fixing

Author: rbSparky

GNNLux/src/layers/conv.jl

@@ -666,11 +666,8 @@ function (l::NNConv)(g, x, edge_weight, ps, st)
     return y, stnew
 end
 
-LuxCore.outputsize(d::NNConv) = (d.out_dims,)
-
 function Base.show(io::IO, l::NNConv)
     print(io, "NNConv($(l.nn)")
-    print(io, ", $(l.ϵ)")
     l.σ == identity || print(io, ", ", l.σ)
     l.use_bias || print(io, ", use_bias=false")
     l.add_self_loops || print(io, ", add_self_loops=false")

temp.jl

@@ -0,0 +1,129 @@
+
+using StableRNGs
+using LuxTestUtils: test_gradients, AutoReverseDiff, AutoTracker, AutoForwardDiff, AutoEnzyme
+
+using Lux: Lux, Chain, Dense, GRUCell,
+           glorot_uniform, zeros32  , 
+           StatefulLuxLayer
+
+import Reexport: @reexport
+
+@reexport using Test
+@reexport using GNNLux
+@reexport using Lux
+@reexport using StableRNGs
+@reexport using Random, Statistics
+
+using LuxTestUtils: test_gradients, AutoReverseDiff, AutoTracker, AutoForwardDiff, AutoEnzyme
+
+
+rng = StableRNG(1234)
+    edim = 10
+    g = rand_graph(10, 40)
+    in_dims = 3
+    out_dims = 5
+    x = randn(Float32, in_dims, 10)
+
+    g2 = GNNGraph(g, edata = rand(Float32, edim, g.num_edges)) 
+
+@testset "GINConv" begin
+    nn = Chain(Dense(in_dims => out_dims, relu), Dense(out_dims => out_dims))
+    l = GINConv(nn, 0.5)
+    test_lux_layer(rng, l, g, x, sizey=(out_dims,g.num_nodes), container=true)
+end
+
+
+
+function test_lux_layer(rng::AbstractRNG, l, g::GNNGraph, x; 
+            outputsize=nothing, sizey=nothing, container=false,
+            atol=1.0f-2, rtol=1.0f-2, edge_weight=nothing) 
+
+    if container
+        @test l isa GNNContainerLayer
+    else
+        @test l isa GNNLayer
+    end
+
+    ps = LuxCore.initialparameters(rng, l)
+    st = LuxCore.initialstates(rng, l)
+    @test LuxCore.parameterlength(l) == LuxCore.parameterlength(ps)
+    @test LuxCore.statelength(l) == LuxCore.statelength(st)
+    
+    if edge_weight !== nothing
+        y, st′ = l(g, x, edge_weight, ps, st)  
+    else
+        y, st′ = l(g, x, ps, st)  
+    end
+    @test eltype(y) == eltype(x)
+    if outputsize !== nothing
+        @test LuxCore.outputsize(l) == outputsize
+    end
+    if sizey !== nothing
+        @test size(y) == sizey
+    elseif outputsize !== nothing
+        @test size(y) == (outputsize..., g.num_nodes)
+    end
+    
+    loss = (x, ps) -> sum(first(l(g, x, ps, st)))
+    test_gradients(loss, x, ps; atol, rtol, skip_backends=[AutoReverseDiff(), AutoTracker(), AutoForwardDiff(), AutoEnzyme()])
+end
+
+
+"""
+MEGNetConv{Flux.Chain{Tuple{Flux.Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}, Flux.Chain{Tuple{Flux.Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}, typeof(mean)}(Chain(Dense(9 => 5, relu), Dense(5 => 5)), Chain(Dense(8 => 5, relu), Dense(5 => 5)), Statistics.mean)
+"""
+
+g = rand_graph(10, 40, seed=1234)
+    in_dims = 3
+    out_dims = 5
+    x = randn(Float32, in_dims, 10)
+    rng = StableRNG(1234)
+    l = MEGNetConv(in_dims => out_dims)
+    l
+    l isa GNNContainerLayer
+    test_lux_layer(rng, l, g, x, sizey=((out_dims, g.num_nodes), (out_dims, g.num_edges)), container=true)
+
+
+        ps = LuxCore.initialparameters(rng, l)
+        st = LuxCore.initialstates(rng, l)
+        edata = rand(T, in_channel, g.num_edges)
+
+        (x_new, e_new), st_new = l(g, x, ps, st)
+
+        @test size(x_new) == (out_dims, g.num_nodes)
+        @test size(e_new) == (out_dims, g.num_edges)
+
+
+
+
+           edim = 10
+           in_dims = 3 # Example
+           out_dims = 5 # Example
+using Flux           
+    g2 = GNNGraph(g, edata = rand(Float32, edim, g.num_edges))
+           nn = Dense(edim, out_dims * in_dims) 
+           l = NNConv(in_dims => out_dims, nn, tanh) 
+           test_lux_layer(rng, l, g2, x, sizey=(out_dims, g2.num_nodes), container=true, edge_weight=g2.edata.e) 
+
+
+
+    hin = 6
+    hout = 7
+    hidden = 8
+    l = EGNNConv(hin => hout, hidden)
+    ps = LuxCore.initialparameters(rng, l)
+    st = LuxCore.initialstates(rng, l)
+    h = randn(rng, Float32, hin, g.num_nodes)
+    (hnew, xnew), stnew = l(g, h, x, ps, st)
+    @test size(hnew) == (hout, g.num_nodes)
+    @test size(xnew) == (in_dims, g.num_nodes)
+
+
+    l = MEGNetConv(in_dims => out_dims)
+    l
+    l isa GNNContainerLayer
+    test_lux_layer(rng, l, g, x, sizey=((out_dims, g.num_nodes), (out_dims, g.num_edges)), container=true)
+
+
+        ps = LuxCore.initialparameters(rng, l)
+        st = LuxCore.initialstates(rng, l)