From bfcd18aff82abc98b78415e585df7cb5d1fce39c Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Fri, 25 Oct 2024 11:25:56 +0200
Subject: [PATCH] Tests parameterize models directly with dicts, not pydantic
models, which are reserved for API calls
---
model_server/base/pipelines/segment.py | 2 +-
model_server/base/session.py | 2 +-
model_server/extensions/ilastik/models.py | 18 ++--------
model_server/extensions/ilastik/router.py | 29 ++++++++++-----
tests/test_ilastik/test_ilastik.py | 43 +++++++++++++----------
5 files changed, 50 insertions(+), 44 deletions(-)
diff --git a/model_server/base/pipelines/segment.py b/model_server/base/pipelines/segment.py
index 887ccea5..e0180b55 100644
--- a/model_server/base/pipelines/segment.py
+++ b/model_server/base/pipelines/segment.py
@@ -36,7 +36,7 @@ def segment_pipeline(
model = models.get('model')
if not isinstance(model, SemanticSegmentationModel):
- raise IncompatibleModelsError('Expecting a pixel classification model')
+ raise IncompatibleModelsError('Expecting a semantic segmentation model')
if ch := k.get('channel') is not None:
d['mono'] = d['input'].get_mono(ch)
diff --git a/model_server/base/session.py b/model_server/base/session.py
index 8f25520d..4aa88a1e 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)
+ mi = ModelClass(params=params.dict())
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 75e0434c..dccb269a 100644
--- a/model_server/extensions/ilastik/models.py
+++ b/model_server/extensions/ilastik/models.py
@@ -2,11 +2,9 @@ import json
from logging import getLogger
import os
from pathlib import Path
-from typing import Union
import warnings
import numpy as np
-from pydantic import BaseModel, Field
import vigra
import model_server.extensions.ilastik.conf
@@ -14,17 +12,10 @@ from ...base.accessors import PatchStack
from ...base.accessors import GenericImageDataAccessor, InMemoryDataAccessor
from ...base.models import Model, ImageToImageModel, InstanceSegmentationModel, InvalidInputImageError, ParameterExpectedError, SemanticSegmentationModel
-# TODO: move params models to router only; model classes to be created only with dict
-class IlastikParams(BaseModel):
- project_file: str = Field(description='(*.ilp) ilastik project filename')
- duplicate: bool = Field(
- True,
- description='Load another instance of the same project file if True; return existing one if False'
- )
class IlastikModel(Model):
- def __init__(self, params: IlastikParams, autoload=True, enforce_embedded=True):
+ def __init__(self, params: dict, autoload=True, enforce_embedded=True):
"""
Base class for models that run via ilastik shell API
:param params:
@@ -33,7 +24,7 @@ class IlastikModel(Model):
:param enforce_embedded:
raise an error if all input data are not embedded in the project file, i.e. on the filesystem
"""
- pf = Path(params.project_file)
+ pf = Path(params['project_file'])
self.enforce_embedded = enforce_embedded
if pf.is_absolute():
pap = pf
@@ -103,14 +94,11 @@ class IlastikModel(Model):
def model_3d(self):
return self.model_shape_dict['Z'] > 1
-class IlastikPixelClassifierParams(IlastikParams):
- px_class: int = 0
- px_prob_threshold: float = 0.5
class IlastikPixelClassifierModel(IlastikModel, SemanticSegmentationModel):
operations = ['segment', ]
- def __init__(self, params: IlastikPixelClassifierParams, **kwargs):
+ def __init__(self, params: dict, **kwargs):
super(IlastikPixelClassifierModel, self).__init__(params, **kwargs)
@staticmethod
diff --git a/model_server/extensions/ilastik/router.py b/model_server/extensions/ilastik/router.py
index ea852798..e4412221 100644
--- a/model_server/extensions/ilastik/router.py
+++ b/model_server/extensions/ilastik/router.py
@@ -1,4 +1,5 @@
from fastapi import APIRouter
+from pydantic import BaseModel, Field
from model_server.base.session import session
@@ -13,8 +14,20 @@ router = APIRouter(
import model_server.extensions.ilastik.pipelines.px_then_ob
router.include_router(model_server.extensions.ilastik.pipelines.px_then_ob.router)
+# TODO: move params models to router only; model classes to be created only with dict
+class IlastikParams(BaseModel):
+ project_file: str = Field(description='(*.ilp) ilastik project filename')
+ duplicate: bool = Field(
+ True,
+ description='Load another instance of the same project file if True; return existing one if False'
+ )
+
+class IlastikPixelClassifierParams(IlastikParams):
+ px_class: int = 0
+ px_prob_threshold: float = 0.5
+
@router.put('/seg/load/')
-def load_px_model(p: ilm.IlastikPixelClassifierParams, model_id=None) -> dict:
+def load_px_model(p: IlastikPixelClassifierParams, model_id=None) -> dict:
"""
Load an ilastik pixel classifier model from its project file
"""
@@ -25,7 +38,7 @@ def load_px_model(p: ilm.IlastikPixelClassifierParams, model_id=None) -> dict:
)
@router.put('/pxmap_to_obj/load/')
-def load_pxmap_to_obj_model(p: ilm.IlastikParams, model_id=None) -> dict:
+def load_pxmap_to_obj_model(p: IlastikParams, model_id=None) -> dict:
"""
Load an ilastik object classifier from pixel predictions model from its project file
"""
@@ -36,7 +49,7 @@ def load_pxmap_to_obj_model(p: ilm.IlastikParams, model_id=None) -> dict:
)
@router.put('/seg_to_obj/load/')
-def load_seg_to_obj_model(p: ilm.IlastikParams, model_id=None) -> dict:
+def load_seg_to_obj_model(p: IlastikParams, model_id=None) -> dict:
"""
Load an ilastik object classifier from segmentation model from its project file
"""
@@ -46,13 +59,13 @@ def load_seg_to_obj_model(p: ilm.IlastikParams, model_id=None) -> dict:
model_id=model_id,
)
-def load_ilastik_model(model_class: ilm.IlastikModel, p: ilm.IlastikParams, model_id=None) -> dict:
- project_file = p.project_file
+def load_ilastik_model(model_class: ilm.IlastikModel, p: IlastikParams, model_id=None) -> dict:
+ pf = p.project_file
if not p.duplicate:
- existing_model_id = session.find_param_in_loaded_models('project_file', project_file, is_path=True)
+ existing_model_id = session.find_param_in_loaded_models('project_file', pf, is_path=True)
if existing_model_id is not None:
- session.log_info(f'An ilastik model from {project_file} already existing exists; did not load a duplicate')
+ session.log_info(f'An ilastik model from {pf} already existing exists; did not load a duplicate')
return {'model_id': existing_model_id}
result = session.load_model(model_class, key=model_id, params=p)
- session.log_info(f'Loaded ilastik model {result} from {project_file}')
+ session.log_info(f'Loaded ilastik model {result} from {pf}')
return {'model_id': result}
\ No newline at end of file
diff --git a/tests/test_ilastik/test_ilastik.py b/tests/test_ilastik/test_ilastik.py
index 40ba7e0b..a6f467f9 100644
--- a/tests/test_ilastik/test_ilastik.py
+++ b/tests/test_ilastik/test_ilastik.py
@@ -29,14 +29,18 @@ class TestIlastikPixelClassification(unittest.TestCase):
self.cf = CziImageFileAccessor(czifile['path'])
self.channel = 0
self.model = ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px']['path'].__str__())
+ params={
+ 'project_file': ilastik_classifiers['px']['path'].__str__(),
+ 'px_class': 0,
+ 'px_prob_threshold': 0.5,
+ }
)
self.mono_image = self.cf.get_mono(self.channel)
def test_raise_error_if_autoload_disabled(self):
model = ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px']['path'].__str__()),
+ params={'project_file': ilastik_classifiers['px']['path'].__str__()},
autoload=False
)
w = 512
@@ -80,11 +84,12 @@ class TestIlastikPixelClassification(unittest.TestCase):
def test_label_pixels_with_params(self):
def _run_seg(tr, sig):
mod = ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(
- project_file=ilastik_classifiers['px']['path'].__str__(),
- px_prob_threshold=tr,
- px_smoothing=sig,
- ),
+ params={
+ 'project_file': ilastik_classifiers['px']['path'].__str__(),
+ 'px_class': 0,
+ 'px_prob_threshold': tr,
+ 'px_smoothing': sig,
+ },
)
mask = mod.label_pixel_class(self.mono_image)
write_accessor_data_to_file(
@@ -154,7 +159,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
self.test_run_pixel_classifier()
fp = czifile['path']
model = ilm.IlastikObjectClassifierFromPixelPredictionsModel(
- params=ilm.IlastikParams(project_file=ilastik_classifiers['pxmap_to_obj']['path'].__str__())
+ 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)
@@ -172,7 +177,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
self.test_run_pixel_classifier()
fp = czifile['path']
model = ilm.IlastikObjectClassifierFromSegmentationModel(
- params=ilm.IlastikParams(project_file=ilastik_classifiers['seg_to_obj']['path'].__str__())
+ params={'project_file': ilastik_classifiers['seg_to_obj']['path'].__str__()}
)
mask = self.model.label_pixel_class(self.mono_image)
objmap = model.label_instance_class(self.mono_image, mask)
@@ -191,11 +196,11 @@ class TestIlastikPixelClassification(unittest.TestCase):
'accessor': generate_file_accessor(czifile['path'])
},
models={
- 'model': ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(
- project_file=ilastik_classifiers['px']['path'].__str__()
- ),
- ),
+ 'model': ilm.IlastikPixelClassifierModel({
+ 'project_file': ilastik_classifiers['px']['path'].__str__(),
+ 'px_class': 0,
+ 'px_prob_threshold': 0.5,
+ }),
},
channel=0,
)
@@ -327,7 +332,7 @@ class TestIlastikOnMultichannelInputs(TestServerTestCase):
def test_classify_pixels(self):
img = generate_file_accessor(self.pa_input_image)
self.assertGreater(img.chroma, 1)
- mod = ilm.IlastikPixelClassifierModel(ilm.IlastikPixelClassifierParams(project_file=self.pa_px_classifier.__str__()))
+ mod = ilm.IlastikPixelClassifierModel({'project_file': self.pa_px_classifier.__str__()})
pxmap = mod.infer(img)[0]
self.assertEqual(pxmap.hw, img.hw)
self.assertEqual(pxmap.nz, img.nz)
@@ -337,7 +342,7 @@ class TestIlastikOnMultichannelInputs(TestServerTestCase):
pxmap = self.test_classify_pixels()
img = generate_file_accessor(self.pa_input_image)
mod = ilm.IlastikObjectClassifierFromPixelPredictionsModel(
- ilm.IlastikParams(project_file=self.pa_ob_pxmap_classifier.__str__())
+ {'project_file': self.pa_ob_pxmap_classifier.__str__()}
)
obmap = mod.infer(img, pxmap)[0]
self.assertEqual(obmap.hw, img.hw)
@@ -354,10 +359,10 @@ class TestIlastikOnMultichannelInputs(TestServerTestCase):
},
models={
'px_model': ilm.IlastikPixelClassifierModel(
- ilm.IlastikParams(project_file=self.pa_px_classifier.__str__()),
+ {'project_file': self.pa_px_classifier.__str__()},
),
'ob_model': ilm.IlastikObjectClassifierFromPixelPredictionsModel(
- ilm.IlastikParams(project_file=self.pa_ob_pxmap_classifier.__str__()),
+ {'project_file': self.pa_ob_pxmap_classifier.__str__()}
)
},
channel=channel,
@@ -428,7 +433,7 @@ class TestIlastikObjectClassification(unittest.TestCase):
)
self.classifier = ilm.IlastikObjectClassifierFromSegmentationModel(
- params=ilm.IlastikParams(project_file=ilastik_classifiers['seg_to_obj']['path'].__str__()),
+ params={'project_file': ilastik_classifiers['seg_to_obj']['path'].__str__()},
)
self.raw = self.roiset.get_patches_acc()
self.masks = self.roiset.get_patch_masks_acc()
--
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