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3ac1d94 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_sum
from torch_geometric.nn import radius_graph, knn_graph
from .common import GaussianSmearing, MLP, batch_hybrid_edge_connection, NONLINEARITIES
class EnBaseLayer(nn.Module):
def __init__(self, hidden_dim, edge_feat_dim, num_r_gaussian, update_x=True, act_fn='silu', norm=False):
super().__init__()
self.r_min = 0.
self.r_max = 10.
self.hidden_dim = hidden_dim
self.num_r_gaussian = num_r_gaussian
self.edge_feat_dim = edge_feat_dim
self.update_x = update_x
self.act_fn = act_fn
self.norm = norm
if num_r_gaussian > 1:
self.distance_expansion = GaussianSmearing(self.r_min, self.r_max, num_gaussians=num_r_gaussian)
self.edge_mlp = MLP(2 * hidden_dim + edge_feat_dim + num_r_gaussian, hidden_dim, hidden_dim,
num_layer=2, norm=norm, act_fn=act_fn, act_last=True)
self.edge_inf = nn.Sequential(nn.Linear(hidden_dim, 1), nn.Sigmoid())
if self.update_x:
# self.x_mlp = MLP(hidden_dim, 1, hidden_dim, num_layer=2, norm=norm, act_fn=act_fn)
x_mlp = [nn.Linear(hidden_dim, hidden_dim), NONLINEARITIES[act_fn]]
layer = nn.Linear(hidden_dim, 1, bias=False)
torch.nn.init.xavier_uniform_(layer.weight, gain=0.001)
x_mlp.append(layer)
x_mlp.append(nn.Tanh())
self.x_mlp = nn.Sequential(*x_mlp)
self.node_mlp = MLP(2 * hidden_dim, hidden_dim, hidden_dim, num_layer=2, norm=norm, act_fn=act_fn)
def forward(self, h, x, edge_index, mask_ligand, edge_attr=None):
src, dst = edge_index
hi, hj = h[dst], h[src]
# \phi_e in Eq(3)
rel_x = x[dst] - x[src]
d_sq = torch.sum(rel_x ** 2, -1, keepdim=True)
if self.num_r_gaussian > 1:
d_feat = self.distance_expansion(torch.sqrt(d_sq + 1e-8))
else:
d_feat = d_sq
if edge_attr is not None:
edge_feat = torch.cat([d_feat, edge_attr], -1)
else:
edge_feat = d_sq
mij = self.edge_mlp(torch.cat([hi, hj, edge_feat], -1))
eij = self.edge_inf(mij)
mi = scatter_sum(mij * eij, dst, dim=0, dim_size=h.shape[0])
# h update in Eq(6)
h = h + self.node_mlp(torch.cat([mi, h], -1))
if self.update_x:
# x update in Eq(4)
xi, xj = x[dst], x[src]
# (xi - xj) / (\|xi - xj\| + C) to make it more stable
delta_x = scatter_sum((xi - xj) / (torch.sqrt(d_sq + 1e-8) + 1) * self.x_mlp(mij), dst, dim=0)
x = x + delta_x * mask_ligand[:, None] # only ligand positions will be updated
return h, x
class EGNN(nn.Module):
def __init__(self, num_layers, hidden_dim, edge_feat_dim, num_r_gaussian, k=32, cutoff=10.0, cutoff_mode='knn',
update_x=True, act_fn='silu', norm=False):
super().__init__()
# Build the network
self.num_layers = num_layers
self.hidden_dim = hidden_dim
self.edge_feat_dim = edge_feat_dim
self.num_r_gaussian = num_r_gaussian
self.update_x = update_x
self.act_fn = act_fn
self.norm = norm
self.k = k
self.cutoff = cutoff
self.cutoff_mode = cutoff_mode
self.distance_expansion = GaussianSmearing(stop=cutoff, num_gaussians=num_r_gaussian)
self.net = self._build_network()
def _build_network(self):
# Equivariant layers
layers = []
for l_idx in range(self.num_layers):
layer = EnBaseLayer(self.hidden_dim, self.edge_feat_dim, self.num_r_gaussian,
update_x=self.update_x, act_fn=self.act_fn, norm=self.norm)
layers.append(layer)
return nn.ModuleList(layers)
# todo: refactor
def _connect_edge(self, x, mask_ligand, batch):
# if self.cutoff_mode == 'radius':
# edge_index = radius_graph(x, r=self.r, batch=batch, flow='source_to_target')
if self.cutoff_mode == 'knn':
edge_index = knn_graph(x, k=self.k, batch=batch, flow='source_to_target')
elif self.cutoff_mode == 'hybrid':
edge_index = batch_hybrid_edge_connection(
x, k=self.k, mask_ligand=mask_ligand, batch=batch, add_p_index=True)
else:
raise ValueError(f'Not supported cutoff mode: {self.cutoff_mode}')
return edge_index
# todo: refactor
@staticmethod
def _build_edge_type(edge_index, mask_ligand):
src, dst = edge_index
edge_type = torch.zeros(len(src)).to(edge_index)
n_src = mask_ligand[src] == 1
n_dst = mask_ligand[dst] == 1
edge_type[n_src & n_dst] = 0
edge_type[n_src & ~n_dst] = 1
edge_type[~n_src & n_dst] = 2
edge_type[~n_src & ~n_dst] = 3
edge_type = F.one_hot(edge_type, num_classes=4)
return edge_type
def forward(self, h, x, mask_ligand, batch, return_all=False):
all_x = [x]
all_h = [h]
for l_idx, layer in enumerate(self.net):
edge_index = self._connect_edge(x, mask_ligand, batch)
edge_type = self._build_edge_type(edge_index, mask_ligand)
h, x = layer(h, x, edge_index, mask_ligand, edge_attr=edge_type)
all_x.append(x)
all_h.append(h)
outputs = {'x': x, 'h': h}
if return_all:
outputs.update({'all_x': all_x, 'all_h': all_h})
return outputs
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