Add support for PCAM dataset

.circleci/unittest/linux/scripts/environment.yml

@@ -9,6 +9,7 @@ dependencies:
   - libpng
   - jpeg
   - ca-certificates
+  - h5py
   - pip:
     - future
     - pillow >=5.3.0, !=8.3.*

.circleci/unittest/windows/scripts/environment.yml

@@ -9,6 +9,7 @@ dependencies:
   - libpng
   - jpeg
   - ca-certificates
+  - h5py
   - pip:
     - future
     - pillow >=5.3.0, !=8.3.*

docs/source/datasets.rst

@@ -66,6 +66,7 @@ You can also create your own datasets using the provided :ref:`base classes <bas
     MNIST
     Omniglot
     OxfordIIITPet
+    PCAM
     PhotoTour
     Places365
     QMNIST

test/datasets_utils.py

@@ -61,6 +61,7 @@ class LazyImporter:
         "requests",
         "scipy.io",
         "scipy.sparse",
+        "h5py",
     )
 
     def __init__(self):

test/test_datasets.py

@@ -2577,5 +2577,28 @@ def inject_fake_data(self, tmpdir: str, config):
         return num_images_per_split[config["split"]]
 
 
+class PCAMTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.PCAM
+
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(split=("train", "val", "test"))
+    REQUIRED_PACKAGES = ("h5py",)
+
+    def inject_fake_data(self, tmpdir: str, config):
+        base_folder = pathlib.Path(tmpdir) / "pcam"
+        base_folder.mkdir()
+
+        num_images = {"train": 2, "test": 3, "val": 4}[config["split"]]
+
+        images_file = datasets.PCAM._FILES[config["split"]]["images"][0]
+        with datasets_utils.lazy_importer.h5py.File(str(base_folder / images_file), "w") as f:
+            f["x"] = np.random.randint(0, 256, size=(num_images, 10, 10, 3), dtype=np.uint8)
+
+        targets_file = datasets.PCAM._FILES[config["split"]]["targets"][0]
+        with datasets_utils.lazy_importer.h5py.File(str(base_folder / targets_file), "w") as f:
+            f["y"] = np.random.randint(0, 2, size=(num_images, 1, 1, 1), dtype=np.uint8)
+
+        return num_images
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

@@ -25,6 +25,7 @@
 from .mnist import MNIST, EMNIST, FashionMNIST, KMNIST, QMNIST
 from .omniglot import Omniglot
 from .oxford_iiit_pet import OxfordIIITPet
+from .pcam import PCAM
 from .phototour import PhotoTour
 from .places365 import Places365
 from .sbd import SBDataset

torchvision/datasets/oxford_iiit_pet.py

@@ -27,8 +27,8 @@ class OxfordIIITPet(VisionDataset):
         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/dtd``. If
-            dataset is already downloaded, it is not downloaded again.
+        download (bool, optional): If True, downloads the dataset from the internet and puts it into
+            ``root/oxford-iiit-pet``. If dataset is already downloaded, it is not downloaded again.
     """
 
     _RESOURCES = (

torchvision/datasets/pcam.py

@@ -0,0 +1,136 @@
+import os
+import os.path
+import pathlib
+from typing import Any, Callable, Optional, Tuple
+
+from PIL import Image
+
+from .utils import download_file_from_google_drive, extract_archive, verify_str_arg
+from .vision import VisionDataset
+
+
+class PCAM(VisionDataset):
+    """`PCAM Dataset   <https://github.com/basveeling/pcam>`_.
+
+    The PatchCamelyon dataset is a binray classification dataset with 327,680
+    color images (96 x 96px), extracted from histopathologic scans of lymph node
+    sections. Each image is annoted 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 ``"trai"`` (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_",
+                "d5b63470df7cfa627aeec8b9dc0c066e",
+            ),
+            "targets": (
+                "camelyonpatch_level_2_split_test_y.h5",
+                "17BHrSrwWKjYsOgTMmoqrIjDy6Fa2o_gP",
+                "2b85f58b927af9964a4c15b8f7e8f179",
+            ),
+        },
+        "val": {
+            "images": (
+                "camelyonpatch_level_2_split_valid_x.h5",
+                "1hgshYGWK8V-eGRy8LToWJJgDU_rXWVJ3",
+                "d8c2d60d490dbd479f8199bdfa0cf6ec",
+            ),
+            "targets": (
+                "camelyonpatch_level_2_split_valid_y.h5",
+                "1bH8ZRbhSVAhScTS0p9-ZzGnX91cHT3uO",
+                "60a7035772fbdb7f34eb86d4420cf66a",
+            ),
+        },
+    }
+
+    def __init__(
+        self,
+        root: str,
+        split: str = "train",
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        download: bool = True,
+    ):
+        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")
+
+        self.classes = ["neg", "pos"]
+        self.classes_to_idx = {"neg": 0, "pos": 1}
+
+    def __len__(self) -> int:
+        images_file = self._FILES[self._split]["images"][0]
+        with self.h5py.File(self._base_folder / images_file) as images_data:
+            print(images_data.keys())
+            return images_data["x"].shape[0]
+
+    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((os.path.exists(str(self._base_folder / h5_file)) 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)
+            extract_archive(str(self._base_folder / archive_name))