lr in zip(self.optimizer.param_groups, warmup_lr): param_group['lr'] = lr else: if epoch is None: self.after_scheduler.step(metrics, None) else: self.after_scheduler.step(metrics, epoch - self.total_epoch) def step(self, epoch=None, metrics=None): if type(self.after_scheduler) !
lr: 0.002 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range:...
from torch.optim.lr_scheduler import CosineAnnealingLR, MultiStepLR from warmup_scheduler import GradualWarmupScheduler def get_scheduler(hparams, optimizer): eps = 1e-8 if hparams.lr_scheduler == 'steplr': scheduler = MultiStepLR(optimizer, milestones=hparams.decay_step, gamma=hparams...
--learning_rate 2e-5 \ --lr_scheduler_type constant \ --adam_beta10.9 \ --adam_beta2 0.98 \ --adam_epsilon1e-8 \ --max_grad_norm 1.0 \ --weight_decay 1e-4 \ --warmup_ratio0.0 \ --logging_steps 1 \ --gradient_checkpointing True \ --deepspeed ds_config.json \ --bf16 T...
'--unet_lr=1e-4', '--text_encoder_lr=1e-5', '--lr_scheduler=cosine_with_restarts', '--lr_warmup_steps=0', '--network_dim=32', '--network_alpha=32', '--output_name=shoujilihui', '--train_batch_size=1', '--save_every_n_epochs=2', '--mixed_precision=fp16', '--sa...
CONSTANT_WITH_WARMUP = "constant_with_warmup" class OptimizerType(BaseEnum): """ Stores the acceptable string identifiers for optimizers.""" # supported item for test, will be delete in the future.ADAMWEIGHTDECAY = 'AdamWeightDecay' # will be support item for future.ADAMW...
adversarial_nets_lr_scheduler after_kernel agile_modeling al_for_fep albert algae_dice aloe alx amortized_bo android_in_the_wild anthea aptamers_mlpd aqt aquadem ara_optimization arithmetic_sampling arxiv_latex_cleaner assemblenet assessment_plan_modeling attentional_adapters at...
def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0): warmup_schedule = np.array([]) warmup_iters = warmup_epochs * niter_per_ep if warmup_epochs > 0: warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_it...
class WarmUpLR(_LRScheduler): """warmup_training learning rate scheduler Args: optimizer: optimzier(e.g. SGD) total_iters: totoal_iters of warmup phase """ def __init__(self, optimizer, total_iters, last_epoch=-1): self.total_iters = total_iters super().__init__(opt...
class WarmUpLR(_LRScheduler): """warmup_training learning rate scheduler Args: optimizer: optimzier(e.g. SGD) total_iters: totoal_iters of warmup phase """ def __init__(self, optimizer, total_iters, last_epoch=-1): self.total_iters = total_iters super().__init__(opt...