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import os.path as osp
from typing import Any, Dict
import torch
from torch_geometric.data import Data, OnDiskDataset
from torch_geometric.testing import withPackage
@withPackage('sqlite3')
def test_pickle(tmp_path):
dataset = OnDiskDataset(tmp_path)
assert len(dataset) == 0
assert str(dataset) == 'OnDiskDataset(0)'
assert osp.exists(osp.join(tmp_path, 'processed', 'sqlite.db'))
data_list = [
Data(
x=torch.randn(5, 8),
edge_index=torch.randint(0, 5, (2, 16)),
num_nodes=5,
) for _ in range(4)
]
dataset.append(data_list[0])
assert len(dataset) == 1
dataset.extend(data_list[1:])
assert len(dataset) == 4
out = dataset.get(0)
assert torch.equal(out.x, data_list[0].x)
assert torch.equal(out.edge_index, data_list[0].edge_index)
assert out.num_nodes == data_list[0].num_nodes
out_list = dataset.multi_get([1, 2, 3])
for out, data in zip(out_list, data_list[1:]):
assert torch.equal(out.x, data.x)
assert torch.equal(out.edge_index, data.edge_index)
assert out.num_nodes == data.num_nodes
dataset.close()
# Test persistence of datasets:
dataset = OnDiskDataset(tmp_path)
assert len(dataset) == 4
out = dataset.get(0)
assert torch.equal(out.x, data_list[0].x)
assert torch.equal(out.edge_index, data_list[0].edge_index)
assert out.num_nodes == data_list[0].num_nodes
dataset.close()
@withPackage('sqlite3')
def test_custom_schema(tmp_path):
class CustomSchemaOnDiskDataset(OnDiskDataset):
def __init__(self, root: str):
schema = {
'x': dict(dtype=torch.float, size=(-1, 8)),
'edge_index': dict(dtype=torch.long, size=(2, -1)),
'num_nodes': int,
}
self.serialize_count = 0
self.deserialize_count = 0
super().__init__(root, schema=schema)
def serialize(self, data: Data) -> Dict[str, Any]:
self.serialize_count += 1
return data.to_dict()
def deserialize(self, mapping: Dict[str, Any]) -> Any:
self.deserialize_count += 1
return Data.from_dict(mapping)
dataset = CustomSchemaOnDiskDataset(tmp_path)
assert len(dataset) == 0
assert str(dataset) == 'CustomSchemaOnDiskDataset(0)'
assert osp.exists(osp.join(tmp_path, 'processed', 'sqlite.db'))
data_list = [
Data(
x=torch.randn(5, 8),
edge_index=torch.randint(0, 5, (2, 16)),
num_nodes=5,
) for _ in range(4)
]
dataset.append(data_list[0])
assert dataset.serialize_count == 1
assert len(dataset) == 1
dataset.extend(data_list[1:])
assert dataset.serialize_count == 4
assert len(dataset) == 4
out = dataset.get(0)
assert dataset.deserialize_count == 1
assert torch.equal(out.x, data_list[0].x)
assert torch.equal(out.edge_index, data_list[0].edge_index)
assert out.num_nodes == data_list[0].num_nodes
out_list = dataset.multi_get([1, 2, 3])
assert dataset.deserialize_count == 4
for out, data in zip(out_list, data_list[1:]):
assert torch.equal(out.x, data.x)
assert torch.equal(out.edge_index, data.edge_index)
assert out.num_nodes == data.num_nodes
dataset.close()
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