def _make_examples_nfeats_tpl(data_type, src_data_iter, src_path, src_dir,
src_seq_length_trunc, sample_rate,
window_size, window_stride, window,
normalize_audio):
"""
Process the corpus into (example_dict iterator, num_feats) tuple
on source side for different 'data_type'.
"""
if data_type == 'text':
src_examples_iter, num_src_feats = \
TextDataset.make_text_examples_nfeats_tpl(
src_data_iter, src_path, src_seq_length_trunc, "src")
elif data_type == 'img':
src_examples_iter, num_src_feats = \
ImageDataset.make_image_examples_nfeats_tpl(
src_data_iter, src_path, src_dir)
elif data_type == 'audio':
if src_data_iter:
raise ValueError("""Data iterator for AudioDataset isn't
implemented""")
if src_path is None:
raise ValueError("AudioDataset requires a non None path")
src_examples_iter, num_src_feats = \
AudioDataset.make_audio_examples_nfeats_tpl(
src_path, src_dir, sample_rate,
window_size, window_stride, window,
normalize_audio)
return src_examples_iter, num_src_feats
def build_dataset(fields,
data_type,
src,
src_dir=None,
tgt=None,
src_seq_len=50,
tgt_seq_len=50,
sample_rate=0,
window_size=0,
window_stride=0,
window=None,
normalize_audio=True,
use_filter_pred=True,
image_channel_size=3):
"""
src: path to corpus file or iterator over source data
tgt: path to corpus file, iterator over target data, or None
"""
dataset_classes = {
'text': TextDataset,
'img': ImageDataset,
'audio': AudioDataset
}
assert data_type in dataset_classes
assert src is not None
if data_type == 'text':
src_examples_iter = TextDataset.make_examples(src, "src")
elif data_type == 'img':
# there is a truncate argument as well, but it was never set to
# anything besides None before
src_examples_iter = ImageDataset.make_examples(
src, src_dir, 'src', channel_size=image_channel_size)
else:
src_examples_iter = AudioDataset.make_examples(src, src_dir, "src",
sample_rate,
window_size,
window_stride, window,
normalize_audio, None)
if tgt is None:
tgt_examples_iter = None
else:
tgt_examples_iter = TextDataset.make_examples(tgt, "tgt")
# the second conjunct means nothing will be filtered at translation time
# if there is no target data
if use_filter_pred and tgt_examples_iter is not None:
filter_pred = partial(filter_example,
use_src_len=data_type == 'text',
max_src_len=src_seq_len,
max_tgt_len=tgt_seq_len)
else:
filter_pred = None
dataset_cls = dataset_classes[data_type]
dataset = dataset_cls(fields,
src_examples_iter,
tgt_examples_iter,
filter_pred=filter_pred)
return dataset
def build_dataset(fields, data_type, src_data_iter=None, src_path=None,
src_dir=None, tgt_data_iter=None, tgt_path=None,
src_seq_len=0, tgt_seq_len=0,
src_seq_length_trunc=0, tgt_seq_length_trunc=0,
dynamic_dict=True, sample_rate=0,
window_size=0, window_stride=0, window=None,
normalize_audio=True, use_filter_pred=True,
image_channel_size=3):
"""
Build src/tgt examples iterator from corpus files, also extract
number of features.
"""
if data_type == 'text':
src_examples_iter = TextDataset.make_text_examples(
src_data_iter, src_path, src_seq_length_trunc, "src"
)
elif data_type == 'img':
src_examples_iter = ImageDataset.make_image_examples(
src_data_iter, src_path, src_dir, image_channel_size)
elif data_type == 'audio':
if src_data_iter:
raise ValueError("""Data iterator for AudioDataset isn't
implemented""")
if src_path is None:
raise ValueError("AudioDataset requires a non None path")
src_examples_iter = AudioDataset.make_audio_examples(
src_path, src_dir, sample_rate, window_size, window_stride,
window, normalize_audio
)
# For all data types, the tgt side corpus is in form of text.
tgt_examples_iter = TextDataset.make_text_examples(
tgt_data_iter, tgt_path, tgt_seq_length_trunc, "tgt"
)
# I'm not certain about the practical utility of the second part
if use_filter_pred and tgt_examples_iter is not None:
filter_pred = partial(
filter_example, use_src_len=data_type == 'text',
max_src_len=src_seq_len, max_tgt_len=tgt_seq_len
)
else:
filter_pred = None
if data_type == 'text':
dataset = TextDataset(
fields, src_examples_iter, tgt_examples_iter,
dynamic_dict=dynamic_dict, filter_pred=filter_pred)
else:
dataset_cls = ImageDataset if data_type == 'img' else AudioDataset
dataset = dataset_cls(
fields, src_examples_iter, tgt_examples_iter,
filter_pred=filter_pred
)
return dataset
def get_fields(data_type, n_src_features, n_tgt_features):
"""
Args:
data_type: type of the source input. Options are [text|img|audio].
n_src_features: the number of source features to
create `torchtext.data.Field` for.
n_tgt_features: the number of target features to
create `torchtext.data.Field` for.
Returns:
A dictionary whose keys are strings and whose values are the
corresponding Field objects.
"""
if data_type == 'text':
return TextDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'img':
return ImageDataset.get_fields(n_src_features, n_tgt_features)
elif data_type == 'audio':
return AudioDataset.get_fields(n_src_features, n_tgt_features)
else:
raise ValueError("Data type not implemented")
def get_num_features(data_type, corpus_file, side):
"""
Args:
data_type (str): type of the source input.
Options are [text|img|audio].
corpus_file (str): file path to get the features.
side (str): for source or for target.
Returns:
number of features on `side`.
"""
assert side in ["src", "tgt"]
if data_type == 'text':
return TextDataset.get_num_features(corpus_file, side)
elif data_type == 'img':
return ImageDataset.get_num_features(corpus_file, side)
elif data_type == 'audio':
return AudioDataset.get_num_features(corpus_file, side)
else:
raise ValueError("Data type not implemented")
def build_dataset(fields,
data_type,
src_data_iter=None,
src_path=None,
src_dir=None,
tgt_data_iter=None,
tgt_path=None,
src_seq_length=0,
tgt_seq_length=0,
src_seq_length_trunc=0,
tgt_seq_length_trunc=0,
dynamic_dict=True,
sample_rate=0,
window_size=0,
window_stride=0,
window=None,
normalize_audio=True,
use_filter_pred=True,
image_channel_size=3):
"""
Build src/tgt examples iterator from corpus files, also extract
number of features.
"""
def _make_examples_nfeats_tpl(data_type,
src_data_iter,
src_path,
src_dir,
src_seq_length_trunc,
sample_rate,
window_size,
window_stride,
window,
normalize_audio,
image_channel_size=3):
"""
Process the corpus into (example_dict iterator, num_feats) tuple
on source side for different 'data_type'.
"""
if data_type == 'text':
src_examples_iter, num_src_feats = \
TextDataset.make_text_examples_nfeats_tpl(
src_data_iter, src_path, src_seq_length_trunc, "src")
elif data_type == 'img':
src_examples_iter, num_src_feats = \
ImageDataset.make_image_examples_nfeats_tpl(
src_data_iter, src_path, src_dir, image_channel_size)
elif data_type == 'audio':
if src_data_iter:
raise ValueError("""Data iterator for AudioDataset isn't
implemented""")
if src_path is None:
raise ValueError("AudioDataset requires a non None path")
src_examples_iter, num_src_feats = \
AudioDataset.make_audio_examples_nfeats_tpl(
src_path, src_dir, sample_rate,
window_size, window_stride, window,
normalize_audio)
return src_examples_iter, num_src_feats
src_examples_iter, num_src_feats = \
_make_examples_nfeats_tpl(data_type, src_data_iter, src_path, src_dir,
src_seq_length_trunc, sample_rate,
window_size, window_stride,
window, normalize_audio,
image_channel_size=image_channel_size)
# For all data types, the tgt side corpus is in form of text.
tgt_examples_iter, num_tgt_feats = \
TextDataset.make_text_examples_nfeats_tpl(
tgt_data_iter, tgt_path, tgt_seq_length_trunc, "tgt")
if data_type == 'text':
dataset = TextDataset(fields,
src_examples_iter,
tgt_examples_iter,
num_src_feats,
num_tgt_feats,
src_seq_length=src_seq_length,
tgt_seq_length=tgt_seq_length,
dynamic_dict=dynamic_dict,
use_filter_pred=use_filter_pred)
elif data_type == 'img':
dataset = ImageDataset(fields,
src_examples_iter,
tgt_examples_iter,
num_src_feats,
num_tgt_feats,
tgt_seq_length=tgt_seq_length,
use_filter_pred=use_filter_pred,
image_channel_size=image_channel_size)
elif data_type == 'audio':
dataset = AudioDataset(fields,
src_examples_iter,
tgt_examples_iter,
num_src_feats,
num_tgt_feats,
tgt_seq_length=tgt_seq_length,
sample_rate=sample_rate,
window_size=window_size,
window_stride=window_stride,
window=window,
normalize_audio=normalize_audio,
use_filter_pred=use_filter_pred)
return dataset
def build_dataset(fields, data_type, src, knl,
src_dir=None, tgt=None,
knl_seq_len=800, src_seq_len=150, tgt_seq_len=50,
knl_seq_length_trunc=200, src_seq_length_trunc=50, tgt_seq_length_trunc=0,
dynamic_dict=False, sample_rate=0,
window_size=0, window_stride=0, window=None,
normalize_audio=True, use_filter_pred=True,
image_channel_size=3, corpus_type='train', model_mode='default'):
"""
src: path to corpus file or iterator over source data
tgt: path to corpus file, iterator over target data, or None
"""
dataset_classes = {
'text': TextDataset, 'img': ImageDataset, 'audio': AudioDataset
}
assert data_type in dataset_classes
assert src is not None
assert not dynamic_dict or data_type == 'text', \
'it is not possible to use dynamic_dict with non-text input'
if data_type == 'text':
src_examples_iter = TextDataset.make_examples(
src, src_seq_length_trunc, "src", corpus_type, model_mode
)
knl_examples_iter = TextDataset.make_examples(
knl, knl_seq_length_trunc, "knl", corpus_type, model_mode
)
elif data_type == 'img':
# there is a truncate argument as well, but it was never set to
# anything besides None before
src_examples_iter = ImageDataset.make_examples(
src, src_dir, 'src', channel_size=image_channel_size
)
else:
src_examples_iter = AudioDataset.make_examples(
src, src_dir, "src", sample_rate,
window_size, window_stride, window,
normalize_audio, None)
if tgt is None:
tgt_examples_iter = None
else:
tgt_examples_iter = TextDataset.make_examples(
tgt, tgt_seq_length_trunc, "tgt", corpus_type, model_mode)
# the second conjunct means nothing will be filtered at translation time
# if there is no target data
if use_filter_pred and tgt_examples_iter is not None:
filter_pred = partial(
filter_example, use_src_len=data_type == 'text', use_knl_len=data_type == 'text',
max_src_len=src_seq_len, max_tgt_len=tgt_seq_len, max_knl_len=knl_seq_len
)
else:
filter_pred = None
dataset_cls = dataset_classes[data_type]
dataset = dataset_cls(
fields, src_examples_iter, tgt_examples_iter, knl_examples_iter,
dynamic_dict=dynamic_dict, filter_pred=filter_pred
)
print("[onmt.inputters.inputter.py] dataset_cls:{}".format(dataset_cls))
print("[onmt.inputters.inputter.py] dataset:{}".format(dataset))
return dataset
