@@ -0,0 +1,68 @@ | |||||
from fastNLP.io.dataset_loader import JsonLoader,DataSet,Instance | |||||
from fastNLP.io.file_reader import _read_json | |||||
from fastNLP.core.vocabulary import Vocabulary | |||||
from fastNLP.io.base_loader import DataInfo | |||||
from reproduction.coreference_resolution.model.config import Config | |||||
import reproduction.coreference_resolution.model.preprocess as preprocess | |||||
class CRLoader(JsonLoader): | |||||
def __init__(self, fields=None, dropna=False): | |||||
super().__init__(fields, dropna) | |||||
def _load(self, path): | |||||
""" | |||||
加载数据 | |||||
:param path: | |||||
:return: | |||||
""" | |||||
dataset = DataSet() | |||||
for idx, d in _read_json(path, fields=self.fields_list, dropna=self.dropna): | |||||
if self.fields: | |||||
ins = {self.fields[k]: v for k, v in d.items()} | |||||
else: | |||||
ins = d | |||||
dataset.append(Instance(**ins)) | |||||
return dataset | |||||
def process(self, paths, **kwargs): | |||||
data_info = DataInfo() | |||||
for name in ['train', 'test', 'dev']: | |||||
data_info.datasets[name] = self.load(paths[name]) | |||||
config = Config() | |||||
vocab = Vocabulary().from_dataset(*data_info.datasets.values(), field_name='sentences') | |||||
vocab.build_vocab() | |||||
word2id = vocab.word2idx | |||||
char_dict = preprocess.get_char_dict(config.char_path) | |||||
data_info.vocabs = vocab | |||||
genres = {g: i for i, g in enumerate(["bc", "bn", "mz", "nw", "pt", "tc", "wb"])} | |||||
for name, ds in data_info.datasets.items(): | |||||
ds.apply(lambda x: preprocess.doc2numpy(x['sentences'], word2id, char_dict, max(config.filter), | |||||
config.max_sentences, is_train=name=='train')[0], | |||||
new_field_name='doc_np') | |||||
ds.apply(lambda x: preprocess.doc2numpy(x['sentences'], word2id, char_dict, max(config.filter), | |||||
config.max_sentences, is_train=name=='train')[1], | |||||
new_field_name='char_index') | |||||
ds.apply(lambda x: preprocess.doc2numpy(x['sentences'], word2id, char_dict, max(config.filter), | |||||
config.max_sentences, is_train=name=='train')[2], | |||||
new_field_name='seq_len') | |||||
ds.apply(lambda x: preprocess.speaker2numpy(x["speakers"], config.max_sentences, is_train=name=='train'), | |||||
new_field_name='speaker_ids_np') | |||||
ds.apply(lambda x: genres[x["doc_key"][:2]], new_field_name='genre') | |||||
ds.set_ignore_type('clusters') | |||||
ds.set_padder('clusters', None) | |||||
ds.set_input("sentences", "doc_np", "speaker_ids_np", "genre", "char_index", "seq_len") | |||||
ds.set_target("clusters") | |||||
# train_dev, test = self.ds.split(348 / (2802 + 343 + 348), shuffle=False) | |||||
# train, dev = train_dev.split(343 / (2802 + 343), shuffle=False) | |||||
return data_info | |||||
@@ -0,0 +1,54 @@ | |||||
class Config(): | |||||
def __init__(self): | |||||
self.is_training = True | |||||
# path | |||||
self.glove = 'data/glove.840B.300d.txt.filtered' | |||||
self.turian = 'data/turian.50d.txt' | |||||
self.train_path = "data/train.english.jsonlines" | |||||
self.dev_path = "data/dev.english.jsonlines" | |||||
self.test_path = "data/test.english.jsonlines" | |||||
self.char_path = "data/char_vocab.english.txt" | |||||
self.cuda = "0" | |||||
self.max_word = 1500 | |||||
self.epoch = 200 | |||||
# config | |||||
# self.use_glove = True | |||||
# self.use_turian = True #No | |||||
self.use_elmo = False | |||||
self.use_CNN = True | |||||
self.model_heads = True #Yes | |||||
self.use_width = True # Yes | |||||
self.use_distance = True #Yes | |||||
self.use_metadata = True #Yes | |||||
self.mention_ratio = 0.4 | |||||
self.max_sentences = 50 | |||||
self.span_width = 10 | |||||
self.feature_size = 20 #宽度信息emb的size | |||||
self.lr = 0.001 | |||||
self.lr_decay = 1e-3 | |||||
self.max_antecedents = 100 # 这个参数在mention detection中没有用 | |||||
self.atten_hidden_size = 150 | |||||
self.mention_hidden_size = 150 | |||||
self.sa_hidden_size = 150 | |||||
self.char_emb_size = 8 | |||||
self.filter = [3,4,5] | |||||
# decay = 1e-5 | |||||
def __str__(self): | |||||
d = self.__dict__ | |||||
out = 'config==============\n' | |||||
for i in list(d): | |||||
out += i+":" | |||||
out += str(d[i])+"\n" | |||||
out+="config==============\n" | |||||
return out | |||||
if __name__=="__main__": | |||||
config = Config() | |||||
print(config) |
@@ -0,0 +1,163 @@ | |||||
from fastNLP.core.metrics import MetricBase | |||||
import numpy as np | |||||
from collections import Counter | |||||
from sklearn.utils.linear_assignment_ import linear_assignment | |||||
""" | |||||
Mostly borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py | |||||
""" | |||||
class CRMetric(MetricBase): | |||||
def __init__(self): | |||||
super().__init__() | |||||
self.evaluators = [Evaluator(m) for m in (muc, b_cubed, ceafe)] | |||||
# TODO 改名为evaluate,输入也 | |||||
def evaluate(self, predicted, mention_to_predicted,clusters): | |||||
for e in self.evaluators: | |||||
e.update(predicted,mention_to_predicted, clusters) | |||||
def get_f1(self): | |||||
return sum(e.get_f1() for e in self.evaluators) / len(self.evaluators) | |||||
def get_recall(self): | |||||
return sum(e.get_recall() for e in self.evaluators) / len(self.evaluators) | |||||
def get_precision(self): | |||||
return sum(e.get_precision() for e in self.evaluators) / len(self.evaluators) | |||||
# TODO 原本的getprf | |||||
def get_metric(self,reset=False): | |||||
res = {"pre":self.get_precision(), "rec":self.get_recall(), "f":self.get_f1()} | |||||
self.evaluators = [Evaluator(m) for m in (muc, b_cubed, ceafe)] | |||||
return res | |||||
class Evaluator(): | |||||
def __init__(self, metric, beta=1): | |||||
self.p_num = 0 | |||||
self.p_den = 0 | |||||
self.r_num = 0 | |||||
self.r_den = 0 | |||||
self.metric = metric | |||||
self.beta = beta | |||||
def update(self, predicted,mention_to_predicted,gold): | |||||
gold = gold[0].tolist() | |||||
gold = [tuple(tuple(m) for m in gc) for gc in gold] | |||||
mention_to_gold = {} | |||||
for gc in gold: | |||||
for mention in gc: | |||||
mention_to_gold[mention] = gc | |||||
if self.metric == ceafe: | |||||
pn, pd, rn, rd = self.metric(predicted, gold) | |||||
else: | |||||
pn, pd = self.metric(predicted, mention_to_gold) | |||||
rn, rd = self.metric(gold, mention_to_predicted) | |||||
self.p_num += pn | |||||
self.p_den += pd | |||||
self.r_num += rn | |||||
self.r_den += rd | |||||
def get_f1(self): | |||||
return f1(self.p_num, self.p_den, self.r_num, self.r_den, beta=self.beta) | |||||
def get_recall(self): | |||||
return 0 if self.r_num == 0 else self.r_num / float(self.r_den) | |||||
def get_precision(self): | |||||
return 0 if self.p_num == 0 else self.p_num / float(self.p_den) | |||||
def get_prf(self): | |||||
return self.get_precision(), self.get_recall(), self.get_f1() | |||||
def get_counts(self): | |||||
return self.p_num, self.p_den, self.r_num, self.r_den | |||||
def b_cubed(clusters, mention_to_gold): | |||||
num, dem = 0, 0 | |||||
for c in clusters: | |||||
if len(c) == 1: | |||||
continue | |||||
gold_counts = Counter() | |||||
correct = 0 | |||||
for m in c: | |||||
if m in mention_to_gold: | |||||
gold_counts[tuple(mention_to_gold[m])] += 1 | |||||
for c2, count in gold_counts.items(): | |||||
if len(c2) != 1: | |||||
correct += count * count | |||||
num += correct / float(len(c)) | |||||
dem += len(c) | |||||
return num, dem | |||||
def muc(clusters, mention_to_gold): | |||||
tp, p = 0, 0 | |||||
for c in clusters: | |||||
p += len(c) - 1 | |||||
tp += len(c) | |||||
linked = set() | |||||
for m in c: | |||||
if m in mention_to_gold: | |||||
linked.add(mention_to_gold[m]) | |||||
else: | |||||
tp -= 1 | |||||
tp -= len(linked) | |||||
return tp, p | |||||
def phi4(c1, c2): | |||||
return 2 * len([m for m in c1 if m in c2]) / float(len(c1) + len(c2)) | |||||
def ceafe(clusters, gold_clusters): | |||||
clusters = [c for c in clusters if len(c) != 1] | |||||
scores = np.zeros((len(gold_clusters), len(clusters))) | |||||
for i in range(len(gold_clusters)): | |||||
for j in range(len(clusters)): | |||||
scores[i, j] = phi4(gold_clusters[i], clusters[j]) | |||||
matching = linear_assignment(-scores) | |||||
similarity = sum(scores[matching[:, 0], matching[:, 1]]) | |||||
return similarity, len(clusters), similarity, len(gold_clusters) | |||||
def lea(clusters, mention_to_gold): | |||||
num, dem = 0, 0 | |||||
for c in clusters: | |||||
if len(c) == 1: | |||||
continue | |||||
common_links = 0 | |||||
all_links = len(c) * (len(c) - 1) / 2.0 | |||||
for i, m in enumerate(c): | |||||
if m in mention_to_gold: | |||||
for m2 in c[i + 1:]: | |||||
if m2 in mention_to_gold and mention_to_gold[m] == mention_to_gold[m2]: | |||||
common_links += 1 | |||||
num += len(c) * common_links / float(all_links) | |||||
dem += len(c) | |||||
return num, dem | |||||
def f1(p_num, p_den, r_num, r_den, beta=1): | |||||
p = 0 if p_den == 0 else p_num / float(p_den) | |||||
r = 0 if r_den == 0 else r_num / float(r_den) | |||||
return 0 if p + r == 0 else (1 + beta * beta) * p * r / (beta * beta * p + r) |
@@ -0,0 +1,576 @@ | |||||
import torch | |||||
import numpy as np | |||||
import torch.nn as nn | |||||
import torch.nn.functional as F | |||||
from allennlp.commands.elmo import ElmoEmbedder | |||||
from fastNLP.models.base_model import BaseModel | |||||
from fastNLP.modules.encoder.variational_rnn import VarLSTM | |||||
from reproduction.coreference_resolution.model import preprocess | |||||
from fastNLP.io.embed_loader import EmbedLoader | |||||
import random | |||||
# 设置seed | |||||
torch.manual_seed(0) # cpu | |||||
torch.cuda.manual_seed(0) # gpu | |||||
np.random.seed(0) # numpy | |||||
random.seed(0) | |||||
class ffnn(nn.Module): | |||||
def __init__(self, input_size, hidden_size, output_size): | |||||
super(ffnn, self).__init__() | |||||
self.f = nn.Sequential( | |||||
# 多少层数 | |||||
nn.Linear(input_size, hidden_size), | |||||
nn.ReLU(inplace=True), | |||||
nn.Dropout(p=0.2), | |||||
nn.Linear(hidden_size, hidden_size), | |||||
nn.ReLU(inplace=True), | |||||
nn.Dropout(p=0.2), | |||||
nn.Linear(hidden_size, output_size) | |||||
) | |||||
self.reset_param() | |||||
def reset_param(self): | |||||
for name, param in self.named_parameters(): | |||||
if param.dim() > 1: | |||||
nn.init.xavier_normal_(param) | |||||
# param.data = torch.tensor(np.random.randn(*param.shape)).float() | |||||
else: | |||||
nn.init.zeros_(param) | |||||
def forward(self, input): | |||||
return self.f(input).squeeze() | |||||
class Model(BaseModel): | |||||
def __init__(self, vocab, config): | |||||
word2id = vocab.word2idx | |||||
super(Model, self).__init__() | |||||
vocab_num = len(word2id) | |||||
self.word2id = word2id | |||||
self.config = config | |||||
self.char_dict = preprocess.get_char_dict('data/char_vocab.english.txt') | |||||
self.genres = {g: i for i, g in enumerate(["bc", "bn", "mz", "nw", "pt", "tc", "wb"])} | |||||
self.device = torch.device("cuda:" + config.cuda) | |||||
self.emb = nn.Embedding(vocab_num, 350) | |||||
emb1 = EmbedLoader().load_with_vocab(config.glove, vocab,normalize=False) | |||||
emb2 = EmbedLoader().load_with_vocab(config.turian, vocab ,normalize=False) | |||||
pre_emb = np.concatenate((emb1, emb2), axis=1) | |||||
pre_emb /= (np.linalg.norm(pre_emb, axis=1, keepdims=True) + 1e-12) | |||||
if pre_emb is not None: | |||||
self.emb.weight = nn.Parameter(torch.from_numpy(pre_emb).float()) | |||||
for param in self.emb.parameters(): | |||||
param.requires_grad = False | |||||
self.emb_dropout = nn.Dropout(inplace=True) | |||||
if config.use_elmo: | |||||
self.elmo = ElmoEmbedder(options_file='data/elmo/elmo_2x4096_512_2048cnn_2xhighway_options.json', | |||||
weight_file='data/elmo/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5', | |||||
cuda_device=int(config.cuda)) | |||||
print("elmo load over.") | |||||
self.elmo_args = torch.randn((3), requires_grad=True).to(self.device) | |||||
self.char_emb = nn.Embedding(len(self.char_dict), config.char_emb_size) | |||||
self.conv1 = nn.Conv1d(config.char_emb_size, 50, 3) | |||||
self.conv2 = nn.Conv1d(config.char_emb_size, 50, 4) | |||||
self.conv3 = nn.Conv1d(config.char_emb_size, 50, 5) | |||||
self.feature_emb = nn.Embedding(config.span_width, config.feature_size) | |||||
self.feature_emb_dropout = nn.Dropout(p=0.2, inplace=True) | |||||
self.mention_distance_emb = nn.Embedding(10, config.feature_size) | |||||
self.distance_drop = nn.Dropout(p=0.2, inplace=True) | |||||
self.genre_emb = nn.Embedding(7, config.feature_size) | |||||
self.speaker_emb = nn.Embedding(2, config.feature_size) | |||||
self.bilstm = VarLSTM(input_size=350+150*config.use_CNN+config.use_elmo*1024,hidden_size=200,bidirectional=True,batch_first=True,hidden_dropout=0.2) | |||||
# self.bilstm = nn.LSTM(input_size=500, hidden_size=200, bidirectional=True, batch_first=True) | |||||
self.h0 = nn.init.orthogonal_(torch.empty(2, 1, 200)).to(self.device) | |||||
self.c0 = nn.init.orthogonal_(torch.empty(2, 1, 200)).to(self.device) | |||||
self.bilstm_drop = nn.Dropout(p=0.2, inplace=True) | |||||
self.atten = ffnn(input_size=400, hidden_size=config.atten_hidden_size, output_size=1) | |||||
self.mention_score = ffnn(input_size=1320, hidden_size=config.mention_hidden_size, output_size=1) | |||||
self.sa = ffnn(input_size=3980+40*config.use_metadata, hidden_size=config.sa_hidden_size, output_size=1) | |||||
self.mention_start_np = None | |||||
self.mention_end_np = None | |||||
def _reorder_lstm(self, word_emb, seq_lens): | |||||
sort_ind = sorted(range(len(seq_lens)), key=lambda i: seq_lens[i], reverse=True) | |||||
seq_lens_re = [seq_lens[i] for i in sort_ind] | |||||
emb_seq = self.reorder_sequence(word_emb, sort_ind, batch_first=True) | |||||
packed_seq = nn.utils.rnn.pack_padded_sequence(emb_seq, seq_lens_re, batch_first=True) | |||||
h0 = self.h0.repeat(1, len(seq_lens), 1) | |||||
c0 = self.c0.repeat(1, len(seq_lens), 1) | |||||
packed_out, final_states = self.bilstm(packed_seq, (h0, c0)) | |||||
lstm_out, _ = nn.utils.rnn.pad_packed_sequence(packed_out, batch_first=True) | |||||
back_map = {ind: i for i, ind in enumerate(sort_ind)} | |||||
reorder_ind = [back_map[i] for i in range(len(seq_lens_re))] | |||||
lstm_out = self.reorder_sequence(lstm_out, reorder_ind, batch_first=True) | |||||
return lstm_out | |||||
def reorder_sequence(self, sequence_emb, order, batch_first=True): | |||||
""" | |||||
sequence_emb: [T, B, D] if not batch_first | |||||
order: list of sequence length | |||||
""" | |||||
batch_dim = 0 if batch_first else 1 | |||||
assert len(order) == sequence_emb.size()[batch_dim] | |||||
order = torch.LongTensor(order) | |||||
order = order.to(sequence_emb).long() | |||||
sorted_ = sequence_emb.index_select(index=order, dim=batch_dim) | |||||
del order | |||||
return sorted_ | |||||
def flat_lstm(self, lstm_out, seq_lens): | |||||
batch = lstm_out.shape[0] | |||||
seq = lstm_out.shape[1] | |||||
dim = lstm_out.shape[2] | |||||
l = [j + i * seq for i, seq_len in enumerate(seq_lens) for j in range(seq_len)] | |||||
flatted = torch.index_select(lstm_out.view(batch * seq, dim), 0, torch.LongTensor(l).to(self.device)) | |||||
return flatted | |||||
def potential_mention_index(self, word_index, max_sent_len): | |||||
# get mention index [3,2]:the first sentence is 3 and secend 2 | |||||
# [0,0,0,1,1] --> [[0, 0], [0, 1], [1, 1], [1, 2], [2, 2], [3, 3], [3, 4], [4, 4]] (max =2) | |||||
potential_mention = [] | |||||
for i in range(len(word_index)): | |||||
for j in range(i, i + max_sent_len): | |||||
if (j < len(word_index) and word_index[i] == word_index[j]): | |||||
potential_mention.append([i, j]) | |||||
return potential_mention | |||||
def get_mention_start_end(self, seq_lens): | |||||
# 序列长度转换成mention | |||||
# [3,2] --> [0,0,0,1,1] | |||||
word_index = [0] * sum(seq_lens) | |||||
sent_index = 0 | |||||
index = 0 | |||||
for length in seq_lens: | |||||
for l in range(length): | |||||
word_index[index] = sent_index | |||||
index += 1 | |||||
sent_index += 1 | |||||
# [0,0,0,1,1]-->[[0,0],[0,1],[0,2]....] | |||||
mention_id = self.potential_mention_index(word_index, self.config.span_width) | |||||
mention_start = np.array(mention_id, dtype=int)[:, 0] | |||||
mention_end = np.array(mention_id, dtype=int)[:, 1] | |||||
return mention_start, mention_end | |||||
def get_mention_emb(self, flatten_lstm, mention_start, mention_end): | |||||
mention_start_tensor = torch.from_numpy(mention_start).to(self.device) | |||||
mention_end_tensor = torch.from_numpy(mention_end).to(self.device) | |||||
emb_start = flatten_lstm.index_select(dim=0, index=mention_start_tensor) # [mention_num,embed] | |||||
emb_end = flatten_lstm.index_select(dim=0, index=mention_end_tensor) # [mention_num,embed] | |||||
return emb_start, emb_end | |||||
def get_mask(self, mention_start, mention_end): | |||||
# big mask for attention | |||||
mention_num = mention_start.shape[0] | |||||
mask = np.zeros((mention_num, self.config.span_width)) # [mention_num,span_width] | |||||
for i in range(mention_num): | |||||
start = mention_start[i] | |||||
end = mention_end[i] | |||||
# 实际上是宽度 | |||||
for j in range(end - start + 1): | |||||
mask[i][j] = 1 | |||||
mask = torch.from_numpy(mask) # [mention_num,max_mention] | |||||
# 0-->-inf 1-->0 | |||||
log_mask = torch.log(mask) | |||||
return log_mask | |||||
def get_mention_index(self, mention_start, max_mention): | |||||
# TODO 后面可能要改 | |||||
assert len(mention_start.shape) == 1 | |||||
mention_start_tensor = torch.from_numpy(mention_start) | |||||
num_mention = mention_start_tensor.shape[0] | |||||
mention_index = mention_start_tensor.expand(max_mention, num_mention).transpose(0, | |||||
1) # [num_mention,max_mention] | |||||
assert mention_index.shape[0] == num_mention | |||||
assert mention_index.shape[1] == max_mention | |||||
range_add = torch.arange(0, max_mention).expand(num_mention, max_mention).long() # [num_mention,max_mention] | |||||
mention_index = mention_index + range_add | |||||
mention_index = torch.min(mention_index, torch.LongTensor([mention_start[-1]]).expand(num_mention, max_mention)) | |||||
return mention_index.to(self.device) | |||||
def sort_mention(self, mention_start, mention_end, candidate_mention_emb, candidate_mention_score, seq_lens): | |||||
# 排序记录,高分段在前面 | |||||
mention_score, mention_ids = torch.sort(candidate_mention_score, descending=True) | |||||
preserve_mention_num = int(self.config.mention_ratio * sum(seq_lens)) | |||||
mention_ids = mention_ids[0:preserve_mention_num] | |||||
mention_score = mention_score[0:preserve_mention_num] | |||||
mention_start_tensor = torch.from_numpy(mention_start).to(self.device).index_select(dim=0, | |||||
index=mention_ids) # [lamda*word_num] | |||||
mention_end_tensor = torch.from_numpy(mention_end).to(self.device).index_select(dim=0, | |||||
index=mention_ids) # [lamda*word_num] | |||||
mention_emb = candidate_mention_emb.index_select(index=mention_ids, dim=0) # [lamda*word_num,emb] | |||||
assert mention_score.shape[0] == preserve_mention_num | |||||
assert mention_start_tensor.shape[0] == preserve_mention_num | |||||
assert mention_end_tensor.shape[0] == preserve_mention_num | |||||
assert mention_emb.shape[0] == preserve_mention_num | |||||
# TODO 不交叉没做处理 | |||||
# 对start进行再排序,实际位置在前面 | |||||
# TODO 这里只考虑了start没有考虑end | |||||
mention_start_tensor, temp_index = torch.sort(mention_start_tensor) | |||||
mention_end_tensor = mention_end_tensor.index_select(dim=0, index=temp_index) | |||||
mention_emb = mention_emb.index_select(dim=0, index=temp_index) | |||||
mention_score = mention_score.index_select(dim=0, index=temp_index) | |||||
return mention_start_tensor, mention_end_tensor, mention_score, mention_emb | |||||
def get_antecedents(self, mention_starts, max_antecedents): | |||||
num_mention = mention_starts.shape[0] | |||||
max_antecedents = min(max_antecedents, num_mention) | |||||
# mention和它是第几个mention之间的对应关系 | |||||
antecedents = np.zeros((num_mention, max_antecedents), dtype=int) # [num_mention,max_an] | |||||
# 记录长度 | |||||
antecedents_len = [0] * num_mention | |||||
for i in range(num_mention): | |||||
ante_count = 0 | |||||
for j in range(max(0, i - max_antecedents), i): | |||||
antecedents[i, ante_count] = j | |||||
ante_count += 1 | |||||
# 补位操作 | |||||
for j in range(ante_count, max_antecedents): | |||||
antecedents[i, j] = 0 | |||||
antecedents_len[i] = ante_count | |||||
assert antecedents.shape[1] == max_antecedents | |||||
return antecedents, antecedents_len | |||||
def get_antecedents_score(self, span_represent, mention_score, antecedents, antecedents_len, mention_speakers_ids, | |||||
genre): | |||||
num_mention = mention_score.shape[0] | |||||
max_antecedent = antecedents.shape[1] | |||||
pair_emb = self.get_pair_emb(span_represent, antecedents, mention_speakers_ids, genre) # [span_num,max_ant,emb] | |||||
antecedent_scores = self.sa(pair_emb) | |||||
mask01 = self.sequence_mask(antecedents_len, max_antecedent) | |||||
maskinf = torch.log(mask01).to(self.device) | |||||
assert maskinf.shape[1] <= max_antecedent | |||||
assert antecedent_scores.shape[0] == num_mention | |||||
antecedent_scores = antecedent_scores + maskinf | |||||
antecedents = torch.from_numpy(antecedents).to(self.device) | |||||
mention_scoreij = mention_score.unsqueeze(1) + torch.gather( | |||||
mention_score.unsqueeze(0).expand(num_mention, num_mention), dim=1, index=antecedents) | |||||
antecedent_scores += mention_scoreij | |||||
antecedent_scores = torch.cat([torch.zeros([mention_score.shape[0], 1]).to(self.device), antecedent_scores], | |||||
1) # [num_mentions, max_ant + 1] | |||||
return antecedent_scores | |||||
############################## | |||||
def distance_bin(self, mention_distance): | |||||
bins = torch.zeros(mention_distance.size()).byte().to(self.device) | |||||
rg = [[1, 1], [2, 2], [3, 3], [4, 4], [5, 7], [8, 15], [16, 31], [32, 63], [64, 300]] | |||||
for t, k in enumerate(rg): | |||||
i, j = k[0], k[1] | |||||
b = torch.LongTensor([i]).unsqueeze(-1).expand(mention_distance.size()).to(self.device) | |||||
m1 = torch.ge(mention_distance, b) | |||||
e = torch.LongTensor([j]).unsqueeze(-1).expand(mention_distance.size()).to(self.device) | |||||
m2 = torch.le(mention_distance, e) | |||||
bins = bins + (t + 1) * (m1 & m2) | |||||
return bins.long() | |||||
def get_distance_emb(self, antecedents_tensor): | |||||
num_mention = antecedents_tensor.shape[0] | |||||
max_ant = antecedents_tensor.shape[1] | |||||
assert max_ant <= self.config.max_antecedents | |||||
source = torch.arange(0, num_mention).expand(max_ant, num_mention).transpose(0,1).to(self.device) # [num_mention,max_ant] | |||||
mention_distance = source - antecedents_tensor | |||||
mention_distance_bin = self.distance_bin(mention_distance) | |||||
distance_emb = self.mention_distance_emb(mention_distance_bin) | |||||
distance_emb = self.distance_drop(distance_emb) | |||||
return distance_emb | |||||
def get_pair_emb(self, span_emb, antecedents, mention_speakers_ids, genre): | |||||
emb_dim = span_emb.shape[1] | |||||
num_span = span_emb.shape[0] | |||||
max_ant = antecedents.shape[1] | |||||
assert span_emb.shape[0] == antecedents.shape[0] | |||||
antecedents = torch.from_numpy(antecedents).to(self.device) | |||||
# [num_span,max_ant,emb] | |||||
antecedent_emb = torch.gather(span_emb.unsqueeze(0).expand(num_span, num_span, emb_dim), dim=1, | |||||
index=antecedents.unsqueeze(2).expand(num_span, max_ant, emb_dim)) | |||||
# [num_span,max_ant,emb] | |||||
target_emb_tiled = span_emb.expand((max_ant, num_span, emb_dim)) | |||||
target_emb_tiled = target_emb_tiled.transpose(0, 1) | |||||
similarity_emb = antecedent_emb * target_emb_tiled | |||||
pair_emb_list = [target_emb_tiled, antecedent_emb, similarity_emb] | |||||
# get speakers and genre | |||||
if self.config.use_metadata: | |||||
antecedent_speaker_ids = mention_speakers_ids.unsqueeze(0).expand(num_span, num_span).gather(dim=1, | |||||
index=antecedents) | |||||
same_speaker = torch.eq(mention_speakers_ids.unsqueeze(1).expand(num_span, max_ant), | |||||
antecedent_speaker_ids) # [num_mention,max_ant] | |||||
speaker_embedding = self.speaker_emb(same_speaker.long().to(self.device)) # [mention_num.max_ant,emb] | |||||
genre_embedding = self.genre_emb( | |||||
torch.LongTensor([genre]).expand(num_span, max_ant).to(self.device)) # [mention_num,max_ant,emb] | |||||
pair_emb_list.append(speaker_embedding) | |||||
pair_emb_list.append(genre_embedding) | |||||
# get distance emb | |||||
if self.config.use_distance: | |||||
distance_emb = self.get_distance_emb(antecedents) | |||||
pair_emb_list.append(distance_emb) | |||||
pair_emb = torch.cat(pair_emb_list, 2) | |||||
return pair_emb | |||||
def sequence_mask(self, len_list, max_len): | |||||
x = np.zeros((len(len_list), max_len)) | |||||
for i in range(len(len_list)): | |||||
l = len_list[i] | |||||
for j in range(l): | |||||
x[i][j] = 1 | |||||
return torch.from_numpy(x).float() | |||||
def logsumexp(self, value, dim=None, keepdim=False): | |||||
"""Numerically stable implementation of the operation | |||||
value.exp().sum(dim, keepdim).log() | |||||
""" | |||||
# TODO: torch.max(value, dim=None) threw an error at time of writing | |||||
if dim is not None: | |||||
m, _ = torch.max(value, dim=dim, keepdim=True) | |||||
value0 = value - m | |||||
if keepdim is False: | |||||
m = m.squeeze(dim) | |||||
return m + torch.log(torch.sum(torch.exp(value0), | |||||
dim=dim, keepdim=keepdim)) | |||||
else: | |||||
m = torch.max(value) | |||||
sum_exp = torch.sum(torch.exp(value - m)) | |||||
return m + torch.log(sum_exp) | |||||
def softmax_loss(self, antecedent_scores, antecedent_labels): | |||||
antecedent_labels = torch.from_numpy(antecedent_labels * 1).to(self.device) | |||||
gold_scores = antecedent_scores + torch.log(antecedent_labels.float()) # [num_mentions, max_ant + 1] | |||||
marginalized_gold_scores = self.logsumexp(gold_scores, 1) # [num_mentions] | |||||
log_norm = self.logsumexp(antecedent_scores, 1) # [num_mentions] | |||||
return torch.sum(log_norm - marginalized_gold_scores) # [num_mentions]reduce_logsumexp | |||||
def get_predicted_antecedents(self, antecedents, antecedent_scores): | |||||
predicted_antecedents = [] | |||||
for i, index in enumerate(np.argmax(antecedent_scores.detach(), axis=1) - 1): | |||||
if index < 0: | |||||
predicted_antecedents.append(-1) | |||||
else: | |||||
predicted_antecedents.append(antecedents[i, index]) | |||||
return predicted_antecedents | |||||
def get_predicted_clusters(self, mention_starts, mention_ends, predicted_antecedents): | |||||
mention_to_predicted = {} | |||||
predicted_clusters = [] | |||||
for i, predicted_index in enumerate(predicted_antecedents): | |||||
if predicted_index < 0: | |||||
continue | |||||
assert i > predicted_index | |||||
predicted_antecedent = (int(mention_starts[predicted_index]), int(mention_ends[predicted_index])) | |||||
if predicted_antecedent in mention_to_predicted: | |||||
predicted_cluster = mention_to_predicted[predicted_antecedent] | |||||
else: | |||||
predicted_cluster = len(predicted_clusters) | |||||
predicted_clusters.append([predicted_antecedent]) | |||||
mention_to_predicted[predicted_antecedent] = predicted_cluster | |||||
mention = (int(mention_starts[i]), int(mention_ends[i])) | |||||
predicted_clusters[predicted_cluster].append(mention) | |||||
mention_to_predicted[mention] = predicted_cluster | |||||
predicted_clusters = [tuple(pc) for pc in predicted_clusters] | |||||
mention_to_predicted = {m: predicted_clusters[i] for m, i in mention_to_predicted.items()} | |||||
return predicted_clusters, mention_to_predicted | |||||
def evaluate_coref(self, mention_starts, mention_ends, predicted_antecedents, gold_clusters, evaluator): | |||||
gold_clusters = [tuple(tuple(m) for m in gc) for gc in gold_clusters] | |||||
mention_to_gold = {} | |||||
for gc in gold_clusters: | |||||
for mention in gc: | |||||
mention_to_gold[mention] = gc | |||||
predicted_clusters, mention_to_predicted = self.get_predicted_clusters(mention_starts, mention_ends, | |||||
predicted_antecedents) | |||||
evaluator.update(predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold) | |||||
return predicted_clusters | |||||
def forward(self, sentences, doc_np, speaker_ids_np, genre, char_index, seq_len): | |||||
""" | |||||
实际输入都是tensor | |||||
:param sentences: 句子,被fastNLP转化成了numpy, | |||||
:param doc_np: 被fastNLP转化成了Tensor | |||||
:param speaker_ids_np: 被fastNLP转化成了Tensor | |||||
:param genre: 被fastNLP转化成了Tensor | |||||
:param char_index: 被fastNLP转化成了Tensor | |||||
:param seq_len: 被fastNLP转化成了Tensor | |||||
:return: | |||||
""" | |||||
# change for fastNLP | |||||
sentences = sentences[0].tolist() | |||||
doc_tensor = doc_np[0] | |||||
speakers_tensor = speaker_ids_np[0] | |||||
genre = genre[0].item() | |||||
char_index = char_index[0] | |||||
seq_len = seq_len[0].cpu().numpy() | |||||
# 类型 | |||||
# doc_tensor = torch.from_numpy(doc_np).to(self.device) | |||||
# speakers_tensor = torch.from_numpy(speaker_ids_np).to(self.device) | |||||
mention_emb_list = [] | |||||
word_emb = self.emb(doc_tensor) | |||||
word_emb_list = [word_emb] | |||||
if self.config.use_CNN: | |||||
# [batch, length, char_length, char_dim] | |||||
char = self.char_emb(char_index) | |||||
char_size = char.size() | |||||
# first transform to [batch *length, char_length, char_dim] | |||||
# then transpose to [batch * length, char_dim, char_length] | |||||
char = char.view(char_size[0] * char_size[1], char_size[2], char_size[3]).transpose(1, 2) | |||||
# put into cnn [batch*length, char_filters, char_length] | |||||
# then put into maxpooling [batch * length, char_filters] | |||||
char_over_cnn, _ = self.conv1(char).max(dim=2) | |||||
# reshape to [batch, length, char_filters] | |||||
char_over_cnn = torch.tanh(char_over_cnn).view(char_size[0], char_size[1], -1) | |||||
word_emb_list.append(char_over_cnn) | |||||
char_over_cnn, _ = self.conv2(char).max(dim=2) | |||||
char_over_cnn = torch.tanh(char_over_cnn).view(char_size[0], char_size[1], -1) | |||||
word_emb_list.append(char_over_cnn) | |||||
char_over_cnn, _ = self.conv3(char).max(dim=2) | |||||
char_over_cnn = torch.tanh(char_over_cnn).view(char_size[0], char_size[1], -1) | |||||
word_emb_list.append(char_over_cnn) | |||||
# word_emb = torch.cat(word_emb_list, dim=2) | |||||
# use elmo or not | |||||
if self.config.use_elmo: | |||||
# 如果确实被截断了 | |||||
if doc_tensor.shape[0] == 50 and len(sentences) > 50: | |||||
sentences = sentences[0:50] | |||||
elmo_embedding, elmo_mask = self.elmo.batch_to_embeddings(sentences) | |||||
elmo_embedding = elmo_embedding.to( | |||||
self.device) # [sentence_num,max_sent_len,3,1024]--[sentence_num,max_sent,1024] | |||||
elmo_embedding = elmo_embedding[:, 0, :, :] * self.elmo_args[0] + elmo_embedding[:, 1, :, :] * \ | |||||
self.elmo_args[1] + elmo_embedding[:, 2, :, :] * self.elmo_args[2] | |||||
word_emb_list.append(elmo_embedding) | |||||
# print(word_emb_list[0].shape) | |||||
# print(word_emb_list[1].shape) | |||||
# print(word_emb_list[2].shape) | |||||
# print(word_emb_list[3].shape) | |||||
# print(word_emb_list[4].shape) | |||||
word_emb = torch.cat(word_emb_list, dim=2) | |||||
word_emb = self.emb_dropout(word_emb) | |||||
# word_emb_elmo = self.emb_dropout(word_emb_elmo) | |||||
lstm_out = self._reorder_lstm(word_emb, seq_len) | |||||
flatten_lstm = self.flat_lstm(lstm_out, seq_len) # [word_num,emb] | |||||
flatten_lstm = self.bilstm_drop(flatten_lstm) | |||||
# TODO 没有按照论文写 | |||||
flatten_word_emb = self.flat_lstm(word_emb, seq_len) # [word_num,emb] | |||||
mention_start, mention_end = self.get_mention_start_end(seq_len) # [mention_num] | |||||
self.mention_start_np = mention_start # [mention_num] np | |||||
self.mention_end_np = mention_end | |||||
mention_num = mention_start.shape[0] | |||||
emb_start, emb_end = self.get_mention_emb(flatten_lstm, mention_start, mention_end) # [mention_num,emb] | |||||
# list | |||||
mention_emb_list.append(emb_start) | |||||
mention_emb_list.append(emb_end) | |||||
if self.config.use_width: | |||||
mention_width_index = mention_end - mention_start | |||||
mention_width_tensor = torch.from_numpy(mention_width_index).to(self.device) # [mention_num] | |||||
mention_width_emb = self.feature_emb(mention_width_tensor) | |||||
mention_width_emb = self.feature_emb_dropout(mention_width_emb) | |||||
mention_emb_list.append(mention_width_emb) | |||||
if self.config.model_heads: | |||||
mention_index = self.get_mention_index(mention_start, self.config.span_width) # [mention_num,max_mention] | |||||
log_mask_tensor = self.get_mask(mention_start, mention_end).float().to( | |||||
self.device) # [mention_num,max_mention] | |||||
alpha = self.atten(flatten_lstm).to(self.device) # [word_num] | |||||
# 得到attention | |||||
mention_head_score = torch.gather(alpha.expand(mention_num, -1), 1, | |||||
mention_index).float().to(self.device) # [mention_num,max_mention] | |||||
mention_attention = F.softmax(mention_head_score + log_mask_tensor, dim=1) # [mention_num,max_mention] | |||||
# TODO flatte lstm | |||||
word_num = flatten_lstm.shape[0] | |||||
lstm_emb = flatten_lstm.shape[1] | |||||
emb_num = flatten_word_emb.shape[1] | |||||
# [num_mentions, max_mention_width, emb] | |||||
mention_text_emb = torch.gather( | |||||
flatten_word_emb.unsqueeze(1).expand(word_num, self.config.span_width, emb_num), | |||||
0, mention_index.unsqueeze(2).expand(mention_num, self.config.span_width, | |||||
emb_num)) | |||||
# [mention_num,emb] | |||||
mention_head_emb = torch.sum( | |||||
mention_attention.unsqueeze(2).expand(mention_num, self.config.span_width, emb_num) * mention_text_emb, | |||||
dim=1) | |||||
mention_emb_list.append(mention_head_emb) | |||||
candidate_mention_emb = torch.cat(mention_emb_list, 1) # [candidate_mention_num,emb] | |||||
candidate_mention_score = self.mention_score(candidate_mention_emb) # [candidate_mention_num] | |||||
antecedent_scores, antecedents, mention_start_tensor, mention_end_tensor = (None, None, None, None) | |||||
mention_start_tensor, mention_end_tensor, mention_score, mention_emb = \ | |||||
self.sort_mention(mention_start, mention_end, candidate_mention_emb, candidate_mention_score, seq_len) | |||||
mention_speakers_ids = speakers_tensor.index_select(dim=0, index=mention_start_tensor) # num_mention | |||||
antecedents, antecedents_len = self.get_antecedents(mention_start_tensor, self.config.max_antecedents) | |||||
antecedent_scores = self.get_antecedents_score(mention_emb, mention_score, antecedents, antecedents_len, | |||||
mention_speakers_ids, genre) | |||||
ans = {"candidate_mention_score": candidate_mention_score, "antecedent_scores": antecedent_scores, | |||||
"antecedents": antecedents, "mention_start_tensor": mention_start_tensor, | |||||
"mention_end_tensor": mention_end_tensor} | |||||
return ans | |||||
def predict(self, sentences, doc_np, speaker_ids_np, genre, char_index, seq_len): | |||||
ans = self(sentences, | |||||
doc_np, | |||||
speaker_ids_np, | |||||
genre, | |||||
char_index, | |||||
seq_len) | |||||
predicted_antecedents = self.get_predicted_antecedents(ans["antecedents"], ans["antecedent_scores"]) | |||||
predicted_clusters, mention_to_predicted = self.get_predicted_clusters(ans["mention_start_tensor"], | |||||
ans["mention_end_tensor"], | |||||
predicted_antecedents) | |||||
return {'predicted':predicted_clusters,"mention_to_predicted":mention_to_predicted} | |||||
if __name__ == '__main__': | |||||
pass |
@@ -0,0 +1,225 @@ | |||||
import json | |||||
import numpy as np | |||||
from . import util | |||||
import collections | |||||
def load(path): | |||||
""" | |||||
load the file from jsonline | |||||
:param path: | |||||
:return: examples with many example(dict): {"clusters":[[[mention],[mention]],[another cluster]], | |||||
"doc_key":"str","speakers":[[,,,],[]...],"sentence":[[][]]} | |||||
""" | |||||
with open(path) as f: | |||||
train_examples = [json.loads(jsonline) for jsonline in f.readlines()] | |||||
return train_examples | |||||
def get_vocab(): | |||||
""" | |||||
从所有的句子中得到最终的字典,被main调用,不止是train,还有dev和test | |||||
:param examples: | |||||
:return: word2id & id2word | |||||
""" | |||||
word2id = {'PAD':0,'UNK':1} | |||||
id2word = {0:'PAD',1:'UNK'} | |||||
index = 2 | |||||
data = [load("../data/train.english.jsonlines"),load("../data/dev.english.jsonlines"),load("../data/test.english.jsonlines")] | |||||
for examples in data: | |||||
for example in examples: | |||||
for sent in example["sentences"]: | |||||
for word in sent: | |||||
if(word not in word2id): | |||||
word2id[word]=index | |||||
id2word[index] = word | |||||
index += 1 | |||||
return word2id,id2word | |||||
def normalize(v): | |||||
norm = np.linalg.norm(v) | |||||
if norm > 0: | |||||
return v / norm | |||||
else: | |||||
return v | |||||
# 加载glove得到embedding | |||||
def get_emb(id2word,embedding_size): | |||||
glove_oov = 0 | |||||
turian_oov = 0 | |||||
both = 0 | |||||
glove_emb_path = "../data/glove.840B.300d.txt.filtered" | |||||
turian_emb_path = "../data/turian.50d.txt" | |||||
word_num = len(id2word) | |||||
emb = np.zeros((word_num,embedding_size)) | |||||
glove_emb_dict = util.load_embedding_dict(glove_emb_path,300,"txt") | |||||
turian_emb_dict = util.load_embedding_dict(turian_emb_path,50,"txt") | |||||
for i in range(word_num): | |||||
if id2word[i] in glove_emb_dict: | |||||
word_embedding = glove_emb_dict.get(id2word[i]) | |||||
emb[i][0:300] = np.array(word_embedding) | |||||
else: | |||||
# print(id2word[i]) | |||||
glove_oov += 1 | |||||
if id2word[i] in turian_emb_dict: | |||||
word_embedding = turian_emb_dict.get(id2word[i]) | |||||
emb[i][300:350] = np.array(word_embedding) | |||||
else: | |||||
# print(id2word[i]) | |||||
turian_oov += 1 | |||||
if id2word[i] not in glove_emb_dict and id2word[i] not in turian_emb_dict: | |||||
both += 1 | |||||
emb[i] = normalize(emb[i]) | |||||
print("embedding num:"+str(word_num)) | |||||
print("glove num:"+str(glove_oov)) | |||||
print("glove oov rate:"+str(glove_oov/word_num)) | |||||
print("turian num:"+str(turian_oov)) | |||||
print("turian oov rate:"+str(turian_oov/word_num)) | |||||
print("both num:"+str(both)) | |||||
return emb | |||||
def _doc2vec(doc,word2id,char_dict,max_filter,max_sentences,is_train): | |||||
max_len = 0 | |||||
max_word_length = 0 | |||||
docvex = [] | |||||
length = [] | |||||
if is_train: | |||||
sent_num = min(max_sentences,len(doc)) | |||||
else: | |||||
sent_num = len(doc) | |||||
for i in range(sent_num): | |||||
sent = doc[i] | |||||
length.append(len(sent)) | |||||
if (len(sent) > max_len): | |||||
max_len = len(sent) | |||||
sent_vec =[] | |||||
for j,word in enumerate(sent): | |||||
if len(word)>max_word_length: | |||||
max_word_length = len(word) | |||||
if word in word2id: | |||||
sent_vec.append(word2id[word]) | |||||
else: | |||||
sent_vec.append(word2id["UNK"]) | |||||
docvex.append(sent_vec) | |||||
char_index = np.zeros((sent_num, max_len, max_word_length),dtype=int) | |||||
for i in range(sent_num): | |||||
sent = doc[i] | |||||
for j,word in enumerate(sent): | |||||
char_index[i, j, :len(word)] = [char_dict[c] for c in word] | |||||
return docvex,char_index,length,max_len | |||||
# TODO 修改了接口,确认所有该修改的地方都修改好 | |||||
def doc2numpy(doc,word2id,chardict,max_filter,max_sentences,is_train): | |||||
docvec, char_index, length, max_len = _doc2vec(doc,word2id,chardict,max_filter,max_sentences,is_train) | |||||
assert max(length) == max_len | |||||
assert char_index.shape[0]==len(length) | |||||
assert char_index.shape[1]==max_len | |||||
doc_np = np.zeros((len(docvec), max_len), int) | |||||
for i in range(len(docvec)): | |||||
for j in range(len(docvec[i])): | |||||
doc_np[i][j] = docvec[i][j] | |||||
return doc_np,char_index,length | |||||
# TODO 没有测试 | |||||
def speaker2numpy(speakers_raw,max_sentences,is_train): | |||||
if is_train and len(speakers_raw)> max_sentences: | |||||
speakers_raw = speakers_raw[0:max_sentences] | |||||
speakers = flatten(speakers_raw) | |||||
speaker_dict = {s: i for i, s in enumerate(set(speakers))} | |||||
speaker_ids = np.array([speaker_dict[s] for s in speakers]) | |||||
return speaker_ids | |||||
def flat_cluster(clusters): | |||||
flatted = [] | |||||
for cluster in clusters: | |||||
for item in cluster: | |||||
flatted.append(item) | |||||
return flatted | |||||
def get_right_mention(clusters,mention_start_np,mention_end_np): | |||||
flatted = flat_cluster(clusters) | |||||
cluster_num = len(flatted) | |||||
mention_num = mention_start_np.shape[0] | |||||
right_mention = np.zeros(mention_num,dtype=int) | |||||
for i in range(mention_num): | |||||
if [mention_start_np[i],mention_end_np[i]] in flatted: | |||||
right_mention[i]=1 | |||||
return right_mention,cluster_num | |||||
def handle_cluster(clusters): | |||||
gold_mentions = sorted(tuple(m) for m in flatten(clusters)) | |||||
gold_mention_map = {m: i for i, m in enumerate(gold_mentions)} | |||||
cluster_ids = np.zeros(len(gold_mentions), dtype=int) | |||||
for cluster_id, cluster in enumerate(clusters): | |||||
for mention in cluster: | |||||
cluster_ids[gold_mention_map[tuple(mention)]] = cluster_id | |||||
gold_starts, gold_ends = tensorize_mentions(gold_mentions) | |||||
return cluster_ids, gold_starts, gold_ends | |||||
# 展平 | |||||
def flatten(l): | |||||
return [item for sublist in l for item in sublist] | |||||
# 把mention分成start end | |||||
def tensorize_mentions(mentions): | |||||
if len(mentions) > 0: | |||||
starts, ends = zip(*mentions) | |||||
else: | |||||
starts, ends = [], [] | |||||
return np.array(starts), np.array(ends) | |||||
def get_char_dict(path): | |||||
vocab = ["<UNK>"] | |||||
with open(path) as f: | |||||
vocab.extend(c.strip() for c in f.readlines()) | |||||
char_dict = collections.defaultdict(int) | |||||
char_dict.update({c: i for i, c in enumerate(vocab)}) | |||||
return char_dict | |||||
def get_labels(clusters,mention_starts,mention_ends,max_antecedents): | |||||
cluster_ids, gold_starts, gold_ends = handle_cluster(clusters) | |||||
num_mention = mention_starts.shape[0] | |||||
num_gold = gold_starts.shape[0] | |||||
max_antecedents = min(max_antecedents, num_mention) | |||||
mention_indices = {} | |||||
for i in range(num_mention): | |||||
mention_indices[(mention_starts[i].detach().item(), mention_ends[i].detach().item())] = i | |||||
# 用来记录哪些mention是对的,-1表示错误,正数代表这个mention实际上对应哪个gold cluster的id | |||||
mention_cluster_ids = [-1] * num_mention | |||||
# test | |||||
right_mention_count = 0 | |||||
for i in range(num_gold): | |||||
right_mention = mention_indices.get((gold_starts[i], gold_ends[i])) | |||||
if (right_mention != None): | |||||
right_mention_count += 1 | |||||
mention_cluster_ids[right_mention] = cluster_ids[i] | |||||
# i j 是否属于同一个cluster | |||||
labels = np.zeros((num_mention, max_antecedents + 1), dtype=bool) # [num_mention,max_an+1] | |||||
for i in range(num_mention): | |||||
ante_count = 0 | |||||
null_label = True | |||||
for j in range(max(0, i - max_antecedents), i): | |||||
if (mention_cluster_ids[i] >= 0 and mention_cluster_ids[i] == mention_cluster_ids[j]): | |||||
labels[i, ante_count + 1] = True | |||||
null_label = False | |||||
else: | |||||
labels[i, ante_count + 1] = False | |||||
ante_count += 1 | |||||
for j in range(ante_count, max_antecedents): | |||||
labels[i, j + 1] = False | |||||
labels[i, 0] = null_label | |||||
return labels | |||||
# test=========================== | |||||
if __name__=="__main__": | |||||
word2id,id2word = get_vocab() | |||||
get_emb(id2word,350) | |||||
@@ -0,0 +1,32 @@ | |||||
from fastNLP.core.losses import LossBase | |||||
from reproduction.coreference_resolution.model.preprocess import get_labels | |||||
from reproduction.coreference_resolution.model.config import Config | |||||
import torch | |||||
class SoftmaxLoss(LossBase): | |||||
""" | |||||
交叉熵loss | |||||
允许多标签分类 | |||||
""" | |||||
def __init__(self, antecedent_scores=None, clusters=None, mention_start_tensor=None, mention_end_tensor=None): | |||||
""" | |||||
:param pred: | |||||
:param target: | |||||
""" | |||||
super().__init__() | |||||
self._init_param_map(antecedent_scores=antecedent_scores, clusters=clusters, | |||||
mention_start_tensor=mention_start_tensor, mention_end_tensor=mention_end_tensor) | |||||
def get_loss(self, antecedent_scores, clusters, mention_start_tensor, mention_end_tensor): | |||||
antecedent_labels = get_labels(clusters[0], mention_start_tensor, mention_end_tensor, | |||||
Config().max_antecedents) | |||||
antecedent_labels = torch.from_numpy(antecedent_labels*1).to(torch.device("cuda:" + Config().cuda)) | |||||
gold_scores = antecedent_scores + torch.log(antecedent_labels.float()).to(torch.device("cuda:" + Config().cuda)) # [num_mentions, max_ant + 1] | |||||
marginalized_gold_scores = gold_scores.logsumexp(dim=1) # [num_mentions] | |||||
log_norm = antecedent_scores.logsumexp(dim=1) # [num_mentions] | |||||
return torch.sum(log_norm - marginalized_gold_scores) |
@@ -0,0 +1,101 @@ | |||||
import os | |||||
import errno | |||||
import collections | |||||
import torch | |||||
import numpy as np | |||||
import pyhocon | |||||
# flatten the list | |||||
def flatten(l): | |||||
return [item for sublist in l for item in sublist] | |||||
def get_config(filename): | |||||
return pyhocon.ConfigFactory.parse_file(filename) | |||||
# safe make directions | |||||
def mkdirs(path): | |||||
try: | |||||
os.makedirs(path) | |||||
except OSError as exception: | |||||
if exception.errno != errno.EEXIST: | |||||
raise | |||||
return path | |||||
def load_char_dict(char_vocab_path): | |||||
vocab = ["<unk>"] | |||||
with open(char_vocab_path) as f: | |||||
vocab.extend(c.strip() for c in f.readlines()) | |||||
char_dict = collections.defaultdict(int) | |||||
char_dict.update({c: i for i, c in enumerate(vocab)}) | |||||
return char_dict | |||||
# 加载embedding | |||||
def load_embedding_dict(embedding_path, embedding_size, embedding_format): | |||||
print("Loading word embeddings from {}...".format(embedding_path)) | |||||
default_embedding = np.zeros(embedding_size) | |||||
embedding_dict = collections.defaultdict(lambda: default_embedding) | |||||
skip_first = embedding_format == "vec" | |||||
with open(embedding_path) as f: | |||||
for i, line in enumerate(f.readlines()): | |||||
if skip_first and i == 0: | |||||
continue | |||||
splits = line.split() | |||||
assert len(splits) == embedding_size + 1 | |||||
word = splits[0] | |||||
embedding = np.array([float(s) for s in splits[1:]]) | |||||
embedding_dict[word] = embedding | |||||
print("Done loading word embeddings.") | |||||
return embedding_dict | |||||
# safe devide | |||||
def maybe_divide(x, y): | |||||
return 0 if y == 0 else x / float(y) | |||||
def shape(x, dim): | |||||
return x.get_shape()[dim].value or torch.shape(x)[dim] | |||||
def normalize(v): | |||||
norm = np.linalg.norm(v) | |||||
if norm > 0: | |||||
return v / norm | |||||
else: | |||||
return v | |||||
class RetrievalEvaluator(object): | |||||
def __init__(self): | |||||
self._num_correct = 0 | |||||
self._num_gold = 0 | |||||
self._num_predicted = 0 | |||||
def update(self, gold_set, predicted_set): | |||||
self._num_correct += len(gold_set & predicted_set) | |||||
self._num_gold += len(gold_set) | |||||
self._num_predicted += len(predicted_set) | |||||
def recall(self): | |||||
return maybe_divide(self._num_correct, self._num_gold) | |||||
def precision(self): | |||||
return maybe_divide(self._num_correct, self._num_predicted) | |||||
def metrics(self): | |||||
recall = self.recall() | |||||
precision = self.precision() | |||||
f1 = maybe_divide(2 * recall * precision, precision + recall) | |||||
return recall, precision, f1 | |||||
if __name__=="__main__": | |||||
print(load_char_dict("../data/char_vocab.english.txt")) | |||||
embedding_dict = load_embedding_dict("../data/glove.840B.300d.txt.filtered",300,"txt") | |||||
print("hello") |
@@ -0,0 +1,49 @@ | |||||
# 共指消解复现 | |||||
## 介绍 | |||||
Coreference resolution是查找文本中指向同一现实实体的所有表达式的任务。 | |||||
对于涉及自然语言理解的许多更高级别的NLP任务来说, | |||||
这是一个重要的步骤,例如文档摘要,问题回答和信息提取。 | |||||
代码的实现主要基于[ End-to-End Coreference Resolution (Lee et al, 2017)](https://arxiv.org/pdf/1707.07045). | |||||
## 数据获取与预处理 | |||||
论文在[OntoNote5.0](https://allennlp.org/models)数据集上取得了当时的sota结果。 | |||||
由于版权问题,本文无法提供数据集的下载,请自行下载。 | |||||
原始数据集的格式为conll格式,详细介绍参考数据集给出的官方介绍页面。 | |||||
代码实现采用了论文作者Lee的预处理方法,具体细节参加[链接](https://github.com/kentonl/e2e-coref/blob/e2e/setup_training.sh)。 | |||||
处理之后的数据集为json格式,例子: | |||||
``` | |||||
{ | |||||
"clusters": [], | |||||
"doc_key": "nw", | |||||
"sentences": [["This", "is", "the", "first", "sentence", "."], ["This", "is", "the", "second", "."]], | |||||
"speakers": [["spk1", "spk1", "spk1", "spk1", "spk1", "spk1"], ["spk2", "spk2", "spk2", "spk2", "spk2"]] | |||||
} | |||||
``` | |||||
### embedding 数据集下载 | |||||
[turian emdedding](https://lil.cs.washington.edu/coref/turian.50d.txt) | |||||
[glove embedding]( https://nlp.stanford.edu/data/glove.840B.300d.zip) | |||||
## 运行 | |||||
```python | |||||
# 训练代码 | |||||
CUDA_VISIBLE_DEVICES=0 python train.py | |||||
# 测试代码 | |||||
CUDA_VISIBLE_DEVICES=0 python valid.py | |||||
``` | |||||
## 结果 | |||||
原论文作者在测试集上取得了67.2%的结果,AllenNLP复现的结果为 [63.0%](https://allennlp.org/models)。 | |||||
其中allenNLP训练时没有加入speaker信息,没有variational dropout以及只使用了100的antecedents而不是250。 | |||||
在与allenNLP使用同样的超参和配置时,本代码复现取得了63.6%的F1值。 | |||||
## 问题 | |||||
如果您有什么问题或者反馈,请提issue或者邮件联系我: | |||||
yexu_i@qq.com |
@@ -0,0 +1,14 @@ | |||||
import unittest | |||||
from ..data_load.cr_loader import CRLoader | |||||
class Test_CRLoader(unittest.TestCase): | |||||
def test_cr_loader(self): | |||||
train_path = 'data/train.english.jsonlines.mini' | |||||
dev_path = 'data/dev.english.jsonlines.minid' | |||||
test_path = 'data/test.english.jsonlines' | |||||
cr = CRLoader() | |||||
data_info = cr.process({'train':train_path,'dev':dev_path,'test':test_path}) | |||||
print(data_info.datasets['train'][0]) | |||||
print(data_info.datasets['dev'][0]) | |||||
print(data_info.datasets['test'][0]) |
@@ -0,0 +1,69 @@ | |||||
import sys | |||||
sys.path.append('../..') | |||||
import torch | |||||
from torch.optim import Adam | |||||
from fastNLP.core.callback import Callback, GradientClipCallback | |||||
from fastNLP.core.trainer import Trainer | |||||
from reproduction.coreference_resolution.data_load.cr_loader import CRLoader | |||||
from reproduction.coreference_resolution.model.config import Config | |||||
from reproduction.coreference_resolution.model.model_re import Model | |||||
from reproduction.coreference_resolution.model.softmax_loss import SoftmaxLoss | |||||
from reproduction.coreference_resolution.model.metric import CRMetric | |||||
from fastNLP import SequentialSampler | |||||
from fastNLP import cache_results | |||||
# torch.backends.cudnn.benchmark = False | |||||
# torch.backends.cudnn.deterministic = True | |||||
class LRCallback(Callback): | |||||
def __init__(self, parameters, decay_rate=1e-3): | |||||
super().__init__() | |||||
self.paras = parameters | |||||
self.decay_rate = decay_rate | |||||
def on_step_end(self): | |||||
if self.step % 100 == 0: | |||||
for para in self.paras: | |||||
para['lr'] = para['lr'] * (1 - self.decay_rate) | |||||
if __name__ == "__main__": | |||||
config = Config() | |||||
print(config) | |||||
@cache_results('cache.pkl') | |||||
def cache(): | |||||
cr_train_dev_test = CRLoader() | |||||
data_info = cr_train_dev_test.process({'train': config.train_path, 'dev': config.dev_path, | |||||
'test': config.test_path}) | |||||
return data_info | |||||
data_info = cache() | |||||
print("数据集划分:\ntrain:", str(len(data_info.datasets["train"])), | |||||
"\ndev:" + str(len(data_info.datasets["dev"])) + "\ntest:" + str(len(data_info.datasets["test"]))) | |||||
# print(data_info) | |||||
model = Model(data_info.vocabs, config) | |||||
print(model) | |||||
loss = SoftmaxLoss() | |||||
metric = CRMetric() | |||||
optim = Adam(model.parameters(), lr=config.lr) | |||||
lr_decay_callback = LRCallback(optim.param_groups, config.lr_decay) | |||||
trainer = Trainer(model=model, train_data=data_info.datasets["train"], dev_data=data_info.datasets["dev"], | |||||
loss=loss, metrics=metric, check_code_level=-1,sampler=None, | |||||
batch_size=1, device=torch.device("cuda:" + config.cuda), metric_key='f', n_epochs=config.epoch, | |||||
optimizer=optim, | |||||
save_path='/remote-home/xxliu/pycharm/fastNLP/fastNLP/reproduction/coreference_resolution/save', | |||||
callbacks=[lr_decay_callback, GradientClipCallback(clip_value=5)]) | |||||
print() | |||||
trainer.train() |
@@ -0,0 +1,24 @@ | |||||
import torch | |||||
from reproduction.coreference_resolution.model.config import Config | |||||
from reproduction.coreference_resolution.model.metric import CRMetric | |||||
from reproduction.coreference_resolution.data_load.cr_loader import CRLoader | |||||
from fastNLP import Tester | |||||
import argparse | |||||
if __name__=='__main__': | |||||
parser = argparse.ArgumentParser() | |||||
parser.add_argument('--path') | |||||
args = parser.parse_args() | |||||
cr_loader = CRLoader() | |||||
config = Config() | |||||
data_info = cr_loader.process({'train': config.train_path, 'dev': config.dev_path, | |||||
'test': config.test_path}) | |||||
metirc = CRMetric() | |||||
model = torch.load(args.path) | |||||
tester = Tester(data_info.datasets['test'],model,metirc,batch_size=1,device="cuda:0") | |||||
tester.test() | |||||
print('test over') | |||||