pytorch-image-models/timm/utils/model_ema.py

""" Exponential Moving Average (EMA) of model updates

Hacked together by / Copyright 2020 Ross Wightman
"""
from copy import deepcopy

import torch
import torch.nn as nn


class ModelEma(nn.Module):
    """ Model Exponential Moving Average
    Keep a moving average of everything in the model state_dict (parameters and buffers).

    This is intended to allow functionality like
    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage

    A smoothed version of the weights is necessary for some training schemes to perform well.
    E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
    RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
    smoothing of weights to match results. Pay attention to the decay constant you are using
    relative to your update count per epoch.

    To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
    disable validation of the EMA weights. Validation will have to be done manually in a separate
    process, or after the training stops converging.

    This class is sensitive where it is initialized in the sequence of model init,
    GPU assignment and distributed training wrappers.
    I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU.
    """
    def __init__(self, model, decay=0.9999, device=None):
        super(ModelEma, self).__init__()
        # make a copy of the model for accumulating moving average of weights
        self.module = deepcopy(model)
        self.module.eval()
        self.decay = decay
        self.device = device  # perform ema on different device from model if set
        if device is not None:
            self.module.to(device=device)

    def update(self, model):
        with torch.no_grad():
            for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
                assert ema_v.shape == model_v.shape
                if self.device:
                    model_v = model_v.to(device=self.device)
                ema_v.copy_(ema_v * self.decay + (1. - self.decay) * model_v)