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# Copyright (c) OpenMMLab. All rights reserved.
from ...parallel import is_module_wrapper
from ..hooks.hook import HOOKS, Hook
@HOOKS.register_module()
class EMAHook(Hook):
r"""Exponential Moving Average Hook.
Use Exponential Moving Average on all parameters of model in training
process. All parameters have a ema backup, which update by the formula
as below. EMAHook takes priority over EvalHook and CheckpointSaverHook.
.. math::
\text{Xema\_{t+1}} = (1 - \text{momentum}) \times
\text{Xema\_{t}} + \text{momentum} \times X_t
Args:
momentum (float): The momentum used for updating ema parameter.
Defaults to 0.0002.
interval (int): Update ema parameter every interval iteration.
Defaults to 1.
warm_up (int): During first warm_up steps, we may use smaller momentum
to update ema parameters more slowly. Defaults to 100.
resume_from (str): The checkpoint path. Defaults to None.
"""
def __init__(self,
momentum=0.0002,
interval=1,
warm_up=100,
resume_from=None):
assert isinstance(interval, int) and interval > 0
self.warm_up = warm_up
self.interval = interval
assert momentum > 0 and momentum < 1
self.momentum = momentum**interval
self.checkpoint = resume_from
def before_run(self, runner):
"""To resume model with it's ema parameters more friendly.
Register ema parameter as ``named_buffer`` to model
"""
model = runner.model
if is_module_wrapper(model):
model = model.module
self.param_ema_buffer = {}
self.model_parameters = dict(model.named_parameters(recurse=True))
for name, value in self.model_parameters.items():
# "." is not allowed in module's buffer name
buffer_name = f"ema_{name.replace('.', '_')}"
self.param_ema_buffer[name] = buffer_name
model.register_buffer(buffer_name, value.data.clone())
self.model_buffers = dict(model.named_buffers(recurse=True))
if self.checkpoint is not None:
runner.resume(self.checkpoint)
def after_train_iter(self, runner):
"""Update ema parameter every self.interval iterations."""
curr_step = runner.iter
# We warm up the momentum considering the instability at beginning
momentum = min(self.momentum,
(1 + curr_step) / (self.warm_up + curr_step))
if curr_step % self.interval != 0:
return
for name, parameter in self.model_parameters.items():
buffer_name = self.param_ema_buffer[name]
buffer_parameter = self.model_buffers[buffer_name]
buffer_parameter.mul_(1 - momentum).add_(momentum, parameter.data)
def after_train_epoch(self, runner):
"""We load parameter values from ema backup to model before the
EvalHook."""
self._swap_ema_parameters()
def before_train_epoch(self, runner):
"""We recover model's parameter from ema backup after last epoch's
EvalHook."""
self._swap_ema_parameters()
def _swap_ema_parameters(self):
"""Swap the parameter of model with parameter in ema_buffer."""
for name, value in self.model_parameters.items():
temp = value.data.clone()
ema_buffer = self.model_buffers[self.param_ema_buffer[name]]
value.data.copy_(ema_buffer.data)
ema_buffer.data.copy_(temp)
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