from typing import List
import torch.nn as nn
from greatx.nn.layers import Sequential, SSGConv, activations
from greatx.utils import wrapper
[docs]class SSGC(nn.Module):
r"""The Simple Spectra Graph Convolution Network (SSGC)
from paper `"Simple Spectral Graph Convolution"
<https://openreview.net/forum?id=CYO5T-YjWZV>`_ paper (ICLR'21)
Parameters
----------
in_channels : int,
the input dimensions of model
out_channels : int,
the output dimensions of model
hids : List[int], optional
the number of hidden units for each hidden layer,
by default []
acts : List[str], optional
the activation function for each hidden layer,
by default []
K : int, optional
the number of propagation steps, by default 5
alpha : float
Teleport probability :math:`\alpha`, by default 0.1
dropout : float, optional
the dropout ratio of model, by default 0.
bias : bool, optional
whether to use bias in the layers, by default True
cached : bool, optional
whether the layer will cache
the computation of :math:`(\mathbf{\hat{D}}^{-1/2}
\mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2})^K`
on first execution, and will use the
cached version for further executions, by default True
bn: bool, optional
whether to use :class:`BatchNorm1d` after the convolution layer,
by default False
Note
----
To accept a different graph as inputs, please call :meth:`cache_clear`
first to clear cached results.
Examples
--------
>>> # SSGC without hidden layer
>>> model = SSGC(100, 10)
>>> # SSGC with two hidden layers
>>> model = SSGC(100, 10, hids=[32, 16], acts=['relu', 'elu'])
>>> # SSGC with two hidden layers, without first activation
>>> model = SSGC(100, 10, hids=[32, 16], acts=[None, 'relu'])
>>> # SSGC with deep architectures, each layer has elu activation
>>> model = SSGC(100, 10, hids=[16]*8, acts=['elu'])
See also
--------
:class:`greatx.nn.layers.SSGConv`
"""
@wrapper
def __init__(self, in_channels, out_channels, hids: List[int] = [],
acts: List[str] = [], dropout: float = 0., K: int = 5,
alpha: float = 0.1, bias: bool = True, cached: bool = True,
bn: bool = False):
super().__init__()
assert len(hids) == len(acts)
conv = []
for i, (hid, act) in enumerate(zip(hids, acts)):
if i == 0:
conv.append(
SSGConv(in_channels, hid, bias=bias, K=K, alpha=alpha,
cached=cached))
else:
conv.append(nn.Linear(in_channels, hid, bias=bias))
if bn:
conv.append(nn.BatchNorm1d(hid))
conv.append(activations.get(act))
conv.append(nn.Dropout(dropout))
in_channels = hid
if not hids:
conv.append(
SSGConv(in_channels, out_channels, bias=bias, K=K, alpha=alpha,
cached=cached))
else:
conv.append(nn.Linear(in_channels, out_channels, bias=bias))
self.conv = Sequential(*conv)
[docs] def reset_parameters(self):
self.conv.reset_parameters()
[docs] def cache_clear(self):
"""Clear cached inputs or intermediate results."""
for layer in self.conv:
if hasattr(layer, 'cache_clear'):
layer.cache_clear()
return self
[docs] def forward(self, x, edge_index, edge_weight=None):
""""""
return self.conv(x, edge_index, edge_weight)