|
import torch
|
|
from torch import nn
|
|
|
|
|
|
class LitEma(nn.Module):
|
|
def __init__(self, model, decay=0.9999, use_num_upates=True):
|
|
super().__init__()
|
|
if decay < 0.0 or decay > 1.0:
|
|
raise ValueError('Decay must be between 0 and 1')
|
|
|
|
self.m_name2s_name = {}
|
|
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
|
|
self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
|
|
else torch.tensor(-1,dtype=torch.int))
|
|
|
|
for name, p in model.named_parameters():
|
|
if p.requires_grad:
|
|
|
|
s_name = name.replace('.','')
|
|
self.m_name2s_name.update({name:s_name})
|
|
self.register_buffer(s_name,p.clone().detach().data)
|
|
|
|
self.collected_params = []
|
|
|
|
def forward(self,model):
|
|
decay = self.decay
|
|
|
|
if self.num_updates >= 0:
|
|
self.num_updates += 1
|
|
decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
|
|
|
|
one_minus_decay = 1.0 - decay
|
|
|
|
with torch.no_grad():
|
|
m_param = dict(model.named_parameters())
|
|
shadow_params = dict(self.named_buffers())
|
|
|
|
for key in m_param:
|
|
if m_param[key].requires_grad:
|
|
sname = self.m_name2s_name[key]
|
|
shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
|
|
shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
|
|
else:
|
|
assert not key in self.m_name2s_name
|
|
|
|
def copy_to(self, model):
|
|
m_param = dict(model.named_parameters())
|
|
shadow_params = dict(self.named_buffers())
|
|
for key in m_param:
|
|
if m_param[key].requires_grad:
|
|
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
|
|
else:
|
|
assert not key in self.m_name2s_name
|
|
|
|
def store(self, parameters):
|
|
"""
|
|
Save the current parameters for restoring later.
|
|
Args:
|
|
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
|
|
temporarily stored.
|
|
"""
|
|
self.collected_params = [param.clone() for param in parameters]
|
|
|
|
def restore(self, parameters):
|
|
"""
|
|
Restore the parameters stored with the `store` method.
|
|
Useful to validate the model with EMA parameters without affecting the
|
|
original optimization process. Store the parameters before the
|
|
`copy_to` method. After validation (or model saving), use this to
|
|
restore the former parameters.
|
|
Args:
|
|
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
|
|
updated with the stored parameters.
|
|
"""
|
|
for c_param, param in zip(self.collected_params, parameters):
|
|
param.data.copy_(c_param.data) |
