changelog

Author: rusty1s

test/nn/conv/test_point_conv.py

@@ -15,7 +15,7 @@ def test_point_net_conv():
     edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
     row, col = edge_index
     adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
 
     local_nn = Seq(Lin(16 + 3, 32), ReLU(), Lin(32, 32))
     global_nn = Seq(Lin(32, 32))
@@ -43,7 +43,7 @@ def test_point_net_conv():
         assert torch.allclose(jit(x1, pos1, adj1.t()), out, atol=1e-6)
 
     adj1 = adj1.sparse_resize((4, 2))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
     out = conv(x1, (pos1, pos2), edge_index)
     assert out.size() == (2, 32)
     assert conv((x1, None), (pos1, pos2), edge_index).tolist() == out.tolist()

test/nn/conv/test_point_gnn_conv.py

@@ -5,13 +5,13 @@
 from torch_geometric.testing import is_full_test
 
 
-def test_pointgnn_conv():
+def test_point_gnn_conv():
     x = torch.randn(6, 8)
     pos = torch.randn(6, 3)
     edge_index = torch.tensor([[0, 1, 1, 1, 2, 5], [1, 2, 3, 4, 3, 4]])
     row, col = edge_index
     adj1 = SparseTensor(row=row, col=col, sparse_sizes=(6, 6))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
 
     conv = PointGNNConv(
         mlp_h=MLP([8, 16, 3]),

test/nn/conv/test_point_transformer_conv.py

@@ -14,7 +14,7 @@ def test_point_transformer_conv():
     edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
     row, col = edge_index
     adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
 
     conv = PointTransformerConv(in_channels=16, out_channels=32)
     assert str(conv) == 'PointTransformerConv(16, 32)'
@@ -44,7 +44,7 @@ def test_point_transformer_conv():
 
     conv = PointTransformerConv((16, 8), 32)
     adj1 = adj1.sparse_resize((4, 2))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
 
     out = conv((x1, x2), (pos1, pos2), edge_index)
     assert out.size() == (2, 32)

test/nn/conv/test_ppf_conv.py

@@ -18,7 +18,7 @@ def test_ppf_conv():
     edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
     row, col = edge_index
     adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
 
     local_nn = Seq(Lin(16 + 4, 32), ReLU(), Lin(32, 32))
     global_nn = Seq(Lin(32, 32))
@@ -46,7 +46,7 @@ def test_ppf_conv():
         assert torch.allclose(jit(x1, pos1, n1, adj1.t()), out, atol=1e-6)
 
     adj1 = adj1.sparse_resize((4, 2))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
     out = conv(x1, (pos1, pos2), (n1, n2), edge_index)
     assert out.size() == (2, 32)
     assert torch.allclose(conv((x1, None), (pos1, pos2), (n1, n2), edge_index),

test/nn/conv/test_res_gated_graph_conv.py

@@ -11,7 +11,7 @@ def test_res_gated_graph_conv():
     edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
     row, col = edge_index
     adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
 
     conv = ResGatedGraphConv(8, 32)
     assert str(conv) == 'ResGatedGraphConv(8, 32)'
@@ -30,7 +30,7 @@ def test_res_gated_graph_conv():
         assert torch.allclose(jit(x1, adj1.t()), out, atol=1e-6)
 
     adj1 = adj1.sparse_resize((4, 2))
-    adj2 = adj1.to_torch_sparse_coo_tensor()
+    adj2 = adj1.to_torch_sparse_csc_tensor()
     conv = ResGatedGraphConv((8, 32), 32)
     assert str(conv) == 'ResGatedGraphConv((8, 32), 32)'
     out = conv((x1, x2), edge_index)