Update conftest, test prediction functions with any_model, fix issues in prediction impl

Author: constantinpape

bioimageio/core/prediction.py

@@ -318,6 +318,8 @@ def predict_with_tiling(prediction_pipeline: PredictionPipeline, inputs, tiling)
 
 
 def parse_padding(padding, model):
+    if padding is None:  # no padding
+        return padding
     if len(model.inputs) > 1:
         raise NotImplementedError("Padding for multiple inputs not yet implemented")
 
@@ -327,9 +329,7 @@ def parse_padding(padding, model):
     def check_padding(padding):
         assert all(k in pad_keys for k in padding.keys())
 
-    if padding is None:  # no padding
-        return padding
-    elif isinstance(padding, dict):  # pre-defined padding
+    if isinstance(padding, dict):  # pre-defined padding
         check_padding(padding)
     elif isinstance(padding, bool):  # determine padding from spec
         if padding:
@@ -351,6 +351,8 @@ def check_padding(padding):
 
 
 def parse_tiling(tiling, model):
+    if tiling is None:  # no tiling
+        return tiling
     if len(model.inputs) > 1:
         raise NotImplementedError("Tiling for multiple inputs not yet implemented")
 
@@ -363,9 +365,7 @@ def parse_tiling(tiling, model):
     def check_tiling(tiling):
         assert "halo" in tiling and "tile" in tiling
 
-    if tiling is None:  # no tiling
-        return tiling
-    elif isinstance(tiling, dict):
+    if isinstance(tiling, dict):
         check_tiling(tiling)
     elif isinstance(tiling, bool):
         if tiling:

tests/conftest.py

@@ -11,14 +11,14 @@
 torch_models_pre_3_10 = ["unet2d_fixed_shape", "unet2d_multi_tensor", "unet2d_nuclei_broad_model"]
 torchscript_models = ["unet2d_multi_tensor", "unet2d_nuclei_broad_model"]
 onnx_models = ["unet2d_multi_tensor", "unet2d_nuclei_broad_model", "hpa_densenet"]
-tensorflow1_models = ["FruNet_model", "stardist"]
+tensorflow1_models = ["stardist"]
 tensorflow2_models = []
-keras_models = ["FruNet_model"]
-tensorflow_js_models = ["FruNet_model"]
+keras_models = []
+tensorflow_js_models = []
 
 model_sources = {
-    "FruNet_model": "https://sandbox.zenodo.org/record/894498/files/rdf.yaml",
-    # "FruNet_model": "https://raw.githubusercontent.com/deepimagej/models/master/fru-net_sev_segmentation/model.yaml",
+    # TODO add unet2d_keras_tf from https://github.com/bioimage-io/spec-bioimage-io/pull/267
+    # "unet2d_keras_tf": (""),
     "unet2d_nuclei_broad_model": (
         "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models/"
         "unet2d_nuclei_broad/rdf.yaml"
@@ -35,10 +35,12 @@
         "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models/hpa-densenet/rdf.yaml"
     ),
     "stardist": (
-        "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models/stardist_example_model/rdf.yaml"
+        "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models"
+        "/stardist_example_model/rdf.yaml"
     ),
     "stardist_wrong_shape": (
-        "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models/stardist_example_model/rdf_wrong_shape.yaml"
+        "https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_specs/models/"
+        "stardist_example_model/rdf_wrong_shape.yaml"
     ),
 }
 
@@ -50,7 +52,6 @@
 except ImportError:
     torch = None
     torch_version = None
-
 skip_torch = torch is None
 
 try:
@@ -66,17 +67,15 @@
 except ImportError:
     tensorflow = None
     tf_major_version = None
-
 skip_tensorflow = tensorflow is None
-skip_tensorflow = True  # todo: update FruNet and remove this
-skip_tensorflow_js = True  # todo: update FruNet and figure out how to test tensorflow_js weights in python
+skip_tensorflow_js = True  # TODO: add a tensorflow_js example model
 
 try:
     import keras
 except ImportError:
     keras = None
 skip_keras = keras is None
-skip_keras = True  # FruNet requires update
+skip_keras = True  # TODO add unet2d_keras_tf to have a model for keras tests
 
 # load all model packages we need for testing
 load_model_packages = set()
@@ -120,14 +119,14 @@ def unet2d_nuclei_broad_model(request):
 
 
 # written as model group to automatically skip on missing tensorflow 1
-@pytest.fixture(params=[] if skip_tensorflow or tf_major_version != 1 else ["FruNet_model"])
-def FruNet_model(request):
+@pytest.fixture(params=[] if skip_tensorflow or tf_major_version != 1 else ["stardist_wrong_shape"])
+def stardist_wrong_shape(request):
     return pytest.model_packages[request.param]
 
 
 # written as model group to automatically skip on missing tensorflow 1
-@pytest.fixture(params=[] if skip_tensorflow or tf_major_version != 1 else ["stardist_wrong_shape"])
-def stardist_wrong_shape(request):
+@pytest.fixture(params=[] if skip_tensorflow or tf_major_version != 1 else ["stardist"])
+def stardist(request):
     return pytest.model_packages[request.param]
 
 
@@ -164,7 +163,9 @@ def any_tensorflow_js_model(request):
 
 
 # fixture to test with all models that should run in the current environment
-@pytest.fixture(params=load_model_packages)
+# we exclude stardist_wrong_shape here because it is not a valid model
+# and included only to test that validation for this model fails
+@pytest.fixture(params=load_model_packages - {"stardist_wrong_shape"})
 def any_model(request):
     return pytest.model_packages[request.param]
 

tests/test_prediction.py

@@ -8,20 +8,19 @@
 from bioimageio.core.resource_io.nodes import Model
 
 
-def test_test_model(unet2d_nuclei_broad_model):
+def test_test_model(any_model):
     from bioimageio.core.resource_tests import test_model
 
-    assert test_model(unet2d_nuclei_broad_model)
+    assert test_model(any_model)
 
 
-def test_test_resource(unet2d_nuclei_broad_model):
+def test_test_resource(any_model):
     from bioimageio.core.resource_tests import test_resource
 
-    assert test_resource(unet2d_nuclei_broad_model)
+    assert test_resource(any_model)
 
 
-def test_predict_image(unet2d_fixed_shape_or_not, tmpdir):
-    any_model = unet2d_fixed_shape_or_not  # todo: replace 'unet2d_fixed_shape_or_not' with 'any_model'
+def test_predict_image(any_model, tmpdir):
     from bioimageio.core.prediction import predict_image
 
     spec = load_resource_description(any_model)
@@ -92,15 +91,14 @@ def check_result():
     check_result()
 
 
-# prediction with padding with the parameters above may not be suted for any model
+# prediction with padding with the parameters above may not be suited for any model
 # so we only run it for the pytorch unet2d here
 def test_predict_image_with_padding(unet2d_fixed_shape_or_not, tmp_path):
     _test_predict_with_padding(unet2d_fixed_shape_or_not, tmp_path)
 
 
-# TODO need stardist model
-# def test_predict_image_with_padding_channel_last(stardist_model, tmp_path):
-#     _test_predict_with_padding(stardist_model, tmp_path)
+def test_predict_image_with_padding_channel_last(stardist, tmp_path):
+    _test_predict_with_padding(stardist, tmp_path)
 
 
 def _test_predict_image_with_tiling(model, tmp_path):
@@ -132,13 +130,14 @@ def check_result():
     check_result()
 
 
+# prediction with tiling with the parameters above may not be suited for any model
+# so we only run it for the pytorch unet2d here
 def test_predict_image_with_tiling(unet2d_nuclei_broad_model, tmp_path):
     _test_predict_image_with_tiling(unet2d_nuclei_broad_model, tmp_path)
 
 
-# TODO need stardist model
-# def test_predict_image_with_tiling_channel_last(stardist_model, tmp_path):
-#     _test_predict_image_with_tiling(stardist_model, tmp_path)
+def test_predict_image_with_tiling_channel_last(stardist, tmp_path):
+    _test_predict_image_with_tiling(stardist, tmp_path)
 
 
 def test_predict_images(unet2d_nuclei_broad_model, tmp_path):