dgl.dataloading.BlockSampler¶
-
class
dgl.dataloading.
BlockSampler
(prefetch_node_feats=None, prefetch_labels=None, prefetch_edge_feats=None, output_device=None)[source]¶ Base class for sampling mini-batches in the form of Message-passing Flow Graphs (MFGs).
It provides prefetching options to fetch the node features for the first MFG’s
srcdata
, the node labels for the last MFG’sdstdata
and the edge features of all MFG’sedata
.- Parameters
prefetch_node_feats (list[str] or dict[str, list[str]], optional) –
The node data to prefetch for the first MFG.
DGL will populate the first layer’s MFG’s
srcnodes
andsrcdata
with the node data of the given names from the original graph.prefetch_labels (list[str] or dict[str, list[str]], optional) –
The node data to prefetch for the last MFG.
DGL will populate the last layer’s MFG’s
dstnodes
anddstdata
with the node data of the given names from the original graph.prefetch_edge_feats (list[str] or dict[etype, list[str]], optional) –
The edge data names to prefetch for all the MFGs.
DGL will populate every MFG’s
edges
andedata
with the edge data of the given names from the original graph.output_device (device, optional) – The device of the output subgraphs or MFGs. Default is the same as the minibatch of seed nodes.
-
__init__
(prefetch_node_feats=None, prefetch_labels=None, prefetch_edge_feats=None, output_device=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([prefetch_node_feats, …])Initialize self.
assign_lazy_features
(result)Assign lazy features for prefetching.
sample
(g, seed_nodes[, exclude_eids])Sample a list of blocks from the given seed nodes.
sample_blocks
(g, seed_nodes[, exclude_eids])Generates a list of blocks from the given seed nodes.