def main():
system_param, input_data_param = user_parameters_parser.run()
if util.has_bad_inputs(system_param):
return -1
# print all parameters to txt file for future reference
all_param = {}
all_param.update(system_param)
all_param.update(input_data_param)
txt_file = 'settings_{}.txt'.format(system_param['SYSTEM'].action)
model_folder = touch_folder(system_param['SYSTEM'].model_dir)
txt_file = os.path.join(model_folder, txt_file)
util.print_save_input_parameters(all_param, txt_file)
# keep all commandline outputs
log_file_name = os.path.join(
model_folder, '{}_{}'.format(all_param['SYSTEM'].action,
'niftynet_log'))
set_logger(file_name=log_file_name)
# start application
app_driver = ApplicationDriver()
app_driver.initialise_application(system_param, input_data_param)
app_driver.run_application()
return 0
def main():
system_param, input_data_param = user_parameters_parser.run()
if util.has_bad_inputs(system_param):
return -1
# print all parameters to txt file for future reference
all_param = {}
all_param.update(system_param)
all_param.update(input_data_param)
# Set up path for niftynet model_root
# (rewriting user input with an absolute path)
system_param['SYSTEM'].model_dir = resolve_module_dir(
system_param['SYSTEM'].model_dir,
create_new=system_param['SYSTEM'].action == "train")
# writing all params for future reference
txt_file = 'settings_{}.txt'.format(system_param['SYSTEM'].action)
txt_file = os.path.join(system_param['SYSTEM'].model_dir, txt_file)
util.print_save_input_parameters(all_param, txt_file)
# keep all commandline outputs to model_root
log_file_name = os.path.join(
system_param['SYSTEM'].model_dir,
'{}_{}'.format(all_param['SYSTEM'].action, 'niftynet_log'))
set_logger(file_name=log_file_name)
# set up all model folder related parameters here
# see https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/issues/168
# 1. resolve mapping file:
try:
if system_param['NETWORK'].histogram_ref_file:
system_param['NETWORK'].histogram_ref_file = to_absolute_path(
input_path=system_param['NETWORK'].histogram_ref_file,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# 2. resolve output file:
try:
if system_param['INFERENCE'].save_seg_dir:
system_param['INFERENCE'].save_seg_dir = to_absolute_path(
input_path=system_param['INFERENCE'].save_seg_dir,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# start application
app_driver = ApplicationDriver()
app_driver.initialise_application(system_param, input_data_param)
app_driver.run_application()
return 0
# with self.assertRaisesRegexp(
# tf.errors.NotFoundError, 'Failed to find'):
# ModelRestorer(**vars(test_driver)).restore_model(None)
def test_from_file_initialisation(self):
test_driver = get_initialised_driver(40, False)
expected_init = np.array([[
-0.23192197, 0.60880029, -0.24921742, -0.00186354, -0.3345384,
0.16067748, -0.2210995, -0.19460233, -0.3035436, -0.42839912,
-0.0489039, -0.90753943, -0.12664583, -0.23129687, 0.01584663,
-0.43854219, 0.40412974, 0.0396539, -0.1590578, -0.53759819
]],
dtype=np.float32)
graph = test_driver.create_graph(test_driver.app, 1, True)
with self.test_session(graph=graph) as sess:
test_tensor = tf.get_default_graph().get_tensor_by_name(
"G/conv_bn_selu/conv_/w:0")
with self.assertRaisesRegexp(tf.errors.FailedPreconditionError,
'uninitialized value'):
_ = sess.run(test_tensor)
ModelRestorer(**vars(test_driver)).restore_model(None)
after_init = sess.run(test_tensor)
self.assertAllClose(after_init[0], expected_init)
_ = sess.run(tf.global_variables())
if __name__ == "__main__":
set_logger()
# _run_test_application()
tf.test.main()
def main():
set_logger()
system_param, input_data_param = user_parameters_parser.run()
if util.has_bad_inputs(system_param):
return -1
# print all parameters to txt file for future reference
all_param = {}
all_param.update(system_param)
all_param.update(input_data_param)
# Set up path for niftynet model_root
# (rewriting user input with an absolute path)
system_param['SYSTEM'].model_dir = resolve_module_dir(
system_param['SYSTEM'].model_dir,
create_new=system_param['SYSTEM'].action == "train")
# writing all params for future reference
txt_file = 'settings_{}.txt'.format(system_param['SYSTEM'].action)
txt_file = os.path.join(system_param['SYSTEM'].model_dir, txt_file)
try:
util.print_save_input_parameters(all_param, txt_file)
except IOError:
tf.logging.fatal(
'Unable to write %s,\nplease check '
'model_dir parameter, current value: %s',
txt_file, system_param['SYSTEM'].model_dir)
raise
# keep all commandline outputs to model_root
log_file_name = os.path.join(
system_param['SYSTEM'].model_dir,
'{}_{}'.format(all_param['SYSTEM'].action, 'niftynet_log'))
set_logger(file_name=log_file_name)
# set up all model folder related parameters here
# see https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/issues/168
# 1. resolve mapping file:
try:
if system_param['NETWORK'].histogram_ref_file:
system_param['NETWORK'].histogram_ref_file = to_absolute_path(
input_path=system_param['NETWORK'].histogram_ref_file,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# 2. resolve output file:
try:
if system_param['INFERENCE'].save_seg_dir:
system_param['INFERENCE'].save_seg_dir = to_absolute_path(
input_path=system_param['INFERENCE'].save_seg_dir,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# 3. resolve dataset splitting file:
try:
if system_param['SYSTEM'].dataset_split_file:
system_param['SYSTEM'].dataset_split_file = to_absolute_path(
input_path=system_param['SYSTEM'].dataset_split_file,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# start application
app_driver = ApplicationDriver()
app_driver.initialise_application(system_param, input_data_param)
app_driver.run_application()
return 0
def main():
set_logger()
print("Hola como vai estoy en el init de niftynet ################")
system_param, input_data_param = user_parameters_parser.run()
if util.has_bad_inputs(system_param):
return -1
# print all parameters to txt file for future reference
all_param = {}
all_param.update(system_param)
all_param.update(input_data_param)
# Set up path for niftynet model_root
# (rewriting user input with an absolute path)
system_param['SYSTEM'].model_dir = resolve_module_dir(
system_param['SYSTEM'].model_dir,
create_new=system_param['SYSTEM'].action == "train")
# writing all params for future reference
txt_file = 'settings_{}.txt'.format(system_param['SYSTEM'].action)
txt_file = os.path.join(system_param['SYSTEM'].model_dir, txt_file)
try:
util.print_save_input_parameters(all_param, txt_file)
except IOError:
tf.logging.fatal(
'Unable to write %s,\nplease check '
'model_dir parameter, current value: %s',
txt_file, system_param['SYSTEM'].model_dir)
raise
# keep all commandline outputs to model_root
log_file_name = os.path.join(
system_param['SYSTEM'].model_dir,
'{}_{}'.format(all_param['SYSTEM'].action, 'niftynet_log'))
set_logger(file_name=log_file_name)
# set up all model folder related parameters here
# see https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/issues/168
# 1. resolve mapping file:
try:
if system_param['NETWORK'].histogram_ref_file:
system_param['NETWORK'].histogram_ref_file = to_absolute_path(
input_path=system_param['NETWORK'].histogram_ref_file,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# 2. resolve output file:
try:
if system_param['INFERENCE'].save_seg_dir:
system_param['INFERENCE'].save_seg_dir = to_absolute_path(
input_path=system_param['INFERENCE'].save_seg_dir,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# 3. resolve dataset splitting file:
try:
if system_param['SYSTEM'].dataset_split_file:
system_param['SYSTEM'].dataset_split_file = to_absolute_path(
input_path=system_param['SYSTEM'].dataset_split_file,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# start application
app_driver = ApplicationDriver()
print("Initializing application$$$$$$$$$$$$$$$$$$$$$BEGIN")
app_driver.initialise_application(system_param, input_data_param)
print("Initializing application$$$$$$$$$$$$$$$$$$$$$END")
print("Initializing RUN application$$$$$$$$$$$$$$$$$$$$$BEGIN")
app_driver.run_application()
print("Initializing RUN application$$$$$$$$$$$$$$$$$$$$$END")
return 0
def main():
system_param, input_data_param = user_parameters_parser.run()
if util.has_bad_inputs(system_param):
return -1
# print all parameters to txt file for future reference
all_param = {}
all_param.update(system_param)
all_param.update(input_data_param)
# Set up path for niftynet model_root
# (rewriting user input with an absolute path)
system_param['SYSTEM'].model_dir = resolve_module_dir(
system_param['SYSTEM'].model_dir,
create_new=system_param['SYSTEM'].action == TRAIN)
# writing all params for future reference
txt_file = 'settings_{}.txt'.format(system_param['SYSTEM'].action)
txt_file = os.path.join(system_param['SYSTEM'].model_dir, txt_file)
try:
util.print_save_input_parameters(all_param, txt_file)
except IOError:
tf.logging.fatal(
'Unable to write %s,\nplease check '
'model_dir parameter, current value: %s',
txt_file, system_param['SYSTEM'].model_dir)
raise
# keep all commandline outputs to model_root
log_file_name = os.path.join(
system_param['SYSTEM'].model_dir,
'{}_{}'.format(all_param['SYSTEM'].action, 'niftynet_log'))
set_logger(file_name=log_file_name)
# set up all model folder related parameters here
# see https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/issues/168
# 1. resolve mapping file:
try:
if system_param['NETWORK'].histogram_ref_file:
system_param['NETWORK'].histogram_ref_file = to_absolute_path(
input_path=system_param['NETWORK'].histogram_ref_file,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# 2. resolve output file:
try:
if system_param['INFERENCE'].save_seg_dir:
system_param['INFERENCE'].save_seg_dir = to_absolute_path(
input_path=system_param['INFERENCE'].save_seg_dir,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# 3. resolve dataset splitting file:
try:
if system_param['SYSTEM'].dataset_split_file:
system_param['SYSTEM'].dataset_split_file = to_absolute_path(
input_path=system_param['SYSTEM'].dataset_split_file,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# 4. resolve evaluation dir:
try:
if system_param['EVALUATION'].save_csv_dir:
system_param['EVALUATION'].save_csv_dir = to_absolute_path(
input_path=system_param['EVALUATION'].save_csv_dir,
model_root=system_param['SYSTEM'].model_dir)
except (AttributeError, KeyError):
pass
# start application
driver_table = {
TRAIN: ApplicationDriver,
INFER: ApplicationDriver,
EVAL: EvaluationApplicationDriver,
EXPORT: ApplicationDriver}
app_driver = driver_table[system_param['SYSTEM'].action]()
app_driver.initialise_application(system_param, input_data_param)
app_driver.run(app_driver.app)
if tf.get_default_session() is not None:
tf.get_default_session().close()
tf.reset_default_graph()
close_logger()
return 0
'output_prob':False})
eval_param=NS({'evaluations':'Dice'})
mask = np.reshape(np.abs(np.linspace(0.,2.,64)-1)>.8,[4,4,4,1,1])
data_dict = {'label':mask,'inferred':mask}
interp_orders = {'label':0,'inferred':0}
image_folder = '.'
e = SegmentationEvaluator(None, segmentation_param, eval_param)
def generator():
yield ('test',data_dict,interp_orders)
result_dict = e.evaluate_from_generator(generator())
self.assertIn(('subject_id', 'label'), result_dict)
self.assertIn(('subject_id', 'cc_id'), result_dict)
self.assertEqual(tuple(result_dict[('subject_id', 'label')].index.names),
('subject_id', 'label'))
self.assertEqual(tuple(result_dict[('subject_id', 'cc_id')].index.names),
('subject_id', 'cc_id'))
print(result_dict[('subject_id', 'cc_id')].to_dict('index'))
self.assertEqual(result_dict[('subject_id', 'label')].to_dict('index'),
{('test', 1): {'dice': 1.}})
self.assertEqual(result_dict[('subject_id', 'cc_id')].to_dict('index'),
{('test', 'r1_s1'): {'dice': 1.},
('test', 'r2_s2'): {'dice': 1.}})
if __name__ == "__main__":
set_logger()
# _run_test_application()
tf.test.main()
