mindspore_gl.nn.glob.weightandsum 源代码

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"""Weight And Sum Layer"""
import mindspore as ms
from mindspore_gl import BatchedGraph
from .. import GNNCell


[文档]class WeightAndSum(GNNCell): """ Calculates importance weights for nodes and performs weighted sums. Args: in_feat_size (int): input feature size. Inputs: - **x** (Tensor) - The input node features to be updated. The shape is :math:`(N, D)` where :math:`N` is the number of nodes, and :math:`D` is the feature size of nodes. - **g** (BatchedGraph) - The input graph. Outputs: - **x** (Tensor) - The output representation for graphs. The shape is :math:`(2, D_{out})` where :math:`D_{out}` is the feature size of nodes Raises: TypeError: If `in_feat_size` is not an int. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import numpy as np >>> import mindspore as ms >>> from mindspore_gl.nn import WeightAndSum >>> from mindspore_gl import BatchedGraphField >>> n_nodes = 7 >>> n_edges = 8 >>> src_idx = ms.Tensor([0, 2, 2, 3, 4, 5, 5, 6], ms.int32) >>> dst_idx = ms.Tensor([1, 0, 1, 5, 3, 4, 6, 4], ms.int32) >>> ver_subgraph_idx = ms.Tensor([0, 0, 0, 1, 1, 1, 1], ms.int32) >>> edge_subgraph_idx = ms.Tensor([0, 0, 0, 1, 1, 1, 1, 1], ms.int32) >>> graph_mask = ms.Tensor([1, 1], ms.int32) >>> batched_graph_field = BatchedGraphField(src_idx, dst_idx, n_nodes, n_edges, ver_subgraph_idx, ... edge_subgraph_idx, graph_mask) >>> node_feat = np.random.random((n_nodes, 4)) >>> node_feat = ms.Tensor(node_feat, ms.float32) >>> net = WeightAndSum(4) >>> ret = net(node_feat, *batched_graph_field.get_batched_graph()) >>> print(ret.shape) (2, 4) """ def __init__(self, in_feat_size): super().__init__() assert isinstance(in_feat_size, int) and in_feat_size > 0, "in_feat_size must be positive int" self.in_feat_size = in_feat_size self.atom_weighting = ms.nn.SequentialCell( ms.nn.Dense(in_feat_size, 1), ms.nn.Sigmoid() ) # pylint: disable=arguments-differ def construct(self, x, g: BatchedGraph): """ Construct function for WeightAndSum. """ w = self.atom_weighting(x) return g.sum_nodes(x * w)