forked from tensorflow/models
-
Notifications
You must be signed in to change notification settings - Fork 0
/
classifier_utils.py
163 lines (136 loc) · 5.37 KB
/
classifier_utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities for pre-processing classification data."""
from absl import logging
from official.nlp.xlnet import data_utils
SEG_ID_A = 0
SEG_ID_B = 1
class PaddingInputExample(object):
"""Fake example so the num input examples is a multiple of the batch size.
When running eval/predict on the TPU, we need to pad the number of examples
to be a multiple of the batch size, because the TPU requires a fixed batch
size. The alternative is to drop the last batch, which is bad because it means
the entire output data won't be generated.
We use this class instead of `None` because treating `None` as padding
battches could cause silent errors.
"""
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
input_ids,
input_mask,
segment_ids,
label_id,
is_real_example=True):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.is_real_example = is_real_example
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def convert_single_example(example_index, example, label_list, max_seq_length,
tokenize_fn, use_bert_format):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(
input_ids=[0] * max_seq_length,
input_mask=[1] * max_seq_length,
segment_ids=[0] * max_seq_length,
label_id=0,
is_real_example=False)
if label_list is not None:
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
tokens_a = tokenize_fn(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenize_fn(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for two [SEP] & one [CLS] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for one [SEP] & one [CLS] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:max_seq_length - 2]
tokens = []
segment_ids = []
for token in tokens_a:
tokens.append(token)
segment_ids.append(SEG_ID_A)
tokens.append(data_utils.SEP_ID)
segment_ids.append(SEG_ID_A)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(SEG_ID_B)
tokens.append(data_utils.SEP_ID)
segment_ids.append(SEG_ID_B)
if use_bert_format:
tokens.insert(0, data_utils.CLS_ID)
segment_ids.insert(0, data_utils.SEG_ID_CLS)
else:
tokens.append(data_utils.CLS_ID)
segment_ids.append(data_utils.SEG_ID_CLS)
input_ids = tokens
# The mask has 0 for real tokens and 1 for padding tokens. Only real
# tokens are attended to.
input_mask = [0] * len(input_ids)
# Zero-pad up to the sequence length.
if len(input_ids) < max_seq_length:
delta_len = max_seq_length - len(input_ids)
if use_bert_format:
input_ids = input_ids + [0] * delta_len
input_mask = input_mask + [1] * delta_len
segment_ids = segment_ids + [data_utils.SEG_ID_PAD] * delta_len
else:
input_ids = [0] * delta_len + input_ids
input_mask = [1] * delta_len + input_mask
segment_ids = [data_utils.SEG_ID_PAD] * delta_len + segment_ids
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
if label_list is not None:
label_id = label_map[example.label]
else:
label_id = example.label
if example_index < 5:
logging.info("*** Example ***")
logging.info("guid: %s", (example.guid))
logging.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
logging.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
logging.info("label: %d (id = %d)", example.label, label_id)
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id)
return feature