[Sparse] Add conversion between DGLGraph and SparseMatrix. (#5553)

tests/python/pytorch/nn/test_nn.py

@@ -35,7 +35,7 @@ def _AXWb(A, X, W, b):
 def test_graph_conv0(out_dim):
     g = dgl.DGLGraph(nx.path_graph(3)).to(F.ctx())
     ctx = F.ctx()
-    adj = g.adjacency_matrix(transpose=True, ctx=ctx)
+    adj = g.adj_external(transpose=True, ctx=ctx)
 
     conv = nn.GraphConv(5, out_dim, norm="none", bias=True)
     conv = conv.to(ctx)
@@ -220,7 +220,7 @@ def test_tagconv(out_dim):
     g = dgl.DGLGraph(nx.path_graph(3))
     g = g.to(F.ctx())
     ctx = F.ctx()
-    adj = g.adjacency_matrix(transpose=True, ctx=ctx)
+    adj = g.adj_external(transpose=True, ctx=ctx)
     norm = th.pow(g.in_degrees().float(), -0.5)
 
     conv = nn.TAGConv(5, out_dim, bias=True)
@@ -1140,7 +1140,7 @@ def test_dense_graph_conv(norm_type, g, idtype, out_dim):
     g = g.astype(idtype).to(F.ctx())
     ctx = F.ctx()
     # TODO(minjie): enable the following option after #1385
-    adj = g.adjacency_matrix(transpose=True, ctx=ctx).to_dense()
+    adj = g.adj_external(transpose=True, ctx=ctx).to_dense()
     conv = nn.GraphConv(5, out_dim, norm=norm_type, bias=True)
     dense_conv = nn.DenseGraphConv(5, out_dim, norm=norm_type, bias=True)
     dense_conv.weight.data = conv.weight.data
@@ -1159,7 +1159,7 @@ def test_dense_graph_conv(norm_type, g, idtype, out_dim):
 def test_dense_sage_conv(g, idtype, out_dim):
     g = g.astype(idtype).to(F.ctx())
     ctx = F.ctx()
-    adj = g.adjacency_matrix(transpose=True, ctx=ctx).to_dense()
+    adj = g.adj_external(transpose=True, ctx=ctx).to_dense()
     sage = nn.SAGEConv(5, out_dim, "gcn")
     dense_sage = nn.DenseSAGEConv(5, out_dim)
     dense_sage.fc.weight.data = sage.fc_neigh.weight.data
@@ -1258,7 +1258,7 @@ def test_dense_cheb_conv(out_dim):
         ctx = F.ctx()
         g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
         g = g.to(F.ctx())
-        adj = g.adjacency_matrix(transpose=True, ctx=ctx).to_dense()
+        adj = g.adj_external(transpose=True, ctx=ctx).to_dense()
         cheb = nn.ChebConv(5, out_dim, k, None)
         dense_cheb = nn.DenseChebConv(5, out_dim, k)
         # for i in range(len(cheb.fc)):

tests/python/tensorflow/test_nn.py

@@ -31,7 +31,7 @@ def test_graph_conv(out_dim):
     g = dgl.DGLGraph(nx.path_graph(3)).to(F.ctx())
     ctx = F.ctx()
     adj = tf.sparse.to_dense(
-        tf.sparse.reorder(g.adjacency_matrix(transpose=True, ctx=ctx))
+        tf.sparse.reorder(g.adj_external(transpose=True, ctx=ctx))
     )
 
     conv = nn.GraphConv(5, out_dim, norm="none", bias=True)
@@ -610,7 +610,7 @@ def test_dense_cheb_conv(out_dim):
         g = g.to(ctx)
 
         adj = tf.sparse.to_dense(
-            tf.sparse.reorder(g.adjacency_matrix(transpose=True, ctx=ctx))
+            tf.sparse.reorder(g.adj_external(transpose=True, ctx=ctx))
         )
         cheb = nn.ChebConv(5, out_dim, k, None, bias=True)
         dense_cheb = nn.DenseChebConv(5, out_dim, k, bias=True)