something_something_v2 / something_something_v2.py
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Leyo
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fix label/text swap
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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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
#
# http://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.
# Lint as: python3
"""The Something-Something dataset (version 2) is a collection of 220,847 labeled video clips of humans performing pre-defined, basic actions with everyday objects."""
import csv
import json
import os
import datasets
from .classes import SOMETHING_SOMETHING_V2_CLASSES
_CITATION = """
@inproceedings{goyal2017something,
title={The" something something" video database for learning and evaluating visual common sense},
author={Goyal, Raghav and Ebrahimi Kahou, Samira and Michalski, Vincent and Materzynska, Joanna and Westphal, Susanne and Kim, Heuna and Haenel, Valentin and Fruend, Ingo and Yianilos, Peter and Mueller-Freitag, Moritz and others},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={5842--5850},
year={2017}
}
"""
_DESCRIPTION = """\
The Something-Something dataset (version 2) is a collection of 220,847 labeled video clips of humans performing pre-defined, basic actions with everyday objects. It is designed to train machine learning models in fine-grained understanding of human hand gestures like putting something into something, turning something upside down and covering something with something.
"""
class SomethingSomethingV2(datasets.GeneratorBasedBuilder):
"""Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk"""
BUILDER_CONFIGS = [datasets.BuilderConfig(name="default")]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"video_id": datasets.Value("string"),
"video": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(
num_classes=len(SOMETHING_SOMETHING_V2_CLASSES),
names=SOMETHING_SOMETHING_V2_CLASSES,
),
"placeholders": datasets.Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
@property
def manual_download_instructions(self):
return (
"To use Something-Something-v2, please download the 19 data files and the labels file "
"from 'https://developer.qualcomm.com/software/ai-datasets/something-something'. "
"Unzip the 19 files and concatenate the extracts in order into a tar file named '20bn-something-something-v2.tar.gz. "
"Use command like `cat 20bn-something-something-v2-?? >> 20bn-something-something-v2.tar.gz` "
"Place the `labels.zip` file and the tar file into a folder '/path/to/data/' and load the dataset using "
"`load_dataset('something-something-v2', data_dir='/path/to/data')`"
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.manual_dir
labels_path = os.path.join(data_dir, "labels.zip")
videos_path = os.path.join(data_dir, "20bn-something-something-v2.tar.gz")
if not os.path.exists(labels_path):
raise FileNotFoundError(
f"labels.zip doesn't exist in {data_dir}. Please follow manual download instructions."
)
if not os.path.exists(videos_path):
raise FileNotFoundError(
f"20bn-something-sokmething-v2.tar.gz doesn't exist in {data_dir}. Please follow manual download instructions."
)
labels_path = dl_manager.extract(labels_path)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotation_file": os.path.join(
labels_path, "labels", "train.json"
),
"video_files": dl_manager.iter_archive(videos_path),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"annotation_file": os.path.join(
labels_path, "labels", "validation.json"
),
"video_files": dl_manager.iter_archive(videos_path),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotation_file": os.path.join(labels_path, "labels", "test.json"),
"video_files": dl_manager.iter_archive(videos_path),
"labels_file": os.path.join(
labels_path, "labels", "test-answers.csv"
),
},
),
]
def _generate_examples(self, annotation_file, video_files, labels_file=None):
data = {}
labels = None
if labels_file is not None:
with open(labels_file, "r", encoding="utf-8") as fobj:
labels = {}
for label in fobj.readlines():
label = label.strip().split(";")
labels[label[0]] = label[1]
with open(annotation_file, "r", encoding="utf-8") as fobj:
annotations = json.load(fobj)
for annotation in annotations:
if "template" in annotation:
annotation["template"] = (
annotation["template"].replace("[", "").replace("]", "")
)
if labels:
annotation["template"] = labels[annotation["id"]]
data[annotation["id"]] = annotation
idx = 0
for path, file in video_files:
video_id = os.path.splitext(os.path.split(path)[1])[0]
if video_id not in data:
continue
info = data[video_id]
yield idx, {
"video_id": video_id,
"video": file,
"placeholders": info.get("placeholders", []),
"label": info["label"] if "label" in info else -1,
"text": info["template"],
}
idx += 1