fix tests

GNNGraphs/src/convert.jl

@@ -4,27 +4,24 @@ function to_coo(data::EDict; num_nodes = nothing, kws...)
     graph = EDict{COO_T}()
     _num_nodes = NDict{Int}()
     num_edges = EDict{Int}()
-    if !isempty(data)
-        for k in keys(data)
-            d = data[k]
-            @assert d isa Tuple
-            if length(d) == 2
-                d = (d..., nothing)
-            end
-            if num_nodes !== nothing
-                n1 = get(num_nodes, k[1], nothing)
-                n2 = get(num_nodes, k[3], nothing)
-            else
-                n1 = nothing
-                n2 = nothing
-            end
-            g, nnodes, nedges = to_coo(d; hetero = true, num_nodes = (n1, n2), kws...)
-            graph[k] = g
-            num_edges[k] = nedges
-            _num_nodes[k[1]] = max(get(_num_nodes, k[1], 0), nnodes[1])
-            _num_nodes[k[3]] = max(get(_num_nodes, k[3], 0), nnodes[2])
+    for k in keys(data)
+        d = data[k]
+        @assert d isa Tuple
+        if length(d) == 2
+            d = (d..., nothing)
         end
-        graph = Dict([k => v for (k, v) in pairs(graph)]...) # try to restrict the key/value types
+        if num_nodes !== nothing
+            n1 = get(num_nodes, k[1], nothing)
+            n2 = get(num_nodes, k[3], nothing)
+        else
+            n1 = nothing
+            n2 = nothing
+        end
+        g, nnodes, nedges = to_coo(d; hetero = true, num_nodes = (n1, n2), kws...)
+        graph[k] = g
+        num_edges[k] = nedges
+        _num_nodes[k[1]] = max(get(_num_nodes, k[1], 0), nnodes[1])
+        _num_nodes[k[3]] = max(get(_num_nodes, k[3], 0), nnodes[2])
     end
     return graph, _num_nodes, num_edges
 end

GNNGraphs/src/generate.jl

@@ -59,7 +59,7 @@ function rand_graph(rng::AbstractRNG, n::Integer, m::Integer; bidirected = true,
     else
         s, t, _ = _rand_edges(rng, n, m; directed=true, self_loops=false)
     end
-    return GNNGraph((s, t, edge_weight); kws...)
+    return GNNGraph((s, t, edge_weight); num_nodes=n, kws...)
 end
 
 """

GNNGraphs/src/transform.jl

@@ -57,7 +57,8 @@ then all new self loops will have no weight.
 If `edge_t` is not passed as argument, for the entire graph self-loop is added to each node for every edge type in the graph where the source and destination node types are the same. 
 This iterates over all edge types present in the graph, applying the self-loop addition logic to each applicable edge type.
 """
-function add_self_loops(g::GNNHeteroGraph{Tuple{T, T, V}}, edge_t::EType) where {T <: AbstractVector{<:Integer}, V}
+function add_self_loops(g::GNNHeteroGraph{<:COO_T}, edge_t::EType)
+
     function get_edge_weight_nullable(g::GNNHeteroGraph{<:COO_T}, edge_t::EType)
         get(g.graph, edge_t, (nothing, nothing, nothing))[3]
     end
@@ -69,13 +70,17 @@ function add_self_loops(g::GNNHeteroGraph{Tuple{T, T, V}}, edge_t::EType) where
     n = get(g.num_nodes, src_t, 0)
 
     if haskey(g.graph, edge_t)
-        x = g.graph[edge_t]
-        s, t = x[1:2]
+        s, t = g.graph[edge_t][1:2]
         nodes = convert(typeof(s), [1:n;])
         s = [s; nodes]
         t = [t; nodes]
     else
-        nodes = convert(T, [1:n;])
+        if !isempty(g.graph)
+            T = typeof(first(values(g.graph))[1])
+            nodes = convert(T, [1:n;])
+        else
+            nodes = [1:n;]
+        end
         s = nodes
         t = nodes
     end

GNNGraphs/test/utils.jl

@@ -93,7 +93,7 @@
     end
 end
 
-@testset "color_refinment" begin
+@testset "color_refinement" begin
     rng = MersenneTwister(17)
     g = rand_graph(rng, 10, 20, graph_type = GRAPH_T)
     x0 = ones(Int, 10)
@@ -104,4 +104,4 @@ end
 
     x2, _, _ = color_refinement(g)
     @test x2 == x
-end
+end