def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
check_existing_pt_files(opt)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = count_features(opt.train_src) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, src_reader,
tgt_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def main():
opt = parse_args()
if opt.max_shard_size > 0:
raise AssertionError("-max_shard_size is deprecated, please use \
-shard_size (number of examples) instead.")
if opt.shuffle > 0:
raise AssertionError("-shuffle is not implemented, please make sure \
you shuffle your data before pre-processing.")
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
"src")
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
"tgt")
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset("train", fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset("valid", fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def main():
opt = parse_args()
f = open("data/corpus.json", "r")
data = f.readlines()
samples = []
for i in data:
samples.append(json.loads(i)["sents"])
corpus = []
for i in samples:
corpus.append(" ".join([" ".join(sent) for sent in i]))
vectorizer = TfidfVectorizer()
tfidf = vectorizer.fit(corpus)
init_logger(opt.log_file)
logger.info("Extracting features...")
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, tfidf, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, tfidf, opt)
logger.info("Building & saving vocabulary...")
# train_dataset_files = 'data/processed.train.pt'
build_save_vocab(train_dataset_files, opt.data_type, fields, opt)
def preprocess(opt):
# 参数的验证
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
tgt_nfeats = 0
fields = inputters.get_fields(
src_nfeats,
tgt_nfeats,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
align_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
build_save_dataset(
'train', fields, src_reader, tgt_reader, align_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset(
'valid', fields, src_reader, tgt_reader, align_reader, opt)
def main():
opt = parse_args()
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_dir,
'src')
qa_nfeats = inputters.get_num_features(opt.data_type, opt.train_dir, 'qa')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_dir,
'tgt')
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of qa features: %d." % qa_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(src_nfeats, qa_nfeats, tgt_nfeats,
opt.data_type)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
# train_dataset_files = '/research/king3/yfgao/pycharm_deployment/CoQG/data/coref_flow/processed/coqg.turn3.train.pt'
build_save_vocab(train_dataset_files, opt.data_type, fields, opt)
def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
if not (opt.overwrite):
check_existing_pt_files(opt)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
# tgt_nfeats = 0
for src in opt.train_src:
src_nfeats += count_features(src) if opt.data_type == 'text' \
else 0
logger.info(" * number of source features: %d." % src_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, src_reader, opt)
if opt.valid_src:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, opt)
def main():
#pdb.set_trace()
opt = parse_args()
init_logger(opt.log_file)
logger.info("Extracting features...")
# If there are special features added -- not in our case
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
'src')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
'tgt')
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
valid_dataset_files = build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files + valid_dataset_files, fields, opt)
def main():
opt = parse_args()
assert opt.max_shard_size == 0, \
"-max_shard_size is deprecated. Please use \
-shard_size (number of examples) instead."
assert opt.shuffle == 0, \
"-shuffle is not implemented. Please shuffle \
your data before pre-processing."
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = count_features(opt.train_src) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
def main():
opt = parse_args()
if (opt.max_shard_size > 0):
raise AssertionError("-max_shard_size is deprecated, please use \
-shard_size (number of examples) instead.")
init_logger(opt.log_file)
logger.info("Extracting features...")
# 下面的代码是尝试解决多进程prepare失败的问题,但是没有效果
torch.multiprocessing.set_sharing_strategy('file_system')
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (65535, rlimit[1]))
# END
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
'src')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
'tgt')
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
myutils.add_more_field(fields)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def main():
# Options are parsed and stored in opt object
opt = parse_args()
# Logging of data
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
'src')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
'tgt')
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
#Generation of traing, validation and dictionary data
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
valid_dataset_files = build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files + valid_dataset_files, fields, opt)
def main():
opt = parse_args()
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
'src')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
'tgt')
ans_nfeats = inputters.get_num_features(opt.data_type, opt.train_ans,
"ans")
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info(" * number of answer features: %d." % ans_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats,
ans_nfeats)
logger.info("fields src")
logger.info(fields.src.__dict__)
logger.info(fields.tgt.__dict__)
logger.info(fields.src_map.__dict__)
logger.info(fields.ans.__dict__)
logger.info(fields.indices.__dict__)
logger.info(fields.alignment.__dict__)
'''
def main():
opt = parse_args()
if (opt.max_shard_size > 0):
raise AssertionError("-max_shard_size is deprecated, please use \
-shard_size (number of examples) instead.")
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
'src')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
'tgt')
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def _get_fields(data_type, train_src, train_tgt):
logger.info("Extracting features...")
src_nfeats = inputters.get_num_features(data_type, train_src, 'src')
tgt_nfeats = inputters.get_num_features(data_type, train_tgt, 'tgt')
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(data_type, src_nfeats, tgt_nfeats)
return fields
def preprocess(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
tgt_nfeats = 0
src_nfeats = count_features(opt.train_src[0]) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt[0]) # tgt always text so far
if len(opt.train_src) > 1 and opt.data_type == 'text':
for src, tgt in zip(opt.train_src[1:], opt.train_tgt[1:]):
assert src_nfeats == count_features(src),\
"%s seems to mismatch features of "\
"the other source datasets" % src
assert tgt_nfeats == count_features(tgt),\
"%s seems to mismatch features of "\
"the other target datasets" % tgt
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
if opt.disable_eos_sampling:
eos_token = "<blank>"
logger.info("Using NO eos token")
else:
eos_token = "</s>"
logger.info("Using standard eos token")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
with_align=opt.train_align[0] is not None,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc,
eos=eos_token)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
align_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, src_reader, tgt_reader, align_reader,
opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader,
align_reader, opt)
def main():
opt = parse_args()
assert opt.max_shard_size == 0, \
"-max_shard_size is deprecated. Please use \
-shard_size (number of examples) instead."
assert opt.shuffle == 0, \
"-shuffle is not implemented. Please shuffle \
your data before pre-processing."
assert os.path.isfile(opt.train_src) and os.path.isfile(opt.train_tgt), \
"Please check path of your train src and tgt files!"
assert not opt.valid_src or os.path.isfile(opt.valid_src), \
"Please check path of your valid src file!"
assert not opt.valid_tgt or os.path.isfile(opt.valid_tgt), \
"Please check path of your valid tgt file!"
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = count_features(opt.train_src) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(
opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset(
'train', fields, src_reader, tgt_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def preprocess(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
tgt_nfeats = 0
for src, tgt in zip(opt.train_src, opt.train_tgt):
src_nfeats += count_features(src) if opt.data_type == 'text' \
else 0
tgt_nfeats += count_features(tgt) # tgt always text so far
# print(src_nfeats)
# exit()
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
# exit()
##################=======================================
tt_nfeats = tgt_nfeats
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tt_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
with_align=opt.train_align[0] is not None,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
########===============================================================
tt_reader = inputters.str2reader["text"].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
align_reader = inputters.str2reader["text"].from_opt(opt)
# for k,v in fields.items():
# if(k in ['src','tgt','tt']):
# print(("preprocess .py preprocess() fields_item",k,v.fields[0][1].include_lengths))
logger.info("Building & saving training data...")
build_save_dataset('train', fields, src_reader, tt_reader, tgt_reader,
align_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tt_reader, tgt_reader,
align_reader, opt)
def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
check_existing_pt_files(opt)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = count_features(
opt.train_src) if opt.data_type == 'text' else 0
tgt_nfeats = count_features(opt.train_tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
if len(opt.src_vocab) > 0:
assert len(opt.src_vocab) == len(
opt.train_src
), "you should provide src vocab for each dataset if you want to use your own vocab"
for i, (train_src,
train_tgt) in enumerate(zip(opt.train_src, opt.train_tgt)):
valid_src = opt.valid_src[i]
valid_tgt = opt.valid_tgt[i]
logger.info("Working on %d dataset..." % i)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, src_reader,
tgt_reader, opt, i, train_src,
train_tgt, valid_src,
valid_tgt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt, i,
train_src, train_tgt, valid_src, valid_tgt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt, i)
def preprocess(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
tgt_nfeats = 0
confnet_nfeats = 0
for src, tgt, cnet in zip(opt.train_src, opt.train_tgt, opt.train_confnet):
src_nfeats += count_features(src) if opt.data_type == 'text' or opt.data_type == 'lattice' \
else 0
tgt_nfeats += count_features(tgt) # tgt always text so far
#confnet_nfeats += count_features(cnet) if opt.data_type == 'lattice' \
# else 0
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info(" * number of confnet features: %d." % confnet_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
confnet_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
with_align=opt.train_align[0] is not None,
ans_truncate=opt.src_seq_length_trunc,
ques_truncate=opt.confnet_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
#print('fields done')
ans_reader = inputters.str2reader["text"].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
align_reader = inputters.str2reader["text"].from_opt(opt)
ques_reader = inputters.str2reader["lattice"].from_opt(opt)
#print('src_reader', ques_reader)
#print('tgt_reader', tgt_reader)
#print('aglign_reader', align_reader)
#print('confnet_reader', ans_reader)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, ques_reader, ans_reader, tgt_reader,
align_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, ques_reader, ans_reader,
tgt_reader, align_reader, opt)
def preprocess(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
tgt_nfeats = 0
for src, tgt in zip(opt.train_src, opt.train_tgt):
src_nfeats += count_features(src) if opt.data_type == 'text' \
else 0
tgt_nfeats += count_features(tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
print("haha")
logger.info("haha")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
with_align=opt.train_align[0] is not None,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
logger.info("please...")
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
align_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
is_train_dataset_finished = build_save_dataset('train', fields, src_reader,
tgt_reader, align_reader,
opt)
#if is_train_dataset_finished: # For Debugging(From sehun)
# logger.info("Train data is finished") # For Debugging(From sehun)
#else: # For Debugging(From sehun)
# logger.info("f**k") # For Debugging(From sehun)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader,
align_reader, opt)
def dump_dataset(savepath, save_dev=False):
src_corpus = savepath + '/src-train.txt'
tgt_corpus = savepath + '/tgt-train.txt'
src_nfeats = inputters.get_num_features('text', src_corpus, 'src')
tgt_nfeats = inputters.get_num_features('text', tgt_corpus, 'tgt')
fields = inputters.get_fields('text', src_nfeats, tgt_nfeats)
fields['graph'] = torchtext.data.Field(sequential=False)
train_dataset_files = build_save_dataset('train', fields, src_corpus,
tgt_corpus, savepath, args)
if save_dev:
src_corpus = savepath + '/src-dev.txt'
tgt_corpus = savepath + '/tgt-dev.txt'
build_save_dataset('dev', fields, src_corpus, tgt_corpus, savepath,
args)
build_save_vocab(train_dataset_files, fields, savepath, args)
def main():
opt = parse_args()
init_logger(opt.log_file)
logger.info("Extracting features...")
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
# train_dataset_files = 'data/processed.train.pt'
build_save_vocab(train_dataset_files, opt.data_type, fields, opt)
def main():
opt = parse_args()
print("[preprocess.py] opt.model_mode: {}".format(opt.model_mode))
assert opt.max_shard_size == 0, \
"-max_shard_size is deprecated. Please use \
-shard_size (number of examples) instead."
assert opt.shuffle == 0, \
"-shuffle is not implemented. Please shuffle \
your data before pre-processing."
assert os.path.isfile(opt.train_src) and os.path.isfile(opt.train_tgt), \
"Please check path of your train src and tgt files!"
assert os.path.isfile(opt.valid_src) and os.path.isfile(opt.valid_tgt), \
"Please check path of your valid src and tgt files!"
init_logger(opt.log_file)
logger.info("Extracting features...")
knl_nfeats = count_features(opt.train_knl) if opt.data_type == 'text' \
else 0
src_nfeats = count_features(opt.train_src) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt) # tgt always text so far
logger.info(" * number of knowledge features: %d." % knl_nfeats)
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats,
knl_nfeats)
# fields = inputters.get_fields(opt.data_type, 1, 1, knl_nfeats)
# {'src', 'src_feat_0', 'knl', 'src_map', 'alignment', 'tgt', 'tgt_feat_0', 'indices'}
# {'src' 'knl', 'src_map', 'alignment', 'tgt', 'indices', 'src_da_label', 'tgt_da_label'}
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def preprocess(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
cue_nfeats = 0
tgt_nfeats = 0
for src, cue, tgt in zip(opt.train_src, opt.train_cue, opt.train_tgt):
src_nfeats += count_features(src) if opt.data_type == 'text' \
else 0
cue_nfeats += count_features(cue) if opt.data_type == 'text' else 0
tgt_nfeats += count_features(tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info("* number of knowledge features: %d" % cue_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
cue_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
cue_truncate=opt.cue_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
cue_reader = inputters.str2reader["text"].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, src_reader, cue_reader, tgt_reader,
opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, cue_reader, tgt_reader,
opt)
def preprocess_main(opt):
logger = get_logger(opt.log_file)
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
'src')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
'tgt')
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt, logger)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt, logger)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt, logger)
def main():
opt = parse_args()
if (opt.max_shard_size > 0):
raise AssertionError("-max_shard_size is deprecated, please use \
-shard_size (number of examples) instead.")
init_logger(opt.log_file)
logger.info("Extracting features...")
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, 0, 0)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(fields, opt)
def main():
opt = parse_args()
print("Extracting features...")
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
'src')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
'tgt')
print(" * number of source features: %d." % src_nfeats)
print(" * number of target features: %d." % tgt_nfeats)
print("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
print("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
print("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
print("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
if not (opt.overwrite):
check_existing_pt_files(opt)
init_logger(opt.log_file)
shutil.copy2(opt.config, os.path.dirname(opt.log_file))
logger.info(opt)
logger.info("Extracting features...")
src_nfeats = 0
tgt_nfeats = 0
for src, tgt in zip(opt.train_src, opt.train_tgt):
src_nfeats += count_features(src) if opt.data_type == 'text' \
else 0
tgt_nfeats += count_features(tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader[opt.data_type].from_opt(opt)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, src_reader, tgt_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
def main():
opt = parse_args()
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = inputters.get_num_features(
opt.data_type, opt.train_src, 'src')
tgt_nfeats = inputters.get_num_features(
opt.data_type, opt.train_tgt, 'tgt')
ans_nfeats = inputters.get_num_features(
opt.data_type, opt.train_ans, "ans")
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info(" * number of answer features: %d." % ans_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats, ans_nfeats)
logger.info("fields src")
logger.info(fields['src'].__dict__)
logger.info(fields['tgt'].__dict__)
logger.info(fields['src_map'].__dict__)
logger.info(fields['ans'].__dict__)
logger.info(fields['indices'].__dict__)
logger.info(fields['alignment'].__dict__)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info(train_dataset_files)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, opt)
def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
check_existing_pt_files(opt)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = count_features(opt.train_src) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(
opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset(
'train', fields, src_reader, tgt_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
check_existing_pt_files(opt)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = count_features(opt.train_src) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
if opt.fixed_vocab:
tgt_bos = '<|endoftext|>'
tgt_eos = '\u0120GDDR'
tgt_pad = '\u0120SHALL'
tgt_unk = '\u0120RELE'
if opt.no_spec_src:
src_pad = None
src_unk = None
elif opt.free_src:
src_pad = '<blank>'
src_unk = '<unk>'
else:
src_pad = '\u0120SHALL'
src_unk = '\u0120RELE'
else:
tgt_bos = '<s>'
tgt_eos = '</s>'
tgt_pad = '<blank>'
tgt_unk = '<unk>'
src_pad = '<blank>'
src_unk = '<unk>'
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc,
src_pad=src_pad,
src_unk=src_unk,
tgt_pad=tgt_pad,
tgt_unk=tgt_unk,
tgt_bos=tgt_bos,
tgt_eos=tgt_eos,
include_ptrs=opt.pointers_file is not None)
if opt.data_type == 'none':
src_reader = None
else:
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, src_reader,
tgt_reader, opt)
if (opt.valid_src or opt.data_type == 'none') and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
if not (opt.overwrite):
check_existing_pt_files(opt)
init_logger(opt.log_file)
shutil.copy2(opt.config, os.path.dirname(opt.log_file))
logger.info(opt)
logger.info("Extracting features...")
#Prepares the document embedding to initialize memory vectors.
embedder = SentenceTransformer('bert-base-nli-mean-tokens')
kpcorpus = []
files_path = [ #'data/keyphrase/json/kp20k/kp20k_train.json',
'data/keyphrase/json/kp20k/kp20k_valid.json',
'data/keyphrase/json/kp20k/kp20k_test.json',
'data/keyphrase/json/inspec/inspec_valid.json',
'data/keyphrase/json/inspec/inspec_test.json',
'data/keyphrase/json/krapivin/krapivin_valid.json',
'data/keyphrase/json/krapivin/krapivin_test.json',
'data/keyphrase/json/nus/split/nus_valid.json',
'data/keyphrase/json/nus/split/nus_test.json',
'data/keyphrase/json/semeval/semeval_valid.json',
'data/keyphrase/json/semeval/semeval_test.json',
'data/keyphrase/json/duc/split/duc_valid.json',
'data/keyphrase/json/duc/split/duc_test.json'
]
for file_path in files_path:
file = open(file_path, 'r')
for line in file.readlines():
dic = json.loads(line)
# print(dic)
kpcorpus.append(dic['title'] + ' ' + dic['abstract'])
# print(kpcorpus)
num_of_example = len(kpcorpus)
print("number of examples in corpus: ", num_of_example)
time_a = time.time()
corpus_embeddings = embedder.encode(kpcorpus[:num_of_example])
print("elapsed time: ", time.time() - time_a)
alldocs_emb = torch.Tensor(corpus_embeddings)
torch.save(alldocs_emb, './data/alldocs_emb')
src_nfeats = 0
tgt_nfeats = 0
for src, tgt in zip(opt.train_src, opt.train_tgt):
src_nfeats += count_features(src) if opt.data_type == 'text' \
else 0
tgt_nfeats += count_features(tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader[opt.data_type].from_opt(opt)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, src_reader, tgt_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
