def train(config, model, train_iter, dev_iter, test_iter):
start_time = time.time()
# EMA初始化
ema = EMA(model, 0.999)
ema.register()
model.train()
param_optimizer = list(model.named_parameters())
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': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}]
# optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
total_batch = 0 # 记录进行到多少batch
dev_best_loss = float('inf')
last_improve = 0 # 记录上次验证集loss下降的batch数
flag = False # 记录是否很久没有效果提升
model.train()
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
ema.update() # 训练过程中，更新完参数后，同步update shadow weights
if total_batch % 100 == 0:
# 每多少轮输出在训练集和验证集上的效果
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predic)
ema.apply_shadow() # model应用shadow weights，进行验证、保存模型
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.save_path)
improve = '*'
last_improve = total_batch
else:
improve = ''
ema.restore() # 下一次训练之前，恢复模型参数
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, 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(), train_acc, dev_loss, dev_acc, time_dif, improve))
model.train()
total_batch += 1
if total_batch - last_improve > config.require_improvement:
# 验证集loss超过1000batch没下降，结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break
if flag:
break
test(config, model, test_iter)
def train(config, model, train_iter, dev_iter, test_iter):
param_optimizer = list(model.named_parameters())
#定义不需要衰减的参数，bert是基于transformer的encoder，add&norm层的参数不需要衰减
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
#需要衰减
{'params':[p for n,p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay':0.01},
#不需要衰减
{'params':[p for n,p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_deacy':0.0}
]
optimizer = BertAdam(params=optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
total_batch = 0
dev_best_loss = float('inf')
last_imporve = 0 # 上次校验集loss下降的batch数
flag = False # 记录是否有效果提升
model.train()
for epoch in range(config.num_epochs):
print('Epoch [{}/{}'.format(epoch+1, config.num_epochs))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
if total_batch % 100 == 0:
true = labels.data.cpu()
predit = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predit)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.save_path)
imporve = '*'
last_imporve = total_batch
else:
imporve = ''
msg = 'Iter:{0:>6}, Train Loss:{1:>5.2}, Train Acc:{2:>6.2}, Val Loss:{3:>5.2}, Val Acc:{4:>6.2%}, {6}'
print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, imporve))
model.train()
total_batch = total_batch + 1
if total_batch - last_imporve > config.require_improvement:
print('在校验数据集合上已经很长时间没有提升了，模型自动停止训练')
flag = True
break
if flag:
break
test(config, model, test_iter)
def train(self, config, model, train_iter, dev_iter, test_iter):
start_time = time.time()
model.train()
param_optimizer = list(model.named_parameters())
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': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}]
# optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
total_batch = 0 # 记录进行到多少batch
dev_best_loss = float('inf')
last_improve = 0 # 记录上次验证集loss下降的batch数
flag = False # 记录是否很久没有效果提升
model.train()
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
for i, (trains, labels) in enumerate(train_iter):
predicts, predicts_list = model(trains, labels, mode="train")
losses = list(map(self._get_loss, predicts, labels))
loss = torch.mean(torch.stack(losses))
model.zero_grad()
#loss = F.cross_entropy(losses, labels)
loss.backward()
optimizer.step()
if total_batch % 1000 == 0:
dev_acc_lev1, dev_acc_lev2, dev_loss = self.evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.save_path)
improve = '*'
last_improve = total_batch
else:
improve = ''
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Val Loss: {2:>5.2}, Val_lev1 Acc: {3:>6.2%}, Val_lev2 Acc: {4:>6.2%}, Time: {5} {6}'
print(msg.format(total_batch, loss.item(), dev_loss, dev_acc_lev1, dev_acc_lev2, time_dif, improve))
model.train()
if total_batch > 10 and total_batch % 90000 == 0:
self.test(config, model, train_iter)
total_batch += 1
if total_batch - last_improve > config.require_improvement:
# 验证集loss超过1000batch没下降，结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break
if flag:
break
self.test(config, model, test_iter)
def train(config, model, train_iter, dev_iter, test_iter):
"""
模型训练方法
:param config:
:param model:
:param train_iter:
:param dev_iter:
:param test_iter:
:return:
"""
start_time = time.time()
#启动 BatchNormalization 和 dropout
model.train()
#拿到所有mode种的参数
param_optimizer = list(model.named_parameters())
# 不需要衰减的参数
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':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_deacy':
0.0
}]
optimizer = BertAdam(params=optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
total_batch = 0 #记录进行多少batch
dev_best_loss = float('inf') #记录校验集合最好的loss
last_imporve = 0 #记录上次校验集loss下降的batch数
flag = False #记录是否很久没有效果提升，停止训练
model.train()
for epoch in range(config.num_epochs):
print('Epoch [{}/{}'.format(epoch + 1, config.num_epochs))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward(retain_graph=False)
optimizer.step()
if total_batch % 100 == 0: #每多少次输出在训练集和校验集上的效果
true = labels.data.cpu()
predit = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predit)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.save_path)
imporve = '*'
last_imporve = total_batch
else:
imporve = ''
time_dif = utils.get_time_dif(start_time)
msg = 'Iter:{0:>6}, Train Loss:{1:>5.2}, 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(), train_acc, dev_loss,
dev_acc, time_dif, imporve))
model.train()
total_batch = total_batch + 1
if total_batch - last_imporve > config.require_improvement:
#在验证集合上loss超过1000batch没有下降，结束训练
print('在校验数据集合上已经很长时间没有提升了，模型自动停止训练')
flag = True
break
if flag:
break
test(config, model, test_iter)
def train(config, model, train_iter, dev_iter, test_iter):
"""
模型训练方法
:param config:
:param model:
:param train_iter:
:param dev_iter:
:param test_iter:
:return:
"""
start_time = time.time()
# 启动 BatchNormalization 和 dropout
model.train()
# 拿到 model 中的参数
param_optimizer = list(model.named_parameters())
# 不需要衰减的参数
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':
0.01
},
{
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
},
]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
# 记录进行到了多少batch
total_batch = 0
# 记录校验集合最好的loss
dev_best_loss = float('inf')
# 记录上次验证集loss下降的batch数
last_improve = 0
# 记录是否很久没有效果提升
flag = False
model.train()
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
# 梯度清零
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
# 每多少轮输出在训练集和校验集上的效果
if total_batch % 100 == 0:
true = labels.data.cpu()
predict = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predict)
# 评估校验集
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
# 保存模型
torch.save(model.state_dict(), config.save_path)
improve = '*'
last_improve = total_batch
else:
improve = ''
time_dif = get_time_dif(start_time)
msg = 'Iter:{0:>6}, Train Loss:{1:>5.2}, 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(), train_acc, dev_loss,
dev_acc, time_dif, improve))
model.train()
total_batch += 1
# 校验集loss超过1000batch没下降，结束训练
if total_batch - last_improve > config.require_improvement:
print("No optimization for a long time, auto-stopping...")
flag = True
break
if flag:
break
print("开始测试模型")
test(config, model, test_iter)
def train_(self):
batch_size = self.arg_parser.batch_size
epochs = self.arg_parser.num_epochs
self.model.train()
iter_counter = 0
# Adadelta, Adagrad, Adam, AdamW, SGD, SparseAdam, Adamax, ASGD, RMSprop, LBFGS, Rprop
best_dev_loss = float("inf")
best_dev_accuracy = 0
train_loss_info = {} # collection loss data to draw the loss curve
train_loss_info["num_epochs"] = epochs
train_loss_info["batch_size"] = batch_size
if self.arg_parser.bert:
param_optimizer = list(self.model.named_parameters())
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':
0.01
}, {
'params': [
p for n, p in param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=self.lr,
warmup=0.05,
t_total=epochs * len(self.train_batch))
else:
optimizer = getattr(torch.optim, self.arg_parser.optimizer)(
self.model.parameters(), lr=self.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode="min",
factor=self.lr_decay,
patience=self.lr_patience,
verbose=True)
for epoch in range(epochs):
logger.info(f"-" * 35 + ">" + f"Training {epoch}th epoch" + "<" +
"-" * 35)
#optimizer = getattr(torch.optim, self.arg_parser.optimizer)(self.model.parameters(), lr = self.lr * (self.lr_decay ** epoch))
last_epoch_dev_loss = float(
"inf") if epoch == 0 else epoch_dev_loss
last_epoch_dev_accuracy = 0 if epoch == 0 else epoch_dev_accuracy
epoch_train_loss = 0
epoch_train_accuracy = 0
epoch_dev_loss = 0
epoch_dev_accuracy = 0
print_counter = 0
for minibatch in self.train_batch:
if self.arg_parser.bert:
input_x = minibatch[0]
masks = minibatch[2]
input_ = (input_x, masks)
else:
input_ = minibatch[0]
label_ = minibatch[1]
output_ = self.model(input_)
self.model.zero_grad()
loss = F.cross_entropy(output_, label_)
loss.backward()
optimizer.step()
iter_counter += 1
if iter_counter % 100 == 0:
predict = output_.max(1)[1].cpu()
label_cpu = label_.cpu()
train_loss = loss.cpu().item()
train_loss = round(train_loss, 5)
#train_accuracy = round(accuracy_score(label_cpu, predict), 4)
train_accuracy = accuracy_score(label_cpu, predict)
dev_loss, dev_accuracy, dev_f1_macro, dev_f1_micro, dev_weighted = self.evaluation(
self.model, self.dev_batch)
epoch_train_loss += train_loss
epoch_train_accuracy += train_accuracy
epoch_dev_loss += dev_loss
epoch_dev_accuracy += dev_accuracy
logger.info(
f"Iter: {iter_counter}, train loss: {train_loss}, train accuracy: {train_accuracy}, val loss: {dev_loss}, val accuracy: {dev_accuracy}"
)
if self.unbalanced == True:
logger.info(
f"val F1 macro: {dev_f1_macro}, val F1 micro: {dev_f1_micro}, val F1 weighted: {dev_weighted}"
)
self.model.train()
if dev_loss < best_dev_loss and dev_accuracy > best_dev_accuracy:
best_dev_loss = dev_loss
best_dev_accuracy = dev_accuracy
#logger.info(f"Best validation loss updated: {best_dev_loss}")
torch.save(self.model, self.model_save_path)
else:
self.iter_patience += 1
print_counter += 1
epoch_train_loss = round(epoch_train_loss / print_counter, 5)
epoch_train_accuracy = round(epoch_train_accuracy / print_counter,
5)
epoch_dev_loss = round(epoch_dev_loss / print_counter, 5)
epoch_dev_accuracy = round(epoch_dev_accuracy / print_counter, 5)
scheduler.step(epoch_dev_loss)
logger.info(
f"{epoch}th epoch finished, val epoch loss:{epoch_dev_loss}, val epoch accuracy:{epoch_dev_accuracy}"
)
self.EarlyStopping(epoch_dev_loss, epoch_dev_accuracy,
last_epoch_dev_loss, last_epoch_dev_accuracy)
if self.EarlyStopping_triggered == True:
logger.info("=" * 70)
logger.info(
f"Early Stopping triggered after {epoch + 1} epoches, calculating test accuracy..."
)
break
if self.EarlyStopping_triggered == False:
logger.info(
"Training fnished, full epoch, calculating test accuracy...")
self.evaluate_test()
def train(config, model, train_iter, dev_iter, test_iter):
start_time = time.time()
ema = EMA(model, 0.999)
ema.register()
model.train()
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias',
'LayerNorm.weight'] # bert官方将此三类免于正则化
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
# optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
total_batch = 0 # 记录进行到多少batch
dev_best_loss = float('inf') # 正无穷
last_improve = 0 # 记录上次验证集loss下降的batch数
flag = False # 记录是否很久没有效果提升
dev_f1_score = []
model.train()
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
for i, (trains, labels) in enumerate(
train_iter): # trains, labels ==> (x, seq_len, mask), y
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss = loss / config.acc_grad
loss.backward()
if (i + 1) % config.acc_grad == 0: # 梯度累加
optimizer.step()
ema.update()
if total_batch % 100 == 0:
# 每100轮输出在训练集和验证集上的效果
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_f1 = metrics.f1_score(true, predic, average='macro')
ema.apply_shadow()
dev_f1, dev_loss = evaluate(config, model, dev_iter)
dev_f1_score.append(dev_f1)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.save_path) # 单gpu
improve = '*'
last_improve = total_batch
else:
improve = ''
ema.restore()
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Train F1: {2:>6.2%}, Val Loss: {3:>5.2}, Val F1: {4:>6.2%}, Time: {5} {6}'
print(
msg.format(total_batch, loss.item(), train_f1, dev_loss,
dev_f1, time_dif, improve))
model.train()
total_batch += 1
if total_batch - last_improve > config.require_improvement:
# 验证集loss超过1000batch没下降，结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break
# print(logits_res)
print('Epoch {} Average F1-Score: {}'.format(epoch + 1,
np.mean(dev_f1_score)))
if flag:
break
test(config, model, test_iter)
def train(config, model, train_iter, dev_iter, test_iter):
start_time = time.time()
model.train()
param_optimizer = list(model.named_parameters())
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':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
# optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
total_batch = 0 # 记录进行到多少batch
dev_best_loss = float('inf')
last_improve = 0 # 记录上次验证集loss下降的batch数
flag = False # 记录是否很久没有效果提升
model.train()
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
if total_batch % 5 == 0:
# 每多少轮输出在训练集和验证集上的效果
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predic)
train_f1 = metrics.f1_score(true, predic, average='macro')
dev_acc, dev_loss, dev_f1 = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.save_path)
improve = '*'
last_improve = total_batch
else:
improve = ''
time_dif = get_time_dif(start_time)
print(
'Iter:{}， Train acc:{:.2f} , Train loss:{:.2f} , train-f1: {:.2f}'
.format(total_batch, train_acc, loss.item(), train_f1))
print('dev_loss:{:.2f}, dev_acc:{:.2f},dev_f1:{:.2f}'.format(
dev_loss, dev_acc, dev_f1))
print('using Time', time_dif)
print('----' * 10)
print('\n')
# msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Train Acc: {2:>6.2%},Train f1: {2:>6.2%}, Val Loss: {3:>5.2}, Val Acc: {4:>6.2%}, Val f1: {4:>6.2%},Time: {5} {6}'
# print(msg.format(total_batch, loss.item(), train_acc, train_f1, dev_loss, dev_acc, dev_f1, time_dif, improve))
model.train()
total_batch += 1
if total_batch - last_improve > config.require_improvement:
# 验证集loss超过1000batch没下降，结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break
if flag:
break
test(config, model, test_iter)
def train(config, model, train_iter, dev_iter):
"""
模型的训练
:param config:
:param model:
:param train_iter:
:param dev_iter:
:return:
"""
start_time = time.time()
# 启动模型的训练模式
model.train()
# 拿到所有参数
param_optimizer = list(model.named_parameters())
# 定义不需要衰减的参数
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': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight': 0.0}
]
optimizer = BertAdam(params=optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.epochs)
total_batch = 0 # 记录进行多少batch
dev_best_loss = float('inf') # 记录上次最好的验证集loss
last_improve = 0 # 记录上次提升的batch
flag = False # 停止位的标志, 是否很久没提升
for epoch in range(config.epochs):
print('Epoch [{}/{}]'.format(epoch+1, config.epochs))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
if total_batch % 50 == 0: # 每训练50次输出在训练集和验证集上的效果
torch.cuda.empty_cache()
true = labels.data.cpu()
predict = torch.max(outputs.data, 1)[1].cpu()
score = metrics.accuracy_score(true, predict)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_best_loss > dev_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.model_save)
improve = '+'
last_improve = total_batch
else:
improve = ''
time_idf = utils.get_time_idf(start_time)
msg = 'Iter:{0:>6}, Train Loss:{1:>5.2}, 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(), score, dev_loss, dev_acc, time_idf, improve))
model.train()
total_batch = total_batch + 1
if total_batch - last_improve > config.require_improvement:
# 在验证集上loss超过1000batch没有下降, 结束训练
print('在验证集上loss超过1000batch没有下降, 结束训练')
flag = True
break
if flag:
break
def train(config, model, train_iter, dev_iter, test_iter):
"""
Train model
"""
# get the model's parameters
param_optimizer = list(model.named_parameters())
# which parameters do not need to be decay
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':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}
]
optimizer = BertAdam(params=optimizer_grouped_parameters,
schedule = None,
lr = config.learning_rate,
warmup=config.warmup,
t_total=len(train_iter) * config.num_epochs)
config.hvd.broadcast_parameters(model.state_dict(), root_rank=0)
config.hvd.broadcast_optimizer_state(optimizer, root_rank=0)
optimizer = config.hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
start_time = time.time()
# activate BatchNormalization & dropout
model.train()
flag = False
# progress
total_batch = 0
# Best loss in dev
dev_best_loss = float('inf')
# Last time loss was decreased's batch number in dev
last_improve = 0
# no improvement in long time, it's ok to stop train
for epoch in range(config.num_epochs):
if config.hvd.rank() == 0:
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
for i, (token_ids, label, seq_len, mask) in enumerate(train_iter):
token_ids = token_ids.to(config.device)
label_gpu = label.to(config.device)
seq_len = seq_len.to(config.device)
mask = mask.to(config.device)
outputs = model(token_ids, seq_len, mask)
model.zero_grad()
loss = F.cross_entropy(outputs, label_gpu)
loss.backward()
optimizer.step()
#Every n batches, go dev
if total_batch % 100 == 0:
# Get the highest one from Softmax()
predit = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(label, predit)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
if config.hvd.rank() == 0:
torch.save(model.state_dict(), config.save_path)
improve = '*'
last_improve = total_batch
else:
improve = ''
time_dif = utils.get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Train Acc:{2:>6.2}, Val Loss: {3:>5.2}, Val Acc: {4:>6.2%}, Time: {5} {6}'
if config.hvd.rank() == 0:
print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve))
model.train()
total_batch = total_batch + 1
if total_batch - last_improve > config.require_improvement:
# No improvement for too long (longer than config.require_improvement)
print('No improvement for too long. Stop training automatically.')
flag = True
break
if flag:
break
def train(config, model, train_iter, dev_iter, test_iter):
start_time = time.time()
TIMESTAMP = "{}_bs{}_lr{}_ps{}_{:%Y_%m_%d_%H_%M_%S/}".format(
config.model_name, config.batch_size, config.learning_rate,
config.pad_size, datetime.now())
writer = SummaryWriter('/data-output/{}'.format(TIMESTAMP))
model.train()
param_optimizer = list(model.named_parameters())
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':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
# optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
# optimizer = BertAdam(optimizer_grouped_parameters, lr=config.learning_rate, warmup=0.05)
model, optimizer = amp.initialize(model, optimizer, opt_level="O1")
# dev_best_loss = float('inf')
dev_best_acc = 0
last_improve = 0 # 记录上次验证集loss下降的batch数
flag = False # 记录是否很久没有效果提升
model.train()
sum_batch = 0
for epoch in range(config.num_epochs):
total_batch = 0
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
pbar = tqdm(total=len(train_iter))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
# loss = FocalLoss(gamma=config.gamma, num_class=config.num_classes)(outputs, labels)
# loss.backward()
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
# if i % 2 == 1:
# optimizer.step()
# model.zero_grad()
optimizer.step()
# model.zero_grad()
if total_batch % 50 == 0 and total_batch != 0:
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predic)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_acc > dev_best_acc:
dev_best_acc = dev_acc
# torch.save(model.state_dict(), config.save_path)
# torch.save(model.state_dict(), 'out/model/epoch{}_{}_pytorch_model.bin'.format(epoch, config.model_name))
torch.save(
model.state_dict(),
'out/model/best_{}_pytorch_model.bin'.format(
config.model_name))
improve = '*'
last_improve = total_batch
else:
improve = ''
# time_dif = get_time_dif(start_time)
print(
'\n Iter: {}, Train Loss: {:.3f} Train Acc: {:.3f}% Val Loss: {:.3f} Val Acc: {:.3f}% {} '
.format(total_batch, loss.item(), train_acc * 100,
dev_loss, dev_acc * 100, improve))
writer.add_scalar('Loss/train', loss.item(), sum_batch)
writer.add_scalar('Loss/dev', dev_loss, sum_batch)
writer.add_scalar('Acc/train', train_acc, sum_batch)
writer.add_scalar('Acc/dev', dev_acc, sum_batch)
writer.flush()
model.train()
pbar.update(1)
sum_batch += 1
total_batch += 1
if total_batch - last_improve > config.require_improvement:
print("No optimization for a long time, auto-stopping...")
flag = True
break
pbar.close()
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predic)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
writer.add_scalar('Loss/train', loss.item(), sum_batch)
writer.add_scalar('Loss/dev', dev_loss, sum_batch)
writer.add_scalar('Acc/train', train_acc, sum_batch)
writer.add_scalar('Acc/dev', dev_acc, sum_batch)
writer.flush()
print(
'\n Epoch{}, Train Loss: {:.3f} Train Acc: {:.3f} Val Loss: {:.3f} Val Acc: {:.3f} '
.format(epoch + 1, loss.item(), train_acc, dev_loss, dev_acc))
torch.save(model.state_dict(),
'out/model/{}_pytorch_model.bin'.format(config.model_name))
if flag:
break
# torch.save(model.state_dict(), 'out/model/{}_pytorch_model.bin'.format(config.model_name))
predict(config, model, test_iter, dev_acc)
writer.close()
def train(config, model, train_iter, dev_iter, test_iter):
start_time = time.time()
model.train()
param_optimizer = list(model.named_parameters())
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': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}]
# optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
total_batch = 0 # 记录进行到多少batch
dev_best_loss = float('inf')
last_improve = 0 # 记录上次验证集loss下降的batch数
flag = False # 记录是否很久没有效果提升
model.train()
# 初始化
pgd = PGD(model)
K = 3
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
# model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
pgd.backup_grad()
# 对抗训练
for t in range(K):
pgd.attack(is_first_attack=(t == 0)) # 在embedding上添加对抗扰动, first attack时备份param.data
if t != K - 1:
model.zero_grad()
else:
pgd.restore_grad()
outputs_adv = model(trains)
loss_adv = F.cross_entropy(outputs_adv, labels)
loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度
pgd.restore() # 恢复embedding参数
optimizer.step()
model.zero_grad()
if total_batch % 100 == 0:
# 每多少轮输出在训练集和验证集上的效果
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predic)
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
dev_best_loss = dev_loss
torch.save(model.state_dict(), config.save_path)
improve = '*'
last_improve = total_batch
else:
improve = ''
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, 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(), train_acc, dev_loss, dev_acc, time_dif, improve))
model.train()
total_batch += 1
if total_batch - last_improve > config.require_improvement:
# 验证集loss超过1000batch没下降，结束训练
print("No optimization for a long time, auto-stopping...")
flag = True
break
if flag:
break
test(config, model, test_iter)