def test_eval(self):
data = DATACQA(
debug=False,
data_dir=self.data_dir
)
test_examples = data.read_examples_test(os.path.join(self.data_dir, 'test.csv'))
print('eval_examples的数量', len(test_examples))
questions = [x.text_a 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)
all_label = torch.tensor([f.label for f in test_features], dtype=torch.long)
test_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
# 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, num_labels=self.num_labels)
model = BertForSequenceClassification.from_pretrained(
os.path.join(self.output_dir, "pytorch_model_0.bin"), self.args, config=config)
model.to(self.device)
model.eval()
inference_labels = []
gold_labels = []
scores = []
for input_ids, input_mask, segment_ids, label_ids 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_ids = label_ids.to(self.device)
with torch.no_grad():
logits = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask
).detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
scores.append(logits)
inference_labels.append(np.argmax(logits, axis=1))
gold_labels.append(label_ids)
gold_labels = np.concatenate(gold_labels, 0)
scores = np.concatenate(scores, 0)
logits = np.concatenate(inference_labels, 0)
# eval_accuracy = accuracyCQA(inference_logits, gold_labels)
eval_mrr = compute_MRR_CQA(scores, gold_labels, questions)
eval_5R20 = compute_5R20(scores, gold_labels, questions)
print('eval_mrr',eval_mrr)
print('eval_5R20',eval_5R20)
def train(self):
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
data_splitList = DATACQA.load_data(os.path.join(self.data_dir, 'train.csv'),n_splits=5)
for split_index,each_data in enumerate(data_splitList):
# Prepare model
config = BertConfig.from_pretrained(self.model_name_or_path, num_labels=self.num_labels)
model = BertForSequenceClassification.from_pretrained(self.model_name_or_path, self.args, config=config)
model.to(self.device)
logger.info(f'Fold {split_index + 1}')
train_dataloader, eval_dataloader, train_examples, eval_examples = self.create_dataloader(each_data)
num_train_optimization_steps = self.train_steps
# 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
model.train()
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, label_ids = batch
loss = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
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:
scheduler.step()
optimizer.step()
optimizer.zero_grad()
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 ['dev.csv']:
inference_labels = []
gold_labels = []
inference_logits = []
scores = []
questions = [x.text_a 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)
# Run prediction for full data
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
input_ids = input_ids.to(self.device)
input_mask = input_mask.to(self.device)
segment_ids = segment_ids.to(self.device)
label_ids = label_ids.to(self.device)
with torch.no_grad():
tmp_eval_loss = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids)
logits = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
inference_labels.append(np.argmax(logits, axis=1))
scores.append(logits)
gold_labels.append(label_ids)
inference_logits.append(logits)
eval_loss += tmp_eval_loss.mean().item()
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
gold_labels = np.concatenate(gold_labels, 0)
inference_logits = np.concatenate(inference_logits, 0)
scores = np.concatenate(scores, 0)
model.train()
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = accuracyCQA(inference_logits, gold_labels)
eval_mrr = compute_MRR_CQA(scores,gold_labels,questions)
eval_5R20 = compute_5R20(scores,gold_labels,questions)
result = {'eval_loss': eval_loss,
'eval_F1': eval_accuracy,
'eval_MRR':eval_mrr,
'eval_5R20':eval_5R20,
'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_accuracy > best_acc :
print("=" * 80)
print("Best F1", eval_accuracy)
print("Saving Model......")
best_acc = eval_accuracy
# 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".format(split_index))
torch.save(model_to_save.state_dict(), output_model_file)
print("=" * 80)
else:
print("=" * 80)
del model
gc.collect()