Merge pull request #394 from bioimage-io/update_usage

Update model usage example

README.md

@@ -124,6 +124,10 @@ The model specification and its validation tools can be found at <https://github
 
 ## Changelog
 
+### 0.6.7
+
+* `predict()` argument `inputs` may be sample
+
 ### 0.6.6
 
 * add aliases to match previous API more closely

bioimageio/core/VERSION

@@ -1,3 +1,3 @@
 {
-    "version": "0.6.6"
+    "version": "0.6.7"
 }

bioimageio/core/io.py

@@ -47,9 +47,10 @@ def save_sample(path: Union[Path, str], sample: Sample) -> None:
     `path` must contain `{member_id}` and may contain `{sample_id}`,
     which are resolved with the `sample` object.
     """
-    path = str(path).format(sample_id=sample.id)
     if "{member_id}" not in path:
         raise ValueError(f"missing `{{member_id}}` in path {path}")
 
+    path = str(path).format(sample_id=sample.id, member_id="{member_id}")
+
     for m, t in sample.members.items():
         save_tensor(Path(path.format(member_id=m)), t)

bioimageio/core/prediction.py

@@ -39,7 +39,7 @@ def predict(
     model: Union[
         PermissiveFileSource, v0_4.ModelDescr, v0_5.ModelDescr, PredictionPipeline
     ],
-    inputs: PerMember[Union[Tensor, xr.DataArray, NDArray[Any], Path]],
+    inputs: Union[Sample, PerMember[Union[Tensor, xr.DataArray, NDArray[Any], Path]]],
     sample_id: Hashable = "sample",
     blocksize_parameter: Optional[
         Union[
@@ -56,7 +56,7 @@ def predict(
     Args:
         model: model to predict with.
             May be given as RDF source, model description or prediction pipeline.
-        inputs: the named input(s) for this model as a dictionary
+        inputs: the input sample or the named input(s) for this model as a dictionary
         sample_id: the sample id.
         blocksize_parameter: (optional) tile the input into blocks parametrized by
             blocksize according to any parametrized axis sizes defined in the model RDF
@@ -82,9 +82,12 @@ def predict(
 
         pp = create_prediction_pipeline(model)
 
-    sample = create_sample_for_model(
-        pp.model_description, inputs=inputs, sample_id=sample_id
-    )
+    if isinstance(inputs, Sample):
+        sample = inputs
+    else:
+        sample = create_sample_for_model(
+            pp.model_description, inputs=inputs, sample_id=sample_id
+        )
 
     if blocksize_parameter is None:
         output = pp.predict_sample_without_blocking(

bioimageio/core/proc_setup.py

@@ -11,10 +11,18 @@
 
 from typing_extensions import assert_never
 
+from bioimageio.core.common import MemberId
+from bioimageio.core.digest_spec import get_member_ids
 from bioimageio.spec.model import AnyModelDescr, v0_4, v0_5
 from bioimageio.spec.model.v0_5 import TensorId
 
-from .proc_ops import AddKnownDatasetStats, Processing, UpdateStats, get_proc_class
+from .proc_ops import (
+    AddKnownDatasetStats,
+    EnsureDtype,
+    Processing,
+    UpdateStats,
+    get_proc_class,
+)
 from .sample import Sample
 from .stat_calculators import StatsCalculator
 from .stat_measures import DatasetMeasure, Measure, MeasureValue
@@ -87,12 +95,8 @@ def _prepare_setup_pre_and_postprocessing(model: AnyModelDescr) -> _SetupProcess
     pre_measures: Set[Measure] = set()
     post_measures: Set[Measure] = set()
 
-    if isinstance(model, v0_4.ModelDescr):
-        input_ids = {TensorId(str(d.name)) for d in model.inputs}
-        output_ids = {TensorId(str(d.name)) for d in model.outputs}
-    else:
-        input_ids = {d.id for d in model.inputs}
-        output_ids = {d.id for d in model.outputs}
+    input_ids = set(get_member_ids(model.inputs))
+    output_ids = set(get_member_ids(model.outputs))
 
     def prepare_procs(tensor_descrs: Sequence[TensorDescr]):
         procs: List[Processing] = []