mindspore_gl.nn.conv.agnnconv 源代码

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"""AGNNConv Layer."""
import mindspore as ms
from mindspore_gl import Graph
from .. import GNNCell


[文档]class AGNNConv(GNNCell): r""" Attention Based Graph Neural Network. From the paper `Attention-based Graph Neural Network for Semi-Supervised Learning <https://arxiv.org/abs/1803.03735>`_ . .. math:: H^{l+1} = P H^{l} Computation of :math:`P` is: .. math:: P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l)) :math:`\beta` is a single scalar parameter. Args: init_beta (float, optional): Init :math:`\beta`, a single scalar parameter. Default: 1.0. learn_beta (bool, optional): Whether :math:`\beta` is learnable. 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. - **g** (Graph): The input graph. Outputs: - Tensor, output node features, where the shape should be the same as input 'x'. Raises: TypeError: If 'init_beta' is not a float. TypeError: If 'learn_beta' is not a bool. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore as ms >>> from mindspore_gl.nn import AGNNConv >>> from mindspore_gl import GraphField >>> n_nodes = 4 >>> n_edges = 8 >>> feat_size = 16 >>> src_idx = ms.Tensor([0, 0, 0, 1, 1, 1, 2, 3], ms.int32) >>> dst_idx = ms.Tensor([0, 1, 3, 1, 2, 3, 3, 2], ms.int32) >>> ones = ms.ops.Ones() >>> feat = ones((n_nodes, feat_size), ms.float32) >>> graph_field = GraphField(src_idx, dst_idx, n_nodes, n_edges) >>> conv = AGNNConv() >>> ret = conv(feat, *graph_field.get_graph()) >>> print(ret.shape) (4, 16) """ def __init__(self, init_beta: float = 1.0, learn_beta: bool = True): super().__init__() assert isinstance(init_beta, float), "init_beta must be float" assert isinstance(learn_beta, bool), "learn_beta must be bool" if learn_beta: self.beta = ms.Parameter(ms.Tensor([init_beta], ms.float32)) else: self.beta = ms.Tensor([init_beta], ms.float32) # pylint: disable=arguments-differ def construct(self, x, g: Graph): """ Construct function for AGNNConv. """ g.set_vertex_attr({"h": x, "norm_h": ms.ops.L2Normalize()(x)}) for v in g.dst_vertex: cosine_dis = [ms.ops.Exp()(self.beta * g.dot(u.norm_h, v.norm_h)) for u in v.innbs] a = cosine_dis / g.sum(cosine_dis) v.h = g.sum([u.h for u in v.innbs] * a) return [v.h for v in g.dst_vertex]