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']
