def main(args):
# --------------------------------------------------------------------------
# DATA
logger.info('-' * 100)
logger.info('Load data files')
train_exs = []
for t_file in args.train_file:
train_exs += utils.load_data(args, t_file, skip_no_answer=True)
# Shuffle training examples
np.random.shuffle(train_exs)
logger.info('Num train examples = %d' % len(train_exs))
dev_exs = utils.load_data(args, args.dev_file)
logger.info('Num dev examples = %d' % len(dev_exs))
# If we are doing offician evals then we need to:
# 1) Load the original text to retrieve spans from offsets.
# 2) Load the (multiple) text answers for each question.
if args.official_eval:
dev_texts = utils.load_text(args.dev_json)
dev_offsets = {ex['id']: ex['offsets'] for ex in dev_exs}
dev_answers = utils.load_answers(args.dev_json)
# --------------------------------------------------------------------------
# MODEL
logger.info('-' * 100)
start_epoch = 0
if args.checkpoint and os.path.isfile(args.model_file + '.checkpoint'):
# Just resume training, no modifications.
logger.info('Found a checkpoint...')
checkpoint_file = args.model_file + '.checkpoint'
model, start_epoch = SentenceSelector.load_checkpoint(checkpoint_file, args)
else:
# Training starts fresh. But the model state is either pretrained or
# newly (randomly) initialized.
if args.pretrained:
logger.info('Using pretrained model...')
model = SentenceSelector.load(args.pretrained, args)
if args.expand_dictionary:
logger.info('Expanding dictionary for new data...')
# Add words in training + dev examples
words = utils.load_words(args, train_exs + dev_exs)
added = model.expand_dictionary(words)
# Load pretrained embeddings for added words
if args.embedding_file:
model.load_embeddings(added, args.embedding_file)
else:
logger.info('Training model from scratch...')
model = init_from_scratch(args, train_exs, dev_exs)
# Set up partial tuning of embeddings
if args.tune_partial > 0:
logger.info('-' * 100)
logger.info('Counting %d most frequent question words' %
args.tune_partial)
top_words = utils.top_question_words(
args, train_exs, model.word_dict
)
for word in top_words[:5]:
logger.info(word)
logger.info('...')
for word in top_words[-6:-1]:
logger.info(word)
model.tune_embeddings([w[0] for w in top_words])
# Set up optimizer
model.init_optimizer()
# Use the GPU?
if args.cuda:
model.cuda()
# Use multiple GPUs?
if args.parallel:
model.parallelize()
# --------------------------------------------------------------------------
# DATA ITERATORS
# Two datasets: train and dev. If we sort by length it's faster.
logger.info('-' * 100)
logger.info('Make data loaders')
train_dataset = data.SentenceSelectorDataset(train_exs, model, single_answer=True)
if args.sort_by_len:
train_sampler = data.SortedBatchSampler(train_dataset.lengths(),
args.batch_size,
shuffle=True)
else:
train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=args.data_workers,
collate_fn=vector.batchify,
pin_memory=args.cuda,
)
dev_dataset = data.SentenceSelectorDataset(dev_exs, model, single_answer=False)
if args.sort_by_len:
dev_sampler = data.SortedBatchSampler(dev_dataset.lengths(),
args.test_batch_size,
shuffle=False)
else:
dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset)
dev_loader = torch.utils.data.DataLoader(
dev_dataset,
batch_size=args.test_batch_size,
sampler=dev_sampler,
num_workers=args.data_workers,
collate_fn=vector.batchify,
pin_memory=args.cuda,
)
# -------------------------------------------------------------------------
# PRINT CONFIG
logger.info('-' * 100)
logger.info('CONFIG:\n%s' %
json.dumps(vars(args), indent=4, sort_keys=True))
# --------------------------------------------------------------------------
# TRAIN/VALID LOOP
logger.info('-' * 100)
logger.info('Starting training...')
stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0}
## allow toggle mode that will let you evaluate on whatever dev set you give it; preload model
if args.global_mode == "test":
result = validate_unofficial(args, dev_loader, model, stats, mode='dev')
print(result[args.valid_metric])
exit(0)
for epoch in range(start_epoch, args.num_epochs):
stats['epoch'] = epoch
# Train
train(args, train_loader, model, stats)
# Validate unofficial (train)
validate_unofficial(args, train_loader, model, stats, mode='train')
# Validate unofficial (dev)
result = validate_unofficial(args, dev_loader, model, stats, mode='dev')
# Validate official
if args.official_eval:
result = validate_official(args, dev_loader, model, stats,
dev_offsets, dev_texts, dev_answers)
# Save best valid
if result[args.valid_metric] > stats['best_valid']:
logger.info('Best valid: %s = %.2f (epoch %d, %d updates)' %
(args.valid_metric, result[args.valid_metric],
stats['epoch'], model.updates))
model.save(args.model_file)
stats['best_valid'] = result[args.valid_metric]
if epoch % 5 == 0:
model.save(args.model_file + ".dummy")
def main(args):
# --------------------------------------------------------------------------
# DATA
logger.info('-' * 100)
logger.info('Load data files')
train_exs = []
for t_file in args.train_file:
train_exs += utils.load_data(args, t_file, skip_no_answer=True)
np.random.shuffle(train_exs)
logger.info('Num train examples = %d' % len(train_exs))
dev_exs = utils.load_data(args, args.dev_file)
logger.info('Num dev examples = %d' % len(dev_exs))
# If we are doing offician evals then we need to:
# 1) Load the original text to retrieve spans from offsets.
# 2) Load the (multiple) text answers for each question.
if args.official_eval:
dev_texts = utils.load_text(args.dev_json)
dev_offsets = {ex['id']: ex['offsets'] for ex in dev_exs}
dev_answers = utils.load_answers(args.dev_json)
## OFFSET comes from the gold sentence; the predicted sentence value shoule be maintained and sent to official validation set
# --------------------------------------------------------------------------
# MODEL
logger.info('-' * 100)
start_epoch = 0
if args.checkpoint and os.path.isfile(args.model_file + '.checkpoint'):
# Just resume training, no modifications.
logger.info('Found a checkpoint...')
checkpoint_file = args.model_file + '.checkpoint'
model, start_epoch = DocReader.load_checkpoint(checkpoint_file, args)
else:
# Training starts fresh. But the model state is either pretrained or
# newly (randomly) initialized.
if args.pretrained:
logger.info('Using pretrained model...')
model = DocReader.load(args.pretrained, args)
if args.expand_dictionary:
logger.info('Expanding dictionary for new data...')
# Add words in training + dev examples
words = utils.load_words(args, train_exs + dev_exs)
added = model.expand_dictionary(words)
# Load pretrained embeddings for added words
if args.embedding_file:
model.load_embeddings(added, args.embedding_file)
else:
logger.info('Training model from scratch...')
model = init_from_scratch(args, train_exs, dev_exs)
# Set up partial tuning of embeddings
if args.tune_partial > 0:
logger.info('-' * 100)
logger.info('Counting %d most frequent question words' %
args.tune_partial)
top_words = utils.top_question_words(
args, train_exs, model.word_dict
)
for word in top_words[:5]:
logger.info(word)
logger.info('...')
for word in top_words[-6:-1]:
logger.info(word)
model.tune_embeddings([w[0] for w in top_words])
# Set up optimizer
model.init_optimizer()
# Use the GPU?
if args.cuda:
model.cuda()
# Use multiple GPUs?
if args.parallel:
model.parallelize()
# --------------------------------------------------------------------------
# DATA ITERATORS
# Two datasets: train and dev. If we sort by length it's faster.
# Sentence selection objective : run the sentence selector as a submodule
logger.info('-' * 100)
logger.info('Make data loaders')
train_dataset = reader_data.ReaderDataset(train_exs, model, single_answer=True)
# Filter out None examples in training dataset (where sentence selection fails)
#train_dataset.examples = [t for t in train_dataset.examples if t is not None]
if args.sort_by_len:
train_sampler = reader_data.SortedBatchSampler(train_dataset.lengths(),
args.batch_size,
shuffle=True)
else:
train_sampler = torch.utils.data.sampler.RandomSampler(train_dataset)
if args.use_sentence_selector:
train_batcher = reader_vector.sentence_batchifier(model, single_answer=True)
# batching_function = train_batcher.batchify
batching_function = reader_vector.batchify
else:
batching_function = reader_vector.batchify
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=args.data_workers,
collate_fn=batching_function,
pin_memory=args.cuda,
)
dev_dataset = reader_data.ReaderDataset(dev_exs, model, single_answer=False)
#dev_dataset.examples = [t for t in dev_dataset.examples if t is not None]
if args.sort_by_len:
dev_sampler = reader_data.SortedBatchSampler(dev_dataset.lengths(),
args.test_batch_size,
shuffle=False)
else:
dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset)
if args.use_sentence_selector:
dev_batcher = reader_vector.sentence_batchifier(model, single_answer=False)
# batching_function = dev_batcher.batchify
batching_function = reader_vector.batchify
else:
batching_function = reader_vector.batchify
dev_loader = torch.utils.data.DataLoader(
dev_dataset,
batch_size=args.test_batch_size,
sampler=dev_sampler,
num_workers=args.data_workers,
collate_fn=batching_function,
pin_memory=args.cuda,
)
## Dev dataset for measuring performance of the trained sentence selector
if args.use_sentence_selector:
dev_dataset1 = selector_data.SentenceSelectorDataset(dev_exs, model.sentence_selector, single_answer=False)
#dev_dataset1.examples = [t for t in dev_dataset.examples if t is not None]
if args.sort_by_len:
dev_sampler1 = selector_data.SortedBatchSampler(dev_dataset1.lengths(),
args.test_batch_size,
shuffle=False)
else:
dev_sampler1 = torch.utils.data.sampler.SequentialSampler(dev_dataset1)
dev_loader1 = torch.utils.data.DataLoader(
dev_dataset1,
#batch_size=args.test_batch_size,
#sampler=dev_sampler1,
batch_sampler = dev_sampler1,
num_workers=args.data_workers,
collate_fn=selector_vector.batchify,
pin_memory=args.cuda,
)
# -------------------------------------------------------------------------
# PRINT CONFIG
logger.info('-' * 100)
logger.info('CONFIG:\n%s' %
json.dumps(vars(args), indent=4, sort_keys=True))
# --------------------------------------------------------------------------
# TRAIN/VALID LOOP
logger.info('-' * 100)
logger.info('Starting training...')
stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0}
# --------------------------------------------------------------------------
# QUICKLY VALIDATE ON PRETRAINED MODEL
if args.global_mode == "test":
result1 = validate_unofficial(args, dev_loader, model, stats, mode='dev')
result2 = validate_official(args, dev_loader, model, stats,
dev_offsets, dev_texts, dev_answers)
print(result2[args.valid_metric])
print(result1["exact_match"])
if args.use_sentence_selector:
sent_stats = {'timer': utils.Timer(), 'epoch': 0, 'best_valid': 0}
#sent_selector_results = validate_selector(model.sentence_selector.args, dev_loader1, model.sentence_selector, sent_stats, mode="dev")
#print("Sentence Selector model acheives:")
#print(sent_selector_results["accuracy"])
if len(args.adv_dev_json) > 0:
validate_adversarial(args, model, stats, mode="dev")
exit(0)
valid_history = []
bad_counter = 0
for epoch in range(start_epoch, args.num_epochs):
stats['epoch'] = epoch
# Train
train(args, train_loader, model, stats)
# Validate unofficial (train)
validate_unofficial(args, train_loader, model, stats, mode='train')
# Validate unofficial (dev)
result = validate_unofficial(args, dev_loader, model, stats, mode='dev')
# Validate official
if args.official_eval:
result = validate_official(args, dev_loader, model, stats,
dev_offsets, dev_texts, dev_answers)
# Save best valid
if result[args.valid_metric] >= stats['best_valid']:
logger.info('Best valid: %s = %.2f (epoch %d, %d updates)' %
(args.valid_metric, result[args.valid_metric],
stats['epoch'], model.updates))
model.save(args.model_file)
stats['best_valid'] = result[args.valid_metric]
bad_counter = 0
else:
bad_counter += 1
if bad_counter > args.patience:
logger.info("Early Stopping at epoch: %d" % epoch)
exit(0)