niftynet.application.base_application module

Interface of NiftyNet application

class SingletonApplication

Bases: type

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

is_training = True

is_validation = None

readers = None

sampler = None

net = None

optimiser = None

gradient_op = None

output_decoder = None

check_initialisations()

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

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: