# Copyright (c) OpenMMLab. All rights reserved.
import annotator.uniformer.mmcv as mmcv
from .hook import HOOKS, Hook
from .lr_updater import annealing_cos, annealing_linear, format_param
class MomentumUpdaterHook(Hook):
def __init__(self,
by_epoch=True,
warmup=None,
warmup_iters=0,
warmup_ratio=0.9):
# validate the "warmup" argument
if warmup is not None:
if warmup not in ['constant', 'linear', 'exp']:
raise ValueError(
f'"{warmup}" is not a supported type for warming up, valid'
' types are "constant" and "linear"')
if warmup is not None:
assert warmup_iters > 0, \
'"warmup_iters" must be a positive integer'
assert 0 < warmup_ratio <= 1.0, \
'"warmup_momentum" must be in range (0,1]'
self.by_epoch = by_epoch
self.warmup = warmup
self.warmup_iters = warmup_iters
self.warmup_ratio = warmup_ratio
self.base_momentum = [] # initial momentum for all param groups
self.regular_momentum = [
] # expected momentum if no warming up is performed
def _set_momentum(self, runner, momentum_groups):
if isinstance(runner.optimizer, dict):
for k, optim in runner.optimizer.items():
for param_group, mom in zip(optim.param_groups,
momentum_groups[k]):
if 'momentum' in param_group.keys():
param_group['momentum'] = mom
elif 'betas' in param_group.keys():
param_group['betas'] = (mom, param_group['betas'][1])
else:
for param_group, mom in zip(runner.optimizer.param_groups,
momentum_groups):
if 'momentum' in param_group.keys():
param_group['momentum'] = mom
elif 'betas' in param_group.keys():
param_group['betas'] = (mom, param_group['betas'][1])
def get_momentum(self, runner, base_momentum):
raise NotImplementedError
def get_regular_momentum(self, runner):
if isinstance(runner.optimizer, dict):
momentum_groups = {}
for k in runner.optimizer.keys():
_momentum_group = [
self.get_momentum(runner, _base_momentum)
for _base_momentum in self.base_momentum[k]
]
momentum_groups.update({k: _momentum_group})
return momentum_groups
else:
return [
self.get_momentum(runner, _base_momentum)
for _base_momentum in self.base_momentum
]
def get_warmup_momentum(self, cur_iters):
def _get_warmup_momentum(cur_iters, regular_momentum):
if self.warmup == 'constant':
warmup_momentum = [
_momentum / self.warmup_ratio
for _momentum in self.regular_momentum
]
elif self.warmup == 'linear':
k = (1 - cur_iters / self.warmup_iters) * (1 -
self.warmup_ratio)
warmup_momentum = [
_momentum / (1 - k) for _momentum in self.regular_mom
]
elif self.warmup == 'exp':
k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
warmup_momentum = [
_momentum / k for _momentum in self.regular_mom
]
return warmup_momentum
if isinstance(self.regular_momentum, dict):
momentum_groups = {}
for key, regular_momentum in self.regular_momentum.items():
momentum_groups[key] = _get_warmup_momentum(
cur_iters, regular_momentum)
return momentum_groups
else:
return _get_warmup_momentum(cur_iters, self.regular_momentum)
def before_run(self, runner):
# NOTE: when resuming from a checkpoint,
# if 'initial_momentum' is not saved,
# it will be set according to the optimizer params
if isinstance(runner.optimizer, dict):
self.base_momentum = {}
for k, optim in runner.optimizer.items():
for group in optim.param_groups:
if 'momentum' in group.keys():
group.setdefault('initial_momentum', group['momentum'])
else:
group.setdefault('initial_momentum', group['betas'][0])
_base_momentum = [
group['initial_momentum'] for group in optim.param_groups
]
self.base_momentum.update({k: _base_momentum})
else:
for group in runner.optimizer.param_groups:
if 'momentum' in group.keys():
group.setdefault('initial_momentum', group['momentum'])
else:
group.setdefault('initial_momentum', group['betas'][0])
self.base_momentum = [
group['initial_momentum']
for group in runner.optimizer.param_groups
]
def before_train_epoch(self, runner):
if not self.by_epoch:
return
self.regular_mom = self.get_regular_momentum(runner)
self._set_momentum(runner, self.regular_mom)
def before_train_iter(self, runner):
cur_iter = runner.iter
if not self.by_epoch:
self.regular_mom = self.get_regular_momentum(runner)
if self.warmup is None or cur_iter >= self.warmup_iters:
self._set_momentum(runner, self.regular_mom)
else:
warmup_momentum = self.get_warmup_momentum(cur_iter)
self._set_momentum(runner, warmup_momentum)
elif self.by_epoch:
if self.warmup is None or cur_iter > self.warmup_iters:
return
elif cur_iter == self.warmup_iters:
self._set_momentum(runner, self.regular_mom)
else:
warmup_momentum = self.get_warmup_momentum(cur_iter)
self._set_momentum(runner, warmup_momentum)
@HOOKS.register_module()
class StepMomentumUpdaterHook(MomentumUpdaterHook):
"""Step momentum scheduler with min value clipping.
Args:
step (int | list[int]): Step to decay the momentum. If an int value is
given, regard it as the decay interval. If a list is given, decay
momentum at these steps.
gamma (float, optional): Decay momentum ratio. Default: 0.5.
min_momentum (float, optional): Minimum momentum value to keep. If
momentum after decay is lower than this value, it will be clipped
accordingly. If None is given, we don't perform lr clipping.
Default: None.
"""
def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
if isinstance(step, list):
assert mmcv.is_list_of(step, int)
assert all([s > 0 for s in step])
elif isinstance(step, int):
assert step > 0
else:
raise TypeError('"step" must be a list or integer')
self.step = step
self.gamma = gamma
self.min_momentum = min_momentum
super(StepMomentumUpdaterHook, self).__init__(**kwargs)
def get_momentum(self, runner, base_momentum):
progress = runner.epoch if self.by_epoch else runner.iter
# calculate exponential term
if isinstance(self.step, int):
exp = progress // self.step
else:
exp = len(self.step)
for i, s in enumerate(self.step):
if progress < s:
exp = i
break
momentum = base_momentum * (self.gamma**exp)
if self.min_momentum is not None:
# clip to a minimum value
momentum = max(momentum, self.min_momentum)
return momentum
@HOOKS.register_module()
class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
assert (min_momentum is None) ^ (min_momentum_ratio is None)
self.min_momentum = min_momentum
self.min_momentum_ratio = min_momentum_ratio
super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
def get_momentum(self, runner, base_momentum):
if self.by_epoch:
progress = runner.epoch
max_progress = runner.max_epochs
else:
progress = runner.iter
max_progress = runner.max_iters
if self.min_momentum_ratio is not None:
target_momentum = base_momentum * self.min_momentum_ratio
else:
target_momentum = self.min_momentum
return annealing_cos(base_momentum, target_momentum,
progress / max_progress)
@HOOKS.register_module()
class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
"""Cyclic momentum Scheduler.
Implement the cyclical momentum scheduler policy described in
https://arxiv.org/pdf/1708.07120.pdf
This momentum scheduler usually used together with the CyclicLRUpdater
to improve the performance in the 3D detection area.
Attributes:
target_ratio (tuple[float]): Relative ratio of the lowest momentum and
the highest momentum to the initial momentum.
cyclic_times (int): Number of cycles during training
step_ratio_up (float): The ratio of the increasing process of momentum
in the total cycle.
by_epoch (bool): Whether to update momentum by epoch.
"""
def __init__(self,
by_epoch=False,
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4,
**kwargs):
if isinstance(target_ratio, float):
target_ratio = (target_ratio, target_ratio / 1e5)
elif isinstance(target_ratio, tuple):
target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
if len(target_ratio) == 1 else target_ratio
else:
raise ValueError('target_ratio should be either float '
f'or tuple, got {type(target_ratio)}')
assert len(target_ratio) == 2, \
'"target_ratio" must be list or tuple of two floats'
assert 0 <= step_ratio_up < 1.0, \
'"step_ratio_up" must be in range [0,1)'
self.target_ratio = target_ratio
self.cyclic_times = cyclic_times
self.step_ratio_up = step_ratio_up
self.momentum_phases = [] # init momentum_phases
# currently only support by_epoch=False
assert not by_epoch, \
'currently only support "by_epoch" = False'
super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
def before_run(self, runner):
super(CyclicMomentumUpdaterHook, self).before_run(runner)
# initiate momentum_phases
# total momentum_phases are separated as up and down
max_iter_per_phase = runner.max_iters // self.cyclic_times
iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
self.momentum_phases.append(
[0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
self.momentum_phases.append([
iter_up_phase, max_iter_per_phase, max_iter_per_phase,
self.target_ratio[0], self.target_ratio[1]
])
def get_momentum(self, runner, base_momentum):
curr_iter = runner.iter
for (start_iter, end_iter, max_iter_per_phase, start_ratio,
end_ratio) in self.momentum_phases:
curr_iter %= max_iter_per_phase
if start_iter <= curr_iter < end_iter:
progress = curr_iter - start_iter
return annealing_cos(base_momentum * start_ratio,
base_momentum * end_ratio,
progress / (end_iter - start_iter))
@HOOKS.register_module()
class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
"""OneCycle momentum Scheduler.
This momentum scheduler usually used together with the OneCycleLrUpdater
to improve the performance.
Args:
base_momentum (float or list): Lower momentum boundaries in the cycle
for each parameter group. Note that momentum is cycled inversely
to learning rate; at the peak of a cycle, momentum is
'base_momentum' and learning rate is 'max_lr'.
Default: 0.85
max_momentum (float or list): Upper momentum boundaries in the cycle
for each parameter group. Functionally,
it defines the cycle amplitude (max_momentum - base_momentum).
Note that momentum is cycled inversely
to learning rate; at the start of a cycle, momentum is
'max_momentum' and learning rate is 'base_lr'
Default: 0.95
pct_start (float): The percentage of the cycle (in number of steps)
spent increasing the learning rate.
Default: 0.3
anneal_strategy (str): {'cos', 'linear'}
Specifies the annealing strategy: 'cos' for cosine annealing,
'linear' for linear annealing.
Default: 'cos'
three_phase (bool): If three_phase is True, use a third phase of the
schedule to annihilate the learning rate according to
final_div_factor instead of modifying the second phase (the first
two phases will be symmetrical about the step indicated by
pct_start).
Default: False
"""
def __init__(self,
base_momentum=0.85,
max_momentum=0.95,
pct_start=0.3,
anneal_strategy='cos',
three_phase=False,
**kwargs):
# validate by_epoch, currently only support by_epoch=False
if 'by_epoch' not in kwargs:
kwargs['by_epoch'] = False
else:
assert not kwargs['by_epoch'], \
'currently only support "by_epoch" = False'
if not isinstance(base_momentum, (float, list, dict)):
raise ValueError('base_momentum must be the type among of float,'
'list or dict.')
self._base_momentum = base_momentum
if not isinstance(max_momentum, (float, list, dict)):
raise ValueError('max_momentum must be the type among of float,'
'list or dict.')
self._max_momentum = max_momentum
# validate pct_start
if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
raise ValueError('Expected float between 0 and 1 pct_start, but '
f'got {pct_start}')
self.pct_start = pct_start
# validate anneal_strategy
if anneal_strategy not in ['cos', 'linear']:
raise ValueError('anneal_strategy must by one of "cos" or '
f'"linear", instead got {anneal_strategy}')
elif anneal_strategy == 'cos':
self.anneal_func = annealing_cos
elif anneal_strategy == 'linear':
self.anneal_func = annealing_linear
self.three_phase = three_phase
self.momentum_phases = [] # init momentum_phases
super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
def before_run(self, runner):
if isinstance(runner.optimizer, dict):
for k, optim in runner.optimizer.items():
if ('momentum' not in optim.defaults
and 'betas' not in optim.defaults):
raise ValueError('optimizer must support momentum with'
'option enabled')
self.use_beta1 = 'betas' in optim.defaults
_base_momentum = format_param(k, optim, self._base_momentum)
_max_momentum = format_param(k, optim, self._max_momentum)
for group, b_momentum, m_momentum in zip(
optim.param_groups, _base_momentum, _max_momentum):
if self.use_beta1:
_, beta2 = group['betas']
group['betas'] = (m_momentum, beta2)
else:
group['momentum'] = m_momentum
group['base_momentum'] = b_momentum
group['max_momentum'] = m_momentum
else:
optim = runner.optimizer
if ('momentum' not in optim.defaults
and 'betas' not in optim.defaults):
raise ValueError('optimizer must support momentum with'
'option enabled')
self.use_beta1 = 'betas' in optim.defaults
k = type(optim).__name__
_base_momentum = format_param(k, optim, self._base_momentum)
_max_momentum = format_param(k, optim, self._max_momentum)
for group, b_momentum, m_momentum in zip(optim.param_groups,
_base_momentum,
_max_momentum):
if self.use_beta1:
_, beta2 = group['betas']
group['betas'] = (m_momentum, beta2)
else:
group['momentum'] = m_momentum
group['base_momentum'] = b_momentum
group['max_momentum'] = m_momentum
if self.three_phase:
self.momentum_phases.append({
'end_iter':
float(self.pct_start * runner.max_iters) - 1,
'start_momentum':
'max_momentum',
'end_momentum':
'base_momentum'
})
self.momentum_phases.append({
'end_iter':
float(2 * self.pct_start * runner.max_iters) - 2,
'start_momentum':
'base_momentum',
'end_momentum':
'max_momentum'
})
self.momentum_phases.append({
'end_iter': runner.max_iters - 1,
'start_momentum': 'max_momentum',
'end_momentum': 'max_momentum'
})
else:
self.momentum_phases.append({
'end_iter':
float(self.pct_start * runner.max_iters) - 1,
'start_momentum':
'max_momentum',
'end_momentum':
'base_momentum'
})
self.momentum_phases.append({
'end_iter': runner.max_iters - 1,
'start_momentum': 'base_momentum',
'end_momentum': 'max_momentum'
})
def _set_momentum(self, runner, momentum_groups):
if isinstance(runner.optimizer, dict):
for k, optim in runner.optimizer.items():
for param_group, mom in zip(optim.param_groups,
momentum_groups[k]):
if 'momentum' in param_group.keys():
param_group['momentum'] = mom
elif 'betas' in param_group.keys():
param_group['betas'] = (mom, param_group['betas'][1])
else:
for param_group, mom in zip(runner.optimizer.param_groups,
momentum_groups):
if 'momentum' in param_group.keys():
param_group['momentum'] = mom
elif 'betas' in param_group.keys():
param_group['betas'] = (mom, param_group['betas'][1])
def get_momentum(self, runner, param_group):
curr_iter = runner.iter
start_iter = 0
for i, phase in enumerate(self.momentum_phases):
end_iter = phase['end_iter']
if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
pct = (curr_iter - start_iter) / (end_iter - start_iter)
momentum = self.anneal_func(
param_group[phase['start_momentum']],
param_group[phase['end_momentum']], pct)
break
start_iter = end_iter
return momentum
def get_regular_momentum(self, runner):
if isinstance(runner.optimizer, dict):
momentum_groups = {}
for k, optim in runner.optimizer.items():
_momentum_group = [
self.get_momentum(runner, param_group)
for param_group in optim.param_groups
]
momentum_groups.update({k: _momentum_group})
return momentum_groups
else:
momentum_groups = []
for param_group in runner.optimizer.param_groups:
momentum_groups.append(self.get_momentum(runner, param_group))
return momentum_groups