use GNNlib in GNN.jl

.github/workflows/test_GraphNeuralNetworks.yml

@@ -37,7 +37,7 @@ jobs:
           # dev mono repo versions
           pkg"registry up"
           Pkg.update()
-          pkg"dev ./GNNGraphs ."
+          pkg"dev ./GNNGraphs ./GNNlib ."
           Pkg.test("GraphNeuralNetworks"; coverage=true)
       - uses: julia-actions/julia-processcoverage@v1
         with:

GNNlib/Project.toml

@@ -7,7 +7,6 @@ version = "0.2.0"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
-Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"

GNNlib/src/GNNlib.jl

@@ -12,70 +12,65 @@ using .GNNGraphs: COO_T, ADJMAT_T, SPARSE_T,
                   check_num_nodes, check_num_edges,
                   EType, NType # for heteroconvs
 
-export
-      # utils
-      reduce_nodes,
-      reduce_edges,
-      softmax_nodes,
-      softmax_edges,
-      broadcast_nodes,
-      broadcast_edges,
-      softmax_edge_neighbors,
-      # msgpass
-      apply_edges,
-      aggregate_neighbors,
-      propagate,
-      copy_xj,
-      copy_xi,
-      xi_dot_xj,
-      xi_sub_xj,
-      xj_sub_xi,
-      e_mul_xj,
-      w_mul_xj
+include("utils.jl")
+export reduce_nodes,
+       reduce_edges,
+       softmax_nodes,
+       softmax_edges,
+       broadcast_nodes,
+       broadcast_edges,
+       softmax_edge_neighbors
 
+include("msgpass.jl")
+export apply_edges,
+       aggregate_neighbors,
+       propagate,
+       copy_xj,
+       copy_xi,
+       xi_dot_xj,
+       xi_sub_xj,
+       xj_sub_xi,
+       e_mul_xj,
+       w_mul_xj
+
 ## The following methods are defined but not exported
 
-# # layers/basic
-#       dot_decoder,
-
-# # layers/conv
-#       agnn_conv,
-#       cg_conv,
-#       cheb_conv,
-#       edge_conv,
-#       egnn_conv,
-#       gat_conv,
-#       gatv2_conv,
-#       gated_graph_conv,
-#       gcn_conv,
-#       gin_conv,
-#       gmm_conv,
-#       graph_conv,
-#       megnet_conv,
-#       nn_conv,
-#       res_gated_graph_conv,
-#       sage_conv,
-#       sg_conv,
-#       transformer_conv,
+include("layers/basic.jl")
+export dot_decoder
 
-# # layers/temporalconv
-#       a3tgcn_conv,
+include("layers/conv.jl")
+export agnn_conv,
+       cg_conv,
+       cheb_conv,
+       d_conv,
+       edge_conv,
+       egnn_conv,
+       gat_conv,
+       gatv2_conv,
+       gated_graph_conv,
+       gcn_conv,
+       gin_conv,
+       gmm_conv,
+       graph_conv,
+       megnet_conv,
+       nn_conv,
+       res_gated_graph_conv,
+       sage_conv,
+       sg_conv,
+       tag_conv,
+       transformer_conv
 
-# # layers/pool
-#       global_pool,
-#       global_attention_pool,
-#       set2set_pool,
-#       topk_pool,
-#       topk_index,
+include("layers/temporalconv.jl")
+export a3tgcn_conv
 
+include("layers/pool.jl")
+export global_pool,
+       global_attention_pool,
+       set2set_pool,
+       topk_pool,
+       topk_index
 
-include("utils.jl")
-include("layers/basic.jl")
-include("layers/conv.jl")
 # include("layers/heteroconv.jl") # no functional part at the moment
-include("layers/temporalconv.jl")
-include("layers/pool.jl")
-include("msgpass.jl")
 
 end #module