add tests, release note

NEWS.md

@@ -1,5 +1,8 @@
 # Flux Release Notes
 
+## v0.13.7
+* Added [`@autosize` macro](https://github.com/FluxML/Flux.jl/pull/2078)
+
 ## v0.13.4
 * Added [`PairwiseFusion` layer](https://github.com/FluxML/Flux.jl/pull/1983)
 

src/outputsize.jl

@@ -261,16 +261,6 @@ function _underscoredepth(ex::Expr)
 end
 _underscoredepth(ex) = Int(ex == :_)
 
-#=
-
-@autosize (3,) Chain(one = Dense(_ => 4), two = softmax)  # needs kw
-@autosize (3, 45) Maxout(() -> Dense(_ => 6, tanh), 2)    # needs ->, block
-
-# here Parallel gets two inputs, no problem:
-@autosize (3,) Chain(SkipConnection(Dense(_ => 4), Parallel(vcat, Dense(_ => 5), Dense(_ => 6))), Flux.Scale(_))
-
-=#
-
 function _makefun(ex)
   T = Meta.isexpr(ex, :call) ? ex.args[1] : Type
   @gensym x s

test/outputsize.jl

@@ -142,16 +142,81 @@ end
   m = LayerNorm(32)
   @test outputsize(m, (32, 32, 3, 16)) == (32, 32, 3, 16)
   @test outputsize(m, (32, 32, 3); padbatch=true) == (32, 32, 3, 1)
+  m2 = LayerNorm(3, 2)
+  @test outputsize(m2, (3, 2)) == (3, 2) == size(m2(randn(3, 2)))
+  @test outputsize(m2, (3,)) == (3, 2) == size(m2(randn(3, 2)))
 
   m = BatchNorm(3)
   @test outputsize(m, (32, 32, 3, 16)) == (32, 32, 3, 16)
   @test outputsize(m, (32, 32, 3); padbatch=true) == (32, 32, 3, 1)
+  @test_throws Exception m(randn(Float32, 32, 32, 5, 1))
+  @test_throws DimensionMismatch outputsize(m, (32, 32, 5, 1))
 
   m = InstanceNorm(3)
   @test outputsize(m, (32, 32, 3, 16)) == (32, 32, 3, 16)
   @test outputsize(m, (32, 32, 3); padbatch=true) == (32, 32, 3, 1)
+  @test_throws Exception m(randn(Float32, 32, 32, 5, 1))
+  @test_throws DimensionMismatch outputsize(m, (32, 32, 5, 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)
+  @test_throws Exception m(randn(Float32, 32, 32, 15, 4))
+  @test_throws DimensionMismatch outputsize(m, (32, 32, 15, 4))
 end
+
+@testset "autosize macro" begin
+  m = @autosize (3,) Dense(_ => 4)
+  @test randn(3) |> m |> size == (4,)
+
+  m = @autosize (3, 1) Chain(Dense(_ => 4), Dense(4 => 10), softmax)
+  @test randn(3, 5) |> m |> size == (10, 5)
+
+  m = @autosize (2, 3, 4, 5) Dense(_ => 10)  # goes by first dim, not 2nd-last
+  @test randn(2, 3, 4, 5) |> m |> size == (10, 3, 4, 5)
+
+  m = @autosize (9,) Dense(_ => div(_,2))
+  @test randn(9) |> m |> size == (4,)
+
+  m = @autosize (3,) Chain(one = Dense(_ => 4), two = softmax)  # needs kw
+  @test randn(3) |> m |> size == (4,)
+
+  m = @autosize (3, 45) Maxout(() -> Dense(_ => 6, tanh), 2)    # needs ->, block
+  @test randn(3, 45) |> m |> size == (6, 45)
+
+  # here Parallel gets two inputs, no problem:
+  m = @autosize (3,) Chain(SkipConnection(Dense(_ => 4), Parallel(vcat, Dense(_ => 5), Dense(_ => 6))), Flux.Scale(_))
+  @test randn(3) |> m |> size == (11,)
+
+  # like Dense, LayerNorm goes by the first dimension:
+  m = @autosize (3, 4, 5) LayerNorm(_)
+  @test rand(3, 6, 7) |> m |> size == (3, 6, 7)
+
+  m = @autosize (3, 3, 10) LayerNorm(_, _)  # does not check that sizes match
+  @test rand(3, 3, 10) |> m |> size == (3, 3, 10)
+
+  m = @autosize (3,) Flux.Bilinear(_ => 10)
+  @test randn(3) |> m |> size == (10,)
+
+  m = @autosize (3, 1) Flux.Bilinear(_ => 10)
+  @test randn(3, 4) |> m |> size == (10, 4)
+
+  @test_throws Exception @eval @autosize (3,) Flux.Bilinear((_,3) => 10)
+
+  # first docstring example
+  m = @autosize (3, 1) Chain(Dense(_ => 2, sigmoid), BatchNorm(_, affine=false))
+  @test randn(3, 4) |> m |> size == (2, 4)
+
+  # evil docstring example
+  img = [28, 28];
+  m = @autosize (img..., 1, 32) Chain(              # size is only needed at runtime
+         Chain(c = Conv((3,3), _ => 5; stride=2, pad=SamePad()),
+               p = MeanPool((3,3)),
+               b = BatchNorm(_),
+               f = Flux.flatten),
+         Dense(_ => _÷4, relu, init=Flux.rand32),   # can calculate output size _÷4
+         SkipConnection(Dense(_ => _, relu), +),
+         Dense(_ => 10),
+      ) |> gpu                                      # moves to GPU after initialisation
+  @test randn(Float32, img..., 1, 32) |> gpu |> m |> size == (10, 32)
+end