# -*- 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.crop import CropLayer
from niftynet.layer.histogram_normalisation import \
HistogramNormalisationLayer
from niftynet.layer.loss_regression 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.regression_evaluator import RegressionEvaluator
from niftynet.layer.rand_elastic_deform import RandomElasticDeformationLayer
from niftynet.engine.windows_aggregator_identity import WindowAsImageAggregator
SUPPORTED_INPUT = set(['image', 'output', 'weight', 'sampler', 'inferred'])
[docs]class RegressionApplication(BaseApplication):
REQUIRED_CONFIG_SECTION = "REGRESSION"
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting regression application')
self.action = action
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.regression_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.regression_param = task_param
# initialise input image readers
if self.is_training:
reader_names = ('image', 'output', 'weight', 'sampler')
elif self.is_inference:
# in the inference process use `image` input only
reader_names = ('image',)
elif self.is_evaluation:
reader_names = ('image', 'output', '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
mean_var_normaliser = MeanVarNormalisationLayer(image_name='image') \
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,
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
normalisation_layers = []
if histogram_normaliser is not None:
normalisation_layers.append(histogram_normaliser)
if mean_var_normaliser is not None:
normalisation_layers.append(mean_var_normaliser)
if rgb_normaliser is not None:
normalisation_layers.append(rgb_normaliser)
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
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:
rotation_layer = RandomRotationLayer()
if train_param.rotation_angle:
rotation_layer.init_uniform_angle(
train_param.rotation_angle)
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)
[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_identity_aggregator(self):
self.output_decoder = WindowAsImageAggregator(
image_reader=self.readers[0],
output_path=self.action_param.save_seg_dir,
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):
if self.net_param.force_output_identity_resizing:
self.initialise_identity_aggregator()
else:
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=1,
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:
self.patience = self.action_param.patience
self.mode = self.action_param.early_stopping_mode
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(loss_type=self.action_param.loss_type)
weight_map = data_dict.get('weight', None)
border=self.regression_param.loss_border
if border == None or tf.reduce_sum(tf.abs(border)) == 0:
data_loss = loss_func(
prediction=net_out,
ground_truth=data_dict['output'],
weight_map=weight_map)
else:
crop_layer = CropLayer(border)
weight_map = None if weight_map is None else crop_layer(weight_map)
data_loss = loss_func(
prediction=crop_layer(net_out),
ground_truth=crop_layer(data_dict['output']),
weight_map=weight_map)
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 are fixed (--vars_to_freeze %s)",
len(to_optimise),
len(tf.trainable_variables()),
vars_to_freeze)
self.total_loss = loss
grads = self.optimiser.compute_gradients(
loss, var_list=to_optimise, colocate_gradients_with_ops=True)
# 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)
elif self.is_inference:
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)
net_out = PostProcessingLayer('IDENTITY')(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_reg':batch_output['window']}, batch_output['location'])
return True
[docs] def initialise_evaluator(self, eval_param):
self.eval_param = eval_param
self.evaluator = RegressionEvaluator(self.readers[0],
self.regression_param,
eval_param)
[docs] def add_inferred_output(self, data_param, task_param):
return self.add_inferred_output_like(data_param, task_param, 'output')