def _create_app(app_type_string):
"""
Import the application module
"""
app_class = ApplicationFactory.create(app_type_string)
app_class.clear()
return app_class
def _create_app(app_type_string):
"""
Import the application module
"""
app_class = ApplicationFactory.create(app_type_string)
app_class.clear()
return app_class
def initialise_application(self, workflow_param, data_param):
"""
This function receives all parameters from user config file,
create an instance of application.
:param workflow_param: a dictionary of user parameters,
keys correspond to sections in the config file
:param data_param: a dictionary of input image parameters,
keys correspond to data properties to be used by image_reader
:return:
"""
try:
system_param = workflow_param.get('SYSTEM', None)
net_param = workflow_param.get('NETWORK', None)
infer_param = workflow_param.get('INFERENCE', None)
eval_param = workflow_param.get('EVALUATION', None)
app_param = workflow_param.get('CUSTOM', None)
except AttributeError:
tf.logging.fatal('parameters should be dictionaries')
raise
self.num_threads = 1
# self.num_threads = max(system_param.num_threads, 1)
# self.num_gpus = system_param.num_gpus
# set_cuda_device(system_param.cuda_devices)
# set output TF model folders
self.model_dir = touch_folder(
os.path.join(system_param.model_dir, 'models'))
self.session_prefix = os.path.join(self.model_dir, FILE_PREFIX)
assert infer_param, 'inference parameters not specified'
# create an application instance
assert app_param, 'application specific param. not specified'
self.app_param = app_param
app_module = ApplicationFactory.create(app_param.name)
self.app = app_module(net_param, infer_param,
system_param.action)
self.eval_param = eval_param
data_param, self.app_param = \
self.app.add_inferred_output(data_param, self.app_param)
# initialise data input
data_partitioner = ImageSetsPartitioner()
# clear the cached file lists
data_partitioner.reset()
if data_param:
data_partitioner.initialise(
data_param=data_param,
new_partition=False,
ratios=None,
data_split_file=system_param.dataset_split_file)
# initialise data input
self.app.initialise_dataset_loader(data_param, self.app_param,
data_partitioner)
self.app.initialise_evaluator(eval_param)
def initialise_application(self, workflow_param, data_param):
"""
This function receives all parameters from user config file,
create an instance of application.
:param workflow_param: a dictionary of user parameters,
keys correspond to sections in the config file
:param data_param: a dictionary of input image parameters,
keys correspond to data properties to be used by image_reader
:return:
"""
try:
system_param = workflow_param.get('SYSTEM', None)
net_param = workflow_param.get('NETWORK', None)
infer_param = workflow_param.get('INFERENCE', None)
eval_param = workflow_param.get('EVALUATION', None)
app_param = workflow_param.get('CUSTOM', None)
except AttributeError:
tf.logging.fatal('parameters should be dictionaries')
raise
self.num_threads = 1
# self.num_threads = max(system_param.num_threads, 1)
# self.num_gpus = system_param.num_gpus
# set_cuda_device(system_param.cuda_devices)
# set output TF model folders
self.model_dir = touch_folder(
os.path.join(system_param.model_dir, 'models'))
self.session_prefix = os.path.join(self.model_dir, FILE_PREFIX)
assert infer_param, 'inference parameters not specified'
# create an application instance
assert app_param, 'application specific param. not specified'
self.app_param = app_param
app_module = ApplicationFactory.create(app_param.name)
self.app = app_module(net_param, infer_param, system_param.action)
self.eval_param = eval_param
data_param, self.app_param = \
self.app.add_inferred_output(data_param, self.app_param)
# initialise data input
data_partitioner = ImageSetsPartitioner()
# clear the cached file lists
data_partitioner.reset()
if data_param:
data_partitioner.initialise(
data_param=data_param,
new_partition=False,
ratios=None,
data_split_file=system_param.dataset_split_file)
# initialise data input
self.app.initialise_dataset_loader(data_param, self.app_param,
data_partitioner)
self.app.initialise_evaluator(eval_param)
def _create_app(app_type_string):
"""
Import the application module
"""
return ApplicationFactory.create(app_type_string)
def run():
"""
meta_parser is first used to find out location
of the configuration file. Based on the application_name
or meta_parser.prog name, the section parsers are organised
to find system parameters and application specific
parameters.
:return: system parameters is a group of parameters including
SYSTEM_SECTIONS and app_module.REQUIRED_CONFIG_SECTION
input_data_args is a group of input data sources to be
used by niftynet.io.ImageReader
"""
meta_parser = argparse.ArgumentParser(
description="Launch a NiftyNet application.",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=textwrap.dedent(EPILOG_STRING))
version_string = get_niftynet_version_string()
meta_parser.add_argument("action",
help="train networks, run inferences "
"or evaluate inferences",
metavar='ACTION',
choices=list(ACTIONS))
meta_parser.add_argument("-v",
"--version",
action='version',
version=version_string)
meta_parser.add_argument("-c",
"--conf",
help="specify configurations from a file",
metavar="CONFIG_FILE")
meta_parser.add_argument("-a",
"--application_name",
help="specify an application module name",
metavar='APPLICATION_NAME',
default="")
meta_args, args_from_cmdline = meta_parser.parse_known_args()
print(version_string)
# read configurations, to be parsed by sections
config_file_name = __resolve_config_file_path(meta_args.conf)
config = NiftyNetLaunchConfig()
config.read([config_file_name])
# infer application name from command
app_name = None
try:
parser_prog = meta_parser.prog.replace('.py', '')
app_name = parser_prog if parser_prog in SUPPORTED_APP \
else meta_args.application_name
assert app_name
except (AttributeError, AssertionError):
raise ValueError("\nUnknown application {}, or did you forget '-a' "
"command argument?{}".format(app_name, EPILOG_STRING))
# load application by name
app_module = ApplicationFactory.create(app_name)
try:
assert app_module.REQUIRED_CONFIG_SECTION, \
"\nREQUIRED_CONFIG_SECTION should be static variable " \
"in {}".format(app_module)
has_section_in_config(config, app_module.REQUIRED_CONFIG_SECTION)
except AttributeError:
raise AttributeError(
"Application code doesn't have REQUIRED_CONFIG_SECTION property. "
"{} should be an instance of "
"niftynet.application.base_application".format(app_module))
except ValueError:
raise ValueError(
"\n{} requires [{}] section in the config file.{}".format(
app_name, app_module.REQUIRED_CONFIG_SECTION, EPILOG_STRING))
# check keywords in configuration file
_check_config_file_keywords(config)
# using configuration as default, and parsing all command line arguments
# command line args override the configure file options
all_args = {}
for section in config.sections():
# try to rename user-specified sections for consistency
section = standardise_section_name(config, section)
section_defaults = dict(config.items(section))
section_args, args_from_cmdline = \
_parse_arguments_by_section([],
section,
section_defaults,
args_from_cmdline,
app_module.REQUIRED_CONFIG_SECTION)
all_args[section] = section_args
# check if any args from command line not recognised
_check_cmd_remaining_keywords(list(args_from_cmdline))
# split parsed results in all_args
# into dictionaries of system_args and input_data_args
system_args, input_data_args = {}, {}
for section in all_args:
# copy system default sections to ``system_args``
if section in SYSTEM_SECTIONS:
system_args[section] = all_args[section]
continue
# copy application specific sections to ``system_args``
if section == app_module.REQUIRED_CONFIG_SECTION:
system_args['CUSTOM'] = all_args[section]
vars(system_args['CUSTOM'])['name'] = app_name
continue
# copy non-default sections to ``input_data_args``
input_data_args[section] = all_args[section]
# set the output path of csv list if not exists
try:
csv_path = resolve_file_name(
input_data_args[section].csv_file,
(os.path.dirname(config_file_name), NIFTYNET_HOME))
input_data_args[section].csv_file = csv_path
# don't search files if csv specified in config
try:
delattr(input_data_args[section], 'path_to_search')
except AttributeError:
pass
except (IOError, TypeError):
input_data_args[section].csv_file = ''
# preserve ``config_file`` and ``action parameter`` from the meta_args
system_args['CONFIG_FILE'] = argparse.Namespace(path=config_file_name)
# mapping the captured action argument to a string in ACTIONS
system_args['SYSTEM'].action = \
look_up_operations(meta_args.action, ACTIONS)
if not system_args['SYSTEM'].model_dir:
system_args['SYSTEM'].model_dir = os.path.join(
os.path.dirname(config_file_name), 'model')
return system_args, input_data_args
def run():
"""
meta_parser is first used to find out location
of the configuration file. based on the application_name
or meta_parser.prog name, the section parsers are organised
to find system parameters and application specific
parameters.
:return: system parameters is a group of parameters including
SYSTEM_SECTIONS and app_module.REQUIRED_CONFIG_SECTION
input_data_args is a group of input data sources to be
used by niftynet.io.ImageReader
"""
meta_parser = argparse.ArgumentParser(
description="Launch a NiftyNet application.",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=textwrap.dedent(epilog_string))
version_string = get_niftynet_version_string()
meta_parser.add_argument("action",
help="train networks or run inferences",
metavar='ACTION',
choices=['train', 'inference'])
meta_parser.add_argument("-v",
"--version",
action='version',
version=version_string)
meta_parser.add_argument("-c",
"--conf",
help="specify configurations from a file",
metavar="CONFIG_FILE")
meta_parser.add_argument("-a",
"--application_name",
help="specify an application module name",
metavar='APPLICATION_NAME',
default="")
meta_args, args_from_cmdline = meta_parser.parse_known_args()
print(version_string)
# read configurations, to be parsed by sections
if not meta_args.conf:
print("\nNo configuration file has been provided, did you "
"forget '-c' command argument?{}".format(epilog_string))
raise IOError
# Resolve relative configuration file location
config_path = meta_args.conf
if not os.path.isfile(config_path):
relative_conf_file = os.path.join(
NiftyNetGlobalConfig().get_default_examples_folder(), config_path,
config_path + "_config.ini")
if os.path.isfile(relative_conf_file):
config_path = relative_conf_file
os.chdir(os.path.dirname(config_path))
else:
print("\nConfiguration file not found: {}.{}".format(
config_path, epilog_string))
raise IOError
config = configparser.ConfigParser()
config.read([config_path])
app_module = None
module_name = None
try:
if meta_parser.prog[:-3] in SUPPORTED_APP:
module_name = meta_parser.prog[:-3]
elif meta_parser.prog in SUPPORTED_APP:
module_name = meta_parser.prog
else:
module_name = meta_args.application_name
app_module = ApplicationFactory.create(module_name)
assert app_module.REQUIRED_CONFIG_SECTION, \
"\nREQUIRED_CONFIG_SECTION should be static variable " \
"in {}".format(app_module)
has_section_in_config(config, app_module.REQUIRED_CONFIG_SECTION)
except ValueError:
if app_module:
section_name = app_module.REQUIRED_CONFIG_SECTION
print('\n{} requires [{}] section in the config file.{}'.format(
module_name, section_name, epilog_string))
if not module_name:
print("\nUnknown application {}, or did you forget '-a' "
"command argument?{}".format(module_name, epilog_string))
raise
# check keywords in configuration file
check_keywords(config)
# using configuration as default, and parsing all command line arguments
all_args = {}
for section in config.sections():
# try to rename user-specified sections for consistency
section = standardise_section_name(config, section)
section_defaults = dict(config.items(section))
section_args, args_from_cmdline = \
_parse_arguments_by_section([],
section,
section_defaults,
args_from_cmdline,
app_module.REQUIRED_CONFIG_SECTION)
all_args[section] = section_args
# command line parameters should be valid
assert not args_from_cmdline, \
'\nUnknown parameter: {}{}'.format(args_from_cmdline, epilog_string)
# split parsed results in all_args
# into dictionaries of system_args and input_data_args
system_args = {}
input_data_args = {}
# copy system default sections to ``system_args``
for section in all_args:
if section in SYSTEM_SECTIONS:
system_args[section] = all_args[section]
elif section == app_module.REQUIRED_CONFIG_SECTION:
system_args['CUSTOM'] = all_args[section]
vars(system_args['CUSTOM'])['name'] = module_name
if all_args['SYSTEM'].model_dir is None:
all_args['SYSTEM'].model_dir = os.path.join(
os.path.dirname(meta_args.conf), 'model')
# copy non-default sections to ``input_data_args``
for section in all_args:
if section in SYSTEM_SECTIONS:
continue
if section == app_module.REQUIRED_CONFIG_SECTION:
continue
input_data_args[section] = all_args[section]
# set the output path of csv list if not exists
csv_path = input_data_args[section].csv_file
if os.path.isfile(csv_path):
# don't search files if csv specified in config
try:
delattr(input_data_args[section], 'path_to_search')
except AttributeError:
pass
else:
input_data_args[section].csv_file = ''
# preserve ``config_file`` and ``action parameter`` from the meta_args
system_args['CONFIG_FILE'] = argparse.Namespace(path=meta_args.conf)
system_args['SYSTEM'].action = meta_args.action
return system_args, input_data_args
def run():
"""
meta_parser is first used to find out location
of the configuration file. based on the application_name
or meta_parser.prog name, the section parsers are organised
to find system parameters and application specific
parameters.
:return: system parameters is a group of parameters including
SYSTEM_SECTIONS and app_module.REQUIRED_CONFIG_SECTION
input_data_args is a group of input data sources to be
used by niftynet.io.ImageReader
"""
meta_parser = argparse.ArgumentParser(
description="Launch a NiftyNet application.",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=textwrap.dedent(epilog_string))
version_string = get_niftynet_version_string()
meta_parser.add_argument("action",
help="train networks or run inferences",
metavar='ACTION',
choices=['train', 'inference'])
meta_parser.add_argument("-v", "--version",
action='version',
version=version_string)
meta_parser.add_argument("-c", "--conf",
help="specify configurations from a file",
metavar="CONFIG_FILE")
meta_parser.add_argument("-a", "--application_name",
help="specify an application module name",
metavar='APPLICATION_NAME',
default="")
meta_args, args_from_cmdline = meta_parser.parse_known_args()
print(version_string)
# read configurations, to be parsed by sections
if not meta_args.conf:
print("\nNo configuration file has been provided, did you "
"forget '-c' command argument?{}".format(epilog_string))
raise IOError
# Resolve relative configuration file location
config_path = os.path.expanduser(meta_args.conf)
if not os.path.isfile(config_path):
relative_conf_file = os.path.join(
NiftyNetGlobalConfig().get_default_examples_folder(),
config_path,
config_path + "_config.ini")
if os.path.isfile(relative_conf_file):
config_path = relative_conf_file
os.chdir(os.path.dirname(config_path))
else:
print("\nConfiguration file not found: {}.{}".format(
config_path, epilog_string))
raise IOError
config = configparser.ConfigParser()
config.read([config_path])
app_module = None
module_name = None
try:
if meta_parser.prog[:-3] in SUPPORTED_APP:
module_name = meta_parser.prog[:-3]
elif meta_parser.prog in SUPPORTED_APP:
module_name = meta_parser.prog
else:
module_name = meta_args.application_name
app_module = ApplicationFactory.create(module_name)
assert app_module.REQUIRED_CONFIG_SECTION, \
"\nREQUIRED_CONFIG_SECTION should be static variable " \
"in {}".format(app_module)
has_section_in_config(config, app_module.REQUIRED_CONFIG_SECTION)
except ValueError:
if app_module:
section_name = app_module.REQUIRED_CONFIG_SECTION
print('\n{} requires [{}] section in the config file.{}'.format(
module_name, section_name, epilog_string))
if not module_name:
print("\nUnknown application {}, or did you forget '-a' "
"command argument?{}".format(module_name, epilog_string))
raise
# check keywords in configuration file
check_keywords(config)
# using configuration as default, and parsing all command line arguments
all_args = {}
for section in config.sections():
# try to rename user-specified sections for consistency
section = standardise_section_name(config, section)
section_defaults = dict(config.items(section))
section_args, args_from_cmdline = \
_parse_arguments_by_section([],
section,
section_defaults,
args_from_cmdline,
app_module.REQUIRED_CONFIG_SECTION)
all_args[section] = section_args
# command line parameters should be valid
assert not args_from_cmdline, \
'\nUnknown parameter: {}{}'.format(args_from_cmdline, epilog_string)
# split parsed results in all_args
# into dictionaries of system_args and input_data_args
system_args = {}
input_data_args = {}
# copy system default sections to ``system_args``
for section in all_args:
if section in SYSTEM_SECTIONS:
system_args[section] = all_args[section]
elif section == app_module.REQUIRED_CONFIG_SECTION:
system_args['CUSTOM'] = all_args[section]
vars(system_args['CUSTOM'])['name'] = module_name
if all_args['SYSTEM'].model_dir is None:
all_args['SYSTEM'].model_dir = os.path.join(
os.path.dirname(meta_args.conf), 'model')
# copy non-default sections to ``input_data_args``
for section in all_args:
if section in SYSTEM_SECTIONS:
continue
if section == app_module.REQUIRED_CONFIG_SECTION:
continue
input_data_args[section] = all_args[section]
# set the output path of csv list if not exists
csv_path = input_data_args[section].csv_file
if os.path.isfile(csv_path):
# don't search files if csv specified in config
try:
delattr(input_data_args[section], 'path_to_search')
except AttributeError:
pass
else:
input_data_args[section].csv_file = ''
# preserve ``config_file`` and ``action parameter`` from the meta_args
system_args['CONFIG_FILE'] = argparse.Namespace(path=meta_args.conf)
system_args['SYSTEM'].action = meta_args.action
return system_args, input_data_args
def initialise_application(self, workflow_param, data_param=None):
"""
This function receives all parameters from user config file,
create an instance of application.
:param workflow_param: a dictionary of user parameters,
keys correspond to sections in the config file
:param data_param: a dictionary of input image parameters,
keys correspond to data properties to be used by image_reader
:return:
"""
try:
system_param = workflow_param.get('SYSTEM', None)
net_param = workflow_param.get('NETWORK', None)
train_param = workflow_param.get('TRAINING', None)
infer_param = workflow_param.get('INFERENCE', None)
app_param = workflow_param.get('CUSTOM', None)
except AttributeError:
tf.logging.fatal('parameters should be dictionaries')
raise
assert os.path.exists(system_param.model_dir), \
'Model folder not exists {}'.format(system_param.model_dir)
self.model_dir = system_param.model_dir
self.is_training_action = TRAIN.startswith(system_param.action.lower())
# hardware-related parameters
self.num_threads = max(system_param.num_threads, 1) \
if self.is_training_action else 1
self.num_gpus = system_param.num_gpus \
if self.is_training_action else min(system_param.num_gpus, 1)
set_cuda_device(system_param.cuda_devices)
# set training params.
if self.is_training_action:
assert train_param, 'training parameters not specified'
self.initial_iter = train_param.starting_iter
self.final_iter = max(train_param.max_iter, self.initial_iter)
self.save_every_n = train_param.save_every_n
self.tensorboard_every_n = train_param.tensorboard_every_n
self.max_checkpoints = max(self.max_checkpoints,
train_param.max_checkpoints)
self.validation_every_n = train_param.validation_every_n
if self.validation_every_n > 0:
self.validation_max_iter = max(self.validation_max_iter,
train_param.validation_max_iter)
action_param = train_param
else: # set inference params.
assert infer_param, 'inference parameters not specified'
self.initial_iter = infer_param.inference_iter
action_param = infer_param
# infer the initial iteration from model files
if self.initial_iter < 0:
self.initial_iter = infer_latest_model_file(
os.path.join(self.model_dir, 'models'))
# create an application instance
assert app_param, 'application specific param. not specified'
app_module = ApplicationFactory.create(app_param.name)
self.app = app_module(net_param, action_param, system_param.action)
# clear the cached file lists
self.data_partitioner.reset()
if data_param:
do_new_partition = \
self.is_training_action and self.initial_iter == 0 and \
(not os.path.isfile(system_param.dataset_split_file)) and \
(train_param.exclude_fraction_for_validation > 0 or
train_param.exclude_fraction_for_inference > 0)
data_fractions = (train_param.exclude_fraction_for_validation,
train_param.exclude_fraction_for_inference) \
if do_new_partition else None
self.data_partitioner.initialise(
data_param=data_param,
new_partition=do_new_partition,
ratios=data_fractions,
data_split_file=system_param.dataset_split_file)
assert self.data_partitioner.has_validation or \
self.validation_every_n <= 0, \
'validation_every_n is set to {}, ' \
'but train/validation splitting not available.\nPlease ' \
'check dataset partition list {} ' \
'(remove file to generate a new dataset partition), ' \
'check "exclude_fraction_for_validation" ' \
'(current config value: {}).\nAlternatively, ' \
'set "validation_every_n" to -1.'.format(
self.validation_every_n,
system_param.dataset_split_file,
train_param.exclude_fraction_for_validation)
# initialise readers
self.app.initialise_dataset_loader(data_param, app_param,
self.data_partitioner)
# make the list of initialised event handler instances.
self.load_event_handlers(system_param.event_handler
or DEFAULT_EVENT_HANDLERS)
self._generator = IteratorFactory.create(
system_param.iteration_generator or DEFAULT_ITERATION_GENERATOR)
def run():
"""
meta_parser is first used to find out location
of the configuration file. based on the application_name
or meta_parser.prog name, the section parsers are organised
to find system parameters and application specific
parameters
:return: system parameters is a group of parameters including
SYSTEM_SECTIONS and app_module.REQUIRED_CONFIG_SECTION
input_data_args is a group of input data sources to be
used by niftynet.io.ImageReader
"""
meta_parser = argparse.ArgumentParser(
epilog=
'Please visit https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/tree/dev/demos '
'for more info.')
version_string = get_niftynet_version_string()
meta_parser.add_argument("-v",
"--version",
action='version',
version=version_string)
meta_parser.add_argument(
"-c",
"--conf",
help="Specify configurations from a file",
metavar="File",
)
meta_parser.add_argument(
"-a",
"--application_name",
help="Specify application name",
default="",
)
meta_args, args_from_cmdline = meta_parser.parse_known_args()
print(version_string)
# read configurations, to be parsed by sections
if (meta_args.conf is None) or (not os.path.isfile(meta_args.conf)):
raise IOError("Configuration file not found {}".format(meta_args.conf))
config = configparser.ConfigParser()
config.read([meta_args.conf])
try:
if meta_parser.prog[:-3] in SUPPORTED_APP:
module_name = meta_parser.prog[:-3]
elif meta_parser.prog in SUPPORTED_APP:
module_name = meta_parser.prog
else:
module_name = meta_args.application_name
app_module = ApplicationFactory.create(module_name)
assert app_module.REQUIRED_CONFIG_SECTION, \
"REQUIRED_CONFIG_SECTION should be static variable " \
"in {}".format(app_module)
has_section_in_config(config, app_module.REQUIRED_CONFIG_SECTION)
except ValueError:
raise ValueError('{} requires [{}] section in the config file'.format(
module_name, app_module.REQUIRED_CONFIG_SECTION))
# check keywords in configuration file
check_keywords(config)
# using configuration as default, and parsing all command line arguments
all_args = {}
for section in config.sections():
# try to rename user-specified sections for consistency
section = standardise_section_name(config, section)
section_defaults = dict(config.items(section))
section_args, args_from_cmdline = \
_parse_arguments_by_section([],
section,
section_defaults,
args_from_cmdline,
app_module.REQUIRED_CONFIG_SECTION)
all_args[section] = section_args
# command line parameters should be valid
assert not args_from_cmdline, \
'unknown parameter: {}'.format(args_from_cmdline)
# split parsed results in all_args
# into dictionary of system_args and input_data_args
system_args = {}
input_data_args = {}
for section in all_args:
if section in SYSTEM_SECTIONS:
system_args[section] = all_args[section]
elif section == app_module.REQUIRED_CONFIG_SECTION:
system_args['CUSTOM'] = all_args[section]
vars(system_args['CUSTOM'])['name'] = module_name
else:
input_data_args[section] = all_args[section]
# set the output path of csv list if not exists
csv_path = input_data_args[section].csv_file
if not os.path.isfile(csv_path):
csv_filename = os.path.join(all_args['SYSTEM'].model_dir,
'{}.csv'.format(section))
input_data_args[section].csv_file = csv_filename
else:
# don't search files if csv specified in config
try:
delattr(input_data_args[section], 'path_to_search')
except AttributeError:
pass
# update conf path
system_args['CONFIG_FILE'] = argparse.Namespace(path=meta_args.conf)
return system_args, input_data_args
def run():
"""
meta_parser is first used to find out location
of the configuration file. Based on the application_name
or meta_parser.prog name, the section parsers are organised
to find system parameters and application specific
parameters.
:return: system parameters is a group of parameters including
SYSTEM_SECTIONS and app_module.REQUIRED_CONFIG_SECTION
input_data_args is a group of input data sources to be
used by niftynet.io.ImageReader
"""
meta_parser = argparse.ArgumentParser(
description="Launch a NiftyNet application.",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=textwrap.dedent(EPILOG_STRING))
version_string = get_niftynet_version_string()
meta_parser.add_argument("action",
help="train networks, run inferences "
"or evaluate inferences",
metavar='ACTION',
choices=['train', 'inference', 'evaluation'])
meta_parser.add_argument("-v", "--version",
action='version',
version=version_string)
meta_parser.add_argument("-c", "--conf",
help="specify configurations from a file",
metavar="CONFIG_FILE")
meta_parser.add_argument("-a", "--application_name",
help="specify an application module name",
metavar='APPLICATION_NAME',
default="")
meta_args, args_from_cmdline = meta_parser.parse_known_args()
print(version_string)
# read configurations, to be parsed by sections
config_file_name = __resolve_config_file_path(meta_args.conf)
config = configparser.ConfigParser()
config.read([config_file_name])
# infer application name from command
app_name = None
try:
parser_prog = meta_parser.prog.replace('.py', '')
app_name = parser_prog if parser_prog in SUPPORTED_APP \
else meta_args.application_name
assert app_name
except (AttributeError, AssertionError):
raise ValueError(
"\nUnknown application {}, or did you forget '-a' "
"command argument?{}".format(app_name, EPILOG_STRING))
# load application by name
app_module = ApplicationFactory.create(app_name)
try:
assert app_module.REQUIRED_CONFIG_SECTION, \
"\nREQUIRED_CONFIG_SECTION should be static variable " \
"in {}".format(app_module)
has_section_in_config(config, app_module.REQUIRED_CONFIG_SECTION)
except AttributeError:
raise AttributeError(
"Application code doesn't have REQUIRED_CONFIG_SECTION property. "
"{} should be an instance of "
"niftynet.application.base_application".format(app_module))
except ValueError:
raise ValueError(
"\n{} requires [{}] section in the config file.{}".format(
app_name, app_module.REQUIRED_CONFIG_SECTION, EPILOG_STRING))
# check keywords in configuration file
_check_config_file_keywords(config)
# using configuration as default, and parsing all command line arguments
# command line args override the configure file options
all_args = {}
for section in config.sections():
# try to rename user-specified sections for consistency
section = standardise_section_name(config, section)
section_defaults = dict(config.items(section))
section_args, args_from_cmdline = \
_parse_arguments_by_section([],
section,
section_defaults,
args_from_cmdline,
app_module.REQUIRED_CONFIG_SECTION)
all_args[section] = section_args
# check if any args from command line not recognised
_check_cmd_remaining_keywords(list(args_from_cmdline))
# split parsed results in all_args
# into dictionaries of system_args and input_data_args
system_args, input_data_args = {}, {}
for section in all_args:
# copy system default sections to ``system_args``
if section in SYSTEM_SECTIONS:
system_args[section] = all_args[section]
continue
# copy application specific sections to ``system_args``
if section == app_module.REQUIRED_CONFIG_SECTION:
system_args['CUSTOM'] = all_args[section]
vars(system_args['CUSTOM'])['name'] = app_name
continue
# copy non-default sections to ``input_data_args``
input_data_args[section] = all_args[section]
# set the output path of csv list if not exists
try:
csv_path = resolve_file_name(
input_data_args[section].csv_file,
(os.path.dirname(config_file_name), NIFTYNET_HOME))
input_data_args[section].csv_file = csv_path
# don't search files if csv specified in config
try:
delattr(input_data_args[section], 'path_to_search')
except AttributeError:
pass
except (IOError, TypeError):
input_data_args[section].csv_file = ''
# preserve ``config_file`` and ``action parameter`` from the meta_args
system_args['CONFIG_FILE'] = argparse.Namespace(path=config_file_name)
system_args['SYSTEM'].action = meta_args.action
if not system_args['SYSTEM'].model_dir:
system_args['SYSTEM'].model_dir = os.path.join(
os.path.dirname(config_file_name), 'model')
return system_args, input_data_args