Confirmed ilastik pipeline runs with new module format

model_server/base/accessors.py

@@ -316,7 +316,7 @@ def write_accessor_data_to_file(fpath: Path, acc: GenericImageDataAccessor, mkdi
         raise FileWriteError(f'Unable to write data to file of extension {ext}')
     return True
 
-# TODO: deprecate and move into GenericImageFile
+
 def generate_file_accessor(fpath):
     """
     Given an image file path, return an image accessor, assuming the file is a supported format and represents

model_server/base/api.py

 from fastapi import FastAPI, HTTPException
 from model_server.base.session import session, InvalidPathError
-from model_server.extensions.ilastik.pipelines.px_then_ob import infer_px_then_ob_model
 
 app = FastAPI(debug=True)
 
@@ -53,16 +52,6 @@ def restart_session(root: str = None) -> dict:
 def list_session_log() -> list:
     return session.get_log_data()
 
-# TODO: endpoints to load image data via file
-@app.get('/data/file/import')
-def import_data_from_file() -> str:
-    pass
-
-# TODO: endpoints to load image data via file
-@app.get('/data/file/import')
-def import_data_from_file():
-    pass
-
 @app.get('/models')
 def list_active_models():
     return session.describe_loaded_models()

model_server/base/pipelines/segment.py

@@ -49,7 +49,7 @@ def segment(p: SegmentParams) -> SegmentRecord:
     steps = pipeline(
         generate_file_accessor(inpath),
         session.models[p.model_id]['object'],
-        **p,
+        **p.dict(),
     )
     outpath = session.paths['outbound_images'] / (p.model_id + '_' + inpath.stem + '.tif')
     write_accessor_data_to_file(outpath, steps.last)
@@ -66,10 +66,10 @@ def segment(p: SegmentParams) -> SegmentRecord:
 def pipeline(acc_in: GenericImageDataAccessor, model: SemanticSegmentationModel, **k) -> PipelineTrace:
     d = PipelineTrace()
     d['input'] = acc_in
-    if ch := k.get('channel'):
+    if ch := k.get('channel') is not None:
         d['mono'] = acc_in.get_mono(ch)
     d['inference'] = model.label_pixel_class(d.last)
-    if sm := k.get('smooth'):
+    if sm := k.get('smooth') is not None:
         d['smooth'] = d.last.apply(lambda x: smooth(x, sm))
     d['output'] = d.last
     return d
\ No newline at end of file

model_server/base/session.py

@@ -131,8 +131,6 @@ class _Session(object):
     def log_error(self, msg):
         logger.error(msg)
 
-    # TODO: load, describe, unload data accessors
-
     def load_model(self, ModelClass: Model, params: Union[BaseModel, None] = None) -> dict:
         """
         Load an instance of a given model class and attach to this session's model registry

model_server/extensions/ilastik/pipelines/px_then_ob.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from typing import Dict
 
 from fastapi import APIRouter, HTTPException
-from pydantic import BaseModel, validator
+from pydantic import BaseModel, Field
 
 from model_server.base.session import session
 from model_server.base.util import PipelineTrace
@@ -17,7 +17,15 @@ router = APIRouter(
     prefix='/pipelines',
 )
 
-class Record(BaseModel):
+class PxThenObParams(BaseModel):
+    px_model_id: str = Field(description='ID of model for pixel classification')
+    ob_model_id: str = Field(description='ID of model for object classification')
+    input_filename: str
+    channel: int = Field(None, description='Image channel to pass to pixel classification, or all channels if empty.')
+    mip: bool = Field(False, description='Use maximum intensity projection of input image if True')
+
+
+class PxThenObRecord(BaseModel):
     pixel_model_id: str
     object_model_id: str
     input_filepath: str
@@ -26,16 +34,12 @@ class Record(BaseModel):
     success: bool
     timer_results: Dict[str, float]
 
-
-class FileCallParams(BaseModel):
-    px_model_id: str
-    ob_model_id: str
-    input_filename: str
-    channel: int = None
-    mip: bool = False
-
 @router.put('/pixel_then_object_classification/infer')
-def infer_px_then_ob_maps(p: FileCallParams) -> Record:
+def pixel_then_object_classification(p: PxThenObParams) -> PxThenObRecord:
+    """
+    Workflow that specifically runs an ilastik pixel classifier, then passes results to an object classifier,
+    saving intermediate images
+    """
 
     def _validate(model_ids, inpaths):
         for mid in model_ids:
@@ -54,7 +58,7 @@ def infer_px_then_ob_maps(p: FileCallParams) -> Record:
     inpath = session.paths['inbound_images'] / p.input_filename
     _validate([p.px_model_id, p.ob_model_id], [inpath])
     try:
-        record = infer_px_then_ob_model(
+        record = pipeline(
             inpath,
             session.models[p.px_model_id]['object'],
             session.models[p.ob_model_id]['object'],
@@ -68,27 +72,18 @@ def infer_px_then_ob_maps(p: FileCallParams) -> Record:
     return record
 
 
-def infer_px_then_ob_model(
+def pipeline(
         fpi: Path,
         px_model: IlastikPixelClassifierModel,
         ob_model: IlastikObjectClassifierFromPixelPredictionsModel,
         where_output: Path,
         channel: int = None,
         **kwargs
-) -> Record:
-    """
-    Workflow that specifically runs an ilastik pixel classifier, then passes results to an object classifier,
-    saving intermediate images
-    :param fpi: Path object that references input image file
-    :param px_model: model instance for pixel classification
-    :param ob_model: model instance for object classification
-    :param where_output: Path object that references output image directory
-    :param channel: input image channel to pass to pixel classification, or all channels if None
-    :param kwargs: variable-length keyword arguments
-    :return:
-    """
-    assert isinstance(px_model, IlastikPixelClassifierModel)
-    assert isinstance(ob_model, IlastikObjectClassifierFromPixelPredictionsModel)
+) -> PxThenObRecord:
+    assert isinstance(px_model, IlastikPixelClassifierModel), \
+        'Expecting an ilastik pixel classification model'
+    assert isinstance(ob_model, IlastikObjectClassifierFromPixelPredictionsModel), \
+        'Expecting an ilastik object classification from pixel predictions model'
 
     d = PipelineTrace()
     raw_acc = generate_file_accessor(fpi)
@@ -108,7 +103,7 @@ def infer_px_then_ob_model(
     ob_map_path = where_output / (ob_model.model_id + '_obmap_' + fpi.stem + '.tif')
     write_accessor_data_to_file(ob_map_path, d['ob_map'])
 
-    return Record(
+    return PxThenObRecord(
         pixel_model_id=px_model.model_id,
         object_model_id=ob_model.model_id,
         input_filepath=str(fpi),

tests/test_ilastik/test_ilastik.py

@@ -4,9 +4,9 @@ import numpy as np
 
 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.extensions.ilastik.pipelines import px_then_ob
 from model_server.base.roiset import get_label_ids, RoiSet, RoiSetMetaParams
-from model_server.base.pipelines.segment import classify_pixels
+from model_server.base.pipelines import segment
 import model_server.conf.testing as conf
 
 data = conf.meta['image_files']
@@ -180,7 +180,7 @@ class TestIlastikPixelClassification(unittest.TestCase):
         self.assertEqual(objmap.data.max(), 2)
 
     def test_ilastik_pixel_classification_as_workflow(self):
-        res = classify_pixels(
+        res = segment.pipeline(
             generate_file_accessor(czifile['path']),
             ilm.IlastikPixelClassifierModel(
                 params=ilm.IlastikPixelClassifierParams(project_file=ilastik_classifiers['px']['path'].__str__()),
@@ -342,7 +342,7 @@ class TestIlastikOnMultichannelInputs(conf.TestServerBaseClass):
         self.assertEqual(obmap.nz, img.nz)
 
     def _call_workflow(self, channel):
-        return infer_px_then_ob_model(
+        return px_then_ob.pipeline(
             self.pa_input_image,
             ilm.IlastikPixelClassifierModel(
                 ilm.IlastikParams(project_file=self.pa_px_classifier.__str__()),