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Source code for mmedit.engine.hooks.ema

# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from copy import deepcopy
from functools import partial
from typing import Optional, Sequence

import torch
from mmengine.hooks import Hook
from mmengine.model.wrappers import is_model_wrapper
from mmengine.registry import HOOKS
from mmengine.runner import Runner
from mmengine.utils import is_tuple_of

[docs]DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
[docs]class ExponentialMovingAverageHook(Hook): """Exponential Moving Average Hook. Exponential moving average is a trick that widely used in current GAN literature, e.g., PGGAN, StyleGAN, and BigGAN. This general idea of it is maintaining a model with the same architecture, but its parameters are updated as a moving average of the trained weights in the original model. In general, the model with moving averaged weights achieves better performance. Args: module_keys (str | tuple[str]): The name of the ema model. Note that we require these keys are followed by '_ema' so that we can easily find the original model by discarding the last four characters. interp_mode (str, optional): Mode of the interpolation method. Defaults to 'lerp'. interp_cfg (dict | None, optional): Set arguments of the interpolation function. Defaults to None. interval (int, optional): Evaluation interval (by iterations). Default: -1. start_iter (int, optional): Start iteration for ema. If the start iteration is not reached, the weights of ema model will maintain the same as the original one. Otherwise, its parameters are updated as a moving average of the trained weights in the original model. Default: 0. """ def __init__(self, module_keys, interp_mode='lerp', interp_cfg=None, interval=-1, start_iter=0): super().__init__() assert isinstance(module_keys, str) or is_tuple_of(module_keys, str) self.module_keys = (module_keys, ) if isinstance(module_keys, str) else module_keys # sanity check for the format of module keys for k in self.module_keys: assert k.endswith( '_ema'), 'You should give keys that end with "_ema".' self.interp_mode = interp_mode self.interp_cfg = dict() if interp_cfg is None else deepcopy( interp_cfg) self.interval = interval self.start_iter = start_iter assert hasattr( self, interp_mode ), f'Currently, we do not support {self.interp_mode} for EMA.' self.interp_func = partial( getattr(self, interp_mode), **self.interp_cfg) @staticmethod
[docs] def lerp(a, b, momentum=0.001, momentum_nontrainable=1., trainable=True): """Does a linear interpolation of two parameters/ buffers. Args: a (torch.Tensor): Interpolation start point, refer to orig state. b (torch.Tensor): Interpolation end point, refer to ema state. momentum (float, optional): The weight for the interpolation formula. Defaults to 0.001. momentum_nontrainable (float, optional): The weight for the interpolation formula used for nontrainable parameters. Defaults to 1.. trainable (bool, optional): Whether input parameters is trainable. If set to False, momentum_nontrainable will be used. Defaults to True. Returns: torch.Tensor: Interpolation result. """ assert 0.0 < momentum < 1.0, 'momentum must be in range (0.0, 1.0)'\ f'but got {momentum}' assert 0.0 < momentum_nontrainable <= 1.0, ( 'momentum_nontrainable must be in range (0.0, 1.0] but got ' f'{momentum_nontrainable}') if momentum > 0.5: warnings.warn( 'The value of momentum in EMA is usually a small number,' 'which is different from the conventional notion of ' f'momentum but got {momentum}. Please make sure the ' f'value is correct.') m = momentum if trainable else momentum_nontrainable return b + (a - b) * m
[docs] def every_n_iters(self, runner: Runner, n: int): """This is the function to perform every n iterations. Args: runner (Runner): runner used to drive the whole pipeline n (int): the number of iterations Returns: int: the latest iterations """ if runner.iter < self.start_iter: return True return (runner.iter + 1 - self.start_iter) % n == 0 if n > 0 else False
@torch.no_grad()
[docs] def after_train_iter(self, runner: Runner, batch_idx: int, data_batch: DATA_BATCH = None, outputs: Optional[dict] = None) -> None: """This is the function to perform after each training iteration. Args: runner (Runner): runner to drive the pipeline batch_idx (int): the id of batch data_batch (DATA_BATCH, optional): data batch. Defaults to None. outputs (Optional[dict], optional): output. Defaults to None. """ if not self.every_n_iters(runner, self.interval): return model = runner.model.module if is_model_wrapper( runner.model) else runner.model for key in self.module_keys: # get current ema states ema_net = getattr(model, key) states_ema = ema_net.state_dict(keep_vars=False) # get currently original states net = getattr(model, key[:-4]) states_orig = net.state_dict(keep_vars=True) for k, v in states_orig.items(): if runner.iter < self.start_iter: states_ema[k].data.copy_(v.data) else: states_ema[k] = self.interp_func( v, states_ema[k], trainable=v.requires_grad).detach() ema_net.load_state_dict(states_ema, strict=True)
[docs] def before_run(self, runner: Runner): """This is the function perform before each run. Args: runner (Runner): runner used to drive the whole pipeline Raises: RuntimeError: error message """ model = runner.model.module if is_model_wrapper( runner.model) else runner.model # sanity check for ema model for k in self.module_keys: if not hasattr(model, k) and not hasattr(model, k[:-4]): raise RuntimeError( f'Cannot find both {k[:-4]} and {k} network for EMA hook.') if not hasattr(model, k) and hasattr(model, k[:-4]): setattr(model, k, deepcopy(getattr(model, k[:-4]))) warnings.warn( f'We do not suggest construct and initialize EMA model {k}'
' in hook. You may explicitly define it by yourself.')
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