def on_stage_start(self, state: RunnerState):
optimizer = state.get_key(key="optimizer",
inner_key=self.optimizer_key)
assert optimizer is not None
lr = optimizer.defaults["lr"]
momentum = get_optimizer_momentum(optimizer)
state.set_key(lr, "lr", inner_key=self.optimizer_key)
state.set_key(momentum, "momentum", inner_key=self.optimizer_key)
def on_stage_start(self, state: RunnerState):
self.fp16 = isinstance(state.model, Fp16Wrap)
optimizer = state.get_key(key="optimizer",
inner_key=self.optimizer_key)
assert optimizer is not None
lr = optimizer.defaults["lr"]
momentum = get_optimizer_momentum(optimizer)
state.set_key(lr, "lr", inner_key=self.optimizer_key)
state.set_key(momentum, "momentum", inner_key=self.optimizer_key)
def _update_optimizer(self, optimizer):
new_lr = self.calc_lr()
if new_lr is not None:
self._update_lr(optimizer, new_lr)
new_momentum = self.calc_momentum()
if new_momentum is not None:
self._update_momentum(optimizer, new_momentum)
else:
new_momentum = get_optimizer_momentum(optimizer)
return new_lr, new_momentum
def _scheduler_step(
scheduler,
valid_metric=None,
):
if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
scheduler.step(valid_metric)
lr = safitty.get(scheduler.optimizer.param_groups, 0, "lr")
else:
scheduler.step()
lr = scheduler.get_lr()[0]
momentum = get_optimizer_momentum(scheduler.optimizer)
return lr, momentum
def __init__(
self,
optimizer: Optimizer,
num_steps: int,
lr_range=(1.0, 0.005),
init_lr: float = None,
warmup_steps: int = 0,
warmup_fraction: float = None,
decay_steps: int = 0,
decay_fraction: float = None,
momentum_range=(0.8, 0.99, 0.999),
init_momentum: float = None,
):
"""
Args:
optimizer: PyTorch optimizer
num_steps (int): total number of steps
lr_range: tuple with two or three elements
(max_lr, min_lr, [final_lr])
init_lr (float, optional): initial lr
warmup_steps (int): count of steps for warm-up stage
warmup_fraction (float, optional): fraction in [0; 1) to calculate
number of warmup steps.
Cannot be set together with ``warmup_steps``
decay_steps (int): count of steps for lr decay stage
decay_fraction (float, optional): fraction in [0; 1) to calculate
number of decay steps.
Cannot be set together with ``decay_steps``
momentum_range: tuple with two or three elements
(min_momentum, max_momentum, [final_momentum])
init_momentum (float, optional): initial momentum
"""
if len(lr_range) == 2:
max_lr, min_lr = lr_range
final_lr = min_lr
elif len(lr_range) == 3:
max_lr, min_lr, final_lr = lr_range
if len(momentum_range) == 2:
min_momentum, max_momentum = momentum_range
final_momentum = max_momentum
elif len(momentum_range) == 3:
min_momentum, max_momentum, final_momentum = momentum_range
if init_lr is None:
init_lr = optimizer.defaults["lr"]
if init_momentum is None:
init_momentum = get_optimizer_momentum(optimizer)
warmup_steps = self._calculate_warmup(
num_steps, warmup_steps, warmup_fraction
)
decay_steps = self._calculate_decay(
num_steps, decay_steps, decay_fraction
)
lr_annealing_steps = num_steps - (warmup_steps + decay_steps)
self.warmup_steps = warmup_steps
self.lr_annealing_steps = lr_annealing_steps
self.decay_steps = decay_steps
self.num_steps = warmup_steps + lr_annealing_steps + decay_steps
self.lr_range = init_lr, max_lr, min_lr, final_lr
self.momentum_range = \
init_momentum, min_momentum, max_momentum, final_momentum
self._calculate_lr_momentum(
warmup_steps,
lr_annealing_steps,
decay_steps
)
self.total_groups = len(optimizer.param_groups)
super().__init__(optimizer)