🎨 Format Python code with psf/black

dacapo/experiments/tasks/__init__.py

@@ -5,4 +5,4 @@
 from .one_hot_task_config import OneHotTaskConfig, OneHotTask  # noqa
 from .pretrained_task_config import PretrainedTaskConfig, PretrainedTask  # noqa
 from .affinities_task_config import AffinitiesTaskConfig, AffinitiesTask  # noqa
-from .hot_distance_task_config import HotDistanceTaskConfig, HotDistanceTask  # noqa
+from .hot_distance_task_config import HotDistanceTaskConfig, HotDistanceTask  # noqa

dacapo/experiments/tasks/hot_distance_task_config.py

@@ -5,6 +5,7 @@
 
 from typing import List
 
+
 @attr.s
 class HotDistanceTaskConfig(TaskConfig):
     """This is a Hot Distance task config used for generating and

dacapo/experiments/tasks/losses/hot_distance_loss.py

@@ -14,17 +14,19 @@ def compute(self, prediction, target, weight):
         return self.hot_loss(
             prediction_hot, target_hot, weight_hot
         ) + self.distance_loss(prediction_distance, target_distance, weight_distance)
-    
+
     def hot_loss(self, prediction, target, weight):
-        loss = torch.nn.BCEWithLogitsLoss(reduction='none')
-        return torch.mean(loss(prediction , target) * weight)
-    
+        loss = torch.nn.BCEWithLogitsLoss(reduction="none")
+        return torch.mean(loss(prediction, target) * weight)
+
     def distance_loss(self, prediction, target, weight):
         loss = torch.nn.MSELoss()
         return loss(prediction * weight, target * weight)
-    
+
     def split(self, x):
         # Shape[0] is the batch size and Shape[1] is the number of channels.
-        assert x.shape[1] % 2 == 0, f"First dimension (Channels) of target {x.shape} must be even to be splitted in hot and distance."
+        assert (
+            x.shape[1] % 2 == 0
+        ), f"First dimension (Channels) of target {x.shape} must be even to be splitted in hot and distance."
         mid = x.shape[1] // 2
-        return torch.split(x,mid,dim=1)
+        return torch.split(x, mid, dim=1)

dacapo/experiments/tasks/predictors/__init__.py

@@ -3,4 +3,4 @@
 from .one_hot_predictor import OneHotPredictor  # noqa
 from .predictor import Predictor  # noqa
 from .affinities_predictor import AffinitiesPredictor  # noqa
-from .hot_distance_predictor import HotDistancePredictor  # noqa
+from .hot_distance_predictor import HotDistancePredictor  # noqa

dacapo/experiments/tasks/predictors/hot_distance_predictor.py

@@ -76,7 +76,9 @@ def create_weight(self, gt, target, mask, moving_class_counts=None):
             2,
             slab=tuple(1 if c == "c" else -1 for c in gt.axes),
             masks=[mask[target.roi]],
-            moving_counts=None if moving_class_counts is None else moving_class_counts[: self.classes],
+            moving_counts=None
+            if moving_class_counts is None
+            else moving_class_counts[: self.classes],
         )
 
         if self.mask_distances:
@@ -95,7 +97,9 @@ def create_weight(self, gt, target, mask, moving_class_counts=None):
             2,
             slab=tuple(1 if c == "c" else -1 for c in gt.axes),
             masks=[mask[target.roi], distance_mask],
-            moving_counts=None if moving_class_counts is None else moving_class_counts[-self.classes :],
+            moving_counts=None
+            if moving_class_counts is None
+            else moving_class_counts[-self.classes :],
         )
 
         weights = np.concatenate((one_hot_weights, distance_weights))

dacapo/train.py

@@ -98,7 +98,7 @@ def train_run(
 
             weights_store.retrieve_weights(run, iteration=trained_until)
 
-        elif latest_weights_iteration > trained_until:          
+        elif latest_weights_iteration > trained_until:
             logger.warn(
                 f"Found weights for iteration {latest_weights_iteration}, but "
                 f"run {run.name} was only trained until {trained_until}. "