import copy
import re
from collections import defaultdict, namedtuple
from collections.abc import Mapping
from itertools import chain
from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
import torch
from torch import Tensor
from torch_sparse import SparseTensor
from torch_geometric.data.data import BaseData, Data, size_repr
from torch_geometric.data.storage import BaseStorage, EdgeStorage, NodeStorage
from torch_geometric.typing import EdgeType, NodeType, QueryType
NodeOrEdgeType = Union[NodeType, EdgeType]
NodeOrEdgeStorage = Union[NodeStorage, EdgeStorage]
[docs]class HeteroData(BaseData):
r"""A data object describing a heterogeneous graph, holding multiple node
and/or edge types in disjunct storage objects.
Storage objects can hold either node-level, link-level or graph-level
attributes.
In general, :class:`~torch_geometric.data.HeteroData` tries to mimic the
behaviour of a regular **nested** Python dictionary.
In addition, it provides useful functionality for analyzing graph
structures, and provides basic PyTorch tensor functionalities.
.. code-block::
from torch_geometric.data import HeteroData
data = HeteroData()
# Create two node types "paper" and "author" holding a feature matrix:
data['paper'].x = torch.randn(num_papers, num_paper_features)
data['author'].x = torch.randn(num_authors, num_authors_features)
# Create an edge type "(author, writes, paper)" and building the
# graph connectivity:
data['author', 'writes', 'paper'].edge_index = ... # [2, num_edges]
data['paper'].num_nodes
>>> 23
data['author', 'writes', 'paper'].num_edges
>>> 52
# PyTorch tensor functionality:
data = data.pin_memory()
data = data.to('cuda:0', non_blocking=True)
Note that there exists multiple ways to create a heterogeneous graph data,
*e.g.*:
* To initialize a node of type :obj:`"paper"` holding a node feature
matrix :obj:`x_paper` named :obj:`x`:
.. code-block:: python
from torch_geometric.data import HeteroData
data = HeteroData()
data['paper'].x = x_paper
data = HeteroData(paper={ 'x': x_paper })
data = HeteroData({'paper': { 'x': x_paper }})
* To initialize an edge from source node type :obj:`"author"` to
destination node type :obj:`"paper"` with relation type :obj:`"writes"`
holding a graph connectivity matrix :obj:`edge_index_author_paper` named
:obj:`edge_index`:
.. code-block:: python
data = HeteroData()
data['author', 'writes', 'paper'].edge_index = edge_index_author_paper
data = HeteroData(author__writes__paper={
'edge_index': edge_index_author_paper
})
data = HeteroData({
('author', 'writes', 'paper'):
{ 'edge_index': edge_index_author_paper }
})
"""
DEFAULT_REL = 'to'
def __init__(self, _mapping: Optional[Dict[str, Any]] = None, **kwargs):
self.__dict__['_global_store'] = BaseStorage(_parent=self)
self.__dict__['_node_store_dict'] = {}
self.__dict__['_edge_store_dict'] = {}
for key, value in chain((_mapping or {}).items(), kwargs.items()):
if '__' in key and isinstance(value, Mapping):
key = tuple(key.split('__'))
if isinstance(value, Mapping):
self[key].update(value)
else:
setattr(self, key, value)
def __getattr__(self, key: str) -> Any:
# `data.*_dict` => Link to node and edge stores.
# `data.*` => Link to the `_global_store`.
# Using `data.*_dict` is the same as using `collect()` for collecting
# nodes and edges features.
if hasattr(self._global_store, key):
return getattr(self._global_store, key)
elif bool(re.search('_dict$', key)):
return self.collect(key[:-5])
raise AttributeError(f"'{self.__class__.__name__}' has no "
f"attribute '{key}'")
def __setattr__(self, key: str, value: Any):
# NOTE: We aim to prevent duplicates in node or edge types.
if key in self.node_types:
raise AttributeError(f"'{key}' is already present as a node type")
elif key in self.edge_types:
raise AttributeError(f"'{key}' is already present as an edge type")
setattr(self._global_store, key, value)
def __delattr__(self, key: str):
delattr(self._global_store, key)
def __getitem__(self, *args: Tuple[QueryType]) -> Any:
# `data[*]` => Link to either `_global_store`, _node_store_dict` or
# `_edge_store_dict`.
# If neither is present, we create a new `Storage` object for the given
# node/edge-type.
key = self._to_canonical(*args)
out = self._global_store.get(key, None)
if out is not None:
return out
if isinstance(key, tuple):
return self.get_edge_store(*key)
else:
return self.get_node_store(key)
def __setitem__(self, key: str, value: Any):
if key in self.node_types:
raise AttributeError(f"'{key}' is already present as a node type")
elif key in self.edge_types:
raise AttributeError(f"'{key}' is already present as an edge type")
self._global_store[key] = value
def __delitem__(self, *args: Tuple[QueryType]):
# `del data[*]` => Link to `_node_store_dict` or `_edge_store_dict`.
key = self._to_canonical(*args)
if key in self.edge_types:
del self._edge_store_dict[key]
elif key in self.node_types:
del self._node_store_dict[key]
else:
del self._global_store[key]
def __copy__(self):
out = self.__class__.__new__(self.__class__)
for key, value in self.__dict__.items():
out.__dict__[key] = value
out.__dict__['_global_store'] = copy.copy(self._global_store)
out._global_store._parent = out
out.__dict__['_node_store_dict'] = {}
for key, store in self._node_store_dict.items():
out._node_store_dict[key] = copy.copy(store)
out._node_store_dict[key]._parent = out
out.__dict__['_edge_store_dict'] = {}
for key, store in self._edge_store_dict.items():
out._edge_store_dict[key] = copy.copy(store)
out._edge_store_dict[key]._parent = out
return out
def __deepcopy__(self, memo):
out = self.__class__.__new__(self.__class__)
for key, value in self.__dict__.items():
out.__dict__[key] = copy.deepcopy(value, memo)
out._global_store._parent = out
for key in self._node_store_dict.keys():
out._node_store_dict[key]._parent = out
for key in out._edge_store_dict.keys():
out._edge_store_dict[key]._parent = out
return out
def __repr__(self) -> str:
info1 = [size_repr(k, v, 2) for k, v in self._global_store.items()]
info2 = [size_repr(k, v, 2) for k, v in self._node_store_dict.items()]
info3 = [size_repr(k, v, 2) for k, v in self._edge_store_dict.items()]
info = ',\n'.join(info1 + info2 + info3)
info = f'\n{info}\n' if len(info) > 0 else info
return f'{self.__class__.__name__}({info})'
def stores_as(self, data: 'HeteroData'):
for node_type in data.node_types:
self.get_node_store(node_type)
for edge_type in data.edge_types:
self.get_edge_store(*edge_type)
return self
@property
def stores(self) -> List[BaseStorage]:
r"""Returns a list of all storages of the graph."""
return ([self._global_store] + list(self.node_stores) +
list(self.edge_stores))
@property
def node_types(self) -> List[NodeType]:
r"""Returns a list of all node types of the graph."""
return list(self._node_store_dict.keys())
@property
def node_stores(self) -> List[NodeStorage]:
r"""Returns a list of all node storages of the graph."""
return list(self._node_store_dict.values())
@property
def edge_types(self) -> List[EdgeType]:
r"""Returns a list of all edge types of the graph."""
return list(self._edge_store_dict.keys())
@property
def edge_stores(self) -> List[EdgeStorage]:
r"""Returns a list of all edge storages of the graph."""
return list(self._edge_store_dict.values())
[docs] def to_dict(self) -> Dict[str, Any]:
out = self._global_store.to_dict()
for key, store in chain(self._node_store_dict.items(),
self._edge_store_dict.items()):
out[key] = store.to_dict()
return out
[docs] def to_namedtuple(self) -> NamedTuple:
field_names = list(self._global_store.keys())
field_values = list(self._global_store.values())
field_names += [
'__'.join(key) if isinstance(key, tuple) else key
for key in self.node_types + self.edge_types
]
field_values += [
store.to_namedtuple()
for store in self.node_stores + self.edge_stores
]
DataTuple = namedtuple('DataTuple', field_names)
return DataTuple(*field_values)
[docs] def __cat_dim__(self, key: str, value: Any,
store: Optional[NodeOrEdgeStorage] = None, *args,
**kwargs) -> Any:
if isinstance(value, SparseTensor) and 'adj' in key:
return (0, 1)
elif 'index' in key or 'face' in key:
return -1
return 0
[docs] def __inc__(self, key: str, value: Any,
store: Optional[NodeOrEdgeStorage] = None, *args,
**kwargs) -> Any:
if 'batch' in key:
return int(value.max()) + 1
elif isinstance(store, EdgeStorage) and 'index' in key:
return torch.tensor(store.size()).view(2, 1)
else:
return 0
@property
def num_nodes(self) -> Optional[int]:
r"""Returns the number of nodes in the graph."""
return super().num_nodes
def debug(self):
pass # TODO
###########################################################################
def _to_canonical(self, *args: Tuple[QueryType]) -> NodeOrEdgeType:
# Converts a given `QueryType` to its "canonical type":
# 1. `relation_type` will get mapped to the unique
# `(src_node_type, relation_type, dst_node_type)` tuple.
# 2. `(src_node_type, dst_node_type)` will get mapped to the unique
# `(src_node_type, *, dst_node_type)` tuple, and
# `(src_node_type, 'to', dst_node_type)` otherwise.
if len(args) == 1:
args = args[0]
if isinstance(args, str):
node_types = [key for key in self.node_types if key == args]
if len(node_types) == 1:
args = node_types[0]
return args
# Try to map to edge type based on unique relation type:
edge_types = [key for key in self.edge_types if key[1] == args]
if len(edge_types) == 1:
args = edge_types[0]
return args
elif len(args) == 2:
# Try to find the unique source/destination node tuple:
edge_types = [
key for key in self.edge_types
if key[0] == args[0] and key[-1] == args[-1]
]
if len(edge_types) == 1:
args = edge_types[0]
return args
else:
args = (args[0], self.DEFAULT_REL, args[1])
return args
return args
[docs] def collect(self, key: str) -> Dict[NodeOrEdgeType, Any]:
r"""Collects the attribute :attr:`key` from all node and edge types.
.. code-block:: python
data = HeteroData()
data['paper'].x = ...
data['author'].x = ...
print(data.collect('x'))
>>> { 'paper': ..., 'author': ...}
.. note::
This is equivalent to writing :obj:`data.x_dict`.
"""
mapping = {}
for subtype, store in chain(self._node_store_dict.items(),
self._edge_store_dict.items()):
if hasattr(store, key):
mapping[subtype] = getattr(store, key)
return mapping
[docs] def get_node_store(self, key: NodeType) -> NodeStorage:
r"""Gets the :class:`~torch_geometric.data.storage.NodeStorage` object
of a particular node type :attr:`key`.
If the storage is not present yet, will create a new
:class:`torch_geometric.data.storage.NodeStorage` object for the given
node type.
.. code-block:: python
data = HeteroData()
node_storage = data.get_node_store('paper')
"""
out = self._node_store_dict.get(key, None)
if out is None:
out = NodeStorage(_parent=self, _key=key)
self._node_store_dict[key] = out
return out
[docs] def get_edge_store(self, src: str, rel: str, dst: str) -> EdgeStorage:
r"""Gets the :class:`~torch_geometric.data.storage.EdgeStorage` object
of a particular edge type given by the tuple :obj:`(src, rel, dst)`.
If the storage is not present yet, will create a new
:class:`torch_geometric.data.storage.EdgeStorage` object for the given
edge type.
.. code-block:: python
data = HeteroData()
edge_storage = data.get_edge_store('author', 'writes', 'paper')
"""
key = (src, rel, dst)
out = self._edge_store_dict.get(key, None)
if out is None:
out = EdgeStorage(_parent=self, _key=key)
self._edge_store_dict[key] = out
return out
[docs] def to_homogeneous(self, node_attrs: Optional[List[str]] = None,
edge_attrs: Optional[List[str]] = None,
add_node_type: bool = True,
add_edge_type: bool = True) -> Data:
"""Converts a :class:`~torch_geometric.data.HeteroData` object to a
homogeneous :class:`~torch_geometric.data.Data` object.
By default, all features with same feature dimensionality across
different types will be merged into a single representation, unless
otherwise specified via the :obj:`node_attrs` and :obj:`edge_attrs`
arguments.
Furthermore, attributes named :obj:`node_type` and :obj:`edge_type`
will be added to the returned :class:`~torch_geometric.data.Data`
object, denoting node-level and edge-level vectors holding the
node and edge type as integers, respectively.
Args:
node_attrs (List[str], optional): The node features to combine
across all node types. These node features need to be of the
same feature dimensionality. If set to :obj:`None`, will
automatically determine which node features to combine.
(default: :obj:`None`)
edge_attrs (List[str], optional): The edge features to combine
across all edge types. These edge features need to be of the
same feature dimensionality. If set to :obj:`None`, will
automatically determine which edge features to combine.
(default: :obj:`None`)
add_node_type (bool, optional): If set to :obj:`False`, will not
add the node-level vector :obj:`node_type` to the returned
:class:`~torch_geometric.data.Data` object.
(default: :obj:`True`)
add_edge_type (bool, optional): If set to :obj:`False`, will not
add the edge-level vector :obj:`edge_type` to the returned
:class:`~torch_geometric.data.Data` object.
(default: :obj:`True`)
"""
def _consistent_size(stores: List[BaseStorage]) -> List[str]:
sizes_dict = defaultdict(list)
for store in stores:
for key, value in store.items():
if key in ['edge_index', 'adj_t']:
continue
if isinstance(value, Tensor):
dim = self.__cat_dim__(key, value, store)
size = value.size()[:dim] + value.size()[dim + 1:]
sizes_dict[key].append(tuple(size))
return [
k for k, sizes in sizes_dict.items()
if len(sizes) == len(stores) and len(set(sizes)) == 1
]
data = Data(**self._global_store.to_dict())
# Iterate over all node stores and record the slice information:
node_slices, cumsum = {}, 0
node_type_names, node_types = [], []
for i, (node_type, store) in enumerate(self._node_store_dict.items()):
num_nodes = store.num_nodes
node_slices[node_type] = (cumsum, cumsum + num_nodes)
node_type_names.append(node_type)
cumsum += num_nodes
if add_node_type:
kwargs = {'dtype': torch.long}
node_types.append(torch.full((num_nodes, ), i, **kwargs))
data._node_type_names = node_type_names
if len(node_types) > 1:
data.node_type = torch.cat(node_types, dim=0)
elif len(node_types) == 1:
data.node_type = node_types[0]
# Combine node attributes into a single tensor:
if node_attrs is None:
node_attrs = _consistent_size(self.node_stores)
for key in node_attrs:
values = [store[key] for store in self.node_stores]
dim = self.__cat_dim__(key, values[0], self.node_stores[0])
value = torch.cat(values, dim) if len(values) > 1 else values[0]
data[key] = value
if len([
key for key in node_attrs
if (key in {'x', 'pos', 'batch'} or 'node' in key)
]) == 0 and not add_node_type:
data.num_nodes = cumsum
# Iterate over all edge stores and record the slice information:
edge_slices, cumsum = {}, 0
edge_indices, edge_type_names, edge_types = [], [], []
for i, (edge_type, store) in enumerate(self._edge_store_dict.items()):
src, _, dst = edge_type
num_edges = store.num_edges
edge_slices[edge_type] = (cumsum, cumsum + num_edges)
edge_type_names.append(edge_type)
cumsum += num_edges
kwargs = {'dtype': torch.long, 'device': store.edge_index.device}
offset = [[node_slices[src][0]], [node_slices[dst][0]]]
offset = torch.tensor(offset, **kwargs)
edge_indices.append(store.edge_index + offset)
if add_edge_type:
edge_types.append(torch.full((num_edges, ), i, **kwargs))
data._edge_type_names = edge_type_names
if len(edge_indices) > 1:
data.edge_index = torch.cat(edge_indices, dim=-1)
elif len(edge_indices) == 1:
data.edge_index = edge_indices[0]
if len(edge_types) > 1:
data.edge_type = torch.cat(edge_types, dim=0)
elif len(edge_types) == 1:
data.edge_type = edge_types[0]
# Combine edge attributes into a single tensor:
if edge_attrs is None:
edge_attrs = _consistent_size(self.edge_stores)
for key in edge_attrs:
values = [store[key] for store in self.edge_stores]
dim = self.__cat_dim__(key, values[0], self.edge_stores[0])
value = torch.cat(values, dim) if len(values) > 1 else values[0]
data[key] = value
return data