Format Python code with psf/black push

dacapo/experiments/datasplits/datasets/arrays/zarr_array.py

@@ -108,7 +108,6 @@ def __init__(self, array_config):
         self._attributes = self.data.attrs
         self._axes = array_config._axes
         self.snap_to_grid = array_config.snap_to_grid
-
 
     def __str__(self):
         """
@@ -144,7 +143,7 @@ def __repr__(self):
 
         """
         return f"ZarrArray({self.file_name}, {self.dataset})"
-    
+
     @property
     def mode(self):
         if not hasattr(self, "_mode"):
@@ -421,7 +420,7 @@ def create_from_array_identifier(
         num_channels,
         voxel_size,
         dtype,
-        mode = "a",
+        mode="a",
         write_size=None,
         name=None,
         overwrite=False,

dacapo/experiments/datasplits/datasplit_generator.py

@@ -125,7 +125,7 @@ def get_right_resolution_array_config(
         file_name=container,
         dataset=str(current_dataset_path),
         snap_to_grid=target_resolution,
-        mode = "r"
+        mode="r",
     )
     zarr_array = ZarrArray(zarr_config)
     while (
@@ -138,7 +138,7 @@ def get_right_resolution_array_config(
             file_name=container,
             dataset=str(Path(dataset, f"s{level}")),
             snap_to_grid=target_resolution,
-            mode = "r"
+            mode="r",
         )
 
         zarr_array = ZarrArray(zarr_config)

dacapo/experiments/run.py

@@ -93,6 +93,7 @@ def __init__(self, run_config, load_starter_model: bool = True):
 
         """
         self.name = run_config.name
+        self._config = run_config
         self.train_until = run_config.num_iterations
         self.validation_interval = run_config.validation_interval
 
@@ -106,17 +107,17 @@ def __init__(self, run_config, load_starter_model: bool = True):
         self.task = task_type(run_config.task_config)
         self.architecture = architecture_type(run_config.architecture_config)
         self.trainer = trainer_type(run_config.trainer_config)
-        self.datasplit = datasplit_type(run_config.datasplit_config)
+
+        # lazy load datasplit
+        self._datasplit = None
 
         # combined pieces
         self.model = self.task.create_model(self.architecture)
         self.optimizer = self.trainer.create_optimizer(self.model)
 
         # tracking
         self.training_stats = TrainingStats()
-        self.validation_scores = ValidationScores(
-            self.task.parameters, self.datasplit.validate, self.task.evaluation_scores
-        )
+        self._validation_scores = None
 
         if not load_starter_model:
             self.start = None
@@ -142,6 +143,24 @@ def __init__(self, run_config, load_starter_model: bool = True):
 
         self.start.initialize_weights(self.model, new_head=new_head)
 
+    @property
+    def datasplit(self):
+        if self._datasplit is None:
+            self._datasplit = self._config.datasplit_config.datasplit_type(
+                self._config.datasplit_config
+            )
+        return self._datasplit
+
+    @property
+    def validation_scores(self):
+        if self._validation_scores is None:
+            self._validation_scores = ValidationScores(
+                self.task.parameters,
+                self.datasplit.validate,
+                self.task.evaluation_scores,
+            )
+        return self._validation_scores
+
     @staticmethod
     def get_validation_scores(run_config) -> ValidationScores:
         """