black reformatting

dacapo/experiments/architectures/architecture.py

@@ -37,7 +37,6 @@ def module(self) -> torch.nn.Module:
         """
         pass
 
-
     @property
     @abstractmethod
     def input_shape(self) -> Coordinate:
@@ -78,4 +77,4 @@ def scale(self, input_voxel_size: Coordinate) -> Coordinate:
         """
         Method to scale the input voxel size as required by the architecture.
         """
-        return input_voxel_size
+        return input_voxel_size

dacapo/experiments/architectures/cnnectome_unet.py

@@ -130,12 +130,15 @@ class CNNectomeUNetConfig(ArchitectureConfig):
         default=False,
         metadata={"help_text": "Whether to use batch normalization."},
     )
+
     @property
     def input_shape(self) -> Coordinate:
         return self._input_shape
+
     @property
     def input_shape(self) -> Coordinate:
         return Coordinate(self._input_shape)
+
     @input_shape.setter
     def input_shape(self, value: Coordinate):
         self._input_shape = value
@@ -147,15 +150,11 @@ def module(self) -> torch.nn.Module:
         if self.kernel_size_down is not None:
             kernel_size_down = self.kernel_size_down
         else:
-            kernel_size_down = [
-                [(3,) * self.dims, (3,) * self.dims]
-            ] * levels
+            kernel_size_down = [[(3,) * self.dims, (3,) * self.dims]] * levels
         if self.kernel_size_up is not None:
             kernel_size_up = self.kernel_size_up
         else:
-            kernel_size_up = [
-                [(3,) * self.dims, (3,) * self.dims]
-            ] * (levels - 1)
+            kernel_size_up = [[(3,) * self.dims, (3,) * self.dims]] * (levels - 1)
 
         # downsample factors has to be a list of tuples
         downsample_factors = [tuple(x) for x in self.downsample_factors]

dacapo/experiments/architectures/cnnectome_unet_impl.py

@@ -3,6 +3,7 @@
 
 import math
 
+
 class CNNectomeUNetModule(torch.nn.Module):
     """
     A U-Net module for 3D or 4D data. The U-Net expects 3D or 4D tensors shaped

dacapo/experiments/architectures/model_zoo_config.py

@@ -91,16 +91,16 @@ def model_description(self) -> ModelDescr:
 
     @property
     def input_desc(self) -> InputTensorDescr:
-        assert len(self.model_description.inputs) == 1, (
-            f"Only models with one input are supported, found {self.model_description.inputs}"
-        )
+        assert (
+            len(self.model_description.inputs) == 1
+        ), f"Only models with one input are supported, found {self.model_description.inputs}"
         return self.model_description.inputs[0]
 
     @property
     def output_desc(self) -> OutputTensorDescr:
-        assert len(self.model_description.outputs) == 1, (
-            f"Only models with one output are supported, found {self.model_description.outputs}"
-        )
+        assert (
+            len(self.model_description.outputs) == 1
+        ), f"Only models with one output are supported, found {self.model_description.outputs}"
         return self.model_description.outputs[0]
 
     @property
@@ -115,19 +115,19 @@ def input_shape(self):
     @property
     def num_in_channels(self) -> int:
         channel_axes = [axis for axis in self.input_desc.axes if axis.type == "channel"]
-        assert len(channel_axes) == 1, (
-            f"Only models with one input channel axis are supported, found {channel_axes}"
-        )
+        assert (
+            len(channel_axes) == 1
+        ), f"Only models with one input channel axis are supported, found {channel_axes}"
         return channel_axes[0].size
 
     @property
     def num_out_channels(self) -> int:
         channel_axes = [
             axis for axis in self.output_desc.axes if axis.type == "channel"
         ]
-        assert len(channel_axes) == 1, (
-            f"Only models with one output channel axis are supported, found {channel_axes}"
-        )
+        assert (
+            len(channel_axes) == 1
+        ), f"Only models with one output channel axis are supported, found {channel_axes}"
         return channel_axes[0].size
 
     def scale(self, input_voxel_size: Coordinate) -> Coordinate:

dacapo/experiments/tasks/affinities_task.py

@@ -50,8 +50,10 @@ def __init__(self, task_config):
         self.post_processor = WatershedPostProcessor(offsets=task_config.neighborhood)
         self.evaluator = InstanceEvaluator()
 
-        self._channels = [f"aff_{'.'.join(map(str, n))}" for n in task_config.neighborhood]
+        self._channels = [
+            f"aff_{'.'.join(map(str, n))}" for n in task_config.neighborhood
+        ]
 
     @property
     def channels(self) -> list[str]:
-        return self._channels
+        return self._channels

dacapo/experiments/tasks/distance_task.py

@@ -58,4 +58,4 @@ def __init__(self, task_config):
 
     @property
     def channels(self) -> list[str]:
-        return self._channels
+        return self._channels

dacapo/experiments/tasks/dummy_task.py

@@ -54,4 +54,4 @@ def __init__(self, task_config):
 
     @property
     def channels(self) -> list[str]:
-        return self._channels
+        return self._channels

dacapo/experiments/tasks/hot_distance_task.py

@@ -62,4 +62,4 @@ def __init__(self, task_config):
 
     @property
     def channels(self) -> list[str]:
-        return self._channels
+        return self._channels

dacapo/experiments/tasks/inner_distance_task.py

@@ -51,4 +51,4 @@ def __init__(self, task_config):
 
     @property
     def channels(self) -> list[str]:
-        return self._channels
+        return self._channels

dacapo/experiments/tasks/one_hot_task.py

@@ -51,4 +51,4 @@ def __init__(self, task_config):
 
     @property
     def channels(self) -> list[str]:
-        return self._classes
+        return self._classes

dacapo/experiments/tasks/task.py

@@ -23,7 +23,7 @@ def evaluation_scores(self) -> EvaluationScores:
 
     def create_model(self, architecture):
         return self.predictor.create_model(architecture=architecture)
-    
+
     @property
     @abstractmethod
     def channels(self) -> list[str]:

dacapo/store/local_weights_store.py

@@ -72,7 +72,11 @@ def save_trace(self, run: RunConfig):
         if not trace_file.parent.exists():
             trace_file.parent.mkdir(parents=True, exist_ok=True)
         if not trace_file.exists():
-            in_shape = (1, run.architecture.num_in_channels, *run.architecture.input_shape)
+            in_shape = (
+                1,
+                run.architecture.num_in_channels,
+                *run.architecture.input_shape,
+            )
             in_data = torch.randn(in_shape)
             try:
                 torch.jit.save(
@@ -82,7 +86,6 @@ def save_trace(self, run: RunConfig):
             except SystemError as e:
                 print(f"Error saving trace: {e}, this model will not be traced")
                 trace_file.touch()
-
 
     def latest_iteration(self, run: str) -> Optional[int]:
         """

tests/components/test_architectures.py

@@ -120,13 +120,15 @@ def build_test_architecture_config(
             tmp_path / "dacapo_modelzoo_test.zip", authors=[Author(name="Test")]
         )
         return ModelZooConfig(
-            model_id = tmp_path / "dacapo_modelzoo_test.zip", name="test_model_zoo"
+            model_id=tmp_path / "dacapo_modelzoo_test.zip", name="test_model_zoo"
         )
 
 
 # TODO: Move unet parameters that don't affect interaction with other modules
 # to a separate architcture test
-@pytest.mark.filterwarnings("ignore::FutureWarning")  # pytest treats this as an error but we don't care for now
+@pytest.mark.filterwarnings(
+    "ignore::FutureWarning"
+)  # pytest treats this as an error but we don't care for now
 @pytest.mark.parametrize("data_dims", [2, 3])
 @pytest.mark.parametrize("channels", [True, False])
 @pytest.mark.parametrize("architecture_dims", [2, 3])
@@ -155,7 +157,7 @@ def test_architectures(
         use_attention,
         padding,
         source,
-        tmp_path
+        tmp_path,
     )
 
     in_data = torch.rand(

tests/operations/test_architecture.py

@@ -35,9 +35,7 @@ def test_stored_architecture(
 
     architecture = retrieved_arch_config
 
-    assert (
-        architecture.dims is not None
-    ), f"Architecture dims are None {architecture}"
+    assert architecture.dims is not None, f"Architecture dims are None {architecture}"
 
 
 @pytest.mark.parametrize(