update documentation

dacapo/experiments/architectures/model_zoo_config.py

@@ -6,7 +6,7 @@
 
 from funlib.geometry import Coordinate
 
-from bioimageio.core import load_description_and_test
+from bioimageio.core import load_description
 from bioimageio.core.model_adapters._pytorch_model_adapter import PytorchModelAdapter
 from bioimageio.spec import InvalidDescr
 from bioimageio.spec.model.v0_5 import (
@@ -28,7 +28,7 @@ class ModelZooConfig(ArchitectureConfig):
     Support is currently limited to models saved with the `PytorchStateDictWeightsDescr`.
     See more info here: https://bioimage-io.github.io/spec-bioimage-io/bioimageio/spec/model/v0_5.html#PytorchStateDictWeightsDescr
 
-    We try to support all model_id formats that are supported by the `bioimageio.core` `load_description_and_test` function.
+    We try to support all model_id formats that are supported by the `bioimageio.core` `load_description` function.
     However there seem to be some cases that fail. You may receive an `InvalidDescr` error when trying to load a model from
     a downloaded rdf file. In this case downloading the zipped model, or using the models name e.g. "affable-shark"
     should work.
@@ -80,11 +80,11 @@ def model_description(self) -> ModelDescr:
             if isinstance(self.model_id, Path) and self.model_id.suffix == ".zip":
                 with zipfile.ZipFile(self.model_id, "r") as zip_ref:
                     zip_ref.extractall(Path(f"{self.model_id}.unzip"))
-                self._model_description = load_description_and_test(
+                self._model_description = load_description(
                     Path(f"{self.model_id}.unzip")
                 )
             else:
-                self._model_description = load_description_and_test(self.model_id)
+                self._model_description = load_description(self.model_id)
             if isinstance(self._model_description, InvalidDescr):
                 raise Exception("Invalid model description")
         return self._model_description

dacapo/experiments/tasks/predictors/predictor.py

@@ -31,19 +31,9 @@ class Predictor(ABC):
     def create_model(self, architecture: "Architecture") -> "Model":
         """
         Given a training architecture, create a model for this predictor.
-        This is usually done by appending extra layers to the output of the
+        This is usually done by appending a single final conv layer to the output of the
         architecture to get the output tensor of the architecture into the
         right shape for this predictor.
-
-        Args:
-            architecture: The training architecture.
-        Returns:
-            The model.
-        Raises:
-            NotImplementedError: This method is not implemented.
-        Examples:
-            >>> predictor.create_model(architecture)
-
         """
         pass
 
@@ -58,7 +48,7 @@ def create_target(self, gt: Array) -> Array:
         compared to the prediction (i.e., the output of the model). Depending
         on the predictor, the target can therefore be different from the
         ground-truth (e.g., an instance segmentation ground-truth would have to
-        be converted into boundaries, if the model is predicting boundaries).
+        be converted into affinities, if the model is predicting affinities).
 
         By default, it is assumed that the spatial dimensions of ground-truth
         and target are the same.
@@ -67,16 +57,6 @@ def create_target(self, gt: Array) -> Array:
         (e.g., because it predicts the distance to a boundary, up to a certain
         threshold), you can request a larger ground-truth region. See method
         ``gt_region_for_roi``.
-
-        Args:
-            gt: The ground-truth array.
-        Returns:
-            The target array.
-        Raises:
-            NotImplementedError: This method is not implemented.
-        Examples:
-            >>> predictor.create_target(gt)
-
         """
         pass
 
@@ -119,31 +99,11 @@ def gt_region_for_roi(self, target_spec):
 
         Overwrite this method to request ground-truth in a larger ROI, as
         needed.
-
-        Args:
-            target_spec: The ROI for which the target is requested.
-        Returns:
-            The ROI for which the ground-truth is requested.
-        Raises:
-            NotImplementedError: This method is not implemented.
-        Examples:
-            >>> predictor.gt_region_for_roi(target_spec)
-
-
         """
         return target_spec
 
     def padding(self, gt_voxel_size: Coordinate) -> Coordinate:
         """
         Return the padding needed for the ground-truth array.
-
-        Args:
-            gt_voxel_size: The voxel size of the ground-truth array.
-        Returns:
-            The padding needed for the ground-truth array.
-        Raises:
-            NotImplementedError: This method is not implemented.
-        Examples:
-            >>> predictor.padding(gt_voxel_size)
         """
         return Coordinate((0,) * gt_voxel_size.dims)