def test_build_train_datasets_valid(data_opts_17):
datasets = build_training_datasets(
fieldset=build_fieldset(), **vars(data_opts_17)
)
assert len(datasets) == 2
for dataset in datasets:
assert type(dataset) == QEDataset
def test_extend_vocabs(extend_vocab):
options = extend_vocab
OOV_SRC = 'oov_word_src'
OOV_TGT = 'oov_word_tgt'
fieldset = Predictor.fieldset(wmt18_format=options.wmt18_format)
vocabs_fieldset = extend_vocabs_fieldset.build_fieldset(fieldset)
dataset, _ = build_training_datasets(
fieldset, extend_vocabs_fieldset=vocabs_fieldset, **vars(options))
assert OOV_SRC in dataset.fields[constants.SOURCE].vocab.stoi
assert OOV_TGT in dataset.fields[constants.TARGET].vocab.stoi
fieldset = Predictor.fieldset(wmt18_format=options.wmt18_format)
options.extend_source_vocab = None
options.extend_target_vocab = None
dataset, _ = build_training_datasets(fieldset, **vars(options))
assert OOV_SRC not in dataset.fields[constants.SOURCE].vocab.stoi
assert OOV_TGT not in dataset.fields[constants.TARGET].vocab.stoi
def test_build_train_datasets_no_valid(data_opts_no_validation, atol):
datasets = build_training_datasets(
fieldset=build_fieldset(), **vars(data_opts_no_validation)
)
assert len(datasets) == 2
for dataset in datasets:
assert type(dataset) == QEDataset
train_size, dev_size = len(datasets[0]), len(datasets[1])
np.testing.assert_allclose(
train_size / (train_size + dev_size),
data_opts_no_validation.split,
atol=atol,
)
def check_qe_dataset(options):
train_dataset, dev_dataset = build_training_datasets(
fieldset=build_fieldset(), **vars(options)
)
train_iter = build_bucket_iterator(
train_dataset, batch_size=8, is_train=True, device=None
)
dev_iter = build_bucket_iterator(
dev_dataset, batch_size=8, is_train=False, device=None
)
for batch_train, batch_dev in zip(train_iter, dev_iter):
train_source = getattr(batch_train, constants.SOURCE)
if isinstance(train_source, tuple):
train_source, lenghts = train_source
train_source.t()
train_prev_len = train_source.shape[1]
# buckets should be sorted in decreasing length order
# so we can use pack/padded sequences
for train_sample in train_source:
train_mask = train_sample != constants.PAD_ID
train_cur_len = train_mask.int().sum().item()
assert train_cur_len <= train_prev_len
train_prev_len = train_cur_len
source_field = train_dataset.fields[constants.SOURCE]
target_field = train_dataset.fields[constants.TARGET]
target_tags_field = train_dataset.fields[constants.TARGET_TAGS]
# check if each token is in the vocab
for train_sample, dev_sample in zip(train_dataset, dev_dataset):
for word in getattr(train_sample, constants.SOURCE):
assert word in source_field.vocab.stoi
for word in getattr(train_sample, constants.TARGET):
assert word in target_field.vocab.stoi
for tag in getattr(train_sample, constants.TARGET_TAGS):
assert tag in target_tags_field.vocab.stoi
for word in getattr(dev_sample, constants.SOURCE):
assert word in source_field.vocab.stoi
for word in getattr(dev_sample, constants.TARGET):
assert word in target_field.vocab.stoi
for tag in getattr(dev_sample, constants.TARGET_TAGS):
assert tag in target_tags_field.vocab.stoi
def retrieve_datasets(fieldset, pipeline_options, model_options, output_dir):
"""
Creates `Dataset` objects for the training and validation sets.
Parses files according to pipeline and model options.
Args:
fieldset
pipeline_options (Namespace): Generic training options
load_data (str): Input directory for loading preprocessed data
files.
load_model (str): Directory containing model.torch for loading
pre-created model.
resume (boolean): Indicates if you should resume training from a
previous run.
load_vocab (str): Directory containing vocab.torch file to be
loaded.
model_options (Namespace): Model specific options.
output_dir (str): Path to directory where experiment files should be
saved.
Returns:
datasets (Dataset): Training and validation datasets
"""
if pipeline_options.load_data:
datasets = utils.load_training_datasets(pipeline_options.load_data,
fieldset)
else:
load_vocab = None
if pipeline_options.resume:
load_vocab = Path(output_dir, const.VOCAB_FILE)
elif pipeline_options.load_model:
load_vocab = pipeline_options.load_model
elif model_options.__dict__.get("load_pred_source"):
load_vocab = model_options.load_pred_source
elif model_options.__dict__.get("load_pred_target"):
load_vocab = model_options.load_pred_target
elif pipeline_options.load_vocab:
load_vocab = pipeline_options.load_vocab
datasets = builders.build_training_datasets(fieldset,
load_vocab=load_vocab,
**vars(model_options))
return datasets
