Datasets:

HuggingFaceM4
/

something_something_v2

	# 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"),
	"labels": datasets.features.ClassLabel(
	num_classes=len(SOMETHING_SOMETHING_V2_CLASSES), names=SOMETHING_SOMETHING_V2_CLASSES
	),
	"objects": 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):
	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, "train.json"),
	"videos_files": dl_manager.iter_archive(videos_path),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"annotation_file": os.path.join(labels_path, "validation.json"),
	"videos_files": dl_manager.iter_archive(videos_path),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"annotation_file": os.path.join(labels_path, "test.json"),
	"videos_files": dl_manager.iter_archive(videos_path),
	"labels_file": os.path.join(labels_path, "test_labels.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("[something]", "something")
	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,
	"objects": info["objects"],
	"label": data["template"],
	"text": data["text"] if "text" in data else -1
	}

	idx += 1