mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
9c3859fb9c
* All models have 'default_cfgs' dict * load/resume/pretrained helpers factored out * pretrained load operates on state_dict based on default_cfg * test all models in validate * schedule, optim factor factored out * test time pool wrapper applied based on default_cfg
31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
from torch import optim as optim
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from optim import Nadam, AdaBound
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def create_optimizer(args, parameters):
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if args.opt.lower() == 'sgd':
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optimizer = optim.SGD(
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parameters, lr=args.lr,
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momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
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elif args.opt.lower() == 'adam':
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optimizer = optim.Adam(
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parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
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elif args.opt.lower() == 'nadam':
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optimizer = Nadam(
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parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
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elif args.opt.lower() == 'adabound':
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optimizer = AdaBound(
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parameters, lr=args.lr / 100, weight_decay=args.weight_decay, eps=args.opt_eps,
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final_lr=args.lr)
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elif args.opt.lower() == 'adadelta':
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optimizer = optim.Adadelta(
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parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
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elif args.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=args.weight_decay)
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else:
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assert False and "Invalid optimizer"
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raise ValueError
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return optimizer
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