Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
# 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, | |
) | |
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["template"], | |
"text": info["label"] if "label" in info else -1 | |
} | |
idx += 1 | |