code: #    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 numpy as np
  from batchgenerators.utilities.file_and_folder_operations import *
  from tuframework.network_architecture.generic_UNet import Generic_UNet
  from tuframework.network_architecture.initialization import InitWeights_He
  from tuframework.network_architecture.neural_network import SegmentationNetwork
  from tuframework.training.data_augmentation.data_augmentation_moreDA import get_moreDA_augmentation
  from tuframework.training.data_augmentation.default_data_augmentation import default_3D_augmentation_params, \
      default_2D_augmentation_params, get_patch_size
  from tuframework.training.dataloading.dataset_loading import unpack_dataset
  from tuframework.training.loss_functions.dice_loss import DC_and_CE_loss
  from tuframework.training.network_training.tuTrainer import tuframeworkTrainer
  from tuframework.training.network_training.tuTrainerV2 import tuframeworkTrainerV2
  from tuframework.utilities.nd_softmax import softmax_helper
  from torch import nn
  import torch
  
  
  class tuframeworkTrainerV2_noDeepSupervision(tuframeworkTrainerV2):
      def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
                   unpack_data=True, deterministic=True, fp16=False):
          super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
                           deterministic, fp16)
          self.loss = DC_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {})
  
      def setup_DA_params(self):
          """
          we leave out the creation of self.deep_supervision_scales, so it remains None
          :return:
          """
          if self.threeD:
              self.data_aug_params = default_3D_augmentation_params
              self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
              self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
              self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
              if self.do_dummy_2D_aug:
                  self.data_aug_params["dummy_2D"] = True
                  self.print_to_log_file("Using dummy2d data augmentation")
                  self.data_aug_params["elastic_deform_alpha"] = \
                      default_2D_augmentation_params["elastic_deform_alpha"]
                  self.data_aug_params["elastic_deform_sigma"] = \
                      default_2D_augmentation_params["elastic_deform_sigma"]
                  self.data_aug_params["rotation_x"] = default_2D_augmentation_params["rotation_x"]
          else:
              self.do_dummy_2D_aug = False
              if max(self.patch_size) / min(self.patch_size) > 1.5:
                  default_2D_augmentation_params['rotation_x'] = (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi)
              self.data_aug_params = default_2D_augmentation_params
          self.data_aug_params["mask_was_used_for_normalization"] = self.use_mask_for_norm
  
          if self.do_dummy_2D_aug:
              self.basic_generator_patch_size = get_patch_size(self.patch_size[1:],
                                                               self.data_aug_params['rotation_x'],
                                                               self.data_aug_params['rotation_y'],
                                                               self.data_aug_params['rotation_z'],
                                                               self.data_aug_params['scale_range'])
              self.basic_generator_patch_size = np.array([self.patch_size[0]] + list(self.basic_generator_patch_size))
              patch_size_for_spatialtransform = self.patch_size[1:]
          else:
              self.basic_generator_patch_size = get_patch_size(self.patch_size, self.data_aug_params['rotation_x'],
                                                               self.data_aug_params['rotation_y'],
                                                               self.data_aug_params['rotation_z'],
                                                               self.data_aug_params['scale_range'])
              patch_size_for_spatialtransform = self.patch_size
  
          self.data_aug_params["scale_range"] = (0.7, 1.4)
          self.data_aug_params["do_elastic"] = False
          self.data_aug_params['selected_seg_channels'] = [0]
          self.data_aug_params['patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
  
      def initialize(self, training=True, force_load_plans=False):
          """
          removed deep supervision
          :return:
          """
          if not self.was_initialized:
              maybe_mkdir_p(self.output_folder)
  
              if force_load_plans or (self.plans is None):
                  self.load_plans_file()
  
              self.process_plans(self.plans)
  
              self.setup_DA_params()
  
              self.folder_with_preprocessed_data = join(self.dataset_directory, self.plans['data_identifier'] +
                                                        "_stage%d" % self.stage)
              if training:
                  self.dl_tr, self.dl_val = self.get_basic_generators()
                  if self.unpack_data:
                      print("unpacking dataset")
                      unpack_dataset(self.folder_with_preprocessed_data)
                      print("done")
                  else:
                      print(
                          "INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
                          "will wait all winter for your model to finish!")
  
                  assert self.deep_supervision_scales is None
                  self.tr_gen, self.val_gen = get_moreDA_augmentation(self.dl_tr, self.dl_val,
                                                                      self.data_aug_params[
                                                                          'patch_size_for_spatialtransform'],
                                                                      self.data_aug_params,
                                                                      deep_supervision_scales=self.deep_supervision_scales,
                                                                      classes=None,
                                                                      pin_memory=self.pin_memory)
  
                  self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())),
                                         also_print_to_console=False)
                  self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())),
                                         also_print_to_console=False)
              else:
                  pass
  
              self.initialize_network()
              self.initialize_optimizer_and_scheduler()
  
              assert isinstance(self.network, (SegmentationNetwork, nn.DataParallel))
          else:
              self.print_to_log_file('self.was_initialized is True, not running self.initialize again')
          self.was_initialized = True
  
      def initialize_network(self):
          """
          changed deep supervision to False
          :return:
          """
          if self.threeD:
              conv_op = nn.Conv3d
              dropout_op = nn.Dropout3d
              norm_op = nn.InstanceNorm3d
  
          else:
              conv_op = nn.Conv2d
              dropout_op = nn.Dropout2d
              norm_op = nn.InstanceNorm2d
  
          norm_op_kwargs = {'eps': 1e-5, 'affine': True}
          dropout_op_kwargs = {'p': 0, 'inplace': True}
          net_nonlin = nn.LeakyReLU
          net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
          self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes,
                                      len(self.net_num_pool_op_kernel_sizes),
                                      self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
                                      net_nonlin, net_nonlin_kwargs, False, False, lambda x: x, InitWeights_He(1e-2),
                                      self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
          if torch.cuda.is_available():
              self.network.cuda()
          self.network.inference_apply_nonlin = softmax_helper
  
      def run_online_evaluation(self, output, target):
          return tuframeworkTrainer.run_online_evaluation(self, output, target)
  
apis: torch.cuda.is_available