with open(str_mixing, 'rb') as fp: mixing = cPickle.load(fp) str_settings_resnet = str(nb_cl) + 'settings_resnet.pickle' with open(str_settings_resnet, 'rb') as fp: order = cPickle.load(fp) files_valid = cPickle.load(fp) files_train = cPickle.load(fp) # Load class means str_class_means = str(nb_cl) + 'class_means.pickle' with open(str_class_means, 'rb') as fp: class_means = cPickle.load(fp) # Loading the labels labels_dic, label_names, validation_ground_truth = utils_data.parse_devkit_meta( devkit_path) # Initialization acc_list = np.zeros((nb_groups, 3)) for itera in range(nb_groups): print("Processing network after {} increments\t".format(itera)) # Evaluation on cumul of classes or original classes if is_cumul == 'cumul': eval_groups = np.array(range(itera + 1)) else: eval_groups = [0] print("Evaluation on batches {} \t".format(eval_groups)) # Load the evaluation files files_from_cl = []
with open(str_mixing,'rb') as fp: mixing = cPickle.load(fp) str_settings_resnet = str(nb_cl)+'settings_resnet.pickle' with open(str_settings_resnet,'rb') as fp: order = cPickle.load(fp) files_valid = cPickle.load(fp) files_train = cPickle.load(fp) # Load class means str_class_means = str(nb_cl)+'class_means.pickle' with open(str_class_means,'rb') as fp: class_means = cPickle.load(fp) # Loading the labels labels_dic, label_names, validation_ground_truth = utils_data.parse_devkit_meta(devkit_path) # Initialization acc_list = np.zeros((nb_groups,3)) # Load the evaluation files print("Processing network after {} increments\t".format(itera)) print("Evaluation on batches {} \t".format(eval_groups)) files_from_cl = [] for i in eval_groups: files_from_cl.extend(files_valid[i]) inits,scores,label_batch,loss_class,file_string_batch,op_feature_map = utils_icarl.reading_data_and_preparing_network(files_from_cl, gpu, itera, batch_size, train_path, labels_dic, mixing, nb_groups, nb_cl, save_path) with tf.Session(config=config) as sess: