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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 zipfile
from multiprocessing.pool import Pool
from batchgenerators.utilities.file_and_folder_operations import *
import shutil
from nnunet.paths import default_cascade_trainer, default_plans_identifier, default_trainer, network_training_output_dir
from nnunet.utilities.task_name_id_conversion import convert_id_to_task_name
from subprocess import call
def copy_fold(in_folder: str, out_folder: str):
shutil.copy(join(in_folder, "debug.json"), join(out_folder, "debug.json"))
shutil.copy(join(in_folder, "model_final_checkpoint.model"), join(out_folder, "model_final_checkpoint.model"))
shutil.copy(join(in_folder, "model_final_checkpoint.model.pkl"),
join(out_folder, "model_final_checkpoint.model.pkl"))
shutil.copy(join(in_folder, "progress.png"), join(out_folder, "progress.png"))
if isfile(join(in_folder, "network_architecture.pdf")):
shutil.copy(join(in_folder, "network_architecture.pdf"), join(out_folder, "network_architecture.pdf"))
def copy_model(directory: str, output_directory: str):
"""
:param directory: must have the 5 fold_X subfolders as well as a postprocessing.json and plans.pkl
:param output_directory:
:return:
"""
expected_folders = ["fold_%d" % i for i in range(5)]
assert all([isdir(join(directory, i)) for i in expected_folders]), "not all folds present"
assert isfile(join(directory, "plans.pkl")), "plans.pkl missing"
assert isfile(join(directory, "postprocessing.json")), "postprocessing.json missing"
for e in expected_folders:
maybe_mkdir_p(join(output_directory, e))
copy_fold(join(directory, e), join(output_directory, e))
shutil.copy(join(directory, "plans.pkl"), join(output_directory, "plans.pkl"))
shutil.copy(join(directory, "postprocessing.json"), join(output_directory, "postprocessing.json"))
def copy_pretrained_models_for_task(task_name: str, output_directory: str,
models: tuple = ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
nnunet_trainer=default_trainer,
nnunet_trainer_cascade=default_cascade_trainer,
plans_identifier=default_plans_identifier):
trainer_output_dir = nnunet_trainer + "__" + plans_identifier
trainer_output_dir_cascade = nnunet_trainer_cascade + "__" + plans_identifier
for m in models:
to = trainer_output_dir_cascade if m == "3d_cascade_fullres" else trainer_output_dir
expected_output_folder = join(network_training_output_dir, m, task_name, to)
if not isdir(expected_output_folder):
if m == "3d_lowres" or m == "3d_cascade_fullres":
print("Task", task_name, "does not seem to have the cascade")
continue
else:
raise RuntimeError("missing folder! %s" % expected_output_folder)
output_here = join(output_directory, m, task_name, to)
maybe_mkdir_p(output_here)
copy_model(expected_output_folder, output_here)
def check_if_valid(ensemble: str, valid_models, valid_trainers, valid_plans):
ensemble = ensemble[len("ensemble_"):]
mb1, mb2 = ensemble.split("--")
c1, tr1, p1 = mb1.split("__")
c2, tr2, p2 = mb2.split("__")
if c1 not in valid_models: return False
if c2 not in valid_models: return False
if tr1 not in valid_trainers: return False
if tr2 not in valid_trainers: return False
if p1 not in valid_plans: return False
if p2 not in valid_plans: return False
return True
def copy_ensembles(taskname, output_folder, valid_models=('2d', '3d_fullres', '3d_lowres', '3d_cascade_fullres'),
valid_trainers=(default_trainer, default_cascade_trainer),
valid_plans=(default_plans_identifier,)):
ensemble_dir = join(network_training_output_dir, 'ensembles', taskname)
if not isdir(ensemble_dir):
print("No ensemble directory found for task", taskname)
return
subd = subdirs(ensemble_dir, join=False)
valid = []
for s in subd:
v = check_if_valid(s, valid_models, valid_trainers, valid_plans)
if v:
valid.append(s)
output_ensemble = join(output_folder, 'ensembles', taskname)
maybe_mkdir_p(output_ensemble)
for v in valid:
this_output = join(output_ensemble, v)
maybe_mkdir_p(this_output)
shutil.copy(join(ensemble_dir, v, 'postprocessing.json'), this_output)
def compress_everything(output_base, num_processes=8):
p = Pool(num_processes)
tasks = subfolders(output_base, join=False)
tasknames = [i.split('/')[-1] for i in tasks]
args = []
for t, tn in zip(tasks, tasknames):
args.append((join(output_base, tn + ".zip"), join(output_base, t)))
p.starmap(compress_folder, args)
p.close()
p.join()
def compress_folder(zip_file, folder):
"""inspired by https://stackoverflow.com/questions/1855095/how-to-create-a-zip-archive-of-a-directory-in-python"""
zipf = zipfile.ZipFile(zip_file, 'w', zipfile.ZIP_DEFLATED)
for root, dirs, files in os.walk(folder):
for file in files:
zipf.write(join(root, file), os.path.relpath(join(root, file), folder))
def export_one_task(taskname, models, output_folder, nnunet_trainer=default_trainer,
nnunet_trainer_cascade=default_cascade_trainer,
plans_identifier=default_plans_identifier):
copy_pretrained_models_for_task(taskname, output_folder, models, nnunet_trainer, nnunet_trainer_cascade,
plans_identifier)
copy_ensembles(taskname, output_folder, models, (nnunet_trainer, nnunet_trainer_cascade), (plans_identifier,))
compress_folder(join(output_folder, taskname + '.zip'), join(output_folder, taskname))
def export_pretrained_model(task_name: str, output_file: str,
models: tuple = ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"),
nnunet_trainer=default_trainer,
nnunet_trainer_cascade=default_cascade_trainer,
plans_identifier=default_plans_identifier,
folds=(0, 1, 2, 3, 4), strict=True):
zipf = zipfile.ZipFile(output_file, 'w', zipfile.ZIP_DEFLATED)
trainer_output_dir = nnunet_trainer + "__" + plans_identifier
trainer_output_dir_cascade = nnunet_trainer_cascade + "__" + plans_identifier
for m in models:
to = trainer_output_dir_cascade if m == "3d_cascade_fullres" else trainer_output_dir
expected_output_folder = join(network_training_output_dir, m, task_name, to)
if not isdir(expected_output_folder):
if strict:
raise RuntimeError("Task %s is missing the model %s" % (task_name, m))
else:
continue
expected_folders = ["fold_%d" % i if i != 'all' else i for i in folds]
assert all([isdir(join(expected_output_folder, i)) for i in expected_folders]), "not all requested folds " \
"present, " \
"Task %s model %s" % \
(task_name, m)
assert isfile(join(expected_output_folder, "plans.pkl")), "plans.pkl missing, Task %s model %s" % (task_name, m)
for e in expected_folders:
zipf.write(join(expected_output_folder, e, "debug.json"),
os.path.relpath(join(expected_output_folder, e, "debug.json"),
network_training_output_dir))
zipf.write(join(expected_output_folder, e, "model_final_checkpoint.model"),
os.path.relpath(join(expected_output_folder, e, "model_final_checkpoint.model"),
network_training_output_dir))
zipf.write(join(expected_output_folder, e, "model_final_checkpoint.model.pkl"),
os.path.relpath(join(expected_output_folder, e, "model_final_checkpoint.model.pkl"),
network_training_output_dir))
zipf.write(join(expected_output_folder, e, "progress.png"),
os.path.relpath(join(expected_output_folder, e, "progress.png"), network_training_output_dir))
if isfile(join(expected_output_folder, e, "network_architecture.pdf")):
zipf.write(join(expected_output_folder, e, "network_architecture.pdf"),
os.path.relpath(join(expected_output_folder, e, "network_architecture.pdf"),
network_training_output_dir))
zipf.write(join(expected_output_folder, "plans.pkl"),
os.path.relpath(join(expected_output_folder, "plans.pkl"), network_training_output_dir))
if not isfile(join(expected_output_folder, "postprocessing.json")):
if strict:
raise RuntimeError('postprocessing.json missing. Run nnUNet_determine_postprocessing or disable strict')
else:
print('WARNING: postprocessing.json missing')
else:
zipf.write(join(expected_output_folder, "postprocessing.json"),
os.path.relpath(join(expected_output_folder, "postprocessing.json"), network_training_output_dir))
ensemble_dir = join(network_training_output_dir, 'ensembles', task_name)
if not isdir(ensemble_dir):
print("No ensemble directory found for task", task_name)
return
subd = subdirs(ensemble_dir, join=False)
valid = []
for s in subd:
v = check_if_valid(s, models, (nnunet_trainer, nnunet_trainer_cascade), (plans_identifier))
if v:
valid.append(s)
for v in valid:
zipf.write(join(ensemble_dir, v, 'postprocessing.json'),
os.path.relpath(join(ensemble_dir, v, 'postprocessing.json'),
network_training_output_dir))
zipf.close()
def export_entry_point():
import argparse
parser = argparse.ArgumentParser(description="Use this script to export models to a zip file for sharing with "
"others. You can upload the zip file and then either share the url "
"for usage with nnUNet_download_pretrained_model_by_url, or share the "
"zip for usage with nnUNet_install_pretrained_model_from_zip")
parser.add_argument('-t', type=str, help='task name or task id')
parser.add_argument('-o', type=str, help='output file name. Should end with .zip')
parser.add_argument('-m', nargs='+',
help='list of model configurations. Default: 2d 3d_lowres 3d_fullres 3d_cascade_fullres. Must '
'be adapted to fit the available models of a task',
default=("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres"), required=False)
parser.add_argument('-tr', type=str, help='trainer class used for 2d 3d_lowres and 3d_fullres. '
'Default: %s' % default_trainer, required=False, default=default_trainer)
parser.add_argument('-trc', type=str, help='trainer class used for 3d_cascade_fullres. '
'Default: %s' % default_cascade_trainer, required=False,
default=default_cascade_trainer)
parser.add_argument('-pl', type=str, help='nnunet plans identifier. Default: %s' % default_plans_identifier,
required=False, default=default_plans_identifier)
parser.add_argument('--disable_strict', action='store_true', help='set this if you want to allow skipping '
'missing things', required=False)
parser.add_argument('-f', nargs='+', help='Folds. Default: 0 1 2 3 4', required=False, default=[0, 1, 2, 3, 4])
args = parser.parse_args()
folds = args.f
folds = [int(i) if i != 'all' else i for i in folds]
taskname = args.t
if taskname.startswith("Task"):
pass
else:
try:
taskid = int(taskname)
except Exception as e:
print('-t must be either a Task name (TaskXXX_YYY) or a task id (integer)')
raise e
taskname = convert_id_to_task_name(taskid)
export_pretrained_model(taskname, args.o, args.m, args.tr, args.trc, args.pl, strict=not args.disable_strict,
folds=folds)
def export_for_paper():
output_base = "/media/fabian/DeepLearningData/nnunet_trained_models"
task_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 17, 24, 27, 29, 35, 48, 55, 61, 38]
for t in task_ids:
if t == 61:
models = ("3d_fullres",)
else:
models = ("2d", "3d_lowres", "3d_fullres", "3d_cascade_fullres")
taskname = convert_id_to_task_name(t)
print(taskname)
output_folder = join(output_base, taskname)
maybe_mkdir_p(output_folder)
copy_pretrained_models_for_task(taskname, output_folder, models)
copy_ensembles(taskname, output_folder)
compress_everything(output_base, 8)
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