fix

Author: CarloLucibello

src/gnngraph.jl

@@ -149,14 +149,14 @@ GNNGraph((s, t)::NTuple{2}; kws...) = GNNGraph((s, t, nothing); kws...)
 
 # GNNGraph(g::AbstractGraph; kws...) = GNNGraph(adjacency_matrix(g, dir=:out); kws...)
 
-function GNNGraph(g::AbstractGraph; edata=(;), kws...)
+function GNNGraph(g::AbstractGraph; kws...)
     s = LightGraphs.src.(LightGraphs.edges(g))
     t = LightGraphs.dst.(LightGraphs.edges(g))
     if !LightGraphs.is_directed(g) 
         # add reverse edges since GNNGraph are directed
         s, t = [s; t], [t; s]    
     end
-    GNNGraph((s, t); edata, num_nodes=LightGraphs.nv(g), kws...)
+    GNNGraph((s, t); num_nodes=LightGraphs.nv(g), kws...)
 end
 
 

src/utils.jl

@@ -29,23 +29,41 @@ normalize_graphdata(data::Nothing; kws...) = NamedTuple()
 normalize_graphdata(data; default_name::Symbol, kws...) = 
     normalize_graphdata(NamedTuple{(default_name,)}((data,)); default_name, kws...) 
 
-function normalize_graphdata(data::NamedTuple; default_name=:z, n, duplicate_if_needed=false)
+function normalize_graphdata(data::NamedTuple; default_name, n, duplicate_if_needed=false)
+    # This had to workaround two Zygote bugs with NamedTuples
+    # https://github.com/FluxML/Zygote.jl/issues/1071
+    # https://github.com/FluxML/Zygote.jl/issues/1072
+    
+    if n == 1
+        # If last array dimension is not 1, add a new dimension. 
+        # This is mostly usefule to reshape globale feature vectors
+        # of size D to Dx1 matrices.
+        function unsqz(v)
+            if v isa AbstractArray && size(v)[end] != 1
+                v = reshape(v, size(v)..., 1)
+            end
+            v
+        end
+
+        data = NamedTuple{keys(data)}(unsqz.(values(data)))
+    end
+    
     sz = map(x -> x isa AbstractArray ? size(x)[end] : 0, data)
 
-    if duplicate_if_needed # used to copy edge features on reverse edges    
+    if duplicate_if_needed 
+        # Used to copy edge features on reverse edges    
         @assert all(s -> s == 0 ||  s == n || s == n÷2, sz)
 
-        function replace(k, v)
+        function duplicate(v)
             if v isa AbstractArray && size(v)[end] == n÷2
                 v = cat(v, v, dims=ndims(v))
             end
-            k => v
+            v
         end
 
-        data = NamedTuple(replace(k,v) for (k,v) in pairs(data))
+        data = NamedTuple{keys(data)}(duplicate.(values(data)))
     else
         @assert all(s -> s == 0 ||  s == n, sz)
     end
     return data
 end
-

test/layers/conv.jl

@@ -34,8 +34,10 @@
         for g in test_graphs
             test_layer(l, g, rtol=1e-5)
         end
-    end
 
+        l = GCNConv(in_channel => out_channel, add_self_loops=false)
+        test_layer(l, g1, rtol=1e-5)
+    end
 
     @testset "ChebConv" begin
         k = 6

test/msgpass.jl

@@ -101,7 +101,7 @@ import GraphNeuralNetworks: compute_message, update_node, update_edge, propagate
         @test all(adjacency_matrix(g_) .== adj)
         @test size(node_features(g_)) == (2*out_channel, num_V)
         @test size(edge_features(g_)) == (out_channel, num_E)
-        @test size(graph_features(g_)) == (in_channel,)
+        @test size(graph_features(g_)) == (in_channel, 1)
     end
 
     @testset "message and update with weights" begin
@@ -124,7 +124,7 @@ import GraphNeuralNetworks: compute_message, update_node, update_edge, propagate
         @test adjacency_matrix(g_) == adj
         @test size(node_features(g_)) == (out_channel, num_V)
         @test edge_features(g_) === E
-        @test graph_features(g_) === U
+        @test vec(graph_features(g_)) ≈ U
     end
 
     @testset "NamedTuples" begin