Source code for torchvision.datasets.lsun

import io
import os.path
import pickle
import string
from collections.abc import Iterable
from pathlib import Path
from typing import Any, Callable, cast, List, Optional, Tuple, Union

from PIL import Image

from .utils import iterable_to_str, verify_str_arg
from .vision import VisionDataset


class LSUNClass(VisionDataset):
    def __init__(
        self, root: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None
    ) -> None:
        import lmdb

        super().__init__(root, transform=transform, target_transform=target_transform)

        self.env = lmdb.open(root, max_readers=1, readonly=True, lock=False, readahead=False, meminit=False)
        with self.env.begin(write=False) as txn:
            self.length = txn.stat()["entries"]
        cache_file = "_cache_" + "".join(c for c in root if c in string.ascii_letters)
        if os.path.isfile(cache_file):
            self.keys = pickle.load(open(cache_file, "rb"))
        else:
            with self.env.begin(write=False) as txn:
                self.keys = [key for key in txn.cursor().iternext(keys=True, values=False)]
            pickle.dump(self.keys, open(cache_file, "wb"))

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        img, target = None, None
        env = self.env
        with env.begin(write=False) as txn:
            imgbuf = txn.get(self.keys[index])

        buf = io.BytesIO()
        buf.write(imgbuf)
        buf.seek(0)
        img = Image.open(buf).convert("RGB")

        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 __len__(self) -> int:
        return self.length


class LSUN(VisionDataset):
    """`LSUN <https://www.yf.io/p/lsun>`_ dataset.

    You will need to install the ``lmdb`` package to use this dataset: run
    ``pip install lmdb``

    Args:
        root (str or ``pathlib.Path``): Root directory for the database files.
        classes (string or list): One of {'train', 'val', 'test'} or a list of
            categories to load. e,g. ['bedroom_train', 'church_outdoor_train'].
        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.
    """

    def __init__(
        self,
        root: Union[str, Path],
        classes: Union[str, List[str]] = "train",
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
    ) -> None:
        super().__init__(root, transform=transform, target_transform=target_transform)
        self.classes = self._verify_classes(classes)

        # for each class, create an LSUNClassDataset
        self.dbs = []
        for c in self.classes:
            self.dbs.append(LSUNClass(root=os.path.join(root, f"{c}_lmdb"), transform=transform))

        self.indices = []
        count = 0
        for db in self.dbs:
            count += len(db)
            self.indices.append(count)

        self.length = count

    def _verify_classes(self, classes: Union[str, List[str]]) -> List[str]:
        categories = [
            "bedroom",
            "bridge",
            "church_outdoor",
            "classroom",
            "conference_room",
            "dining_room",
            "kitchen",
            "living_room",
            "restaurant",
            "tower",
        ]
        dset_opts = ["train", "val", "test"]

        try:
            classes = cast(str, classes)
            verify_str_arg(classes, "classes", dset_opts)
            if classes == "test":
                classes = [classes]
            else:
                classes = [c + "_" + classes for c in categories]
        except ValueError:
            if not isinstance(classes, Iterable):
                msg = "Expected type str or Iterable for argument classes, but got type {}."
                raise ValueError(msg.format(type(classes)))

            classes = list(classes)
            msg_fmtstr_type = "Expected type str for elements in argument classes, but got type {}."
            for c in classes:
                verify_str_arg(c, custom_msg=msg_fmtstr_type.format(type(c)))
                c_short = c.split("_")
                category, dset_opt = "_".join(c_short[:-1]), c_short[-1]

                msg_fmtstr = "Unknown value '{}' for {}. Valid values are {{{}}}."
                msg = msg_fmtstr.format(category, "LSUN class", iterable_to_str(categories))
                verify_str_arg(category, valid_values=categories, custom_msg=msg)

                msg = msg_fmtstr.format(dset_opt, "postfix", iterable_to_str(dset_opts))
                verify_str_arg(dset_opt, valid_values=dset_opts, custom_msg=msg)

        return classes

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

        Returns:
            tuple: Tuple (image, target) where target is the index of the target category.
        """
        target = 0
        sub = 0
        for ind in self.indices:
            if index < ind:
                break
            target += 1
            sub = ind

        db = self.dbs[target]
        index = index - sub

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

        img, _ = db[index]
        return img, target

    def __len__(self) -> int:
        return self.length

    def extra_repr(self) -> str:
        return "Classes: {classes}".format(**self.__dict__)