image-match/python/py/Lib/site-packages/timm/optim/sgdp.py

"""
SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py

Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP

References for added functionality:
    Cautious Optimizers: https://arxiv.org/abs/2411.16085
    Spherical Cautious Optimizers: https://openreview.net/forum?id=OyT2CJ4fh7 
Copyright (c) 2020-present NAVER Corp.
MIT license
"""

import torch
import torch.nn.functional as F
from torch.optim.optimizer import Optimizer, required
import math

from .adamp import projection


class SGDP(Optimizer):
    def __init__(
            self,
            params,
            lr=required,
            momentum=0,
            dampening=0,
            weight_decay=0,
            nesterov=False,
            eps=1e-8,
            delta=0.1,
            wd_ratio=0.1,
            caution=False  
    ):
        defaults = dict(
            lr=lr,
            momentum=momentum,
            dampening=dampening,
            weight_decay=weight_decay,
            nesterov=nesterov,
            eps=eps,
            delta=delta,
            wd_ratio=wd_ratio,
            caution=caution,
        )
        super(SGDP, self).__init__(params, defaults)

    @torch.no_grad()
    def step(self, closure=None):
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        for group in self.param_groups:
            weight_decay = group['weight_decay']
            momentum = group['momentum']
            dampening = group['dampening']
            nesterov = group['nesterov']
            caution = group.get('caution', False)  

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad
                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['momentum'] = torch.zeros_like(p)

                # SGD
                buf = state['momentum']
                buf.mul_(momentum).add_(grad, alpha=1. - dampening)
                if nesterov:
                    d_p = grad + momentum * buf
                else:
                    d_p = buf.clone()

                # Projection
                wd_ratio = 1.
                if len(p.shape) > 1:
                    d_p, wd_ratio = projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps'], caution)
                elif caution:
                    mask = (d_p * grad > 0).to(grad.dtype)
                    mask.div_(mask.mean().clamp_(min=1e-3))
                    d_p.mul_(mask)

                # Weight decay
                if weight_decay != 0:
                    p.mul_(1. - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum))

                # Step
                p.add_(d_p, alpha=-group['lr'])

        return loss