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gvae.py
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gvae.py
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import torch
import torch.nn as nn
from torch.nn import Linear
from torch_geometric.nn.conv import TransformerConv
from torch_geometric.nn import Set2Set
from torch_geometric.nn import BatchNorm
from config import SUPPORTED_ATOMS, SUPPORTED_EDGES, MAX_MOLECULE_SIZE, ATOMIC_NUMBERS
from utils import graph_representation_to_molecule, to_one_hot
from tqdm import tqdm
class GVAE(nn.Module):
def __init__(self, feature_size):
super(GVAE, self).__init__()
self.encoder_embedding_size = 64
self.edge_dim = 11
self.latent_embedding_size = 128
self.num_edge_types = len(SUPPORTED_EDGES)
self.num_atom_types = len(SUPPORTED_ATOMS)
self.max_num_atoms = MAX_MOLECULE_SIZE
self.decoder_hidden_neurons = 512
# Encoder layers
self.conv1 = TransformerConv(feature_size,
self.encoder_embedding_size,
heads=4,
concat=False,
beta=True,
edge_dim=self.edge_dim)
self.bn1 = BatchNorm(self.encoder_embedding_size)
self.conv2 = TransformerConv(self.encoder_embedding_size,
self.encoder_embedding_size,
heads=4,
concat=False,
beta=True,
edge_dim=self.edge_dim)
self.bn2 = BatchNorm(self.encoder_embedding_size)
self.conv3 = TransformerConv(self.encoder_embedding_size,
self.encoder_embedding_size,
heads=4,
concat=False,
beta=True,
edge_dim=self.edge_dim)
self.bn3 = BatchNorm(self.encoder_embedding_size)
self.conv4 = TransformerConv(self.encoder_embedding_size,
self.encoder_embedding_size,
heads=4,
concat=False,
beta=True,
edge_dim=self.edge_dim)
# Pooling layers
self.pooling = Set2Set(self.encoder_embedding_size, processing_steps=4)
# Latent transform layers
self.mu_transform = Linear(self.encoder_embedding_size*2,
self.latent_embedding_size)
self.logvar_transform = Linear(self.encoder_embedding_size*2,
self.latent_embedding_size)
# Decoder layers
# --- Shared layers
self.linear_1 = Linear(self.latent_embedding_size, self.decoder_hidden_neurons)
self.linear_2 = Linear(self.decoder_hidden_neurons, self.decoder_hidden_neurons)
# --- Atom decoding (outputs a matrix: (max_num_atoms) * (# atom_types + "none"-type))
atom_output_dim = self.max_num_atoms*(self.num_atom_types + 1)
self.atom_decode = Linear(self.decoder_hidden_neurons, atom_output_dim)
# --- Edge decoding (outputs a triu tensor: (max_num_atoms*(max_num_atoms-1)/2*(#edge_types + 1) ))
edge_output_dim = int(((self.max_num_atoms * (self.max_num_atoms - 1)) / 2) * (self.num_edge_types + 1))
self.edge_decode = Linear(self.decoder_hidden_neurons, edge_output_dim)
def encode(self, x, edge_attr, edge_index, batch_index):
# GNN layers
x = self.conv1(x, edge_index, edge_attr).relu()
x = self.bn1(x)
x = self.conv2(x, edge_index, edge_attr).relu()
x = self.bn2(x)
x = self.conv3(x, edge_index, edge_attr).relu()
x = self.bn3(x)
x = self.conv4(x, edge_index, edge_attr).relu()
# Pool to global representation
x = self.pooling(x, batch_index)
# Latent transform layers
mu = self.mu_transform(x)
logvar = self.logvar_transform(x)
return mu, logvar
def decode_graph(self, graph_z):
"""
Decodes a latent vector into a continuous graph representation
consisting of node types and edge types.
"""
# Pass through shared layers
z = self.linear_1(graph_z).relu()
z = self.linear_2(z).relu()
# Decode atom types
atom_logits = self.atom_decode(z)
# Decode edge types
edge_logits = self.edge_decode(z)
return atom_logits, edge_logits
def decode(self, z, batch_index):
node_logits = []
triu_logits = []
# Iterate over molecules in batch
for graph_id in torch.unique(batch_index):
# Get latent vector for this graph
graph_z = z[graph_id]
# Recover graph from latent vector
atom_logits, edge_logits = self.decode_graph(graph_z)
# Store per graph results
node_logits.append(atom_logits)
triu_logits.append(edge_logits)
# Concatenate all outputs of the batch
node_logits = torch.cat(node_logits)
triu_logits = torch.cat(triu_logits)
return triu_logits, node_logits
def reparameterize(self, mu, logvar):
if self.training:
# Get standard deviation
std = torch.exp(logvar)
# Returns random numbers from a normal distribution
eps = torch.randn_like(std)
# Return sampled values
return eps.mul(std).add_(mu)
else:
return mu
def forward(self, x, edge_attr, edge_index, batch_index):
# Encode the molecule
mu, logvar = self.encode(x, edge_attr, edge_index, batch_index)
# Sample latent vector (per atom)
z = self.reparameterize(mu, logvar)
# Decode latent vector into original molecule
triu_logits, node_logits = self.decode(z, batch_index)
return triu_logits, node_logits, mu, logvar
def sample_mols(self, num=10000):
print("Sampling molecules ... ")
n_valid = 0
# Sample molecules and check if they are valid
for _ in tqdm(range(num)):
# Sample latent space
z = torch.randn(1, self.latent_embedding_size)
# Get model output (this could also be batched)
dummy_batch_index = torch.Tensor([0]).int()
triu_logits, node_logits = self.decode(z, dummy_batch_index)
# Reshape triu predictions
edge_matrix_shape = (int((MAX_MOLECULE_SIZE * (MAX_MOLECULE_SIZE - 1))/2), len(SUPPORTED_EDGES) + 1)
triu_preds_matrix = triu_logits.reshape(edge_matrix_shape)
triu_preds = torch.argmax(triu_preds_matrix, dim=1)
# Reshape node predictions
node_matrix_shape = (MAX_MOLECULE_SIZE, (len(SUPPORTED_ATOMS) + 1))
node_preds_matrix = node_logits.reshape(node_matrix_shape)
node_preds = torch.argmax(node_preds_matrix[:, :9], dim=1)
# Get atomic numbers
node_preds_one_hot = to_one_hot(node_preds, options=ATOMIC_NUMBERS)
atom_numbers_dummy = torch.Tensor(ATOMIC_NUMBERS).repeat(node_preds_one_hot.shape[0], 1)
atom_types = torch.masked_select(atom_numbers_dummy, node_preds_one_hot.bool())
# Attempt to create valid molecule
smiles, _ = graph_representation_to_molecule(atom_types, triu_preds.float())
# A dot means disconnected
if smiles and "." not in smiles:
print("Successfully generated: ", smiles)
n_valid += 1
return n_valid