feat: 🔥 Add Tensorstore based data fetching

src/cellmap_data/__init__.py

@@ -16,6 +16,7 @@
 __email__ = "rhoadesj@hhmi.org"
 
 from .dataloader import CellMapDataLoader
+from .multidataset import CellMapMultiDataset
 from .datasplit import CellMapDataSplit
 from .dataset import CellMapDataset
 from .image import CellMapImage

src/cellmap_data/dataloader.py

@@ -1,17 +1,48 @@
 from torch.utils.data import DataLoader
+
+from .multidataset import CellMapMultiDataset
 from .datasplit import CellMapDataSplit
-from typing import Callable, Iterable
+from typing import Callable, Iterable, Optional
 
 
-class CellMapDataLoader(DataLoader):
-    """This subclasses PyTorch DataLoader to load CellMap data for training. It maintains the same API as the DataLoader class. This includes applying augmentations to the data and returning the data in the correct format for training, such as generating the target arrays (e.g. signed distance transform of labels). It retrieves raw and groundtruth data from a CellMapDataSplit object, which is a subclass of PyTorch Dataset. Training and validation data are split using the CellMapDataSplit object, and separate dataloaders are maintained as `train_loader` and `val_loader` respectively."""
+class CellMapDataLoader:
+    # TODO: docstring corrections
+    """This subclasses PyTorch DataLoader to load CellMap data for training. It maintains the same API as the DataLoader class. This includes applying augmentations to the data and returning the data in the correct format for training, such as generating the target arrays (e.g. signed distance transform of labels). It retrieves raw and groundtruth data from a CellMapDataSplit object, which is a subclass of PyTorch Dataset. Training and validation data are split using the CellMapDataSplit object, and separate dataloaders are maintained as `train_loader` and `validate_loader` respectively."""
 
-    input_arrays: dict[str, dict[str, tuple[int | float]]]
-    target_arrays: dict[str, dict[str, tuple[int | float]]]
-    classes: list[str]
     datasplit: CellMapDataSplit
+    train_datasets: CellMapMultiDataset
+    validate_datasets: CellMapMultiDataset
     train_loader: DataLoader
-    val_loader: DataLoader
+    validate_loader: DataLoader
+    batch_size: int
+    num_workers: int
     is_train: bool
-    augmentations: list[dict[str, any]]
-    to_target: Callable
+
+    def __init__(
+        self,
+        datasplit: CellMapDataSplit,
+        batch_size: int,
+        num_workers: int,
+        is_train: bool,
+    ):
+        self.datasplit = datasplit
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.is_train = is_train
+        self.construct()
+
+    def construct(self):
+        self.train_datasets = self.datasplit.train_datasets_combined
+        self.validate_datasets = self.datasplit.validate_datasets_combined
+        self.train_loader = DataLoader(
+            self.train_datasets,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=self.is_train,
+        )
+        self.validate_loader = DataLoader(
+            self.validate_datasets,
+            batch_size=1,
+            num_workers=self.num_workers,
+            shuffle=False,
+        )

src/cellmap_data/dataset.py

@@ -1,6 +1,6 @@
 # %%
 import csv
-from typing import Callable, Dict, Iterable, Optional
+from typing import Callable, Dict, Sequence, Optional
 import numpy as np
 import torch
 from torch.utils.data import Dataset
@@ -24,49 +24,67 @@ class CellMapDataset(Dataset):
 
     raw_path: str
     gt_path: str
-    classes: Iterable[str]
-    input_arrays: dict[str, dict[str, Iterable[int | float]]]
-    target_arrays: dict[str, dict[str, Iterable[int | float]]]
+    classes: Sequence[str]
+    input_arrays: dict[str, dict[str, Sequence[int | float]]]
+    target_arrays: dict[str, dict[str, Sequence[int | float]]]
     input_sources: dict[str, CellMapImage]
     target_sources: dict[str, dict[str, CellMapImage | EmptyImage]]
+    to_target: Callable
+    transforms: RandomApply | None
+    has_data: bool
+    _bounding_box: Optional[Dict[str, list[int]]]
+    _bounding_box_shape: Optional[Dict[str, int]]
+    _sampling_box: Optional[Dict[str, list[int]]]
+    _sampling_box_shape: Optional[Dict[str, int]]
+    _class_counts: Optional[Dict[str, Dict[str, int]]]
+    _largest_voxel_sizes: Optional[Dict[str, int]]
+    _len: Optional[int]
+    _iter_coords: Optional[...]
 
     def __init__(
         self,
         raw_path: str,
         gt_path: str,
-        classes: Iterable[str],
-        input_arrays: dict[str, dict[str, Iterable[int | float]]],
-        target_arrays: dict[str, dict[str, Iterable[int | float]]],
+        classes: Sequence[str],
+        input_arrays: dict[str, dict[str, Sequence[int | float]]],
+        target_arrays: dict[str, dict[str, Sequence[int | float]]],
+        to_target: Callable,
+        transforms: Optional[Sequence[Callable]] = None,
     ):
         """Initializes the CellMapDataset class.
 
         Args:
             raw_path (str): The path to the raw data.
             gt_path (str): The path to the ground truth data.
-            classes (Iterable[str]): A list of classes for segmentation training. Class order will be preserved in the output arrays. Classes not contained in the dataset will be filled in with zeros.
-            input_arrays (dict[str, dict[str, Iterable[int | float]]]): A dictionary containing the arrays of the dataset to input to the network. The dictionary should have the following structure:
+            classes (Sequence[str]): A list of classes for segmentation training. Class order will be preserved in the output arrays. Classes not contained in the dataset will be filled in with zeros.
+            input_arrays (dict[str, dict[str, Sequence[int | float]]]): A dictionary containing the arrays of the dataset to input to the network. The dictionary should have the following structure:
                 {
                     "array_name": {
                         "shape": typle[int],
-                        "scale": Iterable[float],
+                        "scale": Sequence[float],
                     },
                     ...
                 }
-            target_arrays (dict[str, dict[str, Iterable[int | float]]]): A dictionary containing the arrays of the dataset to use as targets for the network. The dictionary should have the following structure:
+            target_arrays (dict[str, dict[str, Sequence[int | float]]]): A dictionary containing the arrays of the dataset to use as targets for the network. The dictionary should have the following structure:
                 {
                     "array_name": {
                         "shape": typle[int],
-                        "scale": Iterable[float],
+                        "scale": Sequence[float],
                     },
                     ...
                 }
+            to_target (Callable): A function to convert the ground truth data to target arrays. The function should have the following structure:
+                def to_target(gt: torch.Tensor, classes: Sequence[str]) -> dict[str, torch.Tensor]:
+            transforms (Optional[Sequence[Callable]], optional): A sequence of transformations to apply to the data. Defaults to None.
         """
         self.raw_path = raw_path
         self.gt_paths = gt_path
         self.gt_path_str, self.classes_with_path = split_gt_path(gt_path)
         self.classes = classes
         self.input_arrays = input_arrays
         self.target_arrays = target_arrays
+        self.to_target = to_target
+        self.transforms = transforms
         self.construct()
 
     def __len__(self):
@@ -99,7 +117,10 @@ def __getitem__(self, idx):
 
     def __iter__(self):
         """Iterates over the dataset, covering each section of the bounding box. For instance, for calculating validation scores."""
-        ...
+        # TODO
+        if self._iter_coords is None:
+            self._iter_coords = ...
+        yield self.__getitem__(self._iter_coords)
 
     def construct(self):
         """Constructs the input and target sources for the dataset."""
@@ -137,6 +158,7 @@ def construct(self):
         self._class_counts = None
         self._largest_voxel_sizes = None
         self._len = None
+        self._iter_coords = None
 
     @property
     def largest_voxel_sizes(self):

src/cellmap_data/datasplit.py

@@ -1,48 +1,60 @@
 import csv
-from torch.utils.data import Dataset
-from typing import Callable, Dict, Iterable, Optional
+from torchvision.transforms.v2 import RandomApply
+from typing import Callable, Dict, Iterable, Optional, Sequence
+
+from .multidataset import CellMapMultiDataset
 from .dataset import CellMapDataset
 
 
-class CellMapDataSplit(Dataset):
+class CellMapDataSplit:
     """
     This subclasses PyTorch Dataset to split data into training and validation sets. It maintains the same API as the Dataset class. It retrieves raw and groundtruth data from CellMapDataset objects.
     """
 
-    input_arrays: dict[str, dict[str, Iterable[int | float]]]
-    target_arrays: dict[str, dict[str, Iterable[int | float]]]
-    classes: Iterable[str]
+    input_arrays: dict[str, dict[str, Sequence[int | float]]]
+    target_arrays: dict[str, dict[str, Sequence[int | float]]]
+    classes: Sequence[str]
+    to_target: Callable
     datasets: dict[str, Iterable[CellMapDataset]]
+    train_datasets: Iterable[CellMapDataset]
+    validate_datasets: Iterable[CellMapDataset]
+    train_datasets_combined: CellMapMultiDataset
+    validate_datasets_combined: CellMapMultiDataset
+    transforms: RandomApply | None
 
     def __init__(
         self,
-        input_arrays: dict[str, dict[str, Iterable[int | float]]],
-        target_arrays: dict[str, dict[str, Iterable[int | float]]],
-        classes: Iterable[str],
+        input_arrays: dict[str, dict[str, Sequence[int | float]]],
+        target_arrays: dict[str, dict[str, Sequence[int | float]]],
+        classes: Sequence[str],
+        to_target: Callable,
         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,
     ):
         """Initializes the CellMapDatasets class.
 
         Args:
-            input_arrays (dict[str, dict[str, Iterable[int | float]]]): A dictionary containing the arrays of the dataset to input to the network. The dictionary should have the following structure:
+            input_arrays (dict[str, dict[str, Sequence[int | float]]]): A dictionary containing the arrays of the dataset to input to the network. The dictionary should have the following structure:
                 {
                     "array_name": {
                         "shape": typle[int],
-                        "scale": Iterable[float],
+                        "scale": Sequence[float],
                     },
                     ...
                 }
-            target_arrays (dict[str, dict[str, Iterable[int | float]]]): A dictionary containing the arrays of the dataset to use as targets for the network. The dictionary should have the following structure:
+            target_arrays (dict[str, dict[str, Sequence[int | float]]]): A dictionary containing the arrays of the dataset to use as targets for the network. The dictionary should have the following structure:
                 {
                     "array_name": {
                         "shape": typle[int],
-                        "scale": Iterable[float],
+                        "scale": Sequence[float],
                     },
                     ...
                 }
-            classes (Iterable[str]): A list of classes for segmentation training. Class order will be preserved in the output arrays. Classes not contained in the dataset will be filled in with zeros.
+            classes (Sequence[str]): A list of classes for segmentation training. Class order will be preserved in the output arrays. Classes not contained in the dataset will be filled in with zeros.
+            to_target (Callable): A function to convert the ground truth data to target arrays. The function should have the following structure:
+                def to_target(gt: torch.Tensor, classes: Sequence[str]) -> dict[str, torch.Tensor]:
             datasets (Optional[Dict[str, CellMapDataset]], optional): A dictionary containing the dataset objects. The dictionary should have the following structure:
                 {
                     "train": Iterable[CellMapDataset],
@@ -58,6 +70,7 @@ def __init__(
                 }. 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:
         """
         self.input_arrays = input_arrays
         self.target_arrays = target_arrays
@@ -71,13 +84,8 @@ def __init__(
             self.construct(dataset_dict)
         elif csv_path is not None:
             self.from_csv(csv_path)
-
-    def __len__(self):
-        return len(self.train_datasets)
-
-    def __getitem__(self, idx): ...
-
-    def __iter__(self): ...
+        self.to_target = to_target
+        self.transforms = RandomApply(transforms) if transforms is not None else None
 
     def from_csv(self, csv_path):
         # Load file data from csv file
@@ -99,17 +107,40 @@ def construct(self, dataset_dict):
         for raw, gt in zip(dataset_dict["train"]["raw"], dataset_dict["train"]["gt"]):
             self.train_datasets.append(
                 CellMapDataset(
-                    raw, gt, self.classes, self.input_arrays, self.target_arrays
+                    raw,
+                    gt,
+                    self.classes,
+                    self.input_arrays,
+                    self.target_arrays,
+                    self.to_target,
+                    self.transforms,
                 )
             )
         for raw, gt in zip(
             dataset_dict["validate"]["raw"], dataset_dict["validate"]["gt"]
         ):
             self.validate_datasets.append(
                 CellMapDataset(
-                    raw, gt, self.classes, self.input_arrays, self.target_arrays
+                    raw,
+                    gt,
+                    self.classes,
+                    self.input_arrays,
+                    self.target_arrays,
+                    self.to_target,
                 )
             )
+        self.train_datasets_combined = CellMapMultiDataset(
+            self.classes,
+            self.input_arrays,
+            self.target_arrays,
+            [ds for ds in self.train_datasets if ds.has_data],
+        )
+        self.validate_datasets_combined = CellMapMultiDataset(
+            self.classes,
+            self.input_arrays,
+            self.target_arrays,
+            [ds for ds in self.validate_datasets if ds.has_data],
+        )
 
 
 # Example input arrays:

src/cellmap_data/image.py

@@ -1,7 +1,9 @@
 from typing import Iterable, Optional
 import torch
-import tensorstore as ts
 from fibsem_tools.io.core import read_xarray
+import xarray
+import tensorstore
+import xarray_tensorstore as xt
 
 
 class CellMapImage:
@@ -10,14 +12,16 @@ class CellMapImage:
     shape: tuple[float, ...]
     scale: tuple[float, ...]
     label_class: str
-    store: ts.TensorStore
+    array: xarray.DataArray
+    context: Optional[tensorstore.Context] = None
 
     def __init__(
         self,
         path: str,
         target_class: str,
         target_scale: Iterable[float],
         target_voxel_shape: Iterable[int],
+        context: Optional[tensorstore.Context] = None,
     ):
         """Initializes a CellMapImage object.
 
@@ -36,12 +40,29 @@ def __init__(
         self.output_shape = tuple(
             target_voxel_shape
         )  # TODO: this should be a dictionary of shapes for each axis
+        self.context = context
         self.construct()
 
     def construct(self):
         self._bounding_box = None
         self._sampling_box = None
         self._class_counts = None
+        self.ds = read_xarray(self.path)
+        # Find correct multiscale level based on target scale
+        # TODO
+        ...
+        self.array_path = ...
+        # Construct an xarray with Tensorstore backend
+        spec = xt._zarr_spec_from_path(self.array_path)
+        array_future = tensorstore.open(  # type: ignore
+            spec, read=True, write=False, context=self.context
+        )
+        array = array_future.result()
+        new_data = xt._TensorStoreAdapter(array)
+        self.array = ds.copy(data=new_data)  # type: ignore
+        self.xs = self.array.coords["x"]
+        self.ys = self.array.coords["y"]
+        self.zs = self.array.coords["z"]
 
     def __getitem__(self, center: Iterable[float]) -> torch.Tensor:
         """Returns image data centered around the given point, based on the scale and shape of the target output image."""