feat: Enable padding by default in CellMapDataset and update raw valu…

…e transforms in CellMapDataSplit

src/cellmap_data/dataset.py

@@ -113,7 +113,7 @@ def __init__(
         rng: Optional[torch.Generator] = None,
         force_has_data: bool = False,
         empty_value: float | int = torch.nan,
-        pad: bool = False,
+        pad: bool = True,
         device: Optional[str | torch.device] = None,
     ) -> None:
         """Initializes the CellMapDataset class.

src/cellmap_data/datasplit.py

@@ -3,7 +3,9 @@
 from typing import Any, Callable, Mapping, Optional, Sequence
 import tensorstore
 import torch
+import torchvision.transforms.v2 as T
 from tqdm import tqdm
+from .transforms.augment import NaNtoNum, Normalize, Binarize
 from .dataset import CellMapDataset
 from .multidataset import CellMapMultiDataset
 from .subdataset import CellMapSubset
@@ -51,9 +53,9 @@ class CellMapDataSplit:
 
             {transform_name: {transform_args}}
 
-        train_raw_value_transforms (Optional[Callable]): A function to apply to the raw data in training datasets. Defaults to None. Example is to add gaussian noise to the raw data.
-        val_raw_value_transforms (Optional[Callable]): A function to apply to the raw data in validation datasets. Defaults to None. Example is to normalize the raw data.
-        target_value_transforms (Optional[Callable | Sequence[Callable] | Mapping[str, Callable]]): 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.
+        train_raw_value_transforms (Optional[Callable]): A function to apply to the raw data in training datasets. Example is to add gaussian noise to the raw data. Defailts to Normalize, ToDtype, and NaNtoNum.
+        val_raw_value_transforms (Optional[Callable]): A function to apply to the raw data in validation datasets. Example is to normalize the raw data. Defaults to Normalize, ToDtype, and NaNtoNum.
+        target_value_transforms (Optional[Callable | Sequence[Callable] | Mapping[str, Callable]]): A function to convert the ground truth data to target arrays. 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. Defaults to ToDtype and Binarize.
         class_relation_dict (Optional[Mapping[str, Sequence[str]]]): A dictionary containing the class relations. The dictionary should have the following structure::
 
             {
@@ -97,11 +99,23 @@ def __init__(
         dataset_dict: Optional[Mapping[str, Sequence[Mapping[str, str]]]] = None,
         csv_path: Optional[str] = None,
         spatial_transforms: Optional[Mapping[str, Any]] = None,
-        train_raw_value_transforms: Optional[Callable] = None,
-        val_raw_value_transforms: Optional[Callable] = None,
-        target_value_transforms: Optional[
-            Callable | Sequence[Callable] | Mapping[str, Callable]
-        ] = None,
+        train_raw_value_transforms: Optional[T.Transform] = T.Compose(
+            [
+                Normalize(),
+                T.ToDtype(torch.float, scale=True),
+                NaNtoNum({"nan": 0, "posinf": None, "neginf": None}),
+            ],
+        ),
+        val_raw_value_transforms: Optional[T.Transform] = T.Compose(
+            [
+                Normalize(),
+                T.ToDtype(torch.float, scale=True),
+                NaNtoNum({"nan": 0, "posinf": None, "neginf": None}),
+            ],
+        ),
+        target_value_transforms: Optional[T.Transform] = T.Compose(
+            [T.ToDtype(torch.float), Binarize()]
+        ),
         class_relation_dict: Optional[Mapping[str, Sequence[str]]] = None,
         force_has_data: bool = False,
         context: Optional[tensorstore.Context] = None,  # type: ignore