# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""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__()
if in_feat_size <= 0 or not isinstance(in_feat_size, int):
raise ValueError("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)