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from collections import OrderedDict
import torch
@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
"""
Step the EMA model towards the current model.
"""
ema_params = OrderedDict(ema_model.named_parameters())
model_params = OrderedDict(model.named_parameters())
for name, param in model_params.items():
name = name.replace("module.", "")
ema_params[name].mul_(decay).add_(param.detach().cpu().data, alpha=1 - decay)
@torch.no_grad()
def update_cpu_ema(ema_model, model, decay=0.9999):
"""
Step the EMA model towards the current model.
"""
ema_params = OrderedDict(ema_model.named_parameters())
model_params = OrderedDict(model.named_parameters())
for name, param in model_params.items():
name = name.replace("module.", "")
ema_params[name].mul_(decay).add_(param.detach().cpu().data, alpha=1 - decay)
def requires_grad(model, flag=True):
"""
Set requires_grad flag for all parameters in a model.
"""
for p in model.parameters():
p.requires_grad = flag
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