add option save coords file to wsi dataset classes

configs/vision/pathology/offline/classification/camelyon16.yaml

@@ -108,6 +108,7 @@ data:
             height: 224
             target_mpp: 0.25
             split: train
+            coords_path: ${data.init_args.datasets.train.init_args.root}/coords_${.split}.csv
             image_transforms:
               class_path: eva.vision.data.transforms.common.ResizeAndCrop
               init_args:

configs/vision/pathology/offline/classification/camelyon16_small.yaml

@@ -108,6 +108,7 @@ data:
             height: 224
             target_mpp: 0.25
             split: train
+            coords_path: ${data.init_args.datasets.train.init_args.root}/coords_${.split}.csv
             image_transforms:
               class_path: eva.vision.data.transforms.common.ResizeAndCrop
               init_args:

configs/vision/pathology/offline/classification/panda.yaml

@@ -107,6 +107,7 @@ data:
             height: 224
             target_mpp: 0.5
             split: train
+            coords_path: ${data.init_args.datasets.train.init_args.root}/coords_${.split}.csv
             image_transforms:
               class_path: eva.vision.data.transforms.common.ResizeAndCrop
               init_args:

configs/vision/pathology/offline/classification/panda_small.yaml

@@ -107,6 +107,7 @@ data:
             height: 224
             target_mpp: 0.5
             split: train
+            coords_path: ${data.init_args.datasets.train.init_args.root}/coords_${.split}.csv
             image_transforms:
               class_path: eva.vision.data.transforms.common.ResizeAndCrop
               init_args:

src/eva/core/callbacks/writers/embeddings/base.py

@@ -172,15 +172,14 @@ def _get_item_metadata(
 
     def _check_if_exists(self) -> None:
         """Checks if the output directory already exists and if it should be overwritten."""
-        try:
-            os.makedirs(self._output_dir, exist_ok=self._overwrite)
-        except FileExistsError as e:
+        os.makedirs(self._output_dir, exist_ok=True)
+        if os.path.exists(os.path.join(self._output_dir, "manifest.csv")) and not self._overwrite:
             raise FileExistsError(
                 f"The embeddings output directory already exists: {self._output_dir}. This "
                 "either means that they have been computed before or that a wrong output "
                 "directory is being used. Consider using `eva fit` instead, selecting a "
                 "different output directory or setting overwrite=True."
-            ) from e
+            )
         os.makedirs(self._output_dir, exist_ok=True)
 
 

src/eva/vision/data/datasets/classification/camelyon16.py

@@ -87,6 +87,7 @@ def __init__(
         target_mpp: float = 0.5,
         backend: str = "openslide",
         image_transforms: Callable | None = None,
+        coords_path: str | None = None,
         seed: int = 42,
     ) -> None:
         """Initializes the dataset.
@@ -100,6 +101,7 @@ def __init__(
             target_mpp: Target microns per pixel (mpp) for the patches.
             backend: The backend to use for reading the whole-slide images.
             image_transforms: Transforms to apply to the extracted image patches.
+            coords_path: File path to save the patch coordinates as .csv.
             seed: Random seed for reproducibility.
         """
         self._split = split
@@ -119,6 +121,7 @@ def __init__(
             target_mpp=target_mpp,
             backend=backend,
             image_transforms=image_transforms,
+            coords_path=coords_path,
         )
 
     @property
@@ -207,9 +210,7 @@ def load_target(self, index: int) -> torch.Tensor:
 
     @override
     def load_metadata(self, index: int) -> Dict[str, Any]:
-        dataset_index, sample_index = self._get_dataset_idx(index), self._get_sample_idx(index)
-        patch_metadata = self.datasets[dataset_index].load_metadata(sample_index)
-        return {"wsi_id": self.filename(index).split(".")[0]} | patch_metadata
+        return wsi.MultiWsiDataset.load_metadata(self, index)
 
     def _load_file_paths(self, split: Literal["train", "val", "test"] | None = None) -> List[str]:
         """Loads the file paths of the corresponding dataset split."""

src/eva/vision/data/datasets/classification/panda.py

@@ -49,6 +49,7 @@ def __init__(
         target_mpp: float = 0.5,
         backend: str = "openslide",
         image_transforms: Callable | None = None,
+        coords_path: str | None = None,
         seed: int = 42,
     ) -> None:
         """Initializes the dataset.
@@ -62,6 +63,7 @@ def __init__(
             target_mpp: Target microns per pixel (mpp) for the patches.
             backend: The backend to use for reading the whole-slide images.
             image_transforms: Transforms to apply to the extracted image patches.
+            coords_path: File path to save the patch coordinates as .csv.
             seed: Random seed for reproducibility.
         """
         self._split = split
@@ -80,6 +82,7 @@ def __init__(
             target_mpp=target_mpp,
             backend=backend,
             image_transforms=image_transforms,
+            coords_path=coords_path,
         )
 
     @property
@@ -132,9 +135,7 @@ def load_target(self, index: int) -> torch.Tensor:
 
     @override
     def load_metadata(self, index: int) -> Dict[str, Any]:
-        dataset_index, sample_index = self._get_dataset_idx(index), self._get_sample_idx(index)
-        patch_metadata = self.datasets[dataset_index].load_metadata(sample_index)
-        return {"wsi_id": self.filename(index).split(".")[0]} | patch_metadata
+        return wsi.MultiWsiDataset.load_metadata(self, index)
 
     def _load_file_paths(self, split: Literal["train", "val", "test"] | None = None) -> List[str]:
         """Loads the file paths of the corresponding dataset split."""

src/eva/vision/data/datasets/classification/wsi.py

@@ -35,6 +35,7 @@ def __init__(
         split: Literal["train", "val", "test"] | None = None,
         image_transforms: Callable | None = None,
         column_mapping: Dict[str, str] = default_column_mapping,
+        coords_path: str | None = None,
     ):
         """Initializes the dataset.
 
@@ -51,6 +52,7 @@ def __init__(
             split: The split of the dataset to load.
             image_transforms: Transforms to apply to the extracted image patches.
             column_mapping: Mapping of the columns in the manifest file.
+            coords_path: File path to save the patch coordinates as .csv.
         """
         self._split = split
         self._column_mapping = self.default_column_mapping | column_mapping
@@ -66,6 +68,7 @@ def __init__(
             target_mpp=target_mpp,
             backend=backend,
             image_transforms=image_transforms,
+            coords_path=coords_path,
         )
 
     @override
@@ -88,9 +91,7 @@ def load_target(self, index: int) -> np.ndarray:
 
     @override
     def load_metadata(self, index: int) -> Dict[str, Any]:
-        dataset_index, sample_index = self._get_dataset_idx(index), self._get_sample_idx(index)
-        patch_metadata = self.datasets[dataset_index].load_metadata(sample_index)
-        return {"wsi_id": self.filename(index).split(".")[0]} | patch_metadata
+        return wsi.MultiWsiDataset.load_metadata(self, index)
 
     def _load_manifest(self, manifest_path: str) -> pd.DataFrame:
         df = pd.read_csv(manifest_path)

src/eva/vision/data/datasets/wsi.py

@@ -4,6 +4,7 @@
 import os
 from typing import Any, Callable, Dict, List
 
+import pandas as pd
 from loguru import logger
 from torch.utils.data import dataset as torch_datasets
 from torchvision import tv_tensors
@@ -116,6 +117,7 @@ def __init__(
         overwrite_mpp: float | None = None,
         backend: str = "openslide",
         image_transforms: Callable | None = None,
+        coords_path: str | None = None,
     ):
         """Initializes a new dataset instance.
 
@@ -129,6 +131,7 @@ def __init__(
             sampler: The sampler to use for sampling patch coordinates.
             backend: The backend to use for reading the whole-slide images.
             image_transforms: Transforms to apply to the extracted image patches.
+            coords_path: File path to save the patch coordinates as .csv.
         """
         super().__init__()
 
@@ -141,6 +144,7 @@ def __init__(
         self._sampler = sampler
         self._backend = backend
         self._image_transforms = image_transforms
+        self._coords_path = coords_path
 
         self._concat_dataset: torch_datasets.ConcatDataset
 
@@ -157,6 +161,7 @@ def cumulative_sizes(self) -> List[int]:
     @override
     def configure(self) -> None:
         self._concat_dataset = torch_datasets.ConcatDataset(datasets=self._load_datasets())
+        self._save_coords_to_file()
 
     @override
     def __len__(self) -> int:
@@ -170,6 +175,12 @@ def __getitem__(self, index: int) -> tv_tensors.Image:
     def filename(self, index: int) -> str:
         return os.path.basename(self._file_paths[self._get_dataset_idx(index)])
 
+    def load_metadata(self, index: int) -> Dict[str, Any]:
+        """Loads the metadata for the patch at the specified index."""
+        dataset_index, sample_index = self._get_dataset_idx(index), self._get_sample_idx(index)
+        patch_metadata = self.datasets[dataset_index].load_metadata(sample_index)
+        return {"wsi_id": self.filename(index).split(".")[0]} | patch_metadata
+
     def _load_datasets(self) -> list[WsiDataset]:
         logger.info(f"Initializing dataset with {len(self._file_paths)} WSIs ...")
         wsi_datasets = []
@@ -200,3 +211,13 @@ def _get_dataset_idx(self, index: int) -> int:
     def _get_sample_idx(self, index: int) -> int:
         dataset_idx = self._get_dataset_idx(index)
         return index if dataset_idx == 0 else index - self.cumulative_sizes[dataset_idx - 1]
+
+    def _save_coords_to_file(self):
+        if self._coords_path is not None:
+            coords = [
+                {"file": self._file_paths[i]} | dataset._coords.to_dict()
+                for i, dataset in enumerate(self.datasets)
+            ]
+            os.makedirs(os.path.abspath(os.path.join(self._coords_path, os.pardir)), exist_ok=True)
+            pd.DataFrame(coords).to_csv(self._coords_path, index=False)
+            logger.info(f"Saved patch coordinates to: {self._coords_path}")

src/eva/vision/data/wsi/patching/coordinates.py

@@ -2,7 +2,7 @@
 
 import dataclasses
 import functools
-from typing import List, Tuple
+from typing import Any, Dict, List, Tuple
 
 from eva.vision.data.wsi import backends
 from eva.vision.data.wsi.patching import samplers
@@ -75,6 +75,14 @@ def from_file(
 
         return cls(x_y, scaled_width, scaled_height, level_idx, sample_args.get("mask"))
 
+    def to_dict(self, include_keys: List[str] | None = None) -> Dict[str, Any]:
+        """Convert the coordinates to a dictionary."""
+        include_keys = include_keys or ["x_y", "width", "height", "level_idx"]
+        coord_dict = dataclasses.asdict(self)
+        if include_keys:
+            coord_dict = {key: coord_dict[key] for key in include_keys}
+        return coord_dict
+
 
 @functools.lru_cache(LRU_CACHE_SIZE)
 def get_cached_coords(

tests/eva/vision/data/datasets/test_wsi.py

@@ -1,8 +1,10 @@
 """WsiDataset & MultiWsiDataset tests."""
 
 import os
+import pathlib
 from typing import Tuple
 
+import pandas as pd
 import pytest
 
 from eva.vision.data import datasets
@@ -69,14 +71,16 @@ def test_patch_shape(width: int, height: int, target_mpp: float, root: str, back
     assert dataset[0].shape == (3, scaled_width, scaled_height)
 
 
-def test_multi_dataset(root: str):
+def test_multi_dataset(root: str, tmp_path: pathlib.Path):
     """Test MultiWsiDataset with multiple whole-slide image paths."""
     file_paths = [
         os.path.join(root, "0/a.tiff"),
         os.path.join(root, "0/b.tiff"),
         os.path.join(root, "1/a.tiff"),
     ]
 
+    # get tmp csv file path for coords
+    coords_path = (tmp_path / "coords.csv").as_posix()
     width, height = 32, 32
     dataset = datasets.MultiWsiDataset(
         root=root,
@@ -86,6 +90,7 @@ def test_multi_dataset(root: str):
         target_mpp=0.25,
         sampler=samplers.GridSampler(max_samples=None),
         backend="openslide",
+        coords_path=coords_path,
     )
     dataset.setup()
 
@@ -94,6 +99,11 @@ def test_multi_dataset(root: str):
     assert len(dataset) == _expected_n_patches(layer_shape, width, height, (0, 0)) * len(file_paths)
     assert dataset.cumulative_sizes == [64, 128, 192]
 
+    assert os.path.exists(coords_path)
+    df_coords = pd.read_csv(coords_path)
+    assert "file" in df_coords.columns
+    assert "x_y" in df_coords.columns
+
 
 def _expected_n_patches(layer_shape, width, height, overlap):
     """Calculate the expected number of patches."""