metrics_path = os.path.join('results', 'metrics.pickle')
if not os.path.exists(metrics_path):
return (None, None, None, None)
with open(metrics_path, 'rb') as metrics_handle:
metrics_obj = pickle.load(metrics_handle)
return (metrics_obj['token_pairs'],
metrics_obj['decoded_pairs'],
metrics_obj['jaccard_similarities'],
metrics_obj['levenshtein_distances'])
token_pairs, decoded_pairs, jaccard_similarities, levenshtein_distances = load_metrics_obj()
if not token_pairs:
token_pairs = [([tokenizer.id_to_token(x) for x in ocr_tokens[i]], [tokenizer.id_to_token(x) for x in gs_tokens[i]]) for i in range(len(ocr_tokens))]
save_metrics_obj(token_pairs, decoded_pairs, jaccard_similarities, levenshtein_distances)
if not decoded_pairs:
decoded_pairs = [(tokenizer.decode(ocr_tokens[i]), tokenizer.decode(gs_tokens[i])) for i in range(len(ocr_tokens))]
save_metrics_obj(token_pairs, decoded_pairs, jaccard_similarities, levenshtein_distances)
all_pairs = len(token_pairs)
if not jaccard_similarities:
jaccard_similarities = []
for i, token_pair in enumerate(token_pairs):
jaccard_similarities.append(calculate_jaccard_similarity(token_pair[0], token_pair[1]))
save_metrics_obj(token_pairs, decoded_pairs, jaccard_similarities, levenshtein_distances)
if not levenshtein_distances:
class Tokenizer(object):
def __init__(self, vocab_path, do_lower_case=True):
if BertWordPieceTokenizer:
self.tokenizer = BertWordPieceTokenizer(
vocab_path,
lowercase=do_lower_case,
)
else:
self.tokenizer = tokenization.FullTokenizer(
vocab_path, do_lower_case=do_lower_case)
self._do_lower_case = do_lower_case
def tokenize(self, input_text):
if BertWordPieceTokenizer:
return self.tokenizer.encode(input_text,
add_special_tokens=False).tokens
else:
return self.tokenizer.tokenize(input_text)
def encode(self, input_text, add_special_tokens=False):
input_tokens = self.tokenize(input_text)
if add_special_tokens:
input_tokens = ['[CLS]'] + input_tokens + ['[SEP]']
input_token_ids = self.convert_tokens_to_ids(input_tokens)
return input_token_ids
def padded_to_ids(self, input_text, max_length):
if len(input_text) > max_length:
return input_text[:max_length]
else:
return input_text + [0] * (max_length - len(input_text))
def convert_tokens_to_ids(self, input_tokens):
if BertWordPieceTokenizer:
token_ids = [
self.tokenizer.token_to_id(token) for token in input_tokens
]
else:
token_ids = self.tokenizer.convert_tokens_to_ids(input_tokens)
return token_ids
def customize_tokenize(self, input_text):
temp_x = ""
for c in input_text:
if self._is_cjk_character(c) or self._is_punctuation(
c) or self._is_space(c) or self._is_control(c):
temp_x += " " + c + " "
else:
temp_x += c
return temp_x.split()
def convert_ids_to_tokens(self, input_ids):
if BertWordPieceTokenizer:
input_tokens = [
self.tokenizer.id_to_token(ids) for ids in input_ids
]
else:
input_tokens = self.tokenizer.convert_ids_to_tokens(input_ids)
return input_tokens
def decode(self, input_tokens):
text, flag = '', False
for i, token in enumerate(input_tokens):
if token[:2] == '##':
text += token[2:]
elif len(token) == 1 and self._is_cjk_character(token):
text += token
elif len(token) == 1 and self._is_punctuation(token):
text += token
text += ' '
elif i > 0 and self._is_cjk_character(text[-1]):
text += token
else:
text += ' '
text += token
text = re.sub(' +', ' ', text)
text = re.sub('\' (re|m|s|t|ve|d|ll) ', '\'\\1 ', text)
punctuation = self._cjk_punctuation() + '+-/={(<['
punctuation_regex = '|'.join([re.escape(p) for p in punctuation])
punctuation_regex = '(%s) ' % punctuation_regex
text = re.sub(punctuation_regex, '\\1', text)
text = re.sub('(\d\.) (\d)', '\\1\\2', text)
return text.strip()
@staticmethod
def stem(token):
"""
"""
if token[:2] == '##':
return token[2:]
else:
return token
@staticmethod
def _is_space(ch):
"""
"""
return ch == ' ' or ch == '\n' or ch == '\r' or ch == '\t' or \
unicodedata.category(ch) == 'Zs'
@staticmethod
def _is_punctuation(ch):
"""
"""
code = ord(ch)
return 33 <= code <= 47 or \
58 <= code <= 64 or \
91 <= code <= 96 or \
123 <= code <= 126 or \
unicodedata.category(ch).startswith('P')
@staticmethod
def _cjk_punctuation():
return u'\uff02\uff03\uff04\uff05\uff06\uff07\uff08\uff09\uff0a\uff0b\uff0c\uff0d\uff0f\uff1a\uff1b\uff1c\uff1d\uff1e\uff20\uff3b\uff3c\uff3d\uff3e\uff3f\uff40\uff5b\uff5c\uff5d\uff5e\uff5f\uff60\uff62\uff63\uff64\u3000\u3001\u3003\u3008\u3009\u300a\u300b\u300c\u300d\u300e\u300f\u3010\u3011\u3014\u3015\u3016\u3017\u3018\u3019\u301a\u301b\u301c\u301d\u301e\u301f\u3030\u303e\u303f\u2013\u2014\u2018\u2019\u201b\u201c\u201d\u201e\u201f\u2026\u2027\ufe4f\ufe51\ufe54\u00b7\uff01\uff1f\uff61\u3002'
@staticmethod
def _is_cjk_character(ch):
"""
reference：https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
"""
code = ord(ch)
return 0x4E00 <= code <= 0x9FFF or \
0x3400 <= code <= 0x4DBF or \
0x20000 <= code <= 0x2A6DF or \
0x2A700 <= code <= 0x2B73F or \
0x2B740 <= code <= 0x2B81F or \
0x2B820 <= code <= 0x2CEAF or \
0xF900 <= code <= 0xFAFF or \
0x2F800 <= code <= 0x2FA1F
@staticmethod
def _is_control(ch):
"""
"""
return unicodedata.category(ch) in ('Cc', 'Cf')
@staticmethod
def _is_special(ch):
"""
"""
return bool(ch) and (ch[0] == '[') and (ch[-1] == ']')
def rematch(self, text, tokens):
if is_py2:
text = unicode(text)
if self._do_lower_case:
text = text.lower()
normalized_text, char_mapping = '', []
for i, ch in enumerate(text):
if self._do_lower_case:
ch = unicodedata.normalize('NFD', ch)
ch = ''.join(
[c for c in ch if unicodedata.category(c) != 'Mn'])
ch = ''.join([
c for c in ch
if not (ord(c) == 0 or ord(c) == 0xfffd or self._is_control(c))
])
normalized_text += ch
char_mapping.extend([i] * len(ch))
text, token_mapping, offset = normalized_text, [], 0
for token in tokens:
if self._is_special(token):
token_mapping.append([])
else:
token = self.stem(token)
start = text[offset:].index(token) + offset
end = start + len(token)
token_mapping.append(char_mapping[start:end])
offset = end
return token_mapping
class Reader(object):
def __init__(self,
bert_model: str,
tokenizer: BaseTokenizer = None,
cls: str = "[CLS]",
sep: str = "[SEP]",
threshold=6):
self.tokenizer: BaseTokenizer = tokenizer
self.cls = cls
self.sep = sep
if self.tokenizer is None:
vocab_path: str = "tokenization/" + bert_model + ".txt"
self.tokenizer = BertWordPieceTokenizer(vocab_path,
lowercase="-cased"
not in bert_model)
self.threshold = threshold
self.subword_alphabet: Optional[Alphabet] = None
self.label_alphabet: Optional[Alphabet] = None
self.train: Optional[List[SentInst]] = None
self.dev: Optional[List[SentInst]] = None
self.test: Optional[List[SentInst]] = None
def _read_file(self, filename: str, mode: str = 'train') -> List[SentInst]:
sent_list = []
max_len = 0
num_thresh = 0
with open(filename, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line == "": # last few blank lines
break
raw_tokens = line.split(' ')
tokens = raw_tokens
chars = [list(t) for t in raw_tokens]
entities = next(f).strip()
if entities == "": # no entities
sent_inst = SentInst(tokens, chars, [])
else:
entity_list = []
entities = entities.split("|")
for item in entities:
pointers, label = item.split()
pointers = pointers.split(",")
if int(pointers[1]) > len(tokens):
pdb.set_trace()
span_len = int(pointers[1]) - int(pointers[0])
if span_len < 0:
print("Warning! span_len < 0")
continue
if span_len > max_len:
max_len = span_len
if span_len > self.threshold:
num_thresh += 1
new_entity = (int(pointers[0]), int(pointers[1]),
label)
# may be duplicate entities in some datasets
if (mode == 'train' and new_entity
not in entity_list) or (mode != 'train'):
entity_list.append(new_entity)
# assert len(entity_list) == len(set(entity_list)) # check duplicate
sent_inst = SentInst(tokens, chars, entity_list)
assert next(f).strip() == "" # separating line
sent_list.append(sent_inst)
print("Max length: {}".format(max_len))
print("Threshold {}: {}".format(self.threshold, num_thresh))
return sent_list
def _gen_dic(self) -> None:
label_set = set()
for sent_list in [self.train, self.dev, self.test]:
num_mention = 0
for sentInst in sent_list:
for entity in sentInst.entities:
label_set.add(entity[2])
num_mention += len(sentInst.entities)
print("# mentions: {}".format(num_mention))
vocab = [
self.tokenizer.id_to_token(idx)
for idx in range(self.tokenizer.get_vocab_size())
]
self.subword_alphabet = Alphabet(vocab, 0)
self.label_alphabet = Alphabet(label_set, 0)
@staticmethod
def _pad_batches(input_ids_batches: List[List[List[int]]],
first_subtokens_batches: List[List[List[int]]]) \
-> Tuple[List[List[List[int]]],
List[List[List[int]]],
List[List[List[bool]]]]:
padded_input_ids_batches = []
input_mask_batches = []
mask_batches = []
all_batches = list(zip(input_ids_batches, first_subtokens_batches))
for input_ids_batch, first_subtokens_batch in all_batches:
batch_len = len(input_ids_batch)
max_subtokens_num = max(
[len(input_ids) for input_ids in input_ids_batch])
max_sent_len = max([
len(first_subtokens)
for first_subtokens in first_subtokens_batch
])
padded_input_ids_batch = []
input_mask_batch = []
mask_batch = []
for i in range(batch_len):
subtokens_num = len(input_ids_batch[i])
sent_len = len(first_subtokens_batch[i])
padded_subtoken_vec = input_ids_batch[i].copy()
padded_subtoken_vec.extend([0] *
(max_subtokens_num - subtokens_num))
input_mask = [1] * subtokens_num + [0] * (max_subtokens_num -
subtokens_num)
mask = [True] * sent_len + [False] * (max_sent_len - sent_len)
padded_input_ids_batch.append(padded_subtoken_vec)
input_mask_batch.append(input_mask)
mask_batch.append(mask)
padded_input_ids_batches.append(padded_input_ids_batch)
input_mask_batches.append(input_mask_batch)
mask_batches.append(mask_batch)
return padded_input_ids_batches, input_mask_batches, mask_batches
def get_batches(self, sentences: List[SentInst], batch_size: int) -> Tuple:
subtoken_dic_dic = defaultdict(lambda: defaultdict(list))
first_subtoken_dic_dic = defaultdict(lambda: defaultdict(list))
last_subtoken_dic_dic = defaultdict(lambda: defaultdict(list))
label_dic_dic = defaultdict(lambda: defaultdict(list))
this_input_ids_batches = []
this_first_subtokens_batches = []
this_last_subtokens_batches = []
this_label_batches = []
for sentInst in sentences:
subtoken_vec = []
first_subtoken_vec = []
last_subtoken_vec = []
subtoken_vec.append(self.tokenizer.token_to_id(self.cls))
for t in sentInst.tokens:
encoding = self.tokenizer.encode(t)
ids = [
v for v, mask in zip(encoding.ids,
encoding.special_tokens_mask)
if mask == 0
]
first_subtoken_vec.append(len(subtoken_vec))
subtoken_vec.extend(ids)
last_subtoken_vec.append(len(subtoken_vec))
subtoken_vec.append(self.tokenizer.token_to_id(self.sep))
label_list = [(u[0], u[1], self.label_alphabet.get_index(u[2]))
for u in sentInst.entities]
subtoken_dic_dic[len(
sentInst.tokens)][len(subtoken_vec)].append(subtoken_vec)
first_subtoken_dic_dic[len(
sentInst.tokens)][len(subtoken_vec)].append(first_subtoken_vec)
last_subtoken_dic_dic[len(
sentInst.tokens)][len(subtoken_vec)].append(last_subtoken_vec)
label_dic_dic[len(
sentInst.tokens)][len(subtoken_vec)].append(label_list)
input_ids_batches = []
first_subtokens_batches = []
last_subtokens_batches = []
label_batches = []
for length1 in sorted(subtoken_dic_dic.keys(), reverse=True):
for length2 in sorted(subtoken_dic_dic[length1].keys(),
reverse=True):
input_ids_batches.extend(subtoken_dic_dic[length1][length2])
first_subtokens_batches.extend(
first_subtoken_dic_dic[length1][length2])
last_subtokens_batches.extend(
last_subtoken_dic_dic[length1][length2])
label_batches.extend(label_dic_dic[length1][length2])
[
this_input_ids_batches.append(input_ids_batches[i:i + batch_size])
for i in range(0, len(input_ids_batches), batch_size)
]
[
this_first_subtokens_batches.append(
first_subtokens_batches[i:i + batch_size])
for i in range(0, len(first_subtokens_batches), batch_size)
]
[
this_last_subtokens_batches.append(
last_subtokens_batches[i:i + batch_size])
for i in range(0, len(last_subtokens_batches), batch_size)
]
[
this_label_batches.append(label_batches[i:i + batch_size])
for i in range(0, len(label_batches), batch_size)
]
this_input_ids_batches, this_input_mask_batches, this_mask_batches \
= self._pad_batches(this_input_ids_batches, this_first_subtokens_batches)
return (this_input_ids_batches, this_input_mask_batches,
this_first_subtokens_batches, this_last_subtokens_batches,
this_label_batches, this_mask_batches)
def to_batch(self, batch_size: int) -> Tuple:
ret_list = []
for sent_list in [self.train, self.dev, self.test]:
ret_list.append(self.get_batches(sent_list, batch_size))
return tuple(ret_list)
def read_all_data(self, file_path: str, train_file: str, dev_file: str,
test_file: str) -> None:
self.train = self._read_file(file_path + train_file)
self.dev = self._read_file(file_path + dev_file, mode='dev')
self.test = self._read_file(file_path + test_file, mode='test')
self._gen_dic()
def debug_single_sample(self, subtoken: List[int],
label_list: List[Tuple[int, int, int]]) -> None:
print(" ".join(
[self.subword_alphabet.get_instance(t) for t in subtoken]))
for label in label_list:
print(label[0], label[1],
self.label_alphabet.get_instance(label[2]))
