dmlc--dgl
Zihao Ye
93ac29ce34
* upd * upd * upd * lint * fix * fix test * fix * fix * upd * upd * upd * upd * upd * upd * upd * upd * upd * upd * upd tutorial * upd * upd * fix kg * upd doc organization * refresh test * upd * refactor doc * fix lint Co-authored-by: Minjie Wang <minjie.wang@nyu.edu>
1132 行
35 KiB
Python
1132 行
35 KiB
Python
"""Classes and functions for batching multiple graphs together."""
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from __future__ import absolute_import
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import numpy as np
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from .base import DGLError
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from . import backend as F
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__all__ = ['sum_nodes', 'sum_edges', 'mean_nodes', 'mean_edges',
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'max_nodes', 'max_edges', 'softmax_nodes', 'softmax_edges',
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'broadcast_nodes', 'broadcast_edges', 'topk_nodes', 'topk_edges']
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READOUT_ON_ATTRS = {
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'nodes': ('ndata', 'batch_num_nodes', 'number_of_nodes'),
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'edges': ('edata', 'batch_num_edges', 'number_of_edges'),
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}
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def _sum_on(graph, typestr, feat, weight):
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"""Internal function to sum node or edge features.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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typestr : str
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'nodes' or 'edges'
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feat : str
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The feature field name.
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weight : str
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The weight field name.
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Returns
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-------
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tensor
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The (weighted) summed node or edge features.
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"""
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data_attr, batch_num_objs_attr, _ = READOUT_ON_ATTRS[typestr]
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data = getattr(graph, data_attr)
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feat = data[feat]
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if weight is not None:
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weight = data[weight]
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weight = F.reshape(weight, (-1,) + (1,) * (F.ndim(feat) - 1))
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feat = weight * feat
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n_graphs = graph.batch_size
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batch_num_objs = getattr(graph, batch_num_objs_attr)
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seg_id = F.zerocopy_from_numpy(np.arange(n_graphs, dtype='int64').repeat(batch_num_objs))
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seg_id = F.copy_to(seg_id, F.context(feat))
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y = F.unsorted_1d_segment_sum(feat, seg_id, n_graphs, 0)
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return y
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def sum_nodes(graph, feat, weight=None):
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"""Sums all the values of node field :attr:`feat` in :attr:`graph`, optionally
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multiplies the field by a scalar node field :attr:`weight`.
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Parameters
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----------
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graph : DGLGraph.
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The graph.
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feat : str
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The feature field.
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weight : str, optional
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The weight field. If None, no weighting will be performed,
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otherwise, weight each node feature with field :attr:`feat`.
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for summation. The weight feature associated in the :attr:`graph`
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should be a tensor of shape ``[graph.number_of_nodes(), 1]``.
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Returns
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-------
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tensor
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The summed tensor.
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Notes
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-----
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Return a stacked tensor with an extra first dimension whose size equals
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batch size of the input graph.
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The i-th row of the stacked tensor contains the readout result of the
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i-th graph in the batched graph. If a graph has no nodes,
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a zero tensor with the same shape is returned at the corresponding row.
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Examples
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--------
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>>> import dgl
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>>> import torch as th
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Create two :class:`~dgl.DGLGraph` objects and initialize their
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node features.
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>>> g1 = dgl.DGLGraph() # Graph 1
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>>> g1.add_nodes(2)
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>>> g1.ndata['h'] = th.tensor([[1.], [2.]])
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>>> g1.ndata['w'] = th.tensor([[3.], [6.]])
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>>> g2 = dgl.DGLGraph() # Graph 2
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>>> g2.add_nodes(3)
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>>> g2.ndata['h'] = th.tensor([[1.], [2.], [3.]])
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Sum over node attribute :attr:`h` without weighting for each graph in a
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batched graph.
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>>> bg = dgl.batch([g1, g2], node_attrs='h')
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>>> dgl.sum_nodes(bg, 'h')
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tensor([[3.], # 1 + 2
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[6.]]) # 1 + 2 + 3
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Sum node attribute :attr:`h` with weight from node attribute :attr:`w`
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for a single graph.
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>>> dgl.sum_nodes(g1, 'h', 'w')
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tensor([[15.]]) # 1 * 3 + 2 * 6
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See Also
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--------
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mean_nodes
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sum_edges
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mean_edges
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"""
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return _sum_on(graph, 'nodes', feat, weight)
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def sum_edges(graph, feat, weight=None):
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"""Sums all the values of edge field :attr:`feat` in :attr:`graph`,
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optionally multiplies the field by a scalar edge field :attr:`weight`.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : str
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The feature field.
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weight : str, optional
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The weight field. If None, no weighting will be performed,
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otherwise, weight each edge feature with field :attr:`feat`.
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for summation. The weight feature associated in the :attr:`graph`
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should be a tensor of shape ``[graph.number_of_edges(), 1]``.
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Returns
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-------
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tensor
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The summed tensor.
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Notes
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-----
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Return a stacked tensor with an extra first dimension whose size equals
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batch size of the input graph.
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The i-th row of the stacked tensor contains the readout result of the
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i-th graph in the batched graph. If a graph has no edges,
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a zero tensor with the same shape is returned at the corresponding row.
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Examples
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--------
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>>> import dgl
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>>> import torch as th
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Create two :class:`~dgl.DGLGraph` objects and initialize their
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edge features.
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>>> g1 = dgl.DGLGraph() # Graph 1
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>>> g1.add_nodes(2)
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>>> g1.add_edges([0, 1], [1, 0])
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>>> g1.edata['h'] = th.tensor([[1.], [2.]])
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>>> g1.edata['w'] = th.tensor([[3.], [6.]])
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>>> g2 = dgl.DGLGraph() # Graph 2
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>>> g2.add_nodes(3)
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>>> g2.add_edges([0, 1, 2], [1, 2, 0])
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>>> g2.edata['h'] = th.tensor([[1.], [2.], [3.]])
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Sum over edge attribute :attr:`h` without weighting for each graph in a
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batched graph.
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>>> bg = dgl.batch([g1, g2], edge_attrs='h')
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>>> dgl.sum_edges(bg, 'h')
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tensor([[3.], # 1 + 2
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[6.]]) # 1 + 2 + 3
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Sum edge attribute :attr:`h` with weight from edge attribute :attr:`w`
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for a single graph.
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>>> dgl.sum_edges(g1, 'h', 'w')
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tensor([[15.]]) # 1 * 3 + 2 * 6
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See Also
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--------
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sum_nodes
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mean_nodes
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mean_edges
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"""
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return _sum_on(graph, 'edges', feat, weight)
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def _mean_on(graph, typestr, feat, weight):
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"""Internal function to sum node or edge features.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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typestr : str
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'nodes' or 'edges'
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feat : str
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The feature field name.
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weight : str
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The weight field name.
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Returns
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-------
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tensor
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The (weighted) summed node or edge features.
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"""
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data_attr, batch_num_objs_attr, _ = READOUT_ON_ATTRS[typestr]
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data = getattr(graph, data_attr)
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feat = data[feat]
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if weight is not None:
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weight = data[weight]
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weight = F.reshape(weight, (-1,) + (1,) * (F.ndim(feat) - 1))
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feat = weight * feat
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n_graphs = graph.batch_size
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batch_num_objs = getattr(graph, batch_num_objs_attr)
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seg_id = F.zerocopy_from_numpy(np.arange(n_graphs, dtype='int64').repeat(batch_num_objs))
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seg_id = F.copy_to(seg_id, F.context(feat))
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if weight is not None:
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w = F.unsorted_1d_segment_sum(weight, seg_id, n_graphs, 0)
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y = F.unsorted_1d_segment_sum(feat, seg_id, n_graphs, 0)
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y = y / w
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else:
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y = F.unsorted_1d_segment_mean(feat, seg_id, n_graphs, 0)
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return y
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def mean_nodes(graph, feat, weight=None):
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"""Averages all the values of node field :attr:`feat` in :attr:`graph`,
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optionally multiplies the field by a scalar node field :attr:`weight`.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : str
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The feature field.
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weight : str, optional
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The weight field. If None, no weighting will be performed,
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otherwise, weight each node feature with field :attr:`feat`.
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for calculating mean. The weight feature associated in the :attr:`graph`
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should be a tensor of shape ``[graph.number_of_nodes(), 1]``.
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Returns
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-------
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tensor
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The averaged tensor.
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Notes
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-----
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Return a stacked tensor with an extra first dimension whose size equals
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batch size of the input graph.
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The i-th row of the stacked tensor contains the readout result of
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the i-th graph in the batch. If a graph has no nodes,
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a zero tensor with the same shape is returned at the corresponding row.
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Examples
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--------
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>>> import dgl
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>>> import torch as th
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Create two :class:`~dgl.DGLGraph` objects and initialize their
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node features.
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>>> g1 = dgl.DGLGraph() # Graph 1
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>>> g1.add_nodes(2)
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>>> g1.ndata['h'] = th.tensor([[1.], [2.]])
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>>> g1.ndata['w'] = th.tensor([[3.], [6.]])
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>>> g2 = dgl.DGLGraph() # Graph 2
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>>> g2.add_nodes(3)
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>>> g2.ndata['h'] = th.tensor([[1.], [2.], [3.]])
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Average over node attribute :attr:`h` without weighting for each graph in a
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batched graph.
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>>> bg = dgl.batch([g1, g2], node_attrs='h')
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>>> dgl.mean_nodes(bg, 'h')
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tensor([[1.5000], # (1 + 2) / 2
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[2.0000]]) # (1 + 2 + 3) / 3
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Sum node attribute :attr:`h` with normalized weight from node attribute :attr:`w`
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for a single graph.
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>>> dgl.mean_nodes(g1, 'h', 'w') # h1 * (w1 / (w1 + w2)) + h2 * (w2 / (w1 + w2))
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tensor([[1.6667]]) # 1 * (3 / (3 + 6)) + 2 * (6 / (3 + 6))
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See Also
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--------
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sum_nodes
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sum_edges
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mean_edges
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"""
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return _mean_on(graph, 'nodes', feat, weight)
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def mean_edges(graph, feat, weight=None):
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"""Averages all the values of edge field :attr:`feat` in :attr:`graph`,
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optionally multiplies the field by a scalar edge field :attr:`weight`.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : str
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The feature field.
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weight : optional, str
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The weight field. If None, no weighting will be performed,
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otherwise, weight each edge feature with field :attr:`feat`.
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for calculating mean. The weight feature associated in the :attr:`graph`
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should be a tensor of shape ``[graph.number_of_edges(), 1]``.
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Returns
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-------
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tensor
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The averaged tensor.
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Notes
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-----
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Return a stacked tensor with an extra first dimension whose size equals
|
|
batch size of the input graph.
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|
The i-th row of the stacked tensor contains the readout result of
|
|
the i-th graph in the batched graph. If a graph has no edges,
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a zero tensor with the same shape is returned at the corresponding row.
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Examples
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--------
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>>> import dgl
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>>> import torch as th
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Create two :class:`~dgl.DGLGraph` objects and initialize their
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|
edge features.
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>>> g1 = dgl.DGLGraph() # Graph 1
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>>> g1.add_nodes(2)
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>>> g1.add_edges([0, 1], [1, 0])
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>>> g1.edata['h'] = th.tensor([[1.], [2.]])
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>>> g1.edata['w'] = th.tensor([[3.], [6.]])
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>>> g2 = dgl.DGLGraph() # Graph 2
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>>> g2.add_nodes(3)
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>>> g2.add_edges([0, 1, 2], [1, 2, 0])
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>>> g2.edata['h'] = th.tensor([[1.], [2.], [3.]])
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Average over edge attribute :attr:`h` without weighting for each graph in a
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batched graph.
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>>> bg = dgl.batch([g1, g2], edge_attrs='h')
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>>> dgl.mean_edges(bg, 'h')
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tensor([[1.5000], # (1 + 2) / 2
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[2.0000]]) # (1 + 2 + 3) / 3
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Sum edge attribute :attr:`h` with normalized weight from edge attribute :attr:`w`
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for a single graph.
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>>> dgl.mean_edges(g1, 'h', 'w') # h1 * (w1 / (w1 + w2)) + h2 * (w2 / (w1 + w2))
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tensor([[1.6667]]) # 1 * (3 / (3 + 6)) + 2 * (6 / (3 + 6))
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See Also
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--------
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sum_nodes
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mean_nodes
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sum_edges
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"""
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return _mean_on(graph, 'edges', feat, weight)
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def _max_on(graph, typestr, feat):
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"""Internal function to take elementwise maximum
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over node or edge features.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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typestr : str
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'nodes' or 'edges'
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feat : str
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The feature field name.
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Returns
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-------
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tensor
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The (weighted) summed node or edge features.
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"""
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data_attr, batch_num_objs_attr, _ = READOUT_ON_ATTRS[typestr]
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data = getattr(graph, data_attr)
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feat = data[feat]
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# TODO: the current solution pads the different graph sizes to the same,
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# a more efficient way is to use segment max, we need to implement it in
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# the future.
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batch_num_objs = getattr(graph, batch_num_objs_attr)
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feat = F.pad_packed_tensor(feat, batch_num_objs, -float('inf'))
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return F.max(feat, 1)
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def _softmax_on(graph, typestr, feat):
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"""Internal function of applying batch-wise graph-level softmax
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over node or edge features of a given field.
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Parameters
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----------
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graph : DGLGraph
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The graph
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typestr : str
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'nodes' or 'edges'
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feat : str
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The feature field name.
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Returns
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-------
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tensor
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The obtained tensor.
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"""
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data_attr, batch_num_objs_attr, _ = READOUT_ON_ATTRS[typestr]
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data = getattr(graph, data_attr)
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feat = data[feat]
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# TODO: the current solution pads the different graph sizes to the same,
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# a more efficient way is to use segment sum/max, we need to implement
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# it in the future.
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batch_num_objs = getattr(graph, batch_num_objs_attr)
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feat = F.pad_packed_tensor(feat, batch_num_objs, -float('inf'))
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feat = F.softmax(feat, 1)
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return F.pack_padded_tensor(feat, batch_num_objs)
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def _broadcast_on(graph, typestr, feat_data):
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"""Internal function of broadcasting features to all nodes/edges.
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Parameters
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----------
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graph : DGLGraph
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The graph
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typestr : str
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'nodes' or 'edges'
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feat_data : tensor
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The feature to broadcast. Tensor shape is :math:`(*)` for single graph,
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and :math:`(B, *)` for batched graph.
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Returns
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-------
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tensor
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The node/edge features tensor with shape :math:`(N, *)`.
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"""
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_, batch_num_objs_attr, _ = READOUT_ON_ATTRS[typestr]
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batch_num_objs = getattr(graph, batch_num_objs_attr)
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index = []
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for i, num_obj in enumerate(batch_num_objs):
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index.extend([i] * num_obj)
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ctx = F.context(feat_data)
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index = F.copy_to(F.tensor(index), ctx)
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return F.gather_row(feat_data, index)
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def _topk_on(graph, typestr, feat, k, descending=True, idx=None):
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"""Internal function to take graph-wise top-k node/edge features of
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field :attr:`feat` in :attr:`graph` ranked by keys at given
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index :attr:`idx`. If :attr:`descending` is set to False, return the
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k smallest elements instead.
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If idx is set to None, the function would return top-k value of all
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indices, which is equivalent to calling `th.topk(graph.ndata[feat], dim=0)`
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for each single graph of the input batched-graph.
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Parameters
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---------
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graph : DGLGraph
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The graph
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typestr : str
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'nodes' or 'edges'
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feat : str
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The feature field name.
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k : int
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The :math:`k` in "top-:math`k`".
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descending : bool
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Controls whether to return the largest or smallest elements,
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defaults to True.
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idx : int or None, defaults to None
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The key index we sort :attr:`feat` on, if set to None, we sort
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the whole :attr:`feat`.
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Returns
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-------
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tuple of tensors:
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The first tensor returns top-k features of each single graph of
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the input graph:
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a tensor with shape :math:`(B, K, D)` would be returned, where
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:math:`B` is the batch size of the input graph.
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The second tensor returns the top-k indices of each single graph
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of the input graph:
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a tensor with shape :math:`(B, K)`(:math:`(B, K, D)` if` idx
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is set to None) would be returned, where
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:math:`B` is the batch size of the input graph.
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Notes
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-----
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If an example has :math:`n` nodes/edges and :math:`n<k`, in the first
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returned tensor the :math:`n+1` to :math:`k`th rows would be padded
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with all zero; in the second returned tensor, the behavior of :math:`n+1`
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to :math:`k`th elements is not defined.
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"""
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data_attr, batch_num_objs_attr, _ = READOUT_ON_ATTRS[typestr]
|
|
data = getattr(graph, data_attr)
|
|
if F.ndim(data[feat]) > 2:
|
|
raise DGLError('The {} feature `{}` should have dimension less than or'
|
|
' equal to 2'.format(typestr, feat))
|
|
|
|
feat = data[feat]
|
|
hidden_size = F.shape(feat)[-1]
|
|
batch_num_objs = getattr(graph, batch_num_objs_attr)
|
|
batch_size = len(batch_num_objs)
|
|
|
|
length = max(max(batch_num_objs), k)
|
|
fill_val = -float('inf') if descending else float('inf')
|
|
feat_ = F.pad_packed_tensor(feat, batch_num_objs, fill_val, l_min=k)
|
|
|
|
if idx is not None:
|
|
keys = F.squeeze(F.slice_axis(feat_, -1, idx, idx+1), -1)
|
|
order = F.argsort(keys, -1, descending=descending)
|
|
else:
|
|
order = F.argsort(feat_, 1, descending=descending)
|
|
|
|
topk_indices = F.slice_axis(order, 1, 0, k)
|
|
|
|
# zero padding
|
|
feat_ = F.pad_packed_tensor(feat, batch_num_objs, 0, l_min=k)
|
|
|
|
if idx is not None:
|
|
feat_ = F.reshape(feat_, (batch_size * length, -1))
|
|
shift = F.repeat(F.arange(0, batch_size) * length, k, -1)
|
|
shift = F.copy_to(shift, F.context(feat))
|
|
topk_indices_ = F.reshape(topk_indices, (-1,)) + shift
|
|
else:
|
|
feat_ = F.reshape(feat_, (-1,))
|
|
shift = F.repeat(F.arange(0, batch_size), k * hidden_size, -1) * length * hidden_size +\
|
|
F.cat([F.arange(0, hidden_size)] * batch_size * k, -1)
|
|
shift = F.copy_to(shift, F.context(feat))
|
|
topk_indices_ = F.reshape(topk_indices, (-1,)) * hidden_size + shift
|
|
|
|
return F.reshape(F.gather_row(feat_, topk_indices_), (batch_size, k, -1)),\
|
|
topk_indices
|
|
|
|
|
|
def max_nodes(graph, feat):
|
|
"""Take elementwise maximum over all the values of node field
|
|
:attr:`feat` in :attr:`graph`
|
|
|
|
Parameters
|
|
----------
|
|
graph : DGLGraph
|
|
The graph.
|
|
feat : str
|
|
The feature field.
|
|
|
|
Returns
|
|
-------
|
|
tensor
|
|
The tensor obtained.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
|
|
Create two :class:`~dgl.DGLGraph` objects and initialize their
|
|
node features.
|
|
|
|
>>> g1 = dgl.DGLGraph() # Graph 1
|
|
>>> g1.add_nodes(2)
|
|
>>> g1.ndata['h'] = th.tensor([[1.], [2.]])
|
|
|
|
>>> g2 = dgl.DGLGraph() # Graph 2
|
|
>>> g2.add_nodes(3)
|
|
>>> g2.ndata['h'] = th.tensor([[1.], [2.], [3.]])
|
|
|
|
Max over node attribute :attr:`h` in a batched graph.
|
|
|
|
>>> bg = dgl.batch([g1, g2], node_attrs='h')
|
|
>>> dgl.max_nodes(bg, 'h')
|
|
tensor([[2.], # max(1, 2)
|
|
[3.]]) # max(1, 2, 3)
|
|
|
|
Max over node attribute :attr:`h` in a single graph.
|
|
|
|
>>> dgl.max_nodes(g1, 'h')
|
|
tensor([[2.]])
|
|
|
|
Notes
|
|
-----
|
|
Return a stacked tensor with an extra first dimension whose size equals
|
|
batch size of the input graph.
|
|
The i-th row of the stacked tensor contains the readout result of
|
|
the i-th graph in the batched graph. If a graph has no nodes,
|
|
a tensor filed with -inf of the same shape is returned at the
|
|
corresponding row.
|
|
"""
|
|
return _max_on(graph, 'nodes', feat)
|
|
|
|
def max_edges(graph, feat):
|
|
"""Take elementwise maximum over all the values of edge field
|
|
:attr:`feat` in :attr:`graph`
|
|
|
|
Parameters
|
|
----------
|
|
graph : DGLGraph
|
|
The graph.
|
|
feat : str
|
|
The feature field.
|
|
|
|
Returns
|
|
-------
|
|
tensor
|
|
The tensor obtained.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
|
|
Create two :class:`~dgl.DGLGraph` objects and initialize their
|
|
edge features.
|
|
|
|
>>> g1 = dgl.DGLGraph() # Graph 1
|
|
>>> g1.add_nodes(2)
|
|
>>> g1.add_edges([0, 1], [1, 0])
|
|
>>> g1.edata['h'] = th.tensor([[1.], [2.]])
|
|
|
|
>>> g2 = dgl.DGLGraph() # Graph 2
|
|
>>> g2.add_nodes(3)
|
|
>>> g2.add_edges([0, 1, 2], [1, 2, 0])
|
|
>>> g2.edata['h'] = th.tensor([[1.], [2.], [3.]])
|
|
|
|
Max over edge attribute :attr:`h` in a batched graph.
|
|
|
|
>>> bg = dgl.batch([g1, g2], edge_attrs='h')
|
|
>>> dgl.max_edges(bg, 'h')
|
|
tensor([[2.], # max(1, 2)
|
|
[3.]]) # max(1, 2, 3)
|
|
|
|
Max over edge attribute :attr:`h` in a single graph.
|
|
|
|
>>> dgl.max_edges(g1, 'h')
|
|
tensor([[2.]])
|
|
|
|
Notes
|
|
-----
|
|
Return a stacked tensor with an extra first dimension whose size equals
|
|
batch size of the input graph.
|
|
The i-th row of the stacked tensor contains the readout result of
|
|
the i-th graph in the batched graph. If a graph has no edges,
|
|
a tensor filled with -inf of the same shape is returned at the
|
|
corresponding row.
|
|
"""
|
|
return _max_on(graph, 'edges', feat)
|
|
|
|
def softmax_nodes(graph, feat):
|
|
"""Apply batch-wise graph-level softmax over all the values of node field
|
|
:attr:`feat` in :attr:`graph`.
|
|
|
|
Parameters
|
|
----------
|
|
graph : DGLGraph
|
|
The graph.
|
|
feat : str
|
|
The feature field.
|
|
|
|
Returns
|
|
-------
|
|
tensor
|
|
The tensor obtained.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
|
|
Create two :class:`~dgl.DGLGraph` objects and initialize their
|
|
node features.
|
|
|
|
>>> g1 = dgl.DGLGraph() # Graph 1
|
|
>>> g1.add_nodes(2)
|
|
>>> g1.ndata['h'] = th.tensor([[1., 0.], [2., 0.]])
|
|
|
|
>>> g2 = dgl.DGLGraph() # Graph 2
|
|
>>> g2.add_nodes(3)
|
|
>>> g2.ndata['h'] = th.tensor([[1., 0.], [2., 0.], [3., 0.]])
|
|
|
|
Softmax over node attribute :attr:`h` in a batched graph.
|
|
|
|
>>> bg = dgl.batch([g1, g2], node_attrs='h')
|
|
>>> dgl.softmax_nodes(bg, 'h')
|
|
tensor([[0.2689, 0.5000], # [0.2689, 0.7311] = softmax([1., 2.])
|
|
[0.7311, 0.5000], # [0.5000, 0.5000] = softmax([0., 0.])
|
|
[0.0900, 0.3333], # [0.0900, 0.2447, 0.6652] = softmax([1., 2., 3.])
|
|
[0.2447, 0.3333], # [0.3333, 0.3333, 0.3333] = softmax([0., 0., 0.])
|
|
[0.6652, 0.3333]])
|
|
|
|
Softmax over node attribute :attr:`h` in a single graph.
|
|
|
|
>>> dgl.softmax_nodes(g1, 'h')
|
|
tensor([[0.2689, 0.5000], # [0.2689, 0.7311] = softmax([1., 2.])
|
|
[0.7311, 0.5000]]), # [0.5000, 0.5000] = softmax([0., 0.])
|
|
|
|
Notes
|
|
-----
|
|
If the input graph has batch size greater then one, the softmax is applied at
|
|
each single graph in the batched graph.
|
|
"""
|
|
return _softmax_on(graph, 'nodes', feat)
|
|
|
|
|
|
def softmax_edges(graph, feat):
|
|
"""Apply batch-wise graph-level softmax over all the values of edge field
|
|
:attr:`feat` in :attr:`graph`.
|
|
|
|
Parameters
|
|
----------
|
|
graph : DGLGraph
|
|
The graph.
|
|
feat : str
|
|
The feature field.
|
|
|
|
Returns
|
|
-------
|
|
tensor
|
|
The tensor obtained.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
|
|
Create two :class:`~dgl.DGLGraph` objects and initialize their
|
|
edge features.
|
|
|
|
>>> g1 = dgl.DGLGraph() # Graph 1
|
|
>>> g1.add_nodes(2)
|
|
>>> g1.add_edges([0, 1], [1, 0])
|
|
>>> g1.edata['h'] = th.tensor([[1., 0.], [2., 0.]])
|
|
|
|
>>> g2 = dgl.DGLGraph() # Graph 2
|
|
>>> g2.add_nodes(3)
|
|
>>> g2.add_edges([0, 1, 2], [1, 2, 0])
|
|
>>> g2.edata['h'] = th.tensor([[1., 0.], [2., 0.], [3., 0.]])
|
|
|
|
Softmax over edge attribute :attr:`h` in a batched graph.
|
|
|
|
>>> bg = dgl.batch([g1, g2], edge_attrs='h')
|
|
>>> dgl.softmax_edges(bg, 'h')
|
|
tensor([[0.2689, 0.5000], # [0.2689, 0.7311] = softmax([1., 2.])
|
|
[0.7311, 0.5000], # [0.5000, 0.5000] = softmax([0., 0.])
|
|
[0.0900, 0.3333], # [0.0900, 0.2447, 0.6652] = softmax([1., 2., 3.])
|
|
[0.2447, 0.3333], # [0.3333, 0.3333, 0.3333] = softmax([0., 0., 0.])
|
|
[0.6652, 0.3333]])
|
|
|
|
Softmax over edge attribute :attr:`h` in a single graph.
|
|
|
|
>>> dgl.softmax_edges(g1, 'h')
|
|
tensor([[0.2689, 0.5000], # [0.2689, 0.7311] = softmax([1., 2.])
|
|
[0.7311, 0.5000]]), # [0.5000, 0.5000] = softmax([0., 0.])
|
|
|
|
Notes
|
|
-----
|
|
If the input graph has batch size greater then one, the softmax is applied at each
|
|
example in the batch.
|
|
"""
|
|
return _softmax_on(graph, 'edges', feat)
|
|
|
|
def broadcast_nodes(graph, feat_data):
|
|
"""Broadcast :attr:`feat_data` to all nodes in :attr:`graph`, and return a
|
|
tensor of node features.
|
|
|
|
Parameters
|
|
----------
|
|
graph : DGLGraph
|
|
The graph.
|
|
feat_data : tensor
|
|
The feature to broadcast. Tensor shape is :math:`(*)` for single graph, and
|
|
:math:`(B, *)` for batched graph.
|
|
|
|
Returns
|
|
-------
|
|
tensor
|
|
The node features tensor with shape :math:`(N, *)`.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
|
|
Create two :class:`~dgl.DGLGraph` objects and initialize their
|
|
node features.
|
|
|
|
>>> g1 = dgl.DGLGraph() # Graph 1
|
|
>>> g1.add_nodes(2)
|
|
|
|
>>> g2 = dgl.DGLGraph() # Graph 2
|
|
>>> g2.add_nodes(3)
|
|
|
|
>>> bg = dgl.batch([g1, g2])
|
|
>>> feat = th.rand(2, 5)
|
|
>>> feat
|
|
tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
|
|
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014]])
|
|
|
|
Broadcast feature to all nodes in the batched graph, feat[i] is broadcast to nodes
|
|
in the i-th example in the batch.
|
|
|
|
>>> dgl.broadcast_nodes(bg, feat)
|
|
tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
|
|
[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
|
|
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014],
|
|
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014],
|
|
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014]])
|
|
|
|
Broadcast feature to all nodes in the batched graph.
|
|
|
|
>>> dgl.broadcast_nodes(g1, feat[0])
|
|
tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
|
|
[0.4325, 0.7710, 0.5541, 0.0544, 0.9368]])
|
|
|
|
Notes
|
|
-----
|
|
feat[i] is broadcast to the nodes in i-th graph in the batched graph.
|
|
"""
|
|
return _broadcast_on(graph, 'nodes', feat_data)
|
|
|
|
def broadcast_edges(graph, feat_data):
|
|
"""Broadcast :attr:`feat_data` to all edges in :attr:`graph`, and return a
|
|
tensor of edge features.
|
|
|
|
Parameters
|
|
----------
|
|
graph : DGLGraph
|
|
The graph.
|
|
feat_data : tensor
|
|
The feature to broadcast. Tensor shape is :math:`(*)` for single
|
|
graph, and :math:`(B, *)` for batched graph.
|
|
|
|
Returns
|
|
-------
|
|
tensor
|
|
The edge features tensor with shape :math:`(E, *)`
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
|
|
Create two :class:`~dgl.DGLGraph` objects and initialize their
|
|
edge features.
|
|
|
|
>>> g1 = dgl.DGLGraph() # Graph 1
|
|
>>> g1.add_nodes(2)
|
|
>>> g1.add_edges([0, 1], [1, 0])
|
|
|
|
>>> g2 = dgl.DGLGraph() # Graph 2
|
|
>>> g2.add_nodes(3)
|
|
>>> g2.add_edges([0, 1, 2], [1, 2, 0])
|
|
|
|
>>> bg = dgl.batch([g1, g2])
|
|
>>> feat = th.rand(2, 5)
|
|
>>> feat
|
|
tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
|
|
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014]])
|
|
|
|
Broadcast feature to all edges in the batched graph, feat[i] is broadcast to edges
|
|
in the i-th example in the batch.
|
|
|
|
>>> dgl.broadcast_edges(bg, feat)
|
|
tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
|
|
[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
|
|
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014],
|
|
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014],
|
|
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014]])
|
|
|
|
Broadcast feature to all edges in the batched graph.
|
|
|
|
>>> dgl.broadcast_edges(g1, feat[0])
|
|
tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
|
|
[0.4325, 0.7710, 0.5541, 0.0544, 0.9368]])
|
|
|
|
Notes
|
|
-----
|
|
feat[i] is broadcast to the edges in i-th graph in the batched graph.
|
|
"""
|
|
return _broadcast_on(graph, 'edges', feat_data)
|
|
|
|
def topk_nodes(graph, feat, k, descending=True, idx=None):
|
|
"""Return graph-wise top-k node features of field :attr:`feat` in
|
|
:attr:`graph` ranked by keys at given index :attr:`idx`. If :attr:
|
|
`descending` is set to False, return the k smallest elements instead.
|
|
|
|
If idx is set to None, the function would return top-k value of all
|
|
indices, which is equivalent to calling
|
|
:code:`torch.topk(graph.ndata[feat], dim=0)`
|
|
for each example of the input graph.
|
|
|
|
Parameters
|
|
----------
|
|
graph : DGLGraph
|
|
The graph.
|
|
feat : str
|
|
The feature field.
|
|
k : int
|
|
The k in "top-k"
|
|
descending : bool
|
|
Controls whether to return the largest or smallest elements.
|
|
idx : int or None, defaults to None
|
|
The index of keys we rank :attr:`feat` on, if set to None, we sort
|
|
the whole :attr:`feat`.
|
|
|
|
Returns
|
|
-------
|
|
tuple of tensors
|
|
The first tensor returns top-k node features of each single graph of
|
|
the input graph:
|
|
a tensor with shape :math:`(B, K, D)` would be returned, where
|
|
:math:`B` is the batch size of the input graph.
|
|
The second tensor returns the top-k node indices of each single graph
|
|
of the input graph:
|
|
a tensor with shape :math:`(B, K)`(:math:`(B, K, D)` if` idx
|
|
is set to None) would be returned, where
|
|
:math:`B` is the batch size of the input graph.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
|
|
Create two :class:`~dgl.DGLGraph` objects and initialize their
|
|
node features.
|
|
|
|
>>> g1 = dgl.DGLGraph() # Graph 1
|
|
>>> g1.add_nodes(4)
|
|
>>> g1.ndata['h'] = th.rand(4, 5)
|
|
>>> g1.ndata['h']
|
|
tensor([[0.0297, 0.8307, 0.9140, 0.6702, 0.3346],
|
|
[0.5901, 0.3030, 0.9280, 0.6893, 0.7997],
|
|
[0.0880, 0.6515, 0.4451, 0.7507, 0.5297],
|
|
[0.5171, 0.6379, 0.2695, 0.8954, 0.5197]])
|
|
|
|
>>> g2 = dgl.DGLGraph() # Graph 2
|
|
>>> g2.add_nodes(5)
|
|
>>> g2.ndata['h'] = th.rand(5, 5)
|
|
>>> g2.ndata['h']
|
|
tensor([[0.3168, 0.3174, 0.5303, 0.0804, 0.3808],
|
|
[0.1323, 0.2766, 0.4318, 0.6114, 0.1458],
|
|
[0.1752, 0.9105, 0.5692, 0.8489, 0.0539],
|
|
[0.1931, 0.4954, 0.3455, 0.3934, 0.0857],
|
|
[0.5065, 0.5182, 0.5418, 0.1520, 0.3872]])
|
|
|
|
Top-k over node attribute :attr:`h` in a batched graph.
|
|
|
|
>>> bg = dgl.batch([g1, g2], node_attrs='h')
|
|
>>> dgl.topk_nodes(bg, 'h', 3)
|
|
(tensor([[[0.5901, 0.8307, 0.9280, 0.8954, 0.7997],
|
|
[0.5171, 0.6515, 0.9140, 0.7507, 0.5297],
|
|
[0.0880, 0.6379, 0.4451, 0.6893, 0.5197]],
|
|
|
|
[[0.5065, 0.9105, 0.5692, 0.8489, 0.3872],
|
|
[0.3168, 0.5182, 0.5418, 0.6114, 0.3808],
|
|
[0.1931, 0.4954, 0.5303, 0.3934, 0.1458]]]), tensor([[[1, 0, 1, 3, 1],
|
|
[3, 2, 0, 2, 2],
|
|
[2, 3, 2, 1, 3]],
|
|
|
|
[[4, 2, 2, 2, 4],
|
|
[0, 4, 4, 1, 0],
|
|
[3, 3, 0, 3, 1]]]))
|
|
|
|
Top-k over node attribute :attr:`h` along index -1 in a batched graph.
|
|
(used in SortPooling)
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|
|
|
>>> dgl.topk_nodes(bg, 'h', 3, idx=-1)
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(tensor([[[0.5901, 0.3030, 0.9280, 0.6893, 0.7997],
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|
[0.0880, 0.6515, 0.4451, 0.7507, 0.5297],
|
|
[0.5171, 0.6379, 0.2695, 0.8954, 0.5197]],
|
|
|
|
[[0.5065, 0.5182, 0.5418, 0.1520, 0.3872],
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|
[0.3168, 0.3174, 0.5303, 0.0804, 0.3808],
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|
[0.1323, 0.2766, 0.4318, 0.6114, 0.1458]]]), tensor([[1, 2, 3],
|
|
[4, 0, 1]]))
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|
|
|
Top-k over node attribute :attr:`h` in a single graph.
|
|
|
|
>>> dgl.topk_nodes(g1, 'h', 3)
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(tensor([[[0.5901, 0.8307, 0.9280, 0.8954, 0.7997],
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|
[0.5171, 0.6515, 0.9140, 0.7507, 0.5297],
|
|
[0.0880, 0.6379, 0.4451, 0.6893, 0.5197]]]), tensor([[[1, 0, 1, 3, 1],
|
|
[3, 2, 0, 2, 2],
|
|
[2, 3, 2, 1, 3]]]))
|
|
|
|
Notes
|
|
-----
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|
If an example has :math:`n` nodes and :math:`n<k`, in the first
|
|
returned tensor the :math:`n+1` to :math:`k`th rows would be padded
|
|
with all zero; in the second returned tensor, the behavior of :math:`n+1`
|
|
to :math:`k`th elements is not defined.
|
|
"""
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|
return _topk_on(graph, 'nodes', feat, k, descending=descending, idx=idx)
|
|
|
|
def topk_edges(graph, feat, k, descending=True, idx=None):
|
|
"""Return graph-wise top-k edge features of field :attr:`feat` in
|
|
:attr:`graph` ranked by keys at given index :attr:`idx`. If
|
|
:attr:`descending` is set to False, return the k smallest elements
|
|
instead.
|
|
|
|
If idx is set to None, the function would return top-k value of all
|
|
indices, which is equivalent to calling
|
|
:code:`torch.topk(graph.edata[feat], dim=0)`
|
|
for each example of the input graph.
|
|
|
|
Parameters
|
|
----------
|
|
graph : DGLGraph
|
|
The graph.
|
|
feat : str
|
|
The feature field.
|
|
k : int
|
|
The k in "top-k".
|
|
descending : bool
|
|
Controls whether to return the largest or smallest elements.
|
|
idx : int or None, defaults to None
|
|
The key index we sort :attr:`feat` on, if set to None, we sort
|
|
the whole :attr:`feat`.
|
|
|
|
Returns
|
|
-------
|
|
tuple of tensors
|
|
The first tensor returns top-k edge features of each single graph of
|
|
the input graph:
|
|
a tensor with shape :math:`(B, K, D)` would be returned, where
|
|
:math:`B` is the batch size of the input graph.
|
|
The second tensor returns the top-k edge indices of each single graph
|
|
of the input graph:
|
|
a tensor with shape :math:`(B, K)`(:math:`(B, K, D)` if` idx
|
|
is set to None) would be returned, where
|
|
:math:`B` is the batch size of the input graph.
|
|
|
|
Examples
|
|
--------
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
|
|
Create two :class:`~dgl.DGLGraph` objects and initialize their
|
|
edge features.
|
|
|
|
>>> g1 = dgl.DGLGraph() # Graph 1
|
|
>>> g1.add_nodes(4)
|
|
>>> g1.add_edges([0, 1, 2, 3], [1, 2, 3, 0])
|
|
>>> g1.edata['h'] = th.rand(4, 5)
|
|
>>> g1.edata['h']
|
|
tensor([[0.0297, 0.8307, 0.9140, 0.6702, 0.3346],
|
|
[0.5901, 0.3030, 0.9280, 0.6893, 0.7997],
|
|
[0.0880, 0.6515, 0.4451, 0.7507, 0.5297],
|
|
[0.5171, 0.6379, 0.2695, 0.8954, 0.5197]])
|
|
|
|
>>> g2 = dgl.DGLGraph() # Graph 2
|
|
>>> g2.add_nodes(5)
|
|
>>> g2.add_edges([0, 1, 2, 3, 4], [1, 2, 3, 4, 0])
|
|
>>> g2.edata['h'] = th.rand(5, 5)
|
|
>>> g2.edata['h']
|
|
tensor([[0.3168, 0.3174, 0.5303, 0.0804, 0.3808],
|
|
[0.1323, 0.2766, 0.4318, 0.6114, 0.1458],
|
|
[0.1752, 0.9105, 0.5692, 0.8489, 0.0539],
|
|
[0.1931, 0.4954, 0.3455, 0.3934, 0.0857],
|
|
[0.5065, 0.5182, 0.5418, 0.1520, 0.3872]])
|
|
|
|
Top-k over edge attribute :attr:`h` in a batched graph.
|
|
|
|
>>> bg = dgl.batch([g1, g2], edge_attrs='h')
|
|
>>> dgl.topk_edges(bg, 'h', 3)
|
|
(tensor([[[0.5901, 0.8307, 0.9280, 0.8954, 0.7997],
|
|
[0.5171, 0.6515, 0.9140, 0.7507, 0.5297],
|
|
[0.0880, 0.6379, 0.4451, 0.6893, 0.5197]],
|
|
|
|
[[0.5065, 0.9105, 0.5692, 0.8489, 0.3872],
|
|
[0.3168, 0.5182, 0.5418, 0.6114, 0.3808],
|
|
[0.1931, 0.4954, 0.5303, 0.3934, 0.1458]]]), tensor([[[1, 0, 1, 3, 1],
|
|
[3, 2, 0, 2, 2],
|
|
[2, 3, 2, 1, 3]],
|
|
|
|
[[4, 2, 2, 2, 4],
|
|
[0, 4, 4, 1, 0],
|
|
[3, 3, 0, 3, 1]]]))
|
|
|
|
Top-k over edge attribute :attr:`h` along index -1 in a batched graph.
|
|
(used in SortPooling)
|
|
|
|
>>> dgl.topk_edges(bg, 'h', 3, idx=-1)
|
|
(tensor([[[0.5901, 0.3030, 0.9280, 0.6893, 0.7997],
|
|
[0.0880, 0.6515, 0.4451, 0.7507, 0.5297],
|
|
[0.5171, 0.6379, 0.2695, 0.8954, 0.5197]],
|
|
|
|
[[0.5065, 0.5182, 0.5418, 0.1520, 0.3872],
|
|
[0.3168, 0.3174, 0.5303, 0.0804, 0.3808],
|
|
[0.1323, 0.2766, 0.4318, 0.6114, 0.1458]]]), tensor([[1, 2, 3],
|
|
[4, 0, 1]]))
|
|
|
|
Top-k over edge attribute :attr:`h` in a single graph.
|
|
|
|
>>> dgl.topk_edges(g1, 'h', 3)
|
|
(tensor([[[0.5901, 0.8307, 0.9280, 0.8954, 0.7997],
|
|
[0.5171, 0.6515, 0.9140, 0.7507, 0.5297],
|
|
[0.0880, 0.6379, 0.4451, 0.6893, 0.5197]]]), tensor([[[1, 0, 1, 3, 1],
|
|
[3, 2, 0, 2, 2],
|
|
[2, 3, 2, 1, 3]]]))
|
|
|
|
Notes
|
|
-----
|
|
If an example has :math:`n` edges and :math:`n<k`, in the first
|
|
returned tensor the :math:`n+1` to :math:`k`th rows would be padded
|
|
with all zero; in the second returned tensor, the behavior of :math:`n+1`
|
|
to :math:`k`th elements is not defined.
|
|
"""
|
|
return _topk_on(graph, 'edges', feat, k, descending=descending, idx=idx)
|