From f5e4fc4ecad6d5e85432568a7e849608555f8d33 Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Tue, 9 Apr 2024 16:17:20 +0200
Subject: [PATCH] Check input against model dimensionality and chroma in all
ilastik cases
---
model_server/extensions/ilastik/models.py | 20 +++++++++++---------
1 file changed, 11 insertions(+), 9 deletions(-)
diff --git a/model_server/extensions/ilastik/models.py b/model_server/extensions/ilastik/models.py
index 0cb6370c..4126e14e 100644
--- a/model_server/extensions/ilastik/models.py
+++ b/model_server/extensions/ilastik/models.py
@@ -36,7 +36,6 @@ class IlastikModel(Model):
raise ParameterExpectedError('Ilastik model expects a project (*.ilp) file')
self.shell = None
- self.axes = None
super().__init__(autoload, params)
def load(self):
@@ -65,18 +64,10 @@ class IlastikModel(Model):
raise ParameterExpectedError(
f'Ilastik project file {self.project_file} does not describe an instance of {shell.workflow.__class__}'
)
- self.axes = [
- a['key'].upper() for a in json.loads(h5[f'Input Data/infos/lane0000/Raw Data/axistags'][()])['axes']
- ]
self.shell = shell
return True
-
-class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
- model_id = 'ilastik_pixel_classification'
- operations = ['segment', ]
-
@property
def model_shape_dict(self):
raw_info = self.shell.projectManager.currentProjectFile['Input Data']['infos']['lane0000']['Raw Data']
@@ -97,6 +88,11 @@ class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
def model_3d(self):
return self.model_shape_dict['Z'] > 1
+
+class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
+ model_id = 'ilastik_pixel_classification'
+ operations = ['segment', ]
+
@staticmethod
def get_workflow():
from ilastik.workflows import PixelClassificationWorkflow
@@ -145,6 +141,9 @@ class IlastikObjectClassifierFromSegmentationModel(IlastikModel, InstanceSegment
return ObjectClassificationWorkflowBinary
def infer(self, input_img: GenericImageDataAccessor, segmentation_img: GenericImageDataAccessor) -> (np.ndarray, dict):
+ if self.model_chroma != input_img.chroma or self.model_3d != input_img.is_3d():
+ raise IlastikInputShapeError()
+
assert segmentation_img.is_mask()
if isinstance(input_img, PatchStack):
assert isinstance(segmentation_img, PatchStack)
@@ -202,6 +201,9 @@ class IlastikObjectClassifierFromPixelPredictionsModel(IlastikModel, ImageToImag
return ObjectClassificationWorkflowPrediction
def infer(self, input_img: GenericImageDataAccessor, pxmap_img: GenericImageDataAccessor) -> (np.ndarray, dict):
+ if self.model_chroma != input_img.chroma or self.model_3d != input_img.is_3d():
+ raise IlastikInputShapeError()
+
tagged_input_data = vigra.taggedView(input_img.data, 'yxcz')
tagged_pxmap_data = vigra.taggedView(pxmap_img.data, 'yxcz')
--
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