add Upsample and PixelShuffle layers

Manifest.toml

@@ -93,9 +93,9 @@ uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
 version = "0.3.4+0"
 
 [[DataAPI]]
-git-tree-sha1 = "6d64b28d291cb94a0d84e6e41081fb081e7f717f"
+git-tree-sha1 = "8ab70b4de35bb3b8cc19654f6b893cf5164f8ee8"
 uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
-version = "1.5.0"
+version = "1.5.1"
 
 [[DataStructures]]
 deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
@@ -236,9 +236,9 @@ uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
 [[NNlib]]
 deps = ["ChainRulesCore", "Compat", "LinearAlgebra", "Pkg", "Requires", "Statistics"]
-git-tree-sha1 = "573cc0d31f9697b9d2b060130a7a3c05a4f36b78"
+git-tree-sha1 = "df42d0816edfc24f5b82a728f46381613c4dff79"
 uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.7.12"
+version = "0.7.14"
 
 [[NaNMath]]
 git-tree-sha1 = "bfe47e760d60b82b66b61d2d44128b62e3a369fb"

NEWS.md

@@ -12,6 +12,7 @@
 * Moved GPU CI to use buildkite instead of GitLab
 * New [`Parallel` layer](https://github.com/FluxML/Flux.jl/pull/1462) adds inception module-like building blocks.
 * Feature additions and bug fixes for BatchNorm, LayerNorm, InstanceNorm, and GroupNorm [normalization layers](https://github.com/FluxML/Flux.jl/pull/1397)
+* Added [Upsample and PixelShuffle layers](https://github.com/FluxML/Flux.jl/pull/1468)
 
 ## v0.11.2
 

Project.toml

@@ -34,7 +34,7 @@ Colors = "0.12"
 Functors = "0.1, 0.2"
 Juno = "0.8"
 MacroTools = "0.5"
-NNlib = "0.7.10"
+NNlib = "0.7.14"
 Reexport = "0.2, 1.0"
 StatsBase = "0.33"
 ZipFile = "0.9"

docs/src/models/layers.md

@@ -29,6 +29,13 @@ Flux.convfilter
 Flux.depthwiseconvfilter
 ```
 
+## Upsampling Layers
+
+```@docs
+Upsample
+PixelShuffle
+```
+
 ## Recurrent Layers
 
 Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).

docs/src/models/nnlib.md

@@ -51,6 +51,14 @@ NNlib.conv
 NNlib.depthwiseconv
 ```
 
+## Upsampling
+
+```@docs
+NNlib.upsample_nearest
+NNlib.upsample_bilinear
+NNlib.pixel_shuffle
+```
+
 ## Batched Operations
 
 ```@docs

src/Flux.jl

@@ -16,6 +16,7 @@ export Chain, Dense, Maxout, SkipConnection, Parallel, flatten,
        SamePad, Conv, CrossCor, ConvTranspose, DepthwiseConv,
        AdaptiveMaxPool, AdaptiveMeanPool, GlobalMaxPool, GlobalMeanPool, MaxPool, MeanPool,
        Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm,
+       Upsample, PixelShuffle,
        params, fmap, cpu, gpu, f32, f64,
        testmode!, trainmode!
 
@@ -42,6 +43,7 @@ include("layers/basic.jl")
 include("layers/conv.jl")
 include("layers/recurrent.jl")
 include("layers/normalise.jl")
+include("layers/upsample.jl")
 
 include("outputsize.jl")
 

src/layers/upsample.jl

@@ -0,0 +1,81 @@
+"""
+  Upsample(mode = :nearest; scale = nothing, size = nothing)  
+
+An upsampling layer. 
+
+`scale` is a number or a tuple of numbers 
+representing  the output rescaling factor along each spatial dimension.
+For integer `scale`, all but the last 2 dimensions (channel and batch)
+will be rescaled by the same factor. 
+
+It is also possible to directly specify the output spatial `size`,
+as an alternative to using `scale`.
+
+Currently supported upsampling `mode`s 
+and corresponding NNlib's methods are:
+  - `:nearest` -> [`NNlib.upsample_nearest`](@ref) 
+  - `:bilinear` -> [`NNlib.upsample_bilinear`](@ref)
+
+# Examples
+
+```juliarepl
+julia> m = Upsample(scale = (2, 3))
+Upsample(:nearest, scale=(2, 3))
+
+julia> m(ones(2, 2, 1, 1)) |> size
+(4, 6, 1, 1)
+
+julia> m = Upsample(:bilinear, size = (4, 5))
+Upsample(:bilinear, size=(4, 5))
+
+julia> m(ones(2, 2, 1, 1)) |> size
+(4, 5, 1, 1)
+"""
+struct Upsample{Mode,S,T}
+  scale::S
+  size::T
+end
+
+function Upsample(mode = :nearest; scale = nothing, size = nothing)
+  mode in [:nearest, :bilinear] || 
+    throw(ArgumentError("mode=:$mode is not supported."))
+  if ~((scale === nothing) ⊻ (size === nothing))
+    throw(ArgumentError("Either scale or size should be specified."))
+  end
+  return Upsample{mode,typeof(scale),typeof(size)}(scale, size)
+end
+
+(m::Upsample{:nearest})(x::AbstractArray) =
+  NNlib.upsample_nearest(x, m.scale)
+function (m::Upsample{:nearest, Int})(x::AbstractArray{T, N}) where {T, N} 
+  NNlib.upsample_nearest(x, ntuple(i -> m.scale, N-2))
+end
+(m::Upsample{:nearest, Nothing})(x::AbstractArray) =
+  NNlib.upsample_nearest(x; size=m.size)
+
+(m::Upsample{:bilinear})(x::AbstractArray) =
+  NNlib.upsample_bilinear(x, m.scale)
+(m::Upsample{:bilinear, Nothing})(x::AbstractArray) = 
+  NNlib.upsample_bilinear(x; size=m.size)
+
+function Base.show(io::IO, u::Upsample{mode}) where {mode}
+  print(io, "Upsample(")
+  print(io, ":", mode)
+  u.scale !== nothing && print(io, ", scale = $(u.scale)")
+  u.size !== nothing && print(io, ", size = $(u.size)")
+  println(io, ")")
+end
+
+"""
+    PixelShuffle(r::Int)
+
+Pixel shuffling layer with upscale factor `r`.
+
+See [`NNlib.pixel_shuffle`](@ref).
+"""
+struct PixelShuffle 
+  r::Int
+end
+
+(m::PixelShuffle)(x) = NNlib.pixel_shuffle(x, m.r)
+

src/outputsize.jl

@@ -35,7 +35,7 @@ Base.isless(::Nil, ::Number) = true
 Base.isless(::Number, ::Nil) = true
 
 Base.isnan(::Nil) = false
-
+Base.isfinite(::Nil) = true
 Base.typemin(::Type{Nil}) = nil
 Base.typemax(::Type{Nil}) = nil
 

test/cuda/layers.jl

@@ -79,6 +79,15 @@ gpu_gradtest("GroupNorm 3d", groupnorm, rand(Float32, 8, 8, 8, 12, 4), 12, 3, se
 gpu_gradtest("GroupNorm 2d", groupnorm, rand(Float32, 8, 8, 12, 4), 12, 3, setmode=true)
 gpu_gradtest("GroupNorm 1d", groupnorm, rand(Float32, 8, 3, 12, 4), 12, 3, setmode=true)
 
+upsample = [x -> Upsample(scale=x)]
+gpu_gradtest("Upsample 2d", upsample, rand(Float32, 3, 4, 2, 3), (2,2))
+gpu_gradtest("Upsample 1d", upsample, rand(Float32, 3, 4, 2, 3), (2,))
+
+pixelshuffle = [PixelShuffle]
+gpu_gradtest("PixelShuffle 2d", pixelshuffle, rand(Float32, 3, 4, 18, 3), 3)
+gpu_gradtest("PixelShuffle 1d", pixelshuffle, rand(Float32, 3, 18, 3), 3)
+
+
 @testset "function layers" begin
   x = rand(Float32, 3,3)
   gpu_autodiff_test(x -> sum(Flux.normalise(x; dims=1)), x)
@@ -168,4 +177,4 @@ end
   @test sum(l(ip)) ≈ 0.f0  
   gs = gradient(() -> sum(l(ip)), Flux.params(l))
   @test l.b ∉ gs.params 
-end
+end

test/layers/upsample.jl

@@ -0,0 +1,63 @@
+@testset "upsample bilinear" begin
+  m = Upsample(:bilinear, scale=(2, 3))
+  x = rand(Float32, 3, 4, 2, 3)
+  y = m(x)
+  @test y isa Array{Float32, 4} 
+  @test size(y) == (6, 12, 2, 3)
+
+  m = Upsample(:bilinear, scale=3)
+  x = rand(Float32, 3, 4, 2, 3)
+  y = m(x)
+  @test y isa Array{Float32, 4} 
+  @test size(y) == (9, 12, 2, 3)
+
+  m = Upsample(:bilinear, size=(4, 6))
+  x = rand(Float32, 3, 4, 2, 3)
+  y = m(x)
+  @test y isa Array{Float32, 4} 
+  @test size(y) == (4, 6, 2, 3)
+end
+
+@testset "upsample nearest" begin
+  x = rand(Float32, 3, 2, 3)
+  m = Upsample(:nearest, scale=(2,))
+  y = m(x)
+  @test y isa Array{Float32, 3} 
+  @test size(y) == (6, 2, 3)
+
+  x = rand(Float32, 3, 4, 2, 3)
+
+  m = Upsample(:nearest, scale=(2, 3))
+  y = m(x)
+  @test y isa Array{Float32, 4} 
+  @test size(y) == (6, 12, 2, 3)
+
+  m = Upsample(:nearest, scale=(2,))
+  y = m(x)
+  @test y isa Array{Float32, 4} 
+  @test size(y) == (6, 4, 2, 3)
+
+  m = Upsample(:nearest, scale=2)
+  y = m(x)
+  @test y isa Array{Float32, 4} 
+  @test size(y) == (6, 8, 2, 3)
+
+  m = Upsample(:nearest, size=(6,8))
+  y = m(x)
+  @test y isa Array{Float32, 4} 
+  @test size(y) == (6, 8, 2, 3)
+end
+
+@testset "PixelShuffle" begin
+  m = PixelShuffle(2)
+  x = rand(Float32, 3, 18, 3)
+  y = m(x)
+  @test y isa Array{Float32, 3} 
+  @test size(y) == (6, 9, 3)
+
+  m = PixelShuffle(3)
+  x = rand(Float32, 3, 4, 18, 3)
+  y = m(x)
+  @test y isa Array{Float32, 4} 
+  @test size(y) == (9, 12, 2, 3)
+end

test/outputsize.jl

@@ -146,9 +146,7 @@ end
   @test outputsize(m, (32, 32, 3, 16)) == (32, 32, 3, 16)
   @test outputsize(m, (32, 32, 3); padbatch=true) == (32, 32, 3, 1)
 
-  if VERSION >= v"1.1"
-    m = GroupNorm(16, 4)
-    @test outputsize(m, (32, 32, 16, 16)) == (32, 32, 16, 16)
-    @test outputsize(m, (32, 32, 16); padbatch=true) == (32, 32, 16, 1)
-  end
+  m = GroupNorm(16, 4)
+  @test outputsize(m, (32, 32, 16, 16)) == (32, 32, 16, 16)
+  @test outputsize(m, (32, 32, 16); padbatch=true) == (32, 32, 16, 1)
 end

test/runtests.jl

@@ -34,6 +34,7 @@ end
   include("layers/stateless.jl")
   include("layers/recurrent.jl")
   include("layers/conv.jl")
+  include("layers/upsample.jl")
 end
 
 @testset "outputsize" begin