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import pickle
import sys
import time
import numpy as np
import rdkit
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from dgllife.data import JTVAEZINC, JTVAECollator, JTVAEDataset
from dgllife.model import JTNNVAE
from torch.utils.data import DataLoader
from utils import get_timestamp, mkdir_p
def main(args):
print(f"{get_timestamp()}: {args}")
mkdir_p(args.save_path)
lg = rdkit.RDLogger.logger()
lg.setLevel(rdkit.RDLogger.CRITICAL)
if args.use_cpu or not torch.cuda.is_available():
device = torch.device("cpu")
else:
device = torch.device("cuda:0")
with open(args.vocab_path, "rb") as f:
vocab = pickle.load(f)
if args.train_path is None:
dataset = JTVAEZINC("train", vocab)
else:
dataset = JTVAEDataset(args.train_path, vocab, training=True)
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=JTVAECollator(training=True),
drop_last=True,
)
model = JTNNVAE(vocab, args.hidden_size, args.latent_size, args.depth)
if args.model_path is not None:
print(f"Loading model at {args.model_path}")
model.load_state_dict(torch.load(args.model_path, map_location="cpu"))
else:
model.reset_parameters()
model = model.to(device)
print(
"Model #Params: {:d}K".format(
sum([x.nelement() for x in model.parameters()]) // 1000
)
)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = lr_scheduler.ExponentialLR(optimizer, args.gamma)
dur = []
t0 = time.time()
for epoch in range(args.max_epoch):
word_acc, topo_acc, assm_acc, steo_acc = 0, 0, 0, 0
for it, (
batch_trees,
batch_tree_graphs,
batch_mol_graphs,
stereo_cand_batch_idx,
stereo_cand_labels,
batch_stereo_cand_graphs,
) in enumerate(dataloader):
batch_tree_graphs = batch_tree_graphs.to(device)
batch_mol_graphs = batch_mol_graphs.to(device)
stereo_cand_batch_idx = stereo_cand_batch_idx.to(device)
batch_stereo_cand_graphs = batch_stereo_cand_graphs.to(device)
loss, kl_div, wacc, tacc, sacc, dacc = model(
batch_trees,
batch_tree_graphs,
batch_mol_graphs,
stereo_cand_batch_idx,
stereo_cand_labels,
batch_stereo_cand_graphs,
beta=args.beta,
)
optimizer.zero_grad()
loss.backward()
optimizer.step()
word_acc += wacc
topo_acc += tacc
assm_acc += sacc
steo_acc += dacc
if (it + 1) % args.print_iter == 0:
dur.append(time.time() - t0)
word_acc = word_acc / args.print_iter * 100
topo_acc = topo_acc / args.print_iter * 100
assm_acc = assm_acc / args.print_iter * 100
steo_acc = steo_acc / args.print_iter * 100
print(
get_timestamp(),
"Epoch {:d}/{:d} | Iter {:d}/{:d} | KL: {:.1f}, Word: {:.2f}, "
"Topo: {:.2f}, Assm: {:.2f}, Steo: {:.2f} | "
"Estimated time per epoch: {:.4f}s".format(
epoch + 1,
args.max_epoch,
it + 1,
len(dataloader),
kl_div,
word_acc,
topo_acc,
assm_acc,
steo_acc,
np.mean(dur) / args.print_iter * len(dataloader),
),
)
word_acc, topo_acc, assm_acc, steo_acc = 0, 0, 0, 0
sys.stdout.flush()
t0 = time.time()
if (it + 1) % 15000 == 0:
scheduler.step()
if (it + 1) % args.save_iter == 0:
save_path = args.save_path + f"/model.epoch-{epoch}-iter-{it}"
print(get_timestamp(), f"Saving checkpoint at {save_path}")
torch.save(model.state_dict(), save_path)
scheduler.step()
torch.save(model.state_dict(), args.save_path + "/model.iter-" + str(epoch))
return {
"KL": kl_div,
"Word": word_acc,
"Topo": topo_acc,
"Assm": assm_acc,
"Steo": steo_acc,
}
if __name__ == "__main__":
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument(
"-tr",
"--train-path",
type=str,
help="Path to the training molecules, with one SMILES string a line",
)
parser.add_argument(
"-s",
"--save-path",
type=str,
default="vae_model",
help="Directory to save model checkpoints",
)
parser.add_argument(
"-m", "--model-path", type=str, help="Path to pre-trained model checkpoint"
)
parser.add_argument("-b", "--batch-size", type=int, default=40, help="Batch size")
parser.add_argument(
"-w", "--hidden-size", type=int, default=450, help="Hidden size"
)
parser.add_argument("-l", "--latent-size", type=int, default=56, help="Latent size")
parser.add_argument(
"-d", "--depth", type=int, default=3, help="Number of GNN layers"
)
parser.add_argument(
"-z", "--beta", type=float, default=0.001, help="Weight for KL loss term"
)
parser.add_argument("-lr", "--lr", type=float, default=0.0007, help="Learning rate")
parser.add_argument(
"-g",
"--gamma",
type=float,
default=0.9,
help="Multiplicative factor for learning rate decay",
)
parser.add_argument(
"-me",
"--max-epoch",
type=int,
default=7,
help="Maximum number of epochs for training",
)
parser.add_argument(
"-nw",
"--num-workers",
type=int,
default=4,
help="Number of subprocesses for data loading",
)
parser.add_argument(
"-pi",
"--print-iter",
type=int,
default=20,
help="Frequency for printing evaluation metrics",
)
parser.add_argument(
"-cpu",
"--use-cpu",
action="store_true",
help="By default, the script uses GPU whenever available. "
"This flag enforces the use of CPU.",
)
args = parser.parse_args()
main(args)
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