File size: 6,207 Bytes
ecf08bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
# 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 batchgenerators.utilities.file_and_folder_operations import *
import os
from nnunet.evaluation.model_selection.summarize_results_in_one_json import summarize
from nnunet.paths import network_training_output_dir
import numpy as np
def list_to_string(l, delim=","):
st = "%03.3f" % l[0]
for i in l[1:]:
st += delim + "%03.3f" % i
return st
def write_plans_to_file(f, plans_file, stage=0, do_linebreak_at_end=True, override_name=None):
a = load_pickle(plans_file)
stages = list(a['plans_per_stage'].keys())
stages.sort()
patch_size_in_mm = [i * j for i, j in zip(a['plans_per_stage'][stages[stage]]['patch_size'],
a['plans_per_stage'][stages[stage]]['current_spacing'])]
median_patient_size_in_mm = [i * j for i, j in zip(a['plans_per_stage'][stages[stage]]['median_patient_size_in_voxels'],
a['plans_per_stage'][stages[stage]]['current_spacing'])]
if override_name is None:
f.write(plans_file.split("/")[-2] + "__" + plans_file.split("/")[-1])
else:
f.write(override_name)
f.write(";%d" % stage)
f.write(";%s" % str(a['plans_per_stage'][stages[stage]]['batch_size']))
f.write(";%s" % str(a['plans_per_stage'][stages[stage]]['num_pool_per_axis']))
f.write(";%s" % str(a['plans_per_stage'][stages[stage]]['patch_size']))
f.write(";%s" % list_to_string(patch_size_in_mm))
f.write(";%s" % str(a['plans_per_stage'][stages[stage]]['median_patient_size_in_voxels']))
f.write(";%s" % list_to_string(median_patient_size_in_mm))
f.write(";%s" % list_to_string(a['plans_per_stage'][stages[stage]]['current_spacing']))
f.write(";%s" % list_to_string(a['plans_per_stage'][stages[stage]]['original_spacing']))
f.write(";%s" % str(a['plans_per_stage'][stages[stage]]['pool_op_kernel_sizes']))
f.write(";%s" % str(a['plans_per_stage'][stages[stage]]['conv_kernel_sizes']))
if do_linebreak_at_end:
f.write("\n")
if __name__ == "__main__":
summarize((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 24, 27), output_dir=join(network_training_output_dir, "summary_fold0"), folds=(0,))
base_dir = os.environ['RESULTS_FOLDER']
nnunets = ['nnUNetV2', 'nnUNetV2_zspacing']
task_ids = list(range(99))
with open("summary.csv", 'w') as f:
f.write("identifier;stage;batch_size;num_pool_per_axis;patch_size;patch_size(mm);median_patient_size_in_voxels;median_patient_size_in_mm;current_spacing;original_spacing;pool_op_kernel_sizes;conv_kernel_sizes;patient_dc;global_dc\n")
for i in task_ids:
for nnunet in nnunets:
try:
summary_folder = join(base_dir, nnunet, "summary_fold0")
if isdir(summary_folder):
summary_files = subfiles(summary_folder, join=False, prefix="Task%03.0d_" % i, suffix=".json", sort=True)
for s in summary_files:
tmp = s.split("__")
trainer = tmp[2]
expected_output_folder = join(base_dir, nnunet, tmp[1], tmp[0], tmp[2].split(".")[0])
name = tmp[0] + "__" + nnunet + "__" + tmp[1] + "__" + tmp[2].split(".")[0]
global_dice_json = join(base_dir, nnunet, tmp[1], tmp[0], tmp[2].split(".")[0], "fold_0", "validation_tiledTrue_doMirror_True", "global_dice.json")
if not isdir(expected_output_folder) or len(tmp) > 3:
if len(tmp) == 2:
continue
expected_output_folder = join(base_dir, nnunet, tmp[1], tmp[0], tmp[2] + "__" + tmp[3].split(".")[0])
name = tmp[0] + "__" + nnunet + "__" + tmp[1] + "__" + tmp[2] + "__" + tmp[3].split(".")[0]
global_dice_json = join(base_dir, nnunet, tmp[1], tmp[0], tmp[2] + "__" + tmp[3].split(".")[0], "fold_0", "validation_tiledTrue_doMirror_True", "global_dice.json")
assert isdir(expected_output_folder), "expected output dir not found"
plans_file = join(expected_output_folder, "plans.pkl")
assert isfile(plans_file)
plans = load_pickle(plans_file)
num_stages = len(plans['plans_per_stage'])
if num_stages > 1 and tmp[1] == "3d_fullres":
stage = 1
elif (num_stages == 1 and tmp[1] == "3d_fullres") or tmp[1] == "3d_lowres":
stage = 0
else:
print("skipping", s)
continue
g_dc = load_json(global_dice_json)
mn_glob_dc = np.mean(list(g_dc.values()))
write_plans_to_file(f, plans_file, stage, False, name)
# now read and add result to end of line
results = load_json(join(summary_folder, s))
mean_dc = results['results']['mean']['mean']['Dice']
f.write(";%03.3f" % mean_dc)
f.write(";%03.3f\n" % mn_glob_dc)
print(name, mean_dc)
except Exception as e:
print(e)
|