mmcv.runner.hooks.ema 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from ...parallel import is_module_wrapper
from ..hooks.hook import HOOKS, Hook

[文档]@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
[文档] 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, self.model_buffers = dict(model.named_buffers(recurse=True)) if self.checkpoint is not None: runner.resume(self.checkpoint)
[文档] 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,
[文档] def after_train_epoch(self, runner): """We load parameter values from ema backup to model before the EvalHook.""" self._swap_ema_parameters()
[文档] 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 = ema_buffer = self.model_buffers[self.param_ema_buffer[name]]
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