Specify resolution with sequences

dacapo/experiments/datasplits/datasplit_generator.py

@@ -1,9 +1,8 @@
 from dacapo.experiments.tasks import TaskConfig
 from pathlib import Path
-from typing import List
+from typing import List, Union, Optional, Sequence
 from enum import Enum, EnumMeta
 from funlib.geometry import Coordinate
-from typing import Union, Optional
 
 import zarr
 from dacapo.experiments.datasplits.datasets.arrays import (
@@ -159,7 +158,10 @@ def generate_dataspec_from_csv(csv_path: Path):
 
 class DataSplitGenerator:
     """Generates DataSplitConfig for a given task config and datasets.
-    class names in gt_dataset shoulb be within [] e.g. [mito&peroxisome&er] for mutiple classes or [mito] for one class
+
+    Class names in gt_dataset should be within [] e.g. [mito&peroxisome&er] for
+    multiple classes or [mito] for one class.
+
     Currently only supports:
      - semantic segmentation.
      Supports:
@@ -172,8 +174,8 @@ def __init__(
         self,
         name: str,
         datasets: List[DatasetSpec],
-        input_resolution: Coordinate,
-        output_resolution: Coordinate,
+        input_resolution: Union[Sequence[int], Coordinate],
+        output_resolution: Union[Sequence[int], Coordinate],
         targets: Optional[List[str]] = None,
         segmentation_type: Union[str, SegmentationType] = "semantic",
         max_gt_downsample=32,
@@ -187,16 +189,19 @@ def __init__(
         raw_max=255,
         classes_separator_caracter="&",
     ):
+        if not isinstance(input_resolution, Coordinate):
+            input_resolution = Coordinate(input_resolution)
+        if not isinstance(output_resolution, Coordinate):
+            output_resolution = Coordinate(output_resolution)
+        if isinstance(segmentation_type, str):
+            segmentation_type = SegmentationType[segmentation_type.lower()]
+
         self.name = name
         self.datasets = datasets
         self.input_resolution = input_resolution
         self.output_resolution = output_resolution
         self.targets = targets
         self._class_name = None
-
-        if isinstance(segmentation_type, str):
-            segmentation_type = SegmentationType[segmentation_type.lower()]
-
         self.segmentation_type = segmentation_type
         self.max_gt_downsample = max_gt_downsample
         self.max_gt_upsample = max_gt_upsample
@@ -369,8 +374,8 @@ def __generate_semantic_seg_dataset_crop(self, dataset: DatasetSpec):
     @staticmethod
     def generate_from_csv(
         csv_path: Path,
-        input_resolution: Coordinate,
-        output_resolution: Coordinate,
+        input_resolution: Union[Sequence[int], Coordinate],
+        output_resolution: Union[Sequence[int], Coordinate],
         name: Optional[str] = None,
         **kwargs,
     ):