I have a fully-trained 2-stage autocontext ILP, and if I open that project from python and set it loose on a single preloaded array, it works fine. However, if I give it 2 preloaded arrays at once, or one then another, then it processes the first and then retrains the forest before attempting the second. This obviously slows everything down a lot. It’s also set to readonly so the newly trained model is (hopefully) not getting persisted anyway.
Script here:
from collections import OrderedDict
import vigra
import numpy as np
import ilastik_main
from ilastik.applets.dataSelection import DatasetInfo
args = ilastik_main.parse_args([])
args.headless = True
args.readonly = True
args.project = "path/to/autocontext2.ilp"
shell = ilastik_main.main(args)
arrays = [
vigra.taggedView(
np.random.randint(0, 256, (100, 100, 10, 1), dtype=np.uint8),
"xytc"
) for _ in range(2)
]
def two_arrays_at_once():
role_data_dict = OrderedDict([
("Raw Data", [DatasetInfo(preloaded_array=a) for a in arrays])
])
return shell.workflow.batchProcessingApplet.run_export(
role_data_dict, export_to_array=True
)
def one_then_another():
role_data_dict1 = OrderedDict([
("Raw Data", [DatasetInfo(preloaded_array=arrays[0])])
])
preds1 = shell.workflow.batchProcessingApplet.run_export(
role_data_dict1, export_to_array=True
)
role_data_dict2 = OrderedDict([
("Raw Data", [DatasetInfo(preloaded_array=arrays[1])])
])
preds2 = shell.workflow.batchProcessingApplet.run_export(
role_data_dict2, export_to_array=True
)
return preds1 + preds2
if __name__ == '__main__':
two_arrays_at_once()
# one_then_another()