Source code for torch_geometric.loader.neighbor_loader

from typing import Callable, Optional, Tuple, Union

from import Data, HeteroData
from import FeatureStore
from import GraphStore
from torch_geometric.loader.node_loader import NodeLoader
from torch_geometric.loader.utils import get_input_nodes
from torch_geometric.sampler import NeighborSampler
from torch_geometric.typing import InputNodes, NumNeighbors

[docs]class NeighborLoader(NodeLoader): r"""A data loader that performs neighbor sampling as introduced in the `"Inductive Representation Learning on Large Graphs" <>`_ paper. This loader allows for mini-batch training of GNNs on large-scale graphs where full-batch training is not feasible. More specifically, :obj:`num_neighbors` denotes how much neighbors are sampled for each node in each iteration. :class:`~torch_geometric.loader.NeighborLoader` takes in this list of :obj:`num_neighbors` and iteratively samples :obj:`num_neighbors[i]` for each node involved in iteration :obj:`i - 1`. Sampled nodes are sorted based on the order in which they were sampled. In particular, the first :obj:`batch_size` nodes represent the set of original mini-batch nodes. .. code-block:: python from torch_geometric.datasets import Planetoid from torch_geometric.loader import NeighborLoader data = Planetoid(path, name='Cora')[0] loader = NeighborLoader( data, # Sample 30 neighbors for each node for 2 iterations num_neighbors=[30] * 2, # Use a batch size of 128 for sampling training nodes batch_size=128, input_nodes=data.train_mask, ) sampled_data = next(iter(loader)) print(sampled_data.batch_size) >>> 128 By default, the data loader will only include the edges that were originally sampled (:obj:`directed = True`). This option should only be used in case the number of hops is equivalent to the number of GNN layers. In case the number of GNN layers is greater than the number of hops, consider setting :obj:`directed = False`, which will include all edges between all sampled nodes (but is slightly slower as a result). Furthermore, :class:`~torch_geometric.loader.NeighborLoader` works for both **homogeneous** graphs stored via :class:`` as well as **heterogeneous** graphs stored via :class:``. When operating in heterogeneous graphs, up to :obj:`num_neighbors` neighbors will be sampled for each :obj:`edge_type`. However, more fine-grained control over the amount of sampled neighbors of individual edge types is possible: .. code-block:: python from torch_geometric.datasets import OGB_MAG from torch_geometric.loader import NeighborLoader hetero_data = OGB_MAG(path)[0] loader = NeighborLoader( hetero_data, # Sample 30 neighbors for each node and edge type for 2 iterations num_neighbors={key: [30] * 2 for key in hetero_data.edge_types}, # Use a batch size of 128 for sampling training nodes of type paper batch_size=128, input_nodes=('paper', hetero_data['paper'].train_mask), ) sampled_hetero_data = next(iter(loader)) print(sampled_hetero_data['paper'].batch_size) >>> 128 .. note:: For an example of using :class:`~torch_geometric.loader.NeighborLoader`, see `examples/hetero/ < pytorch_geometric/blob/master/examples/hetero/>`_. The :class:`~torch_geometric.loader.NeighborLoader` will return subgraphs where global node indices are mapped to local indices corresponding to this specific subgraph. However, often times it is desired to map the nodes of the current subgraph back to the global node indices. A simple trick to achieve this is to include this mapping as part of the :obj:`data` object: .. code-block:: python # Assign each node its global node index: data.n_id = torch.arange(data.num_nodes) loader = NeighborLoader(data, ...) sampled_data = next(iter(loader)) print(sampled_data.n_id) Args: data ( or The :class:`` or :class:`` graph object. num_neighbors (List[int] or Dict[Tuple[str, str, str], List[int]]): The number of neighbors to sample for each node in each iteration. In heterogeneous graphs, may also take in a dictionary denoting the amount of neighbors to sample for each individual edge type. If an entry is set to :obj:`-1`, all neighbors will be included. input_nodes (torch.Tensor or str or Tuple[str, torch.Tensor]): The indices of nodes for which neighbors are sampled to create mini-batches. Needs to be either given as a :obj:`torch.LongTensor` or :obj:`torch.BoolTensor`. If set to :obj:`None`, all nodes will be considered. In heterogeneous graphs, needs to be passed as a tuple that holds the node type and node indices. (default: :obj:`None`) replace (bool, optional): If set to :obj:`True`, will sample with replacement. (default: :obj:`False`) directed (bool, optional): If set to :obj:`False`, will include all edges between all sampled nodes. (default: :obj:`True`) temporal_strategy (string, optional): The sampling strategy when using temporal sampling (:obj:`"uniform"`, :obj:`"last"`). If set to :obj:`"uniform"`, will sample uniformly across neighbors that fulfill temporal constraints. If set to :obj:`"last"`, will sample the last `num_neighbors` that fulfill temporal constraints. (default: :obj:`"uniform"`) time_attr (str, optional): The name of the attribute that denotes timestamps for the nodes in the graph. If set, temporal sampling will be used such that neighbors are guaranteed to fulfill temporal constraints, *i.e.* neighbors have an earlier timestamp than the center node. (default: :obj:`None`) transform (Callable, optional): A function/transform that takes in a sampled mini-batch and returns a transformed version. (default: :obj:`None`) is_sorted (bool, optional): If set to :obj:`True`, assumes that :obj:`edge_index` is sorted by column. If :obj:`time_attr` is set, additionally requires that rows are sorted according to time within individual neighborhoods. This avoids internal re-sorting of the data and can improve runtime and memory efficiency. (default: :obj:`False`) filter_per_worker (bool, optional): If set to :obj:`True`, will filter the returning data in each worker's subprocess rather than in the main process. Setting this to :obj:`True` is generally not recommended: (1) it may result in too many open file handles, (2) it may slown down data loading, (3) it requires operating on CPU tensors. (default: :obj:`False`) **kwargs (optional): Additional arguments of :class:``, such as :obj:`batch_size`, :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`. """ def __init__( self, data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], num_neighbors: NumNeighbors, input_nodes: InputNodes = None, replace: bool = False, directed: bool = True, temporal_strategy: str = 'uniform', time_attr: Optional[str] = None, transform: Callable = None, is_sorted: bool = False, filter_per_worker: bool = False, neighbor_sampler: Optional[NeighborSampler] = None, **kwargs, ): # Get input type: # TODO(manan): this computation is repeated twice, once here and once # in NodeLoader: node_type, _ = get_input_nodes(data, input_nodes) if neighbor_sampler is None: neighbor_sampler = NeighborSampler( data, num_neighbors=num_neighbors, replace=replace, directed=directed, temporal_strategy=temporal_strategy, input_type=node_type, time_attr=time_attr, is_sorted=is_sorted, share_memory=kwargs.get('num_workers', 0) > 0, ) # A NeighborLoader is simply a NodeLoader that uses the NeighborSampler # sampling implementation: super().__init__( data=data, node_sampler=neighbor_sampler, input_nodes=input_nodes, transform=transform, filter_per_worker=filter_per_worker, **kwargs, )