import tikzplotlib
from tqdm import tqdm, trange
import torch.utils.data as data_utils
from util.plotting import plot_histogram
parser = argparse.ArgumentParser()
parser.add_argument('--randseed', type=int, default=123)
args = parser.parse_args()
train_loader = dl.SVHN(train=True, augm_flag=False)
val_loader, test_loader = dl.SVHN(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_CIFAR10 = dl.CIFAR10(train=False)
test_loader_LSUN = dl.LSUN_CR(train=False)
tab_ood = {
'SVHN - SVHN': [],
'SVHN - CIFAR10': [],
'SVHN - LSUN': [],
'SVHN - FarAway': [],
'SVHN - Adversarial': [],
'SVHN - FarAwayAdv': []
}
tab_cal = {'DKL': ([], [])}
delta = 2000
from tqdm import tqdm, trange
import numpy as np
import argparse
import pickle
import os, sys
import matplotlib.pyplot as plt
import seaborn as sns
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': []
torch.manual_seed(args.randseed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
train_loader = dl.binary_SVHN(3, 9, train=True, augm_flag=False)
val_loader, test_loader = dl.binary_SVHN(3,
9,
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_CIFAR10 = dl.CIFAR10(train=False, augm_flag=False)
test_loader_LSUN = dl.LSUN_CR(train=False, augm_flag=False)
ood_loader = dl.UniformNoise('SVHN', size=1000)
noise_loader = dl.UniformNoise('SVHN', size=2000)
def load_model():
model = resnet.ResNet18(num_classes=2).cuda()
model.load_state_dict(torch.load(f'./pretrained_models/binary_SVHN.pt'))
model.eval()
return model
tab_ood = {
'SVHN - SVHN': [],