Update fashion_mnist_corrupted.py

fashion_mnist_corrupted.py

@@ -24,25 +24,29 @@ This dataset (Fashion-Mnist-Corrupted) provides out-of-distribution data for the
 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, )
+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"
+    tag = "v1.0.0"
 else:
     raise ValueError("Unsupported version.")
 
-_URL = f"https://github.com/testingautomated-usi/fashion-mnist-c/blob/{_CURRENT_VERSION_TAG}/generated/ubyte/"
+_URL = (
+    f"https://github.com/testingautomated-usi/fashion-mnist-c/blob/{tag}/generated/npy/"
+)
 
 _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",
+    "train_images": "fmnist-c-train.npy",
+    "train_labels": "fmnist-c-train-labels.npy",
+    "test_images": "fmnist-c-test.npy",
+    "test_labels": "fmnist-c-test-labels.npy",
 }
 
 _NAMES = [
@@ -62,9 +66,7 @@ _NAMES = [
 class FashionMnistCorrupted(datasets.GeneratorBasedBuilder):
     """FashionMNIST-Corrupted Data Set"""
 
-    BUILDER_CONFIGS = [
-        CONFIG
-    ]
+    BUILDER_CONFIGS = [CONFIG]
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -78,25 +80,35 @@ class FashionMnistCorrupted(datasets.GeneratorBasedBuilder):
             supervised_keys=("image", "label"),
             homepage=_HOMEPAGE,
             citation=_CITATION,
-            task_templates=[ImageClassification(image_column="image", label_column="label")],
+            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()}
+        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"]],
+                    "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"]],
+                    "filepath": [
+                        downloaded_files["test_images"],
+                        downloaded_files["test_labels"],
+                    ],
                     "split": "test",
                 },
             ),
@@ -105,23 +117,14 @@ class FashionMnistCorrupted(datasets.GeneratorBasedBuilder):
     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])}
+        images = np.load(filepath[0])
+        labels = np.load(filepath[1])
+
+        if images.shape[0] != labels.shape[0]:
+            raise ValueError(
+                f"Number of images {images.shape[0]} and labels {labels.shape[0]} do not match."
+            )
 
+        for idx in range(images.shape[0]):
+            yield idx, {"image": images[idx], "label": int(labels[idx])}
 
-# For local development / debugger support only
-if __name__ == '__main__':
-    FashionMnistCorrupted().download_and_prepare()