USPS dataset

test/builtin_dataset_mocks.py

@@ -1,3 +1,4 @@
+import bz2
 import collections.abc
 import csv
 import functools
@@ -1431,3 +1432,21 @@ def stanford_cars(info, root, config):
         make_tar(root, "car_devkit.tgz", devkit, compression="gz")
 
     return num_samples
+
+
+@register_mock
+def usps(info, root, config):
+    num_samples = {"train": 15, "test": 7}[config.split]
+
+    with bz2.open(root / f"usps{'.t' if not config.split == 'train' else ''}.bz2", "wb") as fh:
+        lines = []
+        for _ in range(num_samples):
+            label = make_tensor(1, low=1, high=11, dtype=torch.int)
+            values = make_tensor(256, low=-1, high=1, dtype=torch.float)
+            lines.append(
+                " ".join([f"{int(label)}", *(f"{idx}:{float(value):.6f}" for idx, value in enumerate(values, 1))])
+            )
+
+        fh.write("\n".join(lines).encode())
+
+    return num_samples

test/test_prototype_builtin_datasets.py

@@ -12,7 +12,7 @@
 from torchdata.datapipes.iter import IterDataPipe, Shuffler
 from torchvision._utils import sequence_to_str
 from torchvision.prototype import transforms, datasets
-
+from torchvision.prototype.features import Image, Label
 
 assert_samples_equal = functools.partial(
     assert_equal, pair_types=(TensorLikePair, ObjectPair), rtol=0, atol=0, equal_nan=True
@@ -180,3 +180,20 @@ def test_label_matches_path(self, test_home, dataset_mock, config):
         for sample in dataset:
             label_from_path = int(Path(sample["path"]).parent.name)
             assert sample["label"] == label_from_path
+
+
+@parametrize_dataset_mocks(DATASET_MOCKS["usps"])
+class TestUSPS:
+    def test_sample_content(self, test_home, dataset_mock, config):
+        dataset_mock.prepare(test_home, config)
+
+        dataset = datasets.load(dataset_mock.name, **config)
+
+        for sample in dataset:
+            assert "image" in sample
+            assert "label" in sample
+
+            assert isinstance(sample["image"], Image)
+            assert isinstance(sample["label"], Label)
+
+            assert sample["image"].shape == (1, 16, 16)

torchvision/prototype/datasets/_builtin/__init__.py

@@ -17,4 +17,5 @@
 from .semeion import SEMEION
 from .stanford_cars import StanfordCars
 from .svhn import SVHN
+from .usps import USPS
 from .voc import VOC

torchvision/prototype/datasets/_builtin/usps.py

@@ -0,0 +1,54 @@
+from typing import Any, Dict, List
+
+import torch
+from torchdata.datapipes.iter import IterDataPipe, LineReader, Mapper, Decompressor
+from torchvision.prototype.datasets.utils import Dataset, DatasetInfo, DatasetConfig, OnlineResource, HttpResource
+from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
+from torchvision.prototype.features import Image, Label
+
+
+class USPS(Dataset):
+    def _make_info(self) -> DatasetInfo:
+        return DatasetInfo(
+            "usps",
+            homepage="https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps",
+            valid_options=dict(
+                split=("train", "test"),
+            ),
+            categories=10,
+        )
+
+    _URL = "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass"
+
+    _RESOURCES = {
+        "train": HttpResource(
+            f"{_URL}/usps.bz2", sha256="3771e9dd6ba685185f89867b6e249233dd74652389f263963b3b741e994b034f"
+        ),
+        "test": HttpResource(
+            f"{_URL}/usps.t.bz2", sha256="a9c0164e797d60142a50604917f0baa604f326e9a689698763793fa5d12ffc4e"
+        ),
+    }
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        return [USPS._RESOURCES[config.split]]
+
+    def _prepare_sample(self, line: str) -> Dict[str, Any]:
+        label, *values = line.strip().split(" ")
+        values = [float(value.split(":")[1]) for value in values]
+        pixels = torch.tensor(values).add_(1).div_(2)
+        return dict(
+            image=Image(pixels.reshape(16, 16)),
+            label=Label(int(label) - 1, categories=self.categories),
+        )
+
+    def _make_datapipe(
+        self,
+        resource_dps: List[IterDataPipe],
+        *,
+        config: DatasetConfig,
+    ) -> IterDataPipe[Dict[str, Any]]:
+        dp = Decompressor(resource_dps[0])
+        dp = LineReader(dp, decode=True, return_path=False)
+        dp = hint_sharding(dp)
+        dp = hint_shuffling(dp)
+        return Mapper(dp, self._prepare_sample)