add Country211 prototype dataset

test/builtin_dataset_mocks.py

@@ -878,6 +878,34 @@ def celeba(info, root, config):
     return CelebAMockData.generate(root)[config.split]
 
 
+@register_mock
+def country211(info, root, config):
+    split_name_mapper = {
+        "train": "train",
+        "val": "valid",
+        "test": "test",
+    }
+    split_folder = pathlib.Path(root, "country211", split_name_mapper[config["split"]])
+    split_folder.mkdir(parents=True, exist_ok=True)
+
+    num_examples = {
+        "train": 3,
+        "val": 4,
+        "test": 5,
+    }[config["split"]]
+
+    classes = ("AD", "BS", "GR")
+    for cls in classes:
+        create_image_folder(
+            split_folder,
+            name=cls,
+            file_name_fn=lambda idx: f"{idx}.jpg",
+            num_examples=num_examples,
+        )
+    make_tar(root, f"{split_folder.parent.name}.tgz", split_folder.parent, compression="gz")
+    return num_examples * len(classes)
+
+
 @register_mock
 def dtd(info, root, config):
     data_folder = root / "dtd"

torchvision/prototype/datasets/_builtin/__init__.py

@@ -3,6 +3,7 @@
 from .cifar import Cifar10, Cifar100
 from .clevr import CLEVR
 from .coco import Coco
+from .country211 import Country211
 from .cub200 import CUB200
 from .dtd import DTD
 from .fer2013 import FER2013

torchvision/prototype/datasets/_builtin/country211.categories

@@ -0,0 +1,211 @@
+AD
+AE
+AF
+AG
+AI
+AL
+AM
+AO
+AQ
+AR
+AT
+AU
+AW
+AX
+AZ
+BA
+BB
+BD
+BE
+BF
+BG
+BH
+BJ
+BM
+BN
+BO
+BQ
+BR
+BS
+BT
+BW
+BY
+BZ
+CA
+CD
+CF
+CH
+CI
+CK
+CL
+CM
+CN
+CO
+CR
+CU
+CV
+CW
+CY
+CZ
+DE
+DK
+DM
+DO
+DZ
+EC
+EE
+EG
+ES
+ET
+FI
+FJ
+FK
+FO
+FR
+GA
+GB
+GD
+GE
+GF
+GG
+GH
+GI
+GL
+GM
+GP
+GR
+GS
+GT
+GU
+GY
+HK
+HN
+HR
+HT
+HU
+ID
+IE
+IL
+IM
+IN
+IQ
+IR
+IS
+IT
+JE
+JM
+JO
+JP
+KE
+KG
+KH
+KN
+KP
+KR
+KW
+KY
+KZ
+LA
+LB
+LC
+LI
+LK
+LR
+LT
+LU
+LV
+LY
+MA
+MC
+MD
+ME
+MF
+MG
+MK
+ML
+MM
+MN
+MO
+MQ
+MR
+MT
+MU
+MV
+MW
+MX
+MY
+MZ
+NA
+NC
+NG
+NI
+NL
+NO
+NP
+NZ
+OM
+PA
+PE
+PF
+PG
+PH
+PK
+PL
+PR
+PS
+PT
+PW
+PY
+QA
+RE
+RO
+RS
+RU
+RW
+SA
+SB
+SC
+SD
+SE
+SG
+SH
+SI
+SJ
+SK
+SL
+SM
+SN
+SO
+SS
+SV
+SX
+SY
+SZ
+TG
+TH
+TJ
+TL
+TM
+TN
+TO
+TR
+TT
+TW
+TZ
+UA
+UG
+US
+UY
+UZ
+VA
+VE
+VG
+VI
+VN
+VU
+WS
+XK
+YE
+ZA
+ZM
+ZW

torchvision/prototype/datasets/_builtin/country211.py

@@ -0,0 +1,56 @@
+import pathlib
+from typing import Any, Dict, List, Tuple
+
+from torchdata.datapipes.iter import IterDataPipe, Mapper, Filter
+from torchvision.prototype.datasets.utils import Dataset, DatasetConfig, DatasetInfo, HttpResource, OnlineResource
+from torchvision.prototype.datasets.utils._internal import path_comparator, hint_sharding, hint_shuffling
+from torchvision.prototype.features import EncodedImage, Label
+
+
+class Country211(Dataset):
+    def _make_info(self) -> DatasetInfo:
+        return DatasetInfo(
+            "country211",
+            homepage="https://github.com/openai/CLIP/blob/main/data/country211.md",
+            valid_options=dict(split=("train", "val", "test")),
+        )
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        return [
+            HttpResource(
+                "https://openaipublic.azureedge.net/clip/data/country211.tgz",
+                sha256="c011343cdc1296a8c31ff1d7129cf0b5e5b8605462cffd24f89266d6e6f4da3c",
+            )
+        ]
+
+    _SPLIT_NAME_MAPPER = {
+        "train": "train",
+        "val": "valid",
+        "test": "test",
+    }
+
+    def _prepare_sample(self, data: Tuple[str, Any]) -> Dict[str, Any]:
+        path, buffer = data
+        category = pathlib.Path(path).parent.name
+        return dict(
+            label=Label.from_category(category, categories=self.categories),
+            path=path,
+            image=EncodedImage.from_file(buffer),
+        )
+
+    def _filter_split(self, data: Tuple[str, Any], *, split: str) -> bool:
+        return pathlib.Path(data[0]).parent.parent.name == split
+
+    def _make_datapipe(
+        self, resource_dps: List[IterDataPipe], *, config: DatasetConfig
+    ) -> IterDataPipe[Dict[str, Any]]:
+        dp = resource_dps[0]
+        dp = Filter(dp, path_comparator("parent.parent.name", self._SPLIT_NAME_MAPPER[config.split]))
+        dp = hint_sharding(dp)
+        dp = hint_shuffling(dp)
+        return Mapper(dp, self._prepare_sample)
+
+    def _generate_categories(self, root: pathlib.Path) -> List[str]:
+        resources = self.resources(self.default_config)
+        dp = resources[0].load(root)
+        return sorted({pathlib.Path(path).parent.name for path, _ in dp})