fastNLP/reproduction/chinese_word_segment/train_context.py

from fastNLP.api.pipeline import Pipeline
from fastNLP.api.processor import FullSpaceToHalfSpaceProcessor
from fastNLP.api.processor import SeqLenProcessor
from reproduction.chinese_word_segment.process.cws_processor import CWSCharSegProcessor
from reproduction.chinese_word_segment.process.cws_processor import CWSBMESTagProcessor
from reproduction.chinese_word_segment.process.cws_processor import Pre2Post2BigramProcessor
from reproduction.chinese_word_segment.process.cws_processor import VocabIndexerProcessor
from reproduction.chinese_word_segment.process.cws_processor import InputTargetProcessor

from reproduction.chinese_word_segment.cws_io.cws_reader import ConllCWSReader
from reproduction.chinese_word_segment.models.cws_model import CWSBiLSTMCRF

from reproduction.chinese_word_segment.utils import calculate_pre_rec_f1

ds_name = 'msr'

tr_filename = '/home/hyan/ctb3/train.conllx'
dev_filename = '/home/hyan/ctb3/dev.conllx'


reader = ConllCWSReader()

tr_dataset = reader.load(tr_filename, cut_long_sent=True)
dev_dataset = reader.load(dev_filename)

print("Train {}. Dev: {}".format(len(tr_dataset), len(dev_dataset)))

# 1. 准备processor
fs2hs_proc = FullSpaceToHalfSpaceProcessor('raw_sentence')

char_proc = CWSCharSegProcessor('raw_sentence', 'chars_lst')
tag_proc = CWSBMESTagProcessor('raw_sentence', 'target')

bigram_proc = Pre2Post2BigramProcessor('chars_lst', 'bigrams_lst')

char_vocab_proc = VocabIndexerProcessor('chars_lst', new_added_filed_name='chars')
bigram_vocab_proc = VocabIndexerProcessor('bigrams_lst', new_added_filed_name='bigrams', min_freq=4)

seq_len_proc = SeqLenProcessor('chars')

input_target_proc = InputTargetProcessor(input_fields=['chars', 'bigrams', 'seq_lens', "target"],
                                         target_fields=['target', 'seq_lens'])
# 2. 使用processor
fs2hs_proc(tr_dataset)

char_proc(tr_dataset)
tag_proc(tr_dataset)
bigram_proc(tr_dataset)

char_vocab_proc(tr_dataset)
bigram_vocab_proc(tr_dataset)
seq_len_proc(tr_dataset)

# 2.1 处理dev_dataset
fs2hs_proc(dev_dataset)

char_proc(dev_dataset)
tag_proc(dev_dataset)
bigram_proc(dev_dataset)

char_vocab_proc(dev_dataset)
bigram_vocab_proc(dev_dataset)
seq_len_proc(dev_dataset)

input_target_proc(tr_dataset)
input_target_proc(dev_dataset)

print("Finish preparing data.")

# 3. 得到数据集可以用于训练了
# TODO pretrain的embedding是怎么解决的？

import torch
from torch import optim


tag_size = tag_proc.tag_size

cws_model = CWSBiLSTMCRF(char_vocab_proc.get_vocab_size(), embed_dim=100,
                            bigram_vocab_num=bigram_vocab_proc.get_vocab_size(),
                            bigram_embed_dim=100, num_bigram_per_char=8,
                            hidden_size=200, bidirectional=True, embed_drop_p=0.2,
                            num_layers=1, tag_size=tag_size)
cws_model.cuda()

num_epochs = 5
optimizer = optim.Adagrad(cws_model.parameters(), lr=0.005)

from fastNLP.core.trainer import Trainer
from fastNLP.core.sampler import BucketSampler
from fastNLP.core.metrics import BMESF1PreRecMetric

metric = BMESF1PreRecMetric(target='tags')
trainer = Trainer(train_data=tr_dataset, model=cws_model, loss=None, metrics=metric, n_epochs=num_epochs,
                  batch_size=32, print_every=50, validate_every=-1, dev_data=dev_dataset, save_path=None,
                 optimizer=optimizer, check_code_level=0, metric_key='f', sampler=BucketSampler(), use_tqdm=True)

trainer.train()

# 4. 组装需要存下的内容
pp = Pipeline()
pp.add_processor(fs2hs_proc)
# pp.add_processor(sp_proc)
pp.add_processor(char_proc)
pp.add_processor(tag_proc)
pp.add_processor(bigram_proc)
pp.add_processor(char_vocab_proc)
pp.add_processor(bigram_vocab_proc)
pp.add_processor(seq_len_proc)
pp.add_processor(input_target_proc)

# te_filename = '/hdd/fudanNLP/CWS/CWS_semiCRF/all_data/{}/middle_files/{}_test.txt'.format(ds_name, ds_name)
te_filename = '/home/hyan/ctb3/test.conllx'
te_dataset = reader.load(te_filename)
pp(te_dataset)

from fastNLP.core.tester import Tester

tester = Tester(data=te_dataset, model=cws_model, metrics=metric, batch_size=64, use_cuda=False,
                verbose=1)
tester.test()
#
# batch_size = 64
# te_batcher = Batch(te_dataset, batch_size, SequentialSampler(), use_cuda=False)
# pre, rec, f1 = calculate_pre_rec_f1(cws_model, te_batcher, type='bmes')
# print("f1:{:.2f}, pre:{:.2f}, rec:{:.2f}".format(f1 * 100,
#                                                  pre * 100,
#                                                  rec * 100))

# TODO 这里貌似需要区分test pipeline与infer pipeline

test_context_dict = {'pipeline': pp,
                     'model': cws_model}
# torch.save(test_context_dict, 'models/test_context_crf.pkl')


# 5. dev的pp
# 4. 组装需要存下的内容

from fastNLP.api.processor import ModelProcessor
from reproduction.chinese_word_segment.process.cws_processor import BMES2OutputProcessor

model_proc = ModelProcessor(cws_model)
output_proc = BMES2OutputProcessor()

pp = Pipeline()
pp.add_processor(fs2hs_proc)
# pp.add_processor(sp_proc)
pp.add_processor(char_proc)
pp.add_processor(bigram_proc)
pp.add_processor(char_vocab_proc)
pp.add_processor(bigram_vocab_proc)
pp.add_processor(seq_len_proc)

pp.add_processor(model_proc)
pp.add_processor(output_proc)


# TODO 这里貌似需要区分test pipeline与infer pipeline

infer_context_dict = {'pipeline': pp}
# torch.save(infer_context_dict, 'models/cws_crf.pkl')


# TODO 还需要考虑如何替换回原文的问题？
# 1. 不需要将特殊tag替换
# 2. 需要将特殊tag替换回去