nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_ResencUNet_DA3_BN.py

#    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