mindspore_gl.nn.conv.gcnconv 源代码

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"""GCNConv Layer"""
import mindspore as ms
from mindspore import Tensor
from mindspore.common.initializer import initializer
from mindspore.common.initializer import XavierUniform
from mindspore.nn.cell import Cell
from mindspore_gl import Graph
from .. import GNNCell


[文档]class GCNConv(GNNCell): r""" Graph Convolution Network Layer. From the paper `Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/abs/1609.02907>`_ . .. math:: h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{1}{c_{ji}}h_j^{(l)}W^{(l)}) :math:`\mathcal{N}(i)` represents the neighbour node of :math:`i`. :math:`c_{ji} = \sqrt{|\mathcal{N}(j)|}\sqrt{|\mathcal{N}(i)|}`. .. math:: h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{e_{ji}}{c_{ji}}h_j^{(l)}W^{(l)}) Args: in_feat_size (int): Input node feature size. out_size (int): Output node feature size. activation (Cell, optional): Activation function. Default: None. dropout (float, optional): The dropout rate, greater than 0 and less equal than 1. E.g. dropout=0.1, dropping out 10% of input units. Default: 0.5. 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_size` in `Args`. Raises: TypeError: If `in_feat_size` or `out_size` is not an int. TypeError: If `dropout` is not a float. TypeError: If `activation` is not a mindspore.nn.Cell. ValueError: If `dropout` is not in range (0.0, 1.0] Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore as ms >>> from mindspore_gl.nn import GCNConv >>> from mindspore_gl import GraphField >>> n_nodes = 4 >>> n_edges = 7 >>> feat_size = 4 >>> src_idx = ms.Tensor([0, 1, 1, 2, 2, 3, 3], ms.int32) >>> dst_idx = ms.Tensor([0, 0, 2, 1, 3, 0, 1], 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) >>> in_degree = ms.Tensor([3, 2, 1, 1], ms.int32) >>> out_degree = ms.Tensor([1, 2, 1, 2], ms.int32) >>> gcnconv = GCNConv(in_feat_size=4, out_size=2, activation=None, dropout=1.0) >>> res = gcnconv(feat, in_degree, out_degree, *graph_field.get_graph()) >>> print(res.shape) (4, 2) """ def __init__(self, in_feat_size: int, out_size: int, activation=None, dropout=0.5): super().__init__() assert isinstance(in_feat_size, int) and in_feat_size > 0, "in_feat_size must be positive int" assert isinstance(out_size, int) and out_size > 0, "out_size must be positive int" assert isinstance(dropout, float), "dropout must be float" self.in_feat_size = in_feat_size self.out_size = out_size if dropout < 0.0 or dropout >= 1.0: raise ValueError(f"For '{self.cls_name}', the 'dropout_prob' should be a number in range [0.0, 1.0), " f"but got {dropout}.") if activation is not None and not isinstance(activation, Cell): raise TypeError(f"For '{self.cls_name}', the 'activation' must a mindspore.nn.Cell, but got " f"{type(activation).__name__}.") self.fc = ms.nn.Dense(in_feat_size, out_size, weight_init=XavierUniform(), has_bias=False) self.bias = ms.Parameter(initializer('zero', (out_size), ms.float32), name="bias") self.activation = activation self.min_clip = Tensor(1, ms.int32) self.max_clip = Tensor(100000000, ms.int32) self.drop_out = ms.nn.Dropout(p=dropout) # pylint: disable=arguments-differ def construct(self, x, in_deg, out_deg, g: Graph): """ Construct function for GCNConv. """ 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,)) x = self.drop_out(x) x = ms.ops.Squeeze()(x) x = x * out_deg x = self.fc(x) g.set_vertex_attr({"x": x}) for v in g.dst_vertex: v.x = g.sum([u.x for u in v.innbs]) 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,)) x = [v.x for v in g.dst_vertex] * in_deg x = x + self.bias return x