nnUNet_calvingfront_detection
/
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 | |