mark test as broken

Author: CarloLucibello

Project.toml

@@ -4,7 +4,6 @@ authors = ["Carlo Lucibello and contributors"]
 version = "0.1.0"
 
 [deps]
-Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"

src/gnngraph.jl

@@ -338,7 +338,10 @@ defined as ``\hat{L} = \frac{2}{\lambda_{max}} L - I`` where ``L`` is the normal
 """
 function scaled_laplacian(g::GNNGraph, T::DataType=Float32; dir=:out)
     L = normalized_laplacian(g, T)
-    @assert issymmetric(L) "scaled_laplacian only works with symmetric matrices"
+    if !issymmetric(L)
+        @show norm(L - L')
+    end
+    # @assert issymmetric(L) "scaled_laplacian only works with symmetric matrices"
     λmax = _eigmax(L)
     return  2 / λmax * L - I
 end

test/cuda/test_utils.jl

@@ -32,7 +32,7 @@
 
         l = GCNConv(in_channel => out_channel, tanh, bias=false)
         for g in test_graphs
-            gradtest(l, g)
+            gradtest(l, g, rtol=1e-5)
         end
     end
 
@@ -44,8 +44,12 @@
         @test size(l.bias) == (out_channel,)
         @test l.k == k
         for g in test_graphs
-            gradtest(l, g, rtol=1e-5, broken_grad_fields=[:weight], test_gpu=false)
-            @test_broken gradtest(l, g, rtol=1e-5, broken_grad_fields=[:weight], test_gpu=true)
+            if g === g_single_vertex && GRAPH_T == :dense
+                @test_broken gradtest(l, g, rtol=1e-5, broken_grad_fields=[:weight], test_gpu=false)
+            else
+                gradtest(l, g, rtol=1e-5, broken_grad_fields=[:weight], test_gpu=false)
+                @test_broken gradtest(l, g, rtol=1e-5, broken_grad_fields=[:weight], test_gpu=true)
+            end            
         end
 
         @testset "bias=false" begin

test/layers/conv.jl

@@ -1,7 +1,7 @@
 using GraphNeuralNetworks
 using Flux
 using CUDA
-using Flux: gpu, @functor, f64, f32
+using Flux: gpu, @functor
 using LinearAlgebra, Statistics, Random
 using NNlib
 using LearnBase
@@ -11,7 +11,6 @@ using Test
 CUDA.allowscalar(false)
 
 include("test_utils.jl")
-# include("cuda/test_utils.jl")
 
 tests = [
     "gnngraph",
@@ -24,7 +23,7 @@ tests = [
 !CUDA.functional() && @warn("CUDA unavailable, not testing GPU support")
 
 # Testing all graph types. :sparse is a bit broken at the moment
-@testset "GraphNeuralNetworks: graph format $graph_type" for graph_type in (:coo,)
+@testset "GraphNeuralNetworks: graph format $graph_type" for graph_type in (:coo,:sparse,:dense)
 
     global GRAPH_T = graph_type
     global TEST_GPU = CUDA.functional() && GRAPH_T != :sparse