# print('logits: ', model(x).detach().numpy())
# print('adv logits: ', adv_logits.detach().numpy())
# if batch_id == 0:
# break
# exit(0)
#
# if hps.attack == 'pgdinf':
# linfPGD_attack(model, hps)
# elif hps.attack == 'pgd2':
# l2PGD_attack(model, hps)
# elif hps.attack == 'cw':
# cw_l2_attack(model, hps)
# elif hps.attack == 'fgsm':
# fgsm_attack(model, hps)
model.eval()
fmodel = foolbox.models.PyTorchModel(model, bounds=(0, 1.), num_classes=10)
dataset = get_dataset(data_name=hps.problem, train=False, label_id=0)
# hps.n_batch_test = 1
test_loader = DataLoader(dataset=dataset, batch_size=hps.n_batch_test, shuffle=False)
for batch_id, (x, y) in enumerate(test_loader):
# Note that images are scaled to [0., 1.0]
x, y = x.to(hps.device), y.to(hps.device)
if hps.attack == 'deepfool':
attack = foolbox.attacks.DeepFoolL2Attack(fmodel)
elif hps.attack == 'cw':
attack = foolbox.attacks.CarliniWagnerL2Attack(fmodel)
elif hps.attack == 'boundary':
def inference(hps: DictConfig) -> None:
# This enables a ctr-C without triggering errors
import signal
signal.signal(signal.SIGINT, lambda x, y: sys.exit(0))
logger = logging.getLogger(__name__)
cuda_available = torch.cuda.is_available()
torch.manual_seed(hps.seed)
device = "cuda" if cuda_available and hps.device == 'cuda' else "cpu"
# Models
local_channel = hps.get(hps.base_classifier).last_conv_channel
classifier = get_model(model_name=hps.base_classifier,
in_size=local_channel,
out_size=hps.rep_size).to(hps.device)
logger.info('Base classifier name: {}, # parameters: {}'.format(
hps.base_classifier, cal_parameters(classifier)))
sdim = SDIM(disc_classifier=classifier,
mi_units=hps.mi_units,
n_classes=hps.n_classes,
margin=hps.margin,
rep_size=hps.rep_size,
local_channel=local_channel).to(hps.device)
model_path = 'SDIM_{}.pth'.format(hps.base_classifier)
base_dir = '/userhome/cs/u3003679/generative-classification-with-rejection'
path = os.path.join(base_dir, model_path)
sdim.load_state_dict(torch.load(path)['model_state'])
# logging the SDIM desc.
for desc in sdim.desc():
logger.info(desc)
eval_loader = Loader('eval', batch_size=hps.n_batch_test, device=device)
if cuda_available and hps.n_gpu > 1:
sdim = torch.nn.DataParallel(sdim, device_ids=list(range(hps.n_gpu)))
torch.manual_seed(hps.seed)
np.random.seed(hps.seed)
n_iters = 0
top1 = AverageMeter('Acc@1')
top5 = AverageMeter('Acc@5')
sdim.eval()
for x, y in eval_loader:
n_iters += 1
if n_iters == len(eval_loader):
break
with torch.no_grad():
log_lik = sdim.infer(x)
acc1, acc5 = accuracy(log_lik, y, topk=(1, 5))
top1.update(acc1, x.size(0))
top5.update(acc5, x.size(0))
logger.info('Test Acc@1: {:.3f}, Acc@5: {:.3f}'.format(top1.avg, top5.avg))