diff --git a/model_server/base/session.py b/model_server/base/session.py
index 4aa88a1e212fc5e7b759fd82c3ef69f2e45d1ff3..be4911f09d3e20982328cbe465815225050b56f2 100644
--- a/model_server/base/session.py
+++ b/model_server/base/session.py
@@ -251,7 +251,7 @@ class _Session(object):
:param params: optional parameters that are passed to the model's construct
:return: model_id of loaded model
"""
- mi = ModelClass(params=params.dict())
+ mi = ModelClass(params=params.dict() if params else None)
assert mi.loaded, f'Error loading instance of {ModelClass.__name__}'
ii = 0
diff --git a/model_server/extensions/ilastik/models.py b/model_server/extensions/ilastik/models.py
index dccb269ade99d677fc22336979ccd98c61a1076e..5e45bab4d6e9077c1799a957f041edcf60f1baed 100644
--- a/model_server/extensions/ilastik/models.py
+++ b/model_server/extensions/ilastik/models.py
@@ -137,7 +137,7 @@ class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
[1, 2, 3, 0],
[0, 1, 2, 3]
)
- return InMemoryDataAccessor(data=yxcz), {'success': True}
+ return InMemoryDataAccessor(data=yxcz)
def infer_patch_stack(self, img: PatchStack, **kwargs) -> (np.ndarray, dict):
"""
@@ -150,13 +150,13 @@ class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
for i in range(0, img.count):
sl = img.get_slice_at(i)
try:
- data[i][sl[0], sl[1], :, sl[3]] = self.infer(img.iat(i, crop=True))[0].data
+ data[i][sl[0], sl[1], :, sl[3]] = self.infer(img.iat(i, crop=True)).data
except FeatureSelectionConstraintError: # occurs occasionally on small patches
continue
- return PatchStack(data), {'success': True}
+ return PatchStack(data)
def label_pixel_class(self, img: GenericImageDataAccessor, **kwargs):
- pxmap, _ = self.infer(img)
+ pxmap = self.infer(img)
mask = pxmap.get_mono(self.params['px_class']).apply(lambda x: x > self.params['px_prob_threshold'])
return mask
@@ -219,19 +219,18 @@ class IlastikObjectClassifierFromSegmentationModel(IlastikModel, InstanceSegment
[0, 1, 2, 3, 4],
[0, 4, 1, 2, 3]
)
- return PatchStack(data=pyxcz), {'success': True}
+ return PatchStack(data=pyxcz)
else:
yxcz = np.moveaxis(
obmaps[0],
[1, 2, 3, 0],
[0, 1, 2, 3]
)
- return InMemoryDataAccessor(data=yxcz), {'success': True}
+ return InMemoryDataAccessor(data=yxcz)
def label_instance_class(self, img: GenericImageDataAccessor, mask: GenericImageDataAccessor, **kwargs):
super(IlastikObjectClassifierFromSegmentationModel, self).label_instance_class(img, mask, **kwargs)
- obmap, _ = self.infer(img, mask)
- return obmap
+ return self.infer(img, mask)
class IlastikObjectClassifierFromPixelPredictionsModel(IlastikModel, ImageToImageModel):
@@ -277,14 +276,14 @@ class IlastikObjectClassifierFromPixelPredictionsModel(IlastikModel, ImageToImag
[0, 1, 2, 3, 4],
[0, 4, 1, 2, 3]
)
- return PatchStack(data=pyxcz), {'success': True}
+ return PatchStack(data=pyxcz)
else:
yxcz = np.moveaxis(
obmaps[0],
[1, 2, 3, 0],
[0, 1, 2, 3]
)
- return InMemoryDataAccessor(data=yxcz), {'success': True}
+ return InMemoryDataAccessor(data=yxcz)
def label_instance_class(self, img: GenericImageDataAccessor, pxmap: GenericImageDataAccessor, **kwargs):
@@ -305,8 +304,7 @@ class IlastikObjectClassifierFromPixelPredictionsModel(IlastikModel, ImageToImag
pxch = kwargs.get('pixel_classification_channel', 0)
pxtr = kwargs.get('pixel_classification_threshold', 0.5)
mask = InMemoryDataAccessor(pxmap.get_one_channel_data(pxch).data > pxtr)
- obmap, _ = self.infer(img, mask)
- return obmap
+ return self.infer(img, mask)
class Error(Exception):
diff --git a/model_server/extensions/ilastik/pipelines/px_then_ob.py b/model_server/extensions/ilastik/pipelines/px_then_ob.py
index 1aa64615479cad29f01b7d7e416d1fec2f3c9568..2e9fd6e46d6be10e9b0cdfbdac6bebe506019c68 100644
--- a/model_server/extensions/ilastik/pipelines/px_then_ob.py
+++ b/model_server/extensions/ilastik/pipelines/px_then_ob.py
@@ -57,8 +57,8 @@ def pixel_then_object_classification_pipeline(
else:
channels = range(0, d['input'].chroma)
d['select_channels'] = d.last.get_channels(channels, mip=k.get('mip', False))
- d['pxmap'], _ = models['px_model'].infer(d.last)
- d['ob_map'], _ = models['ob_model'].infer(d['select_channels'], d['pxmap'])
+ d['pxmap'] = models['px_model'].infer(d.last)
+ d['ob_map'] = models['ob_model'].infer(d['select_channels'], d['pxmap'])
return d
diff --git a/tests/test_ilastik/test_ilastik.py b/tests/test_ilastik/test_ilastik.py
index a6f467f9b7da2c30f14f07c09fc1a5f32db40f4a..4eedf981452b7771d9e1b6aa5c61f0fa72368633 100644
--- a/tests/test_ilastik/test_ilastik.py
+++ b/tests/test_ilastik/test_ilastik.py
@@ -148,7 +148,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
self.assertEqual(mask.nz, acc.nz)
self.assertEqual(mask.count, acc.count)
- pxmap, _ = self.model.infer_patch_stack(acc)
+ pxmap = self.model.infer_patch_stack(acc)
self.assertEqual(pxmap.dtype, float)
self.assertEqual(pxmap.chroma, len(self.model.labels))
self.assertEqual(pxmap.hw, acc.hw)
@@ -162,7 +162,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
params={'project_file': ilastik_classifiers['pxmap_to_obj']['path'].__str__()}
)
mask = self.model.label_pixel_class(self.mono_image)
- objmap, _ = model.infer(self.mono_image, mask)
+ objmap = model.infer(self.mono_image, mask)
self.assertTrue(
write_accessor_data_to_file(
@@ -333,7 +333,7 @@ class TestIlastikOnMultichannelInputs(TestServerTestCase):
img = generate_file_accessor(self.pa_input_image)
self.assertGreater(img.chroma, 1)
mod = ilm.IlastikPixelClassifierModel({'project_file': self.pa_px_classifier.__str__()})
- pxmap = mod.infer(img)[0]
+ pxmap = mod.infer(img)
self.assertEqual(pxmap.hw, img.hw)
self.assertEqual(pxmap.nz, img.nz)
return pxmap
diff --git a/tests/test_ilastik/test_roiset_workflow.py b/tests/test_ilastik/test_roiset_workflow.py
index 5f799089a54306fe3f2b5a03d4a1fa030e81a3fe..4adf24bb25a13e9a72bbad1bd0fbf9f0983b88ca 100644
--- a/tests/test_ilastik/test_roiset_workflow.py
+++ b/tests/test_ilastik/test_roiset_workflow.py
@@ -72,18 +72,14 @@ class BaseTestRoiSetMonoProducts(object):
'name': 'ilastik_px_mod',
'project_file': fp_px,
'model': ilm.IlastikPixelClassifierModel(
- ilm.IlastikPixelClassifierParams(
- project_file=fp_px,
- )
+ {'project_file': fp_px},
)
},
'object_classifier': {
'name': 'ilastik_ob_mod',
'project_file': fp_ob,
'model': ilm.IlastikObjectClassifierFromSegmentationModel(
- ilm.IlastikParams(
- project_file=fp_ob
- )
+ {'project_file': fp_ob},
)
},
}