🎨 Format Python code with psf/black

dacapo/predict_local.py

@@ -73,7 +73,9 @@ def predict(
 
     model_device = str(next(model.parameters()).device).split(":")[0]
 
-    assert model_device == str(device), f"Model is not on the right device, Model: {model_device}, Compute device: {device}" 
+    assert model_device == str(
+        device
+    ), f"Model is not on the right device, Model: {model_device}, Compute device: {device}"
 
     def predict_fn(block):
         raw_input = raw_array.to_ndarray(block.read_roi)
@@ -89,7 +91,6 @@ def predict_fn(block):
             raw_input = np.expand_dims(raw_input, 0)
             axis_names = ["c^"] + axis_names
 
-
         with torch.no_grad():
             model.eval()
             predictions = (

tests/fixtures/__init__.py

@@ -16,6 +16,12 @@
 from .losses import dummy_loss
 from .post_processors import argmax, threshold
 from .predictors import distance_predictor, onehot_predictor
-from .runs import dummy_run, distance_run, onehot_run, unet_2d_distance_run, unet_3d_distance_run
+from .runs import (
+    dummy_run,
+    distance_run,
+    onehot_run,
+    unet_2d_distance_run,
+    unet_3d_distance_run,
+)
 from .tasks import dummy_task, distance_task, onehot_task, six_onehot_task
 from .trainers import dummy_trainer, gunpowder_trainer

tests/fixtures/architectures.py

@@ -76,8 +76,8 @@ def unet_architecture_builder(batch_norm, upsample, use_attention, three_d):
             num_fmaps=8,
             fmaps_out=8,
             fmap_inc_factor=2,
-            downsample_factors=[(4, 4), ( 4, 4)],
-            kernel_size_down=[[( 3, 3)] * 2] * 3,
+            downsample_factors=[(4, 4), (4, 4)],
+            kernel_size_down=[[(3, 3)] * 2] * 3,
             kernel_size_up=[[(3, 3)] * 2] * 2,
             constant_upsample=True,
             padding="valid",

tests/fixtures/runs.py

@@ -75,7 +75,6 @@ def unet_2d_distance_run(
     )
 
 
-
 @pytest.fixture()
 def unet_3d_distance_run(
     six_class_datasplit,

tests/operations/test_architecture.py

@@ -82,7 +82,6 @@ def test_2d_conv_unet(
             raise ValueError(f"Conv2d found in 3d unet {name}")
 
 
-
 @pytest.mark.parametrize(
     "run_config",
     [

tests/operations/test_train.py

@@ -110,7 +110,6 @@ def test_train_unet(
         batch_norm, upsample, use_attention, three_d
     )
 
-
     run_config = RunConfig(
         name=f"{architecture_config.name}_run",
         task_config=task,