Update model test to handle expanded output shape and add test model

Author: constantinpape

bioimageio/core/resource_tests.py

@@ -89,7 +89,7 @@ def _validate_output_shape(shape: Tuple[int, ...], shape_spec, input_shapes) ->
         if ref_tensor not in input_shapes:
             raise ValidationError(f"The reference tensor name {ref_tensor} is not in {input_shapes}")
         ipt_shape = numpy.array(input_shapes[ref_tensor])
-        scale = numpy.array(shape_spec.scale)
+        scale = numpy.array([0.0 if sc is None else sc for sc in shape_spec.scale])
         offset = numpy.array(shape_spec.offset)
         exp_shape = numpy.round_(ipt_shape * scale) + 2 * offset
 

tests/conftest.py

@@ -32,6 +32,10 @@
         "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models/"
         "unet2d_nuclei_broad/rdf.yaml"
     ),
+    "unet2d_expand_output_shape": (
+        "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models/"
+        "unet2d_nuclei_broad/rdf_expand_output_shape.yaml"
+    ),
     "unet2d_fixed_shape": (
         "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models/"
         "unet2d_fixed_shape/rdf.yaml"
@@ -205,6 +209,12 @@ def unet2d_diff_output_shape(request):
     return pytest.model_packages[request.param]
 
 
+# written as model group to automatically skip on missing torch
+@pytest.fixture(params=[] if skip_torch else ["unet2d_expand_output_shape"])
+def unet2d_expand_output_shape(request):
+    return pytest.model_packages[request.param]
+
+
 # written as model group to automatically skip on missing torch
 @pytest.fixture(params=[] if skip_torch else ["unet2d_fixed_shape"])
 def unet2d_fixed_shape(request):