:sparkles: add builder

cats_and_dogs.py

@@ -0,0 +1,59 @@
+import pickle
+from pathlib import Path
+from typing import List
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_HOMEPAGE = "https://www.microsoft.com/en-us/download/details.aspx?id=54765"
+_URL = "https://huggingface.co/datasets/nateraw/cats-and-dogs/resolve/main/"
+_URLS = {
+    "train": _URL + "train.pt",
+}
+_DESCRIPTION = "A large set of images of cats and dogs. There are 1738 corrupted images that are dropped."
+_CITATION = """\
+@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
+    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
+    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
+    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
+    year = {2007},
+    month = {October},
+    publisher = {Association for Computing Machinery, Inc.},
+    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},
+    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
+}
+"""
+
+
+class CatsAndDogs(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "img_bytes": datasets.Value("binary"),
+                    "labels": datasets.features.ClassLabel(names=["cat", "dog"]),
+                }
+            ),
+            supervised_keys=("img_bytes", "labels"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+
+        with Path(filepath).open("rb") as f:
+            examples = pickle.load(f)
+
+        for i, ex in enumerate(examples):
+            yield str(i), ex