mindspore_gl.nn.glob.sumpooling 源代码

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"""Sum Pooling Layer"""
# pylint: disable=unused-import
import mindspore
from mindspore_gl import BatchedGraph
from .. import GNNCell


[文档]class SumPooling(GNNCell): r""" Apply sum pooling to the nodes in the batched graph. .. math:: r^{(i)} = \sum_{k=1}^{N_i} x^{(i)}_k 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 Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import numpy as np >>> import mindspore as ms >>> from mindspore_gl.nn import MaxPooling >>> 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 = MaxPooling() >>> ret = net(node_feat, *batched_graph_field.get_batched_graph()) >>> print(ret.shape) (2, 4) """ # pylint: disable=arguments-differ def construct(self, x, g: BatchedGraph): """ Construct function for SumPooling. """ ret = g.sum_nodes(x) return ret