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# coding: UTF-8
from typing import Iterator
from transformers import AdamW, get_linear_schedule_with_warmup
from preprocess import get_time_dif
from sklearn import metrics
import time
import torch
import numpy as np
#评估模型性能
def eval(model, config, iterator, flag=False):
model.eval()
total_loss = 0
all_preds = np.array([], dtype=int)
all_labels = np.array([], dtype=int)
with torch.no_grad():
for batch, labels in iterator:
outputs = model(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
token_type_ids=batch["token_type_ids"],
labels=labels)
loss = outputs[0]
logits = outputs[1]
total_loss += loss
true = labels.data.cpu().numpy()
pred = torch.max(logits.data, 1)[1].cpu().numpy()
all_labels = np.append(all_labels, true)
all_preds = np.append(all_preds, pred)
acc = metrics.accuracy_score(all_labels, all_preds)
if flag:
report = metrics.classification_report(all_labels, all_preds, target_names=config.label_list, digits=4)
confusion = metrics.confusion_matrix(all_labels, all_preds)
return acc, total_loss / len(iterator), report, confusion
return acc, total_loss / len(iterator)
#测试验证模型
def test(model, config, iterator):
model.load_state_dict(torch.load(config.saved_model))
start_time = time.time()
acc, loss, report, confusion = eval(model, config, iterator, flag=True)
msg = "Test Loss: {0:>5.2}, Test Acc: {1:>6.2%}"
print(msg.format(loss, acc))
print("Precision, Recall and F1-Score...")
print(report)
print("Confusion Matrix...")
print(confusion)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
#训练模型并保存
def train(model, config, train_iterator, dev_iterator):
model.train()
start_time = time.time()
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
param_optimizer = model.named_parameters()
optimizer_grouped_parameters = [
{"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': config.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
t_total = len(train_iterator) * config.num_epochs
optimizer = AdamW(optimizer_grouped_parameters, lr=config.learning_rate)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=config.warmup_steps, num_training_steps=t_total)
total_batch = 0
last_improve = 0
break_flag = False
best_dev_loss = float('inf')
for epoch in range(config.num_epochs):
print("Epoch [{}/{}]".format(epoch + 1, config.num_epochs))
for _, (batch, labels) in enumerate(train_iterator):
outputs = model(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
token_type_ids=batch["token_type_ids"],
labels=labels)
loss = outputs[0]
logits = outputs[1]
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), config.max_grad_norm)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
if total_batch % config.log_batch == 0:
true = labels.data.cpu()
pred = torch.max(logits.data, 1)[1].cpu()
acc = metrics.accuracy_score(true, pred)
dev_acc, dev_loss = eval(model, config, dev_iterator)
if dev_loss < best_dev_loss:
best_dev_loss = dev_loss
torch.save(model.state_dict(), config.saved_model)
improve = "*"
last_improve = total_batch
else:
improve = ""
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Batch Train Loss: {1:>5.2}, Batch Train Acc: {2:>6.2%}, Val Loss: {3:>5.2}, Val Acc: {4:>6.2%}, Time: {5} {6}'
print(msg.format(total_batch, loss.item(), acc, dev_loss, dev_acc, time_dif, improve))
model.train()
total_batch += 1
if total_batch - last_improve > config.require_improvement:
print("No improvement for a long time, auto-stopping...")
break_flag = True
break
if break_flag:
break
test(model, config, dev_iterator)
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