ubisoft-laforge-ZeroEGGS-main/ZEGGS/optimizers.py

import math

import torch
from torch.optim.optimizer import 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_(grad, grad, value=1 - beta2)
 exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)

 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

 # more conservative since it's an approximated value
 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

 # more conservative since it's an approximated value
 if N_sma >= 5:
 if group['weight_decay'] != 0:
 p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr'])
 denom = exp_avg_sq.sqrt().add_(group['eps'])
 p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
 p.data.copy_(p_data_fp32)
 elif step_size > 0:
 if group['weight_decay'] != 0:
 p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr'])
 p_data_fp32.add_(exp_avg, alpha=-step_size * group['lr'])
 p.data.copy_(p_data_fp32)

 return loss