from typing import List
import torch.nn as nn
from torch.nn import Linear
from greatx.nn.layers import DAGNNConv, Sequential, activations
from greatx.utils import wrapper
[docs]class DAGNN(nn.Module):
r"""The DAGNN operator from the `"Towards Deeper Graph Neural
Networks" <https://arxiv.org/abs/2007.09296>`_
paper (KDD'20)
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 [64]
K : int, optional
the number of propagation steps, by default 10
acts : List[str], optional
the activation function for each hidden layer, by default ['relu']
dropout : float, optional
the dropout ratio of model, by default 0.5
bias : bool, optional
whether to use bias in the layers, by default True
bn: bool, optional
whether to use :class:`BatchNorm1d` after the convolution layer,
by default False
Examples
--------
>>> # DAGNN with one hidden layer
>>> model = DAGNN(100, 10)
>>> # DAGNN with two hidden layers
>>> model = DAGNN(100, 10, hids=[32, 16], acts=['relu', 'elu'])
>>> # DAGNN with two hidden layers, without first activation
>>> model = DAGNN(100, 10, hids=[32, 16], acts=[None, 'relu'])
>>> # DAGNN with deep architectures, each layer has elu activation
>>> model = DAGNN(100, 10, hids=[16]*8, acts=['elu'])
See also
--------
:class:`greatx.nn.layers.DAGNNConv`
"""
@wrapper
def __init__(self, in_channels: int, out_channels: int,
hids: List[int] = [64], acts: List[str] = ['relu'],
dropout: float = 0.5, K: int = 10, bn: bool = False,
bias: bool = True):
super().__init__()
assert len(hids) > 0
lin = []
for hid, act in zip(hids, acts):
lin.append(nn.Dropout(dropout))
lin.append(Linear(in_channels, hid, bias=bias))
if bn:
lin.append(nn.BatchNorm1d(hid))
lin.append(activations.get(act))
in_channels = hid
lin.append(nn.Dropout(dropout))
lin.append(Linear(in_channels, out_channels, bias=bias))
self.prop = DAGNNConv(out_channels, 1, K=K)
self.lin = Sequential(*lin)
[docs] def reset_parameters(self):
self.prop.reset_parameters()
self.lin.reset_parameters()
[docs] def forward(self, x, edge_index, edge_weight=None):
""""""
x = self.lin(x)
return self.prop(x, edge_index, edge_weight)