ranger优化器pytorch代码

以下是一个使用PyTorch实现的Ranger优化器的代码示例：

import torch
from torch.optim.optimizer import Optimizer


class Ranger(Optimizer):
    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(0.95, 0.999), eps=1e-5, weight_decay=0):
        defaults = dict(lr=lr, alpha=alpha, k=k, N_sma_threshhold=N_sma_threshhold, betas=betas, eps=eps, weight_decay=weight_decay)
        super(Ranger, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(Ranger, self).__setstate__(state)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        # calculate the moving average of the gradient
        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                state = self.state[p]
                if not state:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p.data)
                    state['exp_avg_sq'] = torch.zeros_like(p.data)

                state['step'] += 1
                beta1, beta2 = group['betas']
                eps = group['eps']
                state['exp_avg'] = beta1 * state['exp_avg'] + (1 - beta1) * grad
                state['exp_avg_sq'] = beta2 * state['exp_avg_sq'] + (1 - beta2) * grad ** 2
                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']

                # calculate the variance adapted learning rate
                state['S'] = torch.max(state['S'], torch.abs(grad))
                S_max = state['S'].max()
                state['N_sma'] = state['N_sma'] + 1
                N_sma = state['N_sma']
                radam_step_size = group['lr']
                if N_sma >= group['N_sma_threshhold']:
                    radam_step_size = group['lr'] * S_max / (exp_avg_sq.sqrt() + eps)
                if group['weight_decay'] != 0:
                    p.data.add_(-group['weight_decay'] * group['lr'], p.data)

                # calculate the RAdam adaptive learning rate
                beta2_t = beta2 ** state['step']
                r_t = (exp_avg_sq.sqrt() * (1 - beta2_t)) / (state['exp_avg_sq_hat'].sqrt() + eps)
                if state['step'] > 5:
                    r_t = torch.max(r_t, torch.tensor([group['alpha'] * (S_max / (state['S_hat'].max() + eps))]).to(r_t.device))
                p.data.add_(-radam_step_size * r_t, exp_avg)

                # calculate the smoothed weight denominator
                state['S_hat'] = beta2 * state['S_hat'] + (1 - beta2) * state['S']
                state['exp_avg_sq_hat'] = beta2 * state['exp_avg_sq_hat'] + (1 - beta2) * exp_avg_sq
                state['denom'] = state['exp_avg_sq_hat'].sqrt() + eps

        return loss

这是一个比较复杂的优化器实现，其中涉及了RAdam的一些优化，如计算moving average of the gradient、计算variance adapted learning rate、计算RAdam adaptive learning rate等。代码中有详细的注释，可以帮助理解每个步骤的具体实现。