from nengo import Node, builder
from nengo.base import NengoObject
from nengo.builder.operator import Reset
from nengo.exceptions import ValidationError, SimulationError
from nengo.params import Default, IntParam, Parameter
import numpy as np
import tensorflow as tf
from nengo_dl.builder import Builder, OpBuilder
class TensorFuncParam(Parameter):
"""Performs validation on the function passed to TensorNode, and sets
``size_in``/``size_out`` if necessary."""
def __init__(self, name, readonly=False):
super(TensorFuncParam, self).__init__(
name, optional=False, readonly=readonly)
def __set__(self, node, output):
super(TensorFuncParam, self).validate(node, output)
node.size_in = 0 if node.size_in is None else node.size_in
# We trust user's size_out if set, because calling output
# may have unintended consequences
if node.size_out is None:
node.size_out = self.check_size_out(node, output)
# --- Set output
self.data[node] = output
def check_size_out(self, node, output):
if not callable(output):
raise ValidationError("TensorNode output must be a function",
attr=self.name, obj=node)
t, x = tf.constant(0.0), tf.zeros((1, node.size_in))
args = (t, x) if node.size_in > 0 else (t,)
try:
result = output(*args)
except Exception as e:
raise ValidationError(
"Calling TensorNode function with arguments %s produced an "
"error:\n%s" % (args, e), attr=self.name, obj=node)
if not isinstance(result, tf.Tensor):
raise ValidationError("TensorNode function must return a Tensor",
attr=self.name, obj=node)
if result.get_shape().ndims != 2:
raise ValidationError("Node output must be a minibatched vector "
"(got shape %s)" % result.get_shape(),
attr=self.name, obj=node)
return result.get_shape()[1].value
[docs]class TensorNode(Node):
"""Inserts TensorFlow code into a Nengo model. A TensorNode operates in
much the same was a a :class:`~nengo:nengo.Node`, except its inputs and
outputs are defined using TensorFlow operations.
The Tensorflow code is defined in a function or callable class
(``tensor_func``). This function accepts the current simulation time as
input, or the current simulation time and a Tensor ``x`` if
``node.size_in > 0``. ``x`` will have shape
``(sim.minibatch_size, node.size_in``), and the function should return a
Tensor with shape ``(sim.minibatch_size, node.size_out)``.
``node.size_out`` will be inferred by calling the function once and
checking the output, if it isn't set when the Node is created.
If ``tensor_func`` has a ``pre_build`` attribute, that function will be
called once when the model is constructed. This can be used to compute any
constant values or set up variables -- things that don't need to
execute every simulation timestep.
Parameters
----------
tensor_func : callable
a function that maps node inputs to outputs
size_in : int, optional (Default: None)
the number of elements in the input vector
size_out : int, optional (Default: None)
the number of elements in the output vector (if None, value will be
inferred by calling ``tensor_func``)
label : str, optional (Default: None)
a name for the node, used for debugging and visualization
"""
tensor_func = TensorFuncParam('tensor_func')
size_in = IntParam('size_in', default=None, low=0, optional=True)
size_out = IntParam('size_out', default=None, low=1, optional=True)
def __init__(self, tensor_func, size_in=Default, size_out=Default,
label=Default):
# note: we bypass the Node constructor, because we don't want to
# perform validation on `output`
NengoObject.__init__(self, label=label, seed=None)
self.size_in = size_in
self.size_out = size_out
self.tensor_func = tensor_func
@builder.Builder.register(TensorNode)
def build_tensor_node(model, node):
"""This is the Nengo build function, so that Nengo knows what to do with
TensorNodes."""
# input signal
if node.size_in > 0:
sig_in = builder.Signal(np.zeros(node.size_in), name="%s.in" % node)
model.add_op(Reset(sig_in))
else:
sig_in = None
sig_out = builder.Signal(np.zeros(node.size_out), name="%s.out" % node)
model.sig[node]['in'] = sig_in
model.sig[node]['out'] = sig_out
model.params[node] = None
model.add_op(SimTensorNode(node.tensor_func, model.time, sig_in, sig_out))
class SimTensorNode(builder.Operator):
"""Operator for TensorNodes (constructed by :func:`.build_tensor_node`).
Parameters
----------
func : callable
the TensorNode function (``tensor_func``)
time : :class:`~nengo:nengo.builder.Signal`
Signal representing the current simulation time
input : :class:`~nengo:nengo.builder.Signal` or None
input Signal for the TensorNode (or None if size_in==0)
output : :class:`~nengo:nengo.builder.Signal`
output Signal for the TensorNode
tag : str, optional
a label associated with the operator, for debugging
Notes
-----
1. sets ``[output]``
2. incs ``[]``
3. reads ``[time] if input is None else [time, input]``
4. updates ``[]``
"""
def __init__(self, func, time, input, output, tag=None):
super(SimTensorNode, self).__init__(tag=tag)
self.func = func
self.input = input
self.output = output
self.sets = [output]
self.incs = []
self.reads = [time] if input is None else [time, input]
self.updates = []
def make_step(self, *args, **kwargs):
"""``make_step`` is never called by the NengoDL simulator, so if this
is called it means that someone is trying to execute a TensorNode in
some other Simulator."""
def error():
raise SimulationError("TensorNode can only be simulated in the "
"NengoDL simulator")
return error
[docs]@Builder.register(SimTensorNode)
class SimTensorNodeBuilder(OpBuilder):
"""Builds a :class:`.SimTensorNode` operator into a NengoDL model."""
def __init__(self, ops, signals):
# SimTensorNodes should never be merged
assert len(ops) == 1
op = ops[0]
if op.input is None:
self.src_data = None
else:
self.src_data = signals.sig_map[op.input]
self.src_data.load_indices()
assert self.src_data.ndim == 1
self.dst_data = signals.sig_map[op.output]
self.dst_data.load_indices()
self.func = op.func
if hasattr(self.func, "pre_build"):
self.func.pre_build(
(signals.minibatch_size,) + self.src_data.shape,
(signals.minibatch_size,) + self.dst_data.shape)
[docs] def build_step(self, signals):
if self.src_data is None:
output = self.func(signals.time)
else:
input = signals.gather(self.src_data)
# move minibatch dimension to front
input = tf.transpose(input, (1, 0))
output = self.func(signals.time, input)
# move minibatch dimension back to end
output_dim = output.get_shape().ndims - 1
output = tf.transpose(
output, [output_dim] + [i for i in range(output_dim)])
signals.scatter(self.dst_data, output)