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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 .folder import default_loader |
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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 or torch.Tensor, depends on the given loader, |
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and returns a transformed 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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loader (callable, optional): A function to load an image given its path. |
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By default, it uses PIL as its image loader, but users could also pass in |
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``torchvision.io.decode_image`` for decoding image data into tensors directly. |
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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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loader: Callable[[Union[str, pathlib.Path]], Any] = default_loader, |
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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.loader = loader |
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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 = self.loader(image_file) |
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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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