niftynet.application.base_application module

Interface of NiftyNet application

class SingletonApplication

Bases: type

classmethod clear()

class BaseApplication

Bases: object

BaseApplication represents an interface.

Each application type_str should support to use the standard training and inference driver.

REQUIRED_CONFIG_SECTION = None

SUPPORTED_ACTIONS = set(['inference', 'train', 'evaluation'])

is_validation = None

readers = None

sampler = None

net = None

optimiser = None

gradient_op = None

output_decoder = None

check_initialisations()

get_file_lists(data_partitioner)

This function pull the correct file_lists from the data partitioner depending on the phase :param data_partitioner:

specifies train/valid/infer splitting if needed

Returns:list of file lists of length 2 if validation is needed otherwise 1

initialise_dataset_loader(data_param=None, task_param=None, data_partitioner=None)

this function initialise self.readers

Parameters:

• data_param – input modality specifications
• task_param – contains task keywords for grouping data_param
• data_partitioner – specifies train/valid/infer splitting if needed

Returns:

initialise_sampler()

Samplers take self.reader as input and generates sequences of ImageWindow that will be fed to the networks

This function sets self.sampler.

initialise_network()

This function create an instance of network and sets self.net

Returns:None

connect_data_and_network(outputs_collector=None, gradients_collector=None)

Adding sampler output tensor and network tensors to the graph.

Parameters:

• outputs_collector –
• gradients_collector –

Returns:

interpret_output(batch_output)

Implement output interpretations, e.g., save to hard drive cache output windows.

Parameters:batch_output – outputs by running the tf graph Returns:True indicates the driver should continue the loop False indicates the drive should stop

add_inferred_output_like(data_param, task_param, name)

This function adds entries to parameter objects to enable the evaluation action to automatically read in the output of a previous inference run if inference is not explicitly specified.

This can be used in an application if there is a data section entry in the configuration file that matches the inference output. In supervised learning, the reference data section would often match the inference output and could be used here. Otherwise, a template data section could be used.

Parameters:

• data_param –
• task_param –
• name – name of input parameter to copy parameters from

Returns:

modified data_param and task_param

set_network_gradient_op(gradients)

create gradient op by optimiser.apply_gradients this function sets self.gradient_op.

Override this function for more complex optimisations such as using different optimisers for sub-networks.

Parameters:gradients – processed gradients from the gradient_collector Returns:

stop()

stop the sampling threads if there’s any.

Returns:

set_iteration_update(iteration_message)

At each iteration application_driver calls:

output = tf.session.run(variables_to_eval, feed_dict=data_dict)

to evaluate TF graph elements, where variables_to_eval and data_dict are retrieved from application_iteration.IterationMessage.ops_to_run and application_iteration.IterationMessage.data_feed_dict. in addition to the variables collected by output_collector; implemented in application_driver.run_vars)

This function (is called before tf.session.run by the driver) provides an interface for accessing variables_to_eval and data_dict at each iteration.

Override this function for more complex operations according to application_iteration.IterationMessage.current_iter.

get_sampler()

get samplers of the application

Returns:niftynet.engine.sampler_* instances

add_validation_flag()

add a TF placeholder for switching between train/valid graphs, this function sets self.is_validation. self.is_validation can be used by applications.

Returns:

action

A string indicating the action in train/inference/evaluation

Returns:

is_training

return – boolean value indicating if the phase is in training

is_inference

return – boolean value indicating if the phase is inference

is_evaluation

return – boolean value indicating if the action is evaluation