mirror of https://github.com/JDAI-CV/fast-reid.git
103 lines
4.0 KiB
Python
103 lines
4.0 KiB
Python
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####
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# CODE TAKEN FROM https://github.com/mgrankin/over9000
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####
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import torch, math
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from torch.optim.optimizer import Optimizer
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# RAdam + LARS
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class Ralamb(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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self.buffer = [[None, None, None] for ind in range(10)]
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super(Ralamb, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(Ralamb, self).__setstate__(state)
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def step(self, closure=None):
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad.data.float()
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if grad.is_sparse:
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raise RuntimeError('Ralamb does not support sparse gradients')
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p_data_fp32 = p.data.float()
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state = self.state[p]
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if len(state) == 0:
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state['step'] = 0
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state['exp_avg'] = torch.zeros_like(p_data_fp32)
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state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
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else:
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state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
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state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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# Decay the first and second moment running average coefficient
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# m_t
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exp_avg.mul_(beta1).add_(1 - beta1, grad)
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# v_t
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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state['step'] += 1
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buffered = self.buffer[int(state['step'] % 10)]
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if state['step'] == buffered[0]:
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N_sma, radam_step_size = buffered[1], buffered[2]
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else:
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buffered[0] = state['step']
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beta2_t = beta2 ** state['step']
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N_sma_max = 2 / (1 - beta2) - 1
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N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
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buffered[1] = N_sma
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# more conservative since it's an approximated value
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if N_sma >= 5:
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radam_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'])
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else:
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radam_step_size = 1.0 / (1 - beta1 ** state['step'])
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buffered[2] = radam_step_size
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if group['weight_decay'] != 0:
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p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
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# more conservative since it's an approximated value
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radam_step = p_data_fp32.clone()
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if N_sma >= 5:
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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radam_step.addcdiv_(-radam_step_size * group['lr'], exp_avg, denom)
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else:
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radam_step.add_(-radam_step_size * group['lr'], exp_avg)
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radam_norm = radam_step.pow(2).sum().sqrt()
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weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10)
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if weight_norm == 0 or radam_norm == 0:
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trust_ratio = 1
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else:
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trust_ratio = weight_norm / radam_norm
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state['weight_norm'] = weight_norm
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state['adam_norm'] = radam_norm
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state['trust_ratio'] = trust_ratio
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if N_sma >= 5:
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p_data_fp32.addcdiv_(-radam_step_size * group['lr'] * trust_ratio, exp_avg, denom)
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else:
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p_data_fp32.add_(-radam_step_size * group['lr'] * trust_ratio, exp_avg)
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p.data.copy_(p_data_fp32)
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return loss
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