mindspore_gl.nn.conv.meanconv 源代码

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"""MeanConv Layer"""
import mindspore as ms
from mindspore.ops import operations as P
from mindspore.common.initializer import XavierUniform
from mindspore_gl import Graph
from .. import GNNCell


[文档]class MeanConv(GNNCell): r""" GraphSAGE Layer. From the paper `Inductive Representation Learning on Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`_ . .. math:: h_{\mathcal{N}(i)}^{(l+1)} = \mathrm{aggregate} \left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right) \\ h_{i}^{(l+1)} = \sigma \left(W \cdot \mathrm{concat} (h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1}) \right)\\ h_{i}^{(l+1)} = \mathrm{norm}(h_{i}^{l}) If weights are provided on each edge, the weighted graph convolution is defined as: .. math:: h_{\mathcal{N}(i)}^{(l+1)} = \mathrm{aggregate} \left(\{e_{ji} h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right) Args: in_feat_size (int): Input node feature size. out_feat_size (int): Output node feature size. feat_drop (float, optional): The dropout rate, greater equal than 0 and less than 1. E.g. dropout=0.1, dropping out 10% of input units. Default: 0.6. bias (bool, optional): Whether use bias. Default: False. norm (Cell, optional): Normalization function Cell. Default: None. activation (Cell, optional): Activation 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` could be of any shape. - **self_idx** (Tensor) - The node idx. The shape is :math:`(N\_v,)` where :math:`N\_v` is the number of self nodes. - **g** (Graph) - The input graph. Outputs: - Tensor, the output feature of shape :math:`(N\_v,D\_out)`. where :math:`N\_v` is the number of self nodes and :math:`D\_out` could be of any shape Raises: TypeError: If `in_feat_size` or `out_feat_size` is not an int. TypeError: If `bias` is not a bool. TypeError: If `norm` is not a mindspore.nn.Cell. ValueError: If `dropout` is not in range (0.0, 1.0] ValueError: If `activation` is not tanh or relu. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore as ms >>> from mindspore_gl.nn import MeanConv >>> 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) >>> gmmconv = MeanConv(in_feat_size=4, out_feat_size=2, activation='relu') >>> self_idx = ms.Tensor([0, 1], ms.int32) >>> res = gmmconv(feat, self_idx, *graph_field.get_graph()) >>> print(res.shape) (2, 2) """ def __init__(self, in_feat_size: int, out_feat_size: int, feat_drop=0.4, bias=False, norm=None, activation=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(bias, bool), "bias must be bool" self.in_feat_size = in_feat_size self.out_feat_size = out_feat_size self.norm = norm if activation == "tanh": self.activation = P.Tanh() elif activation == "relu": self.activation = P.ReLU() else: raise ValueError("activation should be tanh or relu") if feat_drop < 0.0 or feat_drop >= 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 {feat_drop}.") 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.feat_drop = ms.nn.Dropout(p=feat_drop) self.concat = P.Concat(axis=1) if bias: self.bias = ms.Parameter(ms.ops.Zeros()(self.out_feat_size, ms.float32)) else: self.bias = None self.dense_self = ms.nn.Dense(self.in_feat_size * 2, self.out_feat_size, has_bias=False, weight_init=XavierUniform()) self.gather = ms.ops.Gather() # pylint: disable=arguments-differ def construct(self, node_feat, self_idx, g: Graph): """ Construct function for MEANConv. """ g.set_vertex_attr({"src": node_feat}) for v in g.dst_vertex: v.rst = self.feat_drop(g.avg([u.src for u in v.innbs])) ret = self.dense_self(self.concat((self.gather([v.src for v in g.dst_vertex], self_idx, 0), self.gather([v.rst for v in g.dst_vertex], self_idx, 0)))) if self.bias is not None: ret = ret + self.bias if self.activation is not None: ret = self.activation(ret) if self.norm is not None: ret = self.norm(self.ret) return ret