mindspore_gl.nn.conv.gatedgraphconv 源代码

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"""GatedGraphConv Layer"""
import math
import mindspore as ms
from mindspore.common.initializer import XavierUniform
from mindspore_gl import Graph
from .. import GNNCell


class HomoGraphConv(GNNCell):
    """
    Homo Graph Conv

    Args:
        out_feat_size (int): Output node feature size.
        bias (bool): Whether use bias.

    Returns:
        Tensor, output node features.
    """

    def __init__(self,
                 out_feat_size: int,
                 bias=True):
        """
        Init HomoGraphConv.
        """
        super().__init__()
        gain = math.sqrt(2)  # gain for relu
        self.dense = ms.nn.Dense(out_feat_size, out_feat_size, has_bias=bias, weight_init=XavierUniform(gain))

    # pylint: disable=arguments-differ
    def construct(self, x, g: Graph):
        """
        Construct function for HomoGraphConv.
        """
        g.set_vertex_attr({"h": self.dense(x)})
        for v in g.dst_vertex:
            v.h = g.sum([u.h for u in v.innbs])
        return [v.h for v in g.dst_vertex]


[文档]class GatedGraphConv(ms.nn.Cell): r""" Gated Graph Convolution Layer. From the paper `Gated Graph Sequence Neural Networks <https://arxiv.org/pdf/1511.05493.pdf>`_ . .. math:: h_{i}^{0} = [ x_i \| \mathbf{0} ] \\ a_{i}^{t} = \sum_{j\in\mathcal{N}(i)} W_{e_{ij}} h_{j}^{t} \\ h_{i}^{t+1} = \mathrm{GRU}(a_{i}^{t}, h_{i}^{t}) Args: in_feat_size (int): Input node feature size. out_feat_size (int): Output node feature size. n_steps (int): Number of steps. n_etype (int): Number of edge types. bias (bool, optional): Whether use bias. Default: True. Inputs: - **x** (Tensor): The input node features. The shape is :math:`(N,*)` where :math:`N` is the number of nodes, and :math:`*` could be of any shape. - **src_idx** (List): The source index for each edge type. - **dst_idx** (List): The destination index for each edge type. - **n_nodes** (int): The number of nodes of the whole graph. - **n_edges** (List): The number of edges for each edge type. Outputs: - Tensor, output node features. The shape is :math:`(N, out\_feat\_size)`. Raises: TypeError: If `in_feat_size` is not a positive int. TypeError: If `out_feat_size` is not a positive int. TypeError: If `n_steps` is not a positive int. TypeError: If `n_etype` is not a positive int. TypeError: If `bias` is not a bool. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore as ms >>> from mindspore_gl.nn import GatedGraphConv >>> from mindspore_gl import GraphField >>> feat_size = 16 >>> n_nodes = 4 >>> h = ms.ops.Ones()((n_nodes, feat_size), ms.float32) >>> src_idx = [ms.Tensor([0, 1, 2, 3], ms.int32), ms.Tensor([0, 0, 1, 1], ms.int32), ... ms.Tensor([0, 0, 1, 2, 3], ms.int32), ms.Tensor([2, 3, 3, 0, 1], ms.int32), ... ms.Tensor([0, 1, 2, 3], ms.int32), ms.Tensor([2, 0, 2, 1], ms.int32)] >>> dst_idx = [ms.Tensor([0, 0, 1, 1], ms.int32), ms.Tensor([0, 1, 2, 3], ms.int32), ... ms.Tensor([2, 3, 3, 0, 1], ms.int32), ms.Tensor([0, 0, 1, 2, 3], ms.int32), ... ms.Tensor([2, 0, 2, 1], ms.int32), ms.Tensor([0, 1, 2, 3], ms.int32)] >>> n_edges = [4, 4, 5, 5, 4, 4] >>> conv = GatedGraphConv(feat_size, 16, 2, 6, True) >>> ret = conv(h, src_idx, dst_idx, n_nodes, n_edges) >>> print(ret.shape) (4, 16) """ def __init__(self, in_feat_size: int, out_feat_size: int, n_steps: int, n_etype: int, bias=True): super().__init__() assert isinstance(in_feat_size, int) and in_feat_size > 0, "in_feat_size must be positive int" assert isinstance(out_feat_size, int) and out_feat_size > 0, "out_feat_size must be positive int" if in_feat_size > out_feat_size: raise TypeError("GatedGraphConv requires input feature size <= out_feature_size") self.in_feat_size = in_feat_size self.out_feat_size = out_feat_size cl = [] for _ in range(n_etype): cl.append(HomoGraphConv(out_feat_size, bias)) self.cell_list = ms.nn.CellList(cl) self.n_etype = n_etype self.n_steps = n_steps self.gru = ms.nn.GRUCell(out_feat_size, out_feat_size) # pylint: disable=arguments-differ def construct(self, x, src_idx, dst_idx, n_nodes, n_edges): """ Construct function for GatedGraphConv. """ if self.in_feat_size < self.out_feat_size: x = ms.ops.Concat(axis=-1)(x, ms.ops.Zeros()((ms.ops.Shape()(x)[0], self.out_feat_size - self.in_feat_size), ms.float32)) for _ in range(self.n_steps): out = self.cell_list[0](x, src_idx[0], dst_idx[0], n_nodes, n_edges) for i in range(1, self.n_etype): out += self.cell_list[i](x, src_idx[i], dst_idx[i], n_nodes, n_edges) if self.gru is not None: out = self.gru(out, x) x = out x = out return x