def test_dataloader():
x = torch.Tensor([[1], [1], [1]])
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
face = torch.tensor([[0], [1], [2]])
y = 2.
z = torch.tensor(0.)
name = 'data'
data = Data(x=x, edge_index=edge_index, y=y, z=z, name=name)
assert data.__repr__() == (
'Data(edge_index=[2, 4], name="data", x=[3, 1], y=2.0, z=0.0)')
data.face = face
loader = DataLoader([data, data], batch_size=2, shuffle=False)
for batch in loader:
assert len(batch) == 8
assert batch.batch.tolist() == [0, 0, 0, 1, 1, 1]
assert batch.ptr.tolist() == [0, 3, 6]
assert batch.x.tolist() == [[1], [1], [1], [1], [1], [1]]
assert batch.edge_index.tolist() == [[0, 1, 1, 2, 3, 4, 4, 5],
[1, 0, 2, 1, 4, 3, 5, 4]]
assert batch.y.tolist() == [2.0, 2.0]
assert batch.z.tolist() == [0.0, 0.0]
assert batch.name == ['data', 'data']
assert batch.face.tolist() == [[0, 3], [1, 4], [2, 5]]
loader = DataLoader([data, data],
batch_size=2,
shuffle=False,
follow_batch=['edge_index'])
for batch in loader:
assert len(batch) == 9
assert batch.edge_index_batch.tolist() == [0, 0, 0, 0, 1, 1, 1, 1]
def test_dataloader():
x = torch.Tensor([[1], [1], [1]])
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
face = torch.tensor([[0], [1], [2]])
data = Data(x=x, edge_index=edge_index, y=2)
assert data.__repr__() == 'Data(edge_index=[2, 4], x=[3, 1], y=2)'
data.face = face
loader = DataLoader([data, data], batch_size=2, shuffle=False)
for batch in loader:
assert len(batch) == 5
assert batch.batch.tolist() == [0, 0, 0, 1, 1, 1]
assert batch.x.tolist() == [[1], [1], [1], [1], [1], [1]]
assert batch.edge_index.tolist() == [[0, 1, 1, 2, 3, 4, 4, 5],
[1, 0, 2, 1, 4, 3, 5, 4]]
assert batch.y.tolist() == [2, 2]
assert batch.face.tolist() == [[0, 3], [1, 4], [2, 5]]
loader = DataLoader([data, data],
batch_size=2,
shuffle=False,
follow_batch=['edge_index'])
for batch in loader:
assert len(batch) == 6
assert batch.edge_index_batch.tolist() == [0, 0, 0, 0, 1, 1, 1, 1]
def test_data():
x = torch.tensor([[1, 3, 5], [2, 4, 6]], dtype=torch.float).t()
edge_index = torch.tensor([[0, 0, 1, 1, 2], [1, 1, 0, 2, 1]])
data = Data(x=x, edge_index=edge_index).to(torch.device('cpu'))
N = data.num_nodes
assert data.x.tolist() == x.tolist()
assert data['x'].tolist() == x.tolist()
assert sorted(data.keys) == ['edge_index', 'x']
assert len(data) == 2
assert 'x' in data and 'edge_index' in data and 'pos' not in data
assert data.__cat_dim__('x', data.x) == 0
assert data.__cat_dim__('edge_index', data.edge_index) == -1
assert data.__cumsum__('x', data.x) is False
assert data.__cumsum__('edge_index', data.edge_index) is True
assert not data.x.is_contiguous()
data.contiguous()
assert data.x.is_contiguous()
assert not data.is_coalesced()
data.edge_index, _ = coalesce(data.edge_index, None, N, N)
assert data.is_coalesced()
clone = data.clone()
assert clone != data
assert len(clone) == len(data)
assert clone.x.tolist() == data.x.tolist()
assert clone.edge_index.tolist() == data.edge_index.tolist()
data['x'] = x + 1
assert data.x.tolist() == (x + 1).tolist()
assert data.__repr__() == 'Data(edge_index=[2, 4], x=[3, 2])'
dictionary = {'x': data.x, 'edge_index': data.edge_index}
data = Data.from_dict(dictionary)
assert sorted(data.keys) == ['edge_index', 'x']
assert not data.contains_isolated_nodes()
assert not data.contains_self_loops()
assert data.is_undirected()
assert not data.is_directed()
assert data.num_nodes == 3
assert data.num_edges == 4
assert data.num_features == 2
data.x = None
assert data.num_nodes == 3
data.edge_index = None
assert data.num_nodes is None
assert data.num_edges is None
def test_geometric(self):
x = torch.tensor([[1, 3, 5], [2, 4, 6]],
dtype=torch.float).t().to(device)
edge_index = torch.tensor([[0, 0, 1, 1, 2], [1, 1, 0, 2,
1]]).to(device)
data = Data(x=x, edge_index=edge_index).to(torch.device('cpu'))
N = data.num_nodes
self.assertCountEqual(data.x.tolist(), x.tolist())
self.assertCountEqual(data['x'].tolist(), x.tolist())
self.assertEqual(['edge_index', 'x'], sorted(data.keys))
self.assertEqual(2, len(data))
self.assertIn('x', data)
self.assertIn('edge_index', data)
self.assertNotIn('pos', data)
self.assertEqual(0, data.__cat_dim__('x', data.x))
self.assertEqual(-1, data.__cat_dim__('edge_index', data.edge_index))
self.assertEqual(0, data.__inc__('x', data.x))
self.assertEqual(data.num_nodes,
data.__inc__('edge_index', data.edge_index))
data.contiguous()
self.assertTrue(data.x.is_contiguous())
data.edge_index, _ = coalesce(data.edge_index, None, N, N)
data = data.coalesce()
self.assertTrue(data.is_coalesced())
clone = data.clone()
self.assertNotEqual(clone, data)
self.assertEqual(len(clone), len(data))
self.assertCountEqual(clone.x.tolist(), data.x.tolist())
self.assertCountEqual(clone.edge_index.tolist(),
data.edge_index.tolist())
data['x'] = x + 1
self.assertCountEqual(data.x.tolist(), (x + 1).tolist())
self.assertEqual('Data(edge_index=[2, 4], x=[3, 2])', data.__repr__())
dictionary = {'x': data.x, 'edge_index': data.edge_index}
data = Data.from_dict(dictionary)
self.assertEqual(['edge_index', 'x'], sorted(data.keys))
self.assertTrue(data.is_undirected())
self.assertEqual(3, data.num_nodes)
self.assertEqual(4, data.num_edges)
self.assertIsNone(data.num_faces)
self.assertEqual(2, data.num_node_features)
self.assertEqual(2, data.num_features)
def test_geometric(self):
x = torch.tensor([[1, 3, 5], [2, 4, 6]], dtype=torch.float).t()
edge_index = torch.tensor([[0, 0, 1, 1, 2], [1, 1, 0, 2, 1]])
data = Data(x=x, edge_index=edge_index).to(torch.device('cpu'))
N = data.num_nodes
self.assertTrue(data.x.tolist() == x.tolist())
self.assertTrue(data['x'].tolist() == x.tolist())
self.assertTrue(sorted(data.keys) == ['edge_index', 'x'])
self.assertTrue(len(data) == 2)
self.assertTrue('x' in data and 'edge_index' in data and 'pos' not in data)
self.assertTrue(data.__cat_dim__('x', data.x) == 0)
self.assertTrue(data.__cat_dim__('edge_index', data.edge_index) == -1)
self.assertTrue(data.__inc__('x', data.x) == 0)
self.assertTrue(data.__inc__('edge_index', data.edge_index) == data.num_nodes)
data.contiguous()
self.assertTrue(data.x.is_contiguous())
data.edge_index, _ = coalesce(data.edge_index, None, N, N)
data = data.coalesce()
self.assertTrue(data.is_coalesced())
clone = data.clone()
self.assertTrue(clone != data)
self.assertTrue(len(clone) == len(data))
self.assertTrue(clone.x.tolist() == data.x.tolist())
self.assertTrue(clone.edge_index.tolist() == data.edge_index.tolist())
data['x'] = x + 1
self.assertTrue(data.x.tolist() == (x + 1).tolist())
self.assertTrue(data.__repr__() == 'Data(edge_index=[2, 4], x=[3, 2])')
dictionary = {'x': data.x, 'edge_index': data.edge_index}
data = Data.from_dict(dictionary)
self.assertTrue(sorted(data.keys) == ['edge_index', 'x'])
self.assertTrue(data.is_undirected())
self.assertTrue(data.num_nodes == 3)
self.assertTrue(data.num_edges == 4)
self.assertTrue(data.num_faces is None)
self.assertTrue(data.num_node_features == 2)
self.assertTrue(data.num_features == 2)
def test_data():
x = torch.tensor([[1, 3, 5], [2, 4, 6]], dtype=torch.float).t()
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
data = Data(x=x, edge_index=edge_index)
assert data.x.tolist() == x.tolist()
assert data['x'].tolist() == x.tolist()
assert sorted(data.keys) == ['edge_index', 'x']
assert len(data) == 2
assert 'x' in data and 'edge_index' in data and 'pos' not in data
assert data.cat_dim('x', data.x) == 0
assert data.cat_dim('edge_index', data.edge_index) == -1
data.to(torch.device('cpu'))
assert not data.x.is_contiguous()
data.contiguous()
assert data.x.is_contiguous()
data['x'] = x + 1
assert data.x.tolist() == (x + 1).tolist()
assert data.__repr__() == 'Data(edge_index=[2, 4], x=[3, 2])'
dictionary = {'x': x, 'edge_index': edge_index}
data = Data.from_dict(dictionary)
assert sorted(data.keys) == ['edge_index', 'x']
assert not data.contains_isolated_nodes()
assert not data.contains_self_loops()
assert data.is_undirected()
assert not data.is_directed()
assert data.num_nodes == 3
assert data.num_edges == 4
data.x = None
assert data.num_nodes == 3
data.edge_index = None
assert data.num_nodes is None
assert data.num_edges is None
def test_data():
torch_geometric.set_debug(True)
x = torch.tensor([[1, 3, 5], [2, 4, 6]], dtype=torch.float).t()
edge_index = torch.tensor([[0, 0, 1, 1, 2], [1, 1, 0, 2, 1]])
data = Data(x=x, edge_index=edge_index).to(torch.device('cpu'))
N = data.num_nodes
assert data.x.tolist() == x.tolist()
assert data['x'].tolist() == x.tolist()
assert sorted(data.keys) == ['edge_index', 'x']
assert len(data) == 2
assert 'x' in data and 'edge_index' in data and 'pos' not in data
assert data.__cat_dim__('x', data.x) == 0
assert data.__cat_dim__('edge_index', data.edge_index) == -1
assert data.__inc__('x', data.x) == 0
assert data.__inc__('edge_index', data.edge_index) == data.num_nodes
assert not data.x.is_contiguous()
data.contiguous()
assert data.x.is_contiguous()
assert not data.is_coalesced()
data.edge_index, _ = coalesce(data.edge_index, None, N, N)
data = data.coalesce()
assert data.is_coalesced()
clone = data.clone()
assert clone != data
assert len(clone) == len(data)
assert clone.x.tolist() == data.x.tolist()
assert clone.edge_index.tolist() == data.edge_index.tolist()
data['x'] = x + 1
assert data.x.tolist() == (x + 1).tolist()
assert data.__repr__() == 'Data(edge_index=[2, 4], x=[3, 2])'
dictionary = {'x': data.x, 'edge_index': data.edge_index}
data = Data.from_dict(dictionary)
assert sorted(data.keys) == ['edge_index', 'x']
assert not data.contains_isolated_nodes()
assert not data.contains_self_loops()
assert data.is_undirected()
assert not data.is_directed()
assert data.num_nodes == 3
assert data.num_edges == 4
assert data.num_faces is None
assert data.num_node_features == 2
assert data.num_features == 2
data.edge_attr = torch.randn(data.num_edges, 2)
assert data.num_edge_features == 2
data.edge_attr = None
data.x = None
assert data.num_nodes == 3
data.edge_index = None
assert data.num_nodes is None
assert data.num_edges is None
data.num_nodes = 4
assert data.num_nodes == 4
data = Data(x=x, attribute=x)
assert len(data) == 2
assert data.x.tolist() == x.tolist()
assert data.attribute.tolist() == x.tolist()
face = torch.tensor([[0, 1], [1, 2], [2, 3]])
data = Data(num_nodes=4, face=face)
assert data.num_faces == 2
assert data.num_nodes == 4
data = Data(title="test")
assert data.__repr__() == 'Data(title="test")'
assert data.num_node_features == 0
assert data.num_edge_features == 0
torch_geometric.set_debug(False)
