def save_vocab():
data_cfg = load_config('configs/sign.yaml')
data_path = data_cfg.get("data_path", "./data")
if isinstance(data_cfg["train"], list):
train_paths = [os.path.join(data_path, x) for x in data_cfg["train"]]
dev_paths = [os.path.join(data_path, x) for x in data_cfg["dev"]]
test_paths = [os.path.join(data_path, x) for x in data_cfg["test"]]
pad_feature_size = sum(data_cfg["feature_size"])
else:
train_paths = os.path.join(data_path, data_cfg["train"])
dev_paths = os.path.join(data_path, data_cfg["dev"])
test_paths = os.path.join(data_path, data_cfg["test"])
pad_feature_size = data_cfg["feature_size"]
level = data_cfg["level"]
txt_lowercase = data_cfg["txt_lowercase"]
max_sent_length = data_cfg["max_sent_length"]
def tokenize_text(text):
if level == "char":
return list(text)
else:
return text.split()
def tokenize_features(features):
ft_list = torch.split(features, 1, dim=0)
return [ft.squeeze() for ft in ft_list]
# NOTE (Cihan): The something was necessary to match the function signature.
def stack_features(features, something):
return torch.stack([torch.stack(ft, dim=0) for ft in features], dim=0)
sequence_field = data.RawField()
signer_field = data.RawField()
sgn_field = data.Field(
use_vocab=False,
init_token=None,
dtype=torch.float32,
preprocessing=tokenize_features,
tokenize=lambda features: features, # TODO (Cihan): is this necessary?
batch_first=True,
include_lengths=True,
postprocessing=stack_features,
pad_token=torch.zeros((pad_feature_size,)),
)
gls_field = data.Field(
pad_token=PAD_TOKEN,
tokenize=tokenize_text,
batch_first=True,
lower=False,
include_lengths=True,
)
txt_field = data.Field(
init_token=BOS_TOKEN,
eos_token=EOS_TOKEN,
pad_token=PAD_TOKEN,
tokenize=tokenize_text,
unk_token=UNK_TOKEN,
batch_first=True,
lower=txt_lowercase,
include_lengths=True,
)
train_data = SignTranslationDataset(
path=train_paths,
fields=(sequence_field, signer_field, sgn_field, gls_field, txt_field),
filter_pred=lambda x: len(vars(x)["sgn"]) <= max_sent_length
and len(vars(x)["txt"]) <= max_sent_length,
)
gls_max_size = data_cfg.get("gls_voc_limit", sys.maxsize)
gls_min_freq = data_cfg.get("gls_voc_min_freq", 1)
txt_max_size = data_cfg.get("txt_voc_limit", sys.maxsize)
txt_min_freq = data_cfg.get("txt_voc_min_freq", 1)
gls_vocab_file = data_cfg.get("gls_vocab", None)
txt_vocab_file = data_cfg.get("txt_vocab", None)
gls_vocab = build_vocab(
field="gls",
min_freq=gls_min_freq,
max_size=gls_max_size,
dataset=train_data,
vocab_file=gls_vocab_file,
)
txt_vocab = build_vocab(
field="txt",
min_freq=txt_min_freq,
max_size=txt_max_size,
dataset=train_data,
vocab_file=txt_vocab_file,
)
if not os.path.exists('data/gls_vocab'):
os.makedirs('data/gls_vocab')
if not os.path.exists('data/txt_vocab'):
os.makedirs('data/txt_vocab')
gls_vocab.to_file('data/gls_vocab/gls_vocab.txt')
txt_vocab.to_file('data/txt_vocab/txt_vocab.txt')
def load_data(
data_cfg: dict) -> (Dataset, Dataset, Dataset, Vocabulary, Vocabulary):
"""
Load train, dev and optionally test data as specified in configuration.
Vocabularies are created from the training set with a limit of `voc_limit`
tokens and a minimum token frequency of `voc_min_freq`
(specified in the configuration dictionary).
The training data is filtered to include sentences up to `max_sent_length`
on source and target side.
If you set ``random_train_subset``, a random selection of this size is used
from the training set instead of the full training set.
If you set ``random_dev_subset``, a random selection of this size is used
from the dev development instead of the full development set.
:param data_cfg: configuration dictionary for data
("data" part of configuration file)
:return:
- train_data: training dataset
- dev_data: development dataset
- test_data: test dataset if given, otherwise None
- gls_vocab: gloss vocabulary extracted from training data
- txt_vocab: spoken text vocabulary extracted from training data
"""
data_path = data_cfg.get("data_path", "./data")
train_paths = [os.path.join(data_path, x) for x in data_cfg["train"]]
dev_paths = os.path.join(data_path, data_cfg["dev"])
test_paths = os.path.join(data_path, data_cfg["test"])
pad_feature_size = data_cfg["feature_size"]
pad_feature_size_keypoints = data_cfg["feature_size_keypoints"]
level = data_cfg["level"]
txt_lowercase = data_cfg["txt_lowercase"]
max_sent_length = data_cfg["max_sent_length"]
lst_names = data_cfg['names']
def tokenize_text(text):
if level == "char":
return list(text)
else:
return text.split()
def tokenize_features(features):
ft_list = torch.split(features, 1, dim=0)
return [ft.squeeze() for ft in ft_list]
# NOTE (Cihan): The something was necessary to match the function signature.
def stack_features(features, something):
return torch.stack([torch.stack(ft, dim=0) for ft in features], dim=0)
sequence_field = data.RawField()
signer_field = data.RawField()
sgn_field = data.Field(
use_vocab=False,
init_token=None,
dtype=torch.float32,
preprocessing=tokenize_features,
tokenize=lambda features: features, # TODO (Cihan): is this necessary?
batch_first=True,
include_lengths=True,
postprocessing=stack_features,
pad_token=torch.zeros((pad_feature_size, )),
)
keypoints_field = data.Field(
use_vocab=False,
init_token=None,
dtype=torch.float32,
preprocessing=tokenize_features,
tokenize=lambda features: features, # TODO (Cihan): is this necessary?
batch_first=True,
include_lengths=True,
postprocessing=stack_features,
pad_token=torch.zeros((pad_feature_size_keypoints, )),
)
gls_field = data.Field(
pad_token=PAD_TOKEN,
tokenize=tokenize_text,
batch_first=True,
lower=False,
include_lengths=True,
)
txt_field = data.Field(
init_token=BOS_TOKEN,
eos_token=EOS_TOKEN,
pad_token=PAD_TOKEN,
tokenize=tokenize_text,
unk_token=UNK_TOKEN,
batch_first=True,
lower=txt_lowercase,
include_lengths=True,
)
train_data = SignTranslationDataset(
path=train_paths,
names=lst_names,
fields=(sequence_field, signer_field, sgn_field, keypoints_field,
gls_field, txt_field),
filter_pred=lambda x: len(vars(x)["sgn"]) <= max_sent_length and len(
vars(x)["txt"]) <= max_sent_length,
)
gls_max_size = data_cfg.get("gls_voc_limit", sys.maxsize)
gls_min_freq = data_cfg.get("gls_voc_min_freq", 1)
txt_max_size = data_cfg.get("txt_voc_limit", sys.maxsize)
txt_min_freq = data_cfg.get("txt_voc_min_freq", 1)
gls_vocab_file = data_cfg.get("gls_vocab", None)
txt_vocab_file = data_cfg.get("txt_vocab", None)
gls_vocab = build_vocab(
field="gls",
min_freq=gls_min_freq,
max_size=gls_max_size,
dataset=train_data,
vocab_file=gls_vocab_file,
)
txt_vocab = build_vocab(
field="txt",
min_freq=txt_min_freq,
max_size=txt_max_size,
dataset=train_data,
vocab_file=txt_vocab_file,
)
random_train_subset = data_cfg.get("random_train_subset", -1)
if random_train_subset > -1:
# select this many training examples randomly and discard the rest
keep_ratio = random_train_subset / len(train_data)
keep, _ = train_data.split(split_ratio=[keep_ratio, 1 - keep_ratio],
random_state=random.getstate())
train_data = keep
dev_data = SignTranslationDataset(
path=dev_paths,
names=lst_names,
fields=(sequence_field, signer_field, sgn_field, keypoints_field,
gls_field, txt_field),
)
random_dev_subset = data_cfg.get("random_dev_subset", -1)
if random_dev_subset > -1:
# select this many development examples randomly and discard the rest
keep_ratio = random_dev_subset / len(dev_data)
keep, _ = dev_data.split(split_ratio=[keep_ratio, 1 - keep_ratio],
random_state=random.getstate())
dev_data = keep
# check if target exists
test_data = SignTranslationDataset(
path=test_paths,
names=lst_names,
fields=(sequence_field, signer_field, sgn_field, keypoints_field,
gls_field, txt_field),
)
gls_field.vocab = gls_vocab
txt_field.vocab = txt_vocab
return train_data, dev_data, test_data, gls_vocab, txt_vocab
def load_feat_data(data_cfg: dict, do_anchoring: bool, sets = ['train', 'dev', 'test'], train_size=0.1, dev_size=1, test_size=1) -> (Dataset, Dataset, Dataset, Vocabulary, Vocabulary):
"""
Load train, dev and optionally test data as specified in configuration.
Vocabularies are created from the training set with a limit of `voc_limit`
tokens and a minimum token frequency of `voc_min_freq`
(specified in the configuration dictionary).
The training data is filtered to include sentences up to `max_sent_length`
on source and target side.
If you set ``random_train_subset``, a random selection of this size is used
from the training set instead of the full training set.
If you set ``random_dev_subset``, a random selection of this size is used
from the dev development instead of the full development set.
:param data_cfg: configuration dictionary for data
("data" part of configuration file)
:return:
- train_data: training dataset
- dev_data: development dataset
- test_data: test dataset if given, otherwise None
- gls_vocab: gloss vocabulary extracted from training data
- txt_vocab: spoken text vocabulary extracted from training data
"""
data_path = data_cfg.get("data_path", "./data")
if isinstance(data_cfg["train"], list):
train_paths = [os.path.join(data_path, x) for x in data_cfg["train"]]
dev_paths = [os.path.join(data_path, x) for x in data_cfg["dev"]]
test_paths = [os.path.join(data_path, x) for x in data_cfg["test"]]
pad_feature_size = sum(data_cfg["feature_size"])
else:
train_paths = os.path.join(data_path, data_cfg["train"])
dev_paths = os.path.join(data_path, data_cfg["dev"])
test_paths = os.path.join(data_path, data_cfg["test"])
pad_feature_size = data_cfg["feature_size"]
level = data_cfg["level"]
txt_lowercase = data_cfg["txt_lowercase"]
max_sent_length = data_cfg["max_sent_length"]
def tokenize_text(text):
if level == "char":
return list(text)
else:
return text.split()
def tokenize_features(features):
ft_list = torch.split(features, 1, dim=0)
return [ft.squeeze() for ft in ft_list]
# NOTE (Cihan): The something was necessary to match the function signature.
def stack_features(features, something):
return torch.stack([torch.stack(ft, dim=0) for ft in features], dim=0)
sequence_field = data.RawField()
signer_field = data.RawField()
gls_field = data.Field(
pad_token=PAD_TOKEN,
tokenize=tokenize_text,
batch_first=True,
lower=False,
include_lengths=True,
)
txt_field = data.Field(
init_token=BOS_TOKEN,
eos_token=EOS_TOKEN,
pad_token=PAD_TOKEN,
tokenize=tokenize_text,
unk_token=UNK_TOKEN,
batch_first=True,
lower=txt_lowercase,
include_lengths=True,
)
field_list = [sequence_field, signer_field, gls_field, txt_field]
feat_fields = [
('body_feat', 2048),
('body_scores', 21),
('body_deltas', 20*13*5),
('face_feat', 2048),
('face_scores', 11),
('face_deltas', 10*84*5),
('hand_feat_1', 2048),
('hand_scores_1', 11),
('hand_deltas_1', 10*21*5),
('hand_feat_2', 2048),
('hand_scores_2', 11),
('hand_deltas_2', 10*21*5)
]
for k in range(len(feat_fields)):
new_field = data.Field(
use_vocab=False,
init_token=None,
dtype=torch.float32,
preprocessing=tokenize_features,
tokenize=lambda features: features, # TODO (Cihan): is this necessary?
batch_first=True,
include_lengths=True,
postprocessing=stack_features,
pad_token=torch.zeros((feat_fields[k][1],)),
)
field_list.append(new_field)
field_list = tuple(field_list)
if 'train' in sets:
train_data = FeatTranslationDataset(
path=train_paths,
fields=field_list,
filter_pred=lambda x: len(vars(x)["body_2d"]) <= max_sent_length
and len(vars(x)["txt"]) <= max_sent_length,
size=train_size,
do_anchoring=do_anchoring
)
random_train_subset = data_cfg.get("random_train_subset", -1)
if random_train_subset > -1:
# select this many training examples randomly and discard the rest
keep_ratio = random_train_subset / len(train_data)
keep, _ = train_data.split(
split_ratio=[keep_ratio, 1 - keep_ratio], random_state=random.getstate()
)
train_data = keep
else:
train_data = None
gls_max_size = data_cfg.get("gls_voc_limit", sys.maxsize)
gls_min_freq = data_cfg.get("gls_voc_min_freq", 1)
txt_max_size = data_cfg.get("txt_voc_limit", sys.maxsize)
txt_min_freq = data_cfg.get("txt_voc_min_freq", 1)
gls_vocab_file = data_cfg.get("gls_vocab", None)
txt_vocab_file = data_cfg.get("txt_vocab", None)
gls_vocab = build_vocab(
field="gls",
min_freq=gls_min_freq,
max_size=gls_max_size,
dataset=None,
vocab_file=gls_vocab_file,
)
txt_vocab = build_vocab(
field="txt",
min_freq=txt_min_freq,
max_size=txt_max_size,
dataset=None,
vocab_file=txt_vocab_file,
)
if 'dev' in sets:
dev_data = FeatTranslationDataset(
path=dev_paths,
fields=field_list,
size=dev_size,
do_anchoring=do_anchoring
)
random_dev_subset = data_cfg.get("random_dev_subset", -1)
if random_dev_subset > -1:
# select this many development examples randomly and discard the rest
keep_ratio = random_dev_subset / len(dev_data)
keep, _ = dev_data.split(
split_ratio=[keep_ratio, 1 - keep_ratio], random_state=random.getstate()
)
dev_data = keep
else:
dev_data = None
if 'test' in sets:
# check if target exists
test_data = FeatTranslationDataset(
path=test_paths,
fields=field_list,
size=test_size,
do_anchoring=do_anchoring
)
else:
test_data = None
gls_field.vocab = gls_vocab
txt_field.vocab = txt_vocab
return train_data, dev_data, test_data, gls_vocab, txt_vocab
