Source code for niftynet.application.segmentation_application

# -*- coding: utf-8 -*-
import tensorflow as tf

from niftynet.application.base_application import BaseApplication
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.sampler_resize_v2 import ResizeSampler
from niftynet.engine.sampler_uniform_v2 import UniformSampler
from niftynet.engine.sampler_weighted_v2 import WeightedSampler
from niftynet.engine.sampler_balanced_v2 import BalancedSampler
from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator
from niftynet.engine.windows_aggregator_resize import ResizeSamplesAggregator
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
from niftynet.layer.rgb_histogram_equilisation import \
    RGBHistogramEquilisationLayer
from niftynet.evaluation.segmentation_evaluator import SegmentationEvaluator
from niftynet.layer.rand_elastic_deform import RandomElasticDeformationLayer

SUPPORTED_INPUT = set(['image', 'label', 'weight', 'sampler', 'inferred'])


[docs]class SegmentationApplication(BaseApplication): REQUIRED_CONFIG_SECTION = "SEGMENTATION" def __init__(self, net_param, action_param, action): super(SegmentationApplication, self).__init__() tf.logging.info('starting segmentation application') self.action = action self.net_param = net_param self.action_param = action_param self.data_param = None self.segmentation_param = None self.SUPPORTED_SAMPLING = { 'uniform': (self.initialise_uniform_sampler, self.initialise_grid_sampler, self.initialise_grid_aggregator), 'weighted': (self.initialise_weighted_sampler, self.initialise_grid_sampler, self.initialise_grid_aggregator), 'resize': (self.initialise_resize_sampler, self.initialise_resize_sampler, self.initialise_resize_aggregator), 'balanced': (self.initialise_balanced_sampler, self.initialise_grid_sampler, self.initialise_grid_aggregator), }
[docs] def initialise_dataset_loader( self, data_param=None, task_param=None, data_partitioner=None): self.data_param = data_param self.segmentation_param = task_param # initialise input image readers if self.is_training: reader_names = ('image', 'label', 'weight', 'sampler') elif self.is_inference: # in the inference process use `image` input only reader_names = ('image',) elif self.is_evaluation: reader_names = ('image', 'label', 'inferred') else: tf.logging.fatal( 'Action `%s` not supported. Expected one of %s', self.action, self.SUPPORTED_PHASES) raise ValueError 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) self.readers = [ ImageReader(reader_names).initialise( data_param, task_param, file_list) for file_list in file_lists] # initialise input preprocessing layers foreground_masking_layer = BinaryMaskingLayer( type_str=self.net_param.foreground_type, multimod_fusion=self.net_param.multimod_foreground_type, threshold=0.0) \ if self.net_param.normalise_foreground_only else None mean_var_normaliser = MeanVarNormalisationLayer( image_name='image', binary_masking_func=foreground_masking_layer) \ if self.net_param.whitening else None 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') \ if (self.net_param.histogram_ref_file and self.net_param.normalisation) else None rgb_normaliser = RGBHistogramEquilisationLayer( image_name='image', name='rbg_norm_layer') if self.net_param.rgb_normalisation else None label_normalisers = None if self.net_param.histogram_ref_file and \ task_param.label_normalisation: label_normalisers = [DiscreteLabelNormalisationLayer( image_name='label', modalities=vars(task_param).get('label'), model_filename=self.net_param.histogram_ref_file)] if self.is_evaluation: label_normalisers.append( DiscreteLabelNormalisationLayer( image_name='inferred', modalities=vars(task_param).get('inferred'), model_filename=self.net_param.histogram_ref_file)) label_normalisers[-1].key = label_normalisers[0].key normalisation_layers = [] if histogram_normaliser is not None: normalisation_layers.append(histogram_normaliser) if rgb_normaliser is not None: normalisation_layers.append(rgb_normaliser) if mean_var_normaliser is not None: normalisation_layers.append(mean_var_normaliser) if task_param.label_normalisation and \ (self.is_training or not task_param.output_prob): normalisation_layers.extend(label_normalisers) 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) ] # initialise training data augmentation layers augmentation_layers = [] if self.is_training: train_param = self.action_param self.patience = train_param.patience self.mode = self.action_param.early_stopping_mode if train_param.random_flipping_axes != -1: augmentation_layers.append(RandomFlipLayer( flip_axes=train_param.random_flipping_axes)) if train_param.scaling_percentage: augmentation_layers.append(RandomSpatialScalingLayer( min_percentage=train_param.scaling_percentage[0], max_percentage=train_param.scaling_percentage[1], antialiasing=train_param.antialiasing, isotropic=train_param.isotropic_scaling)) if train_param.rotation_angle or \ train_param.rotation_angle_x or \ train_param.rotation_angle_y or \ train_param.rotation_angle_z: rotation_layer = RandomRotationLayer() if train_param.rotation_angle: rotation_layer.init_uniform_angle( train_param.rotation_angle) else: rotation_layer.init_non_uniform_angle( train_param.rotation_angle_x, train_param.rotation_angle_y, train_param.rotation_angle_z) augmentation_layers.append(rotation_layer) if train_param.do_elastic_deformation: spatial_rank = list(self.readers[0].spatial_ranks.values())[0] augmentation_layers.append(RandomElasticDeformationLayer( spatial_rank=spatial_rank, num_controlpoints=train_param.num_ctrl_points, std_deformation_sigma=train_param.deformation_sigma, proportion_to_augment=train_param.proportion_to_deform)) # only add augmentation to first reader (not validation reader) self.readers[0].add_preprocessing_layers( volume_padding_layer + normalisation_layers + augmentation_layers) for reader in self.readers[1:]: reader.add_preprocessing_layers( volume_padding_layer + normalisation_layers) # Checking num_classes is set correctly if self.segmentation_param.num_classes <= 1: raise ValueError("Number of classes must be at least 2 for segmentation") for preprocessor in self.readers[0].preprocessors: if preprocessor.name == 'label_norm': if len(preprocessor.label_map[preprocessor.key[0]]) != self.segmentation_param.num_classes: raise ValueError("Number of unique labels must be equal to " "number of classes (check histogram_ref file)")
[docs] def initialise_uniform_sampler(self): self.sampler = [[UniformSampler( reader=reader, window_sizes=self.data_param, batch_size=self.net_param.batch_size, windows_per_image=self.action_param.sample_per_volume, queue_length=self.net_param.queue_length) for reader in self.readers]]
[docs] def initialise_weighted_sampler(self): self.sampler = [[WeightedSampler( reader=reader, window_sizes=self.data_param, batch_size=self.net_param.batch_size, windows_per_image=self.action_param.sample_per_volume, queue_length=self.net_param.queue_length) for reader in self.readers]]
[docs] def initialise_resize_sampler(self): self.sampler = [[ResizeSampler( reader=reader, window_sizes=self.data_param, batch_size=self.net_param.batch_size, shuffle=self.is_training, smaller_final_batch_mode=self.net_param.smaller_final_batch_mode, queue_length=self.net_param.queue_length) for reader in self.readers]]
[docs] 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, smaller_final_batch_mode=self.net_param.smaller_final_batch_mode, queue_length=self.net_param.queue_length) for reader in self.readers]]
[docs] def initialise_balanced_sampler(self): self.sampler = [[BalancedSampler( reader=reader, window_sizes=self.data_param, batch_size=self.net_param.batch_size, windows_per_image=self.action_param.sample_per_volume, queue_length=self.net_param.queue_length) for reader in self.readers]]
[docs] 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, postfix=self.action_param.output_postfix, fill_constant=self.action_param.fill_constant)
[docs] def initialise_resize_aggregator(self): self.output_decoder = ResizeSamplesAggregator( 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, postfix=self.action_param.output_postfix)
[docs] def initialise_sampler(self): if self.is_training: self.SUPPORTED_SAMPLING[self.net_param.window_sampling][0]() elif self.is_inference: self.SUPPORTED_SAMPLING[self.net_param.window_sampling][1]()
[docs] def initialise_aggregator(self): self.SUPPORTED_SAMPLING[self.net_param.window_sampling][2]()
[docs] 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)
[docs] 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_args = {'is_training': self.is_training, 'keep_prob': self.net_param.keep_prob} net_out = self.net(image, **net_args) 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, softmax=self.segmentation_param.softmax) 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 # Get all vars to_optimise = tf.trainable_variables() vars_to_freeze = \ self.action_param.vars_to_freeze or \ self.action_param.vars_to_restore if vars_to_freeze: import re var_regex = re.compile(vars_to_freeze) # Only optimise vars that are not frozen to_optimise = \ [v for v in to_optimise if not var_regex.search(v.name)] tf.logging.info( "Optimizing %d out of %d trainable variables, " "the other variables fixed (--vars_to_freeze %s)", len(to_optimise), len(tf.trainable_variables()), vars_to_freeze) grads = self.optimiser.compute_gradients( loss, var_list=to_optimise, colocate_gradients_with_ops=True) self.total_loss = loss # collecting gradients variables gradients_collector.add_to_collection([grads]) # collecting output variables outputs_collector.add_to_collection( var=self.total_loss, name='total_loss', average_over_devices=True, collection=CONSOLE) outputs_collector.add_to_collection( var=self.total_loss, name='total_loss', average_over_devices=True, summary_type='scalar', collection=TF_SUMMARIES) outputs_collector.add_to_collection( var=data_loss, name='loss', average_over_devices=False, collection=CONSOLE) outputs_collector.add_to_collection( var=data_loss, name='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) elif self.is_inference: # 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_args = {'is_training': self.is_training, 'keep_prob': self.net_param.keep_prob} net_out = self.net(image, **net_args) 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) self.initialise_aggregator()
[docs] def interpret_output(self, batch_output): if self.is_inference: return self.output_decoder.decode_batch( {'window_seg':batch_output['window']}, batch_output['location']) return True
[docs] def initialise_evaluator(self, eval_param): self.eval_param = eval_param self.evaluator = SegmentationEvaluator(self.readers[0], self.segmentation_param, eval_param)
[docs] def add_inferred_output(self, data_param, task_param): return self.add_inferred_output_like(data_param, task_param, 'label')