Delete loading script

kmnist.py

@@ -1,122 +0,0 @@
-import struct
-
-import numpy as np
-
-import datasets
-from datasets.tasks import ImageClassification
-
-_CITATION = R"""
-@article{DBLP:journals/corr/abs-1812-01718,
-  author       = {Tarin Clanuwat and
-                  Mikel Bober{-}Irizar and
-                  Asanobu Kitamoto and
-                  Alex Lamb and
-                  Kazuaki Yamamoto and
-                  David Ha},
-  title        = {Deep Learning for Classical Japanese Literature},
-  journal      = {CoRR},
-  volume       = {abs/1812.01718},
-  year         = {2018},
-  url          = {http://arxiv.org/abs/1812.01718},
-  eprinttype    = {arXiv},
-  eprint       = {1812.01718},
-  timestamp    = {Thu, 14 Oct 2021 09:15:14 +0200},
-  biburl       = {https://dblp.org/rec/journals/corr/abs-1812-01718.bib},
-  bibsource    = {dblp computer science bibliography, https://dblp.org}
-}
-"""
-
-_URL = "./raw/"
-_URLS = {
-    "train_images": "train-images-idx3-ubyte.gz",
-    "train_labels": "train-labels-idx1-ubyte.gz",
-    "test_images": "t10k-images-idx3-ubyte.gz",
-    "test_labels": "t10k-labels-idx1-ubyte.gz",
-}
-
-
-class KMNIST(datasets.GeneratorBasedBuilder):
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="kmnist",
-            version=datasets.Version("1.0.0"),
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.features.ClassLabel(
-                        names=[
-                            "お",
-                            "き",
-                            "す",
-                            "つ",
-                            "な",
-                            "は",
-                            "ま",
-                            "や",
-                            "れ",
-                            "を",
-                        ]
-                    ),
-                }
-            ),
-            supervised_keys=("image", "label"),
-            homepage="https://github.com/rois-codh/kmnist",
-            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_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": str(labels[idx])}