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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# ModelEMA implementation is taken from
# https://github.com/facebookresearch/demucs
from collections import defaultdict
import typing as tp
import torch
import torch.nn as nn
def _get_all_non_persistent_buffers_set(module: nn.Module, root: str = "") -> set:
names: set = set()
for (name, sub_module) in module.named_modules():
if name == '':
buffer_names = module._non_persistent_buffers_set
buffer_names = {f"{root}.{buff_name}" if len(root) > 0 else buff_name
for buff_name in buffer_names}
names.update(buffer_names)
else:
sub_name = f"{root}.{name}" if len(root) > 0 else name
sub_buffer_names = _get_all_non_persistent_buffers_set(sub_module, sub_name)
names.update(sub_buffer_names)
return names
def _get_named_tensors(module: nn.Module):
non_persistent_buffers_set = _get_all_non_persistent_buffers_set(module)
named_buffers = [(name, buffer) for (name, buffer) in module.named_buffers()
if name not in non_persistent_buffers_set]
named_parameters = list(module.named_parameters())
return named_parameters + named_buffers
class ModuleDictEMA:
"""Exponential Moving Average over a nn.ModuleDict.
You can switch to the EMA weights temporarily.
"""
def __init__(self, module_dict: nn.ModuleDict, decay: float = 0.999,
unbias: bool = True, device: tp.Union[torch.device, str] = 'cpu'):
self.decay = decay
self.module_dict = module_dict
self.state: dict = defaultdict(dict)
self.count = 0
self.device = device
self.unbias = unbias
self._init()
def _init(self):
for module_name, module in self.module_dict.items():
for key, val in _get_named_tensors(module):
if not val.is_floating_point():
continue
device = self.device or val.device
if key not in self.state[module_name]:
self.state[module_name][key] = val.detach().to(device, copy=True)
def step(self):
if self.unbias:
self.count = self.count * self.decay + 1
w = 1 / self.count
else:
w = 1 - self.decay
for module_name, module in self.module_dict.items():
for key, val in _get_named_tensors(module):
if not val.is_floating_point():
continue
device = self.device or val.device
self.state[module_name][key].mul_(1 - w)
self.state[module_name][key].add_(val.detach().to(device), alpha=w)
def state_dict(self):
return {'state': self.state, 'count': self.count}
def load_state_dict(self, state):
self.count = state['count']
for module_name, module in state['state'].items():
for key, val in module.items():
self.state[module_name][key].copy_(val)
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