"""Classes and functions for batching multiple graphs together."""
from __future__ import absolute_import
from . import backend as F
from .base import dgl_warning, DGLError
from .ops import segment
__all__ = [
"readout_nodes",
"readout_edges",
"sum_nodes",
"sum_edges",
"mean_nodes",
"mean_edges",
"max_nodes",
"max_edges",
"softmax_nodes",
"softmax_edges",
"broadcast_nodes",
"broadcast_edges",
"topk_nodes",
"topk_edges",
]
[docs]def readout_nodes(graph, feat, weight=None, *, op="sum", ntype=None):
"""Generate a graph-level representation by aggregating node features
:attr:`feat`.
The function is commonly used as a *readout* function on a batch of graphs
to generate graph-level representation. Thus, the result tensor shape
depends on the batch size of the input graph. Given a graph of batch size
:math:`B`, and a feature size of :math:`D`, the result shape will be
:math:`(B, D)`, with each row being the aggregated node features of each
graph.
Parameters
----------
graph : DGLGraph.
Input graph.
feat : str
Node feature name.
weight : str, optional
Node weight name. None means aggregating without weights.
Otherwise, multiply each node feature by node feature :attr:`weight`
before aggregation. The weight feature shape must be compatible with
an element-wise multiplication with the feature tensor.
op : str, optional
Readout operator. Can be 'sum', 'max', 'min', 'mean'.
ntype : str, optional
Node type. Can be omitted if there is only one node type in the graph.
Returns
-------
Tensor
Result tensor.
Examples
--------
>>> import dgl
>>> import torch as th
Create two :class:`~dgl.DGLGraph` objects and initialize their
node features.
>>> g1 = dgl.graph(([0, 1], [1, 0])) # Graph 1
>>> g1.ndata['h'] = th.tensor([1., 2.])
>>> g2 = dgl.graph(([0, 1], [1, 2])) # Graph 2
>>> g2.ndata['h'] = th.tensor([1., 2., 3.])
Sum over one graph:
>>> dgl.readout_nodes(g1, 'h')
tensor([3.]) # 1 + 2
Sum over a batched graph:
>>> bg = dgl.batch([g1, g2])
>>> dgl.readout_nodes(bg, 'h')
tensor([3., 6.]) # [1 + 2, 1 + 2 + 3]
Weighted sum:
>>> bg.ndata['w'] = th.tensor([.1, .2, .1, .5, .2])
>>> dgl.readout_nodes(bg, 'h', 'w')
tensor([.5, 1.7])
Readout by max:
>>> dgl.readout_nodes(bg, 'h', op='max')
tensor([2., 3.])
See Also
--------
readout_edges
"""
x = graph.nodes[ntype].data[feat]
if weight is not None:
x = x * graph.nodes[ntype].data[weight]
return segment.segment_reduce(graph.batch_num_nodes(ntype), x, reducer=op)
[docs]def readout_edges(graph, feat, weight=None, *, op="sum", etype=None):
"""Sum the edge feature :attr:`feat` in :attr:`graph`, optionally
multiplies it by a edge :attr:`weight`.
The function is commonly used as a *readout* function on a batch of graphs
to generate graph-level representation. Thus, the result tensor shape
depends on the batch size of the input graph. Given a graph of batch size
:math:`B`, and a feature size of :math:`D`, the result shape will be
:math:`(B, D)`, with each row being the aggregated edge features of each
graph.
Parameters
----------
graph : DGLGraph.
The input graph.
feat : str
The edge feature name.
weight : str, optional
The edge weight feature name. If None, no weighting will be performed,
otherwise, weight each edge feature with field :attr:`feat`.
for summation. The weight feature shape must be compatible with
an element-wise multiplication with the feature tensor.
op : str, optional
Readout operator. Can be 'sum', 'max', 'min', 'mean'.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor
Result tensor.
Examples
--------
>>> import dgl
>>> import torch as th
Create two :class:`~dgl.DGLGraph` objects and initialize their
edge features.
>>> g1 = dgl.graph(([0, 1], [1, 0])) # Graph 1
>>> g1.edata['h'] = th.tensor([1., 2.])
>>> g2 = dgl.graph(([0, 1], [1, 2])) # Graph 2
>>> g2.edata['h'] = th.tensor([2., 3.])
Sum over one graph:
>>> dgl.readout_edges(g1, 'h')
tensor([3.]) # 1 + 2
Sum over a batched graph:
>>> bg = dgl.batch([g1, g2])
>>> dgl.readout_edges(bg, 'h')
tensor([3., 5.]) # [1 + 2, 2 + 3]
Weighted sum:
>>> bg.edata['w'] = th.tensor([.1, .2, .1, .5])
>>> dgl.readout_edges(bg, 'h', 'w')
tensor([.5, 1.7])
Readout by max:
>>> dgl.readout_edges(bg, 'w', op='max')
tensor([2., 3.])
See Also
--------
readout_nodes
"""
x = graph.edges[etype].data[feat]
if weight is not None:
x = x * graph.edges[etype].data[weight]
return segment.segment_reduce(graph.batch_num_edges(etype), x, reducer=op)
[docs]def sum_nodes(graph, feat, weight=None, *, ntype=None):
"""Syntax sugar for ``dgl.readout_nodes(graph, feat, weight, ntype=ntype, op='sum')``.
See Also
--------
readout_nodes
"""
return readout_nodes(graph, feat, weight, ntype=ntype, op="sum")
[docs]def sum_edges(graph, feat, weight=None, *, etype=None):
"""Syntax sugar for ``dgl.readout_edges(graph, feat, weight, etype=etype, op='sum')``.
See Also
--------
readout_edges
"""
return readout_edges(graph, feat, weight, etype=etype, op="sum")
[docs]def mean_nodes(graph, feat, weight=None, *, ntype=None):
"""Syntax sugar for ``dgl.readout_nodes(graph, feat, weight, ntype=ntype, op='mean')``.
See Also
--------
readout_nodes
"""
return readout_nodes(graph, feat, weight, ntype=ntype, op="mean")
[docs]def mean_edges(graph, feat, weight=None, *, etype=None):
"""Syntax sugar for ``dgl.readout_edges(graph, feat, weight, etype=etype, op='mean')``.
See Also
--------
readout_edges
"""
return readout_edges(graph, feat, weight, etype=etype, op="mean")
[docs]def max_nodes(graph, feat, weight=None, *, ntype=None):
"""Syntax sugar for ``dgl.readout_nodes(graph, feat, weight, ntype=ntype, op='max')``.
See Also
--------
readout_nodes
"""
return readout_nodes(graph, feat, weight, ntype=ntype, op="max")
[docs]def max_edges(graph, feat, weight=None, *, etype=None):
"""Syntax sugar for ``dgl.readout_edges(graph, feat, weight, etype=etype, op='max')``.
See Also
--------
readout_edges
"""
return readout_edges(graph, feat, weight, etype=etype, op="max")
[docs]def softmax_nodes(graph, feat, *, ntype=None):
r"""Perform graph-wise softmax on the node features.
For each node :math:`v\in\mathcal{V}` and its feature :math:`x_v`,
calculate its normalized feature as follows:
.. math::
z_v = \frac{\exp(x_v)}{\sum_{u\in\mathcal{V}}\exp(x_u)}
If the graph is a batch of multiple graphs, each graph computes softmax
independently. The result tensor has the same shape as the original node
feature.
Parameters
----------
graph : DGLGraph.
The input graph.
feat : str
The node feature name.
ntype : str, optional
The node type name. Can be omitted if there is only one node type in the graph.
Returns
-------
Tensor
Result tensor.
Examples
--------
>>> import dgl
>>> import torch as th
Create two :class:`~dgl.DGLGraph` objects and initialize their
node features.
>>> g1 = dgl.graph(([0, 1], [1, 0])) # Graph 1
>>> g1.ndata['h'] = th.tensor([1., 1.])
>>> g2 = dgl.graph(([0, 1], [1, 2])) # Graph 2
>>> g2.ndata['h'] = th.tensor([1., 1., 1.])
Softmax over one graph:
>>> dgl.softmax_nodes(g1, 'h')
tensor([.5000, .5000])
Softmax over a batched graph:
>>> bg = dgl.batch([g1, g2])
>>> dgl.softmax_nodes(bg, 'h')
tensor([.5000, .5000, .3333, .3333, .3333])
See Also
--------
softmax_edges
"""
x = graph.nodes[ntype].data[feat]
return segment.segment_softmax(graph.batch_num_nodes(ntype), x)
[docs]def softmax_edges(graph, feat, *, etype=None):
r"""Perform graph-wise softmax on the edge features.
For each edge :math:`e\in\mathcal{E}` and its feature :math:`x_e`,
calculate its normalized feature as follows:
.. math::
z_e = \frac{\exp(x_e)}{\sum_{e'\in\mathcal{E}}\exp(x_{e'})}
If the graph is a batch of multiple graphs, each graph computes softmax
independently. The result tensor has the same shape as the original edge
feature.
Parameters
----------
graph : DGLGraph.
The input graph.
feat : str
The edge feature name.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor
Result tensor.
Examples
--------
>>> import dgl
>>> import torch as th
Create two :class:`~dgl.DGLGraph` objects and initialize their
edge features.
>>> g1 = dgl.graph(([0, 1], [1, 0])) # Graph 1
>>> g1.edata['h'] = th.tensor([1., 1.])
>>> g2 = dgl.graph(([0, 1, 0], [1, 2, 2])) # Graph 2
>>> g2.edata['h'] = th.tensor([1., 1., 1.])
Softmax over one graph:
>>> dgl.softmax_edges(g1, 'h')
tensor([.5000, .5000])
Softmax over a batched graph:
>>> bg = dgl.batch([g1, g2])
>>> dgl.softmax_edges(bg, 'h')
tensor([.5000, .5000, .3333, .3333, .3333])
See Also
--------
softmax_nodes
"""
x = graph.edges[etype].data[feat]
return segment.segment_softmax(graph.batch_num_edges(etype), x)
[docs]def broadcast_nodes(graph, graph_feat, *, ntype=None):
"""Generate a node feature equal to the graph-level feature :attr:`graph_feat`.
The operation is similar to ``numpy.repeat`` (or ``torch.repeat_interleave``).
It is commonly used to normalize node features by a global vector. For example,
to normalize node features across graph to range :math:`[0~1)`:
>>> g = dgl.batch([...]) # batch multiple graphs
>>> g.ndata['h'] = ... # some node features
>>> h_sum = dgl.broadcast_nodes(g, dgl.sum_nodes(g, 'h'))
>>> g.ndata['h'] /= h_sum # normalize by summation
Parameters
----------
graph : DGLGraph
The graph.
graph_feat : tensor
The feature to broadcast. Tensor shape is :math:`(B, *)` for batched graph,
where :math:`B` is the batch size.
ntype : str, optional
Node type. Can be omitted if there is only one node type.
Returns
-------
Tensor
The node features tensor with shape :math:`(N, *)`, where :math:`N` is the
number of nodes.
Examples
--------
>>> import dgl
>>> import torch as th
Create two :class:`~dgl.DGLGraph` objects and initialize their
node features.
>>> g1 = dgl.graph(([0], [1])) # Graph 1
>>> g2 = dgl.graph(([0, 1], [1, 2])) # Graph 2
>>> 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 single graph (the feature tensor shape
to broadcast should be :math:`(1, *)`).
>>> feat0 = th.unsqueeze(feat[0], 0)
>>> dgl.broadcast_nodes(g1, feat0)
tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368],
[0.4325, 0.7710, 0.5541, 0.0544, 0.9368]])
See Also
--------
broadcast_edges
"""
if F.shape(graph_feat)[0] != graph.batch_size and graph.batch_size == 1:
dgl_warning(
"For a single graph, use a tensor of shape (1, *) for graph_feat."
" The support of shape (*) will be deprecated."
)
graph_feat = F.unsqueeze(graph_feat, dim=0)
return F.repeat(graph_feat, graph.batch_num_nodes(ntype), dim=0)
[docs]def broadcast_edges(graph, graph_feat, *, etype=None):
"""Generate an edge feature equal to the graph-level feature :attr:`graph_feat`.
The operation is similar to ``numpy.repeat`` (or ``torch.repeat_interleave``).
It is commonly used to normalize edge features by a global vector. For example,
to normalize edge features across graph to range :math:`[0~1)`:
>>> g = dgl.batch([...]) # batch multiple graphs
>>> g.edata['h'] = ... # some node features
>>> h_sum = dgl.broadcast_edges(g, dgl.sum_edges(g, 'h'))
>>> g.edata['h'] /= h_sum # normalize by summation
Parameters
----------
graph : DGLGraph
The graph.
graph_feat : tensor
The feature to broadcast. Tensor shape is :math:`(B, *)` for batched graph,
where :math:`B` is the batch size.
etype : str, typle of str, optional
Edge type. Can be omitted if there is only one edge type in the graph.
Returns
-------
Tensor
The edge features tensor with shape :math:`(M, *)`, where :math:`M` is the
number of edges.
Examples
--------
>>> import dgl
>>> import torch as th
Create two :class:`~dgl.DGLGraph` objects and initialize their
edge features.
>>> g1 = dgl.graph(([0], [1])) # Graph 1
>>> g2 = dgl.graph(([0, 1], [1, 2])) # Graph 2
>>> 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],
[0.2721, 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 single graph (the feature tensor shape
to broadcast should be :math:`(1, *)`).
>>> feat1 = th.unsqueeze(feat[1], 0)
>>> dgl.broadcast_edges(g2, feat1)
tensor([[0.2721, 0.4629, 0.7269, 0.0724, 0.1014],
[0.2721, 0.4629, 0.7269, 0.0724, 0.1014]])
See Also
--------
broadcast_nodes
"""
if F.shape(graph_feat)[0] != graph.batch_size and graph.batch_size == 1:
dgl_warning(
"For a single graph, use a tensor of shape (1, *) for graph_feat."
" The support of shape (*) will be deprecated."
)
graph_feat = F.unsqueeze(graph_feat, dim=0)
return F.repeat(graph_feat, graph.batch_num_edges(etype), dim=0)
READOUT_ON_ATTRS = {
"nodes": ("ndata", "batch_num_nodes", "number_of_nodes"),
"edges": ("edata", "batch_num_edges", "number_of_edges"),
}
def _topk_torch(keys, k, descending, x):
"""Internal function to take graph-wise top-k node/edge features according to
the rank given by keys, this function is PyTorch only.
Parameters
----------
keys : Tensor
The key for ranking.
k : int
The :math:`k` in "top-:math:`k`".
descending : bool
Indicates whether to return the feature corresponding to largest or
smallest elements.
x : Tensor
The padded feature with shape (batch, max_len, *)
Returns
-------
sorted_feat : Tensor
A tensor with shape :math:`(batch, k, *)`.
sorted_idx : Tensor
A tensor with shape :math:`(batch, k)`.
"""
import torch as th
batch_size, max_len = x.shape[0], x.shape[1]
topk_indices = keys.topk(k, -1, largest=descending)[1] # (batch_size, k)
x = x.view((batch_size * max_len), -1)
shift = (
th.arange(0, batch_size, device=x.device).view(batch_size, 1) * max_len
)
topk_indices_ = topk_indices + shift
x = x[topk_indices_].view(batch_size, k, -1)
return th.masked_fill(x, th.isinf(x), 0), topk_indices
def _topk_on(graph, typestr, feat, k, descending, sortby, ntype_or_etype):
"""Internal function to take graph-wise top-k node/edge features of
field :attr:`feat` in :attr:`graph` ranked by keys at given
index :attr:`sortby`. If :attr:`descending` is set to False, return the
k smallest elements instead.
Parameters
---------
graph : DGLGraph
The graph
typestr : str
'nodes' or 'edges'
feat : str
The feature field name.
k : int
The :math:`k` in "top-:math`k`".
descending : bool
Controls whether to return the largest or smallest elements,
defaults to True.
sortby : int
The key index we sort :attr:`feat` on, if set to None, we sort
the whole :attr:`feat`.
ntype_or_etype : str, tuple of str
Node/edge type.
Returns
-------
sorted_feat : Tensor
A tensor with shape :math:`(B, K, D)`, where
:math:`B` is the batch size of the input graph.
sorted_idx : Tensor
A tensor with shape :math:`(B, K)`(:math:`(B, K, D)` if sortby
is set to None), where
:math:`B` is the batch size of the input graph, :math:`D`
is the feature size.
Notes
-----
If an example has :math:`n` nodes/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.
"""
_, batch_num_objs_attr, _ = READOUT_ON_ATTRS[typestr]
data = getattr(graph, typestr)[ntype_or_etype].data
if F.ndim(data[feat]) > 2:
raise DGLError(
"Only support {} feature `{}` with 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)(ntype_or_etype)
batch_size = len(batch_num_objs)
length = max(max(F.asnumpy(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
) # (batch_size, l, d)
if F.backend_name == "pytorch" and sortby is not None:
# PyTorch's implementation of top-K
keys = feat_[..., sortby] # (batch_size, l)
return _topk_torch(keys, k, descending, feat_)
else:
# Fallback to framework-agnostic implementation of top-K
if sortby is not None:
keys = F.squeeze(F.slice_axis(feat_, -1, sortby, sortby + 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)
if sortby 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
out = F.reshape(F.gather_row(feat_, topk_indices_), (batch_size, k, -1))
out = F.replace_inf_with_zero(out)
return out, topk_indices
[docs]def topk_nodes(graph, feat, k, *, descending=True, sortby=None, ntype=None):
"""Return a graph-level representation by a graph-wise top-k on
node features :attr:`feat` in :attr:`graph` by feature at index :attr:`sortby`.
If :attr:`descending` is set to False, return the k smallest elements instead.
If :attr:`sortby` is set to None, the function would perform top-k on
all dimensions independently, equivalent to calling
:code:`torch.topk(graph.ndata[feat], dim=0)`.
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.
sortby : int, optional
Sort according to which feature. If is None, all features are sorted independently.
ntype : str, optional
Node type. Can be omitted if there is only one node type in the graph.
Returns
-------
sorted_feat : Tensor
A tensor with shape :math:`(B, K, D)`, where
:math:`B` is the batch size of the input graph.
sorted_idx : Tensor
A tensor with shape :math:`(B, K)`(:math:`(B, K, D)` if sortby
is set to None), where
:math:`B` is the batch size of the input graph, :math:`D`
is the feature size.
Notes
-----
If an example has :math:`n` nodes and :math:`n<k`, the ``sorted_feat``
tensor will pad the :math:`n+1` to :math:`k` th rows with zero;
Examples
--------
>>> import dgl
>>> import torch as th
Create two :class:`~dgl.DGLGraph` objects and initialize their
node features.
>>> g1 = dgl.graph(([0, 1], [2, 3])) # Graph 1
>>> 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.graph(([0, 1, 2], [2, 3, 4])) # Graph 2
>>> 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], ndata=['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 the last dimension in a batched graph.
(used in SortPooling)
>>> dgl.topk_nodes(bg, 'h', 3, sortby=-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 node attribute :attr:`h` in a single graph.
>>> dgl.topk_nodes(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]]]))
"""
return _topk_on(
graph,
"nodes",
feat,
k,
descending=descending,
sortby=sortby,
ntype_or_etype=ntype,
)
[docs]def topk_edges(graph, feat, k, *, descending=True, sortby=None, etype=None):
"""Return a graph-level representation by a graph-wise top-k
on edge features :attr:`feat` in :attr:`graph` by feature at index :attr:`sortby`.
If :attr:`descending` is set to False, return the k smallest elements instead.
If :attr:`sortby` is set to None, the function would perform top-k on
all dimensions independently, equivalent to calling
:code:`torch.topk(graph.edata[feat], dim=0)`.
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.
sortby : int, optional
Sort according to which feature. If is None, all features are sorted independently.
etype : str, typle of str, optional
Edge type. Can be omitted if there is only one edge type in the graph.
Returns
-------
sorted_feat : Tensor
A tensor with shape :math:`(B, K, D)`, where
:math:`B` is the batch size of the input graph.
sorted_idx : Tensor
A tensor with shape :math:`(B, K)`(:math:`(B, K, D)` if sortby
is set to None), where
:math:`B` is the batch size of the input graph, :math:`D`
is the feature size.
Notes
-----
If an example has :math:`n` nodes and :math:`n<k`, the ``sorted_feat``
tensor will pad the :math:`n+1` to :math:`k` th rows with zero;
Examples
--------
>>> import dgl
>>> import torch as th
Create two :class:`~dgl.DGLGraph` objects and initialize their
edge features.
>>> g1 = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 0])) # Graph 1
>>> 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.graph(([0, 1, 2, 3, 4], [1, 2, 3, 4, 0])) # Graph 2
>>> 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], edata=['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, sortby=-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]]]))
"""
return _topk_on(
graph,
"edges",
feat,
k,
descending=descending,
sortby=sortby,
ntype_or_etype=etype,
)