torch_geometric.data.Data

class Data(x: Optional[Tensor] = None, edge_index: Optional[Tensor] = None, edge_attr: Optional[Tensor] = None, y: Optional[Tensor] = None, pos: Optional[Tensor] = None, **kwargs)

Bases: BaseData, FeatureStore, GraphStore

A data object describing a homogeneous graph. The data object can hold node-level, link-level and graph-level attributes. In general, Data tries to mimic the behavior of a regular Python dictionary. In addition, it provides useful functionality for analyzing graph structures, and provides basic PyTorch tensor functionalities. See here for the accompanying tutorial.

from torch_geometric.data import Data

data = Data(x=x, edge_index=edge_index, ...)

# Add additional arguments to `data`:
data.train_idx = torch.tensor([...], dtype=torch.long)
data.test_mask = torch.tensor([...], dtype=torch.bool)

# Analyzing the graph structure:
data.num_nodes
>>> 23

data.is_directed()
>>> False

# PyTorch tensor functionality:
data = data.pin_memory()
data = data.to('cuda:0', non_blocking=True)

Parameters

• x (torch.Tensor, optional) – Node feature matrix with shape [num_nodes, num_node_features]. (default: None)

• edge_index (LongTensor, optional) – Graph connectivity in COO format with shape [2, num_edges]. (default: None)

• edge_attr (torch.Tensor, optional) – Edge feature matrix with shape [num_edges, num_edge_features]. (default: None)

• y (torch.Tensor, optional) – Graph-level or node-level ground-truth labels with arbitrary shape. (default: None)

• pos (torch.Tensor, optional) – Node position matrix with shape [num_nodes, num_dimensions]. (default: None)

• **kwargs (optional) – Additional attributes.

to_dict() → Dict[str, Any]

Returns a dictionary of stored key/value pairs.

to_namedtuple() → NamedTuple

Returns a NamedTuple of stored key/value pairs.

update(data: Union[Data, Dict[str, Any]]) → Data

Updates the data object with the elements from another data object.

__cat_dim__(key: str, value: Any, *args, **kwargs) → Any

Returns the dimension for which the value value of the attribute key will get concatenated when creating mini-batches using torch_geometric.loader.DataLoader.

Note

This method is for internal use only, and should only be overridden in case the mini-batch creation process is corrupted for a specific attribute.

__inc__(key: str, value: Any, *args, **kwargs) → Any

Returns the incremental count to cumulatively increase the value value of the attribute key when creating mini-batches using torch_geometric.loader.DataLoader.

Note

This method is for internal use only, and should only be overridden in case the mini-batch creation process is corrupted for a specific attribute.

validate(raise_on_error: bool = True) → bool

Validates the correctness of the data.

is_node_attr(key: str) → bool

Returns True if the object at key key denotes a node-level tensor attribute.

is_edge_attr(key: str) → bool

Returns True if the object at key key denotes an edge-level tensor attribute.

subgraph(subset: Tensor) → Data

Returns the induced subgraph given by the node indices subset.

Parameters

subset (LongTensor or BoolTensor) – The nodes to keep.

edge_subgraph(subset: Tensor) → Data

Returns the induced subgraph given by the edge indices subset. Will currently preserve all the nodes in the graph, even if they are isolated after subgraph computation.

Parameters

subset (LongTensor or BoolTensor) – The edges to keep.

to_heterogeneous(node_type: Optional[Tensor] = None, edge_type: Optional[Tensor] = None, node_type_names: Optional[List[str]] = None, edge_type_names: Optional[List[Tuple[str, str, str]]] = None)

Converts a Data object to a heterogeneous HeteroData object. For this, node and edge attributes are splitted according to the node-level and edge-level vectors node_type and edge_type, respectively. node_type_names and edge_type_names can be used to give meaningful node and edge type names, respectively. That is, the node_type 0 is given by node_type_names[0]. If the Data object was constructed via to_homogeneous(), the object can be reconstructed without any need to pass in additional arguments.

Parameters

• node_type (torch.Tensor, optional) – A node-level vector denoting the type of each node. (default: None)

• edge_type (torch.Tensor, optional) – An edge-level vector denoting the type of each edge. (default: None)

• node_type_names (List[str], optional) – The names of node types. (default: None)

• edge_type_names (List[Tuple[str, str, str]], optional) – The names of edge types. (default: None)

classmethod from_dict(mapping: Dict[str, Any]) → Data

Creates a Data object from a Python dictionary.

property num_node_features: int

Returns the number of features per node in the graph.

property num_features: int

Returns the number of features per node in the graph. Alias for num_node_features.

property num_edge_features: int

Returns the number of features per edge in the graph.

property num_node_types: int

Returns the number of node types in the graph.

property num_edge_types: int

Returns the number of edge types in the graph.

property num_faces: Optional[int]

Returns the number of faces in the mesh.

get_all_tensor_attrs() → List[TensorAttr]

Obtains all feature attributes stored in Data.

apply(func: Callable, *args: List[str])

Applies the function func, either to all attributes or only the ones given in *args.

apply_(func: Callable, *args: List[str])

Applies the in-place function func, either to all attributes or only the ones given in *args.

clone(*args: List[str])

Performs cloning of tensors, either for all attributes or only the ones given in *args.

coalesce()

Sorts and removes duplicated entries from edge indices edge_index.

contiguous(*args: List[str])

Ensures a contiguous memory layout, either for all attributes or only the ones given in *args.

coo(edge_types: Optional[List[Any]] = None, store: bool = False) → Tuple[Dict[Tuple[str, str, str], Tensor], Dict[Tuple[str, str, str], Tensor], Dict[Tuple[str, str, str], Optional[Tensor]]]

Obtains the edge indices in the GraphStore in COO format.

Parameters

• edge_types (List[Any], optional) – The edge types of edge indices to obtain. If set to None, will return the edge indices of all existing edge types. (default: None)

• store (bool, optional) – Whether to store converted edge indices in the GraphStore. (default: False)

cpu(*args: List[str])

Copies attributes to CPU memory, either for all attributes or only the ones given in *args.

csc(edge_types: Optional[List[Any]] = None, store: bool = False) → Tuple[Dict[Tuple[str, str, str], Tensor], Dict[Tuple[str, str, str], Tensor], Dict[Tuple[str, str, str], Optional[Tensor]]]

Obtains the edge indices in the GraphStore in CSC format.

Parameters

• edge_types (List[Any], optional) – The edge types of edge indices to obtain. If set to None, will return the edge indices of all existing edge types. (default: None)

• store (bool, optional) – Whether to store converted edge indices in the GraphStore. (default: False)

csr(edge_types: Optional[List[Any]] = None, store: bool = False) → Tuple[Dict[Tuple[str, str, str], Tensor], Dict[Tuple[str, str, str], Tensor], Dict[Tuple[str, str, str], Optional[Tensor]]]

Obtains the edge indices in the GraphStore in CSR format.

Parameters

• edge_types (List[Any], optional) – The edge types of edge indices to obtain. If set to None, will return the edge indices of all existing edge types. (default: None)

• store (bool, optional) – Whether to store converted edge indices in the GraphStore. (default: False)

cuda(device: Optional[Union[int, str]] = None, *args: List[str], non_blocking: bool = False)

Copies attributes to CUDA memory, either for all attributes or only the ones given in *args.

detach(*args: List[str])

Detaches attributes from the computation graph by creating a new tensor, either for all attributes or only the ones given in *args.

detach_(*args: List[str])

Detaches attributes from the computation graph, either for all attributes or only the ones given in *args.

edge_attrs() → List[str]

Returns all edge-level tensor attribute names.

generate_ids()

Generates and sets n_id and e_id attributes to assign each node and edge to a continuously ascending and unique ID.

get_edge_index(*args, **kwargs) → Tuple[Tensor, Tensor]

Synchronously obtains an edge_index tuple from the GraphStore.

Parameters

**kwargs (EdgeAttr) – Any relevant edge attributes that correspond to the edge_index tuple. See the EdgeAttr documentation for required and optional attributes.

Raises

KeyError – If the edge_index corresponding to the input EdgeAttr was not found.

get_tensor(*args, convert_type: bool = False, **kwargs) → Union[Tensor, ndarray]

Synchronously obtains a tensor from the FeatureStore.

Parameters

• **kwargs (TensorAttr) – Any relevant tensor attributes that correspond to the feature tensor. See the TensorAttr documentation for required and optional attributes.

• convert_type (bool, optional) – Whether to convert the type of the output tensor to the type of the attribute index. (default: False)

Raises

• ValueError – If the input TensorAttr is not fully specified.

• KeyError – If the tensor corresponding to the input TensorAttr was not found.

get_tensor_size(*args, **kwargs) → Optional[Tuple[int, ...]]

Obtains the size of a tensor given its TensorAttr, or None if the tensor does not exist.

has_isolated_nodes() → bool

Returns True if the graph contains isolated nodes.

has_self_loops() → bool

Returns True if the graph contains self-loops.

is_coalesced() → bool

Returns True if edge indices edge_index are sorted and do not contain duplicate entries.

property is_cuda: bool

Returns True if any torch.Tensor attribute is stored on the GPU, False otherwise.

is_directed() → bool

Returns True if graph edges are directed.

is_undirected() → bool

Returns True if graph edges are undirected.

property keys: List[str]

Returns a list of all graph attribute names.

multi_get_tensor(attrs: List[TensorAttr], convert_type: bool = False) → List[Union[Tensor, ndarray]]

Synchronously obtains a list of tensors from the FeatureStore for each tensor associated with the attributes in attrs.

Note

The default implementation simply iterates over all calls to get_tensor(). Implementor classes that can provide additional, more performant functionality are recommended to to override this method.

Parameters

• attrs (List[TensorAttr]) – A list of input TensorAttr objects that identify the tensors to obtain.

• convert_type (bool, optional) – Whether to convert the type of the output tensor to the type of the attribute index. (default: False)

Raises

• ValueError – If any input TensorAttr is not fully specified.

• KeyError – If any of the tensors corresponding to the input TensorAttr was not found.

node_attrs() → List[str]

Returns all node-level tensor attribute names.

property num_edges: int

Returns the number of edges in the graph. For undirected graphs, this will return the number of bi-directional edges, which is double the amount of unique edges.

property num_nodes: Optional[int]

Returns the number of nodes in the graph.

Note

The number of nodes in the data object is automatically inferred in case node-level attributes are present, e.g., data.x. In some cases, however, a graph may only be given without any node-level attributes. PyG then guesses the number of nodes according to edge_index.max().item() + 1. However, in case there exists isolated nodes, this number does not have to be correct which can result in unexpected behavior. Thus, we recommend to set the number of nodes in your data object explicitly via data.num_nodes = .... You will be given a warning that requests you to do so.

pin_memory(*args: List[str])

Copies attributes to pinned memory, either for all attributes or only the ones given in *args.

put_edge_index(edge_index: Tuple[Tensor, Tensor], *args, **kwargs) → bool

Synchronously adds an edge_index tuple to the GraphStore. Returns whether insertion was successful.

Parameters

• edge_index (Tuple[torch.Tensor, torch.Tensor]) – The edge_index tuple in a format specified in EdgeAttr.

• **kwargs (EdgeAttr) – Any relevant edge attributes that correspond to the edge_index tuple. See the EdgeAttr documentation for required and optional attributes.

put_tensor(tensor: Union[Tensor, ndarray], *args, **kwargs) → bool

Synchronously adds a tensor to the FeatureStore. Returns whether insertion was successful.

Parameters

• tensor (torch.Tensor or np.ndarray) – The feature tensor to be added.

• **kwargs (TensorAttr) – Any relevant tensor attributes that correspond to the feature tensor. See the TensorAttr documentation for required and optional attributes.

Raises

ValueError – If the input TensorAttr is not fully specified.

record_stream(stream: Stream, *args: List[str])

Ensures that the tensor memory is not reused for another tensor until all current work queued on stream has been completed, either for all attributes or only the ones given in *args.

remove_edge_index(*args, **kwargs) → bool

Synchronously deletes an edge_index tuple from the GraphStore. Returns whether deletion was successful.

Parameters

**kwargs (EdgeAttr) – Any relevant edge attributes that correspond to the edge_index tuple. See the EdgeAttr documentation for required and optional attributes.

remove_tensor(*args, **kwargs) → bool

Removes a tensor from the FeatureStore. Returns whether deletion was successful.

Parameters

**kwargs (TensorAttr) – Any relevant tensor attributes that correspond to the feature tensor. See the TensorAttr documentation for required and optional attributes.

Raises

ValueError – If the input TensorAttr is not fully specified.

requires_grad_(*args: List[str], requires_grad: bool = True)

Tracks gradient computation, either for all attributes or only the ones given in *args.

share_memory_(*args: List[str])

Moves attributes to shared memory, either for all attributes or only the ones given in *args.

size(dim: Optional[int] = None) → Optional[Union[Tuple[Optional[int], Optional[int]], int]]

Returns the size of the adjacency matrix induced by the graph.

to(device: Union[int, str], *args: List[str], non_blocking: bool = False)

Performs tensor device conversion, either for all attributes or only the ones given in *args.

update_tensor(tensor: Union[Tensor, ndarray], *args, **kwargs) → bool

Updates a tensor in the FeatureStore with a new value. Returns whether the update was succesful.

Note

Implementor classes can choose to define more efficient update methods; the default performs a removal and insertion.

Parameters

• tensor (torch.Tensor or np.ndarray) – The feature tensor to be updated.

• **kwargs (TensorAttr) – Any relevant tensor attributes that correspond to the feature tensor. See the TensorAttr documentation for required and optional attributes.

view(*args, **kwargs) → AttrView

Returns a view of the FeatureStore given a not yet fully-specified TensorAttr.

get_all_edge_attrs() → List[EdgeAttr]

Obtains all edge attributes stored in the GraphStore.