dtd.py

import datasets
from datasets.data_files import DataFilesDict
from datasets.packaged_modules.imagefolder.imagefolder import ImageFolder, ImageFolderConfig

logger = datasets.logging.get_logger(__name__)


class GTSRB(ImageFolder):
    R"""
    DTD dataset for image classification.
    """

    BUILDER_CONFIG_CLASS = ImageFolderConfig
    BUILDER_CONFIGS = [
        ImageFolderConfig(
            name="default",
            features=("images", "labels"),
            data_files=DataFilesDict({split: f"data/{split}.zip" for split in ["train", "test"]}),
        )
    ]

    classnames = [
        "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",
    ]

    clip_templates = [
        lambda c: f"a photo of a {c} texture.",
        lambda c: f"a photo of a {c} pattern.",
        lambda c: f"a photo of a {c} thing.",
        lambda c: f"a photo of a {c} object.",
        lambda c: f"a photo of the {c} texture.",
        lambda c: f"a photo of the {c} pattern.",
        lambda c: f"a photo of the {c} thing.",
        lambda c: f"a photo of the {c} object.",
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description="DTD dataset for image classification.",
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(names=self.classnames),
                }
            ),
            supervised_keys=("image", "label"),
            task_templates=[datasets.ImageClassification(image_column="image", label_column="label")],
        )