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competition_predict.py
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competition_predict.py
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import os
import json
import torch
from collections import defaultdict
from transformers import BertTokenizer
from src.utils.model_utils import CRFModel, SpanModel, EnsembleCRFModel, EnsembleSpanModel
from src.utils.evaluator import crf_decode, span_decode
from src.utils.functions_utils import load_model_and_parallel, ensemble_vote
from src.preprocess.processor import cut_sent, fine_grade_tokenize
MID_DATA_DIR = "./data/mid_data"
RAW_DATA_DIR = "./data/raw_data_random"
SUBMIT_DIR = "./result"
GPU_IDS = "0"
LAMBDA = 0.3
THRESHOLD = 0.9
MAX_SEQ_LEN = 512
TASK_TYPE = "crf" # choose crf or span
VOTE = True # choose True or False
VERSION = "mixed" # choose single or ensemble or mixed ; if mixed VOTE and TAST_TYPE is useless.
# single_predict
BERT_TYPE = "uer_large" # roberta_wwm / ernie_1 / uer_large
BERT_DIR = f"./bert/torch_{BERT_TYPE}"
with open('./best_ckpt_path.txt', 'r', encoding='utf-8') as f:
CKPT_PATH = f.read().strip()
# ensemble_predict
BERT_DIR_LIST = ["./bert/torch_uer_large", "./bert/torch_roberta_wwm"]
with open('./best_ckpt_path.txt', 'r', encoding='utf-8') as f:
ENSEMBLE_DIR_LIST = f.readlines()
print('ENSEMBLE_DIR_LIST:{}'.format(ENSEMBLE_DIR_LIST))
# mixed_predict
MIX_BERT_DIR = "./bert/torch_uer_large"
with open('./best_ckpt_path.txt', 'r', encoding='utf-8') as f:
MIX_DIR_LIST = f.readlines()
print('MIX_DIR_LIST:{}'.format(MIX_DIR_LIST))
def prepare_info():
info_dict = {}
with open(os.path.join(MID_DATA_DIR, f'{TASK_TYPE}_ent2id.json'), encoding='utf-8') as f:
ent2id = json.load(f)
with open(os.path.join(RAW_DATA_DIR, 'test.json'), encoding='utf-8') as f:
info_dict['examples'] = json.load(f)
info_dict['id2ent'] = {ent2id[key]: key for key in ent2id.keys()}
info_dict['tokenizer'] = BertTokenizer(os.path.join(BERT_DIR, 'vocab.txt'))
return info_dict
def mixed_prepare_info(mixed='crf'):
info_dict = {}
with open(os.path.join(MID_DATA_DIR, f'{mixed}_ent2id.json'), encoding='utf-8') as f:
ent2id = json.load(f)
with open(os.path.join(RAW_DATA_DIR, 'test.json'), encoding='utf-8') as f:
info_dict['examples'] = json.load(f)
info_dict['id2ent'] = {ent2id[key]: key for key in ent2id.keys()}
info_dict['tokenizer'] = BertTokenizer(os.path.join(BERT_DIR, 'vocab.txt'))
return info_dict
def base_predict(model, device, info_dict, ensemble=False, mixed=''):
labels = defaultdict(list)
tokenizer = info_dict['tokenizer']
id2ent = info_dict['id2ent']
with torch.no_grad():
for _ex in info_dict['examples']:
ex_idx = _ex['id']
raw_text = _ex['text']
if not len(raw_text):
labels[ex_idx] = []
print('{}为空'.format(ex_idx))
continue
sentences = cut_sent(raw_text, MAX_SEQ_LEN)
start_index = 0
for sent in sentences:
sent_tokens = fine_grade_tokenize(sent, tokenizer)
encode_dict = tokenizer.encode_plus(text=sent_tokens,
max_length=MAX_SEQ_LEN,
is_pretokenized=True,
pad_to_max_length=False,
return_tensors='pt',
return_token_type_ids=True,
return_attention_mask=True)
model_inputs = {'token_ids': encode_dict['input_ids'],
'attention_masks': encode_dict['attention_mask'],
'token_type_ids': encode_dict['token_type_ids']}
for key in model_inputs:
model_inputs[key] = model_inputs[key].to(device)
if ensemble:
if TASK_TYPE == 'crf':
if VOTE:
decode_entities = model.vote_entities(model_inputs, sent, id2ent, THRESHOLD)
else:
pred_tokens = model.predict(model_inputs)[0]
decode_entities = crf_decode(pred_tokens, sent, id2ent)
else:
if VOTE:
decode_entities = model.vote_entities(model_inputs, sent, id2ent, THRESHOLD)
else:
start_logits, end_logits = model.predict(model_inputs)
start_logits = start_logits[0].cpu().numpy()[1:1 + len(sent)]
end_logits = end_logits[0].cpu().numpy()[1:1 + len(sent)]
decode_entities = span_decode(start_logits, end_logits, sent, id2ent)
else:
if mixed:
if mixed == 'crf':
pred_tokens = model(**model_inputs)[0][0]
decode_entities = crf_decode(pred_tokens, sent, id2ent)
else:
start_logits, end_logits = model(**model_inputs)
start_logits = start_logits[0].cpu().numpy()[1:1 + len(sent)]
end_logits = end_logits[0].cpu().numpy()[1:1 + len(sent)]
decode_entities = span_decode(start_logits, end_logits, sent, id2ent)
else:
if TASK_TYPE == 'crf':
pred_tokens = model(**model_inputs)[0][0]
decode_entities = crf_decode(pred_tokens, sent, id2ent)
else:
start_logits, end_logits = model(**model_inputs)
start_logits = start_logits[0].cpu().numpy()[1:1+len(sent)]
end_logits = end_logits[0].cpu().numpy()[1:1+len(sent)]
decode_entities = span_decode(start_logits, end_logits, sent, id2ent)
for _ent_type in decode_entities:
for _ent in decode_entities[_ent_type]:
tmp_start = _ent[1] + start_index
tmp_end = tmp_start + len(_ent[0])
assert raw_text[tmp_start: tmp_end] == _ent[0]
labels[ex_idx].append((_ent_type, tmp_start, tmp_end, _ent[0]))
start_index += len(sent)
if not len(labels[ex_idx]):
labels[ex_idx] = []
return labels
def single_predict():
save_dir = os.path.join(SUBMIT_DIR, VERSION)
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
info_dict = prepare_info()
if TASK_TYPE == 'crf':
model = CRFModel(bert_dir=BERT_DIR, num_tags=len(info_dict['id2ent']))
else:
model = SpanModel(bert_dir=BERT_DIR, num_tags=len(info_dict['id2ent'])+1)
print(f'Load model from {CKPT_PATH}')
model, device = load_model_and_parallel(model, GPU_IDS, CKPT_PATH)
model.eval()
labels = base_predict(model, device, info_dict)
for key in labels.keys():
with open(os.path.join(save_dir, f'{key}.ann'), 'w', encoding='utf-8') as f:
if not len(labels[key]):
print(key)
f.write("")
else:
for idx, _label in enumerate(labels[key]):
f.write(f'T{idx + 1}\t{_label[0]} {_label[1]} {_label[2]}\t{_label[3]}\n')
def ensemble_predict():
save_dir = os.path.join(SUBMIT_DIR, VERSION)
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
info_dict = prepare_info()
model_path_list = [x.strip() for x in ENSEMBLE_DIR_LIST]
print('model_path_list:{}'.format(model_path_list))
device = torch.device(f'cuda:{GPU_IDS[0]}')
if TASK_TYPE == 'crf':
model = EnsembleCRFModel(model_path_list=model_path_list,
bert_dir_list=BERT_DIR_LIST,
num_tags=len(info_dict['id2ent']),
device=device,
lamb=LAMBDA)
else:
model = EnsembleSpanModel(model_path_list=model_path_list,
bert_dir_list=BERT_DIR_LIST,
num_tags=len(info_dict['id2ent'])+1,
device=device)
labels = base_predict(model, device, info_dict, ensemble=True)
for key in labels.keys():
with open(os.path.join(save_dir, f'{key}.ann'), 'w', encoding='utf-8') as f:
if not len(labels[key]):
print(key)
f.write("")
else:
for idx, _label in enumerate(labels[key]):
f.write(f'T{idx + 1}\t{_label[0]} {_label[1]} {_label[2]}\t{_label[3]}\n')
def mixed_predict():
save_dir = os.path.join(SUBMIT_DIR, VERSION)
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
model_path_list = [x.strip() for x in MIX_DIR_LIST]
print('model_path_list:{}'.format(model_path_list))
all_labels = []
for i, model_path in enumerate(model_path_list):
if i <= 4:
info_dict = mixed_prepare_info(mixed='span')
model = SpanModel(bert_dir=MIX_BERT_DIR, num_tags=len(info_dict['id2ent']) + 1)
print(f'Load model from {model_path}')
model, device = load_model_and_parallel(model, GPU_IDS, model_path)
model.eval()
labels = base_predict(model, device, info_dict, ensemble=False, mixed='span')
else:
info_dict = mixed_prepare_info(mixed='crf')
model = CRFModel(bert_dir=MIX_BERT_DIR, num_tags=len(info_dict['id2ent']))
print(f'Load model from {model_path}')
model, device = load_model_and_parallel(model, GPU_IDS, model_path)
model.eval()
labels = base_predict(model, device, info_dict, ensemble=False, mixed='crf')
all_labels.append(labels)
labels = ensemble_vote(all_labels, THRESHOLD)
# for key in labels.keys():
for key in range(1500, 1997):
with open(os.path.join(save_dir, f'{key}.ann'), 'w', encoding='utf-8') as f:
if not len(labels[key]):
print(key)
f.write("")
else:
for idx, _label in enumerate(labels[key]):
f.write(f'T{idx + 1}\t{_label[0]} {_label[1]} {_label[2]}\t{_label[3]}\n')
if __name__ == '__main__':
assert VERSION in ['single', 'ensemble', 'mixed'], 'VERSION mismatch'
if VERSION == 'single':
single_predict()
elif VERSION == 'ensemble':
if VOTE:
print("————————开始投票：————————")
ensemble_predict()
elif VERSION == 'mixed':
print("————————开始混合投票：————————")
mixed_predict()
# 压缩result.zip
import zipfile
def zip_file(src_dir):
zip_name = src_dir + '.zip'
z = zipfile.ZipFile(zip_name, 'w', zipfile.ZIP_DEFLATED)
for dirpath, dirnames, filenames in os.walk(src_dir):
fpath = dirpath.replace(src_dir, '')
fpath = fpath and fpath + os.sep or ''
for filename in filenames:
z.write(os.path.join(dirpath, filename), fpath + filename)
print('==压缩成功==')
z.close()
zip_file('./result')