IN.β), f(IN.γ), f(IN.μ), f(IN.σ²), IN.ϵ, IN.momentum, IN.active) InstanceNorm(in.λ, f(in.β), f(in.γ), f(in.μ), f(in.σ²), in.ϵ, in.momentum, in.active)
_testmode!(IN::InstanceNorm, test) = (IN.active = !test) _testmode!(in::InstanceNorm, test) = (in.active = !test)
function Base.show(io::IO, l::InstanceNorm) function Base.show(io::IO, l::InstanceNorm)
print(io, "InstanceNorm($(join(size(l.β), ", "))") print(io, "InstanceNorm($(join(size(l.β), ", "))")