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20a49af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import torch.nn as nn
"""
A method that increases the stability of a model’s convergence and helps it reach a better overall solution by preventing convergence to a local minima.
To avoid drastic changes in the model’s weights during training, a copy of the current weights is created before updating the model’s weights.
Then the model’s weights are updated to be the weighted average between the current weights and the post-optimization step weights.
"""
class EMAHelper(object):
def __init__(self, mu=0.999):
self.mu = mu
self.shadow = {}
def register(self, module):
if isinstance(module, nn.DataParallel):
module = module.module
for name, param in module.named_parameters():
if param.requires_grad:
self.shadow[name] = param.data.clone()
def update(self, module):
if isinstance(module, nn.DataParallel):
module = module.module
for name, param in module.named_parameters():
if param.requires_grad:
self.shadow[name].data = (
1. - self.mu) * param.data + self.mu * self.shadow[name].data
def ema(self, module):
if isinstance(module, nn.DataParallel):
module = module.module
for name, param in module.named_parameters():
if param.requires_grad:
param.data.copy_(self.shadow[name].data)
def ema_copy(self, module):
if isinstance(module, nn.DataParallel):
inner_module = module.module
module_copy = type(inner_module)(
inner_module.config).to(inner_module.config.device)
module_copy.load_state_dict(inner_module.state_dict())
module_copy = nn.DataParallel(module_copy)
else:
module_copy = type(module)(module.config).to(module.config.device)
module_copy.load_state_dict(module.state_dict())
# module_copy = copy.deepcopy(module)
self.ema(module_copy)
return module_copy
def state_dict(self):
return self.shadow
def load_state_dict(self, state_dict):
self.shadow = state_dict
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