'b_norm_decay'] = 1. # for avoiding the update of batch normalization moving_mean and moving_variance parameters
conf.decoder_args[
'b_norm_decay'] = 1. # for avoiding the update of batch normalization moving_mean and moving_variance parameters
conf.decoder_args[
'b_norm_decay_finish'] = 1. # for avoiding the update of batch normalization moving_mean and moving_variance parameters
conf.encoder_args['return_layer_before_symmetry'] = True
# sampler configuration
conf.experiment_name = 'sampler'
conf.n_samp = [flags.n_sample_points, 3] # Dimensionality of sampled points.
conf.sampler = sampler_with_convs_and_symmetry_and_fc
conf.similarity_reg_weight = flags.similarity_reg_weight
conf.learning_rate = flags.learning_rate
train_dir = create_dir(osp.join(top_out_dir, flags.train_folder))
conf.train_dir = train_dir
conf.save(osp.join(train_dir, 'configuration'))
# Build Sampler and AE Model
reset_tf_graph()
ae = SNetPointNetAutoEncoder(conf.experiment_name, conf)
# Train the sampler (save output to train_stats.txt)
buf_size = 1 # Make 'training_stats' file to flush each output line regarding training.
fout = open(osp.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size)
train_stats = ae.train(pc_data_train,
conf,
log_file=fout,
held_out_data=pc_data_val)
# Load configuration
train_dir = osp.join(top_out_dir, flags.train_folder)
restore_epoch = 500
conf = Conf.load(osp.join(train_dir, 'configuration'))
conf.encoder_args['return_layer_before_symmetry'] = True
# Reload a saved model
reset_tf_graph()
ae = SNetPointNetAutoEncoder(conf.experiment_name, conf)
ae.restore_model(train_dir, epoch=restore_epoch, verbose=True)
n_sample_points = conf.n_samp[0]
# create evaluation dir
eval_dir = create_dir(osp.join(train_dir, "eval"))
# sample point clouds
_, sampled_pc, sample_idx, _ = ae.get_samples(
pc_data_test.point_clouds) # S-NET sampled points
# save sampled point cloud and sample index
file_name = "_".join(
["sampled_pc", "test_set", flags.object_class,
"%04d" % n_sample_points]) + ".npy"
file_path = osp.join(eval_dir, file_name)
np.save(file_path, sampled_pc)
file_name = "_".join(
["sample_idx", "test_set", flags.object_class,
"%04d" % n_sample_points]) + ".npy"