def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting multioutput test')
self.action = action
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.multioutput_param = None
self.SUPPORTED_SAMPLING = {
'uniform':
(self.initialise_uniform_sampler, self.initialise_grid_sampler,
self.initialise_grid_aggregator),
'weighted':
(self.initialise_weighted_sampler, self.initialise_grid_sampler,
self.initialise_grid_aggregator),
'resize':
(self.initialise_resize_sampler, self.initialise_resize_sampler,
self.initialise_resize_aggregator),
'classifier':
(self.initialise_resize_sampler, self.initialise_resize_sampler,
self.initialise_classifier_aggregator),
'identity':
(self.initialise_uniform_sampler, self.initialise_resize_sampler,
self.initialise_identity_aggregator)
}
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting toy application')
self.action = action
self.net_param = net_param
self.action_param = action_param
self.toy_param = None
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting toy application')
self.action = action
self.net_param = net_param
self.action_param = action_param
self.toy_param = None
def __init__(self, net_param, action_param, is_training):
BaseApplication.__init__(self)
tf.logging.info('starting BRATS segmentation app')
self.is_training = is_training
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.segmentation_param = None
def __init__(self, net_param, action_param, is_training):
BaseApplication.__init__(self)
tf.logging.info('starting GAN application')
self.is_training = is_training
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.gan_param = None
def __init__(self, net_param, action_param, is_training):
BaseApplication.__init__(self)
tf.logging.info('starting BRATS segmentation app')
self.is_training = is_training
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.segmentation_param = None
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting GAN application')
self.action = action
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.gan_param = None
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting label-driven registration')
self.action = action
self.net_param = net_param
self.action_param = action_param
self.registration_param = None
self.data_param = None
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting label-driven registration')
self.action = action
self.net_param = net_param
self.action_param = action_param
self.registration_param = None
self.data_param = None
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting autoencoder application')
self.action = action
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.autoencoder_param = None
def __init__(self, net_param, action_param, is_training):
BaseApplication.__init__(self)
tf.logging.info('starting autoencoder application')
self.is_training = is_training
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.autoencoder_param = None
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting brainVesselSegApp segmentation app')
if action in [True, False]: # compatibility with NN 0.2
# action parameter was previously is_training
self.is_training = action
else:
self.action = action
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.segmentation_param = None
def __init__(self, net_param, action_param, is_training):
BaseApplication.__init__(self)
tf.logging.info('starting segmentation application')
self.is_training = is_training
self.net_param = net_param
self.action_param = action_param
self.data_param = None
self.segmentation_param = None
self.SUPPORTED_SAMPLING = {
'uniform':
(self.initialise_uniform_sampler, self.initialise_grid_sampler,
self.initialise_grid_aggregator),
'resize':
(self.initialise_resize_sampler, self.initialise_resize_sampler,
self.initialise_resize_aggregator),
}
def __init__(self, net_param, action_param, is_training):
BaseApplication.__init__(self)
tf.logging.info('starting regression application')
self.is_training = is_training
self.net_param = net_param
self.action_param = action_param
self.regression_param = None
self.data_param = None
self.SUPPORTED_SAMPLING = {
'uniform': (self.initialise_uniform_sampler,
self.initialise_grid_sampler,
self.initialise_grid_aggregator),
'weighted': (self.initialise_weighted_sampler,
self.initialise_grid_sampler,
self.initialise_grid_aggregator),
'resize': (self.initialise_resize_sampler,
self.initialise_resize_sampler,
self.initialise_resize_aggregator),
}
def __init__(self, net_param, action_param, action):
BaseApplication.__init__(self)
tf.logging.info('starting recursive regression application')
self.action = action
self.net_param = net_param
self.net2_param = copy.deepcopy(net_param)
self.action_param = action_param
self.regression_param = None
self.data_param = None
self.SUPPORTED_SAMPLING = {
'uniform':
(self.initialise_uniform_sampler, self.initialise_grid_sampler,
self.initialise_grid_aggregator),
'weighted':
(self.initialise_weighted_sampler, self.initialise_grid_sampler,
self.initialise_grid_aggregator),
'resize':
(self.initialise_resize_sampler, self.initialise_resize_sampler,
self.initialise_resize_aggregator),
}
