Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
File size: 6,604 Bytes
a4a96fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
# 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
|