def _process_dataset(self, dataset, hparams, data_spec):
name_prefix = PairedTextData._get_name_prefix(
hparams["source_dataset"], hparams["target_dataset"])
tran_fn, data_spec = self._make_processor(hparams["source_dataset"],
hparams["target_dataset"],
data_spec,
name_prefix=name_prefix)
num_parallel_calls = hparams["num_parallel_calls"]
dataset = dataset.map(lambda *args: tran_fn(dsutils.maybe_tuple(args)),
num_parallel_calls=num_parallel_calls)
# Filters by length
src_length_name = dsutils._connect_name(
data_spec.name_prefix[0], data_spec.decoder[0].length_tensor_name)
tgt_length_name = dsutils._connect_name(
data_spec.name_prefix[1], data_spec.decoder[1].length_tensor_name)
filter_fn = self._make_length_filter(hparams["source_dataset"],
hparams["target_dataset"],
src_length_name, tgt_length_name,
data_spec.decoder[0],
data_spec.decoder[1])
if filter_fn:
dataset = dataset.filter(filter_fn)
# Truncates data count
dataset = dataset.take(hparams["max_dataset_size"])
return dataset, data_spec
def text_id_name(self):
"""The name of text index tensor, "text_ids" by default.
"""
name = dsutils._connect_name(
self._data_spec.name_prefix,
self._data_spec.decoder.text_id_tensor_name)
return name
def length_name(self):
"""The name of length tensor, "length" by default.
"""
name = dsutils._connect_name(
self._data_spec.name_prefix,
self._data_spec.decoder.length_tensor_name)
return name
def _get_length_name(i):
if not _is_text_data(hparams["datasets"][i]["data_type"]):
return None
name = dsutils._connect_name(
data_spec.name_prefix[i],
data_spec.decoder[i].length_tensor_name)
return name
def target_text_id_name(self):
"""The name of the target text index tensor, "target_text_ids" by
default.
"""
name = dsutils._connect_name(self._data_spec.name_prefix[1],
self._tgt_decoder.text_id_tensor_name)
return name
def source_text_id_name(self):
"""The name of the source text index tensor, "source_text_ids" by
default.
"""
name = dsutils._connect_name(self._data_spec.name_prefix[0],
self._src_decoder.text_id_tensor_name)
return name
def utterance_cnt_name(self):
"""The name of utterance count tensor, "utterance_cnt" by default.
"""
if not self._hparams.dataset.variable_utterance:
raise ValueError("`utterance_cnt_name` is not defined.")
name = dsutils._connect_name(
self._data_spec.name_prefix,
self._data_spec.decoder.utterance_cnt_tensor_name)
return name
def target_utterance_cnt_name(self):
"""The name of the target text utterance count tensor.
"""
if not self._hparams.target_dataset.variable_utterance:
raise ValueError(
"`utterance_cnt_name` of target data is undefined.")
name = dsutils._connect_name(
self._data_spec.name_prefix[1],
self._tgt_decoder.utterance_cnt_tensor_name)
return name
def source_utterance_cnt_name(self):
"""The name of the source text utterance count tensor.
"""
if not self._hparams.source_dataset.variable_utterance:
raise ValueError(
"`utterance_cnt_name` of source data is undefined.")
name = dsutils._connect_name(
self._data_spec.name_prefix[0],
self._src_decoder.utterance_cnt_tensor_name)
return name
def data_name(self, name_or_id):
"""The name of the data tensor of scalar dataset by its name or id..
If the dataset is not a scalar data, returns `None`.
"""
i = self._maybe_name_to_id(name_or_id)
if not _is_scalar_data(self._hparams.datasets[i]["data_type"]):
return None
name = dsutils._connect_name(
self._data_spec.name_prefix[i],
self._data_spec.decoder[i].data_tensor_name)
return name
def text_id_name(self, name_or_id):
"""The name of length tensor of text dataset by its name or id. If the
dataset is not of text type, returns `None`.
"""
i = self._maybe_name_to_id(name_or_id)
if not _is_text_data(self._hparams.datasets[i]["data_type"]):
return None
name = dsutils._connect_name(
self._data_spec.name_prefix[i],
self._data_spec.decoder[i].text_id_tensor_name)
return name
def utterance_cnt_name(self, name_or_id):
"""The name of utterance count tensor of text dataset by its name or id.
If the dataset is not variable utterance text data, returns `None`.
"""
i = self._maybe_name_to_id(name_or_id)
if not _is_text_data(self._hparams.datasets[i]["data_type"]) or \
not self._hparams.datasets[i]["variable_utterance"]:
return None
name = dsutils._connect_name(
self._data_spec.name_prefix[i],
self._data_spec.decoder[i].utterance_cnt_tensor_name)
return name
def _process_dataset(self, dataset, hparams, data_spec):
chained_tran, data_spec = self._make_processor(
hparams["dataset"],
data_spec,
name_prefix=hparams["dataset"]["data_name"])
num_parallel_calls = hparams["num_parallel_calls"]
dataset = dataset.map(
lambda *args: chained_tran(dsutils.maybe_tuple(args)),
num_parallel_calls=num_parallel_calls)
# Filters by length
length_name = dsutils._connect_name(
data_spec.name_prefix, data_spec.decoder.length_tensor_name)
filter_fn = self._make_length_filter(hparams["dataset"], length_name,
data_spec.decoder)
if filter_fn:
dataset = dataset.filter(filter_fn)
# Truncates data count
dataset = dataset.take(hparams["max_dataset_size"])
return dataset, data_spec
def target_length_name(self):
"""The name of the target length tensor.
"""
name = dsutils._connect_name(self._data_spec.name_prefix[1],
self._tgt_decoder.length_tensor_name)
return name
def source_length_name(self):
"""The name of the source length tensor.
"""
name = dsutils._connect_name(self._data_spec.name_prefix[0],
self._src_decoder.length_tensor_name)
return name
