def ensemble(
experiments=(
'nnUNetTrainerNewCandidate23_FabiansPreActResNet__nnUNetPlans',
'nnUNetTrainerNewCandidate23_FabiansResNet__nnUNetPlans'),
out_dir="/media/fabian/Results/nnUNet/3d_lowres/Task48_KiTS_clean/ensemble_preactres_and_res"
):
from nnunet.inference.ensemble_predictions import merge
folders = [
join(network_training_output_dir, "3d_lowres/Task48_KiTS_clean", i,
'crossval_npz') for i in experiments
]
merge(folders, out_dir, 8)
def run_inference_and_store_results(self,output_file_tag=''):
output_file_base_name = output_file_tag + "_nnunet_seg.nii.gz"
# passing only lists of length one to predict_cases
for inner_list in self.data.inference_loader:
list_of_lists = [inner_list]
# output filenames (list of one) include information about patient folder name
# infering patient folder name from all file paths for a sanity check
# (should give the same answer)
folder_names = [fpath.split('/')[-2] for fpath in inner_list]
if set(folder_names) != set(folder_names[:1]):
raise RuntimeError('Patient file paths: {} were found to come from different folders against expectation.'.format(inner_list))
patient_folder_name = folder_names[0]
output_filename = patient_folder_name + output_file_base_name
final_out_folder = join(self.intermediate_out_folder, patient_folder_name)
intermediate_output_folders = []
for model_name, folds in zip(self.model_list, self.folds_list):
output_model = join(self.intermediate_out_folder, model_name)
intermediate_output_folders.append(output_model)
intermediate_output_filepaths = [join(output_model, output_filename)]
maybe_mkdir_p(output_model)
params_folder_model = join(self.params_folder, model_name)
predict_cases(model=params_folder_model,
list_of_lists=list_of_lists,
output_filenames=intermediate_output_filepaths,
folds=folds,
save_npz=True,
num_threads_preprocessing=1,
num_threads_nifti_save=1,
segs_from_prev_stage=None,
do_tta=True,
mixed_precision=True,
overwrite_existing=True,
all_in_gpu=False,
step_size=0.5)
merge(folders=intermediate_output_folders,
output_folder=final_out_folder,
threads=1,
override=True,
postprocessing_file=None,
store_npz=False)
f = join(final_out_folder, output_filename)
apply_brats_threshold(f, f, self.threshold, self.replace_with)
load_convert_save(f)
_ = [shutil.rmtree(i) for i in intermediate_output_folders]
def convert_variant2_predicted_test_to_submission_format(
folder_with_predictions,
output_folder="/home/fabian/drives/datasets/results/nnUNet/test_sets/Task038_CHAOS_Task_3_5_Variant2/ready_to_submit",
postprocessing_file="/home/fabian/drives/datasets/results/nnUNet/ensembles/Task038_CHAOS_Task_3_5_Variant2/ensemble_2d__nnUNetTrainerV2__nnUNetPlansv2.1--3d_fullres__nnUNetTrainerV2__nnUNetPlansv2.1/postprocessing.json"
):
"""
output_folder is where the extracted template is
:param folder_with_predictions:
:param output_folder:
:return:
"""
postprocessing_file = "/media/fabian/Results/nnUNet/3d_fullres/Task039_CHAOS_Task_3_5_Variant2_highres/" \
"nnUNetTrainerV2__nnUNetPlansfixed/postprocessing.json"
# variant 2 treats in and out phase as two training examples, so we need to ensemble these two again
final_predictions_folder = join(output_folder, "final")
maybe_mkdir_p(final_predictions_folder)
t1_patient_names = [
i.split("_")[-1][:-7] for i in subfiles(
folder_with_predictions, prefix="T1", suffix=".nii.gz", join=False)
]
folder_for_ensembing0 = join(output_folder, "ens0")
folder_for_ensembing1 = join(output_folder, "ens1")
maybe_mkdir_p(folder_for_ensembing0)
maybe_mkdir_p(folder_for_ensembing1)
# now copy all t1 out phases in ens0 and all in phases in ens1. Name them the same.
for t1 in t1_patient_names:
shutil.copy(join(folder_with_predictions, "T1_in_%s.npz" % t1),
join(folder_for_ensembing1, "T1_%s.npz" % t1))
shutil.copy(join(folder_with_predictions, "T1_in_%s.pkl" % t1),
join(folder_for_ensembing1, "T1_%s.pkl" % t1))
shutil.copy(join(folder_with_predictions, "T1_out_%s.npz" % t1),
join(folder_for_ensembing0, "T1_%s.npz" % t1))
shutil.copy(join(folder_with_predictions, "T1_out_%s.pkl" % t1),
join(folder_for_ensembing0, "T1_%s.pkl" % t1))
shutil.copy(join(folder_with_predictions, "plans.pkl"),
join(folder_for_ensembing0, "plans.pkl"))
shutil.copy(join(folder_with_predictions, "plans.pkl"),
join(folder_for_ensembing1, "plans.pkl"))
# there is a problem with T1_35 that I need to correct manually (different crop size, will not negatively impact results)
#ens0_softmax = np.load(join(folder_for_ensembing0, "T1_35.npz"))['softmax']
ens1_softmax = np.load(join(folder_for_ensembing1, "T1_35.npz"))['softmax']
#ens0_props = load_pickle(join(folder_for_ensembing0, "T1_35.pkl"))
#ens1_props = load_pickle(join(folder_for_ensembing1, "T1_35.pkl"))
ens1_softmax = ens1_softmax[:, :, :-1, :]
np.savez_compressed(join(folder_for_ensembing1, "T1_35.npz"),
softmax=ens1_softmax)
shutil.copy(join(folder_for_ensembing0, "T1_35.pkl"),
join(folder_for_ensembing1, "T1_35.pkl"))
# now call my ensemble function
merge((folder_for_ensembing0, folder_for_ensembing1),
final_predictions_folder,
8,
True,
postprocessing_file=postprocessing_file)
# copy t2 files to final_predictions_folder as well
t2_files = subfiles(folder_with_predictions,
prefix="T2",
suffix=".nii.gz",
join=False)
for t2 in t2_files:
shutil.copy(join(folder_with_predictions, t2),
join(final_predictions_folder, t2))
# apply postprocessing
from nnunet.postprocessing.connected_components import apply_postprocessing_to_folder, load_postprocessing
postprocessed_folder = join(output_folder, "final_postprocessed")
for_which_classes, min_valid_obj_size = load_postprocessing(
postprocessing_file)
apply_postprocessing_to_folder(final_predictions_folder,
postprocessed_folder, for_which_classes,
min_valid_obj_size, 8)
# now export the niftis in the weird png format
# task 3
output_dir = join(output_folder, "CHAOS_submission_template_new", "Task3",
"MR")
for t1 in t1_patient_names:
output_folder_here = join(output_dir, t1, "T1DUAL", "Results")
nifti_file = join(postprocessed_folder, "T1_%s.nii.gz" % t1)
write_pngs_from_nifti(nifti_file,
output_folder_here,
converter=convert_seg_to_intensity_task3)
for t2 in t2_files:
patname = t2.split("_")[-1][:-7]
output_folder_here = join(output_dir, patname, "T2SPIR", "Results")
nifti_file = join(postprocessed_folder, "T2_%s.nii.gz" % patname)
write_pngs_from_nifti(nifti_file,
output_folder_here,
converter=convert_seg_to_intensity_task3)
# task 5
output_dir = join(output_folder, "CHAOS_submission_template_new", "Task5",
"MR")
for t1 in t1_patient_names:
output_folder_here = join(output_dir, t1, "T1DUAL", "Results")
nifti_file = join(postprocessed_folder, "T1_%s.nii.gz" % t1)
write_pngs_from_nifti(nifti_file,
output_folder_here,
converter=convert_seg_to_intensity_task5)
for t2 in t2_files:
patname = t2.split("_")[-1][:-7]
output_folder_here = join(output_dir, patname, "T2SPIR", "Results")
nifti_file = join(postprocessed_folder, "T2_%s.nii.gz" % patname)
write_pngs_from_nifti(nifti_file,
output_folder_here,
converter=convert_seg_to_intensity_task5)
