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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 copy import deepcopy
import numpy as np
from nnunet.experiment_planning.common_utils import get_pool_and_conv_props
from nnunet.experiment_planning.experiment_planner_baseline_3DUNet import ExperimentPlanner
from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import ExperimentPlanner3D_v21
from nnunet.network_architecture.generic_UNet import Generic_UNet
from nnunet.paths import *
class ExperimentPlanner3D_v21_3cps(ExperimentPlanner3D_v21):
"""
have 3x conv-in-lrelu per resolution instead of 2 while remaining in the same memory budget
This only works with 3d fullres because we use the same data as ExperimentPlanner3D_v21. Lowres would require to
rerun preprocesing (different patch size = different 3d lowres target spacing)
"""
def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
super(ExperimentPlanner3D_v21_3cps, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
self.plans_fname = join(self.preprocessed_output_folder,
"nnUNetPlansv2.1_3cps_plans_3D.pkl")
self.unet_base_num_features = 32
self.conv_per_stage = 3
def run_preprocessing(self, num_threads):
pass
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