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from pathlib import Path
import json
from typing import Iterable, Any
from contextlib import contextmanager
import torch
from diffusers.training_utils import EMAModel as EMAModel_
def save_args(basepath: Path, args, extra={}):
info = {"args": vars(args)}
info["args"].update(extra)
with open(basepath.joinpath("args.json"), "w") as f:
json.dump(info, f, indent=4)
class AverageMeter:
avg: Any
def __init__(self, name=None):
self.name = name
self.reset()
def reset(self):
self.sum = self.count = self.avg = 0
def update(self, val, n=1):
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class EMAModel(EMAModel_):
@contextmanager
def apply_temporary(self, parameters: Iterable[torch.nn.Parameter]):
parameters = list(parameters)
original_params = [p.clone() for p in parameters]
self.copy_to(parameters)
try:
yield
finally:
for o_param, param in zip(original_params, parameters):
param.data.copy_(o_param.data)
del original_params
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