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='',
event_handler=None,
iteration_generator=None)
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_initialised_driver(starting_iter=0,
model_dir_rand=True,
vars_to_restore='',
application='tests.toy_application.ToyApplication'):
if model_dir_rand:
model_dir = os.path.join('.', 'testing_data', 'tmp', str(uuid.uuid4()))
os.makedirs(model_dir)
else:
model_dir = os.path.join('.', 'testing_data')
system_param = {
'SYSTEM':
ParserNamespace(action='train',
num_threads=2,
num_gpus=4,
cuda_devices='6',
model_dir=model_dir,
dataset_split_file=os.path.join(
'.', 'testing_data', 'testtoyapp.csv'),
event_handler=[
'niftynet.engine.handler_model.ModelRestorer',
'niftynet.engine.handler_sampler.SamplerThreading',
'niftynet.engine.handler_gradient.ApplyGradients'
],
iteration_generator=None),
'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,
vars_to_restore=vars_to_restore,
patience=100,
lr=0.01),
'CUSTOM':
ParserNamespace(vector_size=100,
mean=10.0,
stddev=2.0,
name=application)
}
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.action = 'train'
return app_driver
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},
window_sizes={
'modality1': ParserNamespace(spatial_window_size=(10, 10)),
'modality2': ParserNamespace(spatial_window_size=(10, 10)),
'modality3': ParserNamespace(spatial_window_size=())
})
def get_static_window_param():
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,
'label': tf.float32},
data_param={
'modality1': ParserNamespace(spatial_window_size=(10, 10, 2)),
'modality2': ParserNamespace(spatial_window_size=(10, 10, 2)),
'modality3': ParserNamespace(spatial_window_size=(5, 5, 1))}
)
def get_all_dynamic_image_window():
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,
'label': tf.float32},
data_param={
'modality1': ParserNamespace(spatial_window_size=(-1, -1)),
'modality2': ParserNamespace(spatial_window_size=(-1, -1)),
'modality3': ParserNamespace(spatial_window_size=-1)}
)
def preprocess(
input_path,
model_path,
output_path,
cutoff,
):
input_path = Path(input_path)
output_path = Path(output_path)
input_dir = input_path.parent
DATA_PARAM = {
'Modality0':
ParserNamespace(
path_to_search=str(input_dir),
filename_contains=('nii.gz', ),
interp_order=0,
pixdim=None,
axcodes='RAS',
loader=None,
)
}
TASK_PARAM = ParserNamespace(image=('Modality0', ))
data_partitioner = ImageSetsPartitioner()
file_list = data_partitioner.initialise(DATA_PARAM).get_file_list()
reader = ImageReader(['image'])
reader.initialise(DATA_PARAM, TASK_PARAM, file_list)
binary_masking_func = BinaryMaskingLayer(type_str='mean_plus', )
hist_norm = HistogramNormalisationLayer(
image_name='image',
modalities=['Modality0'],
model_filename=str(model_path),
binary_masking_func=binary_masking_func,
cutoff=cutoff,
name='hist_norm_layer',
)
image = reader.output_list[0]['image']
data = image.get_data()
norm_image_dict, mask_dict = hist_norm({'image': data})
data = norm_image_dict['image']
nii = nib.Nifti1Image(data.squeeze(), image.original_affine[0])
dst = output_path
nii.to_filename(str(dst))
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 os
import tensorflow as tf
from niftynet.contrib.dataset_sampler.sampler_resize_v2 import ResizeSampler
from niftynet.io.image_reader import ImageReader
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
from niftynet.utilities.util_common 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=(7, 10, 2),
loader=None),
'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),
loader=None)
}
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
from niftynet.layer.discrete_label_normalisation import \
DiscreteLabelNormalisationLayer
from niftynet.layer.pad import PadLayer
from niftynet.utilities.util_common import ParserNamespace
from tests.niftynet_testcase import NiftyNetTestCase
NEW_ORDER = (0, 1, 2, 4, 3)
NEW_ORDER_2D = (0, 1, 3, 2)
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=0,
pixdim=(2.4, 5.0, 2.0),
axcodes='LAS',
spatial_window_size=(23, 32, 15),
loader=None),
'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=0,
pixdim=(2.4, 5.0, 2.0),
axcodes='LAS',
spatial_window_size=(23, 32, 15),
loader=None)
}
import os
import tensorflow as tf
from niftynet.engine.sampler_linear_interpolate import LinearInterpolateSampler
from niftynet.io.image_reader import ImageReader
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
from niftynet.utilities.util_common 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=(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)
)
}
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 niftynet.utilities.util_common 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,
loader=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,
loader=None)
}
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 niftynet.utilities.util_common 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
from niftynet.contrib.csv_reader.sampler_csvpatch import CSVPatchSampler
from niftynet.engine.image_window import N_SPATIAL
# from niftynet.engine.sampler_uniform import UniformSampler
from niftynet.engine.sampler_uniform_v2 import rand_spatial_coordinates
from niftynet.io.image_reader import ImageReader
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
from niftynet.utilities.util_common import ParserNamespace
from tests.niftynet_testcase import NiftyNetTestCase
DYNAMIC_MOD_DATA = {
'T1':
ParserNamespace(csv_file='',
path_to_search='data/csv_data',
filename_contains=(),
filename_not_contains=('_', 'csv'),
interp_order=3,
csv_data_file='',
pixdim=None,
axcodes=None,
spatial_window_size=(69, 69, 69),
loader=None),
'sampler':
ParserNamespace(csv_file='',
path_to_search='',
filename_contains=(),
filename_not_contains=(),
interp_order=0,
pixdim=None,
axcodes=None,
spatial_window_size=(),
loader=None,
csv_data_file='data/csv_data/ICBMTest3.csv')
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
from niftynet.layer.discrete_label_normalisation import \
DiscreteLabelNormalisationLayer
from niftynet.layer.pad import PadLayer
from niftynet.utilities.util_common import ParserNamespace
from tests.reader_modular_test import generate_2d_images, SEG_THRESHOLD
from tests.niftynet_testcase import NiftyNetTestCase
generate_2d_images()
# 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,
loader=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,
loader=None)
}
MULTI_MOD_TASK = ParserNamespace(image=('T1', 'FLAIR'))
from niftynet.io.image_reader import ImageReader
from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
from niftynet.layer.binary_masking import BinaryMaskingLayer
from niftynet.layer.histogram_normalisation import \
HistogramNormalisationLayer
from niftynet.layer.mean_variance_normalisation import \
MeanVarNormalisationLayer
from niftynet.utilities.util_common import ParserNamespace
from tests.niftynet_testcase import NiftyNetTestCase
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,
loader=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,
loader=None)
}
TASK_PARAM = ParserNamespace(image=('T1', 'FLAIR'))
MODEL_FILE = os.path.join('testing_data', 'std_models.txt')
