from torch.optim.lr_scheduler import LambdaLR
[docs]def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
"""带warmup的schedule, 源自transformers包optimization.py中
:param num_warmup_steps: 需要warmup的步数, 一般为 num_training_steps * warmup_proportion(warmup的比例, 建议0.05-0.15)
:param num_training_steps: 总的训练步数, 一般为 train_batches * num_epoch
"""
def lr_lambda(current_step: int):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
return max(0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps)))
return LambdaLR(optimizer, lr_lambda, last_epoch)
[docs]def extend_with_exponential_moving_average(model, decay=0.999):
''' 模型权重的指数滑动平均, 不参加梯度更新，只是记录滑动平均的参数，给预测使用
注意区别于类似adam一类的自适应学习率优化器, 针对一阶二阶梯度的指数滑动平均, 两者完全不同
Example:
>>> # 初始化
>>> ema = ExponentialMovingAverage(model, 0.999)
>>> # 训练过程中, 更新完参数后, 同步update ema_weights weights
>>> def train():
>>> optimizer.step()
>>> ema.step()
>>> # eval前, 调用apply_ema_weights(); eval之后, restore_raw_weights()恢复原来模型的参数
>>> def evaluate():
>>> ema.apply_ema_weights()
>>> # evaluate
>>> # 如果想保存ema后的模型, 请在restore方法之前调用torch.save()
>>> ema.restore_raw_weights()
'''
class ExponentialMovingAverage():
def __init__(self, model, decay):
self.model = model
self.decay = decay
# 保存ema权重（当前step的每一层的滑动平均权重）
self.ema_weights = {}
# 在进行evaluate的时候, 保存原始的模型权重, 当执行完evaluate后, 从ema权重恢复到原始权重
self.model_weights = {}
# 初始化ema_weights为model_weights
for name, param in self.model.named_parameters():
if param.requires_grad:
self.ema_weights[name] = param.data.clone()
def step(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.ema_weights
new_average = (1.0 - self.decay) * param.data + self.decay * self.ema_weights[name]
self.ema_weights[name] = new_average.clone()
def apply_ema_weights(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.ema_weights
self.model_weights[name] = param.data
param.data = self.ema_weights[name]
def restore_raw_weights(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.model_weights
param.data = self.model_weights[name]
self.model_weights = {}
return ExponentialMovingAverage(model, decay)