def get_initialised_driver(starting_iter=0):
system_param = {
'SYSTEM':
ParserNamespace(action='train',
num_threads=2,
num_gpus=4,
cuda_devices='6',
model_dir='./testing_data'),
'NETWORK':
ParserNamespace(batch_size=20, name='tests.toy_application.TinyNet'),
'TRAINING':
ParserNamespace(
starting_iter=starting_iter,
max_iter=500,
save_every_n=20,
tensorboard_every_n=1,
max_checkpoints=20,
optimiser='niftynet.engine.application_optimiser.Adagrad',
lr=0.01),
'CUSTOM':
ParserNamespace(vector_size=100,
mean=10.0,
stddev=2.0,
name='tests.toy_application.ToyApplication')
}
app_driver = ApplicationDriver()
app_driver.initialise_application(system_param, {})
return app_driver
def _generate_base_params():
# initialise compulsory params that are irrelevant
# to this unit test
user_param = dict()
user_param['SYSTEM'] = ParserNamespace(model_dir='./testing_data',
num_threads=2,
num_gpus=1,
cuda_devices='')
user_param['NETWORK'] = ParserNamespace(
batch_size=20, name='tests.toy_application.TinyNet')
user_param['TRAINING'] = ParserNamespace(starting_iter=0,
max_iter=2,
save_every_n=2,
tensorboard_every_n=0,
max_checkpoints=100)
user_param['INFERENCE'] = ParserNamespace(inference_iter=-1)
user_param['CUSTOM'] = ParserNamespace(
name='tests.toy_application.ToyApplication',
vector_size=100,
mean=10.0,
stddev=2.0)
return user_param
def _generate_data_param():
user_param = dict()
user_param['modality'] = ParserNamespace(csv_file=os.path.join(
'testing_data', 'mod1test.csv'),
path_to_search='testing_data')
user_param['modality2'] = ParserNamespace(csv_file=os.path.join(
'testing_data', 'mod2test.csv'),
path_to_search='testing_data')
return user_param
def get_ill_image_window_2():
return dict(source_names={
'image': (u'modality1', u'modality2'),
'label': (u'modality3', )
},
image_shapes={
'image': (192, 160, 192, 1, 2),
'label': (192, 160, 192, 1, 1)
},
image_dtypes={'image': tf.float32},
data_param={
'modality1': ParserNamespace(spatial_window_size=(10, 10)),
'modality2': ParserNamespace(spatial_window_size=(10, 10)),
'modality3': ParserNamespace(spatial_window_size=())
})
def get_dynamic_image_window():
window = ImageWindow.from_data_reader_properties(
source_names={
'image': (u'modality1', u'modality2'),
'label': (u'modality3', )
},
image_shapes={
'image': (192, 160, 192, 1, 2),
'label': (192, 160, 192, 1, 1)
},
image_dtypes={
'image': tf.float32,
'label': tf.float32
},
data_param={
'modality1': ParserNamespace(spatial_window_size=(10, 10)),
'modality2': ParserNamespace(spatial_window_size=(10, 10)),
'modality3': ParserNamespace(spatial_window_size=(5, 5, 1))
})
return window
def get_initialised_driver(starting_iter=0):
system_param = {
'SYSTEM': ParserNamespace(
action='train',
num_threads=2,
num_gpus=4,
cuda_devices='6',
model_dir=os.path.join('.', 'testing_data'),
dataset_split_file=os.path.join(
'.', 'testing_data', 'testtoyapp.csv')),
'NETWORK': ParserNamespace(
batch_size=20,
name='tests.toy_application.TinyNet'),
'TRAINING': ParserNamespace(
starting_iter=starting_iter,
max_iter=500,
save_every_n=20,
tensorboard_every_n=1,
max_checkpoints=20,
optimiser='niftynet.engine.application_optimiser.Adagrad',
validation_every_n=-1,
exclude_fraction_for_validation=0.1,
exclude_fraction_for_inference=0.1,
lr=0.01),
'CUSTOM': ParserNamespace(
vector_size=100,
mean=10.0,
stddev=2.0,
name='tests.toy_application.ToyApplication')
}
app_driver = ApplicationDriver()
app_driver.initialise_application(system_param, {})
# set parameters without __init__
app_driver.app.action_param = system_param['TRAINING']
app_driver.app.net_param = system_param['NETWORK']
app_driver.app.is_training = True
return app_driver
import numpy as np
import tensorflow as tf
from niftynet.engine.sampler_grid import GridSampler
from niftynet.engine.sampler_grid import _enumerate_step_points
from niftynet.engine.sampler_grid import grid_spatial_coordinates
from niftynet.io.image_reader import ImageReader
from tests.test_util import ParserNamespace
MULTI_MOD_DATA = {
'T1': ParserNamespace(
csv_file=os.path.join('testing_data', 'T1sampler.csv'),
path_to_search='testing_data',
filename_contains=('_o_T1_time',),
filename_not_contains=('Parcellation',),
interp_order=3,
pixdim=None,
axcodes=None,
spatial_window_size=(8, 10, 2)
),
'FLAIR': ParserNamespace(
csv_file=os.path.join('testing_data', 'FLAIRsampler.csv'),
path_to_search='testing_data',
filename_contains=('FLAIR_',),
filename_not_contains=('Parcellation',),
interp_order=3,
pixdim=None,
axcodes=None,
spatial_window_size=(8, 10, 2)
)
}
import tensorflow as tf
from niftynet.io.image_reader import ImageReader
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
from niftynet.layer.discrete_label_normalisation import \
DiscreteLabelNormalisationLayer
from niftynet.layer.pad import PadLayer
from tests.test_util import ParserNamespace
# test multiple modalities
MULTI_MOD_DATA = {
'T1':
ParserNamespace(csv_file=os.path.join('testing_data', 'T1reader.csv'),
path_to_search='testing_data',
filename_contains=('_o_T1_time', ),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=None,
axcodes=None),
'FLAIR':
ParserNamespace(csv_file=os.path.join('testing_data', 'FLAIRreader.csv'),
path_to_search='testing_data',
filename_contains=('FLAIR_', ),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=None,
axcodes=None)
}
MULTI_MOD_TASK = ParserNamespace(image=('T1', 'FLAIR'))
# test single modalities
import tensorflow as tf
from niftynet.engine.sampler_resize import ResizeSampler
from niftynet.engine.windows_aggregator_resize import ResizeSamplesAggregator
from niftynet.io.image_reader import ImageReader
from niftynet.layer.discrete_label_normalisation import \
DiscreteLabelNormalisationLayer
from niftynet.layer.pad import PadLayer
from tests.test_util import ParserNamespace
MULTI_MOD_DATA = {
'T1':
ParserNamespace(csv_file=os.path.join('testing_data', 'T1sampler.csv'),
path_to_search='testing_data',
filename_contains=('_o_T1_time', '23'),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=(2.4, 5.0, 2.0),
axcodes='LAS',
spatial_window_size=(23, 32, 15)),
'FLAIR':
ParserNamespace(csv_file=os.path.join('testing_data', 'FLAIRsampler.csv'),
path_to_search='testing_data',
filename_contains=('FLAIR_', '23'),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=(2.4, 5.0, 2.0),
axcodes='LAS',
spatial_window_size=(23, 32, 15))
}
MULTI_MOD_TASK = ParserNamespace(image=('T1', 'FLAIR'))
import os
import tensorflow as tf
from niftynet.io.image_sets_partitioner import COLUMN_UNIQ_ID
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
from niftynet.io.image_sets_partitioner import TRAIN, VALID, INFER
from tests.test_util import ParserNamespace
test_sections = {
'T1':
ParserNamespace(csv_file=os.path.join('testing_data', 'test_reader.csv'),
path_to_search='testing_data',
filename_contains=('_o_T1_time', ),
filename_not_contain=('Parcellation', ),
interp_order=3,
pixdim=None,
axcodes=None),
'Flair':
ParserNamespace(csv_file=os.path.join('testing_data',
'test_Flairreader.csv'),
path_to_search='testing_data',
filename_contains=('FLAIR_', ),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=None,
axcodes=None)
}
partition_output = os.path.join('testing_data', 'partition.csv')
from niftynet.io.image_reader import ImageReader
from niftynet.layer.binary_masking import BinaryMaskingLayer
from niftynet.layer.histogram_normalisation import \
HistogramNormalisationLayer
from niftynet.layer.mean_variance_normalisation import \
MeanVarNormalisationLayer
from tests.test_util import ParserNamespace
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
DATA_PARAM = {
'T1':
ParserNamespace(csv_file=os.path.join('testing_data', 'T1.csv'),
path_to_search='testing_data',
filename_contains=('_o_T1_time', ),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=None,
axcodes=None),
'FLAIR':
ParserNamespace(csv_file=os.path.join('testing_data', 'FLAIR.csv'),
path_to_search='testing_data',
filename_contains=('FLAIR_', ),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=None,
axcodes=None)
}
TASK_PARAM = ParserNamespace(image=('T1', 'FLAIR'))
MODEL_FILE = os.path.join('testing_data', 'std_models.txt')
data_partitioner = ImageSetsPartitioner()
else:
if c > num_min:
to_add -= 1
if to_add > 0:
print('to add initial is ', num_labels_add)
print('Not enough in additional labels')
return False
return True
MULTI_MOD_DATA = {
'T1':
ParserNamespace(csv_file=os.path.join('testing_data', 'T1sampler.csv'),
path_to_search='testing_data',
filename_contains=('_o_T1_time', ),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=None,
axcodes=None,
spatial_window_size=(7, 10, 2)),
'FLAIR':
ParserNamespace(csv_file=os.path.join('testing_data', 'FLAIRsampler.csv'),
path_to_search='testing_data',
filename_contains=('FLAIR_', ),
filename_not_contains=('Parcellation', ),
interp_order=3,
pixdim=None,
axcodes=None,
spatial_window_size=(7, 10, 2)),
'Label':
ParserNamespace(csv_file=os.path.join('testing_data', 'lesion.csv'),
path_to_search='testing_data',
