fashion_mnist_ambiguous / fashion_mnist_ambiguous.py
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Create fashion_mnist_ambiguous.py
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"""An ambiguous fashion 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": "fmnist-test.csv",
"test": "fmnist-test.csv",
}
_NAMES = [
"T - shirt / top",
"Trouser",
"Pullover",
"Dress",
"Coat",
"Sandal",
"Shirt",
"Sneaker",
"Bag",
"Ankle boot",
]
class FashionMnistAmbiguous(datasets.GeneratorBasedBuilder):
"""An ambiguous fashion mnist data set"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="fashion_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("fashion_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