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optimizer.py
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optimizer.py
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import math
from typing import List
import torch
from easydict import EasyDict
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.optim.optimizer import Optimizer
def get_optimizer(cfg: EasyDict, params: List):
cfg_name = cfg.name
if cfg.name == 'adam':
optimizer = torch.optim.Adam(
params, lr=cfg.lr, weight_decay=cfg.weight_decay)
elif cfg_name == 'radam':
optimizer = RAdam(
params, lr=cfg.lr, betas=(cfg.momentum, cfg.adam_beta2),
eps=cfg.adam_eps, weight_decay=cfg.weight_decay)
else:
raise Exception(f'Unknown Optimizer {cfg.name}')
for param_group in optimizer.param_groups:
if 'initial_lr' in param_group:
raise ValueError
param_group['initial_lr'] = param_group['lr']
return optimizer
class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, degenerated_to_sgd=True):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError(
"Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError(
"Invalid beta parameter at index 1: {}".format(betas[1]))
self.degenerated_to_sgd = degenerated_to_sgd
if isinstance(params, (list, tuple)) and len(
params) > 0 and isinstance(params[0], dict):
for param in params:
if 'betas' in param and (
param['betas'][0] != betas[0] or param['betas'][1] !=
betas[1]):
param['buffer'] = [[None, None, None] for _ in range(10)]
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
buffer=[[None, None, None] for _ in range(10)])
super(RAdam, self).__init__(params, defaults)
def __setstate__(self, state):
super(RAdam, self).__setstate__(state)
def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data.float()
if grad.is_sparse:
raise RuntimeError(
'RAdam does not support sparse gradients')
p_data_fp32 = p.data.float()
state = self.state[p]
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p_data_fp32)
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
else:
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
p_data_fp32)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
exp_avg.mul_(beta1).add_(1 - beta1, grad)
state['step'] += 1
buffered = group['buffer'][int(state['step'] % 10)]
if state['step'] == buffered[0]:
n_sma, step_size = buffered[1], buffered[2]
else:
buffered[0] = state['step']
beta2_t = beta2 ** state['step']
n_sma_max = 2 / (1 - beta2) - 1
n_sma = n_sma_max - 2 * state['step'] * beta2_t / (
1 - beta2_t)
buffered[1] = n_sma
if n_sma >= 5:
step_size = math.sqrt(
(1 - beta2_t) * (n_sma - 4) / (n_sma_max - 4) * (
n_sma - 2) / n_sma * n_sma_max / (
n_sma_max - 2)) / (
1 - beta1 ** state['step'])
elif self.degenerated_to_sgd:
step_size = 1.0 / (1 - beta1 ** state['step'])
else:
step_size = -1
buffered[2] = step_size
if n_sma >= 5:
if group['weight_decay'] != 0:
p_data_fp32.add_(-group['weight_decay'] * group['lr'],
p_data_fp32)
denom = exp_avg_sq.sqrt().add_(group['eps'])
p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg,
denom)
p.data.copy_(p_data_fp32)
elif step_size > 0:
if group['weight_decay'] != 0:
p_data_fp32.add_(-group['weight_decay'] * group['lr'],
p_data_fp32)
p_data_fp32.add_(-step_size * group['lr'], exp_avg)
p.data.copy_(p_data_fp32)
return loss
# noinspection PyAttributeOutsideInit,PyUnresolvedReferences
class ReduceLROnPlateauWarmup(ReduceLROnPlateau):
def __init__(self, optimizer: Optimizer, warmup_epochs, **kwargs):
self.warmup_epochs = warmup_epochs
super().__init__(optimizer, **kwargs)
self.base_lrs = []
for group in optimizer.param_groups:
self.base_lrs.append(group["lr"])
self.step_rop(self.mode_worse, False, None)
def step_rop(self, metrics, do_eval, epoch=None):
assert epoch is None
epoch = self.last_epoch + 1
if epoch <= self.warmup_epochs:
factor = epoch / self.warmup_epochs
self.last_epoch = epoch
for i, param_group in enumerate(self.optimizer.param_groups):
param_group['lr'] = self.base_lrs[i] * factor
elif not do_eval:
pass
else:
super().step(metrics, epoch=epoch)