def write(cls, path, records: Iterator[ParallelSeqRecord]):
if path.exists():
log.warning(f"Overwriting {path} with new records")
os.remove(str(path))
maybe_tmp = IO.maybe_tmpfs(path)
log.info(f'Creating {maybe_tmp}')
conn = sqlite3.connect(str(maybe_tmp))
cur = conn.cursor()
cur.execute(cls.TABLE_STATEMENT)
cur.execute(cls.INDEX_X_LEN)
cur.execute(cls.INDEX_Y_LEN)
cur.execute(f"PRAGMA user_version = {cls.CUR_VERSION};")
count = 0
for x_seq, y_seq in records:
# use numpy. its a lot efficient
if not isinstance(x_seq, np.ndarray):
x_seq = np.array(x_seq, dtype=np.int32)
if y_seq is not None and not isinstance(y_seq, np.ndarray):
y_seq = np.array(y_seq, dtype=np.int32)
values = (x_seq.tobytes(),
None if y_seq is None else y_seq.tobytes(), len(x_seq),
len(y_seq) if y_seq is not None else -1)
cur.execute(cls.INSERT_STMT, values)
count += 1
cur.close()
conn.commit()
if maybe_tmp != path:
# bring the file back to original location where it should be
IO.copy_file(maybe_tmp, path)
log.info(f"stored {count} rows in {path}")
def get_train_data(self,
batch_size: Union[int, Tuple[int, int]],
steps: int = 0,
sort_by='eq_len_rand_batch',
batch_first=True,
shuffle=False,
fine_tune=False,
keep_in_mem=False,
split_ratio: float = 0.,
dynamic_epoch=False):
data_path = self.train_db if self.train_db.exists(
) else self.train_file
if fine_tune:
if not self.finetune_file.exists():
# user may have added fine tune file later
self._pre_process_parallel('finetune_src', 'finetune_tgt',
self.finetune_file)
log.info("Using Fine tuning corpus instead of training corpus")
data_path = self.finetune_file
if split_ratio > 0:
data_path = IO.maybe_tmpfs(data_path)
train_file = data_path.with_suffix('.db.tmp')
file_creator = partial(self.file_creator,
train_file=train_file,
split_ratio=split_ratio)
train_data = GenerativeBatchIterable(file_creator=file_creator,
batches=steps,
batch_size=batch_size,
field=self.tgt_vocab,
dynamic_epoch=dynamic_epoch,
batch_first=batch_first,
shuffle=shuffle,
sort_by=sort_by,
**self._get_batch_args())
else:
data = BatchIterable(data_path=data_path,
batch_size=batch_size,
field=self.tgt_vocab,
sort_by=sort_by,
batch_first=batch_first,
shuffle=shuffle,
**self._get_batch_args())
train_data = LoopingIterable(data, steps)
return train_data
def get_combo_data(self,
batch_size: int,
steps: int = 0,
sort_desc=False,
batch_first=True,
shuffle=False):
if not self.combo_file.exists():
# user may have added fine tune file later
self._pre_process_parallel('combo_src', 'combo_tgt',
self.combo_file)
combo_file = IO.maybe_tmpfs(self.combo_file)
data = BatchIterable(combo_file,
batch_size=batch_size,
sort_desc=sort_desc,
field=self.tgt_vocab,
batch_first=batch_first,
shuffle=shuffle,
**self._get_batch_args())
if steps > 0:
data = LoopingIterable(data, steps)
return data
