Source code for torchvision.datasets.pcam

import pathlib
from typing import Any, Callable, Optional, Tuple, Union

from PIL import Image

from .utils import _decompress, download_file_from_google_drive, verify_str_arg
from .vision import VisionDataset

[docs]class PCAM(VisionDataset): """`PCAM Dataset <>`_. The PatchCamelyon dataset is a binary classification dataset with 327,680 color images (96px x 96px), extracted from histopathologic scans of lymph node sections. Each image is annotated with a binary label indicating presence of metastatic tissue. This dataset requires the ``h5py`` package which you can install with ``pip install h5py``. Args: root (str or ``pathlib.Path``): Root directory of the dataset. split (string, optional): The dataset split, supports ``"train"`` (default), ``"test"`` or ``"val"``. 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. download (bool, optional): If True, downloads the dataset from the internet and puts it into ``root/pcam``. If dataset is already downloaded, it is not downloaded again. .. warning:: To download the dataset `gdown <>`_ is required. """ _FILES = { "train": { "images": ( "camelyonpatch_level_2_split_train_x.h5", # Data file name "1Ka0XfEMiwgCYPdTI-vv6eUElOBnKFKQ2", # Google Drive ID "1571f514728f59376b705fc836ff4b63", # md5 hash ), "targets": ( "camelyonpatch_level_2_split_train_y.h5", "1269yhu3pZDP8UYFQs-NYs3FPwuK-nGSG", "35c2d7259d906cfc8143347bb8e05be7", ), }, "test": { "images": ( "camelyonpatch_level_2_split_test_x.h5", "1qV65ZqZvWzuIVthK8eVDhIwrbnsJdbg_", "d8c2d60d490dbd479f8199bdfa0cf6ec", ), "targets": ( "camelyonpatch_level_2_split_test_y.h5", "17BHrSrwWKjYsOgTMmoqrIjDy6Fa2o_gP", "60a7035772fbdb7f34eb86d4420cf66a", ), }, "val": { "images": ( "camelyonpatch_level_2_split_valid_x.h5", "1hgshYGWK8V-eGRy8LToWJJgDU_rXWVJ3", "d5b63470df7cfa627aeec8b9dc0c066e", ), "targets": ( "camelyonpatch_level_2_split_valid_y.h5", "1bH8ZRbhSVAhScTS0p9-ZzGnX91cHT3uO", "2b85f58b927af9964a4c15b8f7e8f179", ), }, } def __init__( self, root: Union[str, pathlib.Path], split: str = "train", transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, ): try: import h5py self.h5py = h5py except ImportError: raise RuntimeError( "h5py is not found. This dataset needs to have h5py installed: please run pip install h5py" ) self._split = verify_str_arg(split, "split", ("train", "test", "val")) super().__init__(root, transform=transform, target_transform=target_transform) self._base_folder = pathlib.Path(self.root) / "pcam" if download: self._download() if not self._check_exists(): raise RuntimeError("Dataset not found. You can use download=True to download it") def __len__(self) -> int: images_file = self._FILES[self._split]["images"][0] with self.h5py.File(self._base_folder / images_file) as images_data: return images_data["x"].shape[0]
[docs] def __getitem__(self, idx: int) -> Tuple[Any, Any]: images_file = self._FILES[self._split]["images"][0] with self.h5py.File(self._base_folder / images_file) as images_data: image = Image.fromarray(images_data["x"][idx]).convert("RGB") targets_file = self._FILES[self._split]["targets"][0] with self.h5py.File(self._base_folder / targets_file) as targets_data: target = int(targets_data["y"][idx, 0, 0, 0]) # shape is [num_images, 1, 1, 1] if self.transform: image = self.transform(image) if self.target_transform: target = self.target_transform(target) return image, target
def _check_exists(self) -> bool: images_file = self._FILES[self._split]["images"][0] targets_file = self._FILES[self._split]["targets"][0] return all(self._base_folder.joinpath(h5_file).exists() for h5_file in (images_file, targets_file)) def _download(self) -> None: if self._check_exists(): return for file_name, file_id, md5 in self._FILES[self._split].values(): archive_name = file_name + ".gz" download_file_from_google_drive(file_id, str(self._base_folder), filename=archive_name, md5=md5) _decompress(str(self._base_folder / archive_name))


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