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135 lines
5.2 KiB
135 lines
5.2 KiB
import pathlib
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from typing import Any, Callable, Optional, Tuple, Union
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from PIL import Image
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from .utils import _decompress, download_file_from_google_drive, verify_str_arg
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from .vision import VisionDataset
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class PCAM(VisionDataset):
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"""`PCAM Dataset <https://github.com/basveeling/pcam>`_.
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The PatchCamelyon dataset is a binary classification dataset with 327,680
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color images (96px x 96px), extracted from histopathologic scans of lymph node
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sections. Each image is annotated with a binary label indicating presence of
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metastatic tissue.
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This dataset requires the ``h5py`` package which you can install with ``pip install h5py``.
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Args:
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root (str or ``pathlib.Path``): Root directory of the dataset.
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split (string, optional): The dataset split, supports ``"train"`` (default), ``"test"`` or ``"val"``.
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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 the target and transforms it.
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download (bool, optional): If True, downloads the dataset from the internet and puts it into ``root/pcam``. If
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dataset is already downloaded, it is not downloaded again.
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.. warning::
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To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
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"""
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_FILES = {
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"train": {
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"images": (
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"camelyonpatch_level_2_split_train_x.h5", # Data file name
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"1Ka0XfEMiwgCYPdTI-vv6eUElOBnKFKQ2", # Google Drive ID
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"1571f514728f59376b705fc836ff4b63", # md5 hash
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),
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"targets": (
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"camelyonpatch_level_2_split_train_y.h5",
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"1269yhu3pZDP8UYFQs-NYs3FPwuK-nGSG",
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"35c2d7259d906cfc8143347bb8e05be7",
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),
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},
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"test": {
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"images": (
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"camelyonpatch_level_2_split_test_x.h5",
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"1qV65ZqZvWzuIVthK8eVDhIwrbnsJdbg_",
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"d8c2d60d490dbd479f8199bdfa0cf6ec",
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),
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"targets": (
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"camelyonpatch_level_2_split_test_y.h5",
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"17BHrSrwWKjYsOgTMmoqrIjDy6Fa2o_gP",
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"60a7035772fbdb7f34eb86d4420cf66a",
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),
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},
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"val": {
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"images": (
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"camelyonpatch_level_2_split_valid_x.h5",
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"1hgshYGWK8V-eGRy8LToWJJgDU_rXWVJ3",
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"d5b63470df7cfa627aeec8b9dc0c066e",
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),
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"targets": (
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"camelyonpatch_level_2_split_valid_y.h5",
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"1bH8ZRbhSVAhScTS0p9-ZzGnX91cHT3uO",
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"2b85f58b927af9964a4c15b8f7e8f179",
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),
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},
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}
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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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):
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try:
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import h5py
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self.h5py = h5py
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except ImportError:
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raise RuntimeError(
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"h5py is not found. This dataset needs to have h5py installed: please run pip install h5py"
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)
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self._split = verify_str_arg(split, "split", ("train", "test", "val"))
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super().__init__(root, transform=transform, target_transform=target_transform)
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self._base_folder = pathlib.Path(self.root) / "pcam"
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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. You can use download=True to download it")
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def __len__(self) -> int:
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images_file = self._FILES[self._split]["images"][0]
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with self.h5py.File(self._base_folder / images_file) as images_data:
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return images_data["x"].shape[0]
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def __getitem__(self, idx: int) -> Tuple[Any, Any]:
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images_file = self._FILES[self._split]["images"][0]
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with self.h5py.File(self._base_folder / images_file) as images_data:
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image = Image.fromarray(images_data["x"][idx]).convert("RGB")
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targets_file = self._FILES[self._split]["targets"][0]
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with self.h5py.File(self._base_folder / targets_file) as targets_data:
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target = int(targets_data["y"][idx, 0, 0, 0]) # shape is [num_images, 1, 1, 1]
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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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target = self.target_transform(target)
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return image, target
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def _check_exists(self) -> bool:
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images_file = self._FILES[self._split]["images"][0]
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targets_file = self._FILES[self._split]["targets"][0]
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return all(self._base_folder.joinpath(h5_file).exists() for h5_file in (images_file, targets_file))
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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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for file_name, file_id, md5 in self._FILES[self._split].values():
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archive_name = file_name + ".gz"
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download_file_from_google_drive(file_id, str(self._base_folder), filename=archive_name, md5=md5)
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_decompress(str(self._base_folder / archive_name))
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