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conv_mol_new.py
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conv_mol_new.py
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import torch
import torch.nn as nn
from torch_geometric.nn import MessagePassing
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool
from MolEncoders import AtomEncoder, BondEncoder
from torch_geometric.utils import degree
from torch_scatter import scatter_add
from torch_geometric.nn.inits import reset
import math
import pdb
nn_act = torch.nn.ReLU() #ReLU()
F_act = F.relu
class GINConv(MessagePassing):
def __init__(self, emb_dim):
super(GINConv, self).__init__(aggr = "add")
self.mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), nn_act, torch.nn.Linear(2*emb_dim, emb_dim))
self.eps = torch.nn.Parameter(torch.Tensor([0]))
self.bond_encoder = BondEncoder(emb_dim = emb_dim)
def forward(self, x, edge_index, edge_attr, edge_weight=None):
edge_embedding = self.bond_encoder(edge_attr)
out = self.mlp((1 + self.eps) *x + self.propagate(edge_index, x=x, edge_attr=edge_embedding, edge_weight=edge_weight))
return out
def message(self, x_j, edge_attr, edge_weight=None):
if edge_weight is not None:
mess = F_act((x_j + edge_attr) * edge_weight)
else:
mess = F_act(x_j + edge_attr)
return mess
# return F_act(x_j + edge_attr)
def update(self, aggr_out):
return aggr_out
class GINMolHeadEncoder(torch.nn.Module):
def __init__(self, num_layer, emb_dim, drop_ratio=0.5, JK="last", residual=True):
super(GINMolHeadEncoder, self).__init__()
self.num_layer = num_layer
self.drop_ratio = drop_ratio
self.JK = JK
self.residual = residual
self.atom_encoder = AtomEncoder(emb_dim)
self.convs = torch.nn.ModuleList()
self.batch_norms = torch.nn.ModuleList()
for layer in range(num_layer):
self.convs.append(GINConv(emb_dim))
self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))
def forward(self, x, edge_index, edge_attr, batch):
h_list = [self.atom_encoder(x)]
for layer in range(self.num_layer):
h = self.convs[layer](h_list[layer], edge_index, edge_attr)
h = self.batch_norms[layer](h)
if layer == self.num_layer - 1:
h = F.dropout(h, self.drop_ratio, training = self.training)
else:
h = F.dropout(F_act(h), self.drop_ratio, training = self.training)
if self.residual:
h = h + h_list[layer]
h_list.append(h)
if self.JK == "last":
node_representation = h_list[-1]
elif self.JK == "sum":
node_representation = 0
for layer in range(self.num_layer + 1):
node_representation += h_list[layer]
return node_representation
class GraphMolMasker(torch.nn.Module):
def __init__(self, num_layer, emb_dim):
super(GraphMolMasker, self).__init__()
self.gnn_encoder = GINMolHeadEncoder(num_layer, emb_dim)
self.edge_att_mlp = nn.Linear(emb_dim * 2, 1)
self.node_att_mlp = nn.Linear(emb_dim, 1)
self.reset_parameters()
def reset_parameters(self):
reset(self.gnn_encoder)
reset(self.edge_att_mlp)
reset(self.node_att_mlp)
def forward(self, data):
x, edge_index, edge_attr, batch = data.x, data.edge_index, data.edge_attr, data.batch
node_rep = self.gnn_encoder(x, edge_index, edge_attr, batch)
size = batch[-1].item() + 1
row, col = edge_index
edge_rep = torch.cat([node_rep[row], node_rep[col]], dim=-1)
# if self.gumbel:
# node_key = self.gumbel_sigmoid(self.node_att_mlp(node_rep), temp=self.temp).unsqueeze(1)
# edge_key = self.gumbel_sigmoid(self.edge_att_mlp(edge_rep), temp=self.temp).unsqueeze(1)
# else:
node_key = torch.sigmoid(self.node_att_mlp(node_rep))
edge_key = torch.sigmoid(self.edge_att_mlp(edge_rep))
node_key_num, node_env_num, non_zero_node_ratio = self.reg_mask(node_key, batch, size)
edge_key_num, edge_env_num, non_zero_edge_ratio = self.reg_mask(edge_key, batch[edge_index[0]], size)
self.non_zero_node_ratio = non_zero_node_ratio
self.non_zero_edge_ratio = non_zero_edge_ratio
output = {"node_key": node_key, "edge_key": edge_key,
"node_key_num": node_key_num, "node_env_num": node_env_num,
"edge_key_num": edge_key_num, "edge_env_num": edge_env_num}
return output
def gumbel_sigmoid(self, logits, temp=0.1, bias=1e-4):
device = logits.device
eps = (bias - (1 - bias)) * torch.rand(logits.size()) + (1 - bias)
gate_inputs = torch.log(eps) - torch.log(1 - eps)
gate_inputs = gate_inputs.to(device)
gate_inputs = (gate_inputs + logits) / temp
weight = torch.sigmoid(gate_inputs).squeeze()
return weight
def reg_mask(self, mask, batch, size):
key_num = scatter_add(mask, batch, dim=0, dim_size=size)
env_num = scatter_add((1 - mask), batch, dim=0, dim_size=size)
non_zero_mask = scatter_add((mask > 0).to(torch.float32), batch, dim=0, dim_size=size)
all_mask = scatter_add(torch.ones_like(mask).to(torch.float32), batch, dim=0, dim_size=size)
non_zero_ratio = non_zero_mask / (all_mask + 1e-8)
return key_num + 1e-8, env_num + 1e-8, non_zero_ratio
class GNNMolTailEncoder(torch.nn.Module):
def __init__(self, num_layer, emb_dim, drop_ratio=0.5, JK="last", residual=True):
super(GNNMolTailEncoder, self).__init__()
self.num_layer = num_layer
self.drop_ratio = drop_ratio
self.JK = JK
self.residual = residual
self.convs = torch.nn.ModuleList()
self.batch_norms = torch.nn.ModuleList()
for layer in range(num_layer):
self.convs.append(GINConv(emb_dim))
self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))
def forward(self, x, edge_index, edge_attr, batch, node_adv=None, edge_adv=None):
if node_adv is not None:
x = x * node_adv
h_list = [x]
for layer in range(self.num_layer):
h = self.convs[layer](h_list[layer], edge_index, edge_attr, edge_adv)
h = self.batch_norms[layer](h)
if layer == self.num_layer - 1:
h = F.dropout(h, self.drop_ratio, training = self.training)
else:
h = F.dropout(F_act(h), self.drop_ratio, training = self.training)
if self.residual:
h = h + h_list[layer]
h_list.append(h)
if self.JK == "last":
node_representation = h_list[-1]
elif self.JK == "sum":
node_representation = 0
for layer in range(self.num_layer + 1):
node_representation += h_list[layer]
return node_representation
# class GNNMolTailEncoder(torch.nn.Module):
# def __init__(self, num_layer, emb_dim):
# super(GNNMolTailEncoder, self).__init__()
# self.num_layer = num_layer
# self.emb_dim = emb_dim
# self.dropout_rate = 0.5
# self.relu1 = nn.ReLU()
# self.relus = nn.ModuleList([nn.ReLU() for _ in range(num_layer - 1)])
# self.batch_norm1 = nn.BatchNorm1d(emb_dim)
# self.batch_norms = nn.ModuleList([nn.BatchNorm1d(emb_dim) for _ in range(num_layer - 1)])
# self.dropout1 = nn.Dropout(self.dropout_rate)
# self.dropouts = nn.ModuleList([nn.Dropout(self.dropout_rate) for _ in range(num_layer - 1)])
# self.conv1 = GINEConv(emb_dim)
# self.convs = nn.ModuleList([GINEConv(emb_dim) for _ in range(num_layer - 1)])
# def forward(self, x, edge_index, edge_attr, batch, node_adv=None, edge_adv=None):
# if node_adv is not None:
# x = x * node_adv
# post_conv = self.batch_norm1(self.conv1(x, edge_index, edge_attr, edge_adv))
# if self.num_layer > 1:
# post_conv = self.relu1(post_conv)
# post_conv = self.dropout1(post_conv)
# for i, (conv, batch_norm, relu, dropout) in enumerate(zip(self.convs, self.batch_norms, self.relus, self.dropouts)):
# post_conv = batch_norm(conv(post_conv, edge_index, edge_attr, edge_adv))
# if i != len(self.convs) - 1:
# post_conv = relu(post_conv)
# post_conv = dropout(post_conv)
# return post_conv