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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 typing import Tuple
import numpy as np
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.nnUNetTrainer import nnUNetTrainer
from nnunet.training.network_training.nnUNet_variants.data_augmentation.nnUNetTrainerV2_DA3 import \
nnUNetTrainerV2_DA3
from nnunet.utilities.nd_softmax import softmax_helper
class nnUNetTrainerV2_ResencUNet_DA3(nnUNetTrainerV2_DA3):
def initialize_network(self):
if self.threeD:
cfg = get_default_network_config(3, None, norm_type="in")
else:
cfg = get_default_network_config(1, None, norm_type="in")
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
def setup_DA_params(self):
"""
net_num_pool_op_kernel_sizes is different in resunet
"""
super().setup_DA_params()
self.deep_supervision_scales = [[1, 1, 1]] + list(list(i) for i in 1 / np.cumprod(
np.vstack(self.net_num_pool_op_kernel_sizes[1:]), axis=0))[:-1]
def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, step_size: float = 0.5,
save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True,
validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False,
segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True):
ds = self.network.decoder.deep_supervision
self.network.decoder.deep_supervision = False
ret = nnUNetTrainer.validate(self, do_mirroring=do_mirroring, use_sliding_window=use_sliding_window,
step_size=step_size, save_softmax=save_softmax, use_gaussian=use_gaussian,
overwrite=overwrite, validation_folder_name=validation_folder_name, debug=debug,
all_in_gpu=all_in_gpu, segmentation_export_kwargs=segmentation_export_kwargs,
run_postprocessing_on_folds=run_postprocessing_on_folds)
self.network.decoder.deep_supervision = ds
return ret
def predict_preprocessed_data_return_seg_and_softmax(self, data: np.ndarray, do_mirroring: bool = True,
mirror_axes: Tuple[int] = None,
use_sliding_window: bool = True, step_size: float = 0.5,
use_gaussian: bool = True, pad_border_mode: str = 'constant',
pad_kwargs: dict = None, all_in_gpu: bool = False,
verbose: bool = True, mixed_precision=True) -> Tuple[np.ndarray, np.ndarray]:
ds = self.network.decoder.deep_supervision
self.network.decoder.deep_supervision = False
ret = nnUNetTrainer.predict_preprocessed_data_return_seg_and_softmax(self, data=data,
do_mirroring=do_mirroring,
mirror_axes=mirror_axes,
use_sliding_window=use_sliding_window,
step_size=step_size,
use_gaussian=use_gaussian,
pad_border_mode=pad_border_mode,
pad_kwargs=pad_kwargs,
all_in_gpu=all_in_gpu,
verbose=verbose,
mixed_precision=mixed_precision)
self.network.decoder.deep_supervision = ds
return ret
def run_training(self):
self.maybe_update_lr(self.epoch) # if we dont overwrite epoch then self.epoch+1 is used which is not what we
# want at the start of the training
ds = self.network.decoder.deep_supervision
self.network.decoder.deep_supervision = True
ret = nnUNetTrainer.run_training(self)
self.network.decoder.deep_supervision = ds
return ret
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