def test_eval(self):
data = DATAMultiWOZ(
debug=False,
data_dir=self.data_dir
)
test_examples = data.read_examples(os.path.join(self.data_dir, 'test.json'))
print('eval_examples的数量', len(test_examples))
dialogueID = [x.guid for x in test_examples]
utterance_text = [x.text_history for x in test_examples]
test_features = data.convert_examples_to_features(test_examples, self.tokenizer, self.max_seq_length)
all_input_ids = torch.tensor(data.select_field(test_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(data.select_field(test_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(data.select_field(test_features, 'segment_ids'), dtype=torch.long)
eval_labels_domainslot = torch.tensor([f.labels_domainslot for f in test_features], dtype=torch.float)
eval_labels_domain = torch.tensor([f.labels_domain for f in test_features], dtype=torch.long)
eval_labels_dependcy = torch.tensor([f.labels_dependcy for f in test_features], dtype=torch.long)
test_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,eval_labels_domainslot,eval_labels_domain,eval_labels_dependcy)
# Run prediction for full data
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=self.eval_batch_size)
config = BertConfig.from_pretrained(self.model_name_or_path)
model = BertForTokenClassification.from_pretrained(
os.path.join(self.output_dir, "pytorch_model.bin"), self.args, config=config)
model.to(self.device)
model.eval()
inference_labels = []
gold_labels_domain = []
gold_labels_dependcy = []
gold_labels_domainslot = []
scores_domainslot = []
scores_domain = []
scores_dependcy = []
for input_ids, input_mask, segment_ids,label_domainslot,label_domain,label_dependcy in test_dataloader:
input_ids = input_ids.to(self.device)
input_mask = input_mask.to(self.device)
segment_ids = segment_ids.to(self.device)
label_domainslot = label_domainslot.to(self.device)
label_domain = label_domain.to(self.device)
label_dependcy = label_dependcy.to(self.device)
with torch.no_grad():
logits_domainslot,logits_domain,logits_dependcy = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask
)
logits_domainslot = torch.sigmoid(logits_domainslot)
logits_domainslot = (logits_domainslot > 0.4).float()
logits_domainslot = logits_domainslot.cpu().long().numpy()
logits_domain = logits_domain.view(-1, self.num_labels_domain).cpu().numpy()
logits_dependcy = logits_dependcy.view(-1, self.num_labels_dependcy).cpu().numpy()
label_domainslot = label_domainslot.to('cpu').numpy()
label_domain = label_domain.view(-1).to('cpu').numpy()
label_dependcy = label_dependcy.view(-1).to('cpu').numpy()
scores_domainslot.append(logits_domainslot)
scores_domain.append(logits_domain)
scores_dependcy.append(logits_dependcy)
gold_labels_domainslot.append(label_domainslot)
gold_labels_domain.append(label_domain)
gold_labels_dependcy.append(label_dependcy)
gold_labels_domainslot = np.concatenate(gold_labels_domainslot, 0)
gold_labels_domain = np.concatenate(gold_labels_domain, 0)
gold_labels_dependcy = np.concatenate(gold_labels_dependcy, 0)
scores_domainslot = np.concatenate(scores_domainslot, 0)
scores_domain = np.concatenate(scores_domain, 0)
scores_dependcy = np.concatenate(scores_dependcy, 0)
# 计算评价指标
assert scores_domain.shape[0] == scores_dependcy.shape[0] == gold_labels_domain.shape[0] == gold_labels_dependcy.shape[0]
eval_accuracy_domain = accuracyF1(scores_domain, gold_labels_domain,mode='domain',report=True)
eval_accuracy_dependcy = accuracyF1(scores_dependcy, gold_labels_dependcy,mode='dependcy',report=True)
eval_jointGoal = compute_jointGoal_domainslot(
dialogueID,
utterance_text,
scores_domainslot,
gold_labels_domainslot,
scores_domain,
gold_labels_domain,
scores_dependcy,
gold_labels_dependcy
)
print('eval_accuracy_domain',eval_accuracy_domain)
print('eval_accuracy_dependcy', eval_accuracy_dependcy)
print('eval_jointGoal', eval_jointGoal)
def train(self):
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
# logger.info(f'Fold {split_index + 1}')
train_dataloader, eval_dataloader, train_examples, eval_examples = self.create_dataloader()
num_train_optimization_steps = self.train_steps
# Prepare model
config = BertConfig.from_pretrained(self.model_name_or_path)
model = BertForTokenClassification.from_pretrained(self.model_name_or_path,self.args, config=config)
model.to(self.device)
model.train()
# Prepare optimizer
param_optimizer = list(model.named_parameters())
param_optimizer = [n for n in param_optimizer]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': self.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=self.learning_rate, eps=self.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=self.warmup_steps, t_total=self.train_steps)
global_step = 0
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", self.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
best_acc = 0
best_MRR = 0
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
train_dataloader = cycle(train_dataloader)
for step in range(num_train_optimization_steps):
batch = next(train_dataloader)
batch = tuple(t.to(self.device) for t in batch)
input_ids,input_mask,segment_ids,\
utterance_mask,domain_mask, \
slot_mask,hist_mask,\
label_value_start,label_value_end,\
label_domainslot = batch
loss_tokenstart,loss_tokenend,loss_domainslot = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
utterance_mask = utterance_mask,
domain_mask = domain_mask,
slot_mask = slot_mask,
hist_mask = hist_mask,
label_value_start=label_value_start,
label_value_end = label_value_end,
label_domainslot = label_domainslot
)
loss = loss_tokenstart + loss_tokenend + loss_domainslot
# loss = loss_domainslot
tr_loss += loss.item()
train_loss = round(tr_loss / (nb_tr_steps + 1), 4)
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
loss.backward()
if (nb_tr_steps + 1) % self.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
scheduler.step()
global_step += 1
if (step + 1) % (self.eval_steps * self.gradient_accumulation_steps) == 0:
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
logger.info("***** Report result *****")
logger.info(" %s = %s", 'global_step', str(global_step))
logger.info(" %s = %s", 'train loss', str(train_loss))
if self.do_eval and (step + 1) % (self.eval_steps * self.gradient_accumulation_steps) == 0:
for file in ['de.csv']:
gold_value_start = []
gold_value_end = []
gold_domainslot = []
scores_value_start = []
scores_value_end = []
scores_domainslot = []
dialogueID = [x.guid for x in eval_examples]
utterance_text = [x.text_eachturn for x in eval_examples]
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", self.eval_batch_size)
model.eval()
eval_loss_tokens_start,eval_loss_tokens_end,eval_loss_domainslot = 0,0,0
eval_F1_tokens_start,eval_F1_tokens_end = 0,0
eval_F1_sentence_domainslot,eval_F1_tokens_domainslot = 0,0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids,input_mask, segment_ids,\
utterance_mask,domain_mask, \
slot_mask,hist_mask,\
label_value_start,label_value_end,\
label_domainslot in eval_dataloader:
input_ids = input_ids.to(self.device)
input_mask = input_mask.to(self.device)
segment_ids = segment_ids.to(self.device)
utterance_mask = utterance_mask.to(self.device)
domain_mask = domain_mask.to(self.device)
slot_mask = slot_mask.to(self.device)
hist_mask = hist_mask.to(self.device)
label_value_start = label_value_start.to(self.device)
label_value_end = label_value_end.to(self.device)
label_domainslot = label_domainslot.to(self.device)
with torch.no_grad():
batch_eval_loss_value_start,batch_eval_loss_value_end,batch_eval_loss_domainslot = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
utterance_mask = utterance_mask,
domain_mask = domain_mask,
slot_mask = slot_mask,
hist_mask = hist_mask,
label_value_start = label_value_start,
label_value_end=label_value_end,
label_domainslot=label_domainslot
)
logits_value_start,logits_value_end,logits_domainslot = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
utterance_mask = utterance_mask,
domain_mask = domain_mask,
slot_mask = slot_mask,
hist_mask = hist_mask,
)
logits_value_start = logits_value_start.cpu().numpy()
logits_value_end = logits_value_end.cpu().numpy()
logits_domainslot = logits_domainslot.cpu().numpy()
label_value_start = label_value_start.to('cpu').numpy()
label_value_end = label_value_end.to('cpu').numpy()
label_domainslot = label_domainslot.to('cpu').numpy()
scores_value_start.append(logits_value_start)
scores_value_end.append(logits_value_end)
scores_domainslot.append(logits_domainslot)
gold_value_start.append(label_value_start)
gold_value_end.append(label_value_end)
gold_domainslot.append(label_domainslot)
eval_loss_tokens_start += batch_eval_loss_value_start.mean().item()
eval_loss_tokens_end += batch_eval_loss_value_end.mean().item()
eval_loss_domainslot += batch_eval_loss_domainslot.mean().item()
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
gold_value_start = np.concatenate(gold_value_start,0)
gold_value_end = np.concatenate(gold_value_end,0)
gold_domainslot = np.concatenate(gold_domainslot,0)
scores_value_start = np.concatenate(scores_value_start, 0)
scores_value_end = np.concatenate(scores_value_end, 0)
scores_domainslot = np.concatenate(scores_domainslot,0)
model.train()
eval_loss_tokens_start = eval_loss_tokens_start/nb_eval_steps
eval_loss_tokens_end = eval_loss_tokens_end / nb_eval_steps
eval_loss_domainslot = eval_loss_domainslot /nb_eval_steps
# print(scores_domainslot.shape)
# print(gold_labels_domainslot.shape)
# print(scores_domainslot)
# print(gold_labels_domainslot)
# exit()
# eval_accuracy_token_start = accuracyF1(scores_domain, gold_labels_domain,mode='domain')
# eval_accuracy_token_end = accuracyF1(scores_dependcy, gold_labels_dependcy ,mode= 'dependcy')
eval_F1_valuestart,eval_F1_valueend,F1_domainslot = compute_jointGoal_domainslot(
dialogueID,
utterance_text,
scores_value_start,
scores_value_end,
scores_domainslot,
gold_value_start,
gold_value_end,
gold_domainslot
)
print(
'F1_domainslot',F1_domainslot,
'eval_F1_valuestart',eval_F1_valuestart,
'eval_F1_valueend', eval_F1_valueend,
'global_step',global_step,
'loss',train_loss
)
result = {
'eval_loss_tokens_start':eval_loss_tokens_start,
'eval_loss_tokens_end': eval_loss_tokens_end,
'eval_loss_domainslot':eval_loss_domainslot,
'F1_domainslot': F1_domainslot,
'eval_F1_valuestart': eval_F1_valuestart,
'eval_F1_valueend': eval_F1_valueend,
'global_step': global_step,
'loss': train_loss}
output_eval_file = os.path.join(self.output_dir, "eval_results.txt")
with open(output_eval_file, "a") as writer:
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write('*' * 80)
writer.write('\n')
if eval_F1_valuestart > best_acc :
print("=" * 80)
print("Best jointGoal", eval_F1_valuestart)
print("Saving Model......")
# best_acc = eval_accuracy
best_acc = eval_F1_valuestart
# Save a trained model
model_to_save = model.module if hasattr(model,'module') else model
output_model_file = os.path.join(self.output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
print("=" * 80)
else:
print("=" * 80)