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98 lines
4.1 KiB
98 lines
4.1 KiB
import torch
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from torch import optim as optim
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from timm.optim import Nadam, RMSpropTF, AdamW, RAdam, NovoGrad, NvNovoGrad, Lookahead
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try:
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from apex.optimizers import FusedNovoGrad, FusedAdam, FusedLAMB, FusedSGD
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has_apex = True
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except ImportError:
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has_apex = False
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def add_weight_decay(model, weight_decay=1e-5, skip_list=()):
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decay = []
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no_decay = []
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for name, param in model.named_parameters():
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if not param.requires_grad:
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continue # frozen weights
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if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
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no_decay.append(param)
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else:
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decay.append(param)
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return [
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{'params': no_decay, 'weight_decay': 0.},
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{'params': decay, 'weight_decay': weight_decay}]
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def create_optimizer(args, model, filter_bias_and_bn=True):
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opt_lower = args.opt.lower()
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weight_decay = args.weight_decay
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if 'adamw' in opt_lower or 'radam' in opt_lower:
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# Compensate for the way current AdamW and RAdam optimizers apply LR to the weight-decay
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# I don't believe they follow the paper or original Torch7 impl which schedules weight
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# decay based on the ratio of current_lr/initial_lr
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weight_decay /= args.lr
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if weight_decay and filter_bias_and_bn:
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parameters = add_weight_decay(model, weight_decay)
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weight_decay = 0.
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else:
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parameters = model.parameters()
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if 'fused' in opt_lower:
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assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
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opt_split = opt_lower.split('_')
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opt_lower = opt_split[-1]
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if opt_lower == 'sgd':
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optimizer = optim.SGD(
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parameters, lr=args.lr, momentum=args.momentum, weight_decay=weight_decay, nesterov=True)
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elif opt_lower == 'adam':
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optimizer = optim.Adam(
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parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'adamw':
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optimizer = AdamW(
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parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'nadam':
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optimizer = Nadam(
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parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'radam':
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optimizer = RAdam(
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parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'adadelta':
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optimizer = optim.Adadelta(
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parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'rmsprop':
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optimizer = optim.RMSprop(
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parameters, lr=args.lr, alpha=0.9, eps=args.opt_eps,
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momentum=args.momentum, weight_decay=weight_decay)
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elif opt_lower == 'rmsproptf':
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optimizer = RMSpropTF(
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parameters, lr=args.lr, alpha=0.9, eps=args.opt_eps,
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momentum=args.momentum, weight_decay=weight_decay)
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elif opt_lower == 'novograd':
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optimizer = NovoGrad(parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'nvnovograd':
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optimizer = NvNovoGrad(parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'fusedsgd':
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optimizer = FusedSGD(
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parameters, lr=args.lr, momentum=args.momentum, weight_decay=weight_decay, nesterov=True)
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elif opt_lower == 'fusedadam':
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optimizer = FusedAdam(
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parameters, lr=args.lr, adam_w_mode=False, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'fusedadamw':
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optimizer = FusedAdam(
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parameters, lr=args.lr, adam_w_mode=True, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'fusedlamb':
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optimizer = FusedLAMB(parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
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elif opt_lower == 'fusednovograd':
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optimizer = FusedNovoGrad(
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parameters, lr=args.lr, betas=(0.95, 0.98), weight_decay=weight_decay, eps=args.opt_eps)
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else:
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assert False and "Invalid optimizer"
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raise ValueError
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if len(opt_split) > 1:
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if opt_split[0] == 'lookahead':
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optimizer = Lookahead(optimizer)
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return optimizer
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