ensemble_test_data = None labels_pred_set = [] for directory in data_list: print("#################################") print("Load data saved in {0}".format(directory)) # load tracer failure, data, tracer = load_arrangement(keyword, directory) if not failure: print("Load data and tracer success") if ensemble_test_data == None: ensemble_test_data = [ value for _, value in sorted(zip(tracer.test, data.test.images)) ] # load label_pred failure, labels_pred = load_labels_pred(keyword, directory) if not failure: print("Load labels_pred success") temp_labels_pred = [ value for _, value in sorted(zip(tracer.test, labels_pred)) ] labels_pred_set.append(temp_labels_pred) # load cls_true failure, cls_true = load_cls_true(keyword, directory) if not failure: print("Load cls_true success") if ensemble_cls_true == None: ensemble_cls_true = [ value for _, value in sorted(zip(tracer.test, cls_true)) ] #-----------------------------------
ai_alice = argv[2] ai_bob = argv[3] work_dir = os.getcwd() #---------------------------------------- # Load prediction 1 and true labels print("### Prediction 1 ###") print("AI DIR = {0}".format(ai_alice)) os.chdir(ai_alice) data_list = glob.glob("AI*test_on*{0}".format(main_name)) ensemble_cls_true = None alice_labels_pred_set = [] for directory in data_list: # load tracer failure, data, tracer = load_arrangement(main_name, directory) # load label_pred failure, labels_pred = load_labels_pred(main_name, directory) if not failure: temp_labels_pred = [ value for _, value in sorted(zip(tracer.test, labels_pred)) ] alice_labels_pred_set.append(temp_labels_pred) # load cls_true failure, cls_true = load_cls_true(main_name, directory) if not failure: if ensemble_cls_true == None: ensemble_cls_true = [ value for _, value in sorted(zip(tracer.test, cls_true)) ] alice_labels_pred_set = np.array(alice_labels_pred_set) alice_ensemble_labels_pred = np.mean(alice_labels_pred_set, axis=0)