| import torch |
| from torch.optim.optimizer import Optimizer |
| import pytorch_lightning as pl |
| from torch.optim.lr_scheduler import _LRScheduler |
|
|
|
|
| class BaseScheduler(object): |
| """Base class for the step-wise scheduler logic. |
| |
| Args: |
| optimizer (Optimize): Optimizer instance to apply lr schedule on. |
| |
| Subclass this and overwrite ``_get_lr`` to write your own step-wise scheduler. |
| """ |
|
|
| def __init__(self, optimizer): |
| self.optimizer = optimizer |
| self.step_num = 0 |
|
|
| def zero_grad(self): |
| self.optimizer.zero_grad() |
|
|
| def _get_lr(self): |
| raise NotImplementedError |
|
|
| def _set_lr(self, lr): |
| for param_group in self.optimizer.param_groups: |
| param_group["lr"] = lr |
|
|
| def step(self, metrics=None, epoch=None): |
| """Update step-wise learning rate before optimizer.step.""" |
| self.step_num += 1 |
| lr = self._get_lr() |
| self._set_lr(lr) |
|
|
| def load_state_dict(self, state_dict): |
| self.__dict__.update(state_dict) |
|
|
| def state_dict(self): |
| return {key: value for key, value in self.__dict__.items() if key != "optimizer"} |
|
|
| def as_tensor(self, start=0, stop=100_000): |
| """Returns the scheduler values from start to stop.""" |
| lr_list = [] |
| for _ in range(start, stop): |
| self.step_num += 1 |
| lr_list.append(self._get_lr()) |
| self.step_num = 0 |
| return torch.tensor(lr_list) |
|
|
| def plot(self, start=0, stop=100_000): |
| """Plot the scheduler values from start to stop.""" |
| import matplotlib.pyplot as plt |
|
|
| all_lr = self.as_tensor(start=start, stop=stop) |
| plt.plot(all_lr.numpy()) |
| plt.show() |
|
|
| class DPTNetScheduler(BaseScheduler): |
| """Dual Path Transformer Scheduler used in [1] |
| |
| Args: |
| optimizer (Optimizer): Optimizer instance to apply lr schedule on. |
| steps_per_epoch (int): Number of steps per epoch. |
| d_model(int): The number of units in the layer output. |
| warmup_steps (int): The number of steps in the warmup stage of training. |
| noam_scale (float): Linear increase rate in first phase. |
| exp_max (float): Max learning rate in second phase. |
| exp_base (float): Exp learning rate base in second phase. |
| |
| Schedule: |
| This scheduler increases the learning rate linearly for the first |
| ``warmup_steps``, and then decay it by 0.98 for every two epochs. |
| |
| References |
| [1]: Jingjing Chen et al. "Dual-Path Transformer Network: Direct Context- |
| Aware Modeling for End-to-End Monaural Speech Separation" Interspeech 2020. |
| """ |
|
|
| def __init__( |
| self, |
| optimizer, |
| steps_per_epoch, |
| d_model, |
| warmup_steps=4000, |
| noam_scale=1.0, |
| exp_max=0.0004, |
| exp_base=0.98, |
| ): |
| super().__init__(optimizer) |
| self.noam_scale = noam_scale |
| self.d_model = d_model |
| self.warmup_steps = warmup_steps |
| self.exp_max = exp_max |
| self.exp_base = exp_base |
| self.steps_per_epoch = steps_per_epoch |
| self.epoch = 0 |
|
|
| def _get_lr(self): |
| if self.step_num % self.steps_per_epoch == 0: |
| self.epoch += 1 |
|
|
| if self.step_num > self.warmup_steps: |
| |
| lr = self.exp_max * (self.exp_base ** ((self.epoch - 1) // 2)) |
| else: |
| |
| lr = ( |
| self.noam_scale |
| * self.d_model ** (-0.5) |
| * min(self.step_num ** (-0.5), self.step_num * self.warmup_steps ** (-1.5)) |
| ) |
| return lr |
|
|
| class CustomExponentialLR(_LRScheduler): |
| def __init__(self, optimizer, gamma, step_size, last_epoch=-1): |
| self.gamma = gamma |
| self.step_size = step_size |
| self.base_lrs = list(map(lambda group: group['lr'], optimizer.param_groups)) |
| super(CustomExponentialLR, self).__init__(optimizer, last_epoch) |
|
|
| def get_lr(self): |
| if self.last_epoch == 0 or (self.last_epoch + 1) % self.step_size != 0: |
| return [group['lr'] for group in self.optimizer.param_groups] |
| return [lr * self.gamma for lr in self.base_lrs] |
|
|
|
|
| |
| _BaseScheduler = BaseScheduler |
