def _raw_ocsvm_experiment(dataset_load_fn, dataset_name, single_class_ind): (x_train, y_train), (x_test, y_test) = dataset_load_fn() x_train = x_train.reshape((len(x_train), -1)) x_test = x_test.reshape((len(x_test), -1)) x_train_task = x_train[y_train.flatten() == single_class_ind] if dataset_name in ['cats-vs-dogs']: # OC-SVM is quadratic on the number of examples, so subsample training set subsample_inds = np.random.choice(len(x_train_task), 5000, replace=False) x_train_task = x_train_task[subsample_inds] pg = ParameterGrid({'nu': np.linspace(0.1, 0.9, num=9), 'gamma': np.logspace(-7, 2, num=10, base=2)}) results = Parallel(n_jobs=6)( delayed(_train_ocsvm_and_score)(d, x_train_task, y_test.flatten() == single_class_ind, x_test) for d in pg) best_params, best_auc_score = max(zip(pg, results), key=lambda t: t[-1]) best_ocsvm = OneClassSVM(**best_params).fit(x_train_task) scores = best_ocsvm.decision_function(x_test) labels = y_test.flatten() == single_class_ind res_file_name = '{}_raw-oc-svm_{}_{}.npz'.format(dataset_name, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) save_roc_pr_curve_data(scores, labels, res_file_path)
def _dsebm_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q): gpu_to_use = gpu_q.get() os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use (x_train, y_train), (x_test, y_test) = dataset_load_fn() n_channels = x_train.shape[get_channels_axis()] input_side = x_train.shape[2] # image side will always be at shape[2] encoder_mdl = conv_encoder(input_side, n_channels, representation_activation='relu') energy_mdl = dsebm.create_energy_model(encoder_mdl) reconstruction_mdl = dsebm.create_reconstruction_model(energy_mdl) # optimization parameters batch_size = 128 epochs = 200 reconstruction_mdl.compile('adam', 'mse') x_train_task = x_train[y_train.flatten() == single_class_ind] x_test_task = x_test[y_test.flatten() == single_class_ind] # This is just for visual monitoring reconstruction_mdl.fit(x=x_train_task, y=x_train_task, batch_size=batch_size, epochs=epochs, validation_data=(x_test_task, x_test_task)) scores = -energy_mdl.predict(x_test, batch_size) labels = y_test.flatten() == single_class_ind res_file_name = '{}_dsebm_{}_{}.npz'.format(dataset_name, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) save_roc_pr_curve_data(scores, labels, res_file_path) gpu_q.put(gpu_to_use)
def _RDAE_experiment(x_train, y_train, dataset_name, single_class_ind, gpu_q, p): gpu_to_use = gpu_q.get() cudnn.benchmark = True n_channels = x_train.shape[get_channels_axis()] model = CAE_pytorch(in_channels=n_channels) model = model.cuda() optimizer = optim.Adam(model.parameters(), eps=1e-7, weight_decay=0.0005) criterion = nn.MSELoss() epochs = 20 inner_epochs = 1 lmbda = 0.00065 # train RDAE losses = train_robust_cae(x_train, model, criterion, optimizer, lmbda, inner_epochs, epochs // inner_epochs, False) losses = losses - losses.min() losses = losses / (1e-8 + losses.max()) scores = 1 - losses res_file_name = '{}_rdae-{}_{}_{}.npz'.format( dataset_name, p, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) os.makedirs(os.path.join(RESULTS_DIR, dataset_name), exist_ok=True) save_roc_pr_curve_data(scores, y_train, res_file_path) gpu_q.put(gpu_to_use)
def transformation_cifar10_vs_tinyimagenet(): _, (x_test, y_test) = load_cifar10() x_test_out = load_tinyimagenet('/home/izikgo/Imagenet_resize/Imagenet_resize/') transformer = Transformer(8, 8) n = 16 k = 8 base_mdl = create_wide_residual_network(x_test.shape[1:], 10, n, k) transformations_cls_out = Activation('softmax')(dense(transformer.n_transforms)(base_mdl.get_layer(index=-3).output)) mdl = Model(base_mdl.input, [base_mdl.output, transformations_cls_out]) mdl.load_weights('cifar10_WRN_doublehead-transformations_{}-{}.h5'.format(n, k)) scores_mdl = Model(mdl.input, mdl.output[1]) x_test_all = np.concatenate((x_test, x_test_out)) preds = np.zeros((len(x_test_all), transformer.n_transforms)) for t in range(transformer.n_transforms): preds[:, t] = scores_mdl.predict(transformer.transform_batch(x_test_all, [t] * len(x_test_all)), batch_size=128)[:, t] labels = np.concatenate((np.ones(len(x_test)), np.zeros(len(x_test_out)))) scores = preds.mean(axis=-1) save_roc_pr_curve_data(scores, labels, 'cifar10-vs-tinyimagenet_transformations.npz')
def _DRAE_experiment(x_train, y_train, dataset_name, single_class_ind, gpu_q, p): gpu_to_use = gpu_q.get() n_channels = x_train.shape[get_channels_axis()] model = CAE_pytorch(in_channels=n_channels) batch_size = 128 model = model.cuda() trainset = trainset_pytorch(train_data=x_train, train_labels=y_train, transform=transform_train) trainloader = data.DataLoader(trainset, batch_size=batch_size, shuffle=True) cudnn.benchmark = True criterion = DRAELossAutograd(lamb=0.1) optimizer = optim.Adam(model.parameters(), eps=1e-7, weight_decay=0.0005) epochs = 250 # #########################Training######################## train_cae(trainloader, model, criterion, optimizer, epochs) # #######################Testin############################ testloader = data.DataLoader(trainset, batch_size=1024, shuffle=False) losses, reps = test_cae_pytorch(testloader, model) losses = losses - losses.min() losses = losses / (1e-8+losses.max()) scores = 1 - losses res_file_name = '{}_drae-{}_{}_{}.npz'.format(dataset_name, p, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) os.makedirs(os.path.join(RESULTS_DIR, dataset_name), exist_ok=True) save_roc_pr_curve_data(scores, y_train, res_file_path) gpu_q.put(gpu_to_use)
def _cae_ocsvm_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q): gpu_to_use = gpu_q.get() os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use (x_train, y_train), (x_test, y_test) = dataset_load_fn() n_channels = x_train.shape[get_channels_axis()] input_side = x_train.shape[2] # channel side will always be at shape[2] enc = conv_encoder(input_side, n_channels) dec = conv_decoder(input_side, n_channels) x_in = Input(shape=x_train.shape[1:]) x_rec = dec(enc(x_in)) cae = Model(x_in, x_rec) cae.compile('adam', 'mse') x_train_task = x_train[y_train.flatten() == single_class_ind] x_test_task = x_test[y_test.flatten( ) == single_class_ind] # This is just for visual monitoring cae.fit(x=x_train_task, y=x_train_task, batch_size=128, epochs=200, validation_data=(x_test_task, x_test_task)) x_train_task_rep = enc.predict(x_train_task, batch_size=128) if dataset_name in [ 'cats-vs-dogs' ]: # OC-SVM is quadratic on the number of examples, so subsample training set subsample_inds = np.random.choice(len(x_train_task_rep), 2500, replace=False) x_train_task_rep = x_train_task_rep[subsample_inds] x_test_rep = enc.predict(x_test, batch_size=128) pg = ParameterGrid({ 'nu': np.linspace(0.1, 0.9, num=9), 'gamma': np.logspace(-7, 2, num=10, base=2) }) results = Parallel(n_jobs=6)(delayed(_train_ocsvm_and_score)( d, x_train_task_rep, y_test.flatten() == single_class_ind, x_test_rep) for d in pg) best_params, best_auc_score = max(zip(pg, results), key=lambda t: t[-1]) print(best_params) best_ocsvm = OneClassSVM(**best_params).fit(x_train_task_rep) scores = best_ocsvm.decision_function(x_test_rep) labels = y_test.flatten() == single_class_ind res_file_name = '{}_cae-oc-svm_{}_{}.npz'.format( dataset_name, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) save_roc_pr_curve_data(scores, labels, res_file_path) gpu_q.put(gpu_to_use)
def _adgan_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q): gpu_to_use = gpu_q.get() os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use (x_train, y_train), (x_test, y_test) = dataset_load_fn() if len(x_test) > 5000: # subsample x_test due to runtime complexity chosen_inds = np.random.choice(len(x_test), 5000, replace=False) x_test = x_test[chosen_inds] y_test = y_test[chosen_inds] n_channels = x_train.shape[get_channels_axis()] input_side = x_train.shape[2] # image side will always be at shape[2] critic = conv_encoder(input_side, n_channels, representation_dim=1, representation_activation='linear') noise_size = 256 generator = conv_decoder(input_side, n_channels=n_channels, representation_dim=noise_size) def prior_gen(b_size): return np.random.normal(size=(b_size, noise_size)) batch_size = 128 epochs = 100 x_train_task = x_train[y_train.flatten() == single_class_ind] def data_gen(b_size): chosen_inds = np.random.choice(len(x_train_task), b_size, replace=False) return x_train_task[chosen_inds] adgan.train_wgan_with_grad_penalty(prior_gen, generator, data_gen, critic, batch_size, epochs, grad_pen_coef=20) scores = adgan.scores_from_adgan_generator(x_test, prior_gen, generator) labels = y_test.flatten() == single_class_ind res_file_name = '{}_adgan_{}_{}.npz'.format( dataset_name, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) save_roc_pr_curve_data(scores, labels, res_file_path) gpu_q.put(gpu_to_use)
def _dagmm_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q): gpu_to_use = gpu_q.get() os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use (x_train, y_train), (x_test, y_test) = dataset_load_fn() n_channels = x_train.shape[get_channels_axis()] input_side = x_train.shape[2] # image side will always be at shape[2] enc = conv_encoder(input_side, n_channels, representation_dim=5, representation_activation='linear') dec = conv_decoder(input_side, n_channels=n_channels, representation_dim=enc.output_shape[-1]) n_components = 3 estimation = Sequential([Dense(64, activation='tanh', input_dim=enc.output_shape[-1] + 2), Dropout(0.5), Dense(10, activation='tanh'), Dropout(0.5), Dense(n_components, activation='softmax')] ) batch_size = 256 epochs = 200 lambda_diag = 0.0005 lambda_energy = 0.01 dagmm_mdl = dagmm.create_dagmm_model(enc, dec, estimation, lambda_diag) dagmm_mdl.compile('adam', ['mse', lambda y_true, y_pred: lambda_energy*y_pred]) x_train_task = x_train[y_train.flatten() == single_class_ind] x_test_task = x_test[y_test.flatten() == single_class_ind] # This is just for visual monitoring dagmm_mdl.fit(x=x_train_task, y=[x_train_task, np.zeros((len(x_train_task), 1))], # second y is dummy batch_size=batch_size, epochs=epochs, validation_data=(x_test_task, [x_test_task, np.zeros((len(x_test_task), 1))]), # verbose=0 ) energy_mdl = Model(dagmm_mdl.input, dagmm_mdl.output[-1]) scores = -energy_mdl.predict(x_test, batch_size) scores = scores.flatten() if not np.all(np.isfinite(scores)): min_finite = np.min(scores[np.isfinite(scores)]) scores[~np.isfinite(scores)] = min_finite - 1 labels = y_test.flatten() == single_class_ind res_file_name = '{}_dagmm_{}_{}.npz'.format(dataset_name, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) save_roc_pr_curve_data(scores, labels, res_file_path) gpu_q.put(gpu_to_use)
def _transformations_experiment(dataset_load_fn, dataset_name, single_class_ind, gpu_q): gpu_to_use = gpu_q.get() os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use (x_train, y_train), (x_test, y_test) = dataset_load_fn() if dataset_name in ['cats-vs-dogs']: transformer = Transformer(16, 16) n, k = (16, 8) else: transformer = Transformer(8, 8) n, k = (10, 4) mdl = create_wide_residual_network(x_train.shape[1:], transformer.n_transforms, n, k) mdl.compile('adam', 'categorical_crossentropy', ['acc']) x_train_task = x_train[y_train.flatten() == single_class_ind] transformations_inds = np.tile(np.arange(transformer.n_transforms), len(x_train_task)) x_train_task_transformed = transformer.transform_batch(np.repeat(x_train_task, transformer.n_transforms, axis=0), transformations_inds) batch_size = 128 mdl.fit(x=x_train_task_transformed, y=to_categorical(transformations_inds), batch_size=batch_size, epochs=int(np.ceil(200/transformer.n_transforms)) ) ################################################################################################# # simplified normality score ################################################################################################# # preds = np.zeros((len(x_test), transformer.n_transforms)) # for t in range(transformer.n_transforms): # preds[:, t] = mdl.predict(transformer.transform_batch(x_test, [t] * len(x_test)), # batch_size=batch_size)[:, t] # # labels = y_test.flatten() == single_class_ind # scores = preds.mean(axis=-1) ################################################################################################# def calc_approx_alpha_sum(observations): N = len(observations) f = np.mean(observations, axis=0) return (N * (len(f) - 1) * (-psi(1))) / ( N * np.sum(f * np.log(f)) - np.sum(f * np.sum(np.log(observations), axis=0))) def inv_psi(y, iters=5): # initial estimate cond = y >= -2.22 x = cond * (np.exp(y) + 0.5) + (1 - cond) * -1 / (y - psi(1)) for _ in range(iters): x = x - (psi(x) - y) / polygamma(1, x) return x def fixed_point_dirichlet_mle(alpha_init, log_p_hat, max_iter=1000): alpha_new = alpha_old = alpha_init for _ in range(max_iter): alpha_new = inv_psi(psi(np.sum(alpha_old)) + log_p_hat) if np.sqrt(np.sum((alpha_old - alpha_new) ** 2)) < 1e-9: break alpha_old = alpha_new return alpha_new def dirichlet_normality_score(alpha, p): return np.sum((alpha - 1) * np.log(p), axis=-1) scores = np.zeros((len(x_test),)) observed_data = x_train_task for t_ind in range(transformer.n_transforms): observed_dirichlet = mdl.predict(transformer.transform_batch(observed_data, [t_ind] * len(observed_data)), batch_size=1024) log_p_hat_train = np.log(observed_dirichlet).mean(axis=0) alpha_sum_approx = calc_approx_alpha_sum(observed_dirichlet) alpha_0 = observed_dirichlet.mean(axis=0) * alpha_sum_approx mle_alpha_t = fixed_point_dirichlet_mle(alpha_0, log_p_hat_train) x_test_p = mdl.predict(transformer.transform_batch(x_test, [t_ind] * len(x_test)), batch_size=1024) scores += dirichlet_normality_score(mle_alpha_t, x_test_p) scores /= transformer.n_transforms labels = y_test.flatten() == single_class_ind res_file_name = '{}_transformations_{}_{}.npz'.format(dataset_name, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) save_roc_pr_curve_data(scores, labels, res_file_path) mdl_weights_name = '{}_transformations_{}_{}_weights.h5'.format(dataset_name, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) mdl_weights_path = os.path.join(RESULTS_DIR, dataset_name, mdl_weights_name) mdl.save_weights(mdl_weights_path) gpu_q.put(gpu_to_use)
def _E3Outlier_experiment(x_train, y_train, dataset_name, single_class_ind, gpu_q, p): """Surrogate Supervision Discriminative Network training.""" gpu_to_use = gpu_q.get() n_channels = x_train.shape[get_channels_axis()] if OP_TYPE == 'RA': transformer = RA(8, 8) elif OP_TYPE == 'RA+IA': transformer = RA_IA(8, 8, 12) elif OP_TYPE == 'RA+IA+PR': transformer = RA_IA_PR(8, 8, 12, 23, 2) else: raise NotImplementedError print(transformer.n_transforms) if BACKEND == 'wrn': n, k = (10, 4) model = WideResNet(num_classes=transformer.n_transforms, depth=n, widen_factor=k, in_channel=n_channels) elif BACKEND == 'resnet20': n = 20 model = ResNet(num_classes=transformer.n_transforms, depth=n, in_channels=n_channels) elif BACKEND == 'resnet50': n = 50 model = ResNet(num_classes=transformer.n_transforms, depth=n, in_channels=n_channels) elif BACKEND == 'densenet22': n = 22 model = DenseNet(num_classes=transformer.n_transforms, depth=n, in_channels=n_channels) elif BACKEND == 'densenet40': n = 40 model = DenseNet(num_classes=transformer.n_transforms, depth=n, in_channels=n_channels) else: raise NotImplementedError('Unimplemented backend: {}'.format(BACKEND)) print('Using backend: {} ({})'.format(type(model).__name__, BACKEND)) x_train_task = x_train transformations_inds = np.tile(np.arange(transformer.n_transforms), len(x_train_task)) x_train_task_transformed = transformer.transform_batch( np.repeat(x_train_task, transformer.n_transforms, axis=0), transformations_inds) # parameters for training trainset = trainset_pytorch(train_data=x_train_task_transformed, train_labels=transformations_inds, transform=transform_train) batch_size = 128 trainloader = data.DataLoader(trainset, batch_size=batch_size, shuffle=True) cudnn.benchmark = True criterion = nn.CrossEntropyLoss() model = torch.nn.DataParallel(model).cuda() if dataset_name in ['mnist', 'fashion-mnist']: optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0005) else: optimizer = optim.Adam(model.parameters(), eps=1e-7, weight_decay=0.0005) epochs = int(np.ceil(250 / transformer.n_transforms)) train_pytorch(trainloader, model, criterion, optimizer, epochs) # SSD-IF test_set = testset_pytorch(test_data=x_train_task, transform=transform_test) x_train_task_rep = get_features_pytorch(testloader=data.DataLoader( test_set, batch_size=batch_size, shuffle=False), model=model).numpy() clf = IsolationForest(contamination=p, n_jobs=4).fit(x_train_task_rep) if_scores = clf.decision_function(x_train_task_rep) res_file_name = '{}_ssd-iforest-{}_{}_{}.npz'.format( dataset_name, p, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) os.makedirs(os.path.join(RESULTS_DIR, dataset_name), exist_ok=True) save_roc_pr_curve_data(if_scores, y_train, res_file_path) # E3Outlier if SCORE_MODE == 'pl_mean': preds = np.zeros((len(x_train_task), transformer.n_transforms)) original_preds = np.zeros((transformer.n_transforms, len(x_train_task), transformer.n_transforms)) for t in range(transformer.n_transforms): idx = np.squeeze( np.array([range(x_train_task.shape[0])]) * transformer.n_transforms + t) test_set = testset_pytorch( test_data=x_train_task_transformed[idx, :], transform=transform_test) original_preds[t, :, :] = softmax( test_pytorch(testloader=data.DataLoader(test_set, batch_size=batch_size, shuffle=False), model=model)) preds[:, t] = original_preds[t, :, :][:, t] scores = preds.mean(axis=-1) elif SCORE_MODE == 'max_mean': preds = np.zeros((len(x_train_task), transformer.n_transforms)) original_preds = np.zeros((transformer.n_transforms, len(x_train_task), transformer.n_transforms)) for t in range(transformer.n_transforms): idx = np.squeeze( np.array([range(x_train_task.shape[0])]) * transformer.n_transforms + t) test_set = testset_pytorch( test_data=x_train_task_transformed[idx, :], transform=transform_test) original_preds[t, :, :] = softmax( test_pytorch(testloader=data.DataLoader(test_set, batch_size=batch_size, shuffle=False), model=model)) preds[:, t] = np.max(original_preds[t, :, :], axis=1) scores = preds.mean(axis=-1) elif SCORE_MODE == 'neg_entropy': preds = np.zeros((len(x_train_task), transformer.n_transforms)) original_preds = np.zeros((transformer.n_transforms, len(x_train_task), transformer.n_transforms)) for t in range(transformer.n_transforms): idx = np.squeeze( np.array([range(x_train_task.shape[0])]) * transformer.n_transforms + t) test_set = testset_pytorch( test_data=x_train_task_transformed[idx, :], transform=transform_test) original_preds[t, :, :] = softmax( test_pytorch(testloader=data.DataLoader(test_set, batch_size=batch_size, shuffle=False), model=model)) for s in range(len(x_train_task)): preds[s, t] = neg_entropy(original_preds[t, s, :]) scores = preds.mean(axis=-1) else: raise NotImplementedError res_file_name = '{}_e3outlier-{}_{}_{}.npz'.format( dataset_name, p, get_class_name_from_index(single_class_ind, dataset_name), datetime.now().strftime('%Y-%m-%d-%H%M')) res_file_path = os.path.join(RESULTS_DIR, dataset_name, res_file_name) os.makedirs(os.path.join(RESULTS_DIR, dataset_name), exist_ok=True) save_roc_pr_curve_data(scores, y_train, res_file_path) gpu_q.put(gpu_to_use)
dataset_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=bbox_collate(mode).collate) print('Evaluating {} by {}-criterion:'.format(dataset_name, criterion)) if criterion == 'frame': if dataset_name == 'ShanghaiTech': all_frame_scores = [[] for si in set(dataset.scene_idx)] all_targets = [[] for si in set(dataset.scene_idx)] for idx, (_, target) in enumerate(dataset_loader): print('Processing {}-th frame'.format(idx)) cur_pixel_results = torch.load(os.path.join(results_dir, dataset_name, 'score_mask', '{}'.format(idx))) all_frame_scores[scene_idx[idx] - 1].append(cur_pixel_results.max()) all_targets[scene_idx[idx] - 1].append(target[0].numpy().max()) all_frame_scores = [np.array(all_frame_scores[si]) for si in range(dataset.scene_num)] all_targets = [np.array(all_targets[si]) for si in range(dataset.scene_num)] all_targets = [all_targets[si] > 0 for si in range(dataset.scene_num)] results = [save_roc_pr_curve_data(all_frame_scores[si], all_targets[si], os.path.join(results_dir, dataset_name, '{}_{}_{}_frame_results_scene_{}.npz'.format(modality, foreground_extraction_mode, method, si + 1))) for si in range(dataset.scene_num)] results = np.array(results).mean() print('Average frame-level AUC is {}'.format(results)) else: all_frame_scores = list() all_targets = list() for idx, (_, target) in enumerate(dataset_loader): print('Processing {}-th frame'.format(idx)) cur_pixel_results = torch.load(os.path.join(results_dir, dataset_name, 'score_mask', '{}'.format(idx))) all_frame_scores.append(cur_pixel_results.max()) all_targets.append(target[0].numpy().max()) all_frame_scores = np.array(all_frame_scores) all_targets = np.array(all_targets) all_targets = all_targets > 0 results_path = os.path.join(results_dir, dataset_name, '{}_{}_{}_frame_results.npz'.format(modality, foreground_extraction_mode, method)) print('Results written to {}:'.format(results_path))