optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=0.0004, paramwise_options=dict(bias_lr_mult=2., bias_decay_mult=0.)) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) learning policy lr_config = dict( policy='step', warmup='linear', warmup_ite...
eta_min=config.min_lr)elifconfig.lr_scheduler =='multistep':ifconfig.stepsisNone:returnNoneifisinstance(config.steps, int): config.steps = [config.steps] scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=config.steps, gamma=config.gamma)elifconfig.lr_scheduler =='exp-warmup': lr_...
lr_scheduler == 'multistep': if config.steps is None: return None if isinstance(config.steps, int): config.steps = [config.steps] scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=config.steps, gamma=config.gamma) elif config.lr_scheduler == 'exp-warmup': lr_lambda = exp_warm...
# 需要导入模块: from torch.optim import lr_scheduler [as 别名]# 或者: from torch.optim.lr_scheduler importReduceLROnPlateau[as 别名]def_init_optimizer(self):parameters = [pforpinself.network.parameters()ifp.requires_grad]ifself.config['use_bert']andself.config.get('finetune_bert',None): p...
lr_scheduler == 'multistep': if config.steps is None: return None if isinstance(config.steps, int): config.steps = [config.steps] scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=config.steps, gamma=config.gamma) elif config.lr_scheduler == 'exp-warmup': lr_lambda = exp_warm...
eta_min=config.min_lr)elifconfig.lr_scheduler =='multistep':ifconfig.stepsisNone:returnNoneifisinstance(config.steps, int): config.steps = [config.steps] scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=config.steps, gamma=config.gamma)elifconfig.lr_scheduler =='exp-warmup': ...
# 需要导入模块: from torch.optim import lr_scheduler [as 别名]# 或者: from torch.optim.lr_scheduler importStepLR[as 别名]defget_scheduler(optimizer, opt):ifopt.lr_policy =='lambda':deflambda_rule(epoch):lr_l =1.0- max(0, epoch- ...
lr_policy == 'linear': def lambda_rule(epoch): lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1) return lr_l scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) elif opt.lr_policy == 'step': scheduler = lr_scheduler.Step...
def test_create_lr_scheduler_with_warmup_with_real_model(dummy_model_factory): model = dummy_model_factory(with_grads=False, with_frozen_layer=False) init_lr = 0.01 optimizer = torch.optim.SGD(model.parameters(), lr=init_lr) scaled_lr = 0.02 warmup_duration = 5 step_size = 2 gamma...
# 需要导入模块: from torch.optim import lr_scheduler [as 别名]# 或者: from torch.optim.lr_scheduler importStepLR[as 别名]defget_scheduler(optimizer, opt):ifopt.lr_policy =='lambda':deflambda_rule(epoch):lr_l =1.0- max(0, epoch- ...