Source code for mmpretrain.models.utils.ema

# Copyright (c) OpenMMLab. All rights reserved.
from math import cos, pi
from typing import Optional

import torch
import torch.nn as nn
from mmengine.logging import MessageHub
from mmengine.model import ExponentialMovingAverage

from mmpretrain.registry import MODELS

[docs]@MODELS.register_module() class CosineEMA(ExponentialMovingAverage): r"""CosineEMA is implemented for updating momentum parameter, used in BYOL, MoCoV3, etc. All parameters are updated by the formula as below: .. math:: X'_{t+1} = (1 - m) * X'_t + m * X_t Where :math:`m` the the momentum parameter. And it's updated with cosine annealing, including momentum adjustment following: .. math:: m = m_{end} + (m_{end} - m_{start}) * (\cos\frac{k\pi}{K} + 1) / 2 where :math:`k` is the current step, :math:`K` is the total steps. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, :math:`X'_{t}` is the moving average and :math:`X_t` is the new observed value. The value of momentum is usually a small number, allowing observed values to slowly update the ema parameters. See also :external:py:class:`torch.nn.BatchNorm2d`. Args: model (nn.Module): The model to be averaged. momentum (float): The start momentum value. Defaults to 0.004. end_momentum (float): The end momentum value for cosine annealing. Defaults to 0. interval (int): Interval between two updates. Defaults to 1. device (torch.device, optional): If provided, the averaged model will be stored on the :attr:`device`. Defaults to None. update_buffers (bool): if True, it will compute running averages for both the parameters and the buffers of the model. Defaults to False. """ def __init__(self, model: nn.Module, momentum: float = 0.004, end_momentum: float = 0., interval: int = 1, device: Optional[torch.device] = None, update_buffers: bool = False) -> None: super().__init__( model=model, momentum=momentum, interval=interval, device=device, update_buffers=update_buffers) self.end_momentum = end_momentum
[docs] def avg_func(self, averaged_param: torch.Tensor, source_param: torch.Tensor, steps: int) -> None: """Compute the moving average of the parameters using the cosine momentum strategy. Args: averaged_param (Tensor): The averaged parameters. source_param (Tensor): The source parameters. steps (int): The number of times the parameters have been updated. Returns: Tensor: The averaged parameters. """ message_hub = MessageHub.get_current_instance() max_iters = message_hub.get_info('max_iters') cosine_annealing = (cos(pi * steps / float(max_iters)) + 1) / 2 momentum = self.end_momentum - (self.end_momentum - self.momentum) * cosine_annealing averaged_param.mul_(1 - momentum).add_(source_param, alpha=momentum)
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