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
57
|
from pathlib import Path
import json
import math
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:
def __init__(self, inv_gamma=1.0, power=2 / 3):
self.inv_gamma = inv_gamma
self.power = power
self.reset()
def reset(self):
self.step = 0
self.min = math.inf
self.max = 0.0
self.avg = 0.0
def get_decay(self):
if self.step <= 0:
return 1
return (self.step / self.inv_gamma) ** -self.power
def update(self, val, n=1):
for _ in range(n):
self.step += n
self.avg += (val - self.avg) * self.get_decay()
self.min = min(self.min, self.avg)
self.max = max(self.max, self.avg)
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
|
