Datasets:
File size: 7,733 Bytes
7debf8d d35d646 7debf8d d35d646 7debf8d d35d646 7debf8d d35d646 7debf8d d35d646 7debf8d d35d646 7debf8d 9a9445b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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 ELEVATER benchmark"""
import json
import os
import datasets
from zipfile import ZipFile
from io import BytesIO
from PIL import Image
_VERSION = "1.0.0"
_BASE_URL = "https://cvinthewildeus.blob.core.windows.net/datasets/"
_ELEVATER_CITATION = """\
@article{li2022elevater,
title={ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models},
author={Li, Chunyuan and Liu, Haotian and Li, Liunian Harold and Zhang, Pengchuan and Aneja, Jyoti and Yang, Jianwei and Jin, Ping and Lee, Yong Jae and Hu, Houdong and Liu, Zicheng and Gao, Jianfeng},
journal={Neural Information Processing Systems},
year={2022}
}
Note that each ELEVATER dataset has its own citation. Please see the source to
get the correct citation for each contained dataset.
"""
_VOC_2007_CLASSIFICATION_CITATION="""\
@misc{pascal-voc-2007,
author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2007 {(VOC2007)} {R}esults",
howpublished = "http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html"
}"""
_CIFAR_10_CITATION="""\
@article{krizhevsky2009learning,
title={Learning multiple layers of features from tiny images},
author={Krizhevsky, Alex and Hinton, Geoffrey and others},
year={2009},
publisher={Toronto, ON, Canada}
}"""
class ELEVATERConfig(datasets.BuilderConfig):
"""BuilderConfig for ELEVATER."""
def __init__(self, name, description, contact, version, type_, root_folder, labelmap, num_classes, train, test, citation, url, **kwargs):
"""BuilderConfig for ELEVATER.
Args:
features: `list[string]`, list of the features that will appear in the
feature dict. Should not include "label".
data_url: `string`, url to download the zip file from.
citation: `string`, citation for the data set.
url: `string`, url for information about the data set.
label_classes: `list[string]`, the list of classes for the label if the
label is present as a string. Non-string labels will be cast to either
'False' or 'True'.
**kwargs: keyword arguments forwarded to super.
"""
super(ELEVATERConfig, self).__init__(**kwargs)
self.name = name
self.description = description
self.contact = contact
self.version = version
self.type = type_
self.root_folder = root_folder
self.labelmap = labelmap
self.num_classes = num_classes
self.train = train
self.test = test
self.citation = citation
self.url = url
class ELEVATER(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ELEVATERConfig(
name="cifar-10",
description="The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.",
contact="pinjin",
version=_VERSION,
type_="classification_multiclass",
root_folder="classification/cifar_10_20211007",
labelmap="labels.txt",
num_classes=10,
train={
"index_path": "train.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 50000
},
test={
"index_path": "test.txt",
"files_for_local_usage": ["val.zip"],
"num_images": 10000
},
citation=_CIFAR_10_CITATION,
url=_BASE_URL,
),
ELEVATERConfig(
"name"="voc-2007-classification",
"description"="Voc2007 classification dataset.",
"contact"="pinjin",
"version"=_VERSION,
"type"="classification_multilabel",
"root_folder"="classification/voc2007_20211007",
"train": {
"index_path": "train_ic.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 2501
},
"val": {
"index_path": "val_ic.txt",
"files_for_local_usage": ["train.zip"],
"num_images": 2510
},
"test": {
"index_path": "test_ic.txt",
"files_for_local_usage": ["test.zip"],
"num_images": 4952
},
"labelmap": "labels.txt",
"num_classes": 20,
citation=_VOC_2007_CLASSIFICATION_CITATION,
url=_BASE_URL,
)
]
def _info(self):
features = datasets.Features(
{
"image_file_path": datasets.Value("string"),
"image": datasets.Image(),
"labels": datasets.Value("int32")
}
)
return datasets.DatasetInfo(
description=self.config.description,
features=features,
citation=self.config.citation + '\n' + _ELEVATER_CITATION,
)
def _split_generators(self, dl_manager):
_URL = self.config.url + self.config.root_folder
print(self.config.val)
print(good)
urls_to_download = {
"labelmap": os.path.join(_URL, self.config.labelmap),
"train": {
"images": os.path.join(_URL, self.config.train['files_for_local_usage'][0]),
"index": os.path.join(_URL, self.config.train['index_path']),
},
"test": {
"images": os.path.join(_URL, self.config.test['files_for_local_usage'][0]),
"index": os.path.join(_URL, self.config.test['index_path']),
}
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": downloaded_files["train"]["images"],
"index": downloaded_files["train"]["index"],
"split": datasets.Split.TRAIN,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images": downloaded_files["test"]["images"],
"index": downloaded_files["test"]["index"],
"split": datasets.Split.TEST,
},
)
]
def _generate_examples(self, images, index, split):
image_path_label_list = []
with open(index, "r") as f:
lines = f.readlines()
for i, line in enumerate(lines):
line_split = line[:-1].split(" ")
label = int(line_split[1])
image_path = line_split[0].split('@')[1]
path = images + '/' + image_path
yield i, {
"image_file_path": path,
"image": path,
"labels": label,
} |