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# Copyright 2021 DeepMind Technologies Limited.
#
# 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.
"""Python script to generate TFRecords of SequenceExample from csv."""
import contextlib
import math
import os
from typing import Optional, Sequence
from absl import app
from absl import flags
import numpy as np
import pandas as pd
import tensorflow as tf
from tqdm import tqdm
flags.DEFINE_string("csv_path", None, "Input csv")
flags.DEFINE_string("output_path", None, "Tfrecords output path.")
flags.DEFINE_string(
"features_path",
None,
"In case features are stored in individual files and not in the csv.",
)
flags.DEFINE_integer(
"num_shards",
-1,
(
"Number of shards to output, -1 means"
"it will automatically adapt to the sqrt(num_examples)."
),
)
flags.DEFINE_bool("shuffle_csv", False, "Whether or not to shuffle the csv.")
FLAGS = flags.FLAGS
@contextlib.contextmanager
def _close_on_exit(writers):
"""Call close on all writers on exit."""
try:
yield writers
finally:
for writer in writers:
writer.close()
def add_float_list(key: str, values: Sequence[float],
sequence: tf.train.SequenceExample):
sequence.feature_lists.feature_list[key].feature.add(
).float_list.value[:] = values
def add_bytes_list(key: str, values: Sequence[bytes],
sequence: tf.train.SequenceExample):
sequence.feature_lists.feature_list[key].feature.add(
).bytes_list.value[:] = values
def add_int_list(key: str, values: Sequence[int],
sequence: tf.train.SequenceExample):
sequence.feature_lists.feature_list[key].feature.add(
).int64_list.value[:] = values
def set_context_int_list(key: str, value: Sequence[int],
sequence: tf.train.SequenceExample):
sequence.context.feature[key].int64_list.value[:] = value
def set_context_bytes(key: str, value: bytes,
sequence: tf.train.SequenceExample):
sequence.context.feature[key].bytes_list.value[:] = (value,)
def set_context_float(key: str, value: float,
sequence: tf.train.SequenceExample):
sequence.context.feature[key].float_list.value[:] = (value,)
def set_context_int(key: str, value: int, sequence: tf.train.SequenceExample):
sequence.context.feature[key].int64_list.value[:] = (value,)
def generate_sequence_example(video_id: str,
start: Optional[Sequence[float]],
end: Optional[Sequence[float]],
caption: Optional[Sequence[str]],
asr_start: Sequence[float],
asr_end: Sequence[float],
asr_string: Sequence[str],
features: Sequence[Sequence[float]],
duration: int,
split: Sequence[int] = None):
"""Generate a sequence example."""
# Initiate the sequence example.
seq_example = tf.train.SequenceExample()
# Add dense captioning annotations if these exist.
if caption is not None:
for s, e, c in zip(start, end, caption):
seq_example.context.feature[
"video/timestamps/start"
].int64_list.value.append(s)
seq_example.context.feature[
"video/timestamps/end"
].int64_list.value.append(e)
seq_example.context.feature["caption/string"].bytes_list.value.append(
c.encode()
)
# Add ASR.
if asr_start:
for s, e, c in zip(asr_start, asr_end, asr_string):
seq_example.context.feature[
"ASR/timestamps/start"
].int64_list.value.append(s)
seq_example.context.feature["ASR/timestamps/end"].int64_list.value.append(
e
)
seq_example.context.feature["ASR/string"].bytes_list.value.append(
c.encode()
)
# Add visual features.
for f in features:
add_float_list("image/clip_embeddings", f, seq_example)
if split is not None:
for s in split:
seq_example.context.feature["split"].int64_list.value.append(s)
# Add other metadata.
set_context_bytes("videoid", video_id.encode(), seq_example)
set_context_int("video/duration", duration, seq_example)
return seq_example
def generate(video_info):
# reads the input csv.
# input_csv = pd.read_csv(FLAGS.csv_path)
# if FLAGS.num_shards == -1:
# num_shards = int(math.sqrt(len(video_info)))
# else:
# num_shards = FLAGS.num_shards
num_shards = 1
# Set up the TFRecordWriters.
# basename = os.path.splitext(os.path.basename(FLAGS.csv_path))[0]
basename = video_info['basename']
shard_names = [
os.path.join(video_info['output_path'], f"{basename}-{i:05d}-of-{num_shards:05d}")
for i in range(num_shards)
]
writers = [tf.io.TFRecordWriter(shard_name) for shard_name in shard_names]
with _close_on_exit(writers) as writers:
for i in tqdm(range(len(video_info))):
print(
"Processing example %d of %d (%d%%) \r" %
(i, len(video_info), i * 100 / len(video_info)),
end="")
# no gds needed
start = None
end = None
caption = None
asr_start = video_info["asr_start"]
if isinstance(asr_start, str):
asr_start = eval(asr_start) # pylint:disable=eval-used
asr_end = video_info["asr_end"]
if isinstance(asr_end, str):
asr_end = eval(asr_end) # pylint:disable=eval-used
asr_string = video_info["asr_string"]
if isinstance(asr_string, str):
asr_string = eval(asr_string) # pylint:disable=eval-used
video_id = video_info["video_id"]
split = None
# pylint:disable=eval-used
if "features" not in video_info: # load on the fly
assert video_info['features_path']
features = list(
np.load(os.path.join(video_info['features_path'], video_id + ".npy"))
)
else:
features = video_info["features"] # pylint:disable=eval-used
duration = int(video_info["duration"])
seq_ex = generate_sequence_example(
video_id,
start,
end,
caption,
asr_start,
asr_end,
asr_string,
features,
duration,
split)
writers[i % len(writers)].write(seq_ex.SerializeToString())
def main(*args):
# reads the input csv.
input_csv = pd.read_csv(FLAGS.csv_path)
if FLAGS.num_shards == -1:
num_shards = int(math.sqrt(len(input_csv)))
else:
num_shards = FLAGS.num_shards
# Set up the TFRecordWriters.
basename = os.path.splitext(os.path.basename(FLAGS.csv_path))[0]
shard_names = [
os.path.join(FLAGS.output_path, f"{basename}-{i:05d}-of-{num_shards:05d}")
for i in range(num_shards)
]
writers = [tf.io.TFRecordWriter(shard_name) for shard_name in shard_names]
if FLAGS.shuffle_csv:
input_csv = input_csv.sample(frac=1)
with _close_on_exit(writers) as writers:
for i in tqdm(range(len(input_csv))):
print(
"Processing example %d of %d (%d%%) \r" %
(i, len(input_csv), i * 100 / len(input_csv)),
end="")
if "caption" in input_csv:
start = eval(input_csv["start"].values[i]) # pylint:disable=eval-used
end = eval(input_csv["end"].values[i]) # pylint:disable=eval-used
caption = eval(input_csv["caption"].values[i]) # pylint:disable=eval-used
else:
start = None
end = None
caption = None
asr_start = input_csv["asr_start"].values[i]
if isinstance(asr_start, str):
asr_start = eval(asr_start) # pylint:disable=eval-used
asr_end = input_csv["asr_end"].values[i]
if isinstance(asr_end, str):
asr_end = eval(asr_end) # pylint:disable=eval-used
asr_string = input_csv["asr_string"].values[i]
if isinstance(asr_string, str):
asr_string = eval(asr_string) # pylint:disable=eval-used
video_id = input_csv["video_id"].values[i]
split = None
if "split" in input_csv:
split = input_csv["split"].values[i]
if isinstance(split, str):
split = eval(split) # pylint:disable=eval-used
if "features" not in input_csv: # load on the fly
assert FLAGS.features_path
features = list(
np.load(os.path.join(FLAGS.features_path, video_id + ".npy"))
)
else:
features = eval(input_csv["features"].values[i]) # pylint:disable=eval-used
duration = int(input_csv["duration"].values[i])
seq_ex = generate_sequence_example(
video_id,
start,
end,
caption,
asr_start,
asr_end,
asr_string,
features,
duration,
split)
writers[i % len(writers)].write(seq_ex.SerializeToString())
if __name__ == "__main__":
app.run(main)
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