def test(args):
# 1. get dataset
train_loader, val_loader, test_loader, class_list = return_dataset(args)
# 2. generator
if args.net == 'resnet50':
G = ResBase50()
inc = 2048
elif args.net == 'resnet101':
G = ResBase101()
inc = 2048
elif args.net == "alexnet":
G = AlexNetBase()
inc = 4096
elif args.net == "vgg":
G = VGGBase()
inc = 4096
elif args.net == "inception_v3":
G = models.inception_v3(pretrained=True)
inc = 1000
elif args.net == "googlenet":
G = models.googlenet(pretrained = True)
inc = 1000
elif args.net == "densenet":
G = models.densenet161(pretrained = True)
inc = 1000
elif args.net == "resnext":
G = models.resnext50_32x4d(pretrained = True)
inc = 1000
elif args.net == "squeezenet":
G = models.squeezenet1_0(pretrained = True)
inc = 1000
else:
raise ValueError('Model cannot be recognized.')
G.cuda()
G.train()
# 3. classifier
F = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
F.cuda()
F.train()
# 4. load pre-trained model
G.load_state_dict(torch.load(os.path.join(args.checkpath,
"G_net_{}_loss_{}.pth".format(args.net, args.loss))))
F.load_state_dict(torch.load(os.path.join(args.checkpath,
"F_net_{}_loss_{}.pth".format(args.net, args.loss))))
# 5. testing
acc_test = eval(test_loader, G, F, class_list)
print('Testing accuracy: {:.3f}\n'.format(acc_test))
return acc_test
def test_ensemble(args, alphas=None):
# 1. get dataset
#problem: inception_v3 crops dataset differently than all the others
args.net = args.ensemble[0] #test, might introduce problem
print("args .net when loading: ", args.net)
train_loader, val_loader, test_loader, class_list = return_dataset(args)
print("Loading in ")
# 2. generator
G_list = [] #use a list of models
F_list = [] #use a list of predictors, one for each classifier in args.ensemble
for classifier in args.ensemble:
print("classifier: ", classifier)
if classifier == 'resnet50':
G = ResBase50()
inc = 2048
elif classifier == 'resnet101':
G = ResBase101()
inc = 2048
elif classifier == "alexnet":
G = AlexNetBase()
inc = 4096
elif classifier == "vgg":
G = VGGBase()
inc = 4096
elif classifier == "inception_v3":
G = models.inception_v3(pretrained=True)
inc = 1000
elif classifier == "googlenet":
G = models.googlenet(pretrained = True)
inc = 1000
elif classifier == "densenet":
G = models.densenet161(pretrained = True)
inc = 1000
elif classifier == "resnext":
G = models.resnext50_32x4d(pretrained = True)
inc = 1000
elif classifier == "squeezenet":
G = models.squeezenet1_0(pretrained = True)
inc = 1000
else:
raise ValueError('Model cannot be recognized.')
G.cuda()
G.train()
# 3. classifier
F = Predictor(num_class=len(class_list), inc=inc, temp=args.T)
F.cuda()
F.train()
# 4. load pre-trained model
G.load_state_dict(torch.load(os.path.join(args.checkpath,
"G_net_{}_loss_{}.pth".format(classifier, args.loss))))
F.load_state_dict(torch.load(os.path.join(args.checkpath,
"F_net_{}_loss_{}.pth".format(classifier, args.loss))))
G_list.append(G)
F_list.append(F)
# 5. testing
print("evaluating accuracy")
acc_test = eval_ensemble(args,test_loader, G_list, F_list, class_list,alphas)
print('Testing accuracy: {:.3f}\n'.format(acc_test))
return acc_test
