def test_case2(self):
# 测试CRF是否正常work。
import json
import torch
from fastNLP import seq_len_to_mask
with open('tests/data_for_tests/modules/decoder/crf.json', 'r') as f:
data = json.load(f)
bio_logits = torch.FloatTensor(data['bio_logits'])
bio_scores = data['bio_scores']
bio_path = data['bio_path']
bio_trans_m = torch.FloatTensor(data['bio_trans_m'])
bio_seq_lens = torch.LongTensor(data['bio_seq_lens'])
bmes_logits = torch.FloatTensor(data['bmes_logits'])
bmes_scores = data['bmes_scores']
bmes_path = data['bmes_path']
bmes_trans_m = torch.FloatTensor(data['bmes_trans_m'])
bmes_seq_lens = torch.LongTensor(data['bmes_seq_lens'])
labels = ['O']
for label in ['X', 'Y']:
for tag in 'BI':
labels.append('{}-{}'.format(tag, label))
id2label = {idx: label for idx, label in enumerate(labels)}
num_tags = len(id2label)
mask = seq_len_to_mask(bio_seq_lens)
from fastNLP.modules.decoder.crf import ConditionalRandomField, allowed_transitions
fast_CRF = ConditionalRandomField(num_tags=num_tags, allowed_transitions=allowed_transitions(id2label,
include_start_end=True))
fast_CRF.trans_m.data = bio_trans_m
fast_res = fast_CRF.viterbi_decode(bio_logits, mask, unpad=True)
# score equal
self.assertListEqual(bio_scores, [round(s, 4) for s in fast_res[1].tolist()])
# seq equal
self.assertListEqual(bio_path, fast_res[0])
labels = []
for label in ['X', 'Y']:
for tag in 'BMES':
labels.append('{}-{}'.format(tag, label))
id2label = {idx: label for idx, label in enumerate(labels)}
num_tags = len(id2label)
mask = seq_len_to_mask(bmes_seq_lens)
from fastNLP.modules.decoder.crf import ConditionalRandomField, allowed_transitions
fast_CRF = ConditionalRandomField(num_tags=num_tags, allowed_transitions=allowed_transitions(id2label,
encoding_type='BMES',
include_start_end=True))
fast_CRF.trans_m.data = bmes_trans_m
fast_res = fast_CRF.viterbi_decode(bmes_logits, mask, unpad=True)
# score equal
self.assertListEqual(bmes_scores, [round(s, 4) for s in fast_res[1].tolist()])
# seq equal
self.assertListEqual(bmes_path, fast_res[0])
def __init__(self, vocab_num, embed_dim=100, bigram_vocab_num=None, bigram_embed_dim=100, num_bigram_per_char=None,
hidden_size=200, bidirectional=True, embed_drop_p=0.2, num_layers=1, tag_size=4):
"""
默认使用BMES的标注方式
:param vocab_num:
:param embed_dim:
:param bigram_vocab_num:
:param bigram_embed_dim:
:param num_bigram_per_char:
:param hidden_size:
:param bidirectional:
:param embed_drop_p:
:param num_layers:
:param tag_size:
"""
super(CWSBiLSTMCRF, self).__init__()
self.tag_size = tag_size
self.encoder_model = CWSBiLSTMEncoder(vocab_num, embed_dim, bigram_vocab_num, bigram_embed_dim, num_bigram_per_char,
hidden_size, bidirectional, embed_drop_p, num_layers)
size_layer = [hidden_size, 200, tag_size]
self.decoder_model = MLP(size_layer)
allowed_trans = allowed_transitions({0:'b', 1:'m', 2:'e', 3:'s'}, encoding_type='bmes')
self.crf = ConditionalRandomField(num_tags=tag_size, include_start_end_trans=False,
allowed_transitions=allowed_trans)
def test_case1(self):
# 检查allowed_transitions()能否正确使用
from fastNLP.modules.decoder.crf import allowed_transitions
id2label = {0: 'B', 1: 'I', 2:'O'}
expected_res = {(0, 0), (0, 1), (0, 2), (0, 4), (1, 0), (1, 1), (1, 2), (1, 4), (2, 0), (2, 2),
(2, 4), (3, 0), (3, 2)}
self.assertSetEqual(expected_res, set(allowed_transitions(id2label)))
id2label = {0: 'B', 1:'M', 2:'E', 3:'S'}
expected_res = {(0, 1), (0, 2), (1, 1), (1, 2), (2, 0), (2, 3), (2, 5), (3, 0), (3, 3), (3, 5), (4, 0), (4, 3)}
self.assertSetEqual(expected_res, set(allowed_transitions(id2label, encoding_type='BMES')))
id2label = {0: 'B', 1: 'I', 2:'O', 3: '<pad>', 4:"<unk>"}
allowed_transitions(id2label)
labels = ['O']
for label in ['X', 'Y']:
for tag in 'BI':
labels.append('{}-{}'.format(tag, label))
id2label = {idx:label for idx, label in enumerate(labels)}
expected_res = {(0, 0), (0, 1), (0, 3), (0, 6), (1, 0), (1, 1), (1, 2), (1, 3), (1, 6), (2, 0), (2, 1),
(2, 2), (2, 3), (2, 6), (3, 0), (3, 1), (3, 3), (3, 4), (3, 6), (4, 0), (4, 1), (4, 3),
(4, 4), (4, 6), (5, 0), (5, 1), (5, 3)}
self.assertSetEqual(expected_res, set(allowed_transitions(id2label)))
labels = []
for label in ['X', 'Y']:
for tag in 'BMES':
labels.append('{}-{}'.format(tag, label))
id2label = {idx: label for idx, label in enumerate(labels)}
expected_res = {(0, 1), (0, 2), (1, 1), (1, 2), (2, 0), (2, 3), (2, 4), (2, 7), (2, 9), (3, 0), (3, 3), (3, 4),
(3, 7), (3, 9), (4, 5), (4, 6), (5, 5), (5, 6), (6, 0), (6, 3), (6, 4), (6, 7), (6, 9), (7, 0),
(7, 3), (7, 4), (7, 7), (7, 9), (8, 0), (8, 3), (8, 4), (8, 7)}
self.assertSetEqual(expected_res, set(allowed_transitions(id2label, encoding_type='BMES')))
def __init__(self,
vocab_num,
embed_dim=100,
bigram_vocab_num=None,
bigram_embed_dim=100,
num_bigram_per_char=None,
embed_drop_p=0.3,
hidden_size=200,
kernel_size=3,
dilate='none',
num_layers=1,
num_heads=8,
tag_size=4,
relative_pos_embed_dim=0):
super().__init__()
self.embedding = nn.Embedding(vocab_num, embed_dim)
input_size = embed_dim
if bigram_vocab_num:
self.bigram_embedding = nn.Embedding(bigram_vocab_num,
bigram_embed_dim)
input_size += num_bigram_per_char * bigram_embed_dim
self.drop = nn.Dropout(embed_drop_p, inplace=True)
self.fc1 = nn.Linear(input_size, hidden_size)
# value_size = hidden_size//num_heads
# self.transformer = TransformerEncoder(num_layers, model_size=hidden_size, inner_size=hidden_size,
# key_size=value_size,
# value_size=value_size, num_head=num_heads)
self.transformer = TransformerDilateEncoder(
num_layers=num_layers,
model_size=hidden_size,
num_heads=num_heads,
hidden_size=hidden_size,
kernel_size=kernel_size,
dilate=dilate,
relative_pos_embed_dim=relative_pos_embed_dim)
self.fc2 = nn.Linear(hidden_size, tag_size)
allowed_trans = allowed_transitions({
0: 'b',
1: 'm',
2: 'e',
3: 's'
},
encoding_type='bmes')
self.crf = ConditionalRandomField(num_tags=tag_size,
include_start_end_trans=False,
allowed_transitions=allowed_trans)
def test_case12(self):
# 测试能否通过vocab生成转移矩阵
from fastNLP.modules.decoder.crf import allowed_transitions
id2label = {0: 'B', 1: 'I', 2: 'O'}
vocab = Vocabulary(unknown=None, padding=None)
for idx, tag in id2label.items():
vocab.add_word(tag)
expected_res = {(0, 0), (0, 1), (0, 2), (0, 4), (1, 0), (1, 1), (1, 2), (1, 4), (2, 0), (2, 2),
(2, 4), (3, 0), (3, 2)}
self.assertSetEqual(expected_res, set(allowed_transitions(vocab, include_start_end=True)))
id2label = {0: 'B', 1: 'M', 2: 'E', 3: 'S'}
vocab = Vocabulary(unknown=None, padding=None)
for idx, tag in id2label.items():
vocab.add_word(tag)
expected_res = {(0, 1), (0, 2), (1, 1), (1, 2), (2, 0), (2, 3), (2, 5), (3, 0), (3, 3), (3, 5), (4, 0), (4, 3)}
self.assertSetEqual(expected_res, set(
allowed_transitions(vocab, include_start_end=True)))
id2label = {0: 'B', 1: 'I', 2: 'O', 3: '<pad>', 4: "<unk>"}
vocab = Vocabulary()
for idx, tag in id2label.items():
vocab.add_word(tag)
allowed_transitions(vocab, include_start_end=True)
labels = ['O']
for label in ['X', 'Y']:
for tag in 'BI':
labels.append('{}-{}'.format(tag, label))
id2label = {idx: label for idx, label in enumerate(labels)}
expected_res = {(0, 0), (0, 1), (0, 3), (0, 6), (1, 0), (1, 1), (1, 2), (1, 3), (1, 6), (2, 0), (2, 1),
(2, 2), (2, 3), (2, 6), (3, 0), (3, 1), (3, 3), (3, 4), (3, 6), (4, 0), (4, 1), (4, 3),
(4, 4), (4, 6), (5, 0), (5, 1), (5, 3)}
vocab = Vocabulary(unknown=None, padding=None)
for idx, tag in id2label.items():
vocab.add_word(tag)
self.assertSetEqual(expected_res, set(allowed_transitions(vocab, include_start_end=True)))
labels = []
for label in ['X', 'Y']:
for tag in 'BMES':
labels.append('{}-{}'.format(tag, label))
id2label = {idx: label for idx, label in enumerate(labels)}
vocab = Vocabulary(unknown=None, padding=None)
for idx, tag in id2label.items():
vocab.add_word(tag)
expected_res = {(0, 1), (0, 2), (1, 1), (1, 2), (2, 0), (2, 3), (2, 4), (2, 7), (2, 9), (3, 0), (3, 3), (3, 4),
(3, 7), (3, 9), (4, 5), (4, 6), (5, 5), (5, 6), (6, 0), (6, 3), (6, 4), (6, 7), (6, 9), (7, 0),
(7, 3), (7, 4), (7, 7), (7, 9), (8, 0), (8, 3), (8, 4), (8, 7)}
self.assertSetEqual(expected_res, set(
allowed_transitions(vocab, include_start_end=True)))