# Copyright 2022 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""TAGConv Layer"""
import math
import mindspore as ms
from mindspore.common.initializer import XavierUniform
from mindspore import nn
from mindspore._checkparam import Validator
from mindspore_gl import Graph
from .. import GNNCell
[文档]class TAGConv(GNNCell):
r"""
Topology adaptation graph convolutional layer.
From the paper `Topology Adaptive Graph Convolutional Networks <https://arxiv.org/pdf/1710.10370.pdf>`_ .
.. math::
H^{K} = {\sum}_{k=0}^K (D^{-1/2} A D^{-1/2})^{k} X {\Theta}_{k}
where :math:`{\Theta}_{k}` represents a linear weight to add the results of different hop counts.
Args:
in_feat_size (int): Input node feature size.
out_feat_size (int): Output node feature size.
num_hops (int): Number of hops. Default: 2.
bias (bool): Whether use bias. Default: True.
activation (mindspore.nn.Cell): Activation function. 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}` should be equal to `in_feat_size` in `Args`.
- **in_deg** (Tensor) - In degree for nodes. The shape is :math:`(N, )` where :math:`N` is the number of nodes.
- **out_deg** (Tensor) - Out degree for nodes. The shape is :math:`(N, )`
where :math:`N` is the number of nodes.
- **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_feat_size` in `Args`.
Raises:
TypeError: If `in_feat_size` or `out_feat_size` or `num_hops` is not an int.
TypeError: If `bias` is not a bool.
TypeError: If `activation` is not a mindspore.nn.Cell.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore as ms
>>> from mindspore_gl.nn import TAGConv
>>> 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)
>>> in_degree = ms.Tensor([3, 2, 1, 1], ms.int32)
>>> out_degree = ms.Tensor([1, 2, 1, 2], ms.int32)
>>> tagconv = TAGConv(in_feat_size=4, out_feat_size=2, activation=None, num_hops=3)
>>> res = tagconv(feat, in_degree, out_degree, *graph_field.get_graph())
>>> print(res.shape)
(4, 2)
"""
def __init__(self,
in_feat_size: int,
out_feat_size: int,
num_hops: int = 2,
bias: bool = True,
activation=None):
super().__init__()
in_feat_size = Validator.check_positive_int(in_feat_size, "in_feat_size", self.cls_name)
out_feat_size = Validator.check_positive_int(out_feat_size, "out_feat_size", self.cls_name)
num_hops = Validator.check_positive_int(num_hops, "num_hops", self.cls_name)
bias = Validator.check_bool(bias, "bias", self.cls_name)
if activation is not None and not isinstance(activation, nn.Cell):
raise TypeError(f"For '{self.cls_name}', the 'activation' must a mindspore.nn.Cell, but got "
f"{type(activation).__name__}.")
self.dense = ms.nn.Dense(in_feat_size * (num_hops + 1), out_feat_size, has_bias=bias,
weight_init=XavierUniform(math.sqrt(2)))
self.cached_h = None
self.num_hops = num_hops
self.min_clip = ms.Tensor(1, ms.int32)
self.max_clip = ms.Tensor(100000000, ms.int32)
self.activation = activation
# pylint: disable=arguments-differ
def construct(self, x, in_deg, out_deg, g: Graph):
"""
Construct function for TAGConv.
"""
feat = x
in_deg = ms.ops.clip_by_value(in_deg, self.min_clip, self.max_clip)
in_deg = ms.ops.Reshape()(ms.ops.Pow()(in_deg, -0.5), ms.ops.Shape()(out_deg) + (1,))
out_deg = ms.ops.clip_by_value(out_deg, self.min_clip, self.max_clip)
out_deg = ms.ops.Reshape()(ms.ops.Pow()(out_deg, -0.5), ms.ops.Shape()(out_deg) + (1,))
f_stack = [feat]
for _ in range(self.num_hops):
feat = f_stack[-1] * out_deg
g.set_vertex_attr({"h": feat})
for v in g.dst_vertex:
v.h = g.sum([u.h for u in v.innbs])
feat = [v.h for v in g.dst_vertex] * in_deg
f_stack.append(feat)
rst = self.dense(ms.ops.Concat(-1)(f_stack))
if self.activation:
rst = self.activation(rst)
return rst