"""Torch module for GIN."""
import torch
import torch.nn as nn
import torch.nn.functional as F
[docs]class GIN(nn.Module):
r"""
Description
-----------
Graph Isomorphism Network (`GIN <https://arxiv.org/abs/1810.00826>`__)
Parameters
----------
in_features : int
Dimension of input features.
out_features : int
Dimension of output features.
hidden_features : int or list of int
Dimension of hidden features. List if multi-layer.
layer_norm : bool, optional
Whether to use layer normalization. Default: ``False``.
activation : func of torch.nn.functional, optional
Activation function. Default: ``torch.nn.functional.relu``.
dropout : bool, optional
Whether to dropout during training. Default: ``True``.
"""
def __init__(self, in_features, out_features, hidden_features, activation=F.relu, layer_norm=False, dropout=True):
super(GIN, self).__init__()
self.in_features = in_features
self.out_features = out_features
if type(hidden_features) is int:
hidden_features = [hidden_features]
self.layers = nn.ModuleList()
if layer_norm:
self.layers.append(nn.LayerNorm(in_features))
self.layers.append(GINConv(in_features, hidden_features[0], activation=activation, dropout=dropout))
for i in range(len(hidden_features) - 1):
if layer_norm:
self.layers.append(nn.LayerNorm(hidden_features[i]))
self.layers.append(
GINConv(hidden_features[i], hidden_features[i + 1], activation=activation))
self.linear1 = nn.Linear(hidden_features[-2], hidden_features[-1])
self.linear2 = nn.Linear(hidden_features[-1], out_features)
@property
def model_type(self):
"""Indicate type of implementation."""
return "torch"
[docs] def reset_parameters(self):
"""Reset parameters."""
for layer in self.layers:
layer.reset_parameters()
[docs] def forward(self, x, adj, dropout=0.0):
r"""
Parameters
----------
x : torch.Tensor
Tensor of input features.
adj : torch.SparseTensor
Sparse tensor of adjacency matrix.
dropout : float, optional
Rate of dropout. Default: ``0.0``.
Returns
-------
x : torch.Tensor
Output of model (logits without activation).
"""
for layer in self.layers:
if isinstance(layer, nn.LayerNorm):
x = layer(x)
else:
x = layer(x, adj, dropout=dropout)
x = F.relu(self.linear1(x))
x = F.dropout(x, dropout)
x = self.linear2(x)
return x
[docs]class GINConv(nn.Module):
r"""
Description
-----------
GIN convolutional layer.
Parameters
----------
in_features : int
Dimension of input features.
out_features : int
Dimension of output features.
activation : func of torch.nn.functional, optional
Activation function. Default: ``None``.
eps : float, optional
Hyper-parameter, refer to original paper. Default: ``0.0``.
batchnorm : bool, optional
Whether to apply batch normalization. Default: ``True``.
dropout : bool, optional
Whether to dropout during training. Default: ``False``.
"""
def __init__(self, in_features, out_features, activation=F.relu, eps=0.0, batchnorm=True, dropout=False):
super(GINConv, self).__init__()
self.linear1 = nn.Linear(in_features, out_features)
self.linear2 = nn.Linear(out_features, out_features)
self.activation = activation
self.eps = torch.nn.Parameter(torch.Tensor([eps]))
self.batchnorm = batchnorm
if batchnorm:
self.norm = nn.BatchNorm1d(out_features)
self.dropout = dropout
[docs] def reset_parameters(self):
"""Reset parameters."""
if self.activation == F.leaky_relu:
gain = nn.init.calculate_gain('leaky_relu')
else:
gain = nn.init.calculate_gain('relu')
nn.init.xavier_normal_(self.linear.weights, gain=gain)
[docs] def forward(self, x, adj, dropout=0.0):
r"""
Parameters
----------
x : torch.Tensor
Tensor of input features.
adj : torch.SparseTensor
Sparse tensor of adjacency matrix.
dropout : float, optional
Rate of dropout. Default: ``0.0``.
Returns
-------
x : torch.Tensor
Output of layer.
"""
y = torch.spmm(adj, x)
x = y + (1 + self.eps) * x
x = self.linear1(x)
if self.activation is not None:
x = self.activation(x)
x = self.linear2(x)
if self.batchnorm:
x = self.norm(x)
if self.activation is not None:
x = self.activation(x)
if self.dropout:
x = F.dropout(x, dropout)
return x