Source code for dgl.readout

"""Classes and functions for batching multiple graphs together."""
from __future__ import absolute_import

from .base import DGLError
from . import backend as F
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:`(*)` for single graph, and :math:`(B, *)` for batched graph. 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. >>> 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]]) See Also -------- broadcast_edges """ if len(F.shape(graph_feat)) == 1: 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:`(*)` for single graph, and :math:`(B, *)` for batched graph. 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. >>> dgl.broadcast_edges(g2, feat[1]) 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 len(F.shape(graph_feat)) == 1: 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)