train_dir = create_dir(osp.join(trans_out_dir, 'train')) test_dir = create_dir(osp.join(trans_out_dir, 'test')) samples_dir = create_dir(osp.join(trans_out_dir, 'samples')) print(trans_out_dir) print(train_dir) print(test_dir) print(samples_dir) ## Load pre-trained AE reset_tf_graph() ae_conf_AB = Configuration.load(ae_configuration_AB) print(ae_conf_AB.__str__()) ae_AB = AutoEncoder(ae_conf_AB.experiment_name, ae_conf_AB) ae_AB.restore_model(ae_conf_AB.train_dir, FLAGS.ae_epochs, verbose=True) ae_A = ae_AB ae_B = ae_AB # data folders datafolder = top_in_dir + class_name_A + '-' + class_name_B + '/' train_dir_A = datafolder + class_name_A + '_train' train_dir_B = datafolder + class_name_B + '_train' test_dir_A = datafolder + class_name_A + '_test' test_dir_B = datafolder + class_name_B + '_test' ## Load point-clouds training_pc_data_A = load_point_clouds_under_folder(train_dir_A, n_threads=8, file_ending='.ply',
experiment_name = experiment_name ) conf.save(osp.join(train_dir, 'configuration')) # Build AE Model. reset_tf_graph() ae = AutoEncoder(name=conf.experiment_name, configuration=conf) # load pretrained model if FLAGS.load_pre_trained_ae: conf = Configuration.load(train_dir + '/configuration') reset_tf_graph() ae = AutoEncoder(conf.experiment_name, conf) ae.restore_model(conf.train_dir, epoch=FLAGS.restore_epoch) batch_size = train_params['batch_size'] if FLAGS.mode == 'train' : training_pc_data_A = load_point_clouds_under_folder( train_dir_A, n_threads=8, file_ending='.ply', verbose=True) training_pc_data_B = load_point_clouds_under_folder( train_dir_B, n_threads=8, file_ending='.ply', verbose=True) training_pc_data = training_pc_data_A training_pc_data.merge( training_pc_data_B ) training_pc_data.shuffle_data() print( 'training_pc_data.point_clouds.shape[0] = ' + str(training_pc_data.point_clouds.shape[0]) )