print('Dataset:', args.dataset)
print('No. of target classes:', train_iter.dataset.NUM_CLASSES)
print('No. of train instances', len(train_iter.dataset))
print('No. of dev instances', len(dev_iter.dataset))
print('No. of test instances', len(test_iter.dataset))
if args.resume_snapshot:
if args.cuda:
model = torch.load(
args.resume_snapshot,
map_location=lambda storage, location: storage.cuda(args.gpu))
else:
model = torch.load(args.resume_snapshot,
map_location=lambda storage, location: storage)
else:
model = HAN(config)
if args.cuda:
model.cuda()
if not args.trained_model:
save_path = os.path.join(args.save_path,
dataset_map[args.dataset].NAME)
os.makedirs(save_path, exist_ok=True)
parameter = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(parameter,
lr=args.lr,
weight_decay=args.weight_decay)
train_evaluator = EvaluatorFactory.get_evaluator(dataset_class, model,
None, train_iter,
def run_main(args):
print('Args: ', args)
logger = get_logger()
metrics_dev_json = args.metrics_json + '_dev'
metrics_test_json = args.metrics_json + '_test'
# Set random seed for reproducibility
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
np.random.seed(args.seed)
random.seed(args.seed)
device = torch.device(
"cuda" if torch.cuda.is_available() and args.cuda else "cpu")
if not args.cuda:
args.gpu = -1
if torch.cuda.is_available() and args.cuda:
print('Note: You are using GPU for training')
torch.cuda.manual_seed(args.seed)
if torch.cuda.is_available() and not args.cuda:
print('Warning: Using CPU for training')
dataset_map = {
'Reuters': Reuters,
'CongressionalHearing': CongressionalHearing,
'AAPD': AAPD,
'IMDB': IMDB,
'Yelp2014': Yelp2014
}
if args.dataset not in dataset_map:
raise ValueError('Unrecognized dataset')
else:
dataset_class = dataset_map[args.dataset]
if args.fold_num >= 0:
dataset_name = os.path.join(dataset_class.NAME + 'Folds',
'fold' + str(args.fold_num))
else:
dataset_name = dataset_class.NAME
if args.evaluate_dev:
train_iter, dev_iter = dataset_class.iters_dev(
args.data_dir,
dataset_name,
args.word_vectors_file,
args.word_vectors_dir,
batch_size=args.batch_size,
device=device,
unk_init=UnknownWordVecCache.unk)
if args.evaluate_test:
train_iter, test_iter = dataset_class.iters_test(
args.data_dir,
dataset_name,
args.word_vectors_file,
args.word_vectors_dir,
batch_size=args.batch_size,
device=device,
unk_init=UnknownWordVecCache.unk)
config = deepcopy(args)
config.dataset = train_iter.dataset
config.target_class = train_iter.dataset.NUM_CLASSES
config.words_num = len(train_iter.dataset.TEXT_FIELD.vocab)
print('Dataset:', args.dataset)
print('No. of target classes:', train_iter.dataset.NUM_CLASSES)
print('No. of train instances', len(train_iter.dataset))
if args.evaluate_dev:
print('No. of dev instances', len(dev_iter.dataset))
if args.evaluate_test:
print('No. of test instances', len(test_iter.dataset))
if args.resume_snapshot:
if args.cuda:
model = torch.load(
args.resume_snapshot,
map_location=lambda storage, location: storage.cuda(args.gpu))
else:
model = torch.load(args.resume_snapshot,
map_location=lambda storage, location: storage)
else:
model = HAN(config)
model.to(device)
if not args.trained_model:
save_path = os.path.join(args.save_path, dataset_name)
os.makedirs(save_path, exist_ok=True)
parameter = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(parameter,
lr=args.lr,
weight_decay=args.weight_decay)
train_evaluator = EvaluatorFactory.get_evaluator(dataset_class, model,
None, train_iter,
args.batch_size, device)
if args.evaluate_dev:
dev_evaluator = EvaluatorFactory.get_evaluator(dataset_class, model,
None, dev_iter,
args.batch_size, device)
if hasattr(dev_evaluator, 'is_multilabel'):
dev_evaluator.is_multilabel = dataset_class.IS_MULTILABEL
if hasattr(dev_evaluator, 'ignore_lengths'):
dev_evaluator.ignore_lengths = True
if args.evaluate_test:
test_evaluator = EvaluatorFactory.get_evaluator(
dataset_class, model, None, test_iter, args.batch_size, device)
if hasattr(test_evaluator, 'is_multilabel'):
test_evaluator.is_multilabel = dataset_class.IS_MULTILABEL
if hasattr(test_evaluator, 'ignore_lengths'):
test_evaluator.ignore_lengths = True
if hasattr(train_evaluator, 'is_multilabel'):
train_evaluator.is_multilabel = dataset_class.IS_MULTILABEL
trainer_config = {
'optimizer': optimizer,
'batch_size': args.batch_size,
'log_interval': args.log_every,
'patience': args.patience,
'model_outfile': args.save_path,
'logger': logger,
'is_multilabel': dataset_class.IS_MULTILABEL,
'ignore_lengths': True
}
if args.evaluate_dev:
trainer = TrainerFactory.get_trainer_dev(args.dataset, model, None,
train_iter, trainer_config,
train_evaluator,
dev_evaluator, args)
if args.evaluate_test:
trainer = TrainerFactory.get_trainer_test(args.dataset, model, None,
train_iter, trainer_config,
train_evaluator,
test_evaluator, args)
if not args.trained_model:
trainer.train(args.epochs)
else:
if args.cuda:
model = torch.load(
args.trained_model,
map_location=lambda storage, location: storage.cuda(device))
else:
model = torch.load(args.trained_model,
map_location=lambda storage, location: storage)
# Calculate dev and test metrics
if hasattr(trainer, 'snapshot_path'):
model = torch.load(trainer.snapshot_path)
if args.evaluate_dev:
evaluate_dataset('dev',
dataset_map[args.dataset],
model,
None,
dev_iter,
args.batch_size,
is_multilabel=dataset_class.IS_MULTILABEL,
device=device,
save_file=metrics_dev_json)
if args.evaluate_test:
evaluate_dataset('test',
dataset_map[args.dataset],
model,
None,
test_iter,
args.batch_size,
is_multilabel=dataset_class.IS_MULTILABEL,
device=device,
save_file=metrics_test_json)
print('Dataset:', args.dataset)
print('No. of target classes:', train_iter.dataset.NUM_CLASSES)
print('No. of train instances', len(train_iter.dataset))
print('No. of dev instances', len(dev_iter.dataset))
print('No. of test instances', len(test_iter.dataset))
if args.resume_snapshot:
if args.cuda:
model = torch.load(args.resume_snapshot,
map_location=lambda storage, location: storage.
cuda(args.device))
else:
model = torch.load(args.resume_snapshot,
map_location=lambda storage, location: storage)
else:
model = HAN(config)
if args.cuda:
model.to(device)
if not args.trained_model:
save_path = os.path.join(args.save_path,
dataset_map[args.dataset].NAME)
os.makedirs(save_path, exist_ok=True)
parameter = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(parameter,
lr=args.lr,
weight_decay=args.weight_decay)
train_evaluator = EvaluatorFactory.get_evaluator(dataset_class, model,
None, train_iter,