diff --git a/model_server/base/pipelines/segment.py b/model_server/base/pipelines/segment.py
index 2ab20a649efa33b90072953fae457eccc45b60b8..3e0f76e64dbbb86f8812e6de9d040d91f09a1abf 100644
--- a/model_server/base/pipelines/segment.py
+++ b/model_server/base/pipelines/segment.py
@@ -60,6 +60,7 @@ def file_call(p: FileCallParams) -> FileCallRecord:
timer=steps.times
)
+# TODO: implement smoothing, move this away from model itself
def classify_pixels(acc_in: GenericImageDataAccessor, model: SemanticSegmentationModel, **kwargs) -> PipelineTrace:
"""
Run a semantic segmentation model to compute a binary mask from an input image
diff --git a/model_server/extensions/ilastik/pipelines/px_then_ob.py b/model_server/extensions/ilastik/pipelines/px_then_ob.py
index 2908080403dfc4a951b4ca95dcede47e3bad0d24..bc158591aedf93c09889d37ab45d04cec89a3773 100644
--- a/model_server/extensions/ilastik/pipelines/px_then_ob.py
+++ b/model_server/extensions/ilastik/pipelines/px_then_ob.py
@@ -15,7 +15,6 @@ from model_server.base.accessors import generate_file_accessor, write_accessor_d
router = APIRouter(
prefix='/pipelines',
- tags=['pipelines'],
)
class Record(BaseModel):
diff --git a/tests/base/test_workflow.py b/tests/base/test_pipelines.py
similarity index 100%
rename from tests/base/test_workflow.py
rename to tests/base/test_pipelines.py
diff --git a/model_server/extensions/ilastik/tests/test_ilastik.py b/tests/test_ilastik/test_ilastik.py
similarity index 80%
rename from model_server/extensions/ilastik/tests/test_ilastik.py
rename to tests/test_ilastik/test_ilastik.py
index cedc290d48f2add8a8362999466d3fd6dd1c6109..d428b0ffa77f787135e355567fc60765493e8bfd 100644
--- a/model_server/extensions/ilastik/tests/test_ilastik.py
+++ b/tests/test_ilastik/test_ilastik.py
@@ -1,15 +1,19 @@
-import pathlib
import unittest
import numpy as np
-from model_server.conf.testing import czifile, ilastik_classifiers, output_path, roiset_test_data
from model_server.base.accessors import CziImageFileAccessor, generate_file_accessor, InMemoryDataAccessor, PatchStack, write_accessor_data_to_file
from model_server.extensions.ilastik import models as ilm
from model_server.extensions.ilastik.pipelines.px_then_ob import infer_px_then_ob_model
from model_server.base.roiset import _get_label_ids, RoiSet, RoiSetMetaParams
from model_server.base.pipelines.segment import classify_pixels
-from base.test_api import TestServerBaseClass
+import model_server.conf.testing as conf
+
+data = conf.meta['image_files']
+output_path = conf.meta['output_path']
+params = conf.meta['roiset']
+czifile = conf.meta['image_files']['czifile']
+ilastik_classifiers = conf.meta['ilastik_classifiers']
def _random_int(*args):
return np.random.randint(0, 2 ** 8, size=args, dtype='uint8')
@@ -19,23 +23,14 @@ class TestIlastikPixelClassification(unittest.TestCase):
self.cf = CziImageFileAccessor(czifile['path'])
self.channel = 0
self.model = ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__())
+ params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px']['path'].__str__())
)
self.mono_image = self.cf.get_mono(self.channel)
- def test_faulthandler(self): # recreate error that is messing up ilastik
- import io
- import sys
- import faulthandler
-
- with self.assertRaises(io.UnsupportedOperation):
- faulthandler.enable(file=sys.stdout)
-
-
def test_raise_error_if_autoload_disabled(self):
model = ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__()),
+ params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px']['path'].__str__()),
autoload=False
)
w = 512
@@ -80,7 +75,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
def _run_seg(tr, sig):
mod = ilm.IlastikPixelClassifierModel(
params=ilm.IlastikPixelClassifierParams(
- project_file=ilastik_classifiers['px'].__str__(),
+ project_file=ilastik_classifiers['px']['path'].__str__(),
px_prob_threshold=tr,
px_smoothing=sig,
),
@@ -153,7 +148,7 @@ 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'].__str__())
+ params=ilm.IlastikParams(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)
@@ -171,7 +166,7 @@ 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'].__str__())
+ params=ilm.IlastikParams(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)
@@ -185,16 +180,27 @@ class TestIlastikPixelClassification(unittest.TestCase):
self.assertEqual(objmap.data.max(), 2)
def test_ilastik_pixel_classification_as_workflow(self):
- result = classify_pixels(
+ res = classify_pixels(
generate_file_accessor(czifile['path']),
ilm.IlastikPixelClassifierModel(
- params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px'].__str__()),
+ params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px']['path'].__str__()),
),
channel=0,
)
- self.assertGreater(result.timer['inference'], 1.0)
+ self.assertGreater(res.times['inference'], 0.1)
-class TestIlastikOverApi(TestServerBaseClass):
+class TestIlastikOverApi(conf.TestServerBaseClass):
+
+ def _copy_input_file_to_server(self):
+ from pathlib import Path
+ from shutil import copyfile
+
+ resp = self._get('paths')
+ pa = resp.json()['inbound_images']
+ copyfile(
+ czifile['path'],
+ Path(pa) / czifile['name']
+ )
def test_httpexception_if_incorrect_project_file_loaded(self):
resp_load = self._put(
@@ -207,7 +213,7 @@ class TestIlastikOverApi(TestServerBaseClass):
def test_load_ilastik_pixel_model(self):
resp_load = self._put(
'ilastik/seg/load/',
- body={'project_file': str(ilastik_classifiers['px'])},
+ body={'project_file': str(ilastik_classifiers['px']['path'])},
)
self.assertEqual(resp_load.status_code, 200, resp_load.json())
model_id = resp_load.json()['model_id']
@@ -223,20 +229,20 @@ class TestIlastikOverApi(TestServerBaseClass):
self.assertEqual(len(resp_list_1st), 1, resp_list_1st)
resp_load_2nd = self._put(
'ilastik/seg/load/',
- body={'project_file': str(ilastik_classifiers['px']), 'duplicate': True},
+ body={'project_file': str(ilastik_classifiers['px']['path']), 'duplicate': True},
)
resp_list_2nd = self._get('models').json()
self.assertEqual(len(resp_list_2nd), 2, resp_list_2nd)
resp_load_3rd = self._put(
'ilastik/seg/load/',
- body={'project_file': str(ilastik_classifiers['px']), 'duplicate': False},
+ body={'project_file': str(ilastik_classifiers['px']['path']), 'duplicate': False},
)
resp_list_3rd = self._get('models').json()
self.assertEqual(len(resp_list_3rd), 2, resp_list_3rd)
def test_load_ilastik_pixel_model_with_params(self):
params = {
- 'project_file': str(ilastik_classifiers['px']),
+ 'project_file': str(ilastik_classifiers['px']['path']),
'px_class': 0,
'px_prob_threshold': 0.5
}
@@ -250,42 +256,11 @@ class TestIlastikOverApi(TestServerBaseClass):
self.assertEqual(len(mods), 1)
self.assertEqual(mods[model_id]['params']['px_prob_threshold'], 0.5)
- def test_no_duplicate_model_with_different_path_formats(self):
- self._get('session/restart')
- resp_list_1 = self._get('models').json()
- self.assertEqual(len(resp_list_1), 0)
- ilp = ilastik_classifiers['px']
-
- # create and validate two copies of the same pathname with different string formats
- ilp_win = str(pathlib.PureWindowsPath(ilp))
- self.assertGreater(ilp_win.count('\\'), 0) # i.e. contains backslashes
- self.assertEqual(ilp_win.count('/'), 0)
- ilp_posx = ilastik_classifiers['px'].as_posix()
- self.assertGreater(ilp_posx.count('/'), 0)
- self.assertEqual(ilp_posx.count('\\'), 0)
- self.assertEqual(pathlib.Path(ilp_win), pathlib.Path(ilp_posx))
-
- # load models with these paths
- resp1 = self._put(
- 'ilastik/seg/load/',
- body={'project_file': ilp_win, 'duplicate': False},
- )
- resp2 = self._put(
- 'ilastik/seg/load/',
- body={'project_file': ilp_posx, 'duplicate': False},
- )
- self.assertEqual(resp1.json(), resp2.json())
-
- # assert that only one copy of the model is loaded
- resp_list_2 = self._get('models').json()
- print(resp_list_2)
- self.assertEqual(len(resp_list_2), 1)
-
def test_load_ilastik_pxmap_to_obj_model(self):
resp_load = self._put(
'ilastik/pxmap_to_obj/load/',
- body={'project_file': str(ilastik_classifiers['pxmap_to_obj'])},
+ body={'project_file': str(ilastik_classifiers['pxmap_to_obj']['path'])},
)
model_id = resp_load.json()['model_id']
@@ -299,7 +274,7 @@ class TestIlastikOverApi(TestServerBaseClass):
def test_load_ilastik_seg_to_obj_model(self):
resp_load = self._put(
'ilastik/seg_to_obj/load/',
- body={'project_file': str(ilastik_classifiers['seg_to_obj'])},
+ body={'project_file': str(ilastik_classifiers['seg_to_obj']['path'])},
)
model_id = resp_load.json()['model_id']
@@ -311,18 +286,18 @@ class TestIlastikOverApi(TestServerBaseClass):
return model_id
def test_ilastik_infer_pixel_probability(self):
- self.copy_input_file_to_server()
+ self._copy_input_file_to_server()
model_id = self.test_load_ilastik_pixel_model()
resp_infer = self._put(
- f'workflows/segment',
- query={'model_id': model_id, 'input_filename': czifile['filename'], 'channel': 0},
+ f'pipelines/segment',
+ body={'model_id': model_id, 'input_filename': czifile['name'], 'channel': 0},
)
self.assertEqual(resp_infer.status_code, 200, resp_infer.content.decode())
def test_ilastik_infer_px_then_ob(self):
- self.copy_input_file_to_server()
+ self._copy_input_file_to_server()
px_model_id = self.test_load_ilastik_pixel_model()
ob_model_id = self.test_load_ilastik_pxmap_to_obj_model()
@@ -331,33 +306,21 @@ class TestIlastikOverApi(TestServerBaseClass):
body={
'px_model_id': px_model_id,
'ob_model_id': ob_model_id,
- 'input_filename': czifile['filename'],
+ 'input_filename': czifile['name'],
'channel': 0,
}
)
self.assertEqual(resp_infer.status_code, 200, resp_infer.content.decode())
-class TestIlastikOnMultichannelInputs(TestServerBaseClass):
+class TestIlastikOnMultichannelInputs(conf.TestServerBaseClass):
def setUp(self) -> None:
super(TestIlastikOnMultichannelInputs, self).setUp()
- self.pa_px_classifier = ilastik_classifiers['px_color_zstack']
- self.pa_ob_pxmap_classifier = ilastik_classifiers['ob_pxmap_color_zstack']
- self.pa_ob_seg_classifier = ilastik_classifiers['ob_seg_color_zstack']
- self.pa_input_image = roiset_test_data['multichannel_zstack']['path']
- self.pa_mask = roiset_test_data['multichannel_zstack']['mask_path_3d']
-
- def _copy_input_file_to_server(self):
- from shutil import copyfile
-
- pa_data = roiset_test_data['multichannel_zstack']['path']
- resp = self._get('paths')
- pa = resp.json()['inbound_images']
- outpath = pathlib.Path(pa) / pa_data.name
- copyfile(
- czifile['path'],
- outpath
- )
+ self.pa_px_classifier = ilastik_classifiers['px_color_zstack']['path']
+ self.pa_ob_pxmap_classifier = ilastik_classifiers['ob_pxmap_color_zstack']['path']
+ self.pa_ob_seg_classifier = ilastik_classifiers['ob_seg_color_zstack']['path']
+ self.pa_input_image = data['multichannel_zstack_raw']['path']
+ self.pa_mask = data['multichannel_zstack_mask3d']['path']
def test_classify_pixels(self):
img = generate_file_accessor(self.pa_input_image)
@@ -417,8 +380,8 @@ class TestIlastikOnMultichannelInputs(TestServerBaseClass):
ob_model_id = resp_load.json()['model_id']
resp_infer = self._put(
- 'ilastik/pixel_then_object_classification/infer/',
- query={
+ 'ilastik/pipelines/pixel_then_object_classification/infer/',
+ body={
'px_model_id': px_model_id,
'ob_model_id': ob_model_id,
'input_filename': self.pa_input_image.__str__(),
@@ -432,9 +395,9 @@ class TestIlastikOnMultichannelInputs(TestServerBaseClass):
class TestIlastikObjectClassification(unittest.TestCase):
def setUp(self):
- stack = generate_file_accessor(roiset_test_data['multichannel_zstack']['path'])
- stack_ch_pa = stack.get_mono(roiset_test_data['pipeline_params']['patches_channel'])
- seg_mask = generate_file_accessor(roiset_test_data['multichannel_zstack']['mask_path'])
+ stack = generate_file_accessor(data['multichannel_zstack_raw']['path'])
+ stack_ch_pa = stack.get_mono(conf.meta['roiset']['patches_channel'])
+ seg_mask = generate_file_accessor(data['multichannel_zstack_mask2d']['path'])
self.roiset = RoiSet(
stack_ch_pa,
@@ -447,7 +410,7 @@ class TestIlastikObjectClassification(unittest.TestCase):
)
self.classifier = ilm.IlastikObjectClassifierFromSegmentationModel(
- params=ilm.IlastikParams(project_file=ilastik_classifiers['seg_to_obj'].__str__()),
+ params=ilm.IlastikParams(project_file=ilastik_classifiers['seg_to_obj']['path'].__str__()),
)
self.raw = self.roiset.get_patches_acc()
self.masks = self.roiset.get_patch_masks_acc()