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data_util.py
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data_util.py
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import os
import tarfile
import glob
import random
class SortType(object):
GLOBAL = 'global'
POOL = 'pool'
NONE = "none"
class EndEpoch():
pass
class Pool(object):
def __init__(self, sample_generator, pool_size, sort):
self._pool_size = pool_size
self._pool = []
self._sample_generator = sample_generator()
self._end = False
self._sort = sort
def _fill(self):
while len(self._pool) < self._pool_size and not self._end:
try:
sample = self._sample_generator.next()
self._pool.append(sample)
except StopIteration as e:
self._end = True
break
if self._sort:
self._pool.sort(
key=lambda sample: max(len(sample[0]), len(sample[1]))
if len(sample) > 1 else len(sample[0])
)
if self._end and len(self._pool) < self._pool_size:
self._pool.append(EndEpoch())
def push_back(self, samples):
if len(self._pool) != 0:
raise Exception("Pool should be empty.")
if len(samples) >= self._pool_size:
raise Exception("Capacity of pool should be greater than a batch. "
"Please enlarge `pool_size`.")
for sample in samples:
self._pool.append(sample)
self._fill()
def next(self, look=False):
if len(self._pool) == 0:
return None
else:
return self._pool[0] if look else self._pool.pop(0)
class DataReader(object):
def __init__(self,
src_vocab_fpath,
trg_vocab_fpath,
fpattern,
batch_size,
pool_size,
sort_type=SortType.NONE,
clip_last_batch=True,
tar_fname=None,
min_length=0,
max_length=100,
shuffle=True,
shuffle_batch=False,
use_token_batch=False,
delimiter='\t',
start_mark='<s>',
end_mark='<e>',
unk_mark='<unk>',
seed=0):
self._src_vocab = self._load_dict(src_vocab_fpath)
self._only_src = True
if trg_vocab_fpath is not None:
self._trg_vocab = self._load_dict(trg_vocab_fpath)
self._only_src = False
self._pool_size = pool_size
self._batch_size = batch_size
self._use_token_batch = use_token_batch
self._sort_type = sort_type
self._clip_last_batch = clip_last_batch
self._shuffle = shuffle
self._shuffle_batch = shuffle_batch
self._min_length = min_length
self._max_length = max_length
self._delimiter = delimiter
self._epoch_batches = []
src_seq_words, trg_seq_words = self._load_data(fpattern, tar_fname)
self._src_seq_ids = [[
self._src_vocab.get(word, self._src_vocab.get(unk_mark))
for word in ([start_mark] + src_seq + [end_mark])]
for src_seq in src_seq_words
]
self._sample_count = len(self._src_seq_ids)
if not self._only_src:
self._trg_seq_ids = [[
self._trg_vocab.get(word, self._trg_vocab.get(unk_mark))
for word in ([start_mark] + trg_seq + [end_mark])]
for trg_seq in trg_seq_words
]
if len(self._trg_seq_ids) != self._sample_count:
raise Exception("Inconsistent sample count between "
"source sequences and target sequences.")
else: self._trg_seq_ids = None
self._sample_idxs = [i for i in xrange(self._sample_count)]
self._sorted = False
random.seed(seed)
def _parse_file(self, f_obj):
src_seq_words = []
trg_seq_words = []
for line in f_obj:
fields = line.strip().split(self._delimiter)
if len(fields) != 2 or (self._only_src and len(fields) != 1):
continue
sample_words = []
is_valid_sample = True
max_len = -1
for i, seq in enumerate(fields):
seq_words = seq.split()
max_len = max(max_len, len(seq_words))
if len(seq_words) == 0 or \
len(seq_words) < self._min_length or \
len(seq_words) > self._max_length or \
(self._use_token_batch and max_len > self._batch_size):
is_valid_sample = False
break
sample_words.append(seq_words)
if not is_valid_sample: continue
src_seq_words.append(sample_words[0])
if not self._only_src:
trg_seq_words.append(sample_words[1])
return (src_seq_words, trg_seq_words)
def _load_data(self, fpattern, tar_fname):
fpaths = glob.glob(fpattern)
src_seq_words = []
trg_seq_words = []
if len(fpaths) == 1 and tarfile.is_tarfile(fpaths[0]):
if tar_fname is None:
raise Exception("If tar file provided, please set tar_fname.")
f = tarfile.open(fpaths[0], 'r')
part_file_data = self._parse_file(f.extractfile(tar_fname))
src_seq_words = part_file_data[0]
trg_seq_words = part_file_data[1]
else:
for fpath in fpaths:
if not os.path.isfile(fpath):
raise IOError("Invalid file: %s" % fpath)
part_file_data = self._parse_file(open(fpath, 'r'))
src_seq_words.extend(part_file_data[0])
trg_seq_words.extend(part_file_data[1])
return src_seq_words, trg_seq_words
def _load_dict(self, dict_path, reverse=False):
word_dict = {}
with open(dict_path, "r") as fdict:
for idx, line in enumerate(fdict):
if reverse:
word_dict[idx] = line.strip()
else:
word_dict[line.strip()] = idx
return word_dict
def _sample_generator(self):
if self._sort_type == SortType.GLOBAL:
if not self._sorted:
self._sample_idxs.sort(
key=lambda idx: max(len(self._src_seq_ids[idx]),
len(self._trg_seq_ids[idx] if not self._only_src else 0))
)
self._sorted = True
elif self._shuffle:
random.shuffle(self._sample_idxs)
for sample_idx in self._sample_idxs:
if self._only_src:
yield (self._src_seq_ids[sample_idx])
else:
yield (self._src_seq_ids[sample_idx],
self._trg_seq_ids[sample_idx][:-1],
self._trg_seq_ids[sample_idx][1:])
def batch_generator(self):
pool = Pool(self._sample_generator,
self._pool_size,
True if self._sort_type == SortType.POOL else False)
def next_batch():
batch_data = []
max_len = -1
batch_max_seq_len = -1
while True:
sample = pool.next(look=True)
if sample is None:
pool.push_back(batch_data)
batch_data = []
continue
if isinstance(sample, EndEpoch):
return batch_data, batch_max_seq_len, True
max_len = max(max_len, len(sample[0]))
if not self._only_src:
max_len = max(max_len, len(sample[1]))
if self._use_token_batch:
if max_len * (len(batch_data) + 1) < self._batch_size:
batch_max_seq_len = max_len
batch_data.append(pool.next())
else:
return batch_data, batch_max_seq_len, False
else:
if len(batch_data) < self._batch_size:
batch_max_seq_len = max_len
batch_data.append(pool.next())
else:
return batch_data, batch_max_seq_len, False
if not self._shuffle_batch:
batch_data, batch_max_seq_len, last_batch = next_batch()
while not last_batch:
yield batch_data
batch_data, batch_max_seq_len, last_batch = next_batch()
batch_size = len(batch_data)
if self._use_token_batch:
batch_size *= batch_max_seq_len
if (not self._clip_last_batch and len(batch_data) > 0) \
or (batch_size == self._batch_size):
yield batch_data
else:
# should re-generate batches
if self._sort_type == SortType.POOL \
or len(self._epoch_batches) == 0:
self._epoch_batches = []
batch_data, batch_max_seq_len, last_batch = next_batch()
while not last_batch:
self._epoch_batches.append(batch_data)
batch_data, batch_max_seq_len, last_batch = next_batch()
batch_size = len(batch_data)
if self._use_token_batch:
batch_size *= batch_max_seq_len
if (not self._clip_last_batch and len(batch_data) > 0) \
or (batch_size == self._batch_size):
self._epoch_batches.append(batch_data)
random.shuffle(self._epoch_batches)
for batch_data in self._epoch_batches:
yield batch_data
if __name__ == "__main__":
'''data_loader = DataLoader("/root/workspace/unify_reader/wmt16/en_10000.dict",
"/root/workspace/unify_reader/wmt16/de_10000.dict",
"/root/workspace/unify_reader/wmt16/wmt16.tar.gz",
2, tar_fname="wmt16/train")'''
data_loader = DataReader(
"/root/workspace/unify_reader/nist06n_tiny/cn_30001.dict.unify",
"/root/workspace/unify_reader/nist06n_tiny/en_30001.dict.unify",
"/root/workspace/unify_reader/nist06n_tiny/data/part-*",
30,
100000,
sort_type=SortType.GLOBAL,
use_token_batch=True)
for batch_data in data_loader.batch_generator():
print batch_data
break