# 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,
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# ============================================================================
"""DOTGATConv Layer"""
import mindspore as ms
from mindspore_gl import Graph
from .. import GNNCell
[文档]class DOTGATConv(GNNCell):
r"""
Applying a dot product version of self-attention in GAT.
From the paper `Graph Attention Network <https://arxiv.org/pdf/1710.10903.pdf>`_ .
.. math::
h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i, j} h_j^{(l)}
:math:`\alpha_{i, j}` represents the attention score between node :math:`i` and node :math:`j`.
.. math::
\alpha_{i, j} = \mathrm{softmax_i}(e_{ij}^{l}) \\
e_{ij}^{l} = ({W_i^{(l)} h_i^{(l)}})^T \cdot {W_j^{(l)} h_j^{(l)}}
Args:
in_feat_size (int): Input node feature size.
out_feat_size (int): Output node feature size.
num_heads (int): Number of attention head used in GAT.
bias (bool, optional): Whether use bias. Default: False.
Inputs:
- **x** (Tensor): The input node features. The shape is :math:`(N,*)` where :math:`N` is the number of nodes,
and :math:`*` could be of any shape.
- **g** (Graph): The input graph.
Outputs:
- Tensor, output node features. The shape is :math:`(N, num\_heads, out\_feat\_size)`.
Raises:
TypeError: If 'in_feat_size' is not a positive int.
TypeError: If 'out_feat_size' is not a positive int.
TypeError: If 'num_heads' is not a positive int.
TypeError: If 'bias' is not a bool.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore as ms
>>> from mindspore_gl.nn import DOTGATConv
>>> from mindspore_gl import GraphField
>>> n_nodes = 4
>>> n_edges = 8
>>> feat_size = 16
>>> src_idx = ms.Tensor([0, 0, 0, 1, 1, 1, 2, 3], ms.int32)
>>> dst_idx = ms.Tensor([0, 1, 3, 1, 2, 3, 3, 2], ms.int32)
>>> ones = ms.ops.Ones()
>>> nodes_feat = ones((n_nodes, feat_size), ms.float32)
>>> graph_field = GraphField(src_idx, dst_idx, n_nodes, n_edges)
>>> out_size = 4
>>> conv = DOTGATConv(feat_size, out_size, num_heads=2, bias=True)
>>> ret = conv(nodes_feat, *graph_field.get_graph())
>>> print(ret.shape)
(4, 2, 4)
"""
def __init__(self,
in_feat_size: int,
out_feat_size: int,
num_heads: int,
bias=False):
super().__init__()
assert isinstance(in_feat_size, int) and in_feat_size > 0, "in_feat_size must be positive int"
assert isinstance(out_feat_size, int) and out_feat_size > 0, "out_feat_size must be positive int"
assert isinstance(num_heads, int) and num_heads > 0, "num_heads must be positive int"
assert isinstance(bias, bool), "bias must be bool"
self.dense = ms.nn.Dense(in_feat_size, out_feat_size * num_heads, has_bias=bias)
self.num_heads = num_heads
self.out_feat_size = out_feat_size
# pylint: disable=arguments-differ
def construct(self, x, g: Graph):
"""
Construct function for DOTGATConv.
"""
feat_src = feat_dst = ms.ops.Reshape()(self.dense(x), (-1, self.num_heads, self.out_feat_size))
g.set_vertex_attr({"hsrc": feat_src, "hdst": feat_dst})
for v in g.dst_vertex:
dotted = [g.dot(u.hsrc, v.hdst) for u in v.innbs]
a = dotted / g.sum(dotted)
v.h = g.sum(a * [u.hsrc for u in v.innbs])
return [v.h for v in g.dst_vertex]