niftynet.contrib.deep_boosted_regression.regression_rec_application module

class RegressionRecApplication(net_param, action_param, action)

Bases: niftynet.application.base_application.BaseApplication

REQUIRED_CONFIG_SECTION = 'REGRESSION'

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_uniform_sampler()

initialise_weighted_sampler()

initialise_resize_sampler()

initialise_grid_sampler()

initialise_balanced_sampler()

initialise_grid_aggregator()

initialise_resize_aggregator()

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