CLIP_benchmark/clip_benchmark/datasets/caltech101.py

"""
Code adapted from https://github.com/pytorch/vision/blob/main/torchvision/datasets/caltech.py
Modification of caltech101 from torchvision where the background class is not removed
Thanks to the authors of torchvision
"""
from glob import glob
import os
import os.path
from typing import Any, Callable, List, Optional, Union, Tuple

from PIL import Image

from torchvision.datasets.utils import download_and_extract_archive, verify_str_arg
from torchvision.datasets.vision import VisionDataset


class Caltech101(VisionDataset):
    """`Caltech 101 <http://www.vision.caltech.edu/Image_Datasets/Caltech101/>`_ Dataset.

    .. warning::

        This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.

    Args:
        root (string): Root directory of dataset where directory
            ``caltech101`` exists or will be saved to if download is set to True.
        target_type (string or list, optional): Type of target to use, ``category`` or
            ``annotation``. Can also be a list to output a tuple with all specified
            target types.  ``category`` represents the target class, and
            ``annotation`` is a list of points from a hand-generated outline.
            Defaults to ``category``.
        transform (callable, optional): A function/transform that takes in an 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.
        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.
    """

    def __init__(
        self,
        root: str,
        target_type: Union[List[str], str] = "category",
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
        download: bool = False,
    ) -> None:
        super().__init__(os.path.join(root, "caltech101"), transform=transform, target_transform=target_transform)
        os.makedirs(self.root, exist_ok=True)
        if isinstance(target_type, str):
            target_type = [target_type]
        self.target_type = [verify_str_arg(t, "target_type", ("category", "annotation")) for t in target_type]

        if download:
            self.download()

        if not self._check_integrity():
            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")

        self.categories = sorted(os.listdir(os.path.join(self.root, "101_ObjectCategories")))
        #self.categories.remove("BACKGROUND_Google")  # this is not a real class

        # For some reason, the category names in "101_ObjectCategories" and
        # "Annotations" do not always match. This is a manual map between the
        # two. Defaults to using same name, since most names are fine.
        name_map = {
            "Faces": "Faces_2",
            "Faces_easy": "Faces_3",
            "Motorbikes": "Motorbikes_16",
            "airplanes": "Airplanes_Side_2",
        }
        self.annotation_categories = list(map(lambda x: name_map[x] if x in name_map else x, self.categories))

        self.index: List[int] = []
        self.y = []
        for (i, c) in enumerate(self.categories):
            n = len(glob(os.path.join(self.root, "101_ObjectCategories", c, "*.jpg")))
            self.index.extend(range(1, n + 1))
            self.y.extend(n * [i])

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        """
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where the type of target specified by target_type.
        """
        import scipy.io

        img = Image.open(
            os.path.join(
                self.root,
                "101_ObjectCategories",
                self.categories[self.y[index]],
                f"image_{self.index[index]:04d}.jpg",
            )
        )

        target: Any = []
        for t in self.target_type:
            if t == "category":
                target.append(self.y[index])
            elif t == "annotation":
                data = scipy.io.loadmat(
                    os.path.join(
                        self.root,
                        "Annotations",
                        self.annotation_categories[self.y[index]],
                        f"annotation_{self.index[index]:04d}.mat",
                    )
                )
                target.append(data["obj_contour"])
        target = tuple(target) if len(target) > 1 else target[0]

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

    def _check_integrity(self) -> bool:
        # can be more robust and check hash of files
        return os.path.exists(os.path.join(self.root, "101_ObjectCategories"))

    def __len__(self) -> int:
        return len(self.index)

    def download(self) -> None:
        if self._check_integrity():
            print("Files already downloaded and verified")
            return

        download_and_extract_archive(
            "https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp",
            self.root,
            filename="101_ObjectCategories.tar.gz",
            md5="b224c7392d521a49829488ab0f1120d9",
        )
        download_and_extract_archive(
            "https://drive.google.com/file/d/175kQy3UsZ0wUEHZjqkUDdNVssr7bgh_m",
            self.root,
            filename="Annotations.tar",
            md5="6f83eeb1f24d99cab4eb377263132c91",
        )

    def extra_repr(self) -> str:
        return "Target type: {target_type}".format(**self.__dict__)


class Caltech256(VisionDataset):
    """`Caltech 256 <http://www.vision.caltech.edu/Image_Datasets/Caltech256/>`_ Dataset.

    Args:
        root (string): Root directory of dataset where directory
            ``caltech256`` exists or will be saved to if download is set to True.
        transform (callable, optional): A function/transform that takes in an 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.
        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.
    """

    def __init__(
        self,
        root: str,
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
        download: bool = False,
    ) -> None:
        super().__init__(os.path.join(root, "caltech256"), transform=transform, target_transform=target_transform)
        os.makedirs(self.root, exist_ok=True)

        if download:
            self.download()

        if not self._check_integrity():
            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")

        self.categories = sorted(os.listdir(os.path.join(self.root, "256_ObjectCategories")))
        self.index: List[int] = []
        self.y = []
        for (i, c) in enumerate(self.categories):
            n = len(
                [
                    item
                    for item in os.listdir(os.path.join(self.root, "256_ObjectCategories", c))
                    if item.endswith(".jpg")
                ]
            )
            self.index.extend(range(1, n + 1))
            self.y.extend(n * [i])

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        """
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        img = Image.open(
            os.path.join(
                self.root,
                "256_ObjectCategories",
                self.categories[self.y[index]],
                f"{self.y[index] + 1:03d}_{self.index[index]:04d}.jpg",
            )
        )

        target = self.y[index]

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

    def _check_integrity(self) -> bool:
        # can be more robust and check hash of files
        return os.path.exists(os.path.join(self.root, "256_ObjectCategories"))

    def __len__(self) -> int:
        return len(self.index)

    def download(self) -> None:
        if self._check_integrity():
            print("Files already downloaded and verified")
            return

        download_and_extract_archive(
            "https://drive.google.com/file/d/1r6o0pSROcV1_VwT4oSjA2FBUSCWGuxLK",
            self.root,
            filename="256_ObjectCategories.tar",
            md5="67b4f42ca05d46448c6bb8ecd2220f6d",
        )