# 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
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# ============================================================================
"""GCNConv Layer"""
import mindspore as ms
from mindspore.common.initializer import initializer
from mindspore.common.initializer import XavierUniform
from mindspore_gl import Graph
from .. import GNNCell
[文档]class GCNConv2(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)} = (\sum_{j\in\mathcal{N}(i)}h_j^{(l)}W_1^{(l)}+b^{(l)} )+h_i^{(l)}W_2^{(l)}
:math:`\mathcal{N}(i)` represents the neighbour node of :math:`i`.
:math:`W_1` and :math:`W_2` correspond to fc layers for neighbor nodes and root node.
Args:
in_feat_size (int): Input node feature size.
out_size (int): Output node feature size.
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`.
- **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.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore as ms
>>> from mindspore_gl.nn import GCNConv2
>>> 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)
>>> gcnconv2 = GCNConv2(in_feat_size=4, out_size=2)
>>> res = gcnconv2(feat, *graph_field.get_graph())
>>> print(res.shape)
(4, 2)
"""
def __init__(self,
in_feat_size: int,
out_size: int):
super().__init__()
if (not isinstance(in_feat_size, int)) or in_feat_size <= 0:
raise ValueError("in_feat_size must be positive int")
if (not isinstance(out_size, int)) or out_size <= 0:
raise ValueError("out_size must be positive int")
self.in_feat_size = in_feat_size
self.out_size = out_size
self.fc1 = 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.fc2 = ms.nn.Dense(in_feat_size, out_size, weight_init=XavierUniform(), has_bias=False)
# pylint: disable=arguments-differ
def construct(self, x, g: Graph):
"""
Construct function for GCNConv.
"""
x = ms.ops.Squeeze()(x)
x_r = x
x = self.fc1(x)
g.set_vertex_attr({"x": x})
for v in g.dst_vertex:
v.x = g.sum([u.x for u in v.innbs])
v.x += self.bias
x = [v.x for v in g.dst_vertex]
x = self.fc2(x_r) + x
return x