Enabled to disable multiprocessing for inference + disabled mp for app

demo/app.py

@@ -29,7 +29,7 @@ def nifti_to_glb(path):
 def run_model(input_path):
     from livermask.utils.run import run_analysis
     
-    run_analysis(cpu=True, extension='.nii', path=input_path, output='prediction', verbose=True, vessels=False, name="./model.h5")
+    run_analysis(cpu=True, extension='.nii', path=input_path, output='prediction', verbose=True, vessels=False, name="./model.h5", mp_enabled=False)
     
     #cmd_docker = ["python3", "-m", "livermask.livermask", "--input", input_path, "--output", "prediction", "--verbose"]
     #sp.check_call(cmd_docker, shell=True)  # @FIXME: shell=True here is not optimal -> starts a shell after calling script

livermask/utils/process.py

@@ -37,13 +37,16 @@ def intensity_normalization(volume, intensity_clipping_range):
     return result
 
 
-def liver_segmenter_wrapper(curr, output, cpu, verbose, multiple_flag, name, extension):
-    # run inference in a different process
-    mp.set_start_method('spawn', force=True)
-    with mp.Pool(processes=1, maxtasksperchild=1) as p:  # , initializer=initializer)
-        result = p.map_async(liver_segmenter, ((curr, output, cpu, verbose, multiple_flag, name, extension),))
-        log.info("getting result from process...")
-        ret = result.get()[0]
+def liver_segmenter_wrapper(curr, output, cpu, verbose, multiple_flag, name, extension, mp_enabled=True):
+    if mp_enabled:
+        # run inference in a different process
+        mp.set_start_method('spawn', force=True)
+        with mp.Pool(processes=1, maxtasksperchild=1) as p:  # , initializer=initializer)
+            result = p.map_async(liver_segmenter, ((curr, output, cpu, verbose, multiple_flag, name, extension),))
+            log.info("getting result from process...")
+            ret = result.get()[0]
+    else:
+        ret = liver_segmenter(params=(curr, output, cpu, verbose, multiple_flag, name, extension))
     return ret
 
 

livermask/utils/run.py

@@ -24,7 +24,7 @@ import logging as log
 from .utils import get_model, get_vessel_model
 
 
-def run_analysis(path, output, cpu, verbose, vessels, extension, name=None, name_vessel=None):
+def run_analysis(path, output, cpu, verbose, vessels, extension, name=None, name_vessel=None, mp_enabled=True):
     # fix paths (necessary if called as a package and not CLI)
     path = path.replace("\\", "/")
     output = output.replace("\\", "/")
@@ -74,7 +74,7 @@ def run_analysis(path, output, cpu, verbose, vessels, extension, name=None, name
         # check if current file is a nifti file, if not, skip
         if curr.endswith(".nii") or curr.endswith(".nii.gz"):
             # perform liver parenchyma segmentation, launch it in separate process to properly clear memory
-            pred = liver_segmenter_wrapper(curr, output, cpu, verbose, multiple_flag, name, extension)
+            pred = liver_segmenter_wrapper(curr, output, cpu, verbose, multiple_flag, name, extension, mp_enabled)
 
             if vessels:
                 # perform liver vessel segmentation