pytorch-image-models/optim/optim_factory.py
Ross Wightman bc264269c9 Morph mnasnet impl into a generic mobilenet that covers Mnasnet, MobileNetV1/V2, ChamNet, FBNet, and related
* add an alternate RMSprop opt that applies eps like TF
* add bn params for passing through alternates and changing defaults to TF style
2019-04-21 15:54:28 -07:00

35 lines
1.4 KiB
Python

from torch import optim as optim
from optim import Nadam, AdaBound, RMSpropTF
def create_optimizer(args, parameters):
if args.opt.lower() == 'sgd':
optimizer = optim.SGD(
parameters, lr=args.lr,
momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
elif args.opt.lower() == 'adam':
optimizer = optim.Adam(
parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
elif args.opt.lower() == 'nadam':
optimizer = Nadam(
parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
elif args.opt.lower() == 'adabound':
optimizer = AdaBound(
parameters, lr=args.lr / 100, weight_decay=args.weight_decay, eps=args.opt_eps,
final_lr=args.lr)
elif args.opt.lower() == 'adadelta':
optimizer = optim.Adadelta(
parameters, lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
elif args.opt.lower() == 'rmsprop':
optimizer = optim.RMSprop(
parameters, lr=args.lr, alpha=0.9, eps=args.opt_eps,
momentum=args.momentum, weight_decay=args.weight_decay)
elif args.opt.lower() == 'rmsproptf':
optimizer = RMSpropTF(
parameters, lr=args.lr, alpha=0.9, eps=args.opt_eps,
momentum=args.momentum, weight_decay=args.weight_decay)
else:
assert False and "Invalid optimizer"
raise ValueError
return optimizer