def infer(node: Node): axes_1_value = node.in_port(1).data.get_value() axes_2_value = node.in_port(2).data.get_value() if axes_1_value is None or axes_2_value is None: log.warning('Reduction indices for mean and variance for MVN node {} are not constants'.format(node.name)) return if not (all(axes_1_value == axes_2_value)): log.warning('Reduction indices for mean {} and variance {} do not match'.format( axes_1_value, axes_2_value )) return power_value = node.in_port(3).data.get_value() eps_value = node.in_port(4).data.get_value() if power_value is None or eps_value is None: log.warning('Power or/and epsilon values for MVN node {} are not constants'.format(node.name)) return if power_value != 0.5: log.warning('Power for MVN node {} ({}) is not equal to 0.5'.format(node.name, power_value)) return node['eps'] = eps_value for i in range(2, 5): node.in_port(i).disconnect() node.old_infer(node) node.infer = node.old_infer del node['old_infer']
def infer(node: Node): axes_1_value = node.in_port(1).data.get_value() axes_2_value = node.in_port(2).data.get_value() if axes_1_value is None or axes_2_value is None: log.warning( 'Reduction indices for mean and variance for MVN node {} are not constants' .format(node.name)) return if not (all(axes_1_value == axes_2_value)): log.warning( 'Reduction indices for mean {} and variance {} do not match'. format(axes_1_value, axes_2_value)) return node.in_port(2).disconnect() node.old_infer(node) node.infer = node.old_infer del node['old_infer']