from torch import optim as optim from optim import Nadam, RMSpropTF def add_weight_decay(model, weight_decay=1e-5, skip_list=()): decay = [] no_decay = [] for name, param in model.named_parameters(): if not param.requires_grad: continue # frozen weights if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list: no_decay.append(param) else: decay.append(param) return [ {'params': no_decay, 'weight_decay': 0.}, {'params': decay, 'weight_decay': weight_decay}] def create_optimizer(args, model, filter_bias_and_bn=True): weight_decay = args.weight_decay if weight_decay and filter_bias_and_bn: parameters = add_weight_decay(model, weight_decay) weight_decay = 0. else: parameters = model.parameters() if args.opt.lower() == 'sgd': optimizer = optim.SGD( parameters, lr=args.lr, momentum=args.momentum, weight_decay=weight_decay, nesterov=True) elif args.opt.lower() == 'adam': optimizer = optim.Adam( parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps) elif args.opt.lower() == 'nadam': optimizer = Nadam( parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps) elif args.opt.lower() == 'adadelta': optimizer = optim.Adadelta( parameters, lr=args.lr, weight_decay=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=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=weight_decay) else: assert False and "Invalid optimizer" raise ValueError return optimizer