nnUNet_calvingfront_detection
/
nnunet
/training
/network_training
/nnUNet_variants
/architectural_variants
/nnUNetTrainerV2_ResencUNet_DA3.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. | |
| 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 | |