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]) )