dmlc--dgl/examples/pytorch/jtnn/vaetrain_dgl.py

import math
import random
import sys
from collections import deque
from optparse import OptionParser

import rdkit
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import tqdm
from jtnn import *
from torch.utils.data import DataLoader

torch.multiprocessing.set_sharing_strategy("file_system")


def worker_init_fn(id_):
    lg = rdkit.RDLogger.logger()
    lg.setLevel(rdkit.RDLogger.CRITICAL)


worker_init_fn(None)

parser = OptionParser()
parser.add_option(
    "-t", "--train", dest="train", default="train", help="Training file name"
)
parser.add_option(
    "-v", "--vocab", dest="vocab", default="vocab", help="Vocab file name"
)
parser.add_option("-s", "--save_dir", dest="save_path")
parser.add_option("-m", "--model", dest="model_path", default=None)
parser.add_option("-b", "--batch", dest="batch_size", default=40)
parser.add_option("-w", "--hidden", dest="hidden_size", default=200)
parser.add_option("-l", "--latent", dest="latent_size", default=56)
parser.add_option("-d", "--depth", dest="depth", default=3)
parser.add_option("-z", "--beta", dest="beta", default=1.0)
parser.add_option("-q", "--lr", dest="lr", default=1e-3)
parser.add_option("-T", "--test", dest="test", action="store_true")
opts, args = parser.parse_args()

dataset = JTNNDataset(data=opts.train, vocab=opts.vocab, training=True)
vocab = dataset.vocab

batch_size = int(opts.batch_size)
hidden_size = int(opts.hidden_size)
latent_size = int(opts.latent_size)
depth = int(opts.depth)
beta = float(opts.beta)
lr = float(opts.lr)

model = DGLJTNNVAE(vocab, hidden_size, latent_size, depth)

if opts.model_path is not None:
    model.load_state_dict(torch.load(opts.model_path, weights_only=False))
else:
    for param in model.parameters():
        if param.dim() == 1:
            nn.init.constant(param, 0)
        else:
            nn.init.xavier_normal(param)

model = cuda(model)
print(
    "Model #Params: %dK"
    % (sum([x.nelement() for x in model.parameters()]) / 1000,)
)

optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = lr_scheduler.ExponentialLR(optimizer, 0.9)
scheduler.step()

MAX_EPOCH = 100
PRINT_ITER = 20


def train():
    dataset.training = True
    dataloader = DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=4,
        collate_fn=JTNNCollator(vocab, True),
        drop_last=True,
        worker_init_fn=worker_init_fn,
    )

    for epoch in range(MAX_EPOCH):
        word_acc, topo_acc, assm_acc, steo_acc = 0, 0, 0, 0

        for it, batch in enumerate(tqdm.tqdm(dataloader)):
            model.zero_grad()
            try:
                loss, kl_div, wacc, tacc, sacc, dacc = model(batch, beta)
            except:
                print([t.smiles for t in batch["mol_trees"]])
                raise
            loss.backward()
            optimizer.step()

            word_acc += wacc
            topo_acc += tacc
            assm_acc += sacc
            steo_acc += dacc

            if (it + 1) % PRINT_ITER == 0:
                word_acc = word_acc / PRINT_ITER * 100
                topo_acc = topo_acc / PRINT_ITER * 100
                assm_acc = assm_acc / PRINT_ITER * 100
                steo_acc = steo_acc / PRINT_ITER * 100

                print(
                    "KL: %.1f, Word: %.2f, Topo: %.2f, Assm: %.2f, Steo: %.2f, Loss: %.6f"
                    % (
                        kl_div,
                        word_acc,
                        topo_acc,
                        assm_acc,
                        steo_acc,
                        loss.item(),
                    )
                )
                word_acc, topo_acc, assm_acc, steo_acc = 0, 0, 0, 0
                sys.stdout.flush()

            if (it + 1) % 1500 == 0:  # Fast annealing
                scheduler.step()
                print("learning rate: %.6f" % scheduler.get_lr()[0])
                torch.save(
                    model.state_dict(),
                    opts.save_path + "/model.iter-%d-%d" % (epoch, it + 1),
                )

        scheduler.step()
        print("learning rate: %.6f" % scheduler.get_lr()[0])
        torch.save(
            model.state_dict(), opts.save_path + "/model.iter-" + str(epoch)
        )


def test():
    dataset.training = False
    dataloader = DataLoader(
        dataset,
        batch_size=1,
        shuffle=False,
        num_workers=0,
        collate_fn=JTNNCollator(vocab, False),
        drop_last=True,
        worker_init_fn=worker_init_fn,
    )

    # Just an example of molecule decoding; in reality you may want to sample
    # tree and molecule vectors.
    for it, batch in enumerate(dataloader):
        gt_smiles = batch["mol_trees"][0].smiles
        print(gt_smiles)
        model.move_to_cuda(batch)
        _, tree_vec, mol_vec = model.encode(batch)
        tree_vec, mol_vec, _, _ = model.sample(tree_vec, mol_vec)
        smiles = model.decode(tree_vec, mol_vec)
        print(smiles)


if __name__ == "__main__":
    if opts.test:
        test()
    else:
        train()

    print("# passes:", model.n_passes)
    print("Total # nodes processed:", model.n_nodes_total)
    print("Total # edges processed:", model.n_edges_total)
    print("Total # tree nodes processed:", model.n_tree_nodes_total)
    print("Graph decoder: # passes:", model.jtmpn.n_passes)
    print(
        "Graph decoder: Total # candidates processed:",
        model.jtmpn.n_samples_total,
    )
    print("Graph decoder: Total # nodes processed:", model.jtmpn.n_nodes_total)
    print("Graph decoder: Total # edges processed:", model.jtmpn.n_edges_total)
    print("Graph encoder: # passes:", model.mpn.n_passes)
    print(
        "Graph encoder: Total # candidates processed:",
        model.mpn.n_samples_total,
    )
    print("Graph encoder: Total # nodes processed:", model.mpn.n_nodes_total)
    print("Graph encoder: Total # edges processed:", model.mpn.n_edges_total)