def __init__(self, args, is_training):
df_train, self.num_train = lmdb_dataflow(args.lmdb_train, args.batch_size,
args.num_input_points, args.num_gt_points, is_training=True)
batch_train = get_queued_data(df_train.get_data(), [tf.string, tf.float32, tf.float32],
[[args.batch_size],
[args.batch_size, args.num_input_points, 3],
[args.batch_size, args.num_gt_points, 3]])
df_valid, self.num_valid = lmdb_dataflow(args.lmdb_valid, args.batch_size,
args.num_input_points, args.num_gt_points, is_training=False)
batch_valid = get_queued_data(df_valid.get_data(), [tf.string, tf.float32, tf.float32],
[[args.batch_size],
[args.batch_size, args.num_input_points, 3],
[args.batch_size, args.num_gt_points, 3]])
self.batch_data = tf.cond(is_training, lambda: batch_train, lambda: batch_valid)
def __init__(self, args, is_training):
df_test, self.num_test = lmdb_dataflow(args.lmdb_test,
args.batch_size,
args.num_input_points,
args.num_gt_points,
is_training=False)
batch_test = get_queued_data(
df_test.get_data(), [tf.string, tf.float32, tf.float32],
[[args.batch_size], [args.batch_size, args.num_input_points, 3],
[args.batch_size, args.num_gt_points, 3]])
self.batch_data = batch_test
