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# Copyright 2022 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Beam pipeline that generates UCF101 `interp_test` triplet TFRecords.
UCF101 interpolation evaluation dataset consists of 379 triplets, with the
middle frame being the golden intermediate. The dataset is available here:
https://people.cs.umass.edu/~hzjiang/projects/superslomo/UCF101_results.zip.
Input to the script is the root folder that contains the unzipped
`UCF101_results` folder.
Output TFRecord is a tf.train.Example proto of each image triplet.
The feature_map takes the form:
feature_map {
'frame_0/encoded':
tf.io.FixedLenFeature((), tf.string, default_value=''),
'frame_0/format':
tf.io.FixedLenFeature((), tf.string, default_value='jpg'),
'frame_0/height':
tf.io.FixedLenFeature((), tf.int64, default_value=0),
'frame_0/width':
tf.io.FixedLenFeature((), tf.int64, default_value=0),
'frame_1/encoded':
tf.io.FixedLenFeature((), tf.string, default_value=''),
'frame_1/format':
tf.io.FixedLenFeature((), tf.string, default_value='jpg'),
'frame_1/height':
tf.io.FixedLenFeature((), tf.int64, default_value=0),
'frame_1/width':
tf.io.FixedLenFeature((), tf.int64, default_value=0),
'frame_2/encoded':
tf.io.FixedLenFeature((), tf.string, default_value=''),
'frame_2/format':
tf.io.FixedLenFeature((), tf.string, default_value='jpg'),
'frame_2/height':
tf.io.FixedLenFeature((), tf.int64, default_value=0),
'frame_2/width':
tf.io.FixedLenFeature((), tf.int64, default_value=0),
'path':
tf.io.FixedLenFeature((), tf.string, default_value=''),
}
Usage example:
python3 -m frame_interpolation.datasets.create_ucf101_tfrecord \
--input_dir=<root folder of UCF101_results> \
--output_tfrecord_filepath=<output tfrecord filepath>
"""
import os
from . import util
from absl import app
from absl import flags
from absl import logging
import apache_beam as beam
import tensorflow as tf
_INPUT_DIR = flags.DEFINE_string(
'input_dir',
default='/root/path/to/UCF101_results/ucf101_interp_ours',
help='Path to the root directory of the `UCF101_results` of the UCF101 '
'interpolation evaluation data. '
'We expect the data to have been downloaded and unzipped. \n'
'Folder structures:\n'
'| raw_UCF101_results/\n'
'| ucf101_interp_ours/\n'
'| | 1/\n'
'| | | frame_00.png\n'
'| | | frame_01_gt.png\n'
'| | | frame_01_ours.png\n'
'| | | frame_02.png\n'
'| | 2/\n'
'| | | frame_00.png\n'
'| | | frame_01_gt.png\n'
'| | | frame_01_ours.png\n'
'| | | frame_02.png\n'
'| | ...\n'
'| ucf101_sepconv/\n'
'| ...\n')
_OUTPUT_TFRECORD_FILEPATH = flags.DEFINE_string(
'output_tfrecord_filepath',
default=None,
required=True,
help='Filepath to the output TFRecord file.')
_NUM_SHARDS = flags.DEFINE_integer('num_shards',
default=2,
help='Number of shards used for the output.')
# Image key -> basename for frame interpolator: start / middle / end frames.
_INTERPOLATOR_IMAGES_MAP = {
'frame_0': 'frame_00.png',
'frame_1': 'frame_01_gt.png',
'frame_2': 'frame_02.png',
}
def main(unused_argv):
"""Creates and runs a Beam pipeline to write frame triplets as a TFRecord."""
# Collect the list of folder paths containing the input and golden frames.
triplets_list = tf.io.gfile.listdir(_INPUT_DIR.value)
triplet_dicts = []
for triplet in triplets_list:
triplet_dicts.append({
image_key: os.path.join(_INPUT_DIR.value, triplet, image_basename)
for image_key, image_basename in _INTERPOLATOR_IMAGES_MAP.items()
})
p = beam.Pipeline('DirectRunner')
(p | 'ReadInputTripletDicts' >> beam.Create(triplet_dicts) # pylint: disable=expression-not-assigned
| 'GenerateSingleExample' >> beam.ParDo(
util.ExampleGenerator(_INTERPOLATOR_IMAGES_MAP))
| 'WriteToTFRecord' >> beam.io.tfrecordio.WriteToTFRecord(
file_path_prefix=_OUTPUT_TFRECORD_FILEPATH.value,
num_shards=_NUM_SHARDS.value,
coder=beam.coders.BytesCoder()))
result = p.run()
result.wait_until_finish()
logging.info('Succeeded in creating the output TFRecord file: \'%s@%s\'.',
_OUTPUT_TFRECORD_FILEPATH.value, str(_NUM_SHARDS.value))
if __name__ == '__main__':
app.run(main)
|