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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 torch
from nnunet.network_architecture.generic_modular_residual_UNet import FabiansUNet, get_default_network_config
from nnunet.network_architecture.initialization import InitWeights_He
from nnunet.training.network_training.nnUNet_variants.architectural_variants.nnUNetTrainerV2_ResencUNet_DA3 import \
nnUNetTrainerV2_ResencUNet_DA3
from nnunet.utilities.nd_softmax import softmax_helper
class nnUNetTrainerV2_ResencUNet_DA3_BN(nnUNetTrainerV2_ResencUNet_DA3):
def initialize_network(self):
if self.threeD:
cfg = get_default_network_config(3, None, norm_type="bn")
else:
cfg = get_default_network_config(1, None, norm_type="bn")
stage_plans = self.plans['plans_per_stage'][self.stage]
conv_kernel_sizes = stage_plans['conv_kernel_sizes']
blocks_per_stage_encoder = stage_plans['num_blocks_encoder']
blocks_per_stage_decoder = stage_plans['num_blocks_decoder']
pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes']
self.network = FabiansUNet(self.num_input_channels, self.base_num_features, blocks_per_stage_encoder, 2,
pool_op_kernel_sizes, conv_kernel_sizes, cfg, self.num_classes,
blocks_per_stage_decoder, True, False, 320, InitWeights_He(1e-2))
if torch.cuda.is_available():
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
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