Add DTD dataset (#5115)

* add DTD as prototype dataset

* add old style dataset

* add test for old dataset

* fix tests for windows

* add dataset to docs

* remove properties and use pathlib

* Apply suggestions from code review

Co-authored-by: Nicolas Hug <[email protected]>

* fold -> partition

Co-authored-by: Nicolas Hug <[email protected]>

Author: pmeier

docs/source/datasets.rst

@@ -38,6 +38,7 @@ You can also create your own datasets using the provided :ref:`base classes <bas
     Cityscapes
     CocoCaptions
     CocoDetection
+    DTD
     EMNIST
     FakeData
     FashionMNIST

test/test_datasets.py

@@ -2205,5 +2205,41 @@ def inject_fake_data(self, tmpdir: str, config):
         return len(sampled_classes * n_samples_per_class)
 
 
+class DTDTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.DTD
+    FEATURE_TYPES = (PIL.Image.Image, int)
+
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(
+        split=("train", "test", "val"),
+        # There is no need to test the whole matrix here, since each fold is treated exactly the same
+        partition=(1, 5, 10),
+    )
+
+    def inject_fake_data(self, tmpdir: str, config):
+        data_folder = pathlib.Path(tmpdir) / "dtd" / "dtd"
+
+        num_images_per_class = 3
+        image_folder = data_folder / "images"
+        image_files = []
+        for cls in ("banded", "marbled", "zigzagged"):
+            image_files.extend(
+                datasets_utils.create_image_folder(
+                    image_folder,
+                    cls,
+                    file_name_fn=lambda idx: f"{cls}_{idx:04d}.jpg",
+                    num_examples=num_images_per_class,
+                )
+            )
+
+        meta_folder = data_folder / "labels"
+        meta_folder.mkdir()
+        image_ids = [str(path.relative_to(path.parents[1])).replace(os.sep, "/") for path in image_files]
+        image_ids_in_config = random.choices(image_ids, k=len(image_files) // 2)
+        with open(meta_folder / f"{config['split']}{config['partition']}.txt", "w") as file:
+            file.write("\n".join(image_ids_in_config) + "\n")
+
+        return len(image_ids_in_config)
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

@@ -4,6 +4,7 @@
 from .cifar import CIFAR10, CIFAR100
 from .cityscapes import Cityscapes
 from .coco import CocoCaptions, CocoDetection
+from .dtd import DTD
 from .fakedata import FakeData
 from .flickr import Flickr8k, Flickr30k
 from .folder import ImageFolder, DatasetFolder
@@ -79,4 +80,5 @@
     "FlyingThings3D",
     "HD1K",
     "Food101",
+    "DTD",
 )

torchvision/datasets/dtd.py

@@ -0,0 +1,100 @@
+import os
+import pathlib
+from typing import Optional, Callable
+
+import PIL.Image
+
+from .utils import verify_str_arg, download_and_extract_archive
+from .vision import VisionDataset
+
+
+class DTD(VisionDataset):
+    """`Describable Textures Dataset (DTD) <https://www.robots.ox.ac.uk/~vgg/data/dtd/>`_.
+
+    Args:
+        root (string): Root directory of the dataset.
+        split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
+        partition (int, optional): The dataset partition. Should be ``1 <= partition <= 10``. Defaults to ``1``.
+
+            .. note::
+
+                The partition only changes which split each image belongs to. Thus, regardless of the selected
+                partition, combining all splits will result in all images.
+
+        download (bool, optional): If True, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+        transform (callable, optional): A function/transform that  takes in a PIL image and returns a transformed
+            version. E.g, ``transforms.RandomCrop``.
+        target_transform (callable, optional): A function/transform that takes in the target and transforms it.
+    """
+
+    _URL = "https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz"
+    _MD5 = "fff73e5086ae6bdbea199a49dfb8a4c1"
+
+    def __init__(
+        self,
+        root: str,
+        split: str = "train",
+        partition: int = 1,
+        download: bool = True,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+    ) -> None:
+        self._split = verify_str_arg(split, "split", ("train", "val", "test"))
+        if not isinstance(partition, int) and not (1 <= partition <= 10):
+            raise ValueError(
+                f"Parameter 'partition' should be an integer with `1 <= partition <= 10`, "
+                f"but got {partition} instead"
+            )
+        self._partition = partition
+
+        super().__init__(root, transform=transform, target_transform=target_transform)
+        self._base_folder = pathlib.Path(self.root) / type(self).__name__.lower()
+        self._data_folder = self._base_folder / "dtd"
+        self._meta_folder = self._data_folder / "labels"
+        self._images_folder = self._data_folder / "images"
+
+        if download:
+            self._download()
+
+        if not self._check_exists():
+            raise RuntimeError("Dataset not found. You can use download=True to download it")
+
+        self._image_files = []
+        classes = []
+        with open(self._meta_folder / f"{self._split}{self._partition}.txt") as file:
+            for line in file:
+                cls, name = line.strip().split("/")
+                self._image_files.append(self._images_folder.joinpath(cls, name))
+                classes.append(cls)
+
+        self.classes = sorted(set(classes))
+        self.class_to_idx = dict(zip(self.classes, range(len(self.classes))))
+        self._labels = [self.class_to_idx[cls] for cls in classes]
+
+    def __len__(self) -> int:
+        return len(self._image_files)
+
+    def __getitem__(self, idx):
+        image_file, label = self._image_files[idx], self._labels[idx]
+        image = PIL.Image.open(image_file).convert("RGB")
+
+        if self.transform:
+            image = self.transform(image)
+
+        if self.target_transform:
+            label = self.target_transform(label)
+
+        return image, label
+
+    def extra_repr(self) -> str:
+        return f"split={self._split}, partition={self._partition}"
+
+    def _check_exists(self) -> bool:
+        return os.path.exists(self._data_folder) and os.path.isdir(self._data_folder)
+
+    def _download(self) -> None:
+        if self._check_exists():
+            return
+        download_and_extract_archive(self._URL, download_root=str(self._base_folder), md5=self._MD5)

torchvision/prototype/datasets/_builtin/__init__.py

@@ -2,6 +2,7 @@
 from .celeba import CelebA
 from .cifar import Cifar10, Cifar100
 from .coco import Coco
+from .dtd import DTD
 from .imagenet import ImageNet
 from .mnist import MNIST, FashionMNIST, KMNIST, EMNIST, QMNIST
 from .sbd import SBD

torchvision/prototype/datasets/_builtin/dtd.categories

@@ -0,0 +1,47 @@
+banded
+blotchy
+braided
+bubbly
+bumpy
+chequered
+cobwebbed
+cracked
+crosshatched
+crystalline
+dotted
+fibrous
+flecked
+freckled
+frilly
+gauzy
+grid
+grooved
+honeycombed
+interlaced
+knitted
+lacelike
+lined
+marbled
+matted
+meshed
+paisley
+perforated
+pitted
+pleated
+polka-dotted
+porous
+potholed
+scaly
+smeared
+spiralled
+sprinkled
+stained
+stratified
+striped
+studded
+swirly
+veined
+waffled
+woven
+wrinkled
+zigzagged

torchvision/prototype/datasets/_builtin/dtd.py

@@ -0,0 +1,130 @@
+import io
+import pathlib
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import torch
+from torchdata.datapipes.iter import (
+    IterDataPipe,
+    Mapper,
+    Shuffler,
+    Filter,
+    IterKeyZipper,
+    Demultiplexer,
+    LineReader,
+    CSVParser,
+)
+from torchvision.prototype.datasets.utils import (
+    Dataset,
+    DatasetConfig,
+    DatasetInfo,
+    HttpResource,
+    OnlineResource,
+    DatasetType,
+)
+from torchvision.prototype.datasets.utils._internal import (
+    INFINITE_BUFFER_SIZE,
+    hint_sharding,
+    path_comparator,
+    getitem,
+)
+from torchvision.prototype.features import Label
+
+
+class DTD(Dataset):
+    def _make_info(self) -> DatasetInfo:
+        return DatasetInfo(
+            "dtd",
+            type=DatasetType.IMAGE,
+            homepage="https://www.robots.ox.ac.uk/~vgg/data/dtd/",
+            valid_options=dict(
+                split=("train", "test", "val"),
+                fold=tuple(str(fold) for fold in range(1, 11)),
+            ),
+        )
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        archive = HttpResource(
+            "https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz",
+            sha256="e42855a52a4950a3b59612834602aa253914755c95b0cff9ead6d07395f8e205",
+            decompress=True,
+        )
+        return [archive]
+
+    def _classify_archive(self, data: Tuple[str, Any]) -> Optional[int]:
+        path = pathlib.Path(data[0])
+        if path.parent.name == "labels":
+            if path.name == "labels_joint_anno.txt":
+                return 1
+
+            return 0
+        elif path.parents[1].name == "images":
+            return 2
+        else:
+            return None
+
+    def _image_key_fn(self, data: Tuple[str, Any]) -> str:
+        path = pathlib.Path(data[0])
+        return str(path.relative_to(path.parents[1]))
+
+    def _collate_and_decode_sample(
+        self,
+        data: Tuple[Tuple[str, List[str]], Tuple[str, io.IOBase]],
+        *,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> Dict[str, Any]:
+        (_, joint_categories_data), image_data = data
+        _, *joint_categories = joint_categories_data
+        path, buffer = image_data
+
+        category = pathlib.Path(path).parent.name
+
+        return dict(
+            joint_categories={category for category in joint_categories if category},
+            label=Label(self.info.categories.index(category), category=category),
+            path=path,
+            image=decoder(buffer) if decoder else buffer,
+        )
+
+    def _make_datapipe(
+        self,
+        resource_dps: List[IterDataPipe],
+        *,
+        config: DatasetConfig,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> IterDataPipe[Dict[str, Any]]:
+        archive_dp = resource_dps[0]
+
+        splits_dp, joint_categories_dp, images_dp = Demultiplexer(
+            archive_dp, 3, self._classify_archive, drop_none=True, buffer_size=INFINITE_BUFFER_SIZE
+        )
+
+        splits_dp = Filter(splits_dp, path_comparator("name", f"{config.split}{config.fold}.txt"))
+        splits_dp = LineReader(splits_dp, decode=True, return_path=False)
+        splits_dp = Shuffler(splits_dp, buffer_size=INFINITE_BUFFER_SIZE)
+        splits_dp = hint_sharding(splits_dp)
+
+        joint_categories_dp = CSVParser(joint_categories_dp, delimiter=" ")
+
+        dp = IterKeyZipper(
+            splits_dp,
+            joint_categories_dp,
+            key_fn=getitem(),
+            ref_key_fn=getitem(0),
+            buffer_size=INFINITE_BUFFER_SIZE,
+        )
+        dp = IterKeyZipper(
+            dp,
+            images_dp,
+            key_fn=getitem(0),
+            ref_key_fn=self._image_key_fn,
+            buffer_size=INFINITE_BUFFER_SIZE,
+        )
+        return Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+
+    def _filter_images(self, data: Tuple[str, Any]) -> bool:
+        return self._classify_archive(data) == 2
+
+    def _generate_categories(self, root: pathlib.Path) -> List[str]:
+        dp = self.resources(self.default_config)[0].load(pathlib.Path(root) / self.name)
+        dp = Filter(dp, self._filter_images)
+        return sorted({pathlib.Path(path).parent.name for path, _ in dp})