def AddSequences():
"""Creates one training, validation."""
errors = []
# Generate datasets file lists.
sequences = FindPatternFiles(FLAGS.input_dir, FLAGS.view_pattern, errors)
num_frames = PrintSequencesInfo(sequences,
'Found the following datasets and files:')
# Sharding and randomizing sets.
if FLAGS.max_per_shard > 0:
sequences = ShardSequences(sequences, FLAGS.max_per_shard)
num_frames = PrintSequencesInfo(sequences, 'After sharding:')
tf.logging.info('')
# Process sets.
progress = Progress(num_frames)
output_list = []
for sequence in sequences:
record_name = os.path.join(FLAGS.output_dir,
'%s.tfrecord' % sequence['name'])
if tf.gfile.Exists(record_name) and not FLAGS.overwrite:
ok, num_frames = CheckRecord(record_name, sequence)
if ok:
progress.Add(num_frames)
tf.logging.info('Skipping existing output file: %s' %
record_name)
continue
else:
tf.logging.info(
'File does not match sequence, reprocessing...')
output_dir = os.path.dirname(record_name)
if not tf.gfile.Exists(output_dir):
tf.logging.info('Creating output directory: %s' % output_dir)
tf.gfile.MakeDirs(output_dir)
output_list.append(record_name)
tf.logging.info('Writing to ' + record_name)
writer = tf.python_io.TFRecordWriter(record_name)
AddSequence(sequence, writer, progress, errors)
writer.close()
tf.logging.info('Wrote dataset files: ' + str(output_list))
tf.logging.info('All errors (%d): %s' % (len(errors), str(errors)))
