def get_dict(dict_size, reverse=True):
# if reverse = False, return dict = {'a':'001', 'b':'002', ...}
# else reverse = true, return dict = {'001':'a', '002':'b', ...}
tar_file = download(URL_TRAIN, 'wmt14', MD5_TRAIN)
src_dict, trg_dict = __read_to_dict__(tar_file, dict_size)
if reverse:
src_dict = {v: k for k, v in src_dict.items()}
trg_dict = {v: k for k, v in trg_dict.items()}
return src_dict, trg_dict
def get_dict(dict_size, reverse=True):
# if reverse = False, return dict = {'a':'001', 'b':'002', ...}
# else reverse = true, return dict = {'001':'a', '002':'b', ...}
tar_file = download(URL_TRAIN, 'wmt14', MD5_TRAIN)
src_dict, trg_dict = __read_to_dict__(tar_file, dict_size)
if reverse:
src_dict = {v: k for k, v in src_dict.items()}
trg_dict = {v: k for k, v in trg_dict.items()}
return src_dict, trg_dict
def test(dict_size):
"""
WMT14 test set creator.
It returns a reader creator, each sample in the reader is source language
word ID sequence, target language word ID sequence and next word ID
sequence.
:return: Test reader creator
:rtype: callable
"""
return reader_creator(
download(URL_TRAIN, 'wmt14', MD5_TRAIN), 'test/test', dict_size)
def test(dict_size):
"""
WMT14 test set creator.
It returns a reader creator, each sample in the reader is source language
word ID sequence, target language word ID sequence and next word ID
sequence.
:return: Test reader creator
:rtype: callable
"""
return reader_creator(download(URL_TRAIN, 'wmt14', MD5_TRAIN), 'test/test',
dict_size)
def fetch():
download(URL_TRAIN, 'wmt14', MD5_TRAIN)
download(URL_MODEL, 'wmt14', MD5_MODEL)
def model():
tar_file = download(URL_MODEL, 'wmt14', MD5_MODEL)
with gzip.open(tar_file, 'r') as f:
parameters = Parameters.from_tar(f)
return parameters
def gen(dict_size):
return reader_creator(
download(URL_TRAIN, 'wmt14', MD5_TRAIN), 'gen/gen', dict_size)
def fetch():
download(URL_TRAIN, 'wmt14', MD5_TRAIN)
download(URL_MODEL, 'wmt14', MD5_MODEL)
def model():
tar_file = download(URL_MODEL, 'wmt14', MD5_MODEL)
with gzip.open(tar_file, 'r') as f:
parameters = Parameters.from_tar(f)
return parameters
def gen(dict_size):
return reader_creator(download(URL_TRAIN, 'wmt14', MD5_TRAIN), 'gen/gen',
dict_size)
def val():
"""
Create a val dataset reader containing 1449 images in HWC order.
"""
return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'val')
def test():
"""
Create a test dataset reader containing 1464 images in HWC order.
"""
return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'train')
def train():
"""
Create a train dataset reader containing 2913 images in HWC order.
"""
return reader_creator(download(VOC_URL, CACHE_DIR, VOC_MD5), 'trainval')
def train(dict_size):
return reader_creator(download(URL_TRAIN, 'wmt14', MD5_TRAIN),
'train/train', dict_size)
def fetch():
download(URL_TRAIN, 'wmt14', MD5_TRAIN)
def test(dict_size):
return reader_creator(download(URL_TRAIN, 'wmt14', MD5_TRAIN), 'test/test',
dict_size)