Removing warnings appearing in heterograph tests (#6095)

Co-authored-by: Xin Yao <xiny@nvidia.com>

tests/python/common/test_heterograph-kernel.py

@@ -89,7 +89,7 @@ def generate_feature(g, broadcast="none", binary_op="none"):
 
 def test_copy_src_reduce():
     def _test(red, partial):
-        g = dgl.DGLGraph(nx.erdos_renyi_graph(100, 0.1))
+        g = dgl.from_networkx(nx.erdos_renyi_graph(100, 0.1))
         # NOTE(zihao): add self-loop to avoid zero-degree nodes.
         # https://github.com/dmlc/dgl/issues/761
         g.add_edges(g.nodes(), g.nodes())
@@ -157,7 +157,7 @@ def test_copy_src_reduce():
 
 def test_copy_edge_reduce():
     def _test(red, partial):
-        g = dgl.DGLGraph(nx.erdos_renyi_graph(100, 0.1))
+        g = dgl.from_networkx(nx.erdos_renyi_graph(100, 0.1))
         # NOTE(zihao): add self-loop to avoid zero-degree nodes.
         g.add_edges(g.nodes(), g.nodes())
         g = g.to(F.ctx())
@@ -360,7 +360,7 @@ def test_all_binary_builtins():
             _print_error(rhs_grad_1, rhs_grad_2)
         assert F.allclose(rhs_grad_1, rhs_grad_2, rtol, atol)
 
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g.add_nodes(20)
     # NOTE(zihao): add self-loop to avoid zero-degree nodes.
     g.add_edges(g.nodes(), g.nodes())

tests/python/common/test_heterograph-misc.py

@@ -54,17 +54,13 @@ def scipy_csr_input():
 
 
 def gen_by_mutation():
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     src, dst = edge_pair_input()
     g.add_nodes(10)
     g.add_edges(src, dst)
     return g
 
 
-def gen_from_data(data, readonly, sort):
-    return dgl.DGLGraph(data, readonly=readonly, sort_csr=True)
-
-
 def test_query():
     def _test_one(g):
         assert g.num_nodes() == 10
@@ -275,19 +271,14 @@ def test_query():
         eid = g.edge_ids(0, 4)
 
     _test(gen_by_mutation())
-    _test(gen_from_data(elist_input(), False, False))
-    _test(gen_from_data(elist_input(), True, False))
-    _test(gen_from_data(elist_input(), True, True))
-    _test(gen_from_data(scipy_coo_input(), False, False))
-    _test(gen_from_data(scipy_coo_input(), True, False))
-
-    _test_csr(gen_from_data(scipy_csr_input(), False, False))
-    _test_csr(gen_from_data(scipy_csr_input(), True, False))
+    _test(dgl.graph(elist_input()))
+    _test(dgl.from_scipy(scipy_coo_input()))
+    _test_csr(dgl.from_scipy(scipy_csr_input()))
     _test_edge_ids()
 
 
 def test_mutation():
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g = g.to(F.ctx())
     # test add nodes with data
     g.add_nodes(5)
@@ -304,7 +295,7 @@ def test_mutation():
 
 
 def test_scipy_adjmat():
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g.add_nodes(10)
     g.add_edges(range(9), range(1, 10))
 
@@ -318,7 +309,7 @@ def test_scipy_adjmat():
 
 
 def test_incmat():
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g.add_nodes(4)
     g.add_edges(0, 1)  # 0
     g.add_edges(0, 2)  # 1
@@ -367,7 +358,7 @@ def test_incmat():
 
 
 def test_find_edges():
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g.add_nodes(10)
     g.add_edges(range(9), range(1, 10))
     e = g.find_edges([1, 3, 2, 4])
@@ -394,7 +385,7 @@ def test_find_edges():
 
 
 def test_ismultigraph():
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g.add_nodes(10)
     assert g.is_multigraph == False
     g.add_edges([0], [0])
@@ -406,7 +397,7 @@ def test_ismultigraph():
 
 
 def test_hypersparse_query():
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g = g.to(F.ctx())
     g.add_nodes(1000001)
     g.add_edges([0], [1])
@@ -425,7 +416,7 @@ def test_hypersparse_query():
 
 
 def test_empty_data_initialized():
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g = g.to(F.ctx())
     g.ndata["ha"] = F.tensor([])
     g.add_nodes(1, {"hb": F.tensor([1])})
@@ -460,7 +451,7 @@ def test_is_sorted():
 
 
 def test_default_types():
-    dg = dgl.DGLGraph()
+    dg = dgl.graph([])
     g = dgl.graph(([], []))
     assert dg.ntypes == g.ntypes
     assert dg.etypes == g.etypes

tests/python/common/test_heterograph-remove.py

@@ -5,11 +5,16 @@ import numpy as np
 from utils import parametrize_idtype
 
 
+def create_graph(idtype, num_node):
+    g = dgl.graph([])
+    g = g.astype(idtype).to(F.ctx())
+    g.add_nodes(num_node)
+    return g
+
+
 @parametrize_idtype
 def test_node_removal(idtype):
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(10)
+    g = create_graph(idtype, 10)
     g.add_edges(0, 0)
     assert g.num_nodes() == 10
     g.ndata["id"] = F.arange(0, 10)
@@ -38,9 +43,7 @@ def test_node_removal(idtype):
 
 @parametrize_idtype
 def test_multigraph_node_removal(idtype):
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(5)
+    g = create_graph(idtype, 5)
     for i in range(5):
         g.add_edges(i, i)
         g.add_edges(i, i)
@@ -67,9 +70,7 @@ def test_multigraph_node_removal(idtype):
 
 @parametrize_idtype
 def test_multigraph_edge_removal(idtype):
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(5)
+    g = create_graph(idtype, 5)
     for i in range(5):
         g.add_edges(i, i)
         g.add_edges(i, i)
@@ -95,9 +96,7 @@ def test_multigraph_edge_removal(idtype):
 
 @parametrize_idtype
 def test_edge_removal(idtype):
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(5)
+    g = create_graph(idtype, 5)
     for i in range(5):
         for j in range(5):
             g.add_edges(i, j)
@@ -133,9 +132,7 @@ def test_edge_removal(idtype):
 
 @parametrize_idtype
 def test_node_and_edge_removal(idtype):
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(10)
+    g = create_graph(idtype, 10)
     for i in range(10):
         for j in range(10):
             g.add_edges(i, j)
@@ -173,9 +170,7 @@ def test_node_and_edge_removal(idtype):
 
 @parametrize_idtype
 def test_node_frame(idtype):
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(10)
+    g = create_graph(idtype, 10)
     data = np.random.rand(10, 3)
     new_data = data.take([0, 1, 2, 7, 8, 9], axis=0)
     g.ndata["h"] = F.tensor(data)
@@ -187,9 +182,7 @@ def test_node_frame(idtype):
 
 @parametrize_idtype
 def test_edge_frame(idtype):
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(10)
+    g = create_graph(idtype, 10)
     g.add_edges(list(range(10)), list(range(1, 10)) + [0])
     data = np.random.rand(10, 3)
     new_data = data.take([0, 1, 2, 7, 8, 9], axis=0)
@@ -204,18 +197,14 @@ def test_edge_frame(idtype):
 def test_issue1287(idtype):
     # reproduce https://github.com/dmlc/dgl/issues/1287.
     # setting features after remove nodes
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(5)
+    g = create_graph(idtype, 5)
     g.add_edges([0, 2, 3, 1, 1], [1, 0, 3, 1, 0])
     g.remove_nodes([0, 1])
     g.ndata["h"] = F.randn((g.num_nodes(), 3))
     g.edata["h"] = F.randn((g.num_edges(), 2))
 
     # remove edges
-    g = dgl.DGLGraph()
-    g = g.astype(idtype).to(F.ctx())
-    g.add_nodes(5)
+    g = create_graph(idtype, 5)
     g.add_edges([0, 2, 3, 1, 1], [1, 0, 3, 1, 0])
     g.remove_edges([0, 1])
     g = g.to(F.ctx())

tests/python/common/test_heterograph-specialization.py

@@ -10,7 +10,7 @@ D = 5
 
 
 def generate_graph(idtype):
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g = g.astype(idtype).to(F.ctx())
     g.add_nodes(10)
     # create a graph where 0 is the source and 9 is the sink
@@ -183,7 +183,7 @@ def test_v2v_pull(idtype):
 @parametrize_idtype
 def test_update_all_multi_fallback(idtype):
     # create a graph with zero in degree nodes
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g = g.astype(idtype).to(F.ctx())
     g.add_nodes(10)
     for i in range(1, 9):
@@ -237,7 +237,7 @@ def test_update_all_multi_fallback(idtype):
 @parametrize_idtype
 def test_pull_multi_fallback(idtype):
     # create a graph with zero in degree nodes
-    g = dgl.DGLGraph()
+    g = dgl.graph([])
     g = g.astype(idtype).to(F.ctx())
     g.add_nodes(10)
     for i in range(1, 9):
@@ -317,7 +317,7 @@ def test_spmv_3d_feat(idtype):
     n = 100
     p = 0.1
     a = sp.random(n, n, p, data_rvs=lambda n: np.ones(n))
-    g = dgl.DGLGraph(a)
+    g = dgl.from_scipy(a)
     g = g.astype(idtype).to(F.ctx())
     m = g.num_edges()
 

tests/python/pytorch/test_heterograph-pickle.py

@@ -20,7 +20,7 @@ def test_pickling_batched_graph():
     # NOTE: this is a test for a wierd bug mentioned in
     #   https://github.com/dmlc/dgl/issues/438
     glist = [nx.path_graph(i + 5) for i in range(5)]
-    glist = [dgl.DGLGraph(g) for g in glist]
+    glist = [dgl.from_networkx(g) for g in glist]
     bg = dgl.batch(glist)
     bg.ndata["x"] = torch.randn((35, 5))
     bg.edata["y"] = torch.randn((60, 3))