248 lines
8.8 KiB
Python
248 lines
8.8 KiB
Python
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import bmtrain as bmt
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import math
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class Cosine(bmt.lr_scheduler.WarmupLRScheduler):
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r"""
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After a warmup period during which learning rate increases linearly between 0 and the start_lr,
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The decay period performs :math:`\text{lr}=\text{start_lr}\times \dfrac{1+\cos \left( \pi \cdot \dfrac{\text{num_iter}-\text{warmup_iter}}{\text{end_iter}-\text{warmup_iter}}\right)}{2}`
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"""
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def __init__(
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self, optimizer, start_lr, warmup_iter, end_iter, num_iter=0, lr_end_restart=0, resume_no_optimze=0
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) -> None:
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self.warmup_iter = warmup_iter
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self.end_iter = end_iter
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self.optimizer = optimizer
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self.num_iter = num_iter
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self._current_lr = None
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self._start_lr = start_lr
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self.start_lr = []
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self.lr_end_restart = lr_end_restart
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self.resume_step = num_iter
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self.resume_no_optimze = resume_no_optimze
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for group in self.optimizer.param_groups:
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self.start_lr.append(group["lr"])
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self.step(self.num_iter)
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def get_lr_warmup(self, num_iter, base_lr) -> float:
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return base_lr * num_iter / self.warmup_iter
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def get_lr_decay(self, num_iter, base_lr) -> float:
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progress = (num_iter - self.warmup_iter) / max(1, (self.end_iter - self.warmup_iter))
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if progress > 1:
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if self.lr_end_restart == 0:
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progress = 1
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elif self.lr_end_restart == 1:
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progress = progress
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elif self.lr_end_restart == 2:
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progress = int(progress) * 2 + (progress - 1)
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return max(base_lr * 0.1, base_lr * (0.1 + 0.45 * (1.0 + math.cos(progress * math.pi))))
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def get_lr(self, base_lr):
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assert self.num_iter >= 0
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if self.resume_step + self.resume_no_optimze > self.num_iter:
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bmt.print_rank("resume no optimize")
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return 0
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if self.num_iter < self.warmup_iter:
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return self.get_lr_warmup(self.num_iter, base_lr)
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else:
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return self.get_lr_decay(self.num_iter, base_lr)
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@property
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def current_lr(self):
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return self._current_lr
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def step(self, num_iter=None) -> None:
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if num_iter is None:
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num_iter = self.num_iter + 1
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self.num_iter = num_iter
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self._current_lr = self.get_lr(self._start_lr)
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for group, base_lr in zip(self.optimizer.param_groups, self.start_lr):
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group["lr"] = self.get_lr(base_lr)
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def state_dict(self):
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return {
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"_start_lr": self._start_lr,
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"start_lr": self.start_lr,
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"warmup_iter": self.warmup_iter,
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"end_iter": self.end_iter,
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"num_iter": self.num_iter,
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}
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def load_state_dict(self, state_dict):
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self._start_lr = state_dict["_start_lr"]
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self.start_lr = state_dict["start_lr"]
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self.warmup_iter = state_dict["warmup_iter"]
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self.end_iter = state_dict["end_iter"]
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self.num_iter = state_dict["num_iter"]
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self.step(self.num_iter)
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class WarmupStableDrop(bmt.lr_scheduler.WarmupLRScheduler):
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r"""
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After a warmup period during which learning rate increases linearly between 0 and the start_lr,
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The decay period performs :math:`\text{lr}=\text{start_lr}\times \dfrac{1+\cos \left( \pi \cdot \dfrac{\text{num_iter}-\text{warmup_iter}}{\text{end_iter}-\text{warmup_iter}}\right)}{2}`
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"""
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def __init__(
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self, optimizer, start_lr, warmup_iter, end_iter, drop_iter=0, num_iter=0, resume_no_optimze=0
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) -> None:
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self.warmup_iter = warmup_iter
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self.end_iter = end_iter
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self.drop_iter = drop_iter
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self.optimizer = optimizer
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self.num_iter = num_iter
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self._current_lr = None
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self._start_lr = start_lr
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self.start_lr = []
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self.resume_step = num_iter
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self.resume_no_optimze = resume_no_optimze
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for group in self.optimizer.param_groups:
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self.start_lr.append(group["lr"])
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self.step(self.num_iter)
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def get_lr_warmup(self, num_iter, base_lr, warmup_iter) -> float:
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return base_lr * num_iter / warmup_iter
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def get_lr_stable(self, num_iter, base_lr):
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return base_lr
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def get_lr_drop(self, num_iter, base_lr):
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progress = (self.end_iter - num_iter) / self.drop_iter
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return base_lr * (0.1 + max(0.9 * (self.end_iter - num_iter) / self.drop_iter, 0))
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def get_lr(self, base_lr):
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assert self.num_iter >= 0
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if self.resume_step + self.resume_no_optimze > self.num_iter:
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return self.get_lr_warmup(self.num_iter - self.resume_step, base_lr, self.resume_no_optimze)
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if self.num_iter < self.warmup_iter:
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return self.get_lr_warmup(self.num_iter, base_lr, self.warmup_iter)
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if self.num_iter > self.end_iter - self.drop_iter:
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return self.get_lr_drop(self.num_iter, base_lr)
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return self.get_lr_stable(self.num_iter, base_lr)
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@property
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def current_lr(self):
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return self._current_lr
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def step(self, num_iter=None) -> None:
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if num_iter is None:
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num_iter = self.num_iter + 1
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self.num_iter = num_iter
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self._current_lr = self.get_lr(self._start_lr)
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for group, base_lr in zip(self.optimizer.param_groups, self.start_lr):
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group["lr"] = self.get_lr(base_lr)
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def state_dict(self):
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return {
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"_start_lr": self._start_lr,
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"start_lr": self.start_lr,
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"warmup_iter": self.warmup_iter,
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"end_iter": self.end_iter,
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"num_iter": self.num_iter,
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}
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def load_state_dict(self, state_dict):
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self._start_lr = state_dict["_start_lr"]
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self.start_lr = state_dict["start_lr"]
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self.warmup_iter = state_dict["warmup_iter"]
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self.end_iter = state_dict["end_iter"]
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self.num_iter = state_dict["num_iter"]
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self.step(self.num_iter)
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class WarmupStableExp(bmt.lr_scheduler.WarmupLRScheduler):
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r"""
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After a warmup period during which learning rate increases linearly between 0 and the start_lr,
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The decay period performs :math:`\text{lr}=\text{start_lr}\times \dfrac{1+\cos \left( \pi \cdot \dfrac{\text{num_iter}-\text{warmup_iter}}{\text{end_iter}-\text{warmup_iter}}\right)}{2}`
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"""
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def __init__(
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self, optimizer, start_lr, warmup_iter, drop_begin=-1, drop_rate=0.5, drop_iter=0, num_iter=0, resume_no_optimze=0
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) -> None:
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self.warmup_iter = warmup_iter
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self.drop_iter = drop_iter
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self.optimizer = optimizer
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self.num_iter = num_iter
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self._current_lr = None
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self._start_lr = start_lr
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self.start_lr = []
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self.resume_step = num_iter
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self.resume_no_optimze = resume_no_optimze
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self.drop_begin = drop_begin
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self.drop_iter = drop_iter # here drop_iter is half-life
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self.drop_rate = drop_rate
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for group in self.optimizer.param_groups:
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self.start_lr.append(group["lr"])
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self.step(self.num_iter)
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def get_lr_warmup(self, num_iter, base_lr, warmup_iter) -> float:
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return base_lr * num_iter / warmup_iter
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def get_lr_stable(self, num_iter, base_lr):
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return base_lr
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def get_lr_drop(self, num_iter, base_lr):
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if self.drop_iter < 0:
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return base_lr
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progress = (num_iter - self.drop_begin) / self.drop_iter
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return base_lr * (self.drop_rate ** progress)
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def get_lr(self, base_lr):
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assert self.num_iter >= 0
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if self.resume_step + self.resume_no_optimze > self.num_iter:
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return self.get_lr_warmup(self.num_iter - self.resume_step, base_lr, self.resume_no_optimze)
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if self.num_iter < self.warmup_iter:
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return self.get_lr_warmup(self.num_iter, base_lr, self.warmup_iter)
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if self.num_iter > self.drop_begin:
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return self.get_lr_drop(self.num_iter, base_lr)
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return self.get_lr_stable(self.num_iter, base_lr)
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@property
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def current_lr(self):
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return self._current_lr
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def step(self, num_iter=None) -> None:
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if num_iter is None:
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num_iter = self.num_iter + 1
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self.num_iter = num_iter
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self._current_lr = self.get_lr(self._start_lr)
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for group, base_lr in zip(self.optimizer.param_groups, self.start_lr):
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group["lr"] = self.get_lr(base_lr)
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def state_dict(self):
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return {
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"_start_lr": self._start_lr,
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"start_lr": self.start_lr,
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"warmup_iter": self.warmup_iter,
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"drop_begin": self.drop_begin,
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"num_iter": self.num_iter,
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}
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def load_state_dict(self, state_dict):
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self._start_lr = state_dict["_start_lr"]
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self.start_lr = state_dict["start_lr"]
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self.warmup_iter = state_dict["warmup_iter"]
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self.drop_begin = state_dict["drop_begin"]
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self.num_iter = state_dict["num_iter"]
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self.step(self.num_iter)
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