Datasets:
File size: 5,543 Bytes
93ed774 6da7ec8 93ed774 d0e183d 93ed774 |
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 |
"""An ambiguous mnist data set"""
import csv
import datasets
import numpy as np
from datasets.tasks import ImageClassification
_CITATION = """\
@misc{https://doi.org/10.48550/arxiv.2207.10495,
doi = {10.48550/ARXIV.2207.10495},
url = {https://arxiv.org/abs/2207.10495},
author = {Weiss, Michael and Gómez, André García and Tonella, Paolo},
title = {A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity},
publisher = {arXiv},
year = {2022}
}
"""
_DESCRIPTION = """\
The images were created such that they have an unclear ground truth,
i.e., such that they are similar to multiple - but not all - of the datasets classes.
Robust and uncertainty-aware models should be able to detect and flag these ambiguous images.
As such, the dataset should be merged / mixed with the original dataset and we
provide such 'mixed' splits for convenience. Please refer to the dataset card for details.
"""
_HOMEPAGE = "https://github.com/testingautomated-usi/ambiguous-datasets"
_LICENSE = "https://raw.githubusercontent.com/testingautomated-usi/ambiguous-datasets/main/LICENSE"
_VERSION = "0.1.0"
_URL = f"https://github.com/testingautomated-usi/ambiguous-datasets/releases/download/v{_VERSION}/"
_URLS = {
"train": "mnist-test.csv",
"test": "mnist-test.csv",
}
_NAMES = list(range(10))
class MnistAmbiguous(datasets.GeneratorBasedBuilder):
"""An ambiguous mnist data set"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="mnist_ambiguous",
version=datasets.Version(_VERSION),
description=_DESCRIPTION,
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.features.ClassLabel(names=_NAMES),
"text_label": datasets.Value("string"),
"p_label": datasets.Sequence(datasets.Value("float32"), length=10),
"is_ambiguous": datasets.Value("bool"),
}
),
supervised_keys=("image", "label"),
homepage=_HOMEPAGE,
citation=_CITATION,
task_templates=[ImageClassification(image_column="image", label_column="label")],
)
def _split_generators(self, dl_manager):
urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
downloaded_files = dl_manager.download(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "test",
},
),
datasets.SplitGenerator(
name="train_mixed",
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train_mixed",
},
),
datasets.SplitGenerator(
name="test_mixed",
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "test_mixed",
},
),
]
def _generate_examples(self, filepath, split):
"""This function returns the examples in the raw form."""
def _gen_amb_images():
with open(filepath) as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='"')
for i, row in enumerate(spamreader):
if i == 0:
continue
det_label = int(row[7])
class_1, class_2 = int(row[3]), int(row[4])
p_1, p_2 = float(row[5]), float(row[6])
text_label = f"p({_NAMES[class_1]})={p_1:.2f}, p({_NAMES[class_2]})={p_2:.2f}"
p_label = [0.0] * 10
p_label[class_1] = p_1
p_label[class_2] = p_2
image = np.array(row[9:], dtype=np.uint8).reshape(28, 28)
yield i, {"image": image, "label": det_label,
"text_label": text_label, "p_label": p_label, "is_ambiguous": True}
if split == "test" or split == "train":
yield from _gen_amb_images()
elif split == "test_mixed" or split == "train_mixed":
nominal_samples = []
nom_split = "test" if split == "test_mixed" else "train"
nominal_dataset = datasets.load_dataset("mnist", split=nom_split)
for x in nominal_dataset:
nominal_samples.append({
"image": np.array(x["image"]),
"label": x["label"],
"text_label": f"p({_NAMES[x['label']]})=1",
"p_label": [1.0 if i == x["label"] else 0.0 for i in range(10)],
"is_ambiguous": False
})
ambiguous_samples = list([x for i, x in _gen_amb_images()])
all_samples = nominal_samples + ambiguous_samples
np.random.RandomState(42).shuffle(all_samples)
for i, x in enumerate(all_samples):
yield i, x
|