cugraph_pyg.loader.node_loader.NodeLoader#

class cugraph_pyg.loader.node_loader.NodeLoader(data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], node_sampler: BaseSampler, input_nodes: Union[Tensor, None, str, Tuple[str, Optional[Tensor]]] = None, input_time: Optional[Tensor] = None, transform: Optional[Callable] = None, transform_sampler_output: Optional[Callable] = None, filter_per_worker: Optional[bool] = None, custom_cls: Optional[HeteroData] = None, input_id: Optional[Tensor] = None, batch_size: int = 1, shuffle: bool = False, drop_last: bool = False, **kwargs)[source]#

Duck-typed version of torch_geometric.loader.NodeLoader. Loads samples from batches of input nodes using a ~cugraph_pyg.sampler.BaseSampler.sample_from_nodes function.

__init__(data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], node_sampler: BaseSampler, input_nodes: Union[Tensor, None, str, Tuple[str, Optional[Tensor]]] = None, input_time: Optional[Tensor] = None, transform: Optional[Callable] = None, transform_sampler_output: Optional[Callable] = None, filter_per_worker: Optional[bool] = None, custom_cls: Optional[HeteroData] = None, input_id: Optional[Tensor] = None, batch_size: int = 1, shuffle: bool = False, drop_last: bool = False, **kwargs)[source]#

Parameters:

data: Data, HeteroData, or Tuple[FeatureStore, GraphStore]: See torch_geometric.loader.NodeLoader.
node_sampler: BaseSampler: See torch_geometric.loader.NodeLoader.
input_nodes: InputNodes: See torch_geometric.loader.NodeLoader.
input_time: OptTensor: See torch_geometric.loader.NodeLoader.
transform: Callable (optional, default=None): This argument currently has no effect.
transform_sampler_output: Callable (optional, default=None): This argument currently has no effect.
filter_per_worker: bool (optional, default=False): This argument currently has no effect.
custom_cls: HeteroData: This argument currently has no effect. This loader will always return a Data or HeteroData object.
input_id: OptTensor: See torch_geometric.loader.NodeLoader.

Methods

__init__(data, node_sampler[, input_nodes, ...])

Parameters: