from collections.abc import Sequence
import torch
from torch import nn
from torchdrug import core, layers
from torchdrug.core import Registry as R
[docs]@R.register("models.GIN")
class GraphIsomorphismNetwork(nn.Module, core.Configurable):
"""
Graph Ismorphism Network proposed in `How Powerful are Graph Neural Networks?`_
.. _How Powerful are Graph Neural Networks?:
https://arxiv.org/pdf/1810.00826.pdf
Parameters:
input_dim (int): input dimension
hidden_dims (list of int): hidden dimensions
edge_input_dim (int, optional): dimension of edge features
num_mlp_layer (int, optional): number of MLP layers
eps (int, optional): initial epsilon
learn_eps (bool, optional): learn epsilon or not
short_cut (bool, optional): use short cut or not
batch_norm (bool, optional): apply batch normalization or not
activation (str or function, optional): activation function
concat_hidden (bool, optional): concat hidden representations from all layers as output
readout (str, optional): readout function. Available functions are ``sum`` and ``mean``.
"""
def __init__(self, input_dim, hidden_dims, edge_input_dim=None, num_mlp_layer=2, eps=0, learn_eps=False,
short_cut=False, batch_norm=False, activation="relu", concat_hidden=False,
readout="sum"):
super(GraphIsomorphismNetwork, self).__init__()
if not isinstance(hidden_dims, Sequence):
hidden_dims = [hidden_dims]
self.input_dim = input_dim
self.output_dim = sum(hidden_dims) if concat_hidden else hidden_dims[-1]
self.dims = [input_dim] + list(hidden_dims)
self.short_cut = short_cut
self.concat_hidden = concat_hidden
self.layers = nn.ModuleList()
for i in range(len(self.dims) - 1):
layer_hidden_dims = [self.dims[i + 1]] * (num_mlp_layer - 1)
self.layers.append(layers.GraphIsomorphismConv(self.dims[i], self.dims[i + 1], edge_input_dim,
layer_hidden_dims, eps, learn_eps, batch_norm, activation))
if readout == "sum":
self.readout = layers.SumReadout()
elif readout == "mean":
self.readout = layers.MeanReadout()
else:
raise ValueError("Unknown readout `%s`" % readout)
[docs] def forward(self, graph, input, all_loss=None, metric=None):
"""
Compute the node representations and the graph representation(s).
Parameters:
graph (Graph): :math:`n` graph(s)
input (Tensor): input node representations
all_loss (Tensor, optional): if specified, add loss to this tensor
metric (dict, optional): if specified, output metrics to this dict
Returns:
dict with ``node_feature`` and ``graph_feature`` fields:
node representations of shape :math:`(|V|, d)`, graph representations of shape :math:`(n, d)`
"""
hiddens = []
layer_input = input
for layer in self.layers:
hidden = layer(graph, layer_input)
if self.short_cut and hidden.shape == layer_input.shape:
hidden = hidden + layer_input
hiddens.append(hidden)
layer_input = hidden
if self.concat_hidden:
node_feature = torch.cat(hiddens, dim=-1)
else:
node_feature = hiddens[-1]
graph_feature = self.readout(graph, node_feature)
return {
"graph_feature": graph_feature,
"node_feature": node_feature
}