refactor: ♻️ Refactor transforms.

src/cellmap_data/dataset.py

@@ -30,10 +30,9 @@ class CellMapDataset(Dataset):
     input_sources: dict[str, CellMapImage]
     target_sources: dict[str, dict[str, CellMapImage | EmptyImage]]
     spatial_transforms: Optional[Sequence[dict[str, any]]]  # type: ignore
-    raw_value_transforms: Optional[Callable]  # For instance, normalizing the raw data
-    gt_value_transforms: Optional[
-        Callable | Sequence[Callable] | dict[str, Callable]
-    ]  # For instance, converting the ground truth data to target arrays
+    raw_value_transforms: Optional[Callable | Sequence[Callable]]
+    gt_value_transforms: Optional[Callable | Sequence[Callable] | dict[str, Callable]]
+    context: Optional[tensorstore.Context]  # type: ignore
     has_data: bool
     is_train: bool
     _bounding_box: Optional[Dict[str, list[int]]]
@@ -53,7 +52,7 @@ def __init__(
         input_arrays: dict[str, dict[str, Sequence[int | float]]],
         target_arrays: dict[str, dict[str, Sequence[int | float]]],
         spatial_transforms: Optional[Sequence[dict[str, any]]] = None,  # type: ignore
-        raw_value_transforms: Optional[Callable] = None,
+        raw_value_transforms: Optional[Callable | Sequence[Callable]] = None,
         gt_value_transforms: Optional[
             Callable | Sequence[Callable] | dict[str, Callable]
         ] = None,

src/cellmap_data/datasplit.py

@@ -1,6 +1,7 @@
 import csv
 from torchvision.transforms.v2 import RandomApply
 from typing import Callable, Dict, Iterable, Optional, Sequence
+import tensorstore
 
 from .multidataset import CellMapMultiDataset
 from .dataset import CellMapDataset
@@ -20,8 +21,12 @@ class CellMapDataSplit:
     validate_datasets: Iterable[CellMapDataset]
     train_datasets_combined: CellMapMultiDataset
     validate_datasets_combined: CellMapMultiDataset
-    # TODO: Correct transform passing (value for raw and labels, and spatial)
-    transforms: RandomApply | None
+    spatial_transforms: Optional[Sequence[dict[str, any]]]
+    raw_value_transforms: Optional[Callable | Sequence[Callable]] = None
+    gt_value_transforms: Optional[
+        Callable | Sequence[Callable] | dict[str, Callable]
+    ] = None
+    context: Optional[tensorstore.Context] = None  # type: ignore
 
     def __init__(
         self,
@@ -32,7 +37,12 @@ def __init__(
         datasets: Optional[Dict[str, Iterable[CellMapDataset]]] = None,
         dataset_dict: Optional[Dict[str, Dict[str, str]]] = None,
         csv_path: Optional[str] = None,
-        transforms: Optional[Sequence[Callable]] = None,
+        spatial_transforms: Optional[Sequence[dict[str, any]]] = None,
+        raw_value_transforms: Optional[Callable | Sequence[Callable]] = None,
+        gt_value_transforms: Optional[
+            Callable | Sequence[Callable] | dict[str, Callable]
+        ] = None,
+        context: Optional[tensorstore.Context] = None,  # type: ignore
     ):
         """Initializes the CellMapDatasets class.
 
@@ -71,7 +81,13 @@ def to_target(gt: torch.Tensor, classes: Sequence[str]) -> dict[str, torch.Tenso
                 }. Defaults to None.
             csv_path (Optional[str], optional): A path to a csv file containing the dataset data. Defaults to None. Each row in the csv file should have the following structure:
                 train | validate, raw path, gt path
-            transforms (Optional[Iterable[dict[str, any]]], optional): A list of transforms to apply to the data. Each augmentation should be a dictionary containing the following structure:
+            spatial_transforms (Optional[Sequence[dict[str, any]]], optional): A sequence of dictionaries containing the spatial transformations to apply to the data. The dictionary should have the following structure:
+                {transform_name: {transform_args}}
+                Defaults to None.
+            raw_value_transforms (Optional[Callable], optional): A function to apply to the raw data. Defaults to None. Example is to normalize the raw data.
+            gt_value_transforms (Optional[Callable | Sequence[Callable] | dict[str, Callable]], optional): A function to convert the ground truth data to target arrays. Defaults to None. Example is to convert the ground truth data to a signed distance transform. May be a single function, a list of functions, or a dictionary of functions for each class. In the case of a list of functions, it is assumed that the functions correspond to each class in the classes list in order.
+            is_train (bool, optional): Whether the dataset is for training. Defaults to False.
+            context (Optional[tensorstore.Context], optional): The context for the image data. Defaults to None.
         """
         self.input_arrays = input_arrays
         self.target_arrays = target_arrays
@@ -85,8 +101,11 @@ def to_target(gt: torch.Tensor, classes: Sequence[str]) -> dict[str, torch.Tenso
             self.construct(dataset_dict)
         elif csv_path is not None:
             self.from_csv(csv_path)
-        self.to_target = to_target
-        self.transforms = RandomApply(transforms) if transforms is not None else None
+        self.spatial_transforms = spatial_transforms
+        self.raw_value_transforms = raw_value_transforms
+        self.gt_value_transforms = gt_value_transforms
+        self.context = context
+        self.construct(self.dataset_dict)
 
     def from_csv(self, csv_path):
         # Load file data from csv file
@@ -113,8 +132,9 @@ def construct(self, dataset_dict):
                     self.classes,
                     self.input_arrays,
                     self.target_arrays,
-                    self.to_target,
-                    self.transforms,
+                    self.spatial_transforms,
+                    self.raw_value_transforms,
+                    self.gt_value_transforms,
                 )
             )
         for raw, gt in zip(
@@ -127,7 +147,7 @@ def construct(self, dataset_dict):
                     self.classes,
                     self.input_arrays,
                     self.target_arrays,
-                    self.to_target,
+                    gt_value_transforms=self.gt_value_transforms,
                 )
             )
         self.train_datasets_combined = CellMapMultiDataset(