import re
from collections import defaultdict
import torch
from fastNLP.core.batch import Batch
from fastNLP.core.dataset import DataSet
from fastNLP.core.sampler import SequentialSampler
from fastNLP.core.vocabulary import Vocabulary
class Processor(object):
def __init__(self, field_name, new_added_field_name):
"""
:param field_name: 处理哪个field
:param new_added_field_name: 如果为None，则认为是field_name，即覆盖原有的field
"""
self.field_name = field_name
if new_added_field_name is None:
self.new_added_field_name = field_name
else:
self.new_added_field_name = new_added_field_name
def process(self, *args, **kwargs):
raise NotImplementedError
def __call__(self, *args, **kwargs):
return self.process(*args, **kwargs)
[docs]class FullSpaceToHalfSpaceProcessor(Processor):
"""全角转半角，以字符为处理单元
"""
def __init__(self, field_name, change_alpha=True, change_digit=True, change_punctuation=True,
change_space=True):
super(FullSpaceToHalfSpaceProcessor, self).__init__(field_name, None)
self.change_alpha = change_alpha
self.change_digit = change_digit
self.change_punctuation = change_punctuation
self.change_space = change_space
FH_SPACE = [(u" ", u" ")]
FH_NUM = [
(u"０", u"0"), (u"１", u"1"), (u"２", u"2"), (u"３", u"3"), (u"４", u"4"),
(u"５", u"5"), (u"６", u"6"), (u"７", u"7"), (u"８", u"8"), (u"９", u"9")]
FH_ALPHA = [
(u"ａ", u"a"), (u"ｂ", u"b"), (u"ｃ", u"c"), (u"ｄ", u"d"), (u"ｅ", u"e"),
(u"ｆ", u"f"), (u"ｇ", u"g"), (u"ｈ", u"h"), (u"ｉ", u"i"), (u"ｊ", u"j"),
(u"ｋ", u"k"), (u"ｌ", u"l"), (u"ｍ", u"m"), (u"ｎ", u"n"), (u"ｏ", u"o"),
(u"ｐ", u"p"), (u"ｑ", u"q"), (u"ｒ", u"r"), (u"ｓ", u"s"), (u"ｔ", u"t"),
(u"ｕ", u"u"), (u"ｖ", u"v"), (u"ｗ", u"w"), (u"ｘ", u"x"), (u"ｙ", u"y"),
(u"ｚ", u"z"),
(u"Ａ", u"A"), (u"Ｂ", u"B"), (u"Ｃ", u"C"), (u"Ｄ", u"D"), (u"Ｅ", u"E"),
(u"Ｆ", u"F"), (u"Ｇ", u"G"), (u"Ｈ", u"H"), (u"Ｉ", u"I"), (u"Ｊ", u"J"),
(u"Ｋ", u"K"), (u"Ｌ", u"L"), (u"Ｍ", u"M"), (u"Ｎ", u"N"), (u"Ｏ", u"O"),
(u"Ｐ", u"P"), (u"Ｑ", u"Q"), (u"Ｒ", u"R"), (u"Ｓ", u"S"), (u"Ｔ", u"T"),
(u"Ｕ", u"U"), (u"Ｖ", u"V"), (u"Ｗ", u"W"), (u"Ｘ", u"X"), (u"Ｙ", u"Y"),
(u"Ｚ", u"Z")]
# 谨慎使用标点符号转换, 因为"5．12特大地震"转换后可能就成了"5.12特大地震"
FH_PUNCTUATION = [
(u'％', u'%'), (u'！', u'!'), (u'＂', u'\"'), (u'＇', u'\''), (u'＃', u'#'),
(u'￥', u'$'), (u'＆', u'&'), (u'（', u'('), (u'）', u')'), (u'＊', u'*'),
(u'＋', u'+'), (u'，', u','), (u'－', u'-'), (u'．', u'.'), (u'／', u'/'),
(u'：', u':'), (u'；', u';'), (u'＜', u'<'), (u'＝', u'='), (u'＞', u'>'),
(u'？', u'?'), (u'＠', u'@'), (u'［', u'['), (u'］', u']'), (u'＼', u'\\'),
(u'＾', u'^'), (u'＿', u'_'), (u'｀', u'`'), (u'～', u'~'), (u'｛', u'{'),
(u'｝', u'}'), (u'｜', u'|')]
FHs = []
if self.change_alpha:
FHs = FH_ALPHA
if self.change_digit:
FHs += FH_NUM
if self.change_punctuation:
FHs += FH_PUNCTUATION
if self.change_space:
FHs += FH_SPACE
self.convert_map = {k: v for k, v in FHs}
def process(self, dataset):
assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
def inner_proc(ins):
sentence = ins[self.field_name]
new_sentence = [""] * len(sentence)
for idx, char in enumerate(sentence):
if char in self.convert_map:
char = self.convert_map[char]
new_sentence[idx] = char
return "".join(new_sentence)
dataset.apply(inner_proc, new_field_name=self.field_name)
return dataset
[docs]class PreAppendProcessor(Processor):
"""
向某个field的起始增加data(应该为str类型)。该field需要为list类型。即新增的field为
[data] + instance[field_name]
"""
def __init__(self, data, field_name, new_added_field_name=None):
super(PreAppendProcessor, self).__init__(field_name, new_added_field_name)
self.data = data
def process(self, dataset):
dataset.apply(lambda ins: [self.data] + ins[self.field_name], new_field_name=self.new_added_field_name)
return dataset
[docs]class SliceProcessor(Processor):
"""
从某个field中只取部分内容。等价于instance[field_name][start:end:step]
"""
def __init__(self, start, end, step, field_name, new_added_field_name=None):
super(SliceProcessor, self).__init__(field_name, new_added_field_name)
for o in (start, end, step):
assert isinstance(o, int) or o is None
self.slice = slice(start, end, step)
def process(self, dataset):
dataset.apply(lambda ins: ins[self.field_name][self.slice], new_field_name=self.new_added_field_name)
return dataset
[docs]class Num2TagProcessor(Processor):
"""
将一句话中的数字转换为某个tag。
"""
def __init__(self, tag, field_name, new_added_field_name=None):
"""
:param tag: str, 将数字转换为该tag
:param field_name:
:param new_added_field_name:
"""
super(Num2TagProcessor, self).__init__(field_name, new_added_field_name)
self.tag = tag
self.pattern = r'[-+]?([0-9]+[.]?[0-9]*)+[/eE]?[-+]?([0-9]+[.]?[0-9]*)'
def process(self, dataset):
def inner_proc(ins):
s = ins[self.field_name]
new_s = [None] * len(s)
for i, w in enumerate(s):
if re.search(self.pattern, w) is not None:
w = self.tag
new_s[i] = w
return new_s
dataset.apply(inner_proc, new_field_name=self.new_added_field_name)
return dataset
[docs]class IndexerProcessor(Processor):
"""
给定一个vocabulary , 将指定field转换为index形式。指定field应该是一维的list，比如
['我', '是', xxx]
"""
def __init__(self, vocab, field_name, new_added_field_name, delete_old_field=False, is_input=True):
assert isinstance(vocab, Vocabulary), "Only Vocabulary class is allowed, not {}.".format(type(vocab))
super(IndexerProcessor, self).__init__(field_name, new_added_field_name)
self.vocab = vocab
self.delete_old_field = delete_old_field
self.is_input = is_input
def set_vocab(self, vocab):
assert isinstance(vocab, Vocabulary), "Only Vocabulary class is allowed, not {}.".format(type(vocab))
self.vocab = vocab
def process(self, dataset):
assert isinstance(dataset, DataSet), "Only DataSet class is allowed, not {}.".format(type(dataset))
dataset.apply(lambda ins: [self.vocab.to_index(token) for token in ins[self.field_name]],
new_field_name=self.new_added_field_name)
if self.is_input:
dataset.set_input(self.new_added_field_name)
if self.delete_old_field:
dataset.delete_field(self.field_name)
return dataset
[docs]class VocabProcessor(Processor):
"""
传入若干个DataSet以建立vocabulary。
"""
def __init__(self, field_name, min_freq=1, max_size=None):
super(VocabProcessor, self).__init__(field_name, None)
self.vocab = Vocabulary(min_freq=min_freq, max_size=max_size)
def process(self, *datasets):
for dataset in datasets:
assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
dataset.apply(lambda ins: self.vocab.update(ins[self.field_name]))
def get_vocab(self):
self.vocab.build_vocab()
return self.vocab
[docs]class SeqLenProcessor(Processor):
"""
根据某个field新增一个sequence length的field。取该field的第一维
"""
def __init__(self, field_name, new_added_field_name='seq_lens', is_input=True):
super(SeqLenProcessor, self).__init__(field_name, new_added_field_name)
self.is_input = is_input
def process(self, dataset):
assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
dataset.apply(lambda ins: len(ins[self.field_name]), new_field_name=self.new_added_field_name)
if self.is_input:
dataset.set_input(self.new_added_field_name)
return dataset
from fastNLP.core.utils import _build_args
class ModelProcessor(Processor):
def __init__(self, model, seq_len_field_name='seq_lens', batch_size=32):
"""
传入一个model，在process()时传入一个dataset，该processor会通过Batch将DataSet的内容输出给model.predict或者model.forward.
model输出的内容会被增加到dataset中，field_name由model输出决定。如果生成的内容维度不是(Batch_size, )与
(Batch_size, 1)，则使用seqence length这个field进行unpad
TODO 这个类需要删除对seq_lens的依赖。
:param seq_len_field_name:
:param batch_size:
"""
super(ModelProcessor, self).__init__(None, None)
self.batch_size = batch_size
self.seq_len_field_name = seq_len_field_name
self.model = model
def process(self, dataset):
self.model.eval()
assert isinstance(dataset, DataSet), "Only Dataset class is allowed, not {}.".format(type(dataset))
data_iterator = Batch(dataset, batch_size=self.batch_size, sampler=SequentialSampler())
batch_output = defaultdict(list)
if hasattr(self.model, "predict"):
predict_func = self.model.predict
else:
predict_func = self.model.forward
with torch.no_grad():
for batch_x, _ in data_iterator:
refined_batch_x = _build_args(predict_func, **batch_x)
prediction = predict_func(**refined_batch_x)
seq_lens = batch_x[self.seq_len_field_name].tolist()
for key, value in prediction.items():
tmp_batch = []
value = value.cpu().numpy()
if len(value.shape) == 1 or (len(value.shape) == 2 and value.shape[1] == 1):
batch_output[key].extend(value.tolist())
else:
for idx, seq_len in enumerate(seq_lens):
tmp_batch.append(value[idx, :seq_len])
batch_output[key].extend(tmp_batch)
if not self.seq_len_field_name in prediction:
batch_output[self.seq_len_field_name].extend(seq_lens)
# TODO 当前的实现会导致之后的processor需要知道model输出的output的key是什么
for field_name, fields in batch_output.items():
dataset.add_field(field_name, fields, is_input=True, is_target=False)
return dataset
def set_model(self, model):
self.model = model
def set_model_device(self, device):
device = torch.device(device)
self.model.to(device)
[docs]class Index2WordProcessor(Processor):
"""
将DataSet中某个为index的field根据vocab转换为str
"""
def __init__(self, vocab, field_name, new_added_field_name):
super(Index2WordProcessor, self).__init__(field_name, new_added_field_name)
self.vocab = vocab
def process(self, dataset):
dataset.apply(lambda ins: [self.vocab.to_word(w) for w in ins[self.field_name]],
new_field_name=self.new_added_field_name)
return dataset
class SetTargetProcessor(Processor):
# TODO; remove it.
def __init__(self, *fields, flag=True):
super(SetTargetProcessor, self).__init__(None, None)
self.fields = fields
self.flag = flag
def process(self, dataset):
dataset.set_target(*self.fields, flag=self.flag)
return dataset
class SetInputProcessor(Processor):
def __init__(self, *fields, flag=True):
super(SetInputProcessor, self).__init__(None, None)
self.fields = fields
self.flag = flag
def process(self, dataset):
dataset.set_input(*self.fields, flag=self.flag)
return dataset