File size: 2,218 Bytes
4707555 |
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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import os
import copy
import torch
import torch.nn as nn
class EMA:
"""
Modified version of class fairseq.models.ema.EMAModule.
Args:
model (nn.Module):
cfg (DictConfig):
device (str):
skip_keys (list): The keys to skip assigning averaged weights to.
"""
def __init__(self, model: nn.Module, skip_keys=None):
self.model = self.deepcopy_model(model)
self.model.requires_grad_(False)
# self.device = 'cuda'
self.device = 'cpu'
self.model.to(self.device)
self.skip_keys = skip_keys or set()
self.decay = 0.999
self.num_updates = 0
@staticmethod
def deepcopy_model(model):
try:
model = copy.deepcopy(model)
return model
except RuntimeError:
tmp_path = 'tmp_model_for_ema_deepcopy.pt'
torch.save(model, tmp_path)
model = torch.load(tmp_path)
os.remove(tmp_path)
return model
def step(self, new_model: nn.Module):
"""
One EMA step
Args:
new_model (nn.Module): Online model to fetch new weights from
"""
ema_state_dict = {}
ema_params = self.model.state_dict()
for key, param in new_model.state_dict().items():
ema_param = ema_params[key].float()
if key in self.skip_keys:
ema_param = param.to(dtype=ema_param.dtype).clone()
else:
ema_param.mul_(self.decay)
ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1 - self.decay)
ema_state_dict[key] = ema_param
self.model.load_state_dict(ema_state_dict, strict=False)
self.num_updates += 1
def restore(self, model: nn.Module):
d = self.model.state_dict()
model.load_state_dict(d, strict=False)
return model
def state_dict(self):
return self.model.state_dict()
@staticmethod
def get_annealed_rate(start, end, curr_step, total_steps):
"""
Calculate EMA annealing rate
"""
r = end - start
pct_remaining = 1 - curr_step / total_steps
return end - r * pct_remaining |