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89 lines
3.4 KiB
89 lines
3.4 KiB
import json
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import pathlib
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from typing import Any, Callable, List, Optional, Tuple, Union
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from urllib.parse import urlparse
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from PIL import Image
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from .utils import download_and_extract_archive, verify_str_arg
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from .vision import VisionDataset
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class CLEVRClassification(VisionDataset):
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"""`CLEVR <https://cs.stanford.edu/people/jcjohns/clevr/>`_ classification dataset.
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The number of objects in a scene are used as label.
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Args:
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root (str or ``pathlib.Path``): Root directory of dataset where directory ``root/clevr`` exists or will be saved to if download is
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set to True.
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split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in them target and transforms it.
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download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If
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dataset is already downloaded, it is not downloaded again.
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"""
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_URL = "https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip"
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_MD5 = "b11922020e72d0cd9154779b2d3d07d2"
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def __init__(
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self,
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root: Union[str, pathlib.Path],
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split: str = "train",
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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download: bool = False,
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) -> None:
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self._split = verify_str_arg(split, "split", ("train", "val", "test"))
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super().__init__(root, transform=transform, target_transform=target_transform)
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self._base_folder = pathlib.Path(self.root) / "clevr"
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self._data_folder = self._base_folder / pathlib.Path(urlparse(self._URL).path).stem
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if download:
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self._download()
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if not self._check_exists():
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raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
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self._image_files = sorted(self._data_folder.joinpath("images", self._split).glob("*"))
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self._labels: List[Optional[int]]
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if self._split != "test":
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with open(self._data_folder / "scenes" / f"CLEVR_{self._split}_scenes.json") as file:
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content = json.load(file)
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num_objects = {scene["image_filename"]: len(scene["objects"]) for scene in content["scenes"]}
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self._labels = [num_objects[image_file.name] for image_file in self._image_files]
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else:
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self._labels = [None] * len(self._image_files)
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def __len__(self) -> int:
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return len(self._image_files)
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def __getitem__(self, idx: int) -> Tuple[Any, Any]:
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image_file = self._image_files[idx]
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label = self._labels[idx]
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image = Image.open(image_file).convert("RGB")
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if self.transform:
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image = self.transform(image)
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if self.target_transform:
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label = self.target_transform(label)
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return image, label
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def _check_exists(self) -> bool:
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return self._data_folder.exists() and self._data_folder.is_dir()
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def _download(self) -> None:
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if self._check_exists():
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return
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download_and_extract_archive(self._URL, str(self._base_folder), md5=self._MD5)
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def extra_repr(self) -> str:
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return f"split={self._split}"
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