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train_span_scorer.py
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import argparse
import pyhocon
from sklearn.utils import shuffle
from transformers import AutoTokenizer, AutoModel
from tqdm import tqdm
from corpus import Corpus
from evaluator import Evaluation
from models import SpanEmbedder, SpanScorer
from model_utils import *
from utils import *
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='configs/config_span_scorer.json')
args = parser.parse_args()
def train_topic_mention_extractor(span_repr, span_scorer, start_end, continuous_embeddings,
width, labels, batch_size, criterion, optimizer):
accumulate_loss = 0
idx = list(range(len(width)))
for i in range(0, len(width), batch_size):
indices = idx[i:i+batch_size]
batch_start_end = start_end[indices]
batch_width = width[indices]
batch_continuous_embeddings = [continuous_embeddings[k] for k in indices]
batch_labels = labels[i:i + batch_size]
optimizer.zero_grad()
span = span_repr(batch_start_end, batch_continuous_embeddings, batch_width)
scores = span_scorer(span)
loss = criterion(scores.squeeze(1), batch_labels)
loss.backward()
accumulate_loss += loss.item()
optimizer.step()
return accumulate_loss
def get_span_data_from_topic(config, bert_model, data: Corpus, topic: str):
docs_embeddings, docs_length = pad_and_read_bert(data.bert_tokens[topic], bert_model)
span_meta_data, span_embeddings, num_of_tokens = get_all_candidate_from_topic(
config, data, topic, docs_embeddings, docs_length)
doc_ids, sentence_id, start, end = span_meta_data
labels = data.get_candidate_labels(topic, doc_ids, start, end)
mention_labels = torch.zeros(labels.shape, device=device)
mention_labels[labels.nonzero().squeeze(1)] = 1
return span_meta_data, span_embeddings, mention_labels, num_of_tokens
if __name__ == '__main__':
config = pyhocon.ConfigFactory.parse_file(args.config)
fix_seed(config)
logger = create_logger(config, create_file=True)
logger.info(pyhocon.HOCONConverter.convert(config, "hocon"))
create_folder(config['model_path'])
if torch.cuda.is_available():
device = 'cuda:{}'.format(config['gpu_num'])
torch.cuda.set_device(config['gpu_num'])
else:
device = 'cpu'
# read and tokenize data
bert_tokenizer = AutoTokenizer.from_pretrained(config['bert_model'], add_special_tokens=True)
training_set = Corpus.create_corpus(config, bert_tokenizer, 'train')
dev_set = Corpus.create_corpus(config, bert_tokenizer, 'dev')
# Mention extractor configuration
logger.info('Init models')
bert_model = AutoModel.from_pretrained(config['bert_model']).to(device)
config['bert_hidden_size'] = bert_model.config.hidden_size
span_repr = SpanEmbedder(config, device).to(device)
span_scorer = SpanScorer(config).to(device)
optimizer = get_optimizer(config, [span_scorer, span_repr])
criterion = get_loss_function(config)
logger.info('Number of parameters of mention extractor: {}'.format(
count_parameters(span_repr) + count_parameters(span_scorer)))
span_repr_path = os.path.join(config['model_path'],
'{}_span_repr_{}'.format(config['mention_type'], config['exp_num']))
span_scorer_path = os.path.join(config['model_path'],
'{}_span_scorer_{}'.format(config['mention_type'], config['exp_num']))
logger.info('Number of topics: {}'.format(len(training_set.docs_by_topic)))
max_dev = (0, None)
for epoch in range(config['epochs']):
logger.info('Epoch: {}'.format(epoch))
span_repr.train()
span_scorer.train()
list_of_topics = shuffle(list(training_set.docs_by_topic.keys()))
accumulate_loss = 0
for topic in tqdm(list_of_topics):
span_meta_data, span_embeddings, mention_labels, num_of_tokens = \
get_span_data_from_topic(config, bert_model, training_set, topic)
topic_start_end_embeddings, topic_continuous_embeddings, topic_width = span_embeddings
epoch_loss = train_topic_mention_extractor(span_repr, span_scorer, topic_start_end_embeddings,
topic_continuous_embeddings, topic_width.to(device),
mention_labels, config['batch_size'], criterion, optimizer)
accumulate_loss += epoch_loss
torch.cuda.empty_cache()
logger.info('Accumulate loss: {}'.format(accumulate_loss))
logger.info('Evaluate on the dev set')
span_repr.eval()
span_scorer.eval()
all_scores, all_labels = [], []
dev_num_of_tokens = 0
for topic in tqdm(dev_set.docs_by_topic.keys()):
span_meta_data, span_embeddings, mention_labels, num_of_tokens = \
get_span_data_from_topic(config, bert_model, dev_set, topic)
all_labels.extend(mention_labels)
dev_num_of_tokens += num_of_tokens
topic_start_end_embeddings, topic_continuous_embeddings, topic_width = span_embeddings
with torch.no_grad():
span_emb = span_repr(topic_start_end_embeddings, topic_continuous_embeddings,
topic_width.to(device))
span_score = span_scorer(span_emb)
all_scores.extend(span_score.squeeze(1))
all_scores = torch.stack(all_scores)
all_labels = torch.stack(all_labels)
strict_preds = (all_scores > 0).to(torch.int)
eval = Evaluation(strict_preds, all_labels)
logger.info(
'Recall: {}, Precision: {}, F1: {}'.format(eval.get_recall(),
eval.get_precision(), eval.get_f1()))
eval_range = [0.2, 0.25, 0.3] if config['mention_type'] == 'events' else [0.2, 0.25, 0.3, 0.4, 0.45]
for k in eval_range:
s, i = torch.topk(all_scores, int(k * dev_num_of_tokens), sorted=False)
rank_preds = torch.zeros(len(all_scores), device=device)
rank_preds[i] = 1
eval = Evaluation(rank_preds, all_labels)
recall = eval.get_recall()
if recall > max_dev[0]:
max_dev = (recall, epoch)
torch.save(span_repr.state_dict(), span_repr_path)
torch.save(span_scorer.state_dict(), span_scorer_path)
logger.info(
'K = {}, Recall: {}, Precision: {}, F1: {}'.format(k, eval.get_recall(), eval.get_precision(),
eval.get_f1()))
logger.info('Best Performance: {}'.format(max_dev))