Source code for torchvision.datasets.pcam
import pathlib
from typing import Any, Callable, Optional, Tuple
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 <https://github.com/basveeling/pcam>`_.
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 (string): 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.
"""
_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: str,
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))