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import multiprocessing
import sys
from collections import namedtuple
import pytest
import torch
from torch_geometric import EdgeIndex, Index
from torch_geometric.data import Data, HeteroData, OnDiskDataset
from torch_geometric.loader import DataLoader
from torch_geometric.testing import (
get_random_edge_index,
get_random_tensor_frame,
onlyLinux,
withDevice,
withPackage,
)
with_mp = sys.platform not in ['win32']
num_workers_list = [0, 2] if with_mp else [0]
if sys.platform == 'darwin':
multiprocessing.set_start_method('spawn')
@withDevice
@pytest.mark.parametrize('num_workers', num_workers_list)
def test_dataloader(num_workers, device):
if num_workers > 0 and device != torch.device('cpu'):
return
x = torch.tensor([[1.0], [1.0], [1.0]])
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).to(device)
assert str(data) == ("Data(x=[3, 1], edge_index=[2, 4], y=2.0, z=0.0, "
"name='data')")
data.face = face
loader = DataLoader([data, data, data, data], batch_size=2, shuffle=False,
num_workers=num_workers)
assert len(loader) == 2
for batch in loader:
assert batch.x.device == device
assert batch.edge_index.device == device
assert batch.z.device == device
assert batch.num_graphs == len(batch) == 2
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]]
for store in batch.stores:
assert id(batch) == id(store._parent())
loader = DataLoader([data, data, data, data], batch_size=2, shuffle=False,
follow_batch=['edge_index'], num_workers=num_workers,
collate_fn=None)
assert len(loader) == 2
for batch in loader:
assert batch.num_graphs == len(batch) == 2
assert batch.edge_index_batch.tolist() == [0, 0, 0, 0, 1, 1, 1, 1]
@onlyLinux
@pytest.mark.parametrize('num_workers', num_workers_list)
def test_dataloader_on_disk_dataset(tmp_path, num_workers):
dataset = OnDiskDataset(tmp_path)
data1 = Data(x=torch.randn(3, 8))
data2 = Data(x=torch.randn(4, 8))
dataset.extend([data1, data2])
loader = DataLoader(dataset, batch_size=2, num_workers=num_workers)
assert len(loader) == 1
batch = next(iter(loader))
assert batch.num_nodes == 7
assert torch.equal(batch.x, torch.cat([data1.x, data2.x], dim=0))
assert batch.batch.tolist() == [0, 0, 0, 1, 1, 1, 1]
dataset.close()
def test_dataloader_fallbacks():
# Test inputs of type List[torch.Tensor]:
data_list = [torch.ones(3) for _ in range(4)]
batch = next(iter(DataLoader(data_list, batch_size=4)))
assert torch.equal(batch, torch.ones(4, 3))
# Test inputs of type List[float]:
data_list = [1.0, 1.0, 1.0, 1.0]
batch = next(iter(DataLoader(data_list, batch_size=4)))
assert torch.equal(batch, torch.ones(4))
# Test inputs of type List[int]:
data_list = [1, 1, 1, 1]
batch = next(iter(DataLoader(data_list, batch_size=4)))
assert torch.equal(batch, torch.ones(4, dtype=torch.long))
# Test inputs of type List[str]:
data_list = ['test'] * 4
batch = next(iter(DataLoader(data_list, batch_size=4)))
assert batch == data_list
# Test inputs of type List[Mapping]:
data_list = [{'x': torch.ones(3), 'y': 1}] * 4
batch = next(iter(DataLoader(data_list, batch_size=4)))
assert torch.equal(batch['x'], torch.ones(4, 3))
assert torch.equal(batch['y'], torch.ones(4, dtype=torch.long))
# Test inputs of type List[Tuple]:
DataTuple = namedtuple('DataTuple', 'x y')
data_list = [DataTuple(0.0, 1)] * 4
batch = next(iter(DataLoader(data_list, batch_size=4)))
assert torch.equal(batch.x, torch.zeros(4))
assert torch.equal(batch[1], torch.ones(4, dtype=torch.long))
# Test inputs of type List[Sequence]:
data_list = [[0.0, 1]] * 4
batch = next(iter(DataLoader(data_list, batch_size=4)))
assert torch.equal(batch[0], torch.zeros(4))
assert torch.equal(batch[1], torch.ones(4, dtype=torch.long))
# Test that inputs of unsupported types raise an error:
class DummyClass:
pass
with pytest.raises(TypeError):
data_list = [DummyClass()] * 4
next(iter(DataLoader(data_list, batch_size=4)))
@pytest.mark.skipif(not with_mp, reason='Multi-processing not available')
def test_multiprocessing():
queue = torch.multiprocessing.Manager().Queue()
data = Data(x=torch.randn(5, 16))
data_list = [data, data, data, data]
loader = DataLoader(data_list, batch_size=2)
for batch in loader:
queue.put(batch)
batch = queue.get()
assert batch.num_graphs == len(batch) == 2
batch = queue.get()
assert batch.num_graphs == len(batch) == 2
def test_pin_memory():
x = torch.randn(3, 16)
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
data = Data(x=x, edge_index=edge_index)
loader = DataLoader([data] * 4, batch_size=2, pin_memory=True)
for batch in loader:
assert batch.x.is_pinned() or not torch.cuda.is_available()
assert batch.edge_index.is_pinned() or not torch.cuda.is_available()
@pytest.mark.parametrize('num_workers', num_workers_list)
def test_heterogeneous_dataloader(num_workers):
data = HeteroData()
data['p'].x = torch.randn(100, 128)
data['a'].x = torch.randn(200, 128)
data['p', 'a'].edge_index = get_random_edge_index(100, 200, 500)
data['p'].edge_attr = torch.randn(500, 32)
data['a', 'p'].edge_index = get_random_edge_index(200, 100, 400)
data['a', 'p'].edge_attr = torch.randn(400, 32)
loader = DataLoader([data, data, data, data], batch_size=2, shuffle=False,
num_workers=num_workers)
assert len(loader) == 2
for batch in loader:
assert batch.num_graphs == len(batch) == 2
assert batch.num_nodes == 600
for store in batch.stores:
assert id(batch) == id(store._parent())
@pytest.mark.parametrize('num_workers', num_workers_list)
def test_index_dataloader(num_workers):
index1 = Index([0, 1, 1, 2], dim_size=3, is_sorted=True)
index2 = Index([0, 1, 1, 2, 2, 3], dim_size=4, is_sorted=True)
data1 = Data(index=index1, num_nodes=3)
data2 = Data(index=index2, num_nodes=4)
loader = DataLoader(
[data1, data2, data1, data2],
batch_size=2,
num_workers=num_workers,
)
assert len(loader) == 2
for batch in loader:
assert isinstance(batch.index, Index)
assert batch.index.dtype == torch.long
assert batch.index.dim_size == 7
assert batch.index.is_sorted
@pytest.mark.parametrize('num_workers', num_workers_list)
@pytest.mark.parametrize('sort_order', [None, 'row', 'col'])
def test_edge_index_dataloader(num_workers, sort_order):
if sort_order == 'col':
edge_index = [[1, 0, 2, 1], [0, 1, 1, 2]]
else:
edge_index = [[0, 1, 1, 2], [1, 0, 2, 1]]
edge_index = EdgeIndex(
edge_index,
sparse_size=(3, 3),
sort_order=sort_order,
is_undirected=True,
)
data = Data(edge_index=edge_index)
assert data.num_nodes == 3
loader = DataLoader(
[data, data, data, data],
batch_size=2,
num_workers=num_workers,
)
assert len(loader) == 2
for batch in loader:
assert isinstance(batch.edge_index, EdgeIndex)
assert batch.edge_index.dtype == torch.long
assert batch.edge_index.sparse_size() == (6, 6)
assert batch.edge_index.sort_order == sort_order
assert batch.edge_index.is_undirected
@withPackage('torch_frame')
def test_dataloader_tensor_frame():
tf = get_random_tensor_frame(num_rows=10)
loader = DataLoader([tf, tf, tf, tf], batch_size=2, shuffle=False)
assert len(loader) == 2
for batch in loader:
assert batch.num_rows == 20
data = Data(tf=tf, edge_index=get_random_edge_index(10, 10, 20))
loader = DataLoader([data, data, data, data], batch_size=2, shuffle=False)
assert len(loader) == 2
for batch in loader:
assert batch.num_graphs == len(batch) == 2
assert batch.num_nodes == 20
assert batch.tf.num_rows == 20
assert batch.edge_index.max() >= 10
def test_dataloader_sparse():
adj_t = torch.sparse_coo_tensor(
indices=torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]]),
values=torch.randn(4),
size=(3, 3),
)
data = Data(adj_t=adj_t)
loader = DataLoader([data, data], batch_size=2)
for batch in loader:
assert batch.adj_t.size() == (6, 6)
if __name__ == '__main__':
import argparse
import time
from torch_geometric.datasets import QM9
parser = argparse.ArgumentParser()
parser.add_argument('--num_workers', type=int, default=0)
args = parser.parse_args()
kwargs = dict(batch_size=128, shuffle=True, num_workers=args.num_workers)
in_memory_dataset = QM9('/tmp/QM9')
loader = DataLoader(in_memory_dataset, **kwargs)
print('In-Memory Dataset:')
for _ in range(2):
print(f'Start loading {len(loader)} mini-batches ... ', end='')
t = time.perf_counter()
for batch in loader:
pass
print(f'Done! [{time.perf_counter() - t:.4f}s]')
on_disk_dataset = in_memory_dataset.to_on_disk_dataset()
loader = DataLoader(on_disk_dataset, **kwargs)
print('On-Disk Dataset:')
for _ in range(2):
print(f'Start loading {len(loader)} mini-batches ... ', end='')
t = time.perf_counter()
for batch in loader:
pass
print(f'Done! [{time.perf_counter() - t:.4f}s]')
on_disk_dataset.close()
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