# 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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# ============================================================================
"""EDGEConv Layer"""
import mindspore as ms
from mindspore_gl import Graph
from .. import GNNCell
[文档]class EDGEConv(GNNCell):
r"""
EdgeConv layer. From the paper `Dynamic Graph CNN for Learning on Point Clouds <https://arxiv.org/pdf/1801.07829>`_ .
.. math::
h_i^{(l+1)} = \max_{j \in \mathcal{N}(i)} (
\Theta \cdot (h_j^{(l)} - h_i^{(l)}) + \Phi \cdot h_i^{(l)})
:math:`\mathcal{N}(i)` represents the neighbour node of :math:`i`.
:math:`\Theta` and :math:`\Phi` represents linear layers.
Args:
in_feat_size (int): Input node feature size.
out_feat_size (int): Output node feature size.
batch_norm (bool): Whether use batch norm.
bias (bool, optional): Whether use bias. Default: ``True``.
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, 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 `batch_norm` is not a bool.
TypeError: If `bias` is not a bool.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore as ms
>>> from mindspore_gl.nn import EDGEConv
>>> 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 = EDGEConv(feat_size, out_size, batch_norm=True)
>>> ret = conv(nodes_feat, *graph_field.get_graph())
>>> print(ret.shape)
(4, 4)
"""
# pylint: disable=arguments-differ
def __init__(self,
in_feat_size: int,
out_feat_size: int,
batch_norm: bool,
bias=True):
super().__init__()
if in_feat_size <= 0 or not isinstance(in_feat_size, int):
raise ValueError("in_feat_size must be positive int")
if out_feat_size <= 0 or not isinstance(out_feat_size, int):
raise ValueError("out_feat_size must be positive int")
if not isinstance(batch_norm, int):
raise ValueError("batch_norm must be bool")
if not isinstance(bias, bool):
raise ValueError("bias must be bool")
self.batch_norm = batch_norm
self.theta = ms.nn.Dense(in_feat_size, out_feat_size, has_bias=bias)
self.phi = ms.nn.Dense(in_feat_size, out_feat_size, has_bias=bias)
if batch_norm:
self.bn = ms.nn.BatchNorm1d(out_feat_size)
def construct(self, x, g: Graph):
"""
Construct function for EDGEConv.
"""
g.set_vertex_attr({"x": x, "phi": self.phi(x)})
for v in g.dst_vertex:
if not self.batch_norm:
v.h = g.max([self.theta(u.x - v.x) + v.phi for u in v.innbs])
else:
v.h = g.max([self.bn(self.theta(u.x - v.x) + v.phi) for u in v.innbs])
return [v.h for v in g.dst_vertex]