Don't generate during training, add --quiet to generate.py (8f058ea0) · Commits · Simon Will / fairseq

fairseq/data.py

+5 −5

Changes for fairseq/data.py: 5 added lines, 5 removed lines.
Original line number	Diff line number	Diff line
		@@ -17,7 +17,7 @@ from fairseq.dictionary import Dictionary
		from fairseq.indexed_dataset import IndexedDataset, IndexedInMemoryDataset


		def load_with_check(path, src=None, dst=None):
		def load_with_check(path, load_splits, src=None, dst=None):
		"""Loads the train, valid, and test sets from the specified folder
		and check that training files exist."""

		@@ -43,12 +43,12 @@ def load_with_check(path, src=None, dst=None):
		else:
		raise ValueError('training file not found for {}-{}'.format(src, dst))

		dataset = load(path, src, dst)
		dataset = load(path, load_splits, src, dst)
		return dataset


		def load(path, src, dst):
		"""Loads the train, valid, and test sets from the specified folder."""
		def load(path, load_splits, src, dst):
		"""Loads specified data splits (e.g. test, train or valid) from the path."""

		langcode = '{}-{}'.format(src, dst)

		@@ -59,7 +59,7 @@ def load(path, src, dst):
		dst_dict = Dictionary.load(fmt_path('dict.{}.txt', dst))
		dataset = LanguageDatasets(src, dst, src_dict, dst_dict)

		for split in ['train', 'valid', 'test']:
		for split in load_splits:
		for k in itertools.count():
		prefix = "{}{}".format(split, k if k > 0 else '')
		src_path = fmt_path('{}.{}.{}', prefix, langcode, src)

fairseq/options.py

+2 −0

Changes for fairseq/options.py: 2 added lines, 0 removed lines.
Original line number	Diff line number	Diff line
		@@ -104,6 +104,8 @@ def add_generation_args(parser):
		help='length penalty: <1.0 favors shorter, >1.0 favors longer sentences')
		group.add_argument('--unk-replace-dict', default='', type=str,
		help='performs unk word replacement')
		group.add_argument('--quiet', action='store_true',
		help='Only print final scores')

		return group

fairseq/utils.py

+2 −2

Changes for fairseq/utils.py: 2 added lines, 2 removed lines.
Original line number	Diff line number	Diff line
		@@ -94,7 +94,7 @@ def load_checkpoint(filename, model, optimizer, lr_scheduler, cuda_device=None):
		return epoch, batch_offset


		def load_ensemble_for_inference(filenames, data_path):
		def load_ensemble_for_inference(filenames, data_path, split):
		# load model architectures and weights
		states = []
		for filename in filenames:
		@@ -106,7 +106,7 @@ def load_ensemble_for_inference(filenames, data_path):

		# load dataset
		args = states[0]['args']
		dataset = data.load(data_path, args.source_lang, args.target_lang)
		dataset = data.load(data_path, [split], args.source_lang, args.target_lang)

		# build models
		ensemble = []

generate.py

+3 −1

Changes for generate.py: 3 added lines, 1 removed line.
Original line number	Diff line number	Diff line
		@@ -38,7 +38,7 @@ def main():

		# Load model and dataset
		print('\| loading model(s) from {}'.format(', '.join(args.path)))
		models, dataset = utils.load_ensemble_for_inference(args.path, args.data)
		models, dataset = utils.load_ensemble_for_inference(args.path, args.data, args.gen_subset)

		print('\| [{}] dictionary: {} types'.format(dataset.src, len(dataset.src_dict)))
		print('\| [{}] dictionary: {} types'.format(dataset.dst, len(dataset.dst_dict)))
		@@ -81,6 +81,8 @@ def main():

		bpe_symbol = '@@ ' if args.remove_bpe else None
		def display_hypotheses(id, src, orig, ref, hypos):
		if args.quiet:
		return
		id_str = '' if id is None else '-{}'.format(id)
		src_str = to_sentence(dataset.src_dict, src, bpe_symbol)
		print('S{}\t{}'.format(id_str, src_str))

train.py

+1 −25

Changes for train.py: 1 added line, 25 removed lines.
Original line number	Diff line number	Diff line
		@@ -29,9 +29,6 @@ def main():
		dataset_args.add_argument('--valid-subset', default='valid', metavar='SPLIT',
		help='comma separated list ofdata subsets '
		' to use for validation (train, valid, valid1,test, test1)')
		dataset_args.add_argument('--test-subset', default='test', metavar='SPLIT',
		help='comma separated list ofdata subset '
		'to use for testing (train, valid, test)')
		options.add_optimization_args(parser)
		options.add_checkpoint_args(parser)
		options.add_model_args(parser)
		@@ -48,7 +45,7 @@ def main():
		torch.manual_seed(args.seed)

		# Load dataset
		dataset = data.load_with_check(args.data, args.source_lang, args.target_lang)
		dataset = data.load_with_check(args.data, ['train', 'valid'], args.source_lang, args.target_lang)
		if args.source_lang is None or args.target_lang is None:
		# record inferred languages in args, so that it's saved in checkpoints
		args.source_lang, args.target_lang = dataset.src, dataset.dst
		@@ -100,13 +97,6 @@ def main():
		train_meter.stop()
		print('\| done training in {:.1f} seconds'.format(train_meter.sum))

		# Generate on test set and compute BLEU score
		for beam in [1, 5, 10, 20]:
		for subset in args.test_subset.split(','):
		scorer = score_test(args, trainer.get_model(), dataset, subset, beam,
		cuda_device=(0 if num_gpus > 0 else None))
		print('\| Test on {} with beam={}: {}'.format(subset, beam, scorer.result_string()))

		# Stop multiprocessing
		trainer.stop()

		@@ -192,19 +182,5 @@ def validate(args, epoch, trainer, criterion, dataset, subset, ngpus):
		return val_loss


		def score_test(args, model, dataset, subset, beam, cuda_device):
		"""Evaluate the model on the test set and return the BLEU scorer."""

		translator = SequenceGenerator([model], dataset.dst_dict, beam_size=beam)
		if torch.cuda.is_available():
		translator.cuda()

		scorer = bleu.Scorer(dataset.dst_dict.pad(), dataset.dst_dict.eos(), dataset.dst_dict.unk())
		itr = dataset.dataloader(subset, batch_size=4, max_positions=args.max_positions)
		for _, _, ref, hypos in translator.generate_batched_itr(itr, cuda_device=cuda_device):
		scorer.add(ref.int().cpu(), hypos[0]['tokens'].int().cpu())
		return scorer


		if __name__ == '__main__':
		main()