Clean up warnings

setup.cfg

@@ -17,6 +17,7 @@ test=pytest
 
 [tool:pytest]
 addopts = --capture=no
+filterwarnings=ignore::DeprecationWarning:tensorboard.*
 
 [flake8]
 ignore=F811,W503,W504  # ignore overload redefinition, allow line breaks before/after binary operators

test/data/test_data.py

@@ -1,5 +1,6 @@
 import copy
 
+import pytest
 import torch
 import torch.multiprocessing as mp
 
@@ -80,7 +81,8 @@ def test_data():
 
     assert data.num_nodes == 3
     assert data.num_edges == 4
-    assert data.num_faces is None
+    with pytest.warns(UserWarning, match='deprecated'):
+        assert data.num_faces is None
     assert data.num_node_features == 2
     assert data.num_features == 2
 
@@ -89,10 +91,12 @@ def test_data():
     data.edge_attr = None
 
     data.x = None
-    assert data.num_nodes == 3
+    with pytest.warns(UserWarning, match='Unable to accurately infer'):
+        assert data.num_nodes == 3
 
     data.edge_index = None
-    assert data.num_nodes is None
+    with pytest.warns(UserWarning, match='Unable to accurately infer'):
+        assert data.num_nodes is None
     assert data.num_edges == 0
 
     data.num_nodes = 4
@@ -105,7 +109,8 @@ def test_data():
 
     face = torch.tensor([[0, 1], [1, 2], [2, 3]])
     data = Data(num_nodes=4, face=face)
-    assert data.num_faces == 2
+    with pytest.warns(UserWarning, match='deprecated'):
+        assert data.num_faces == 2
     assert data.num_nodes == 4
 
     data = Data(title='test')
@@ -181,11 +186,9 @@ def test_debug_data():
     torch_geometric.set_debug(True)
 
     Data()
-    Data(edge_index=torch.tensor([[0, 1], [1, 0]])).num_nodes
     Data(edge_index=torch.zeros((2, 0), dtype=torch.long), num_nodes=10)
     Data(face=torch.zeros((3, 0), dtype=torch.long), num_nodes=10)
     Data(edge_index=torch.tensor([[0, 1], [1, 0]]), edge_attr=torch.randn(2))
-    Data(face=torch.tensor([[0], [1], [2]])).num_nodes
     Data(x=torch.torch.randn(5, 3), num_nodes=5)
     Data(pos=torch.torch.randn(5, 3), num_nodes=5)
     Data(norm=torch.torch.randn(5, 3), num_nodes=5)

test/nn/conv/test_spline_conv.py

@@ -1,3 +1,5 @@
+import warnings
+
 import torch
 from torch_sparse import SparseTensor
 
@@ -7,6 +9,8 @@
 
 @withPackage('torch_spline_conv')
 def test_spline_conv():
+    warnings.filterwarnings('ignore', '.*non-optimized CPU version.*')
+
     x1 = torch.randn(4, 8)
     x2 = torch.randn(2, 16)
     edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
@@ -59,6 +63,8 @@ def test_spline_conv():
 
 @withPackage('torch_spline_conv')
 def test_lazy_spline_conv():
+    warnings.filterwarnings('ignore', '.*non-optimized CPU version.*')
+
     x1 = torch.randn(4, 8)
     x2 = torch.randn(2, 16)
     edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])

test/utils/test_train_test_split_edges.py

@@ -1,3 +1,4 @@
+import pytest
 import torch
 
 from torch_geometric.data import Data
@@ -10,7 +11,9 @@ def test_train_test_split_edges():
     edge_attr = torch.arange(edge_index.size(1))
     data = Data(edge_index=edge_index, edge_attr=edge_attr)
     data.num_nodes = edge_index.max().item() + 1
-    data = train_test_split_edges(data, val_ratio=0.2, test_ratio=0.3)
+
+    with pytest.warns(UserWarning, match='deprecated'):
+        data = train_test_split_edges(data, val_ratio=0.2, test_ratio=0.3)
 
     assert len(data) == 10
     assert data.val_pos_edge_index.size() == (2, 2)

torch_geometric/utils/random.py

@@ -1,3 +1,5 @@
+import warnings
+
 import numpy as np
 import torch
 
@@ -20,6 +22,7 @@ def erdos_renyi_graph(num_nodes, edge_prob, directed=False):
         idx = idx + torch.arange(1, num_nodes).view(-1, 1)
         idx = idx.view(-1)
     else:
+        warnings.filterwarnings('ignore', '.*pass the indexing argument.*')
         idx = torch.combinations(torch.arange(num_nodes), r=2)
 
     # Filter edges.