"""Torch Module for NNConv layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch as th
from torch import nn
from torch.nn import init
from .... import function as fn
from ..utils import Identity
from ....utils import expand_as_pair
[docs]class NNConv(nn.Module):
r"""
Description
-----------
Graph Convolution layer introduced in `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:`e_{ij}` is the edge feature, :math:`f_\Theta` is a function
with learnable parameters.
Parameters
----------
in_feats : int
Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`.
NNConv can be applied on homogeneous graph and unidirectional
`bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__.
If the layer is to be applied on a unidirectional bipartite graph, ``in_feats``
specifies the input feature size on both the source and destination nodes. If
a scalar is given, the source and destination node feature size would take the
same value.
out_feats : int
Output feature size; i.e., the number of dimensions of :math:`h_i^{(l+1)}`.
edge_func : callable activation function/layer
Maps each edge feature to a vector of shape
``(in_feats * out_feats)`` as weight to compute
messages.
Also is the :math:`f_\Theta` in the formula.
aggregator_type : str
Aggregator type to use (``sum``, ``mean`` or ``max``).
residual : bool, optional
If True, use residual connection. Default: ``False``.
bias : bool, optional
If True, adds a learnable bias to the output. Default: ``True``.
Examples
--------
>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import NNConv
>>> # Case 1: Homogeneous graph
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> lin = th.nn.Linear(5, 20)
>>> def edge_func(efeat):
... return lin(efeat)
>>> efeat = th.ones(6+6, 5)
>>> conv = NNConv(10, 2, edge_func, 'mean')
>>> res = conv(g, feat, efeat)
>>> res
tensor([[-1.5243, -0.2719],
[-1.5243, -0.2719],
[-1.5243, -0.2719],
[-1.5243, -0.2719],
[-1.5243, -0.2719],
[-1.5243, -0.2719]], grad_fn=<AddBackward0>)
>>> # Case 2: Unidirectional bipartite graph
>>> u = [0, 1, 0, 0, 1]
>>> v = [0, 1, 2, 3, 2]
>>> g = dgl.bipartite((u, v))
>>> u_feat = th.tensor(np.random.rand(2, 10).astype(np.float32))
>>> v_feat = th.tensor(np.random.rand(4, 10).astype(np.float32))
>>> conv = NNConv(10, 2, edge_func, 'mean')
>>> efeat = th.ones(5, 5)
>>> res = conv(g, (u_feat, v_feat), efeat)
>>> res
tensor([[-0.6568, 0.5042],
[ 0.9089, -0.5352],
[ 0.1261, -0.0155],
[-0.6568, 0.5042]], grad_fn=<AddBackward0>)
"""
def __init__(self,
in_feats,
out_feats,
edge_func,
aggregator_type='mean',
residual=False,
bias=True):
super(NNConv, self).__init__()
self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats)
self._out_feats = out_feats
self.edge_func = edge_func
if aggregator_type == 'sum':
self.reducer = fn.sum
elif aggregator_type == 'mean':
self.reducer = fn.mean
elif aggregator_type == 'max':
self.reducer = fn.max
else:
raise KeyError('Aggregator type {} not recognized: '.format(aggregator_type))
self._aggre_type = aggregator_type
if residual:
if self._in_dst_feats != out_feats:
self.res_fc = nn.Linear(self._in_dst_feats, out_feats, bias=False)
else:
self.res_fc = Identity()
else:
self.register_buffer('res_fc', None)
if bias:
self.bias = nn.Parameter(th.Tensor(out_feats))
else:
self.register_buffer('bias', None)
self.reset_parameters()
def reset_parameters(self):
r"""
Description
-----------
Reinitialize learnable parameters.
Note
----
The model parameters are initialized using Glorot uniform initialization
and the bias is initialized to be zero.
"""
gain = init.calculate_gain('relu')
if self.bias is not None:
nn.init.zeros_(self.bias)
if isinstance(self.res_fc, nn.Linear):
nn.init.xavier_normal_(self.res_fc.weight, gain=gain)
[docs] def forward(self, graph, feat, efeat):
r"""Compute MPNN Graph Convolution layer.
Parameters
----------
graph : DGLGraph
The graph.
feat : torch.Tensor or pair of torch.Tensor
The input feature of shape :math:`(N, D_{in})` where :math:`N`
is the number of nodes of the graph and :math:`D_{in}` is the
input feature size.
efeat : torch.Tensor
The edge feature of shape :math:`(E, *)`, which should fit the input
shape requirement of ``edge_func``. :math:`E` is the number of edges
of the graph.
Returns
-------
torch.Tensor
The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
is the output feature size.
"""
with graph.local_scope():
feat_src, feat_dst = expand_as_pair(feat, graph)
# (n, d_in, 1)
graph.srcdata['h'] = feat_src.unsqueeze(-1)
# (n, d_in, d_out)
graph.edata['w'] = self.edge_func(efeat).view(-1, self._in_src_feats, self._out_feats)
# (n, d_in, d_out)
graph.update_all(fn.u_mul_e('h', 'w', 'm'), self.reducer('m', 'neigh'))
rst = graph.dstdata['neigh'].sum(dim=1) # (n, d_out)
# residual connection
if self.res_fc is not None:
rst = rst + self.res_fc(feat_dst)
# bias
if self.bias is not None:
rst = rst + self.bias
return rst