nengo_spinnaker latest unable to find tps.full_transform

[Mohinta2892]

Facing the below issue:

Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/nengo_gui-0.4.5.dev0-py2.7.egg/nengo_gui/page.py", line 488, in build
self.sim = backend.Simulator(self.model)
File "/home/samia/Documents/nengo_spinnaker-master/nengo_spinnaker/simulator.py", line 112, in init
self.model.build(network, **builder_kwargs)
File "/home/samia/Documents/nengo_spinnaker-master/nengo_spinnaker/builder/builder.py", line 211, in build
self.connection_map.insert_and_stack_interposers()
File "/home/samia/Documents/nengo_spinnaker-master/nengo_spinnaker/builder/model.py", line 174, in insert_and_stack_interposers
interposers, cm = self.insert_interposers()
File "/home/samia/Documents/nengo_spinnaker-master/nengo_spinnaker/builder/model.py", line 222, in insert_interposers
trans = tps.full_transform(False, False)
AttributeError: 'object' object has no attribute 'full_transform'

Nengo version being used is 2.8.0

Any help with resolving this issue is much appreciated.

Thanks!

[tcstewar]

Can you post a small version of the model you're trying to run? Does this happen to any nengo model you try to run, or just this one?

[Mohinta2892]

This is the git that I am using: https://github.com/bjkomer/spiking-ratslam/blob/master/ratslam/spinnaker_nengo_posecells_ci.py

It happens with any nengo model I am trying to run. It seems that tps is a EnsembleTransmissionParameters type object and this EnsembleTransmissionParameters in nengo_spinnaker.builder.transmission_parameter has not got any full_transform method.
I assume it calls Transform.full_transform()

However, there is some issue with it.

class EnsembleTransmissionParameters(TransmissionParameters):
"""Parameters describing information transmitted by an ensemble.
Attributes
----------
decoders : ndarray
A matrix describing a decoding of the ensemble (sized N x D).
learning_rule :
Learning rule associated with the decoding.
"""
slots = TransmissionParameters.slots + [
"decoders", "learning_rule"
]

def __init__(self, decoders, transform, learning_rule=None):
    super(EnsembleTransmissionParameters, self).__init__(transform)

    # Copy the decoders into a C-contiguous, read-only array
    self.decoders = np.array(decoders, order='C')
    self.decoders.flags["WRITEABLE"] = False

    # Store the learning rule
    self.learning_rule = learning_rule

def __eq__(self, other):
    # Two parameters are equal only if they are of the same type, both have
    # no learning rule and are equivalent in all other fields.
    return (super(EnsembleTransmissionParameters, self).__eq__(other) and
            np.array_equal(self.decoders, other.decoders) and
            self.learning_rule is None and
            other.learning_rule is None)

def __hash__(self):
    return hash((type(self), self.learning_rule, self._transform,
                 fasthash(self.decoders).hexdigest()))

def concat(self, other):
    """Create new connection parameters which are the result of
    concatenating this connection with others.
    Parameters
    ----------
    other : PassthroughNodeTransmissionParameters
        Connection parameters to add to the end of this connection.
    Returns
    -------
    EnsembleTransmissionParameters or None
        Either a new set of transmission parameters, or None if the
        resulting transform contained no non-zero values.
    """
    # Get the outgoing transformation
    new_transform = self._transform.concat(other._transform)

    # Create a new connection (unless the resulting transform is empty,
    # in which case don't)
    if new_transform is not None:
        return EnsembleTransmissionParameters(
            self.decoders, new_transform, self.learning_rule
        )
    else:
        # The transform consisted entirely of zeros so return None.
        return None

@property
def as_global_inhibition_connection(self):
    """Construct a copy of the connection with the optimisation for global
    inhibition applied.
    """
    assert self.supports_global_inhibition
    transform = self.full_transform(slice_out=False)[0, :]

    return EnsembleTransmissionParameters(
        self.decoders,
        Transform(size_in=self.decoders.shape[0], size_out=1,
                  transform=transform, slice_in=self._transform.slice_in)
    )

@property
def full_decoders(self):
    """Get the matrix corresponding to a combination of the decoders and
    the transform applied by the connection.
    """
    return np.dot(self.full_transform(slice_in=False, slice_out=False),
                  self.decoders)

Thanks.