text: import tensorflow as tf
  
  from niftynet.application.base_application import BaseApplication
  from niftynet.contrib.segmentation_selective_sampler.sampler_selective import \
      SelectiveSampler, Constraint
  from niftynet.engine.application_factory import \
      ApplicationNetFactory, InitializerFactory, OptimiserFactory
  from niftynet.engine.application_variables import \
      CONSOLE, NETWORK_OUTPUT, TF_SUMMARIES
  from niftynet.engine.sampler_grid_v2 import GridSampler
  from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator
  from niftynet.io.image_reader import ImageReader
  from niftynet.layer.binary_masking import BinaryMaskingLayer
  from niftynet.layer.discrete_label_normalisation import \
      DiscreteLabelNormalisationLayer
  from niftynet.layer.histogram_normalisation import \
      HistogramNormalisationLayer
  from niftynet.layer.loss_segmentation import LossFunction
  from niftynet.layer.mean_variance_normalisation import \
      MeanVarNormalisationLayer
  from niftynet.layer.pad import PadLayer
  from niftynet.layer.post_processing import PostProcessingLayer
  from niftynet.layer.rand_flip import RandomFlipLayer
  from niftynet.layer.rand_rotation import RandomRotationLayer
  from niftynet.layer.rand_spatial_scaling import RandomSpatialScalingLayer
  
  SUPPORTED_INPUT = set(['image', 'label', 'weight', 'sampler'])
  
  
  class SelectiveSampling(BaseApplication):
      REQUIRED_CONFIG_SECTION = "SEGMENTATION"
  
      def __init__(self, net_param, action_param, is_training):
          super(SelectiveSampling, self).__init__()
          tf.logging.info('starting segmentation application')
          self.is_training = is_training
  
          self.net_param = net_param
          self.action_param = action_param
  
          self.data_param = None
          self.segmentation_param = None
          self.SUPPORTED_SAMPLING = {
              'selective': (self.initialise_selective_sampler,
                            self.initialise_grid_sampler,
                            self.initialise_grid_aggregator)
          }
  
      def initialise_dataset_loader(
              self, data_param=None, task_param=None, data_partitioner=None):
  
          self.data_param = data_param
          self.segmentation_param = task_param
  
          try:
              reader_phase = self.action_param.dataset_to_infer
          except AttributeError:
              reader_phase = None
          file_lists = data_partitioner.get_file_lists_by(
              phase=reader_phase, action=self.action)
          # read each line of csv files into an instance of Subject
          if self.is_training:
              self.readers = []
              for file_list in file_lists:
                  reader = ImageReader(SUPPORTED_INPUT)
                  reader.initialise(data_param, task_param, file_list)
                  self.readers.append(reader)
  
          else:  # in the inference process use image input only
              inference_reader = ImageReader(['image'])
              inference_reader.initialise(data_param, task_param, file_lists[0])
              self.readers = [inference_reader]
  
          foreground_masking_layer = None
          if self.net_param.normalise_foreground_only:
              foreground_masking_layer = BinaryMaskingLayer(
                  type_str=self.net_param.foreground_type,
                  multimod_fusion=self.net_param.multimod_foreground_type,
                  threshold=0.0)
  
          mean_var_normaliser = MeanVarNormalisationLayer(
              image_name='image', binary_masking_func=foreground_masking_layer)
          histogram_normaliser = None
          if self.net_param.histogram_ref_file:
              histogram_normaliser = HistogramNormalisationLayer(
                  image_name='image',
                  modalities=vars(task_param).get('image'),
                  model_filename=self.net_param.histogram_ref_file,
                  binary_masking_func=foreground_masking_layer,
                  norm_type=self.net_param.norm_type,
                  cutoff=self.net_param.cutoff,
                  name='hist_norm_layer')
  
          label_normaliser = None
          if self.net_param.histogram_ref_file:
              label_normaliser = DiscreteLabelNormalisationLayer(
                  image_name='label',
                  modalities=vars(task_param).get('label'),
                  model_filename=self.net_param.histogram_ref_file)
  
          normalisation_layers = []
          if self.net_param.normalisation:
              normalisation_layers.append(histogram_normaliser)
          if self.net_param.whitening:
              normalisation_layers.append(mean_var_normaliser)
          if task_param.label_normalisation:
              normalisation_layers.append(label_normaliser)
  
          augmentation_layers = []
          if self.is_training:
              if self.action_param.random_flipping_axes != -1:
                  augmentation_layers.append(RandomFlipLayer(
                      flip_axes=self.action_param.random_flipping_axes))
              if self.action_param.scaling_percentage:
                  augmentation_layers.append(RandomSpatialScalingLayer(
                      min_percentage=self.action_param.scaling_percentage[0],
                      max_percentage=self.action_param.scaling_percentage[1]))
              if self.action_param.rotation_angle or \
                      self.action_param.rotation_angle_x or \
                      self.action_param.rotation_angle_y or \
                      self.action_param.rotation_angle_z:
                  rotation_layer = RandomRotationLayer()
                  if self.action_param.rotation_angle:
                      rotation_layer.init_uniform_angle(
                          self.action_param.rotation_angle)
                  else:
                      rotation_layer.init_non_uniform_angle(
                          self.action_param.rotation_angle_x,
                          self.action_param.rotation_angle_y,
                          self.action_param.rotation_angle_z)
                  augmentation_layers.append(rotation_layer)
  
          volume_padding_layer = [PadLayer(
              image_name=SUPPORTED_INPUT,
              border=self.net_param.volume_padding_size,
              mode=self.net_param.volume_padding_mode,
              pad_to=self.net_param.volume_padding_to_size)
          ]
  
          for reader in self.readers:
              reader.add_preprocessing_layers(
                  volume_padding_layer +
                  normalisation_layers +
                  augmentation_layers)
  
      def initialise_selective_sampler(self):
          # print("Initialisation ",
          #       self.segmentation_param.compulsory_labels,
          #       self.segmentation_param.proba_connect)
          # print(self.segmentation_param.num_min_labels,
          #       self.segmentation_param.proba_connect)
          selective_constraints = Constraint(
              self.segmentation_param.compulsory_labels,
              self.segmentation_param.min_sampling_ratio,
              self.segmentation_param.min_numb_labels,
              self.segmentation_param.proba_connect)
          self.sampler = [[
              SelectiveSampler(
                  reader=reader,
                  data_param=self.data_param,
                  batch_size=self.net_param.batch_size,
                  windows_per_image=self.action_param.sample_per_volume,
                  constraint=selective_constraints,
                  random_windows_per_image=self.segmentation_param.rand_samples,
                  queue_length=self.net_param.queue_length)
              for reader in self.readers]]
  
      def initialise_grid_sampler(self):
          self.sampler = [[GridSampler(
              reader=reader,
              window_sizes=self.data_param,
              batch_size=self.net_param.batch_size,
              spatial_window_size=self.action_param.spatial_window_size,
              window_border=self.action_param.border,
              queue_length=self.net_param.queue_length) for reader in
              self.readers]]
  
      def initialise_grid_aggregator(self):
          self.output_decoder = GridSamplesAggregator(
              image_reader=self.readers[0],
              output_path=self.action_param.save_seg_dir,
              window_border=self.action_param.border,
              interp_order=self.action_param.output_interp_order)
  
      def initialise_sampler(self):
          if self.is_training:
              self.SUPPORTED_SAMPLING['selective'][0]()
          else:
              self.SUPPORTED_SAMPLING['selective'][1]()
  
      def initialise_network(self):
          w_regularizer = None
          b_regularizer = None
          reg_type = self.net_param.reg_type.lower()
          decay = self.net_param.decay
          if reg_type == 'l2' and decay > 0:
              from tensorflow.contrib.layers.python.layers import regularizers
              w_regularizer = regularizers.l2_regularizer(decay)
              b_regularizer = regularizers.l2_regularizer(decay)
          elif reg_type == 'l1' and decay > 0:
              from tensorflow.contrib.layers.python.layers import regularizers
              w_regularizer = regularizers.l1_regularizer(decay)
              b_regularizer = regularizers.l1_regularizer(decay)
  
          self.net = ApplicationNetFactory.create(self.net_param.name)(
              num_classes=self.segmentation_param.num_classes,
              w_initializer=InitializerFactory.get_initializer(
                  name=self.net_param.weight_initializer),
              b_initializer=InitializerFactory.get_initializer(
                  name=self.net_param.bias_initializer),
              w_regularizer=w_regularizer,
              b_regularizer=b_regularizer,
              acti_func=self.net_param.activation_function)
  
      def connect_data_and_network(self,
                                   outputs_collector=None,
                                   gradients_collector=None):
  
          def switch_sampler(for_training):
              with tf.name_scope('train' if for_training else 'validation'):
                  sampler = self.get_sampler()[0][0 if for_training else -1]
                  return sampler.pop_batch_op()
  
          if self.is_training:
  
              if self.action_param.validation_every_n > 0:
                  data_dict = tf.cond(tf.logical_not(self.is_validation),
                                      lambda: switch_sampler(for_training=True),
                                      lambda: switch_sampler(for_training=False))
              else:
                  data_dict = switch_sampler(for_training=True)
              image = tf.cast(data_dict['image'], tf.float32)
              net_out = self.net(image, is_training=self.is_training)
  
              with tf.name_scope('Optimiser'):
                  optimiser_class = OptimiserFactory.create(
                      name=self.action_param.optimiser)
                  self.optimiser = optimiser_class.get_instance(
                      learning_rate=self.action_param.lr)
              loss_func = LossFunction(
                  n_class=self.segmentation_param.num_classes,
                  loss_type=self.action_param.loss_type)
              data_loss = loss_func(
                  prediction=net_out,
                  ground_truth=data_dict.get('label', None),
                  weight_map=data_dict.get('weight', None))
              reg_losses = tf.get_collection(
                  tf.GraphKeys.REGULARIZATION_LOSSES)
              if self.net_param.decay > 0.0 and reg_losses:
                  reg_loss = tf.reduce_mean(
                      [tf.reduce_mean(reg_loss) for reg_loss in reg_losses])
                  loss = data_loss + reg_loss
              else:
                  loss = data_loss
              grads = self.optimiser.compute_gradients(loss)
              # collecting gradients variables
              gradients_collector.add_to_collection([grads])
              # collecting output variables
              outputs_collector.add_to_collection(
                  var=data_loss, name='dice_loss',
                  average_over_devices=False, collection=CONSOLE)
              outputs_collector.add_to_collection(
                  var=data_loss, name='dice_loss',
                  average_over_devices=True, summary_type='scalar',
                  collection=TF_SUMMARIES)
  
              # outputs_collector.add_to_collection(
              #    var=image*180.0, name='image',
              #    average_over_devices=False, summary_type='image3_sagittal',
              #    collection=TF_SUMMARIES)
  
              # outputs_collector.add_to_collection(
              #    var=image, name='image',
              #    average_over_devices=False,
              #    collection=NETWORK_OUTPUT)
  
              # outputs_collector.add_to_collection(
              #    var=tf.reduce_mean(image), name='mean_image',
              #    average_over_devices=False, summary_type='scalar',
              #    collection=CONSOLE)
          else:
              # converting logits into final output for
              # classification probabilities or argmax classification labels
              data_dict = switch_sampler(for_training=False)
              image = tf.cast(data_dict['image'], tf.float32)
              net_out = self.net(image, is_training=self.is_training)
  
              output_prob = self.segmentation_param.output_prob
              num_classes = self.segmentation_param.num_classes
              if output_prob and num_classes > 1:
                  post_process_layer = PostProcessingLayer(
                      'SOFTMAX', num_classes=num_classes)
              elif not output_prob and num_classes > 1:
                  post_process_layer = PostProcessingLayer(
                      'ARGMAX', num_classes=num_classes)
              else:
                  post_process_layer = PostProcessingLayer(
                      'IDENTITY', num_classes=num_classes)
              net_out = post_process_layer(net_out)
  
              outputs_collector.add_to_collection(
                  var=net_out, name='window',
                  average_over_devices=False, collection=NETWORK_OUTPUT)
              outputs_collector.add_to_collection(
                  var=data_dict['image_location'], name='location',
                  average_over_devices=False, collection=NETWORK_OUTPUT)
              init_aggregator = \
                  self.SUPPORTED_SAMPLING[self.net_param.window_sampling][2]
              init_aggregator()
  
      def interpret_output(self, batch_output):
          if not self.is_training:
              return self.output_decoder.decode_batch(
                  batch_output['window'], batch_output['location'])
          return True
  