Method apply_edges

python/dgl/heterograph.py:4597–4712

Update the features of the specified edges by the provided function. Parameters ---------- func : dgl.function.BuiltinFunction or callable The function to generate new edge features. It must be either a :ref:`api-built-in` or a :ref:`apiudf`.

(self, func, edges=ALL, etype=None)

        self._set_n_repr(ntid, v, ndata)

    def apply_edges(self, func, edges=ALL, etype=None):
        """Update the features of the specified edges by the provided function.

        Parameters
        ----------
        func : dgl.function.BuiltinFunction or callable
            The function to generate new edge features. It must be either
            a :ref:`api-built-in` or a :ref:`apiudf`.
        edges : edges
            The edges to update features on. The allowed input formats are:

            * ``int``: A single edge ID.
            * Int Tensor: Each element is an edge ID.  The tensor must have the same device type
              and ID data type as the graph's.
            * iterable[int]: Each element is an edge ID.
            * (Tensor, Tensor): The node-tensors format where the i-th elements
              of the two tensors specify an edge.
            * (iterable[int], iterable[int]): Similar to the node-tensors format but
              stores edge endpoints in python iterables.

            Default value specifies all the edges in the graph.

        etype : str or (str, str, str), optional
            The type name 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.

        Notes
        -----
        DGL recommends using DGL's bulit-in function for the :attr:`func` argument,
        because DGL will invoke efficient kernels that avoids copying node features to
        edge features in this case.

        Examples
        --------

        The following example uses PyTorch backend.

        >>> import dgl
        >>> import torch

        **Homogeneous graph**

        >>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
        >>> g.ndata['h'] = torch.ones(5, 2)
        >>> g.apply_edges(lambda edges: {'x' : edges.src['h'] + edges.dst['h']})
        >>> g.edata['x']
        tensor([[2., 2.],
                [2., 2.],
                [2., 2.],
                [2., 2.]])

        Use built-in function