class Predict(object):
def __init__(self,
model_path=config.root_path + '/model/saved_dict/bert.ckpt',
bert_path=config.root_path + '/model/bert-wwm/',
is_cuda=config.is_cuda):
self.model_path = model_path
self.tokenizer = BertTokenizer.from_pretrained(bert_path)
self.is_cuda = is_cuda
config.bert_path = config.root_path + '/model/bert/'
config.hidden_size = 768
self.model = Model(config).to(config.device)
checkpoint = torch.load(self.model_path)
self.model.load_state_dict(checkpoint, strict=False)
self.model.eval()
def process_data(self, text, is_cuda=config.is_cuda):
def padding(indice, max_length, pad_idx=0):
"""
pad 函数
注意 token type id 右侧pad是添加1而不是0，1表示属于句子B
"""
pad_indice = [
item + [pad_idx] * max(0, max_length - len(item))
for item in indice
]
return torch.tensor(pad_indice)
text_dict = self.tokenizer.encode_plus(
text, # Sentence to encode.
add_special_tokens=True, # Add '[CLS]' and '[SEP]'
max_length=config.max_length, # Pad & truncate all sentences.
ad_to_max_length=True,
return_attention_mask=True, # Construct attn. masks.
# return_tensors='pt', # Return pytorch tensors.
)
input_ids, attention_mask, token_type_ids = text_dict[
'input_ids'], text_dict['attention_mask'], text_dict[
'token_type_ids']
token_ids_padded = padding([input_ids], config.max_length)
token_type_ids_padded = padding([token_type_ids], config.max_length)
attention_mask_padded = padding([attention_mask], config.max_length)
return token_ids_padded, token_type_ids_padded, attention_mask_padded
def predict(self, text):
token_ids_padded, token_type_ids_padded, attention_mask_padded = self.process_data(
text)
if self.is_cuda:
token_ids_padded = token_ids_padded.to(torch.device('cuda'))
token_type_ids_padded = token_type_ids_padded.to(
torch.device('cuda'))
attention_mask_padded = attention_mask_padded.to(
torch.device('cuda'))
outputs = self.model(
(token_ids_padded, attention_mask_padded, token_type_ids_padded))
label = torch.max(outputs.data, 1)[1].cpu().numpy()[0]
score = outputs.data[0][torch.max(
outputs.data, 1)[1].cpu().numpy()[0]].cpu().numpy().tolist()
return label, score
def __init__(self,
model_path=config.root_path + '/model/saved_dict/bert.ckpt',
bert_path=config.root_path + '/model/bert-wwm/',
is_cuda=config.is_cuda):
self.model_path = model_path
self.tokenizer = BertTokenizer.from_pretrained(bert_path)
self.is_cuda = is_cuda
config.bert_path = config.root_path + '/model/bert/'
config.hidden_size = 768
self.model = Model(config).to(config.device)
checkpoint = torch.load(self.model_path)
self.model.load_state_dict(checkpoint, strict=False)
self.model.eval()
end_time = time.time()
print('Elasped time: {}'.format(end_time - start_time))
preds = np.argmax(preds, axis=1)
eval_acc = (preds == out_label_ids).mean()
return eval_acc
if __name__ == '__main__':
# Set seed
set_seed(config)
config.bert_path = config.root_path + '/model/bert/'
config.hidden_size = 768
model = Model(config).to(config.device)
checkpoint = torch.load(config.root_path + '/model/saved_dict/bert.ckpt')
model.load_state_dict(checkpoint, strict=False)
tokenizer = BertTokenizer.from_pretrained(
config.root_path + '/model/bert', do_lower_case=config.do_lower_case)
model.to(config.device)
print('finish model load')
if config.visualize > -1:
start_pos = config.visualize
end_pos = start_pos + 1
else:
start_pos = config.start_pos
end_pos = config.end_pos
print('load data')
test_dataset = MyDataset(config.test_file,