Create fashion_mnist_ambiguous.py

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