Datasets:

HuggingFaceM4
/

charades

	# 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
	"""Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk"""


	import csv
	import os

	import datasets

	from .classes import CHARADES_CLASSES

	_CITATION = """
	@article{sigurdsson2016hollywood,
	author = {Gunnar A. Sigurdsson and G{\"u}l Varol and Xiaolong Wang and Ivan Laptev and Ali Farhadi and Abhinav Gupta},
	title = {Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding},
	journal = {ArXiv e-prints},
	eprint = {1604.01753},
	year = {2016},
	url = {http://arxiv.org/abs/1604.01753},
	}
	"""

	_DESCRIPTION = """\
	Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk. 267 different users were presented with a sentence, that includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence (like in a game of Charades). The dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos.
	"""


	_ANNOTATIONS_URL = "https://ai2-public-datasets.s3-us-west-2.amazonaws.com/charades/Charades.zip"
	_VIDEOS_URL = {
	"default": "https://ai2-public-datasets.s3-us-west-2.amazonaws.com/charades/Charades_v1.zip",
	"480p": "https://ai2-public-datasets.s3-us-west-2.amazonaws.com/charades/Charades_v1_480.zip",
	}


	class Charades(datasets.GeneratorBasedBuilder):
	"""Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk"""

	BUILDER_CONFIGS = [datasets.BuilderConfig(name="default"), datasets.BuilderConfig(name="480p")]
	DEFAULT_CONFIG_NAME = "default"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"video_id": datasets.Value("string"),
	"video": datasets.Value("string"),
	"subject": datasets.Value("string"),
	"scene": datasets.Value("string"),
	"quality": datasets.Value("int32"),
	"relevance": datasets.Value("int32"),
	"verified": datasets.Value("string"),
	"script": datasets.Value("string"),
	"objects": datasets.features.Sequence(datasets.Value("string")),
	"descriptions": datasets.features.Sequence(datasets.Value("string")),
	"labels": datasets.Sequence(
	datasets.features.ClassLabel(
	num_classes=len(CHARADES_CLASSES), names=list(CHARADES_CLASSES.values())
	)
	),
	"action_timings": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
	"length": datasets.Value("float32"),
	}
	),
	supervised_keys=None,
	homepage="",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	annotations_path = dl_manager.download_and_extract(_ANNOTATIONS_URL)
	archive = os.path.join(dl_manager.download_and_extract(_VIDEOS_URL[self.config.name]), "Charades_v1")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"annotation_file": os.path.join(annotations_path, "Charades", "Charades_v1_train.csv"),
	"video_folder": archive,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"annotation_file": os.path.join(annotations_path, "Charades", "Charades_v1_test.csv"),
	"video_folder": archive,
	},
	),
	]

	def _generate_examples(self, annotation_file, video_folder):
	"""This function returns the examples."""
	with open(annotation_file, "r", encoding="utf-8") as csv_file:
	reader = csv.DictReader(csv_file)
	idx = 0
	for row in reader:
	path = os.path.join(video_folder, row["id"] + ".mp4")
	labels = []
	action_timings = []
	for class_label in row["actions"].split(";"):
	# Skip empty action labels
	if len(class_label) != 0:
	# format is like: "c123 11.0 13.0"
	labels.append(CHARADES_CLASSES[class_label.split(" ")[0]])
	timings = list(map(float, class_label.split(" ")[1:]))
	action_timings.append(timings)

	yield idx, {
	"video_id": row["id"],
	"video": path,
	"subject": row["subject"],
	"scene": row["scene"],
	"quality": int(row["quality"]) if len(row["quality"]) != 0 else -100,
	"relevance": int(row["relevance"]) if len(row["relevance"]) != 0 else -100,
	"verified": row["verified"],
	"script": row["script"],
	"objects": row["objects"].split(";"),
	"descriptions": row["descriptions"].split(";"),
	"labels": labels,
	"action_timings": action_timings,
	"length": row["length"],
	}

	idx += 1