import tikzplotlib
from tqdm import tqdm, trange
import torch.utils.data as data_utils
parser = argparse.ArgumentParser()
parser.add_argument('--randseed', type=int, default=123)
args = parser.parse_args()
train_loader = dl.CIFAR10(train=True, augm_flag=False)
val_loader, test_loader = dl.CIFAR10(train=False, val_size=2000)
targets = torch.cat([y for x, y in test_loader], dim=0).numpy()
print(len(train_loader.dataset), len(val_loader.dataset),
len(test_loader.dataset))
test_loader_SVHN = dl.SVHN(train=False)
test_loader_LSUN = dl.LSUN_CR(train=False)
tab_ood = {
'CIFAR10 - CIFAR10': [],
'CIFAR10 - SVHN': [],
'CIFAR10 - LSUN': [],
'CIFAR10 - FarAway': [],
'CIFAR10 - Adversarial': [],
'CIFAR10 - FarAwayAdv': []
}
tab_cal = {'DKL': ([], [])}
delta = 2000
np.random.seed(args.randseed)
val_loader, test_loader = dl.binary_CIFAR10(class1,
class2,
train=False,
augm_flag=False,
val_size=1000)
targets = torch.cat([y for x, y in test_loader], dim=0).numpy()
targets_val = torch.cat([y for x, y in val_loader], dim=0).numpy()
print(len(train_loader.dataset), len(val_loader.dataset),
len(test_loader.dataset))
test_loader_SVHN, _ = dl.binary_SVHN(3,
9,
train=False,
augm_flag=False,
val_size=1000)
test_loader_LSUN = dl.LSUN_CR(train=False, augm_flag=False)
ood_loader = dl.UniformNoise('CIFAR10', size=1000)
noise_loader = dl.UniformNoise('CIFAR10', size=2000)
def load_model():
model = resnet.ResNet18(num_classes=2).cuda()
model.load_state_dict(torch.load(f'./pretrained_models/binary_CIFAR10.pt'))
model.eval()
return model
tab_ood = {
'CIFAR10 - CIFAR10': [],
'CIFAR10 - SVHN': [],
