Upload fashion_mnist_corrupted.py

fashion_mnist_corrupted.py

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+"""Corrupted Fashion-Mnist Data Set.
+
+This module contains the huggingface dataset adaptation of
+the Corrupted Fashion-Mnist Data Set.
+Find the full code at `https://github.com/testingautomated-usi/fashion-mnist-c`."""
+import struct
+
+import datasets
+import numpy as np
+from datasets.tasks import ImageClassification
+
+_CITATION = """\
+@inproceedings{Weiss2022SimpleTechniques,
+  title={Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning},
+  author={Weiss, Michael and Tonella, Paolo},
+  booktitle={Proceedings of the 31th ACM SIGSOFT International Symposium on Software Testing and Analysis},
+  year={2022}
+}
+"""
+
+_DESCRIPTION = """\
+Fashion-MNIST is dataset of fashion images, indended as a drop-in replacement for the MNIST dataset.
+This dataset (Fashion-Mnist-Corrupted) provides out-of-distribution data for the Fashion-Mnist
+dataset. Fashion-Mnist-Corrupted is based on a similar project for MNIST, called MNIST-C, by Mu et. al.
+"""
+
+CONFIG = datasets.BuilderConfig(name="fashion_mnist_corrupted",
+                                version=datasets.Version("1.0.0"),
+                                description=_DESCRIPTION, )
+
+_HOMEPAGE = "https://github.com/testingautomated-usi/fashion-mnist-c"
+_LICENSE = "https://github.com/testingautomated-usi/fashion-mnist-c/blob/main/LICENSE"
+
+if CONFIG.version == datasets.Version("1.0.0"):
+    _CURRENT_VERSION_TAG = "e31d36a102cdd8c5e2690533eb2aaec7c296fcb6"
+else:
+    raise ValueError("Unsupported version.")
+
+_URL = f"https://github.com/testingautomated-usi/fashion-mnist-c/blob/{_CURRENT_VERSION_TAG}/generated/ubyte/"
+
+_URLS = {
+    "train_images": "fmnist-c-train-ubyte.gz",
+    "train_labels": "fmnist-c-train-labels-ubyte.gz",
+    "test_images": "fmnist-c-test-ubyte.gz",
+    "test_labels": "fmnist-c-test-labels-ubyte.gz",
+}
+
+_NAMES = [
+    "T - shirt / top",
+    "Trouser",
+    "Pullover",
+    "Dress",
+    "Coat",
+    "Sandal",
+    "Shirt",
+    "Sneaker",
+    "Bag",
+    "Ankle boot",
+]
+
+
+class FashionMnistCorrupted(datasets.GeneratorBasedBuilder):
+    """FashionMNIST-Corrupted Data Set"""
+
+    BUILDER_CONFIGS = [
+        CONFIG
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            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 + "?raw=true" for key, fname in _URLS.items()}
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": [downloaded_files["train_images"], downloaded_files["train_labels"]],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": [downloaded_files["test_images"], downloaded_files["test_labels"]],
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples in the raw form."""
+        # Images
+        with open(filepath[0], "rb") as f:
+            # First 16 bytes contain some metadata
+            _ = f.read(4)
+            size = struct.unpack(">I", f.read(4))[0]
+            _ = f.read(8)
+            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
+
+        # Labels
+        with open(filepath[1], "rb") as f:
+            # First 8 bytes contain some metadata
+            _ = f.read(8)
+            labels = np.frombuffer(f.read(), dtype=np.uint8)
+
+        for idx in range(size):
+            yield idx, {"image": images[idx], "label": int(labels[idx])}
+
+
+# For local development / debugger support only
+if __name__ == '__main__':
+    FashionMnistCorrupted().download_and_prepare()