Migrate PCAM prototype dataset (#5745)

* Port PCAM

* skip_integrity_check

* Update torchvision/prototype/datasets/_builtin/pcam.py

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Address comments

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

Author: NicolasHug

test/builtin_dataset_mocks.py

@@ -1430,13 +1430,13 @@ def svhn(info, root, config):
     return num_samples
 
 
-# @register_mock
-def pcam(info, root, config):
+@register_mock(configs=combinations_grid(split=("train", "val", "test")))
+def pcam(root, config):
     import h5py
 
-    num_images = {"train": 2, "test": 3, "val": 4}[config.split]
+    num_images = {"train": 2, "test": 3, "val": 4}[config["split"]]
 
-    split = "valid" if config.split == "val" else config.split
+    split = "valid" if config["split"] == "val" else config["split"]
 
     images_io = io.BytesIO()
     with h5py.File(images_io, "w") as f:

torchvision/prototype/datasets/_builtin/pcam.py

@@ -1,13 +1,13 @@
 import io
+import pathlib
 from collections import namedtuple
-from typing import Any, Dict, List, Optional, Tuple, Iterator
+from typing import Any, Dict, List, Optional, Tuple, Iterator, Union
+from unicodedata import category
 
 from torchdata.datapipes.iter import IterDataPipe, Mapper, Zipper
 from torchvision.prototype import features
 from torchvision.prototype.datasets.utils import (
-    Dataset,
-    DatasetConfig,
-    DatasetInfo,
+    Dataset2,
     OnlineResource,
     GDriveResource,
 )
@@ -17,6 +17,11 @@
 )
 from torchvision.prototype.features import Label
 
+from .._api import register_dataset, register_info
+
+
+NAME = "pcam"
+
 
 class PCAMH5Reader(IterDataPipe[Tuple[str, io.IOBase]]):
     def __init__(
@@ -40,15 +45,25 @@ def __iter__(self) -> Iterator[Tuple[str, io.IOBase]]:
 _Resource = namedtuple("_Resource", ("file_name", "gdrive_id", "sha256"))
 
 
-class PCAM(Dataset):
-    def _make_info(self) -> DatasetInfo:
-        return DatasetInfo(
-            "pcam",
-            homepage="https://github.com/basveeling/pcam",
-            categories=2,
-            valid_options=dict(split=("train", "test", "val")),
-            dependencies=["h5py"],
-        )
+@register_info(NAME)
+def _info() -> Dict[str, Any]:
+    return dict(categories=["0", "1"])
+
+
+@register_dataset(NAME)
+class PCAM(Dataset2):
+    # TODO write proper docstring
+    """PCAM Dataset
+
+    homepage="https://github.com/basveeling/pcam"
+    """
+
+    def __init__(
+        self, root: Union[str, pathlib.Path], split: str = "train", *, skip_integrity_check: bool = False
+    ) -> None:
+        self._split = self._verify_str_arg(split, "split", {"train", "val", "test"})
+        self._categories = _info()["categories"]
+        super().__init__(root, skip_integrity_check=skip_integrity_check, dependencies=("h5py",))
 
     _RESOURCES = {
         "train": (
@@ -89,23 +104,21 @@ def _make_info(self) -> DatasetInfo:
         ),
     }
 
-    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+    def _resources(self) -> List[OnlineResource]:
         return [  # = [images resource, targets resource]
             GDriveResource(file_name=file_name, id=gdrive_id, sha256=sha256, preprocess="decompress")
-            for file_name, gdrive_id, sha256 in self._RESOURCES[config.split]
+            for file_name, gdrive_id, sha256 in self._RESOURCES[self._split]
         ]
 
     def _prepare_sample(self, data: Tuple[Any, Any]) -> Dict[str, Any]:
         image, target = data  # They're both numpy arrays at this point
 
         return {
             "image": features.Image(image.transpose(2, 0, 1)),
-            "label": Label(target.item()),
+            "label": Label(target.item(), categories=self._categories),
         }
 
-    def _make_datapipe(
-        self, resource_dps: List[IterDataPipe], *, config: DatasetConfig
-    ) -> IterDataPipe[Dict[str, Any]]:
+    def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
 
         images_dp, targets_dp = resource_dps
 
@@ -116,3 +129,6 @@ def _make_datapipe(
         dp = hint_shuffling(dp)
         dp = hint_sharding(dp)
         return Mapper(dp, self._prepare_sample)
+
+    def __len__(self):
+        return 262_144 if self._split == "train" else 32_768