/
data_iterator.py
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/
data_iterator.py
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import sys
import numpy
import logging
import gzip
import subprocess
# ModuleNotFoundError is new in 3.6; older versions will throw SystemError
if sys.version_info < (3, 6):
ModuleNotFoundError = SystemError
try:
from .util import load_dict
from . import shuffle
except (ModuleNotFoundError, ImportError) as e:
from util import load_dict
import shuffle
def fopen(filename, mode='r'):
if filename.endswith('.gz'):
return gzip.open(filename, mode, encoding="UTF-8")
return open(filename, mode, encoding="UTF-8")
class FileWrapper(object):
def __init__(self, fname):
self.pos = 0
self.lines = fopen(fname).readlines()
self.lines = numpy.array(self.lines, dtype=numpy.object)
def __iter__(self):
return self
def __next__(self):
if self.pos >= len(self.lines):
raise StopIteration
l = self.lines[self.pos]
self.pos += 1
return l
def reset(self):
self.pos = 0
def seek(self, pos):
assert pos == 0
self.pos = 0
def readline(self):
return next(self)
def shuffle_lines(self, perm):
self.lines = self.lines[perm]
self.pos = 0
def __len__(self):
return len(self.lines)
class TextIterator:
"""Simple Bitext iterator."""
def __init__(self, source, target,
source_dicts, target_dict,
model_type,
batch_size=128,
maxlen=100,
source_vocab_sizes=None,
target_vocab_size=None,
skip_empty=False,
shuffle_each_epoch=False,
sort_by_length=True,
use_factor=False,
maxibatch_size=20,
token_batch_size=0,
keep_data_in_memory=False,
preprocess_script=None):
self.preprocess_script = preprocess_script
self.source_orig = source
self.target_orig = target
if self.preprocess_script:
logging.info("Executing external preprocessing script...")
proc = subprocess.Popen(self.preprocess_script)
proc.wait()
logging.info("done")
if keep_data_in_memory:
self.source, self.target = FileWrapper(source), FileWrapper(target)
if shuffle_each_epoch:
r = numpy.random.permutation(len(self.source))
self.source.shuffle_lines(r)
self.target.shuffle_lines(r)
elif shuffle_each_epoch:
self.source, self.target = shuffle.jointly_shuffle_files(
[self.source_orig, self.target_orig], temporary=True)
else:
self.source = fopen(source, 'r')
self.target = fopen(target, 'r')
self.source_dicts = []
for source_dict in source_dicts:
self.source_dicts.append(load_dict(source_dict, model_type))
self.target_dict = load_dict(target_dict, model_type)
# Determine the UNK value for each dictionary (the value depends on
# which version of build_dictionary.py was used).
def determine_unk_val(d):
if '<UNK>' in d and d['<UNK>'] == 2:
return 2
return 1
self.source_unk_vals = [determine_unk_val(d)
for d in self.source_dicts]
self.target_unk_val = determine_unk_val(self.target_dict)
self.keep_data_in_memory = keep_data_in_memory
self.batch_size = batch_size
self.maxlen = maxlen
self.skip_empty = skip_empty
self.use_factor = use_factor
self.source_vocab_sizes = source_vocab_sizes
self.target_vocab_size = target_vocab_size
self.token_batch_size = token_batch_size
if self.source_vocab_sizes != None:
assert len(self.source_vocab_sizes) == len(self.source_dicts)
for d, vocab_size in zip(self.source_dicts, self.source_vocab_sizes):
if vocab_size != None and vocab_size > 0:
for key, idx in list(d.items()):
if idx >= vocab_size:
del d[key]
if self.target_vocab_size != None and self.target_vocab_size > 0:
for key, idx in list(self.target_dict.items()):
if idx >= self.target_vocab_size:
del self.target_dict[key]
self.shuffle = shuffle_each_epoch
self.sort_by_length = sort_by_length
self.source_buffer = []
self.target_buffer = []
self.k = batch_size * maxibatch_size
self.end_of_data = False
def __iter__(self):
return self
def reset(self):
if self.preprocess_script:
logging.info("Executing external preprocessing script...")
proc = subprocess.Popen(self.preprocess_script)
proc.wait()
logging.info("done")
if self.keep_data_in_memory:
self.source, self.target = FileWrapper(self.source_orig), FileWrapper(self.target_orig)
else:
self.source = fopen(self.source_orig, 'r')
self.target = fopen(self.target_orig, 'r')
if self.shuffle:
if self.keep_data_in_memory:
r = numpy.random.permutation(len(self.source))
self.source.shuffle_lines(r)
self.target.shuffle_lines(r)
else:
self.source, self.target = shuffle.jointly_shuffle_files(
[self.source_orig, self.target_orig], temporary=True)
else:
self.source.seek(0)
self.target.seek(0)
def __next__(self):
if self.end_of_data:
self.end_of_data = False
self.reset()
raise StopIteration
source = []
target = []
longest_source = 0
longest_target = 0
# fill buffer, if it's empty
assert len(self.source_buffer) == len(self.target_buffer), 'Buffer size mismatch!'
if len(self.source_buffer) == 0:
for ss in self.source:
ss = ss.split()
tt = self.target.readline().split()
if self.skip_empty and (len(ss) == 0 or len(tt) == 0):
continue
if len(ss) > self.maxlen or len(tt) > self.maxlen:
continue
self.source_buffer.append(ss)
self.target_buffer.append(tt)
if len(self.source_buffer) == self.k:
break
if len(self.source_buffer) == 0 or len(self.target_buffer) == 0:
self.end_of_data = False
self.reset()
raise StopIteration
# sort by source/target buffer length
if self.sort_by_length:
tlen = numpy.array([max(len(s),len(t)) for (s,t) in zip(self.source_buffer,self.target_buffer)])
tidx = tlen.argsort()
_sbuf = [self.source_buffer[i] for i in tidx]
_tbuf = [self.target_buffer[i] for i in tidx]
self.source_buffer = _sbuf
self.target_buffer = _tbuf
else:
self.source_buffer.reverse()
self.target_buffer.reverse()
def lookup_token(t, d, unk_val):
return d[t] if t in d else unk_val
try:
# actual work here
while True:
# read from source file and map to word index
try:
ss = self.source_buffer.pop()
except IndexError:
break
tmp = []
for w in ss:
if self.use_factor:
w = [lookup_token(f, self.source_dicts[i],
self.source_unk_vals[i])
for (i, f) in enumerate(w.split('|'))]
else:
w = [lookup_token(w, self.source_dicts[0],
self.source_unk_vals[0])]
tmp.append(w)
ss_indices = tmp
# read from source file and map to word index
tt = self.target_buffer.pop()
tt_indices = [lookup_token(w, self.target_dict,
self.target_unk_val) for w in tt]
if self.target_vocab_size != None:
tt_indices = [w if w < self.target_vocab_size
else self.target_unk_val
for w in tt_indices]
source.append(ss_indices)
target.append(tt_indices)
longest_source = max(longest_source, len(ss_indices))
longest_target = max(longest_target, len(tt_indices))
if self.token_batch_size:
if len(source)*longest_source > self.token_batch_size or \
len(target)*longest_target > self.token_batch_size:
# remove last sentence pair (that made batch over-long)
source.pop()
target.pop()
self.source_buffer.append(ss)
self.target_buffer.append(tt)
break
else:
if len(source) >= self.batch_size or \
len(target) >= self.batch_size:
break
except IOError:
self.end_of_data = True
return source, target