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53 lines
1.9 KiB
53 lines
1.9 KiB
from torch import optim as optim
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from optim import Nadam, RMSpropTF
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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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weight_decay = args.weight_decay
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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 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=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=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=weight_decay, eps=args.opt_eps)
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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=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=weight_decay)
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elif args.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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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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