mindspore_gl.nn.conv.sgconv 源代码

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


[文档]class SGConv(GNNCell): r""" Simplified Graph convolutional layer. From the paper `Simplifying Graph Convolutional Networks <https://arxiv.org/pdf/1902.07153.pdf>`_ . .. math:: H^{K} = (\tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2})^K X \Theta Where :math:`\tilde{A}=A+I`. ..Note: PYNATIVE mode only now. Args: in_feat_size (int): Input node feature size. out_feat_size (int): Output node feature size. num_hops (int, optional): Number of hops. Default: 1. cached (bool, optional): Whether use cached. Default: True. bias (bool, optional): Whether use bias. Default: True. norm (Cell, optional): Normalization function Cell. Default: None. Inputs: - **x** (Tensor) - The input node features. The shape is :math:`(N, D_{in})` where :math:`N` is the number of nodes, and :math:`D_{in}` should be equal to `in_feat_size` in `Args`. - **in_deg** (Tensor) - In degree for nodes. The shape is :math:`(N, )` where :math:`N` is the number of nodes. - **out_deg** (Tensor) - Out degree for nodes. The shape is :math:`(N, )` where :math:`N` is the number of nodes. - **g** (Graph) - The input graph. Outputs: - Tensor, output node features with shape of :math:`(N, D_{out})`, where :math:`(D_{out})` should be the same as `out_feat_size` in `Args`. Raises: TypeError: If `in_feat_size` or `out_feat_size` or `num_hops` is not an int. TypeError: If `bias` or `cached` is not a bool. TypeError: If `norm` is not a Cell. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore as ms >>> import mindspore.context as context >>> from mindspore_gl.nn import SGConv >>> from mindspore_gl import GraphField >>> context.set_context(device_target="GPU", mode=context.PYNATIVE_MODE) >>> n_nodes = 4 >>> n_edges = 8 >>> 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) >>> in_deg = ms.Tensor([1, 2, 2, 3], ms.int32) >>> out_deg = ms.Tensor([3, 3, 1, 1], ms.int32) >>> feat_size = 4 >>> in_feat_size = feat_size >>> nh = ms.ops.Ones()((n_nodes, feat_size), ms.float32) >>> eh = ms.ops.Ones()((n_edges, feat_size), ms.float32) >>> g = GraphField(src_idx, dst_idx, n_nodes, n_edges) >>> in_deg = in_deg >>> out_deg = out_deg >>> sgconv = SGConv(in_feat_size, feat_size) >>> res = sgconv(nh, in_deg, out_deg, *g.get_graph()) >>> print(res.shape) (4, 4) """ def __init__(self, in_feat_size: int, out_feat_size: int, num_hops: int = 1, cached: bool = True, bias: bool = True, norm=None): 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" assert isinstance(num_hops, int) and num_hops > 0, "num_hops must be positive int" assert isinstance(bias, bool), "bias must be bool" assert isinstance(cached, bool), "cached must be bool" self.in_feat_size = in_feat_size self.out_feat_size = out_feat_size self.num_hops = num_hops self.bias = bias self.cached = cached if norm is not None and not isinstance(norm, Cell): raise TypeError(f"For '{self.cls_name}', the 'activation' must a mindspore.nn.Cell, but got " f"{type(norm).__name__}.") self.dense = ms.nn.Dense(self.in_feat_size, self.out_feat_size, has_bias=self.bias, weight_init=XavierUniform(math.sqrt(2))) self.cached_h = None self.norm = norm self.min_clip = ms.Tensor(1, ms.int32) self.max_clip = ms.Tensor(100000000, ms.int32) # pylint: disable=arguments-differ def construct(self, x, in_deg, out_deg, g: Graph): """ Construct function for SGConv. """ feat = x if self.cached_h: feat = self.cached_h else: in_deg = ms.ops.clip_by_value(in_deg, self.min_clip, self.max_clip) in_deg = ms.ops.Reshape()(ms.ops.Pow()(in_deg, -0.5), ms.ops.Shape()(in_deg) + (1,)) out_deg = ms.ops.clip_by_value(out_deg, self.min_clip, self.max_clip) out_deg = ms.ops.Reshape()(ms.ops.Pow()(out_deg, -0.5), ms.ops.Shape()(out_deg) + (1,)) for _ in range(self.num_hops): feat = feat * out_deg g.set_vertex_attr({"h": feat}) for v in g.dst_vertex: v.h = g.sum([u.h for u in v.innbs]) feat = [v.h for v in g.dst_vertex] * in_deg if self.norm is not None: feat = self.norm(feat) if self.cached: self.cached_h = feat return self.dense(feat)