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#-*- encoding:utf-8 -*-
import torch
from pytorch_lightning.callbacks import Callback
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:
#remove as '.'-character is not allowed in buffers
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)
class EMACallback(Callback):
def __init__(self, decay=0.9999):
self.decay = decay
self.shadow_params = {}
def on_train_start(self, trainer, pl_module):
# initialize shadow parameters for original models
total_ema_cnt = 0
for name, param in pl_module.named_parameters():
if name not in self.shadow_params:
self.shadow_params[name] = param.data.clone()
else: # already in dict, maybe load from checkpoint
pass
print('will calc ema for param: %s' % name)
total_ema_cnt += 1
print('total_ema_cnt=%d' % total_ema_cnt)
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
# Update the shadow params at the end of each epoch
for name, param in pl_module.named_parameters():
assert name in self.shadow_params
new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow_params[name]
self.shadow_params[name] = new_average.clone()
def on_save_checkpoint(self, trainer, pl_module, checkpoint):
# Save EMA parameters in the checkpoint
checkpoint['ema_params'] = self.shadow_params
def on_load_checkpoint(self, trainer, pl_module, checkpoint):
# Restore EMA parameters from the checkpoint
if 'ema_params' in checkpoint:
self.shadow_params = checkpoint.get('ema_params', {})
for k in self.shadow_params:
self.shadow_params[k] = self.shadow_params[k].cuda()
print('load shadow params from checkpoint, cnt=%d' % len(self.shadow_params))
else:
print('ema_params is not in checkpoint') |