dmlc--dgl
xnouhz
0b47e86803
* [example] arma * update * update * update * update * update * [example] dimenet * [docs] update dimenet * [docs] update tf results * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update Co-authored-by: Mufei Li <mufeili1996@gmail.com>
96 行
3.7 KiB
Python
96 行
3.7 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import dgl.function as fn
|
|
|
|
from modules.residual_layer import ResidualLayer
|
|
from modules.initializers import GlorotOrthogonal
|
|
|
|
class InteractionBlock(nn.Module):
|
|
def __init__(self,
|
|
emb_size,
|
|
num_radial,
|
|
num_spherical,
|
|
num_bilinear,
|
|
num_before_skip,
|
|
num_after_skip,
|
|
activation=None):
|
|
super(InteractionBlock, self).__init__()
|
|
|
|
self.activation = activation
|
|
# Transformations of Bessel and spherical basis representations
|
|
self.dense_rbf = nn.Linear(num_radial, emb_size, bias=False)
|
|
self.dense_sbf = nn.Linear(num_radial * num_spherical, num_bilinear, bias=False)
|
|
# Dense transformations of input messages
|
|
self.dense_ji = nn.Linear(emb_size, emb_size)
|
|
self.dense_kj = nn.Linear(emb_size, emb_size)
|
|
# Bilinear layer
|
|
bilin_initializer = torch.empty((emb_size, num_bilinear, emb_size)).normal_(mean=0, std=2 / emb_size)
|
|
self.W_bilin = nn.Parameter(bilin_initializer)
|
|
# Residual layers before skip connection
|
|
self.layers_before_skip = nn.ModuleList([
|
|
ResidualLayer(emb_size, activation=activation) for _ in range(num_before_skip)
|
|
])
|
|
self.final_before_skip = nn.Linear(emb_size, emb_size)
|
|
# Residual layers after skip connection
|
|
self.layers_after_skip = nn.ModuleList([
|
|
ResidualLayer(emb_size, activation=activation) for _ in range(num_after_skip)
|
|
])
|
|
|
|
self.reset_params()
|
|
|
|
def reset_params(self):
|
|
GlorotOrthogonal(self.dense_rbf.weight)
|
|
GlorotOrthogonal(self.dense_sbf.weight)
|
|
GlorotOrthogonal(self.dense_ji.weight)
|
|
GlorotOrthogonal(self.dense_kj.weight)
|
|
GlorotOrthogonal(self.final_before_skip.weight)
|
|
|
|
def edge_transfer(self, edges):
|
|
# Transform from Bessel basis to dence vector
|
|
rbf = self.dense_rbf(edges.data['rbf'])
|
|
# Initial transformation
|
|
x_ji = self.dense_ji(edges.data['m'])
|
|
x_kj = self.dense_kj(edges.data['m'])
|
|
if self.activation is not None:
|
|
x_ji = self.activation(x_ji)
|
|
x_kj = self.activation(x_kj)
|
|
|
|
# w: W * e_RBF \bigodot \sigma(W * m + b)
|
|
return {'x_kj': x_kj * rbf, 'x_ji': x_ji}
|
|
|
|
def msg_func(self, edges):
|
|
sbf = self.dense_sbf(edges.data['sbf'])
|
|
# Apply bilinear layer to interactions and basis function activation
|
|
# [None, 8] * [128, 8, 128] * [None, 128] -> [None, 128]
|
|
x_kj = torch.einsum("wj,wl,ijl->wi", sbf, edges.src['x_kj'], self.W_bilin)
|
|
return {'x_kj': x_kj}
|
|
|
|
def forward(self, g, l_g):
|
|
g.apply_edges(self.edge_transfer)
|
|
|
|
# nodes correspond to edges and edges correspond to nodes in the original graphs
|
|
# node: d, rbf, o, rbf_env, x_kj, x_ji
|
|
for k, v in g.edata.items():
|
|
l_g.ndata[k] = v
|
|
|
|
l_g.update_all(self.msg_func, fn.sum('x_kj', 'm_update'))
|
|
|
|
for k, v in l_g.ndata.items():
|
|
g.edata[k] = v
|
|
|
|
# Transformations before skip connection
|
|
g.edata['m_update'] = g.edata['m_update'] + g.edata['x_ji']
|
|
for layer in self.layers_before_skip:
|
|
g.edata['m_update'] = layer(g.edata['m_update'])
|
|
g.edata['m_update'] = self.final_before_skip(g.edata['m_update'])
|
|
if self.activation is not None:
|
|
g.edata['m_update'] = self.activation(g.edata['m_update'])
|
|
|
|
# Skip connection
|
|
g.edata['m'] = g.edata['m'] + g.edata['m_update']
|
|
|
|
# Transformations after skip connection
|
|
for layer in self.layers_after_skip:
|
|
g.edata['m'] = layer(g.edata['m'])
|
|
|
|
return g |