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mmedit.models.base_models.average_model

Module Contents

Classes

ExponentialMovingAverage

Implements the exponential moving average (EMA) of the model.

RampUpEMA

Implements the exponential moving average with ramping up momentum.

class mmedit.models.base_models.average_model.ExponentialMovingAverage(model: torch.nn.Module, momentum: float = 0.0002, interval: int = 1, device: Optional[torch.device] = None, update_buffers: bool = False)[source]

Bases: mmengine.model.BaseAveragedModel

Implements the exponential moving average (EMA) of the model.

All parameters are updated by the formula as below:

\[Xema_{t+1} = (1 - momentum) * Xema_{t} + momentum * X_t\]
Parameters
  • model (nn.Module) – The model to be averaged.

  • momentum (float) – The momentum used for updating ema parameter. Defaults to 0.0002. Ema’s parameter are updated with the formula \(averaged\_param = (1-momentum) * averaged\_param + momentum * source\_param\).

  • interval (int) – Interval between two updates. Defaults to 1.

  • device (torch.device, optional) – If provided, the averaged model will be stored on the 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.

avg_func(averaged_param: torch.Tensor, source_param: torch.Tensor, steps: int) None[source]

Compute the moving average of the parameters using exponential moving average.

Parameters
  • averaged_param (Tensor) – The averaged parameters.

  • source_param (Tensor) – The source parameters.

  • steps (int) – The number of times the parameters have been updated.

_load_from_state_dict(state_dict: dict, prefix: str, local_metadata: dict, strict: bool, missing_keys: list, unexpected_keys: list, error_msgs: List[str]) None[source]

Overrides nn.Module._load_from_state_dict to support loading state_dict without wrap ema module with BaseAveragedModel.

In OpenMMLab 1.0, model will not wrap ema submodule with BaseAveragedModel, and the ema weight key in state_dict will miss module prefix. Therefore, BaseAveragedModel need to automatically add the module prefix if the corresponding key in state_dict misses it.

Parameters
  • state_dict (dict) – A dict containing parameters and persistent buffers.

  • prefix (str) – The prefix for parameters and buffers used in this module

  • local_metadata (dict) – a dict containing the metadata for this module.

  • strict (bool) – Whether to strictly enforce that the keys in state_dict with prefix match the names of parameters and buffers in this module

  • missing_keys (List[str]) – if strict=True, add missing keys to this list

  • unexpected_keys (List[str]) – if strict=True, add unexpected keys to this list

  • error_msgs (List[str]) – error messages should be added to this list, and will be reported together in load_state_dict().

sync_buffers(model: torch.nn.Module) None[source]

Copy buffer from model to averaged model.

Parameters

model (nn.Module) – The model whose parameters will be averaged.

sync_parameters(model: torch.nn.Module) None[source]

Copy buffer and parameters from model to averaged model.

Parameters

model (nn.Module) – The model whose parameters will be averaged.

class mmedit.models.base_models.average_model.RampUpEMA(model: torch.nn.Module, interval: int = 1, ema_kimg: int = 10, ema_rampup: float = 0.05, batch_size: int = 32, eps: float = 1e-08, start_iter: int = 0, device: Optional[torch.device] = None, update_buffers: bool = False)[source]

Bases: mmengine.model.BaseAveragedModel

Implements the exponential moving average with ramping up momentum.

Ref: https://github.com/NVlabs/stylegan3/blob/master/training/training_loop.py # noqa

Parameters
  • model (nn.Module) – The model to be averaged.

  • interval (int) – Interval between two updates. Defaults to 1.

  • ema_kimg (int, optional) – EMA kimgs. Defaults to 10.

  • ema_rampup (float, optional) – Ramp up rate. Defaults to 0.05.

  • batch_size (int, optional) – Global batch size. Defaults to 32.

  • eps (float, optional) – Ramp up epsilon. Defaults to 1e-8.

  • start_iter (int, optional) – EMA start iter. Defaults to 0.

  • device (torch.device, optional) – If provided, the averaged model will be stored on the 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.

static rampup(steps, ema_kimg=10, ema_rampup=0.05, batch_size=4, eps=1e-08)[source]

Ramp up ema momentum.

Ref: https://github.com/NVlabs/stylegan3/blob/a5a69f58294509598714d1e88c9646c3d7c6ec94/training/training_loop.py#L300-L308 # noqa

Parameters
  • steps

  • ema_kimg (int, optional) – Half-life of the exponential moving average of generator weights. Defaults to 10.

  • ema_rampup (float, optional) – EMA ramp-up coefficient.If set to None, then rampup will be disabled. Defaults to 0.05.

  • batch_size (int, optional) – Total batch size for one training iteration. Defaults to 4.

  • eps (float, optional) – Epsiolon to avoid batch_size divided by zero. Defaults to 1e-8.

Returns

Updated momentum.

Return type

dict

avg_func(averaged_param: torch.Tensor, source_param: torch.Tensor, steps: int) None[source]

Compute the moving average of the parameters using exponential moving average.

Parameters
  • averaged_param (Tensor) – The averaged parameters.

  • source_param (Tensor) – The source parameters.

  • steps (int) – The number of times the parameters have been updated.

_load_from_state_dict(state_dict: dict, prefix: str, local_metadata: dict, strict: bool, missing_keys: list, unexpected_keys: list, error_msgs: List[str]) None[source]

Overrides nn.Module._load_from_state_dict to support loading state_dict without wrap ema module with BaseAveragedModel.

In OpenMMLab 1.0, model will not wrap ema submodule with BaseAveragedModel, and the ema weight key in state_dict will miss module prefix. Therefore, BaseAveragedModel need to automatically add the module prefix if the corresponding key in state_dict misses it.

Parameters
  • state_dict (dict) – A dict containing parameters and persistent buffers.

  • prefix (str) – The prefix for parameters and buffers used in this module

  • local_metadata (dict) – a dict containing the metadata for this module.

  • strict (bool) – Whether to strictly enforce that the keys in state_dict with prefix match the names of parameters and buffers in this module

  • missing_keys (List[str]) – if strict=True, add missing keys to this list

  • unexpected_keys (List[str]) – if strict=True, add unexpected keys to this list

  • error_msgs (List[str]) – error messages should be added to this list, and will be reported together in load_state_dict().

sync_buffers(model: torch.nn.Module) None[source]

Copy buffer from model to averaged model.

Parameters

model (nn.Module) – The model whose parameters will be averaged.

sync_parameters(model: torch.nn.Module) None[source]

Copy buffer and parameters from model to averaged model.

Parameters

model (nn.Module) – The model whose parameters will be averaged.

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