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train.py
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train.py
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from __future__ import division
from __future__ import print_function
import datetime
import json
import logging
import os
import pickle
import time
import numpy as np
import optimizers
import torch
from config import parser
from models.base_models import NCModel, LPModel
from utils.data_utils import load_data
from utils.train_utils import get_dir_name, format_metrics
import geoopt.manifolds.poincare.math as pmath
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
def train(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if int(args.double_precision):
torch.set_default_dtype(torch.float64)
if int(args.cuda) >= 0:
torch.cuda.manual_seed(args.seed)
args.device = 'cuda:' + str(args.cuda) if int(args.cuda) >= 0 else 'cpu'
args.patience = args.epochs if not args.patience else int(args.patience)
logging.getLogger().setLevel(logging.INFO)
if args.save:
if not args.save_dir:
dt = datetime.datetime.now()
date = f"{dt.year}_{dt.month}_{dt.day}"
models_dir = os.path.join(os.environ['LOG_DIR'], args.task, date)
save_dir = get_dir_name(models_dir)
else:
save_dir = args.save_dir
logging.basicConfig(level=logging.INFO,
handlers=[
logging.FileHandler(os.path.join(save_dir, 'log.txt')),
logging.StreamHandler()
])
logging.info(f'Using: {args.device}')
logging.info("Using seed {}.".format(args.seed))
# Load data
data = load_data(args, os.path.join(os.environ['DATAPATH'], args.dataset))
args.n_nodes, args.feat_dim = data['features'].shape
if args.task == 'nc':
Model = NCModel
args.n_classes = int(data['labels'].max() + 1)
logging.info(f'Num classes: {args.n_classes}')
else:
args.nb_false_edges = len(data['train_edges_false'])
args.nb_edges = len(data['train_edges'])
if args.task == 'lp':
Model = LPModel
if not args.lr_reduce_freq:
args.lr_reduce_freq = args.epochs
# Model and optimizer
model = Model(args)
logging.info(str(model))
optimizer = getattr(optimizers, args.optimizer)(params=model.parameters(), lr=args.lr,
weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
step_size=int(args.lr_reduce_freq),
gamma=float(args.gamma)
)
tot_params = sum([np.prod(p.size()) for p in model.parameters()])
logging.info(f"Total number of parameters: {tot_params}")
if args.cuda is not None and int(args.cuda) >= 0:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda)
model = model.to(args.device)
for x, val in data.items():
if torch.is_tensor(data[x]):
data[x] = data[x].to(args.device)
# Train model
t_total = time.time()
counter = 0
best_val_metrics = model.init_metric_dict()
best_test_metrics = None
best_emb = None
for epoch in range(args.epochs):
t = time.time()
model.train()
optimizer.zero_grad()
embeddings = model.encode(data['features'], data['adj_train_norm'])
train_metrics = model.compute_metrics(embeddings, data, 'train', args)
train_metrics['loss'].backward()
if args.grad_clip is not None:
max_norm = float(args.grad_clip)
all_params = list(model.parameters())
for param in all_params:
torch.nn.utils.clip_grad_norm_(param, max_norm)
optimizer.step()
lr_scheduler.step()
if (epoch + 1) % args.log_freq == 0:
logging.info(" ".join(['Epoch: {:04d}'.format(epoch + 1),
'lr: {}'.format(lr_scheduler.get_lr()[0]),
format_metrics(train_metrics, 'train'),
'time: {:.4f}s'.format(time.time() - t)
]))
if (epoch + 1) % args.eval_freq == 0:
model.eval()
embeddings = model.encode(data['features'], data['adj_train_norm'])
val_metrics = model.compute_metrics(embeddings, data, 'val', args)
if (epoch + 1) % args.log_freq == 0:
logging.info(" ".join(['Epoch: {:04d}'.format(epoch + 1), format_metrics(val_metrics, 'val')]))
if model.has_improved(best_val_metrics, val_metrics):
best_test_metrics = model.compute_metrics(embeddings, data, 'test', args)
if isinstance(embeddings, tuple):
best_emb = torch.cat((pmath.logmap0(embeddings[0], c=1.0), embeddings[1]), dim=1).cpu()
else:
best_emb = embeddings.cpu()
if args.save:
np.save(os.path.join(save_dir, 'embeddings.npy'), best_emb.detach().numpy())
best_val_metrics = val_metrics
counter = 0
else:
counter += 1
if counter == args.patience and epoch > args.min_epochs:
logging.info("Early stopping")
break
logging.info("Optimization Finished!")
logging.info("Total time elapsed: {:.4f}s".format(time.time() - t_total))
if not best_test_metrics:
model.eval()
best_emb = model.encode(data['features'], data['adj_train_norm'])
best_test_metrics = model.compute_metrics(best_emb, data, 'test', args)
logging.info(" ".join(["Val set results:", format_metrics(best_val_metrics, 'val')]))
logging.info(" ".join(["Test set results:", format_metrics(best_test_metrics, 'test')]))
if args.save:
if isinstance(best_emb, tuple):
best_emb = torch.cat((pmath.logmap0(best_emb[0], c=1.0), best_emb[1]), dim=1).cpu()
else:
best_emb = best_emb.cpu()
np.save(os.path.join(save_dir, 'embeddings.npy'), best_emb.detach().numpy())
if hasattr(model.encoder, 'att_adj'):
filename = os.path.join(save_dir, args.dataset + '_att_adj.p')
pickle.dump(model.encoder.att_adj.cpu().to_dense(), open(filename, 'wb'))
print('Dumped attention adj: ' + filename)
json.dump(vars(args), open(os.path.join(save_dir, 'config.json'), 'w'))
torch.save(model.state_dict(), os.path.join(save_dir, 'model.pth'))
logging.info(f"Saved model in {save_dir}")
if __name__ == '__main__':
args = parser.parse_args()
train(args)
print(args)
import sys
sys.exit(0)