Make outputsize understand multiple inputs

src/outputsize.jl

@@ -50,13 +50,14 @@ using .NilNumber: Nil, nil
 """
     outputsize(m, inputsize::Tuple; padbatch=false)
 
-Calculate the output size of model `m` given the input size. 
+Calculate the size of the output from model `m`, given the size of the input.
 Obeys `outputsize(m, size(x)) == size(m(x))` for valid input `x`.
-Keyword `padbatch=true` is equivalent to using `(inputsize..., 1)`, and 
+
+Keyword `padbatch=true` is equivalent to using `(inputsize..., 1)`, and
 returns the final size including this extra batch dimension.
 
-This should be faster than calling `size(m(x))`. It uses a trivial number type, 
-and thus should work out of the box for custom layers.
+This should be faster than calling `size(m(x))`. It uses a trivial number type,
+which should work out of the box for custom layers.
 
 If `m` is a `Tuple` or `Vector`, its elements are applied in sequence, like `Chain(m...)`.
 
@@ -79,32 +80,70 @@ julia> outputsize(m, (10, 10, 3, 64))
 (6, 6, 32, 64)
 
 julia> try outputsize(m, (10, 10, 7, 64)) catch e println(e) end
+┌ Error: layer Conv((3, 3), 3=>16), index 1 in Chain, gave an error with input of size (10, 10, 7, 64)
+└ @ Flux ~/.julia/dev/Flux/src/outputsize.jl:114
 DimensionMismatch("Input channels must match! (7 vs. 3)")
 
-julia> outputsize([Dense(10, 4), Dense(4, 2)], (10, 1))
+julia> outputsize([Dense(10, 4), Dense(4, 2)], (10, 1)) # Vector of layers becomes a Chain
 (2, 1)
+```
+"""
+function outputsize(m, inputsizes::Tuple...; padbatch=false)
+  x = nil_input(padbatch, inputsizes...)
+  return size(m(x))
+end
+
+nil_input(pad::Bool, s::Tuple{Vararg{Integer}}) = pad ? fill(nil, (s...,1)) : fill(nil, s)
+nil_input(pad::Bool, multi::Tuple{Vararg{Integer}}...) = nil_input.(pad, multi)
+nil_input(pad::Bool, tup::Tuple{Vararg{Tuple}}) = nil_input(pad, tup...)
+
+function outputsize(m::Chain, inputsizes::Tuple{Vararg{Integer}}...; padbatch=false)
+  x = nil_input(padbatch, inputsizes...)
+  for (i,lay) in enumerate(m.layers)
+    try
+      x = lay(x)
+    catch err
+      str = x isa AbstractArray ? "with input of size $(size(x))" : ""
+      @error "layer $lay, index $i in Chain, gave an error $str"
+      rethrow(err)
+    end
+  end
+  return size(x)
+end
 
-julia> using LinearAlgebra: norm
+"""
+    outputsize(m, x_size, y_size, ...; padbatch=false)
+
+For model or layer `m` accepting multiple arrays as input,
+this returns `size(m((x, y, ...)))` given `size_x = size(x)`, etc.
+
+# Examples
+```jldoctest
+julia> x, y = rand(Float32, 5, 64), rand(Float32, 7, 64);
+
+julia> par = Parallel(vcat, Dense(5, 9), Dense(7, 11));
 
-julia> f(x) = x ./ norm.(eachcol(x));
+julia> Flux.outputsize(par, (5, 64), (7, 64))
+(20, 64)
 
-julia> outputsize(f, (10, 1)) # manually specify batch size as 1
-(10, 1)
+julia> m = Chain(par, Dense(20, 13), softmax);
 
-julia> outputsize(f, (10,); padbatch=true) # no need to mention batch size
-(10, 1)
+julia> Flux.outputsize(m, (5,), (7,); padbatch=true)
+(13, 1)
+
+julia> par(x, y) == par((x, y)) == Chain(par, identity)((x, y))
+true
 ```
+Notice that `Chain` only accepts multiple arrays as a tuple,
+while `Parallel` also accepts them as multiple arguments;
+`outputsize` always supplies the tuple.
 """
-function outputsize(m, inputsize::Tuple; padbatch=false)
-  inputsize = padbatch ? (inputsize..., 1) : inputsize
-
-  return size(m(fill(nil, inputsize)))
-end
+outputsize
 
 ## make tuples and vectors be like Chains
 
-outputsize(m::Tuple, inputsize::Tuple; padbatch=false) = outputsize(Chain(m...), inputsize; padbatch=padbatch)
-outputsize(m::AbstractVector, inputsize::Tuple; padbatch=false) = outputsize(Chain(m...), inputsize; padbatch=padbatch)
+outputsize(m::Tuple, input::Tuple...; padbatch=false) = outputsize(Chain(m...), input...; padbatch=padbatch)
+outputsize(m::AbstractVector, input::Tuple...; padbatch=false) = outputsize(Chain(m...), input...; padbatch=padbatch)
 
 ## bypass statistics in normalization layers
 

test/outputsize.jl

@@ -36,6 +36,20 @@
   @test outputsize(m, (10, 10, 3, 1)) == (10, 10, 19, 1)
 end
 
+@testset "multiple inputs" begin
+  m = Parallel(vcat, Dense(2, 4, relu), Dense(3, 6, relu))
+  @test outputsize(m, (2,), (3,)) == (10,)
+  @test outputsize(m, ((2,), (3,))) == (10,)
+  @test outputsize(m, (2,), (3,); padbatch=true) == (10, 1)
+  @test outputsize(m, (2,7), (3,7)) == (10, 7)
+
+  m = Chain(m, Dense(10, 13, tanh), softmax)
+  @test outputsize(m, (2,), (3,)) == (13,)
+  @test outputsize(m, ((2,), (3,))) == (13,)
+  @test outputsize(m, (2,), (3,); padbatch=true) == (13, 1)
+  @test outputsize(m, (2,7), (3,7)) == (13, 7)
+end
+
 @testset "activations" begin
   @testset for f in [celu, elu, gelu, hardsigmoid, hardtanh,
                      leakyrelu, lisht, logcosh, logσ, mish,