o zi@sDddlZddlmZddlmZmZmZmZdZGdddeZ dS)N) Optimizer)OptFloatOptLossClosureParamsState)SGDWcsteZdZdZ     ddedededed ed ed d ffd d Zded d ffdd Z dde d e fddZ Z S)raXImplements SGDW algorithm. It has been proposed in `Decoupled Weight Decay Regularization`__. Arguments: params: iterable of parameters to optimize or dicts defining parameter groups lr: learning rate (default: 1e-3) momentum: momentum factor (default: 0) weight_decay: weight decay (L2 penalty) (default: 0) dampening: dampening for momentum (default: 0) nesterov: enables Nesterov momentum (default: False) Example: >>> import torch_optimizer as optim >>> optimizer = optim.SGDW(model.parameters(), lr=0.1, momentum=0.9) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://arxiv.org/abs/1711.05101 Note: Reference code: https://github.com/pytorch/pytorch/pull/22466 MbP?Fparamslrmomentum dampening weight_decaynesterovreturnNcs|dkr td||dkrtd||dkr!td||dkr,td|t|||||d}|rC|dks?|dkrCtdtt|||dS) Nr zInvalid learning rate: {}zInvalid momentum value: {}zInvalid dampening value: {}zInvalid weight_decay value: {})r r rrrrz8Nesterov momentum requires a momentum and zero dampening) ValueErrorformatdictsuperr__init__)selfr r r rrrdefaults __class__H/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/sgdw.pyr$s, z SGDW.__init__statecs,tt|||jD]}|ddq dS)NrF)rr __setstate__ param_groups setdefault)rrgrouprrrrEs zSGDW.__setstate__closurec Csd}|dur |}|jD]{}|d}|d}|d}|d}|dD]d}|jdur*q"|jj} |jjr8d} t| |dkrl|j|} d | vrQt| } | d <n| d } | |j | d |d |rj| || } n| } |jj | |d  d |dkr|jj ||d  d q"q |S) zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nrr rrr zJSGDW does not support sparse gradients, please consider SparseAdam insteadrmomentum_bufferr)alphar ) rgraddata is_sparse RuntimeErrorrtorchclonedetachmul_add_add) rr"lossr!rr rrpd_pmsg param_statebufrrrstepJsF      z SGDW.step)r r r r F)N)__name__ __module__ __qualname____doc__rfloatboolrrrrrr5 __classcell__rrrrr s0!r) r)torch.optim.optimizerrtypesrrrr__all__rrrrrs