from collections.abc import Sequence
import torch
from torch import nn
from torch_scatter import scatter_add
from torchdrug import core, layers
from torchdrug.core import Registry as R
[docs]@R.register("models.GearNet")
class GeometryAwareRelationalGraphNeuralNetwork(nn.Module, core.Configurable):
"""
Geometry Aware Relational Graph Neural Network proposed in
`Protein Representation Learning by Geometric Structure Pretraining`_.
.. _Protein Representation Learning by Geometric Structure Pretraining:
https://arxiv.org/pdf/2203.06125.pdf
Parameters:
input_dim (int): input dimension
hidden_dims (list of int): hidden dimensions
num_relation (int): number of relations
edge_input_dim (int, optional): dimension of edge features
num_angle_bin (int, optional): number of bins to discretize angles between edges.
The discretized angles are used as relations in edge message passing.
If not provided, edge message passing is disabled.
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, num_relation, edge_input_dim=None, num_angle_bin=None,
short_cut=False, batch_norm=False, activation="relu", concat_hidden=False, readout="sum"):
super(GeometryAwareRelationalGraphNeuralNetwork, 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.edge_dims = [edge_input_dim] + self.dims[:-1]
self.num_relation = num_relation
self.num_angle_bin = num_angle_bin
self.short_cut = short_cut
self.concat_hidden = concat_hidden
self.batch_norm = batch_norm
self.layers = nn.ModuleList()
for i in range(len(self.dims) - 1):
self.layers.append(layers.GeometricRelationalGraphConv(self.dims[i], self.dims[i + 1], num_relation,
None, batch_norm, activation))
if num_angle_bin:
self.spatial_line_graph = layers.SpatialLineGraph(num_angle_bin)
self.edge_layers = nn.ModuleList()
for i in range(len(self.edge_dims) - 1):
self.edge_layers.append(layers.GeometricRelationalGraphConv(
self.edge_dims[i], self.edge_dims[i + 1], num_angle_bin, None, batch_norm, activation))
if batch_norm:
self.batch_norms = nn.ModuleList()
for i in range(len(self.dims) - 1):
self.batch_norms.append(nn.BatchNorm1d(self.dims[i + 1]))
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
if self.num_angle_bin:
line_graph = self.spatial_line_graph(graph)
edge_input = line_graph.node_feature.float()
for i in range(len(self.layers)):
hidden = self.layers[i](graph, layer_input)
if self.short_cut and hidden.shape == layer_input.shape:
hidden = hidden + layer_input
if self.num_angle_bin:
edge_hidden = self.edge_layers[i](line_graph, edge_input)
edge_weight = graph.edge_weight.unsqueeze(-1)
node_out = graph.edge_list[:, 1] * self.num_relation + graph.edge_list[:, 2]
update = scatter_add(edge_hidden * edge_weight, node_out, dim=0,
dim_size=graph.num_node * self.num_relation)
update = update.view(graph.num_node, self.num_relation * edge_hidden.shape[1])
update = self.layers[i].linear(update)
update = self.layers[i].activation(update)
hidden = hidden + update
edge_input = edge_hidden
if self.batch_norm:
hidden = self.batch_norms[i](hidden)
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
}