vaetrain_dgl.py

import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader

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

from jtnn import *

lg = rdkit.RDLogger.logger() 
lg.setLevel(rdkit.RDLogger.CRITICAL)

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)
opts,args = parser.parse_args()

dataset = JTNNDataset(data=opts.train, vocab=opts.vocab)
vocab = Vocab([x.strip("\r\n ") for x in open(dataset.vocab_file)])

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))
else:
    for param in model.parameters():
        if param.dim() == 1:
            nn.init.constant(param, 0)
        else:
            nn.init.xavier_normal(param)

if torch.cuda.is_available():
    model = model.cuda()
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 = 1
PRINT_ITER = 20

@profile
def train():
    dataloader = DataLoader(
            dataset,
            batch_size=batch_size,
            shuffle=True,
            num_workers=0,
            collate_fn=lambda x:x,
            drop_last=True)

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

        for it, batch in enumerate(dataloader):
            for mol_tree in batch:
                for node_id, node in mol_tree.nodes.items():
                    if node['label'] not in node['cands']:
                        node['cands'].append(node['label'])
                        node['cand_mols'].append(node['label_mol'])

            model.zero_grad()
            loss, kl_div, wacc, tacc, sacc, dacc = model(batch, beta)
            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" % (
                    kl_div, word_acc, topo_acc, assm_acc, steo_acc))
                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))

if __name__ == '__main__':
    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)