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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.
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import *
from nnunet.paths import network_training_output_dir
if __name__ == "__main__":
# run collect_all_fold0_results_and_summarize_in_one_csv.py first
summary_files_dir = join(network_training_output_dir, "summary_jsons_fold0_new")
output_file = join(network_training_output_dir, "summary.csv")
folds = (0, )
folds_str = ""
for f in folds:
folds_str += str(f)
plans = "nnUNetPlans"
overwrite_plans = {
'nnUNetTrainerV2_2': ["nnUNetPlans", "nnUNetPlansisoPatchesInVoxels"], # r
'nnUNetTrainerV2': ["nnUNetPlansnonCT", "nnUNetPlansCT2", "nnUNetPlansallConv3x3",
"nnUNetPlansfixedisoPatchesInVoxels", "nnUNetPlanstargetSpacingForAnisoAxis",
"nnUNetPlanspoolBasedOnSpacing", "nnUNetPlansfixedisoPatchesInmm", "nnUNetPlansv2.1"],
'nnUNetTrainerV2_warmup': ["nnUNetPlans", "nnUNetPlansv2.1", "nnUNetPlansv2.1_big", "nnUNetPlansv2.1_verybig"],
'nnUNetTrainerV2_cycleAtEnd': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_cycleAtEnd2': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_reduceMomentumDuringTraining': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_graduallyTransitionFromCEToDice': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_independentScalePerAxis': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_Mish': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_Ranger_lr3en4': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_fp32': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_GN': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_momentum098': ["nnUNetPlans", "nnUNetPlansv2.1"],
'nnUNetTrainerV2_momentum09': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_DP': ["nnUNetPlansv2.1_verybig"],
'nnUNetTrainerV2_DDP': ["nnUNetPlansv2.1_verybig"],
'nnUNetTrainerV2_FRN': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_resample33': ["nnUNetPlansv2.3"],
'nnUNetTrainerV2_O2': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_ResencUNet': ["nnUNetPlans_FabiansResUNet_v2.1"],
'nnUNetTrainerV2_DA2': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_allConv3x3': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_ForceBD': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_ForceSD': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_LReLU_slope_2en1': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_lReLU_convReLUIN': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_ReLU': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_ReLU_biasInSegOutput': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_ReLU_convReLUIN': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_lReLU_biasInSegOutput': ["nnUNetPlansv2.1"],
#'nnUNetTrainerV2_Loss_MCC': ["nnUNetPlansv2.1"],
#'nnUNetTrainerV2_Loss_MCCnoBG': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_Loss_DicewithBG': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_Loss_Dice_LR1en3': ["nnUNetPlansv2.1"],
'nnUNetTrainerV2_Loss_Dice': ["nnUNetPlans", "nnUNetPlansv2.1"],
'nnUNetTrainerV2_Loss_DicewithBG_LR1en3': ["nnUNetPlansv2.1"],
# 'nnUNetTrainerV2_fp32': ["nnUNetPlansv2.1"],
# 'nnUNetTrainerV2_fp32': ["nnUNetPlansv2.1"],
# 'nnUNetTrainerV2_fp32': ["nnUNetPlansv2.1"],
# 'nnUNetTrainerV2_fp32': ["nnUNetPlansv2.1"],
# 'nnUNetTrainerV2_fp32': ["nnUNetPlansv2.1"],
}
trainers = ['nnUNetTrainer'] + ['nnUNetTrainerNewCandidate%d' % i for i in range(1, 28)] + [
'nnUNetTrainerNewCandidate24_2',
'nnUNetTrainerNewCandidate24_3',
'nnUNetTrainerNewCandidate26_2',
'nnUNetTrainerNewCandidate27_2',
'nnUNetTrainerNewCandidate23_always3DDA',
'nnUNetTrainerNewCandidate23_corrInit',
'nnUNetTrainerNewCandidate23_noOversampling',
'nnUNetTrainerNewCandidate23_softDS',
'nnUNetTrainerNewCandidate23_softDS2',
'nnUNetTrainerNewCandidate23_softDS3',
'nnUNetTrainerNewCandidate23_softDS4',
'nnUNetTrainerNewCandidate23_2_fp16',
'nnUNetTrainerNewCandidate23_2',
'nnUNetTrainerVer2',
'nnUNetTrainerV2_2',
'nnUNetTrainerV2_3',
'nnUNetTrainerV2_3_CE_GDL',
'nnUNetTrainerV2_3_dcTopk10',
'nnUNetTrainerV2_3_dcTopk20',
'nnUNetTrainerV2_3_fp16',
'nnUNetTrainerV2_3_softDS4',
'nnUNetTrainerV2_3_softDS4_clean',
'nnUNetTrainerV2_3_softDS4_clean_improvedDA',
'nnUNetTrainerV2_3_softDS4_clean_improvedDA_newElDef',
'nnUNetTrainerV2_3_softDS4_radam',
'nnUNetTrainerV2_3_softDS4_radam_lowerLR',
'nnUNetTrainerV2_2_schedule',
'nnUNetTrainerV2_2_schedule2',
'nnUNetTrainerV2_2_clean',
'nnUNetTrainerV2_2_clean_improvedDA_newElDef',
'nnUNetTrainerV2_2_fixes', # running
'nnUNetTrainerV2_BN', # running
'nnUNetTrainerV2_noDeepSupervision', # running
'nnUNetTrainerV2_softDeepSupervision', # running
'nnUNetTrainerV2_noDataAugmentation', # running
'nnUNetTrainerV2_Loss_CE', # running
'nnUNetTrainerV2_Loss_CEGDL',
'nnUNetTrainerV2_Loss_Dice',
'nnUNetTrainerV2_Loss_DiceTopK10',
'nnUNetTrainerV2_Loss_TopK10',
'nnUNetTrainerV2_Adam', # running
'nnUNetTrainerV2_Adam_nnUNetTrainerlr', # running
'nnUNetTrainerV2_SGD_ReduceOnPlateau', # running
'nnUNetTrainerV2_SGD_lr1en1', # running
'nnUNetTrainerV2_SGD_lr1en3', # running
'nnUNetTrainerV2_fixedNonlin', # running
'nnUNetTrainerV2_GeLU', # running
'nnUNetTrainerV2_3ConvPerStage',
'nnUNetTrainerV2_NoNormalization',
'nnUNetTrainerV2_Adam_ReduceOnPlateau',
'nnUNetTrainerV2_fp16',
'nnUNetTrainerV2', # see overwrite_plans
'nnUNetTrainerV2_noMirroring',
'nnUNetTrainerV2_momentum09',
'nnUNetTrainerV2_momentum095',
'nnUNetTrainerV2_momentum098',
'nnUNetTrainerV2_warmup',
'nnUNetTrainerV2_Loss_Dice_LR1en3',
'nnUNetTrainerV2_NoNormalization_lr1en3',
'nnUNetTrainerV2_Loss_Dice_squared',
'nnUNetTrainerV2_newElDef',
'nnUNetTrainerV2_fp32',
'nnUNetTrainerV2_cycleAtEnd',
'nnUNetTrainerV2_reduceMomentumDuringTraining',
'nnUNetTrainerV2_graduallyTransitionFromCEToDice',
'nnUNetTrainerV2_insaneDA',
'nnUNetTrainerV2_independentScalePerAxis',
'nnUNetTrainerV2_Mish',
'nnUNetTrainerV2_Ranger_lr3en4',
'nnUNetTrainerV2_cycleAtEnd2',
'nnUNetTrainerV2_GN',
'nnUNetTrainerV2_DP',
'nnUNetTrainerV2_FRN',
'nnUNetTrainerV2_resample33',
'nnUNetTrainerV2_O2',
'nnUNetTrainerV2_ResencUNet',
'nnUNetTrainerV2_DA2',
'nnUNetTrainerV2_allConv3x3',
'nnUNetTrainerV2_ForceBD',
'nnUNetTrainerV2_ForceSD',
'nnUNetTrainerV2_ReLU',
'nnUNetTrainerV2_LReLU_slope_2en1',
'nnUNetTrainerV2_lReLU_convReLUIN',
'nnUNetTrainerV2_ReLU_biasInSegOutput',
'nnUNetTrainerV2_ReLU_convReLUIN',
'nnUNetTrainerV2_lReLU_biasInSegOutput',
'nnUNetTrainerV2_Loss_DicewithBG_LR1en3',
#'nnUNetTrainerV2_Loss_MCCnoBG',
'nnUNetTrainerV2_Loss_DicewithBG',
# 'nnUNetTrainerV2_Loss_Dice_LR1en3',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
# 'nnUNetTrainerV2_Ranger_lr3en4',
]
datasets = \
{"Task001_BrainTumour": ("3d_fullres", ),
"Task002_Heart": ("3d_fullres",),
#"Task024_Promise": ("3d_fullres",),
#"Task027_ACDC": ("3d_fullres",),
"Task003_Liver": ("3d_fullres", "3d_lowres"),
"Task004_Hippocampus": ("3d_fullres",),
"Task005_Prostate": ("3d_fullres",),
"Task006_Lung": ("3d_fullres", "3d_lowres"),
"Task007_Pancreas": ("3d_fullres", "3d_lowres"),
"Task008_HepaticVessel": ("3d_fullres", "3d_lowres"),
"Task009_Spleen": ("3d_fullres", "3d_lowres"),
"Task010_Colon": ("3d_fullres", "3d_lowres"),}
expected_validation_folder = "validation_raw"
alternative_validation_folder = "validation"
alternative_alternative_validation_folder = "validation_tiledTrue_doMirror_True"
interested_in = "mean"
result_per_dataset = {}
for d in datasets:
result_per_dataset[d] = {}
for c in datasets[d]:
result_per_dataset[d][c] = []
valid_trainers = []
all_trainers = []
with open(output_file, 'w') as f:
f.write("trainer,")
for t in datasets.keys():
s = t[4:7]
for c in datasets[t]:
s1 = s + "_" + c[3]
f.write("%s," % s1)
f.write("\n")
for trainer in trainers:
trainer_plans = [plans]
if trainer in overwrite_plans.keys():
trainer_plans = overwrite_plans[trainer]
result_per_dataset_here = {}
for d in datasets:
result_per_dataset_here[d] = {}
for p in trainer_plans:
name = "%s__%s" % (trainer, p)
all_present = True
all_trainers.append(name)
f.write("%s," % name)
for dataset in datasets.keys():
for configuration in datasets[dataset]:
summary_file = join(summary_files_dir, "%s__%s__%s__%s__%s__%s.json" % (dataset, configuration, trainer, p, expected_validation_folder, folds_str))
if not isfile(summary_file):
summary_file = join(summary_files_dir, "%s__%s__%s__%s__%s__%s.json" % (dataset, configuration, trainer, p, alternative_validation_folder, folds_str))
if not isfile(summary_file):
summary_file = join(summary_files_dir, "%s__%s__%s__%s__%s__%s.json" % (
dataset, configuration, trainer, p, alternative_alternative_validation_folder, folds_str))
if not isfile(summary_file):
all_present = False
print(name, dataset, configuration, "has missing summary file")
if isfile(summary_file):
result = load_json(summary_file)['results'][interested_in]['mean']['Dice']
result_per_dataset_here[dataset][configuration] = result
f.write("%02.4f," % result)
else:
f.write("NA,")
result_per_dataset_here[dataset][configuration] = 0
f.write("\n")
if True:
valid_trainers.append(name)
for d in datasets:
for c in datasets[d]:
result_per_dataset[d][c].append(result_per_dataset_here[d][c])
invalid_trainers = [i for i in all_trainers if i not in valid_trainers]
num_valid = len(valid_trainers)
num_datasets = len(datasets.keys())
# create an array that is trainer x dataset. If more than one configuration is there then use the best metric across the two
all_res = np.zeros((num_valid, num_datasets))
for j, d in enumerate(datasets.keys()):
ks = list(result_per_dataset[d].keys())
tmp = result_per_dataset[d][ks[0]]
for k in ks[1:]:
for i in range(len(tmp)):
tmp[i] = max(tmp[i], result_per_dataset[d][k][i])
all_res[:, j] = tmp
ranks_arr = np.zeros_like(all_res)
for d in range(ranks_arr.shape[1]):
temp = np.argsort(all_res[:, d])[::-1] # inverse because we want the highest dice to be rank0
ranks = np.empty_like(temp)
ranks[temp] = np.arange(len(temp))
ranks_arr[:, d] = ranks
mn = np.mean(ranks_arr, 1)
for i in np.argsort(mn):
print(mn[i], valid_trainers[i])
print()
print(valid_trainers[np.argmin(mn)])
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