# 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.
# ============================================================================
"""NNConv Layer"""
import math
import mindspore as ms
from mindspore.common.initializer import XavierUniform
from mindspore import nn
from mindspore_gl import Graph
from .. import GNNCell
[文档]class NNConv(GNNCell):
r"""
Graph convolutional layer.
From the paper `Neural Message Passing for Quantum Chemistry <https://arxiv.org/pdf/1704.01212.pdf>`_ .
.. math::
h_{i}^{l+1} = h_{i}^{l} + \mathrm{aggregate}\left(\left\{
f_\Theta (e_{ij}) \cdot h_j^{l}, j\in \mathcal{N}(i) \right\}\right)
Where :math:`f_\Theta` is a function with learnable parameters.
Args:
in_feat_size (int): Input node feature size.
out_feat_size (int): Output node feature size.
edge_embed (mindspore.nn.Cell): Edge embedding function Cell.
aggregator_type (str, optional): Type of aggregator, should be ``'sum'``. Default: ``'sum'``.
residual (bool, optional): Whether use residual. Default: ``False``.
bias (bool, optional): Whether use bias. Default: ``True``.
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.
- **edge_feat** (Tensor) - Edge featutes. The shape is :math:`(N\_e,F\_e)`
where :math:`N\_e` is the number of edges and :math:`F\_e` is the number of edge features.
- **g** (Graph) - The input graph.
Outputs:
- Tensor, the output feature of shape :math:`(N,D\_out)`
where :math:`N` is the number of nodes and :math:`D\_out` could be of any shape.
Raises:
TypeError: if `edge_embed` type is not `mindspore.nn.Cell` or `aggregator_type` is not ``'sum'``.
TypeError: If `in_feat_size` or `out_feat_size` is not an int.
TypeError: If `residual` or `bias` is not a bool.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore as ms
>>> from mindspore_gl.nn import NNConv
>>> from mindspore_gl import GraphField
>>> n_nodes = 4
>>> n_edges = 7
>>> node_feat_size = 7
>>> edge_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()
>>> node_feat = ones((n_nodes, node_feat_size), ms.float32)
>>> edge_feat = ones((n_edges, edge_feat_size), ms.float32)
>>> graph_field = GraphField(src_idx, dst_idx, n_nodes, n_edges)
>>> edge_func = ms.nn.Dense(edge_feat_size, 2)
>>> nnconv = NNConv(in_feat_size=node_feat_size, out_feat_size=2, edge_embed=edge_func)
>>> res = nnconv(node_feat, edge_feat, *graph_field.get_graph())
>>> print(res.shape)
(4, 2)
"""
def __init__(self,
in_feat_size: int,
out_feat_size: int,
edge_embed,
aggregator_type: str = "sum",
residual=False,
bias=True):
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_feat_size, int)) or out_feat_size <= 0:
raise ValueError("out_feat_size must be positive int")
if not isinstance(bias, bool):
raise ValueError("bias must be bool")
if not isinstance(residual, bool):
raise ValueError("residual must be bool")
if edge_embed:
if not isinstance(edge_embed, nn.Cell):
raise TypeError("edge_embed type should be mindspore.nn.Cell")
if aggregator_type != "sum":
raise TypeError("Don't support aggregator type other than sum.")
self.edge_embed = edge_embed
self.agg_type = aggregator_type
self.in_feat_size = in_feat_size
self.out_feat_size = out_feat_size
self.res_dense = None
if residual:
if in_feat_size != out_feat_size:
self.res_dense = ms.nn.Dense(in_feat_size, out_feat_size, weight_init=XavierUniform(math.sqrt(2)))
self.bias = None
if bias:
self.bias = ms.Parameter(ms.ops.Zeros()((out_feat_size), ms.float32))
# pylint: disable=arguments-differ
def construct(self, x, edge_feat, g: Graph):
"""
Construct function for NNConv.
"""
g.set_vertex_attr({"h": ms.ops.ExpandDims()(x, -1)})
g.set_edge_attr(
{"g": ms.ops.Reshape()(self.edge_embed(edge_feat), (-1, self.in_feat_size, self.out_feat_size))})
for v in g.dst_vertex:
e = [s.h * e.g for s, e in v.inedges]
v.rt = g.sum(e)
v.rt = ms.ops.ReduceSum()(v.rt, 1)
if self.res_dense is not None:
v.rt = v.rt + self.res_dense(v.h)
if self.bias is not None:
v.rt = v.rt + self.bias
return [v.rt for v in g.dst_vertex]