test_onnx also with multi tensor again

Author: FynnBe

tests/conftest.py

@@ -139,8 +139,15 @@ def any_model(request):
     return pytest.model_packages[request.param]
 
 
-# temporary fixture to test not with all, but only a manual selection of models
+#
+# temporary fixtures to test not with all, but only a manual selection of models
 # (models/functionality should be improved to get rid of this specific model group)
+#
 @pytest.fixture(params=[] if skip_torch else ["unet2d_nuclei_broad_model", "unet2d_fixed_shape"])
 def unet2d_fixed_shape_or_not(request):
     return pytest.model_packages[request.param]
+
+
+@pytest.fixture(params=[] if skip_torch else ["unet2d_nuclei_broad_model", "unet2d_multi_tensor"])
+def unet2d_multi_tensor_or_not(request):
+    return pytest.model_packages[request.param]

tests/weight_converter/torch/test_onnx.py

@@ -3,11 +3,11 @@
 
 
 # todo: test with 'any_torch_model'
-def test_onnx_converter_from_torch(unet2d_nuclei_broad, tmp_path):
+def test_onnx_converter(unet2d_multi_tensor_or_not, tmp_path):
     from bioimageio.core.weight_converter.torch.onnx import convert_weights_to_onnx
 
     out_path = tmp_path / "weights.onnx"
-    ret_val = convert_weights_to_onnx(unet2d_nuclei_broad, out_path, test_decimal=3)
+    ret_val = convert_weights_to_onnx(unet2d_multi_tensor_or_not, out_path, test_decimal=3)
     assert os.path.exists(out_path)
     if not pytest.skip_onnx:
         assert ret_val == 0  # check for correctness is done in converter and returns 0 if it passes