From e32954f68b69a26175feef2f7b1fa38dcfd86055 Mon Sep 17 00:00:00 2001 From: guru4elephant Date: Sat, 21 Mar 2020 21:25:39 +0800 Subject: [PATCH] add reader --- python/paddle_serving_app/__init__.py | 1 + python/paddle_serving_app/reader/batching.py | 126 +++++ .../reader/bert_base_reader.py | 24 + .../reader/chinese_bert_reader.py | 128 +++++ python/paddle_serving_app/reader/reader.py | 24 + .../paddle_serving_app/reader/tokenization.py | 441 ++++++++++++++++++ python/paddle_serving_app/version.py | 15 + 7 files changed, 759 insertions(+) create mode 100644 python/paddle_serving_app/reader/batching.py create mode 100644 python/paddle_serving_app/reader/bert_base_reader.py create mode 100644 python/paddle_serving_app/reader/chinese_bert_reader.py create mode 100644 python/paddle_serving_app/reader/reader.py create mode 100644 python/paddle_serving_app/reader/tokenization.py create mode 100644 python/paddle_serving_app/version.py diff --git a/python/paddle_serving_app/__init__.py b/python/paddle_serving_app/__init__.py index 847ddc47..968e5582 100644 --- a/python/paddle_serving_app/__init__.py +++ b/python/paddle_serving_app/__init__.py @@ -11,3 +11,4 @@ # 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. +from .reader.chinese_bert_reader import ChineseBertReader diff --git a/python/paddle_serving_app/reader/batching.py b/python/paddle_serving_app/reader/batching.py new file mode 100644 index 00000000..5ec5f320 --- /dev/null +++ b/python/paddle_serving_app/reader/batching.py @@ -0,0 +1,126 @@ +#coding:utf-8 +# Copyright (c) 2019 PaddlePaddle 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. +"""Mask, padding and batching.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + + +def prepare_batch_data(insts, + total_token_num, + max_seq_len=128, + pad_id=None, + cls_id=None, + sep_id=None, + mask_id=None, + return_input_mask=True, + return_max_len=True, + return_num_token=False): + """ + 1. generate Tensor of data + 2. generate Tensor of position + 3. generate self attention mask, [shape: batch_size * max_len * max_len] + """ + + batch_src_ids = [inst[0] for inst in insts] + batch_sent_ids = [inst[1] for inst in insts] + batch_pos_ids = [inst[2] for inst in insts] + labels_list = [] + # compatible with squad, whose example includes start/end positions, + # or unique id + + for i in range(3, len(insts[0]), 1): + labels = [inst[i] for inst in insts] + labels = np.array(labels).astype("int64").reshape([-1, 1]) + labels_list.append(labels) + + out = batch_src_ids + # Second step: padding + src_id, self_input_mask = pad_batch_data( + out, pad_idx=pad_id, max_seq_len=max_seq_len, return_input_mask=True) + pos_id = pad_batch_data( + batch_pos_ids, + pad_idx=pad_id, + max_seq_len=max_seq_len, + return_pos=False, + return_input_mask=False) + sent_id = pad_batch_data( + batch_sent_ids, + pad_idx=pad_id, + max_seq_len=max_seq_len, + return_pos=False, + return_input_mask=False) + + return_list = [src_id, pos_id, sent_id, self_input_mask] + labels_list + + return return_list if len(return_list) > 1 else return_list[0] + + +def pad_batch_data(insts, + pad_idx=0, + max_seq_len=128, + return_pos=False, + return_input_mask=False, + return_max_len=False, + return_num_token=False, + return_seq_lens=False): + """ + Pad the instances to the max sequence length in batch, and generate the + corresponding position data and input mask. + """ + return_list = [] + #max_len = max(len(inst) for inst in insts) + max_len = max_seq_len + # Any token included in dict can be used to pad, since the paddings' loss + # will be masked out by weights and make no effect on parameter gradients. + + inst_data = np.array([ + list(inst) + list([pad_idx] * (max_len - len(inst))) for inst in insts + ]) + return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])] + + # position data + if return_pos: + inst_pos = np.array([ + list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst)) + for inst in insts + ]) + + return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])] + + if return_input_mask: + # This is used to avoid attention on paddings. + input_mask_data = np.array( + [[1] * len(inst) + [0] * (max_len - len(inst)) for inst in insts]) + input_mask_data = np.expand_dims(input_mask_data, axis=-1) + return_list += [input_mask_data.astype("float32")] + + if return_max_len: + return_list += [max_len] + + if return_num_token: + num_token = 0 + for inst in insts: + num_token += len(inst) + return_list += [num_token] + + if return_seq_lens: + seq_lens = np.array([len(inst) for inst in insts]) + return_list += [seq_lens.astype("int64").reshape([-1, 1])] + + return return_list if len(return_list) > 1 else return_list[0] diff --git a/python/paddle_serving_app/reader/bert_base_reader.py b/python/paddle_serving_app/reader/bert_base_reader.py new file mode 100644 index 00000000..9888dbe8 --- /dev/null +++ b/python/paddle_serving_app/reader/bert_base_reader.py @@ -0,0 +1,24 @@ +# Copyright (c) 2020 PaddlePaddle 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. +from .reader import ReaderBase + + +class BertBaseReader(ReaderBase): + def __init__(self): + super(BertBaseReader, self).__init__() + pass + + def process(self, line): + super(BertBaseReader, self).process(line) + pass diff --git a/python/paddle_serving_app/reader/chinese_bert_reader.py b/python/paddle_serving_app/reader/chinese_bert_reader.py new file mode 100644 index 00000000..6c884c2a --- /dev/null +++ b/python/paddle_serving_app/reader/chinese_bert_reader.py @@ -0,0 +1,128 @@ +# Copyright (c) 2020 PaddlePaddle 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. +# coding=utf-8 +from .bert_base_reader import BertBaseReader +from .batching import pad_batch_data +from .tokenization import FullTokenizer, convert_to_unicode + + +class ChineseBertReader(BertBaseReader): + """ + ChineseBertReader handles the most traditional Chinese Bert + preprocessing, a user can define the vocab file through initialization + + Examples: + from paddle_serving_app import ChineseBertReader + + line = ["this is China"] + reader = ChineseBertReader() + reader.process(line[0]) + + """ + + def __init__(self, args={}): + super(ChineseBertReader, self).__init__() + vocab_file = "" + if "vocab_file" in args: + vocab_file = args["vocab_file"] + else: + vocab_file = self._download_or_not() + + self.tokenizer = FullTokenizer(vocab_file=vocab_file) + if "max_seq_len" in args: + self.max_seq_len = args["max_seq_len"] + else: + self.max_seq_len = 20 + self.vocab = self.tokenizer.vocab + self.pad_id = self.vocab["[PAD]"] + self.cls_id = self.vocab["[CLS]"] + self.sep_id = self.vocab["[SEP]"] + self.mask_id = self.vocab["[MASK]"] + self.feed_keys = [ + "input_ids", "position_ids", "segment_ids", "input_mask" + ] + + """ + inner function + """ + + def _download_or_not(self): + import os + import paddle_serving_app + module_path = os.path.dirname(paddle_serving_app.__file__) + full_path = "{}/tmp/chinese_bert".format(module_path) + os.system("mkdir -p {}".format(full_path)) + if os.path.exists("{}/vocab.txt".format(full_path)): + pass + else: + url = "https://paddle-serving.bj.bcebos.com/reader/chinese_bert/vocab.txt" + r = os.system("wget --no-check-certificate " + url) + os.system("mv vocab.txt {}".format(full_path)) + if r != 0: + raise SystemExit('Download failed, please check your network') + return "{}/vocab.txt".format(full_path) + + """ + inner function + """ + + def _pad_batch(self, token_ids, text_type_ids, position_ids): + batch_token_ids = [token_ids] + batch_text_type_ids = [text_type_ids] + batch_position_ids = [position_ids] + + padded_token_ids, input_mask = pad_batch_data( + batch_token_ids, + max_seq_len=self.max_seq_len, + pad_idx=self.pad_id, + return_input_mask=True) + padded_text_type_ids = pad_batch_data( + batch_text_type_ids, + max_seq_len=self.max_seq_len, + pad_idx=self.pad_id) + padded_position_ids = pad_batch_data( + batch_position_ids, + max_seq_len=self.max_seq_len, + pad_idx=self.pad_id) + return padded_token_ids, padded_position_ids, padded_text_type_ids, input_mask + + """ + process function deals with a raw Chinese string as a sentence + this funtion returns a feed_dict + default key of the returned feed_dict: input_ids, position_ids, segment_ids, input_mask + """ + + def process(self, line): + text_a = convert_to_unicode(line) + tokens_a = self.tokenizer.tokenize(text_a) + if len(tokens_a) > self.max_seq_len - 2: + tokens_a = tokens_a[0:(self.max_seq_len - 2)] + tokens = [] + text_type_ids = [] + tokens.append("[CLS]") + text_type_ids.append(0) + for token in tokens_a: + tokens.append(token) + text_type_ids.append(0) + token_ids = self.tokenizer.convert_tokens_to_ids(tokens) + position_ids = list(range(len(token_ids))) + p_token_ids, p_pos_ids, p_text_type_ids, input_mask = \ + self._pad_batch(token_ids, text_type_ids, position_ids) + feed_result = { + self.feed_keys[0]: p_token_ids.reshape(-1).tolist(), + self.feed_keys[1]: p_pos_ids.reshape(-1).tolist(), + self.feed_keys[2]: p_text_type_ids.reshape(-1).tolist(), + self.feed_keys[3]: input_mask.reshape(-1).tolist() + } + return feed_result diff --git a/python/paddle_serving_app/reader/reader.py b/python/paddle_serving_app/reader/reader.py new file mode 100644 index 00000000..0a0fa97b --- /dev/null +++ b/python/paddle_serving_app/reader/reader.py @@ -0,0 +1,24 @@ +# Copyright (c) 2020 PaddlePaddle 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. + + +class ReaderBase(object): + def __init__(self): + self.feed_keys = [] + + def set_feed_keys(self, keys): + self.feed_keys = keys + + def get_feed_keys(self): + return self.feed_keys diff --git a/python/paddle_serving_app/reader/tokenization.py b/python/paddle_serving_app/reader/tokenization.py new file mode 100644 index 00000000..0d84ed38 --- /dev/null +++ b/python/paddle_serving_app/reader/tokenization.py @@ -0,0 +1,441 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# 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. +"""Tokenization classes.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import io +import unicodedata +import six +import sentencepiece as spm +import pickle + + +def convert_to_unicode(text): # pylint: disable=doc-string-with-all-args + """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" + if six.PY3: + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + elif six.PY2: + if isinstance(text, str): + return text.decode("utf-8", "ignore") + elif isinstance(text, unicode): # noqa + return text + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + else: + raise ValueError("Not running on Python2 or Python 3?") + + +def printable_text(text): # pylint: disable=doc-string-with-all-args + """Returns text encoded in a way suitable for print or `tf.logging`.""" + + # These functions want `str` for both Python2 and Python3, but in one case + # it's a Unicode string and in the other it's a byte string. + if six.PY3: + if isinstance(text, str): + return text + elif isinstance(text, bytes): + return text.decode("utf-8", "ignore") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + elif six.PY2: + if isinstance(text, str): + return text + elif isinstance(text, unicode): # noqa + return text.encode("utf-8") + else: + raise ValueError("Unsupported string type: %s" % (type(text))) + else: + raise ValueError("Not running on Python2 or Python 3?") + + +def load_vocab(vocab_file): # pylint: disable=doc-string-with-all-args, doc-string-with-returns + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + fin = io.open(vocab_file, "r", encoding="UTF-8") + for num, line in enumerate(fin): + items = convert_to_unicode(line.strip()).split("\t") + if len(items) > 2: + break + token = items[0] + index = items[1] if len(items) == 2 else num + token = token.strip() + vocab[token] = int(index) + fin.close() + return vocab + + +def convert_by_vocab(vocab, items): + """Converts a sequence of [tokens|ids] using the vocab.""" + output = [] + for item in items: + output.append(vocab[item]) + return output + + +def convert_tokens_to_ids(vocab, tokens): + return convert_by_vocab(vocab, tokens) + + +def convert_ids_to_tokens(inv_vocab, ids): + return convert_by_vocab(inv_vocab, ids) + + +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a peice of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +class FullTokenizer(object): + """Runs end-to-end tokenziation.""" + + def __init__(self, + vocab_file, + do_lower_case=True, + use_sentence_piece_vocab=False): + self.vocab = load_vocab(vocab_file) + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) + self.use_sentence_piece_vocab = use_sentence_piece_vocab + self.wordpiece_tokenizer = WordpieceTokenizer( + vocab=self.vocab, + use_sentence_piece_vocab=self.use_sentence_piece_vocab) + + def tokenize(self, text): + split_tokens = [] + for token in self.basic_tokenizer.tokenize(text): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + + return split_tokens + + def convert_tokens_to_ids(self, tokens): + return convert_by_vocab(self.vocab, tokens) + + def convert_ids_to_tokens(self, ids): + return convert_by_vocab(self.inv_vocab, ids) + + +class CharTokenizer(object): + """Runs end-to-end tokenziation.""" + + def __init__(self, vocab_file, do_lower_case=True): + self.vocab = load_vocab(vocab_file) + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) + + def tokenize(self, text): + split_tokens = [] + for token in text.lower().split(" "): + for sub_token in self.wordpiece_tokenizer.tokenize(token): + split_tokens.append(sub_token) + + return split_tokens + + def convert_tokens_to_ids(self, tokens): + return convert_by_vocab(self.vocab, tokens) + + def convert_ids_to_tokens(self, ids): + return convert_by_vocab(self.inv_vocab, ids) + + +class WSSPTokenizer(object): # pylint: disable=doc-string-missing + def __init__(self, vocab_file, sp_model_dir, word_dict, ws=True, + lower=True): + self.vocab = load_vocab(vocab_file) + self.inv_vocab = {v: k for k, v in self.vocab.items()} + self.ws = ws + self.lower = lower + self.dict = pickle.load(open(word_dict, 'rb')) + self.sp_model = spm.SentencePieceProcessor() + self.window_size = 5 + self.sp_model.Load(sp_model_dir) + + def cut(self, chars): # pylint: disable=doc-string-missing + words = [] + idx = 0 + while idx < len(chars): + matched = False + for i in range(self.window_size, 0, -1): + cand = chars[idx:idx + i] + if cand in self.dict: + words.append(cand) + matched = True + break + if not matched: + i = 1 + words.append(chars[idx]) + idx += i + return words + + def tokenize(self, text, unk_token="[UNK]"): # pylint: disable=doc-string-missing + text = convert_to_unicode(text) + if self.ws: + text = [s for s in self.cut(text) if s != ' '] + else: + text = text.split(' ') + if self.lower: + text = [s.lower() for s in text] + text = ' '.join(text) + tokens = self.sp_model.EncodeAsPieces(text) + in_vocab_tokens = [] + for token in tokens: + if token in self.vocab: + in_vocab_tokens.append(token) + else: + in_vocab_tokens.append(unk_token) + return in_vocab_tokens + + def convert_tokens_to_ids(self, tokens): + return convert_by_vocab(self.vocab, tokens) + + def convert_ids_to_tokens(self, ids): + return convert_by_vocab(self.inv_vocab, ids) + + +class BasicTokenizer(object): + """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" + + def __init__(self, do_lower_case=True): + """Constructs a BasicTokenizer. + + Args: + do_lower_case: Whether to lower case the input. + """ + self.do_lower_case = do_lower_case + + def tokenize(self, text): # pylint: disable=doc-string-with-all-args, doc-string-with-returns + """Tokenizes a piece of text.""" + text = convert_to_unicode(text) + text = self._clean_text(text) + + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + text = self._tokenize_chinese_chars(text) + + orig_tokens = whitespace_tokenize(text) + split_tokens = [] + for token in orig_tokens: + if self.do_lower_case: + token = token.lower() + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text): + """Splits punctuation on a piece of text.""" + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ((cp >= 0x4E00 and cp <= 0x9FFF) or # + (cp >= 0x3400 and cp <= 0x4DBF) or # + (cp >= 0x20000 and cp <= 0x2A6DF) or # + (cp >= 0x2A700 and cp <= 0x2B73F) or # + (cp >= 0x2B740 and cp <= 0x2B81F) or # + (cp >= 0x2B820 and cp <= 0x2CEAF) or + (cp >= 0xF900 and cp <= 0xFAFF) or # + (cp >= 0x2F800 and cp <= 0x2FA1F)): # + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xfffd or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +class WordpieceTokenizer(object): + """Runs WordPiece tokenziation.""" + + def __init__(self, + vocab, + unk_token="[UNK]", + max_input_chars_per_word=100, + use_sentence_piece_vocab=False): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + self.use_sentence_piece_vocab = use_sentence_piece_vocab + + def tokenize(self, text): # pylint: disable=doc-string-with-all-args + """Tokenizes a piece of text into its word pieces. + + This uses a greedy longest-match-first algorithm to perform tokenization + using the given vocabulary. + + For example: + input = "unaffable" + output = ["un", "##aff", "##able"] + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through `BasicTokenizer. + + Returns: + A list of wordpiece tokens. + """ + + text = convert_to_unicode(text) + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start == 0 and self.use_sentence_piece_vocab: + substr = u'\u2581' + substr + if start > 0 and not self.use_sentence_piece_vocab: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens + + +def _is_whitespace(char): + """Checks whether `chars` is a whitespace character.""" + # \t, \n, and \r are technically contorl characters but we treat them + # as whitespace since they are generally considered as such. + if char == " " or char == "\t" or char == "\n" or char == "\r": + return True + cat = unicodedata.category(char) + if cat == "Zs": + return True + return False + + +def _is_control(char): + """Checks whether `chars` is a control character.""" + # These are technically control characters but we count them as whitespace + # characters. + if char == "\t" or char == "\n" or char == "\r": + return False + cat = unicodedata.category(char) + if cat.startswith("C"): + return True + return False + + +def _is_punctuation(char): + """Checks whether `chars` is a punctuation character.""" + cp = ord(char) + # We treat all non-letter/number ASCII as punctuation. + # Characters such as "^", "$", and "`" are not in the Unicode + # Punctuation class but we treat them as punctuation anyways, for + # consistency. + if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or + (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): + return True + cat = unicodedata.category(char) + if cat.startswith("P"): + return True + return False diff --git a/python/paddle_serving_app/version.py b/python/paddle_serving_app/version.py new file mode 100644 index 00000000..80f647be --- /dev/null +++ b/python/paddle_serving_app/version.py @@ -0,0 +1,15 @@ +# Copyright (c) 2020 PaddlePaddle 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. +""" Paddle Serving App version string """ +serving_app_version = "0.0.1" -- GitLab