def clf_fun(self, n_class, device, s=20, m=0.01):
if self.method == 'softMax':
clf = softMax(self.out_dim, n_class).to(device)
elif self.method == 'cosMax':
clf = cosMax(self.out_dim, n_class, s).to(device)
elif self.method == 'arcMax':
clf = arcMax(self.out_dim, n_class, s, m).to(device)
return clf
def clf_optimizer(args, net, device, frozen_net, s = 15, m = 0.01):
if frozen_net: s=5
if args.method == 'softMax':
clf = softMax(args.out_dim, args.test_n_way).to(device)
elif args.method == 'cosMax':
clf = cosMax(args.out_dim, args.test_n_way, s).to(device)
elif args.method == 'arcMax':
clf = arcMax(args.out_dim, args.test_n_way, s, m).to(device)
if frozen_net:
optimizer = torch.optim.Adam(clf.parameters(), lr = args.lr)
else:
optimizer = Adam([{'params': net.parameters()},
{'params': clf.parameters()}],
lr = args.lr)
return clf, optimizer