niftynet.application.base_application module

Interface of NiftyNet application

class SingletonApplication[source]

Bases: type

Maintaining a global application instance.

classmethod clear()[source]

Remove the instance. :return:

class BaseApplication[source]

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_PHASES = set([u'evaluation', u'training', u'inference'])
is_validation = None
readers = None
sampler = None
net = None
optimiser = None
gradient_op = None
output_decoder = None
outputs_collector = None
gradients_collector = None
initialise_dataset_loader(data_param=None, task_param=None, data_partitioner=None)[source]

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()[source]

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()[source]

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

Returns:None
connect_data_and_network(outputs_collector=None, gradients_collector=None)[source]

Adding sampler output tensor and network tensors to the graph.

Parameters:
  • outputs_collector
  • gradients_collector
Returns:

interpret_output(batch_output)[source]

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)[source]

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_iteration_update(iteration_message)[source]

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 iteration_message.ops_to_run and iteration_message.data_feed_dict

(In addition to the variables collected by self.output_collector).

The output of tf.session.run(…) will be stored at iteration_message.current_iter_output, and can be accessed from engine.handler_network_output.OutputInterpreter.

override this function for more complex operations (such as learning rate decay) according to iteration_message.current_iter.

get_sampler()[source]

Get samplers of the application

Returns:niftynet.engine.sampler_* instances
add_validation_flag()[source]

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 training

is_inference

return – boolean value indicating if the phase is inference

is_evaluation

return – boolean value indicating if the action is evaluation

static stop()[source]

remove application instance if there’s any.

Returns: