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# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from nnunet.utilities.folder_names import get_output_folder_name
def get_datasets():
configurations_all = {
"Task01_BrainTumour": ("3d_fullres", "2d"),
"Task02_Heart": ("3d_fullres", "2d",),
"Task03_Liver": ("3d_cascade_fullres", "3d_fullres", "3d_lowres", "2d"),
"Task04_Hippocampus": ("3d_fullres", "2d",),
"Task05_Prostate": ("3d_fullres", "2d",),
"Task06_Lung": ("3d_cascade_fullres", "3d_fullres", "3d_lowres", "2d"),
"Task07_Pancreas": ("3d_cascade_fullres", "3d_fullres", "3d_lowres", "2d"),
"Task08_HepaticVessel": ("3d_cascade_fullres", "3d_fullres", "3d_lowres", "2d"),
"Task09_Spleen": ("3d_cascade_fullres", "3d_fullres", "3d_lowres", "2d"),
"Task10_Colon": ("3d_cascade_fullres", "3d_fullres", "3d_lowres", "2d"),
"Task48_KiTS_clean": ("3d_cascade_fullres", "3d_lowres", "3d_fullres", "2d"),
"Task27_ACDC": ("3d_fullres", "2d",),
"Task24_Promise": ("3d_fullres", "2d",),
"Task35_ISBILesionSegmentation": ("3d_fullres", "2d",),
"Task38_CHAOS_Task_3_5_Variant2": ("3d_fullres", "2d",),
"Task29_LITS": ("3d_cascade_fullres", "3d_lowres", "2d", "3d_fullres",),
"Task17_AbdominalOrganSegmentation": ("3d_cascade_fullres", "3d_lowres", "2d", "3d_fullres",),
"Task55_SegTHOR": ("3d_cascade_fullres", "3d_lowres", "3d_fullres", "2d",),
"Task56_VerSe": ("3d_cascade_fullres", "3d_lowres", "3d_fullres", "2d",),
}
return configurations_all
def get_commands(configurations, regular_trainer="nnUNetTrainerV2", cascade_trainer="nnUNetTrainerV2CascadeFullRes",
plans="nnUNetPlansv2.1"):
node_pool = ["hdf18-gpu%02.0d" % i for i in range(1, 21)] + ["hdf19-gpu%02.0d" % i for i in range(1, 8)] + ["hdf19-gpu%02.0d" % i for i in range(11, 16)]
ctr = 0
for task in configurations:
models = configurations[task]
for m in models:
if m == "3d_cascade_fullres":
trainer = cascade_trainer
else:
trainer = regular_trainer
folder = get_output_folder_name(m, task, trainer, plans, overwrite_training_output_dir="/datasets/datasets_fabian/results/nnUNet")
node = node_pool[ctr % len(node_pool)]
print("bsub -m %s -q gputest -L /bin/bash \"source ~/.bashrc && python postprocessing/"
"consolidate_postprocessing.py -f" % node, folder, "\"")
ctr += 1
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