Merge pull request #49 from DrChainsaw/flux0.10.4

Flux0.10.4

Project.toml

@@ -1,6 +1,6 @@
 name = "NaiveNASflux"
 uuid = "85610aed-7d32-5e57-bb50-4c2e1c9e7997"
-version = "1.1.2"
+version = "1.2.0"
 
 [deps]
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
@@ -14,7 +14,7 @@ Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
-Flux = "=0.10.3"
+Flux = "0.10.4"
 JuMP = "0.19, 0.20, 0.21"
 NaiveNASlib = "1"
 Reexport = "0.2.0"

src/NaiveNASflux.jl

@@ -1,8 +1,8 @@
 module NaiveNASflux
 
 using Reexport
-@reexport using NaiveNASlib
-@reexport using Flux
+using NaiveNASlib
+using Flux
 import Flux.Zygote: hook
 using Statistics
 using Setfield
@@ -37,4 +37,9 @@ include("weightinit.jl")
 include("functor.jl")
 include("zygote.jl")
 
+# Reexporting before include("functor.jl") causes a warning about duplicate name (flatten) in NaiveNASlib and Flux when subtypes are called
+# https://discourse.julialang.org/t/avoid-error-message-for-function-name-conflict/37176/10
+@reexport using NaiveNASlib
+@reexport using Flux
+
 end # module

src/types.jl

@@ -51,6 +51,8 @@ layertype(l::MaxPool) = FluxNoParLayer()
 layertype(l::MeanPool) = FluxNoParLayer()
 layertype(l::Dropout) = FluxNoParLayer()
 layertype(l::AlphaDropout) = FluxNoParLayer()
+layertype(l::GlobalMaxPool) = FluxNoParLayer()
+layertype(l::GlobalMeanPool) = FluxNoParLayer()
 
 # Compositions? Might not have any common methods...
 # MaxOut, Chain?

src/zygote.jl

@@ -28,10 +28,10 @@ end
 
 function output_loop!(memo, v)
     vs = nograd() do
-        # flatten returns all input ancestors to v in topological order
+        # NaiveNASlib.flatten returns all input ancestors to v in topological order
         # We also provide all vertices for which we have the output already in memo
         # so we don't do unnecessary calculations.
-        flatten(v, collect(AbstractVertex, keys(memo)))[length(memo)+1:end]
+        NaiveNASlib.flatten(v, collect(AbstractVertex, keys(memo)))[length(memo)+1:end]
     end
 
     for vn in vs

test/pruning.jl

@@ -81,6 +81,16 @@
         @test size(neuron_value(l)) == (2,)
     end
 
+    @testset "Neuron value GlobalMeanPool act contrib" begin
+        l = ml(GlobalMeanPool(), ActivationContribution, insize=2)
+        @test ismissing(neuron_value(l))
+        tr(l, ones(Float32, 4,4,2,5))
+
+        @test ismissing(minΔninfactor(l))
+        @test ismissing(minΔnoutfactor(l))
+        @test size(neuron_value(l)) == (2,)
+    end
+
     @testset "Elem add ActivationContribution" begin
         ac(l) = ActivationContribution(l)
         v = ac >> ml(Dense(2,3)) + ml(Dense(4,3))

test/vertex.jl

@@ -47,7 +47,7 @@ end
 
         W2exp, b2exp = weights(dl2)[:, inds], bias(dl2)
         W1exp, b1exp = weights(dl1)[inds, :], bias(dl1)[inds]
-        apply_mutation.(flatten(dense2))
+        apply_mutation.(NaiveNASlib.flatten(dense2))
 
         @test size(CompGraph([inpt], [dense2])(collect(Float32, 1:nout(inpt)))) == (3,)
 
@@ -63,7 +63,7 @@ end
         @test nin(bv) == [nout(cv)] == [4]
 
         Δnin(bv, -1)
-        apply_mutation.(flatten(bv))
+        apply_mutation.(NaiveNASlib.flatten(bv))
 
         @test nin(bv) == [nout(cv)] == [3]
     end
@@ -76,7 +76,7 @@ end
         @test nin(bv) == [nout(cv)] == [4]
 
         Δnin(bv, -1)
-        apply_mutation.(flatten(bv))
+        apply_mutation.(NaiveNASlib.flatten(bv))
 
         @test nin(bv) == [nout(cv)] == [3]
     end