def _rotation(model, loaders, device, ind_dataset, val_dataset, ood_datasets, num_classes):
ood_dataset_1, ood_dataset_2, ood_dataset_3 = ood_datasets
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
val_kl_div_ind, val_rot_score_ind, _ = _predict_rotations(model, val_ind_loader, num_classes, device=device)
val_kl_div_ood, val_rot_score_ood, _ = _predict_rotations(model, val_ood_loader, num_classes, device=device)
best_val_auc, best_lamda = 0, 0.25
for lamda in [0.25, 0.5, 0.75, 1]:
anomaly_score_ind = val_kl_div_ind - lamda * val_rot_score_ind
anomaly_score_ood = val_kl_div_ood - lamda * val_rot_score_ood
auc, _, _ = _score_npzs(anomaly_score_ind, anomaly_score_ood)
if auc > best_val_auc:
best_val_auc = auc
best_anomaly_score_ind = anomaly_score_ind
best_anomaly_score_ood = anomaly_score_ood
_, threshold = _find_threshold(best_anomaly_score_ind, best_anomaly_score_ood)
_, _, ind_full = _predict_rotations(model, test_ind_loader, num_classes, lamda=best_lamda, device=device)
_, _, ood_full_1 = _predict_rotations(model, test_ood_loader_1, num_classes, lamda=best_lamda, device=device)
_, _, ood_full_2 = _predict_rotations(model, test_ood_loader_2, num_classes, lamda=best_lamda, device=device)
_, _, ood_full_3 = _predict_rotations(model, test_ood_loader_3, num_classes, lamda=best_lamda, device=device)
ind_savefile_name_full = f'npzs/self_supervision_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}.npz'
ood_savefile_name_full_1 = f'npzs/self_supervision_{ood_dataset_1}_ind_{ind_dataset}_val_{val_dataset}.npz'
ood_savefile_name_full_2 = f'npzs/self_supervision_{ood_dataset_2}_ind_{ind_dataset}_val_{val_dataset}.npz'
ood_savefile_name_full_3 = f'npzs/self_supervision_{ood_dataset_3}_ind_{ind_dataset}_val_{val_dataset}.npz'
auc_1, fpr_1, acc_1 = _score_npzs(ind_full, ood_full_1, threshold)
auc_2, fpr_2, acc_2 = _score_npzs(ind_full, ood_full_2, threshold)
auc_3, fpr_3, acc_3 = _score_npzs(ind_full, ood_full_3, threshold)
aucs = [auc_1, auc_2, auc_3]
fprs = [fpr_1, fpr_2, fpr_3]
accs = [acc_1, acc_2, acc_3]
np.savez(ind_savefile_name_full, ind_full)
np.savez(ood_savefile_name_full_1, ood_full_1)
np.savez(ood_savefile_name_full_2, ood_full_2)
np.savez(ood_savefile_name_full_3, ood_full_3)
print('###############################################')
print()
print(f'Succesfully stored in-distribution ood scores to {ind_savefile_name_full} and out-distribution ood scores to {ood_savefile_name_full_1}, {ood_savefile_name_full_2} and {ood_savefile_name_full_3}')
print()
print('###############################################')
print()
print('###############################################')
print()
print(f"InD dataset: {ind_dataset}")
print(f"Validation dataset: {val_dataset}")
method = f"Self-Supervision "
_verbose(method, ood_dataset_1, ood_dataset_2, ood_dataset_3, aucs, fprs, accs)
def _gen_odin_inference(model, loaders, device, ind_dataset, val_dataset, ood_datasets):
model.eval()
ood_dataset_1, ood_dataset_2, ood_dataset_3 = ood_datasets
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
epsilons = [0, 0.0025, 0.005, 0.01, 0.02, 0.04, 0.08]
best_auc, best_epsilon = 0, 0
for epsilon in epsilons:
val_ind_scores = _process_gen_odin_loader(model, val_ind_loader, device, epsilon)
val_ood_scores = _process_gen_odin_loader(model, val_ood_loader, device, epsilon)
auc, _, _ = _score_npzs(val_ind_scores, val_ood_scores, threshold=0)
if auc > best_auc:
best_auc = auc
best_epsilon = epsilon
best_val_ind_scores = val_ind_scores
best_val_ood_scores = val_ood_scores
_, threshold = _find_threshold(best_val_ind_scores, best_val_ood_scores)
test_ind_scores = _process_gen_odin_loader(model, test_ind_loader, device, best_epsilon)
test_ood_scores_1 = _process_gen_odin_loader(model, test_ood_loader_1, device, best_epsilon)
test_ood_scores_2 = _process_gen_odin_loader(model, test_ood_loader_2, device, best_epsilon)
test_ood_scores_3 = _process_gen_odin_loader(model, test_ood_loader_3, device, best_epsilon)
max_h_ind_savefile_name = f'npzs/max_h_gen_odin_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}.npz'
max_h_ood_savefile_name_1 = f'npzs/max_h_gen_odin_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_1}.npz'
max_h_ood_savefile_name_2 = f'npzs/max_h_gen_odin_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_2}.npz'
max_h_ood_savefile_name_3 = f'npzs/max_h_gen_odin_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_3}.npz'
np.savez(max_h_ind_savefile_name, test_ind_scores)
np.savez(max_h_ood_savefile_name_1, test_ood_scores_1)
np.savez(max_h_ood_savefile_name_2, test_ood_scores_2)
np.savez(max_h_ood_savefile_name_3, test_ood_scores_3)
auc_1, fpr_1, acc_1 = _score_npzs(test_ind_scores, test_ood_scores_1, threshold)
auc_2, fpr_2, acc_2 = _score_npzs(test_ind_scores, test_ood_scores_2, threshold)
auc_3, fpr_3, acc_3 = _score_npzs(test_ind_scores, test_ood_scores_3, threshold)
aucs = [auc_1, auc_2, auc_3]
fprs = [fpr_1, fpr_2, fpr_3]
accs = [acc_1, acc_2, acc_3]
print('###############################################')
print()
print(f'Succesfully stored in-distribution ood scores for maximum h to {max_h_ind_savefile_name} and out-distribution ood scores to {max_h_ood_savefile_name_1}, {max_h_ood_savefile_name_2} and {max_h_ood_savefile_name_3}')
print()
method = "Generalized-Odin results (Cosine Similarity) "
print('###############################################')
print()
print(f"InD dataset: {ind_dataset}")
print(f"Validation dataset: {val_dataset}")
_verbose(method, ood_dataset_1, ood_dataset_2, ood_dataset_3, aucs, fprs, accs)
def _ood_detection_performance(method, val_ind, val_ood, test_ind, test_ood_1,
test_ood_2, test_ood_3, ood_dataset_1,
ood_dataset_2, ood_dataset_3):
_, threshold = _find_threshold(val_ind, val_ood)
auc1, fpr1, acc1 = _score_npzs(test_ind, test_ood_1, threshold)
auc2, fpr2, acc2 = _score_npzs(test_ind, test_ood_2, threshold)
auc3, fpr3, acc3 = _score_npzs(test_ind, test_ood_3, threshold)
aucs = [auc1, auc2, auc3]
fprs = [fpr1, fpr2, fpr3]
accs = [acc1, acc2, acc3]
_verbose(method, ood_dataset_1, ood_dataset_2, ood_dataset_3, aucs, fprs,
accs)
def _gen_odin_inference(model, loaders, device):
model.eval()
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
epsilons = [0, 0.0025, 0.005, 0.01, 0.02, 0.04, 0.08]
best_auc, best_epsilon = 0, 0
for epsilon in epsilons:
val_ind_scores = _process_gen_odin_loader(model, val_ind_loader,
device, epsilon)
val_ood_scores = _process_gen_odin_loader(model, val_ood_loader,
device, epsilon)
auc, _, _ = _score_npzs(val_ind_scores, val_ood_scores, threshold=0)
if auc > best_auc:
best_auc = auc
best_epsilon = epsilon
best_val_ind_scores = val_ind_scores
best_val_ood_scores = val_ood_scores
test_ind_scores = _process_gen_odin_loader(model, test_ind_loader, device,
best_epsilon)
test_ood_scores_1 = _process_gen_odin_loader(model, test_ood_loader_1,
device, best_epsilon)
test_ood_scores_2 = _process_gen_odin_loader(model, test_ood_loader_2,
device, best_epsilon)
test_ood_scores_3 = _process_gen_odin_loader(model, test_ood_loader_3,
device, best_epsilon)
return best_val_ind_scores, best_val_ood_scores, test_ind_scores, test_ood_scores_1, test_ood_scores_2, test_ood_scores_3
def _odin(model, loaders, device):
model.eval()
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
best_auc = 0
for T in [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]:
for epsilon in tqdm(np.arange(0, 0.004, 0.004 / 21, float).tolist()):
val_ind = _get_odin_scores(model,
val_ind_loader,
T,
epsilon,
device=device)
val_ood = _get_odin_scores(model,
val_ood_loader,
T,
epsilon,
device=device)
auc, _, _ = _score_npzs(val_ind, val_ood, threshold=0)
if auc > best_auc:
best_auc = auc
best_epsilon = epsilon
best_T = T
best_val_ind = val_ind
best_val_ood = val_ood
print('###############################################')
print()
print(f'Selected temperature: {best_T}, selected epsilon: {best_epsilon}')
print()
test_ind = _get_odin_scores(model,
test_ind_loader,
best_T,
best_epsilon,
device=device)
test_ood_1 = _get_odin_scores(model,
test_ood_loader_1,
best_T,
best_epsilon,
device=device)
test_ood_2 = _get_odin_scores(model,
test_ood_loader_2,
best_T,
best_epsilon,
device=device)
test_ood_3 = _get_odin_scores(model,
test_ood_loader_3,
best_T,
best_epsilon,
device=device)
return best_val_ind, best_val_ood, test_ind, test_ood_1, test_ood_2, test_ood_3
def _ood_detection_performance(method, val_ind, val_ood, test_ind, test_ood):
_, threshold = _find_threshold(val_ind, val_ood)
auc, fpr, acc = _score_npzs(test_ind, test_ood, threshold)
print()
print(f'Method: {method}')
print(f'Area Under Receiver Operating Characteristic curve: {auc}')
print(f'True Negative Rate @ 95% True Positive Rate: {100-fpr}')
print(f'Detection Accuracy: {acc}')
def _rotation(model, loaders, device, num_classes):
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
val_kl_div_ind, val_rot_score_ind, _ = _predict_rotations(model,
val_ind_loader,
num_classes,
device=device)
val_kl_div_ood, val_rot_score_ood, _ = _predict_rotations(model,
val_ood_loader,
num_classes,
device=device)
best_val_auc, best_lamda = 0, 0.25
for lamda in [0.25, 0.5, 0.75, 1]:
anomaly_score_ind = val_kl_div_ind - lamda * val_rot_score_ind
anomaly_score_ood = val_kl_div_ood - lamda * val_rot_score_ood
auc, _, _ = _score_npzs(anomaly_score_ind, anomaly_score_ood)
if auc > best_val_auc:
best_val_auc = auc
best_lamda = lamda
best_anomaly_score_ind = anomaly_score_ind
best_anomaly_score_ood = anomaly_score_ood
print(f"Chosen lambda: {best_lamda}")
_, threshold = _find_threshold(best_anomaly_score_ind,
best_anomaly_score_ood)
_, _, ind_full = _predict_rotations(model,
test_ind_loader,
num_classes,
lamda=best_lamda,
device=device)
_, _, ood_full_1 = _predict_rotations(model,
test_ood_loader_1,
num_classes,
lamda=best_lamda,
device=device)
_, _, ood_full_2 = _predict_rotations(model,
test_ood_loader_2,
num_classes,
lamda=best_lamda,
device=device)
_, _, ood_full_3 = _predict_rotations(model,
test_ood_loader_3,
num_classes,
lamda=best_lamda,
device=device)
return best_anomaly_score_ind, best_anomaly_score_ood, ind_full, ood_full_1, ood_full_2, ood_full_3
def _ensemble_inference(model_checkpoints, num_classes, loaders, device):
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
models = []
for index, model_checkpoint in enumerate(model_checkpoints):
model = build_model_with_checkpoint('eb0',
model_checkpoint,
device,
out_classes=num_classes[index])
model.eval()
models.append(model)
best_auc = -1e30
for T in [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]:
for epsilon in tqdm(np.arange(0, 0.004, 0.004 / 21, float).tolist()):
for index, model in enumerate(models):
if index == 0:
val_ind = _get_odin_scores(model,
val_ind_loader,
T,
epsilon,
device=device,
score_entropy=True)
val_ood = _get_odin_scores(model,
val_ood_loader,
T,
epsilon,
device=device,
score_entropy=True)
else:
val_ind += _get_odin_scores(model,
val_ind_loader,
T,
epsilon,
device=device,
score_entropy=True)
val_ood += _get_odin_scores(model,
val_ood_loader,
T,
epsilon,
device=device,
score_entropy=True)
val_ind = val_ind / len(models)
val_ood = val_ood / len(models)
auc, _, _ = _score_npzs(val_ind, val_ood)
if auc > best_auc:
best_auc = auc
best_T, best_epsilon = T, epsilon
best_val_ind, best_val_ood = val_ind, val_ood
print(f"Chosen T: {best_T}, epsilon: {best_epsilon}")
for index, model in enumerate(models):
if index == 0:
test_ind = _get_odin_scores(model,
test_ind_loader,
best_T,
best_epsilon,
device=device,
score_entropy=True)
test_ood_1 = _get_odin_scores(model,
test_ood_loader_1,
best_T,
best_epsilon,
device=device,
score_entropy=True)
test_ood_2 = _get_odin_scores(model,
test_ood_loader_2,
best_T,
best_epsilon,
device=device,
score_entropy=True)
test_ood_3 = _get_odin_scores(model,
test_ood_loader_3,
best_T,
best_epsilon,
device=device,
score_entropy=True)
else:
test_ind += _get_odin_scores(model,
test_ind_loader,
best_T,
best_epsilon,
device=device,
score_entropy=True)
test_ood_1 += _get_odin_scores(model,
test_ood_loader_1,
best_T,
best_epsilon,
device=device,
score_entropy=True)
test_ood_2 += _get_odin_scores(model,
test_ood_loader_2,
best_T,
best_epsilon,
device=device,
score_entropy=True)
test_ood_3 += _get_odin_scores(model,
test_ood_loader_3,
best_T,
best_epsilon,
device=device,
score_entropy=True)
test_ind = test_ind / len(models)
test_ood_1 = test_ood_1 / len(models)
test_ood_2 = test_ood_2 / len(models)
test_ood_3 = test_ood_3 / len(models)
return best_val_ind, best_val_ood, test_ind, test_ood_1, test_ood_2, test_ood_3
def _baseline(model, loaders, device, ind_dataset, val_dataset, ood_datasets, monte_carlo_steps=1):
ood_dataset_1, ood_dataset_2, ood_dataset_3 = ood_datasets
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
model.eval()
_score_classification_accuracy(model, testloader=test_ind_loader, device=device, dataset=ind_dataset)
if monte_carlo_steps > 1:
model._dropout.train()
val_ind = _get_baseline_scores(model, val_ind_loader, device, monte_carlo_steps)
val_ood = _get_baseline_scores(model, val_ood_loader, device, monte_carlo_steps)
if monte_carlo_steps > 1:
val_ind = val_ind / monte_carlo_steps
val_ood = val_ood / monte_carlo_steps
acc, threshold = _find_threshold(val_ind, val_ood)
test_ind = _get_baseline_scores(model, test_ind_loader, device, monte_carlo_steps)
test_ood_1 = _get_baseline_scores(model, test_ood_loader_1, device, monte_carlo_steps)
test_ood_2 = _get_baseline_scores(model, test_ood_loader_2, device, monte_carlo_steps)
test_ood_3 = _get_baseline_scores(model, test_ood_loader_3, device, monte_carlo_steps)
if monte_carlo_steps == 1:
ind_savefile_name = f'npzs/baseline_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}.npz'
ood_savefile_name_1 = f'npzs/baseline_{ood_dataset_1}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_1}.npz'
ood_savefile_name_2 = f'npzs/baseline_{ood_dataset_2}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_2}.npz'
ood_savefile_name_3 = f'npzs/baseline_{ood_dataset_3}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_3}.npz'
else:
ind_savefile_name = f'npzs/baseline_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}_ood_monte_carlo_{monte_carlo_steps}.npz'
ood_savefile_name_1 = f'npzs/baseline_{ood_dataset_1}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_1}_monte_carlo_{monte_carlo_steps}.npz'
ood_savefile_name_2 = f'npzs/baseline_{ood_dataset_2}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_2}_monte_carlo_{monte_carlo_steps}.npz'
ood_savefile_name_3 = f'npzs/baseline_{ood_dataset_3}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_3}_monte_carlo_{monte_carlo_steps}.npz'
np.savez(ind_savefile_name, test_ind)
np.savez(ood_savefile_name_1, test_ood_1)
np.savez(ood_savefile_name_2, test_ood_2)
np.savez(ood_savefile_name_3, test_ood_3)
auc_1, fpr_1, acc_1 = _score_npzs(test_ind, test_ood_1, threshold)
auc_2, fpr_2, acc_2 = _score_npzs(test_ind, test_ood_2, threshold)
auc_3, fpr_3, acc_3 = _score_npzs(test_ind, test_ood_3, threshold)
aucs = [auc_1, auc_2, auc_3]
fprs = [fpr_1, fpr_2, fpr_3]
accs = [acc_1, acc_2, acc_3]
print('###############################################')
print()
print(f'Succesfully stored in-distribution ood scores to {ind_savefile_name} and out-distribution ood scores to: {ood_savefile_name_1}, {ood_savefile_name_2} and {ood_savefile_name_3}')
print()
print('###############################################')
print()
print(f"InD dataset: {ind_dataset}")
print(f"Validation dataset: {val_dataset}")
if monte_carlo_steps == 1:
method = f"Baseline"
else:
method = f"Baseline results with MC dropout ({monte_carlo_steps} steps)"
_verbose(method, ood_dataset_1, ood_dataset_2, ood_dataset_3, aucs, fprs, accs)
def _ensemble_inference(model_checkpoints, num_classes, loaders, device, ind_dataset, val_dataset):
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
models = []
for index, model_checkpoint in enumerate(model_checkpoints):
model = build_model_with_checkpoint('eb0', model_checkpoint, device, out_classes=num_classes[index])
model.eval()
models.append(model)
best_auc = -1e30
for T in [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]:
for epsilon in tqdm(np.arange(0, 0.004, 0.004/21, float).tolist()):
for index, model in enumerate(models):
if index == 0:
val_ind = _get_odin_scores(model, val_ind_loader, T, epsilon, device=device, score_entropy=True)
val_ood = _get_odin_scores(model, val_ood_loader, T, epsilon, device=device, score_entropy=True)
else:
val_ind += _get_odin_scores(model, val_ind_loader, T, epsilon, device=device, score_entropy=True)
val_ood += _get_odin_scores(model, val_ood_loader, T, epsilon, device=device, score_entropy=True)
val_ind = val_ind / len(models)
val_ood = val_ood / len(models)
auc, _, _ = _score_npzs(val_ind, val_ood)
if auc > best_auc:
best_auc = auc
best_T, best_epsilon = T, epsilon
best_val_ind, best_val_ood = val_ind, val_ood
_, threshold = _find_threshold(best_val_ind, best_val_ood)
for index, model in enumerate(models):
if index == 0:
test_ind = _get_odin_scores(model, test_ind_loader, best_T, best_epsilon, device=device, score_entropy=True)
test_ood_1 = _get_odin_scores(model, test_ood_loader_1, best_T, best_epsilon, device=device, score_entropy=True)
test_ood_2 = _get_odin_scores(model, test_ood_loader_2, best_T, best_epsilon, device=device, score_entropy=True)
test_ood_3 = _get_odin_scores(model, test_ood_loader_3, best_T, best_epsilon, device=device, score_entropy=True)
else:
test_ind += _get_odin_scores(model, test_ind_loader, best_T, best_epsilon, device=device, score_entropy=True)
test_ood_1 += _get_odin_scores(model, test_ood_loader_1, best_T, best_epsilon, device=device, score_entropy=True)
test_ood_2 += _get_odin_scores(model, test_ood_loader_2, best_T, best_epsilon, device=device, score_entropy=True)
test_ood_3 += _get_odin_scores(model, test_ood_loader_3, best_T, best_epsilon, device=device, score_entropy=True)
test_ind = test_ind / len(models)
test_ood_1 = test_ood_1 / len(models)
test_ood_2 = test_ood_2 / len(models)
test_ood_3 = test_ood_3 / len(models)
test_ind_savefile_name = f'npzs/ensemble_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}.npz'
test_ood_savefile_name_1 = f'npzs/ensemble_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_1}.npz'
test_ood_savefile_name_2 = f'npzs/ensemble_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_2}.npz'
test_ood_savefile_name_3 = f'npzs/ensemble_odin_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_3}.npz'
np.savez(test_ind_savefile_name, test_ind)
np.savez(test_ood_savefile_name_1, test_ood_1)
np.savez(test_ood_savefile_name_2, test_ood_2)
np.savez(test_ood_savefile_name_3, test_ood_3)
auc_1, fpr_1, acc_1 = _score_npzs(test_ind, test_ood_1, threshold)
auc_2, fpr_2, acc_2 = _score_npzs(test_ind, test_ood_2, threshold)
auc_3, fpr_3, acc_3 = _score_npzs(test_ind, test_ood_3, threshold)
aucs = [auc_1, auc_2, auc_3]
fprs = [fpr_1, fpr_2, fpr_3]
accs = [acc_1, acc_2, acc_3]
print('###############################################')
print()
print(f'Succesfully stored in-distribution ood scores for maximum h to {test_ind_savefile_name} and out-distribution ood scores to {test_ood_savefile_name_1}, {test_ood_savefile_name_2} and {test_ood_savefile_name_3}')
print()
method = "Self-Ensemble"
print('###############################################')
print()
print(f"InD dataset: {ind_dataset}")
print(f"Validation dataset: {val_dataset}")
_verbose(method, ood_dataset_1, ood_dataset_2, ood_dataset_3, aucs, fprs, accs)
def _odin(model, loaders, device, ind_dataset, val_dataset, ood_datasets):
model.eval()
ood_dataset_1, ood_dataset_2, ood_dataset_3 = ood_datasets
val_ind_loader, test_ind_loader, val_ood_loader, test_ood_loader_1, test_ood_loader_2, test_ood_loader_3 = loaders
best_auc = 0
for T in [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]:
for epsilon in tqdm(np.arange(0, 0.004, 0.004/21, float).tolist()):
val_ind = _get_odin_scores(model, val_ind_loader, T, epsilon, device=device)
val_ood = _get_odin_scores(model, val_ood_loader, T, epsilon, device=device)
auc, _, _ = _score_npzs(val_ind, val_ood, threshold=0)
if auc > best_auc:
best_auc = auc
best_epsilon = epsilon
best_T = T
best_val_ind = val_ind
best_val_ood = val_ood
print('###############################################')
print()
print(f'Selected temperature: {best_T}, selected epsilon: {best_epsilon}')
print()
_, threshold = _find_threshold(best_val_ind, best_val_ood)
test_ind = _get_odin_scores(model, test_ind_loader, best_T, best_epsilon, device=device)
test_ood_1 = _get_odin_scores(model, test_ood_loader_1, best_T, best_epsilon, device=device)
test_ood_2 = _get_odin_scores(model, test_ood_loader_2, best_T, best_epsilon, device=device)
test_ood_3 = _get_odin_scores(model, test_ood_loader_3, best_T, best_epsilon, device=device)
ind_savefile_name = f'npzs/odin_{ind_dataset}_ind_{ind_dataset}_val_{val_dataset}_temperature_{best_T}_epsilon{best_epsilon}.npz'
ood_savefile_name_1 = f'npzs/odin_{ood_dataset_1}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_1}_temperature_{best_T}_epsilon{best_epsilon}.npz'
ood_savefile_name_2 = f'npzs/odin_{ood_dataset_2}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_2}_temperature_{best_T}_epsilon{best_epsilon}.npz'
ood_savefile_name_3 = f'npzs/odin_{ood_dataset_3}_ind_{ind_dataset}_val_{val_dataset}_ood_{ood_dataset_3}_temperature_{best_T}_epsilon{best_epsilon}.npz'
np.savez(ind_savefile_name, test_ind)
np.savez(ood_savefile_name_1, test_ood_1)
np.savez(ood_savefile_name_2, test_ood_2)
np.savez(ood_savefile_name_3, test_ood_3)
auc_1, fpr_1, acc_1 = _score_npzs(test_ind, test_ood_1, threshold)
auc_2, fpr_2, acc_2 = _score_npzs(test_ind, test_ood_2, threshold)
auc_3, fpr_3, acc_3 = _score_npzs(test_ind, test_ood_3, threshold)
aucs = [auc_1, auc_2, auc_3]
fprs = [fpr_1, fpr_2, fpr_3]
accs = [acc_1, acc_2, acc_3]
print('###############################################')
print()
print(f'Succesfully stored in-distribution ood scores to {ind_savefile_name} and out-distribution ood scores to: {ood_savefile_name_1}, {ood_savefile_name_2} and {ood_savefile_name_3}')
print()
print('###############################################')
print()
print(f"InD dataset: {ind_dataset}")
print(f"Validation dataset: {val_dataset}")
method = f"Odin results with chosen T={best_T}, epsilon={best_epsilon}"
_verbose(method, ood_dataset_1, ood_dataset_2, ood_dataset_3, aucs, fprs, accs)