Got a mismatch result between writing and reading tfrecord files - api

Question

Here I use this func to write multiple tfrecord files:
writer = tf.python_io.TFRecordWriter(save)
for pth, lb in tqdm(zip(piece_p, piece_l)):
# mind that the path should be read into image data first
# to convert the byteslist data format into raw bytes
data = Image.open(pth)
if resize is not None:
data.thumbnail(resize, Image.ANTIALIAS)
features = tf.train.Features(feature={
'image': tf.train.Feature(
bytes_list=tf.train.BytesList(value=[data.tobytes()])),
'label': tf.train.Feature(
int64_list=tf.train.Int64List(value=[lb]))
})
example = tf.train.Example(features=features)
# serialize the constructed data format before writing step
writer.write(example.SerializeToString())
sys.stdout.flush()
writer.close()
And parse the binary file using code as below:
def parse_fn(serialized):
features = {
'image': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64)
}
parse_exp = tf.parse_single_example(serialized=serialized,
features=features)
labels = parse_exp['label']
data = parse_exp['image']
data = tf.decode_raw(data, tf.uint8)
data = tf.cast(data, tf.float32)
del parse_exp
return data, labels
dataset = tf.data.Dataset.list_files(data_list, shuffle=True)
dataset = dataset.interleave(tf.data.TFRecordDataset,
cycle_length=file_num)
# dataset = tf.data.TFRecordDataset(data_list[0])
dataset = dataset.map(parse_fn, num_parallel_calls=4)
But why is the number of labels and data always mismatching...?
every time when adding the following code to make multiple batches or sth
dataset = dataset.batch(12)
dataset = dataset.repeat(1)
iterator = dataset.make_initializable_iterator()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(iterator.initializer)
data, labels = iterator.get_next()
and the labels quantity always remains half of data. are there something wrong with my arguments setting? I pretty sure that there are no wrong with my saving part and reading part separately... but there are some problems when combining them together.