add adaptive pool

NEWS.md

@@ -2,6 +2,7 @@
 * Change to `DataLoader`'s constructor [https://github.com/FluxML/Flux.jl/pull/1152]
 * Use `DataLoader` with `NamedTuple`s, so that tensors can be accessed by name [https://github.com/FluxML/Flux.jl/pull/1221].
 * Error if Dense layers weights and biases are not arrays [https://github.com/FluxML/Flux.jl/pull/1218].
+* Add `Adaptive Pooling` in Flux layers [https://github.com/FluxML/Flux.jl/pull/1239].
 
 # v0.10.5
 * Add option for [same padding](https://github.com/FluxML/Flux.jl/pull/901) to conv and pooling layers by setting `pad=SamePad()`.

docs/src/models/layers.md

@@ -13,8 +13,10 @@ These layers are used to build convolutional neural networks (CNNs).
 
 ```@docs
 Conv
+AdaptiveMaxPool
 MaxPool
 GlobalMaxPool
+AdaptiveMeanPool
 MeanPool
 GlobalMeanPool
 DepthwiseConv

src/Flux.jl

@@ -12,9 +12,10 @@ using Zygote: Params, @adjoint, gradient, pullback, @nograd
 export gradient
 
 export Chain, Dense, Maxout, RNN, LSTM, GRU, SamePad, Conv, CrossCor, ConvTranspose,
-       GlobalMaxPool, GlobalMeanPool, MaxPool, MeanPool, flatten,
-       DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm,
-       SkipConnection, params, fmap, cpu, gpu, f32, f64, testmode!, trainmode!
+       AdaptiveMaxPool, AdaptiveMeanPool, GlobalMaxPool, GlobalMeanPool, MaxPool,
+       MeanPool, flatten, DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm,
+       InstanceNorm, GroupNorm, SkipConnection, params, fmap, cpu, gpu, f32, f64,
+       testmode!, trainmode!
 
 include("optimise/Optimise.jl")
 using .Optimise

src/layers/conv.jl

@@ -480,6 +480,54 @@ end
 outdims(l::CrossCor, isize) =
   output_size(DenseConvDims(_paddims(isize, size(l.weight)), size(l.weight); stride = l.stride, padding = l.pad, dilation = l.dilation))
 
+"""
+    AdaptiveMaxPool(out)
+
+Adaptive max pooling layer. `out` is the desired output size (batch and channel dimension excluded).
+"""
+struct AdaptiveMaxPool{S, O}
+  out::NTuple{O, Int}
+  AdaptiveMaxPool(out::NTuple{O, Int}) where O = new{O + 2, O}(out)
+end
+
+function (a::AdaptiveMaxPool{S})(x::AbstractArray{T, S}) where {S, T}
+  insize = size(x)[1:end-2]
+  outsize = a.out
+  stride = Int.(insize ./ outsize)
+  k = Int.(insize .- (outsize .- 1) .* stride)
+  pad = 0
+  pdims = PoolDims(x, k; padding=pad, stride=stride)
+  return maxpool(x, pdims)
+end
+
+function Base.show(io::IO, a::AdaptiveMaxPool)
+  print(io, "AdaptiveMaxPool(", a.out, ")")
+end
+
+"""
+    AdaptiveMeanPool(out)
+
+Adaptive mean pooling layer. `out` is the desired output size (batch and channel dimension excluded).
+"""
+struct AdaptiveMeanPool{S, O}
+  out::NTuple{O, Int}
+  AdaptiveMeanPool(out::NTuple{O, Int}) where O = new{O + 2, O}(out)
+end
+
+function (a::AdaptiveMeanPool{S})(x::AbstractArray{T, S}) where {S, T}
+  insize = size(x)[1:end-2]
+  outsize = a.out
+  stride = Int.(insize ./ outsize)
+  k = Int.(insize .- (outsize .- 1) .* stride)
+  pad = 0
+  pdims = PoolDims(x, k; padding=pad, stride=stride)
+  return meanpool(x, pdims)
+end
+
+function Base.show(io::IO, a::AdaptiveMeanPool)
+  print(io, "AdaptiveMeanPool(", a.out, ")")
+end
+
 """
     GlobalMaxPool()
 

test/layers/conv.jl

@@ -4,6 +4,15 @@ using Flux: gradient
 
 @testset "Pooling" begin
   x = randn(Float32, 10, 10, 3, 2)
+  y = randn(Float32, 20, 20, 3, 2)
+  ampx = AdaptiveMaxPool((5,5))
+  @test ampx(x) == maxpool(x, PoolDims(x, 2))
+  ampx = AdaptiveMeanPool((5,5))
+  @test ampx(x) == meanpool(x, PoolDims(x, 2))
+  ampy = AdaptiveMaxPool((10, 5))
+  @test ampy(y) == maxpool(y, PoolDims(y, (2, 4)))
+  ampy = AdaptiveMeanPool((10, 5))
+  @test ampy(y) == meanpool(y, PoolDims(y, (2, 4)))
   gmp = GlobalMaxPool()
   @test size(gmp(x)) == (1, 1, 3, 2)
   gmp = GlobalMeanPool()