# 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.
# ============================================================================
"""MeanConv Layer"""
import mindspore as ms
from mindspore.ops import operations as P
from mindspore.common.initializer import XavierUniform
from mindspore_gl import Graph
from .. import GNNCell
[文档]class MeanConv(GNNCell):
r"""
GraphSAGE Layer. From the paper `Inductive Representation Learning on Large Graphs
<https://arxiv.org/pdf/1706.02216.pdf>`_ .
.. math::
h_{\mathcal{N}(i)}^{(l+1)} = \mathrm{aggregate}
\left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right) \\
h_{i}^{(l+1)} = \sigma \left(W \cdot \mathrm{concat}
(h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1}) \right)\\
h_{i}^{(l+1)} = \mathrm{norm}(h_{i}^{l})
If weights are provided on each edge, the weighted graph convolution is defined as:
.. math::
h_{\mathcal{N}(i)}^{(l+1)} = \mathrm{aggregate}
\left(\{e_{ji} h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)
Args:
in_feat_size (int): Input node feature size.
out_feat_size (int): Output node feature size.
feat_drop (float, optional): The dropout rate, greater equal than 0 and less than 1. E.g. dropout=0.1,
dropping out 10% of input units. Default: ``0.4``.
bias (bool, optional): Whether to use bias. Default: ``False``.
norm (Cell, optional): Normalization function Cell. Default: ``None``.
activation (Cell, optional): Activation function Cell. 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` could be of any shape.
- **self_idx** (Tensor) - The node idx. The shape is :math:`(N\_v,)`
where :math:`N\_v` is the number of self nodes.
- **g** (Graph) - The input graph.
Outputs:
- Tensor, the output feature of shape :math:`(N\_v,D\_out)`.
where :math:`N\_v` is the number of self nodes and :math:`D\_out` could be of any shape
Raises:
TypeError: If `in_feat_size` or `out_feat_size` is not an int.
TypeError: If `bias` is not a bool.
TypeError: If `norm` is not a `mindspore.nn.Cell`.
ValueError: If `dropout` is not in range (0.0, 1.0]
ValueError: If `activation` is not `tanh` or `relu`.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore as ms
>>> from mindspore_gl.nn import MeanConv
>>> 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)
>>> gmmconv = MeanConv(in_feat_size=4, out_feat_size=2, activation='relu')
>>> self_idx = ms.Tensor([0, 1], ms.int32)
>>> res = gmmconv(feat, self_idx, *graph_field.get_graph())
>>> print(res.shape)
(2, 2)
"""
def __init__(self,
in_feat_size: int,
out_feat_size: int,
feat_drop=0.4,
bias=False,
norm=None,
activation=None):
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(bias, bool):
raise ValueError("bias must be bool")
self.in_feat_size = in_feat_size
self.out_feat_size = out_feat_size
self.norm = norm
if activation == "tanh":
self.activation = P.Tanh()
elif activation == "relu":
self.activation = P.ReLU()
else:
raise ValueError("activation should be tanh or relu")
if feat_drop < 0.0 or feat_drop >= 1.0:
raise ValueError(f"For '{self.cls_name}', the 'dropout_prob' should be a number in range [0.0, 1.0), "
f"but got {feat_drop}.")
if norm is not None and not isinstance(norm, Cell):
raise TypeError(f"For '{self.cls_name}', the 'activation' must a mindspore.nn.Cell, but got "
f"{type(norm).__name__}.")
self.feat_drop = ms.nn.Dropout(p=feat_drop)
self.concat = P.Concat(axis=1)
if bias:
self.bias = ms.Parameter(ms.ops.Zeros()(self.out_feat_size, ms.float32))
else:
self.bias = None
self.dense_self = ms.nn.Dense(self.in_feat_size * 2, self.out_feat_size, has_bias=False,
weight_init=XavierUniform())
self.gather = ms.ops.Gather()
# pylint: disable=arguments-differ
def construct(self, node_feat, self_idx, g: Graph):
"""
Construct function for MEANConv.
"""
g.set_vertex_attr({"src": node_feat})
for v in g.dst_vertex:
v.rst = self.feat_drop(g.avg([u.src for u in v.innbs]))
ret = self.dense_self(self.concat((self.gather([v.src for v in g.dst_vertex], self_idx, 0),
self.gather([v.rst for v in g.dst_vertex], self_idx, 0))))
if self.bias is not None:
ret = ret + self.bias
if self.activation is not None:
ret = self.activation(ret)
if self.norm is not None:
ret = self.norm(self.ret)
return ret