def test_strings():
block = LoihiBlock(3, label="myBlock")
assert str(block) == "LoihiBlock(myBlock)"
assert str(block.compartment) == "Compartment()"
synapse = Synapse(2, label="mySynapse")
assert str(synapse) == "Synapse(mySynapse)"
axon = Axon(2, label="myAxon")
assert str(axon) == "Axon(myAxon)"
spike = Axon.Spike(axon_id=7, atom=2)
assert str(spike) == "Spike(axon_id=7, atom=2)"
def test_negative_cxbase(request, seed):
n_axons = 3
model = Model()
input = SpikeInput(n_axons)
input.add_spikes(1, list(range(n_axons)))
model.add_input(input)
axon = Axon(n_axons)
input.add_axon(axon)
block = LoihiBlock(3)
block.compartment.configure_relu()
model.add_block(block)
synapse = Synapse(n_axons)
weights = [0.1, 0.1, 0.1]
indices = [0, 1, 2]
axon_to_weight_map = list(range(n_axons))
cx_bases = [0, 1, -1]
synapse.set_population_weights(weights,
indices,
axon_to_weight_map,
cx_bases,
pop_type=32)
axon.target = synapse
block.add_synapse(synapse)
probe = Probe(target=block, key='voltage')
block.add_probe(probe)
discretize_model(model)
n_steps = 2
if request.config.getoption("--target") == 'loihi':
with HardwareInterface(model, use_snips=False, seed=seed) as sim:
sim.run_steps(n_steps)
y = sim.get_probe_output(probe)
else:
with EmulatorInterface(model, seed=seed) as sim:
sim.run_steps(n_steps)
y = sim.get_probe_output(probe)
# Compartments 0 and 2 should change from axons 0 and 1.
# Axon 2 should have no effect, and not change compartment 1 (the sum of
# its cx_base and index), or other compartments (e.g. 2 if cx_base ignored)
assert np.allclose(y[1, 1], 0), "Third axon not ignored"
assert np.allclose(y[1, 0], y[1, 2]), "Third axon targeting another"
assert not np.allclose(y[1], y[0]), "Voltage not changing"
def test_multiple_get_probe_output():
n_steps = 15
n_axons = 3
model = Model()
# n_axons controls number of input spikes and thus amount of overflow
input = SpikeInput(n_axons)
for t in np.arange(1, n_steps + 1):
input.add_spikes(t, np.arange(n_axons)) # send spikes to all axons
model.add_input(input)
block = LoihiBlock(1)
block.compartment.configure_relu()
block.compartment.configure_filter(0.1)
model.add_block(block)
synapse = Synapse(n_axons)
synapse.set_weights(np.ones((n_axons, 1)))
block.add_synapse(synapse)
axon = Axon(n_axons)
axon.target = synapse
input.add_axon(axon)
probe_u = LoihiProbe(target=block, key="current", synapse=Lowpass(0.005))
model.add_probe(probe_u)
probe_v = LoihiProbe(target=block, key="voltage", synapse=Lowpass(0.005))
model.add_probe(probe_v)
probe_s = LoihiProbe(target=block, key="spiked", synapse=Lowpass(0.005))
model.add_probe(probe_s)
discretize_model(model)
# must set these after `discretize` to specify discretized values
block.compartment.vmin = -(2**22) + 1
block.compartment.vth[:] = VTH_MAX
with EmulatorInterface(model) as emu:
emu.run_steps(n_steps)
first_u = emu.get_probe_output(probe_u)
first_v = emu.get_probe_output(probe_v)
first_s = emu.get_probe_output(probe_s)
second_u = emu.get_probe_output(probe_u)
second_v = emu.get_probe_output(probe_v)
second_s = emu.get_probe_output(probe_s)
assert np.all(first_u == second_u)
assert np.all(first_v == second_v)
assert np.all(first_s == second_s)
def test_builder_poptype_errors():
pytest.importorskip("nxsdk")
# Test error in build_synapse
model = Model()
block = LoihiBlock(1)
block.compartment.configure_lif()
model.add_block(block)
synapse = Synapse(1)
synapse.set_weights([[1]])
synapse.pop_type = 8
block.add_synapse(synapse)
discretize_model(model)
allocator = Greedy() # one core per ensemble
board = allocator(model, n_chips=1)
with pytest.raises(ValueError, match="[Ss]ynapse.*[Uu]nrec.*pop.*type"):
build_board(board)
# Test error in collect_axons
model = Model()
block0 = LoihiBlock(1)
block0.compartment.configure_lif()
model.add_block(block0)
block1 = LoihiBlock(1)
block1.compartment.configure_lif()
model.add_block(block1)
axon = Axon(1)
block0.add_axon(axon)
synapse = Synapse(1)
synapse.set_weights([[1]])
synapse.pop_type = 8
axon.target = synapse
block1.add_synapse(synapse)
discretize_model(model)
board = allocator(model, n_chips=1)
with pytest.raises(ValueError, match="[Aa]xon.*[Uu]nrec.*pop.*type"):
build_board(board)
def test_validate_block():
# too many compartments
block = LoihiBlock(1200)
assert block.compartment.n_compartments > 1024
with pytest.raises(BuildError, match="Number of compartments"):
validate_block(block)
# too many input axons
block = LoihiBlock(410)
block.add_synapse(Synapse(5000))
with pytest.raises(BuildError, match="Input axons"):
validate_block(block)
# too many output axons
block = LoihiBlock(410)
synapse = Synapse(2500)
axon = Axon(5000)
axon.target = synapse
block.add_synapse(synapse)
block.add_axon(axon)
with pytest.raises(BuildError, match="Output axons"):
validate_block(block)
# too many synapse bits
block = LoihiBlock(600)
synapse = Synapse(500)
synapse.set_full_weights(np.ones((500, 600)))
axon = Axon(500)
axon.target = synapse
block.add_synapse(synapse)
block.add_axon(axon)
with pytest.raises(BuildError, match="synapse bits"):
validate_block(block)
def _basic_model():
model = Model()
block0 = LoihiBlock(1)
block0.compartment.configure_lif()
model.add_block(block0)
block1 = LoihiBlock(1)
block1.compartment.configure_lif()
model.add_block(block1)
axon1 = Axon(1)
block0.add_axon(axon1)
synapse1 = Synapse(1)
synapse1.set_full_weights([[1]])
axon1.target = synapse1
block1.add_synapse(synapse1)
axon0 = Axon(1)
input = LoihiInput()
input.add_axon(axon0)
model.add_input(input)
synapse0 = Synapse(1)
synapse0.set_full_weights([[1]])
axon0.target = synapse0
block0.add_synapse(synapse0)
discretize_model(model)
return model
def _basic_model(n_blocks=2):
model = Model()
blocks = []
for _ in range(n_blocks):
block = LoihiBlock(1)
block.compartment.configure_lif()
model.add_block(block)
blocks.append(block)
for i in range(n_blocks - 1):
axon = Axon(1)
blocks[i].add_axon(axon)
synapse = Synapse(1)
synapse.set_weights([[1]])
axon.target = synapse
blocks[i + 1].add_synapse(synapse)
axon0 = Axon(1)
input = LoihiInput()
input.add_axon(axon0)
model.add_input(input)
synapse0 = Synapse(1)
synapse0.set_weights([[1]])
axon0.target = synapse0
blocks[0].add_synapse(synapse0)
discretize_model(model)
return model
def test_uv_overflow(n_axons, plt, allclose, monkeypatch):
# TODO: Currently this is not testing the V overflow, since it is higher
# and I haven't been able to figure out a way to make it overflow.
nt = 15
model = Model()
# n_axons controls number of input spikes and thus amount of overflow
input = SpikeInput(n_axons)
for t in np.arange(1, nt + 1):
# send spikes to all axons
input.add_spikes(t, np.arange(n_axons), permanent=True)
model.add_input(input)
block = LoihiBlock(1)
block.compartment.configure_relu()
block.compartment.configure_filter(0.1)
model.add_block(block)
synapse = Synapse(n_axons)
synapse.set_weights(np.ones((n_axons, 1)))
block.add_synapse(synapse)
axon = Axon(n_axons)
axon.target = synapse
input.add_axon(axon)
probe_u = LoihiProbe(target=block, key="current")
model.add_probe(probe_u)
probe_v = LoihiProbe(target=block, key="voltage")
model.add_probe(probe_v)
probe_s = LoihiProbe(target=block, key="spiked")
model.add_probe(probe_s)
discretize_model(model)
# must set these after `discretize` to specify discretized values
block.compartment.vmin = -(2**22) + 1
block.compartment.vth[:] = VTH_MAX
assert EmulatorInterface.strict # Tests should be run in strict mode
monkeypatch.setattr(EmulatorInterface, "strict", False)
overflow_var = "q0" if n_axons == 1000 else "current"
with EmulatorInterface(model) as emu:
with pytest.warns(UserWarning, match=f"Overflow in {overflow_var}"):
emu.run_steps(nt)
emu_u = emu.collect_probe_output(probe_u)
emu_v = emu.collect_probe_output(probe_v)
emu_s = emu.collect_probe_output(probe_s)
with HardwareInterface(model, use_snips=False) as sim:
sim.run_steps(nt)
sim_u = sim.collect_probe_output(probe_u)
sim_v = sim.collect_probe_output(probe_v)
sim_s = sim.collect_probe_output(probe_s)
sim_v[sim_s > 0] = 0 # since Loihi has placeholder voltage after spike
plt.subplot(311)
plt.plot(emu_u)
plt.plot(sim_u)
plt.subplot(312)
plt.plot(emu_v)
plt.plot(sim_v)
plt.subplot(313)
plt.plot(emu_s)
plt.plot(sim_s)
assert allclose(emu_u, sim_u)
assert allclose(emu_v, sim_v)
def build_connection(model, conn):
if nengo_transforms is not None:
if isinstance(conn.transform, nengo_transforms.Convolution):
# TODO: integrate these into the same function
conv.build_conv2d_connection(model, conn)
return
elif not isinstance(conn.transform, nengo_transforms.Dense):
raise NotImplementedError(
"nengo-loihi does not yet support %s transforms"
% conn.transform)
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
pre_cx = model.objs[conn.pre_obj]['out']
post_cx = model.objs[conn.post_obj]['in']
assert isinstance(pre_cx, (LoihiBlock, LoihiInput))
assert isinstance(post_cx, (LoihiBlock, Probe))
weights = None
eval_points = None
solver_info = None
neuron_type = None
post_slice = conn.post_slice
# sample transform (if using a distribution)
transform = sample_transform(conn, rng=rng)
tau_s = 0.0 # `synapse is None` gets mapped to `tau_s = 0.0`
if isinstance(conn.synapse, nengo.synapses.Lowpass):
tau_s = conn.synapse.tau
elif conn.synapse is not None:
raise NotImplementedError("Cannot handle non-Lowpass synapses")
needs_decode_neurons = False
target_encoders = None
if isinstance(conn.pre_obj, Node):
assert conn.pre_slice == slice(None)
if np.array_equal(transform, np.array(1.)):
# TODO: this identity transform may be avoidable
transform = np.eye(conn.pre.size_out)
else:
assert transform.ndim == 2, "transform shape not handled yet"
assert transform.shape[1] == conn.pre.size_out
assert transform.shape[1] == conn.pre.size_out
if isinstance(conn.pre_obj, ChipReceiveNeurons):
weights = transform / model.dt
neuron_type = conn.pre_obj.neuron_type
else:
# input is on-off neuron encoded, so double/flip transform
weights = np.column_stack([transform, -transform])
target_encoders = 'node_encoders'
elif (isinstance(conn.pre_obj, Ensemble)
and isinstance(conn.pre_obj.neuron_type, nengo.Direct)):
raise NotImplementedError()
elif isinstance(conn.pre_obj, Ensemble): # Normal decoded connection
eval_points, decoders, solver_info = model.build(
conn.solver, conn, rng, transform)
if conn.solver.weights and not conn.solver.compositional:
weights = decoders
else:
weights = multiply(transform, decoders)
# the decoder solver assumes a spike height of 1/dt; that isn't the
# case on loihi, so we need to undo that scaling
weights = weights / model.dt
neuron_type = conn.pre_obj.neuron_type
if conn.solver.weights:
# weight solvers only allowed on ensemble->ensemble connections
assert isinstance(conn.post_obj, Ensemble)
if conn.solver.compositional:
encoders = model.params[conn.post_obj].scaled_encoders.T
encoders = encoders[post_slice]
weights = multiply(encoders.T, weights)
# post slice already applied to encoders (either here or in
# `build_decoders`), so don't apply later
post_slice = None
else:
needs_decode_neurons = True
elif isinstance(conn.pre_obj, Neurons):
assert conn.pre_slice == slice(None)
assert transform.ndim == 2, "transform shape not handled yet"
weights = transform / model.dt
neuron_type = conn.pre_obj.ensemble.neuron_type
else:
raise NotImplementedError("Connection from type %r" % (
type(conn.pre_obj),))
if neuron_type is not None and hasattr(neuron_type, 'amplitude'):
weights = weights * neuron_type.amplitude
mid_cx = pre_cx
mid_axon_inds = None
post_tau = tau_s
if needs_decode_neurons and not isinstance(conn.post_obj, Neurons):
# --- add decode neurons
assert weights.ndim == 2
d, n = weights.shape
if isinstance(post_cx, Probe):
# use non-spiking decode neurons for voltage probing
assert post_cx.target is None
assert post_slice == slice(None)
# use the same scaling as the ensemble does, to get good
# decodes. Note that this assumes that the decoded value
# is in the range -radius to radius, which is usually true.
weights = weights / conn.pre_obj.radius
gain = 1
dec_cx = LoihiBlock(2 * d, label='%s' % conn)
dec_cx.compartment.configure_nonspiking(
dt=model.dt, vth=model.vth_nonspiking)
dec_cx.compartment.bias[:] = 0
model.add_block(dec_cx)
model.objs[conn]['decoded'] = dec_cx
dec_syn = Synapse(n, label="probe_decoders")
weights2 = gain * np.vstack([weights, -weights]).T
dec_syn.set_full_weights(weights2)
dec_cx.add_synapse(dec_syn)
model.objs[conn]['decoders'] = dec_syn
else:
# use spiking decode neurons for on-chip connection
if isinstance(conn.post_obj, Ensemble):
# loihi encoders don't include radius, so handle scaling here
weights = weights / conn.post_obj.radius
post_d = conn.post_obj.size_in
post_inds = np.arange(post_d, dtype=np.int32)[post_slice]
assert weights.shape[0] == len(post_inds) == conn.size_out == d
mid_axon_inds = model.decode_neurons.get_post_inds(
post_inds, post_d)
target_encoders = 'decode_neuron_encoders'
dec_cx, dec_syn = model.decode_neurons.get_block(
weights, block_label="%s" % conn, syn_label="decoders")
model.add_block(dec_cx)
model.objs[conn]['decoded'] = dec_cx
model.objs[conn]['decoders'] = dec_syn
# use tau_s for filter into decode neurons, decode_tau for filter out
dec_cx.compartment.configure_filter(tau_s, dt=model.dt)
post_tau = model.decode_tau
dec_ax0 = Axon(n, label="decoders")
dec_ax0.target = dec_syn
pre_cx.add_axon(dec_ax0)
model.objs[conn]['decode_axon'] = dec_ax0
if conn.learning_rule_type is not None:
rule_type = conn.learning_rule_type
if isinstance(rule_type, nengo.PES):
if not isinstance(rule_type.pre_synapse,
nengo.synapses.Lowpass):
raise ValidationError(
"Loihi only supports `Lowpass` pre-synapses for "
"learning rules", attr='pre_synapse', obj=rule_type)
tracing_tau = rule_type.pre_synapse.tau / model.dt
# Nengo builder scales PES learning rate by `dt / n_neurons`
n_neurons = (conn.pre_obj.n_neurons
if isinstance(conn.pre_obj, Ensemble)
else conn.pre_obj.size_in)
learning_rate = rule_type.learning_rate * model.dt / n_neurons
# Account for scaling to put integer error in range [-127, 127]
learning_rate /= model.pes_error_scale
# Tracing mag set so that the magnitude of the pre trace
# is independent of the pre tau. `dt` factor accounts for
# Nengo's `dt` spike scaling. Where is the second `dt` from?
# Maybe the fact that post decode neurons have `vth = 1/dt`?
tracing_mag = -np.expm1(-1. / tracing_tau) / model.dt**2
# learning weight exponent controls the maximum weight
# magnitude/weight resolution
wgt_exp = model.pes_wgt_exp
dec_syn.set_learning(
learning_rate=learning_rate,
tracing_mag=tracing_mag,
tracing_tau=tracing_tau,
wgt_exp=wgt_exp,
)
else:
raise NotImplementedError()
mid_cx = dec_cx
if isinstance(post_cx, Probe):
assert post_cx.target is None
assert post_slice == slice(None)
post_cx.target = mid_cx
mid_cx.add_probe(post_cx)
elif isinstance(conn.post_obj, Neurons):
assert isinstance(post_cx, LoihiBlock)
assert post_slice == slice(None)
if weights is None:
raise NotImplementedError("Need weights for connection to neurons")
else:
assert weights.ndim == 2
n2, n1 = weights.shape
assert post_cx.n_neurons == n2
syn = Synapse(n1, label="neuron_weights")
gain = model.params[conn.post_obj.ensemble].gain
syn.set_full_weights(weights.T * gain)
post_cx.add_synapse(syn)
model.objs[conn]['weights'] = syn
ax = Axon(mid_cx.n_neurons, label="neuron_weights")
ax.target = syn
mid_cx.add_axon(ax)
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
if conn.learning_rule_type is not None:
raise NotImplementedError()
elif isinstance(conn.post_obj, Ensemble) and conn.solver.weights:
assert isinstance(post_cx, LoihiBlock)
assert weights.ndim == 2
n2, n1 = weights.shape
assert post_cx.n_neurons == n2
# loihi encoders don't include radius, so handle scaling here
weights = weights / conn.post_obj.radius
syn = Synapse(n1, label="%s::decoder_weights" % conn)
syn.set_full_weights(weights.T)
post_cx.add_synapse(syn)
model.objs[conn]['weights'] = syn
ax = Axon(n1, label="decoder_weights")
ax.target = syn
mid_cx.add_axon(ax)
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
if conn.learning_rule_type is not None:
raise NotImplementedError()
elif isinstance(conn.post_obj, Ensemble):
assert target_encoders is not None
if target_encoders not in post_cx.named_synapses:
build_decode_neuron_encoders(
model, conn.post_obj, kind=target_encoders)
mid_ax = Axon(mid_cx.n_neurons, label="encoders")
mid_ax.target = post_cx.named_synapses[target_encoders]
mid_ax.set_axon_map(mid_axon_inds)
mid_cx.add_axon(mid_ax)
model.objs[conn]['mid_axon'] = mid_ax
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
else:
# This includes Node, since nodes can't be targets on-chip
raise NotImplementedError()
model.params[conn] = BuiltConnection(
eval_points=eval_points,
solver_info=solver_info,
transform=transform,
weights=weights)
def build_conv2d_connection(model, conn):
if nengo_transforms is None:
# It should not be possible to reach this, because this function is
# only called for a Convolution transform, which can exist only if
# nengo_transforms exists.
raise NotImplementedError("Convolution requires newer Nengo")
if conn.transform.dimensions != 2:
raise NotImplementedError("nengo-loihi only supports 2D convolution")
if conn.transform.padding != "valid":
raise NotImplementedError(
"nengo-loihi only supports convolution with 'valid' padding")
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
pre_cx = model.objs[conn.pre_obj]['out']
post_cx = model.objs[conn.post_obj]['in']
assert isinstance(pre_cx, (LoihiInput, LoihiBlock))
assert isinstance(post_cx, LoihiBlock)
tau_s = 0.0
if isinstance(conn.synapse, nengo.synapses.Lowpass):
tau_s = conn.synapse.tau
elif conn.synapse is not None:
raise NotImplementedError("Cannot handle non-Lowpass synapses")
# --- pre
assert isinstance(conn.pre_obj, (Neurons, ChipReceiveNeurons))
assert conn.pre_slice == slice(None)
assert isinstance(conn.transform, nengo_transforms.Convolution)
weights = conn.transform.sample(rng=rng)
input_shape = conn.transform.input_shape
# Account for nengo spike height of 1/dt
weights = weights / model.dt
if isinstance(conn.pre_obj, ChipReceiveNeurons):
neuron_type = conn.pre_obj.neuron_type
elif isinstance(conn.pre_obj, Neurons):
neuron_type = conn.pre_obj.ensemble.neuron_type
if neuron_type is not None and hasattr(neuron_type, 'amplitude'):
weights = weights * neuron_type.amplitude
# --- post
assert isinstance(conn.post_obj, Neurons)
assert conn.post_slice == slice(None)
gain = model.params[conn.post_obj.ensemble].gain
if not np.all(gain == gain[0]):
# TODO: support this?
raise ValidationError(
"All neurons targeted by a Convolution connection must "
"have the same gain",
"gain",
obj=conn.post_obj.ensemble)
weights = weights * gain[0]
pop_type = 32 # TODO: pick this
new_transform = copy.copy(conn.transform)
type(new_transform).init.data[new_transform] = weights
weights, indices, axon_to_weight_map, cx_bases = conv2d_loihi_weights(
new_transform)
synapse = Synapse(np.prod(input_shape.spatial_shape),
label="conv2d_weights")
synapse.set_population_weights(weights,
indices,
axon_to_weight_map,
cx_bases,
pop_type=pop_type)
post_cx.add_synapse(synapse)
model.objs[conn]['weights'] = synapse
ax = Axon(np.prod(input_shape.spatial_shape), label="conv2d_weights")
ax.target = synapse
ax.cx_to_axon_map = pixel_idxs(input_shape)
ax.cx_atoms = channel_idxs(input_shape)
pre_cx.add_axon(ax)
post_cx.compartment.configure_filter(tau_s, dt=model.dt)
model.params[conn] = BuiltConnection(eval_points=None,
solver_info=None,
transform=None,
weights=weights)
def test_pop_tiny(pop_type, channels_last, nc, request, plt, seed, allclose):
tau_rc = 0.02
tau_ref = 0.001
tau_s = 0.0
dt = 0.001
neuron_bias = 1.
pres_time = 0.4
sti, stj = 1, 1
if nc == 1:
filters = np.array([[-0.5, 2., -0.25], [-0.75, 2., -1.0],
[-0.5, 3., -0.5], [-1.0, 6.,
-0.25]]).reshape(1, 4, 1, 3)
inp_biases = np.array([[1, 5, 1], [2, 1, 2]])
inp_biases = inp_biases[:, :, None]
elif nc == 2:
filters = np.array([[[-0.5, 2., -0.2], [-0.7, 2., -1.0],
[-0.5, 3., -0.5], [-1.0, 6., -0.2]],
[[-1.0, 2., -1.0], [-0.5, 2., -0.5],
[-0.8, 3., -0.2], [-1.0, 4.,
-0.2]]]).reshape(2, 4, 1, 3)
inp_biases = np.array([[[1, 5, 1], [2, 1, 2]], [[0, 3, 1], [4, 2, 1]]])
inp_biases = np.transpose(inp_biases, (1, 2, 0))
# rearrange to (kernel_rows, kernel_cols, in_channels, out_channels)
filters = np.transpose(filters, (2, 3, 0, 1))
inp_biases = inp_biases / (inp_biases.max() + 0.001)
# --- compute nengo_loihi outputs
ni, nj, nk = inp_biases.shape
si, sj, nc, nf = filters.shape
nij = ni * nj
nyi = 1 + (ni - si) // sti
nyj = 1 + (nj - sj) // stj
out_size = nyi * nyj * nf
assert out_size <= 1024
model = Model()
# input block
inp = LoihiBlock(ni * nj * nk, label='inp')
assert inp.n_neurons <= 1024
inp.compartment.configure_relu()
inp.compartment.bias[:] = inp_biases.ravel()
inp_ax = Axon(nij, label='inp_ax')
# we always compute the pixel/channel idxs with channels_last=True
# (not sure why?), and then set it to the correct value afterwards
inp_shape = nengo_transforms.ChannelShape((ni, nj, nk), channels_last=True)
inp_ax.set_compartment_axon_map(target_axons=conv.pixel_idxs(inp_shape),
atoms=conv.channel_idxs(inp_shape))
inp_shape.shape = (ni, nj, nk) if channels_last else (nk, ni, nj)
inp_shape.channels_last = channels_last
inp.add_axon(inp_ax)
model.add_block(inp)
# conv block
neurons = LoihiBlock(out_size, label='neurons')
assert neurons.n_neurons <= 1024
neurons.compartment.configure_lif(tau_rc=tau_rc, tau_ref=tau_ref, dt=dt)
neurons.compartment.configure_filter(tau_s, dt=dt)
neurons.compartment.bias[:] = neuron_bias
synapse = Synapse(np.prod(inp_shape.spatial_shape), label='synapse')
conv2d_transform = nengo_transforms.Convolution(
nf,
inp_shape,
strides=(sti, stj),
channels_last=channels_last,
init=filters,
kernel_size=(1, 3))
weights, indices, axon_to_weight_map, bases = conv.conv2d_loihi_weights(
conv2d_transform)
synapse.set_population_weights(weights,
indices,
axon_to_weight_map,
bases,
pop_type=pop_type)
neurons.add_synapse(synapse)
out_probe = Probe(target=neurons, key='spiked')
neurons.add_probe(out_probe)
inp_ax.target = synapse
model.add_block(neurons)
# simulation
discretize_model(model)
n_steps = int(pres_time / dt)
target = request.config.getoption("--target")
if target == 'loihi':
with HardwareInterface(model, use_snips=False, seed=seed) as sim:
sim.run_steps(n_steps)
sim_out = sim.get_probe_output(out_probe)
else:
with EmulatorInterface(model, seed=seed) as sim:
sim.run_steps(n_steps)
sim_out = sim.get_probe_output(out_probe)
sim_out = np.sum(sim_out, axis=0) * (dt / pres_time)
if channels_last:
sim_out.shape = (nyi, nyj, nf)
sim_out = np.transpose(sim_out, (2, 0, 1))
else:
sim_out.shape = (nf, nyi, nyj)
out_max = sim_out.max()
# --- plot results
rows = 1
cols = 2
ax = plt.subplot(rows, cols, 1)
plt.hist(sim_out.ravel(), bins=11)
ax = plt.subplot(rows, cols, 2)
tile(sim_out, vmin=0, vmax=out_max, grid=True, ax=ax)
# ref_out determined by emulator running code known to work
if nc == 1:
ref_out = np.array([[0.06, 0.02], [0.055, 0.], [0.0825, 0.0225],
[0.125, 0.04]])
elif nc == 2:
ref_out = np.array([[0.0975, 0.02], [0.0825, 0.02], [0.125, 0.055],
[0.2475, 0.0825]])
assert allclose(sim_out[:, :, 0], ref_out, rtol=0, atol=1e-7)
def test_conv2d_weights(channels_last, hw_opts, request, plt, seed, rng,
allclose):
def loihi_rates_n(neuron_type, x, gain, bias, dt):
"""Compute Loihi rates on higher dimensional inputs"""
y = x.reshape(-1, x.shape[-1])
gain = np.asarray(gain)
bias = np.asarray(bias)
if gain.ndim == 0:
gain = gain * np.ones(x.shape[-1])
if bias.ndim == 0:
bias = bias * np.ones(x.shape[-1])
rates = loihi_rates(neuron_type, y, gain, bias, dt)
return rates.reshape(*x.shape)
if channels_last:
plt.saveas = None
pytest.xfail("Blocked by CxBase cannot be > 256 bug")
target = request.config.getoption("--target")
if target != 'loihi' and len(hw_opts) > 0:
pytest.skip("Hardware options only available on hardware")
pop_type = 32
# load data
with open(os.path.join(test_dir, 'mnist10.pkl'), 'rb') as f:
test10 = pickle.load(f)
test_x = test10[0][0].reshape(28, 28)
test_x = test_x[3:24, 3:24]
test_x = 1.999 * test_x - 0.999
filters = Gabor(freq=Uniform(0.5, 1)).generate(8, (7, 7), rng=rng)
sti, stj = 2, 2
tau_rc = 0.02
tau_ref = 0.002
tau_s = 0.005
dt = 0.001
encode_type = nengo.SpikingRectifiedLinear()
encode_gain = 1. / dt
encode_bias = 0.
neuron_type = nengo.LIF(tau_rc=tau_rc, tau_ref=tau_ref)
neuron_gain = 1.
neuron_bias = 1.
pres_time = 0.2
# --- compute ideal outputs
def conv_pm(x, kernel):
y0 = scipy.signal.correlate2d(x[0], kernel, mode='valid')[::sti, ::stj]
y1 = scipy.signal.correlate2d(x[1], kernel, mode='valid')[::sti, ::stj]
return [y0, -y1]
ref_out = np.array([test_x, -test_x])
ref_out = loihi_rates_n(encode_type, ref_out, encode_gain, encode_bias, dt)
ref_out = ref_out / encode_gain
ref_out = np.array([conv_pm(ref_out, kernel) for kernel in filters])
ref_out = ref_out.sum(axis=1) # sum positive and negative parts
ref_out = loihi_rates_n(neuron_type, ref_out, neuron_gain, neuron_bias, dt)
# --- compute nengo_loihi outputs
inp_biases = np.stack([test_x, -test_x], axis=-1 if channels_last else 0)
inp_shape = nengo_transforms.ChannelShape(inp_biases.shape,
channels_last=channels_last)
kernel = np.array([filters, -filters]) # two channels, pos and neg
kernel = np.transpose(kernel, (2, 3, 0, 1))
conv2d_transform = nengo_transforms.Convolution(
8,
inp_shape,
strides=(sti, stj),
channels_last=channels_last,
kernel_size=(7, 7),
init=kernel)
out_size = ref_out.size
nf, nyi, nyj = ref_out.shape
assert out_size <= 1024
model = Model()
# input block
inp = LoihiBlock(inp_shape.size, label='inp')
assert inp.n_neurons <= 1024
inp.compartment.configure_relu()
inp.compartment.bias[:] = inp_biases.ravel()
inp_ax = Axon(np.prod(inp_shape.spatial_shape), label='inp_ax')
inp_ax.set_compartment_axon_map(target_axons=conv.pixel_idxs(inp_shape),
atoms=conv.channel_idxs(inp_shape))
inp.add_axon(inp_ax)
model.add_block(inp)
# conv block
neurons = LoihiBlock(out_size, label='neurons')
assert neurons.n_neurons <= 1024
neurons.compartment.configure_lif(tau_rc=tau_rc, tau_ref=tau_ref, dt=dt)
neurons.compartment.configure_filter(tau_s, dt=dt)
neurons.compartment.bias[:] = neuron_bias
synapse = Synapse(np.prod(inp_shape.spatial_shape), label='synapse')
weights, indices, axon_to_weight_map, bases = conv.conv2d_loihi_weights(
conv2d_transform)
synapse.set_population_weights(weights,
indices,
axon_to_weight_map,
bases,
pop_type=pop_type)
neurons.add_synapse(synapse)
out_probe = Probe(target=neurons, key='spiked')
neurons.add_probe(out_probe)
inp_ax.target = synapse
model.add_block(neurons)
# simulation
discretize_model(model)
n_steps = int(pres_time / dt)
if target == 'loihi':
with HardwareInterface(model, use_snips=False, seed=seed,
**hw_opts) as sim:
sim.run_steps(n_steps)
sim_out = sim.get_probe_output(out_probe)
else:
with EmulatorInterface(model, seed=seed) as sim:
sim.run_steps(n_steps)
sim_out = sim.get_probe_output(out_probe)
sim_out = np.sum(sim_out, axis=0) / pres_time
if channels_last:
sim_out.shape = (nyi, nyj, nf)
sim_out = np.transpose(sim_out, (2, 0, 1))
else:
sim_out.shape = (nf, nyi, nyj)
out_max = max(ref_out.max(), sim_out.max())
# --- plot results
rows = 2
cols = 2
ax = plt.subplot(rows, cols, 1)
tile(filters, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 2)
tile(ref_out, vmin=0, vmax=out_max, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 3)
plt.hist(ref_out.ravel(), bins=31)
plt.hist(sim_out.ravel(), bins=31)
ax = plt.subplot(rows, cols, 4)
# tile(sim_out, vmin=0, vmax=1, cols=8, ax=ax)
tile(sim_out, vmin=0, vmax=out_max, cols=8, ax=ax)
assert allclose(sim_out, ref_out, atol=10, rtol=1e-3)
def test_population_input(request, allclose):
target = request.config.getoption("--target")
dt = 0.001
n_inputs = 3
n_axons = 1
n_cx = 2
steps = 6
spike_times_inds = [(1, [0]), (3, [1]), (5, [2])]
model = Model()
input = SpikeInput(n_inputs)
model.add_input(input)
spikes = [(input, ti, inds) for ti, inds in spike_times_inds]
input_axon = Axon(n_axons)
axon_map = np.zeros(n_inputs, dtype=int)
atoms = np.arange(n_inputs)
input_axon.set_axon_map(axon_map, atoms)
input.add_axon(input_axon)
block = LoihiBlock(n_cx)
block.compartment.configure_lif(tau_rc=0., tau_ref=0., dt=dt)
block.compartment.configure_filter(0, dt=dt)
model.add_block(block)
synapse = Synapse(n_axons)
weights = 0.1 * np.array([[[1, 2], [2, 3], [4, 5]]], dtype=float)
indices = np.array([[[0, 1], [0, 1], [0, 1]]], dtype=int)
axon_to_weight_map = np.zeros(n_axons, dtype=int)
cx_bases = np.zeros(n_axons, dtype=int)
synapse.set_population_weights(weights,
indices,
axon_to_weight_map,
cx_bases,
pop_type=32)
block.add_synapse(synapse)
input_axon.target = synapse
probe = Probe(target=block, key='voltage')
block.add_probe(probe)
discretize_model(model)
if target == 'loihi':
with HardwareInterface(model, use_snips=True) as sim:
sim.run_steps(steps, blocking=False)
for ti in range(1, steps + 1):
spikes_i = [spike for spike in spikes if spike[1] == ti]
sim.host2chip(spikes=spikes_i, errors=[])
sim.chip2host(probes_receivers={})
y = sim.get_probe_output(probe)
else:
for inp, ti, inds in spikes:
inp.add_spikes(ti, inds)
with EmulatorInterface(model) as sim:
sim.run_steps(steps)
y = sim.get_probe_output(probe)
vth = block.compartment.vth[0]
assert (block.compartment.vth == vth).all()
z = y / vth
assert allclose(z[[1, 3, 5]], weights[0], atol=4e-2, rtol=0)
def build_conv2d_connection(model, transform, conn):
assert is_transform_type(transform, ("Convolution", "ConvolutionTranspose"))
if transform.dimensions != 2:
raise NotImplementedError("nengo-loihi only supports 2D convolution")
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
pre_obj = model.objs[conn.pre_obj]["out"]
post_obj = model.objs[conn.post_obj]["in"]
assert isinstance(pre_obj, (LoihiInput, LoihiBlock))
assert isinstance(post_obj, LoihiBlock)
tau_s = 0.0
if isinstance(conn.synapse, nengo.synapses.Lowpass):
tau_s = conn.synapse.tau
elif conn.synapse is not None:
raise NotImplementedError("Cannot handle non-Lowpass synapses")
# --- pre
assert isinstance(conn.pre_obj, (Neurons, ChipReceiveNeurons))
kernel = transform.sample(rng=rng)
input_shape = transform.input_shape
# Account for nengo spike height of 1/dt
kernel = kernel / model.dt
if isinstance(conn.pre_obj, ChipReceiveNeurons):
neuron_type = conn.pre_obj.neuron_type
elif isinstance(conn.pre_obj, Neurons):
neuron_type = conn.pre_obj.ensemble.neuron_type
if neuron_type is not None and hasattr(neuron_type, "amplitude"):
kernel = kernel * neuron_type.amplitude
# --- post
assert isinstance(conn.post_obj, Neurons)
assert conn.post_slice == slice(None)
gain = model.params[conn.post_obj.ensemble].gain
if not np.all(gain == gain[0]):
# Cannot fold gains into kernel, result would not be convolutional.
# Therefore, Loihi does not support this if we want to share weights.
raise ValidationError(
"All neurons targeted by a Convolution connection must "
"have the same gain",
"gain",
obj=conn.post_obj.ensemble,
)
kernel = kernel * gain[0]
kernel = kernel.astype(nengo.rc.float_dtype)
pop_type = model.config[conn].pop_type
new_transform = copy.copy(transform)
type(new_transform).init.data[new_transform] = kernel
weights, indices, axon_to_weight_map, offsets = conv2d_loihi_weights(new_transform)
synapse = Synapse(np.prod(input_shape.spatial_shape), label="conv2d_weights")
synapse.set_population_weights(
weights, indices, axon_to_weight_map, offsets, pop_type=pop_type
)
post_obj.add_synapse(synapse)
model.objs[conn]["weights"] = synapse
if synapse.atom_bits_extra() > 0:
warnings.warn(
"Using more than 32 'populations' (e.g. convolutional filters) with "
"`pop_type=16` axons has not yet been implemented in NxSDK. This feature "
"is therefore emulator-only."
)
target_axons = -np.ones(pre_obj.n_neurons, dtype=np.int32)
target_axons[conn.pre_slice] = pixel_idxs(input_shape)
atoms = np.zeros(pre_obj.n_neurons, dtype=np.int32)
atoms[conn.pre_slice] = channel_idxs(input_shape)
ax = Axon(np.prod(input_shape.spatial_shape), label="conv2d_weights")
ax.target = synapse
ax.set_compartment_axon_map(target_axons, atoms=atoms)
pre_obj.add_axon(ax)
post_obj.compartment.configure_filter(tau_s, dt=model.dt)
model.params[conn] = BuiltConnection(
eval_points=None, solver_info=None, transform=None, weights=kernel
)
def build_full_chip_connection(model, conn): # noqa: C901
"""Build dense or sparse connections on-chip"""
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
pre_obj = model.objs[conn.pre_obj]["out"]
post_obj = model.objs[conn.post_obj]["in"]
assert isinstance(pre_obj, (LoihiBlock, LoihiInput))
assert isinstance(post_obj, (LoihiBlock, LoihiProbe))
weights = None
eval_points = None
solver_info = None
neuron_type = None
pre_slice = conn.pre_slice
post_slice = conn.post_slice
# sample transform (if using a distribution), transform shape (out, in)
transform = sample_transform(conn, rng=rng)
tau_s = 0.0 # `synapse is None` gets mapped to `tau_s = 0.0`
if isinstance(conn.synapse, nengo.synapses.Lowpass):
tau_s = conn.synapse.tau
elif conn.synapse is not None:
raise NotImplementedError("Cannot handle non-Lowpass synapses")
needs_decode_neurons = False
target_encoders = None
is_chip_process = isinstance(conn.pre_obj, Node) and isinstance(
conn.pre_obj.output, ChipProcess
)
if isinstance(conn.pre_obj, Node) and not (
isinstance(conn.pre_obj, ChipReceiveNeurons) or is_chip_process
):
assert conn.pre_slice == slice(None)
weights = expand_matrix(transform, shape=(conn.post.size_in, conn.pre.size_out))
# input is on-off neuron encoded, so double/flip transform
weights = stack_matrices([weights, scale_matrix(weights, -1)], order="h")
target_encoders = "node_encoders"
elif isinstance(conn.pre_obj, Ensemble) and isinstance(
conn.pre_obj.neuron_type, nengo.Direct
):
raise NotImplementedError()
elif isinstance(conn.pre_obj, Ensemble): # Normal decoded connection
if isinstance(transform, scipy.sparse.spmatrix):
raise BuildError(
"Applying a sparse transform to a decoded connection is not supported"
)
eval_points, decoders, solver_info = model.build(
conn.solver, conn, rng, transform
)
pre_slice = slice(None) # taken care of in decoders
if conn.solver.weights and not conn.solver.compositional:
weights = decoders
else:
weights = multiply(transform, decoders)
# the decoder solver assumes a spike height of 1/dt; that isn't the
# case on loihi, so we need to undo that scaling
weights = scale_matrix(weights, 1.0 / model.dt)
neuron_type = conn.pre_obj.neuron_type
if conn.solver.weights:
# weight solvers only allowed on ensemble->ensemble connections
assert isinstance(conn.post_obj, Ensemble)
if conn.solver.compositional:
encoders = model.params[conn.post_obj].scaled_encoders
weights = multiply(encoders[:, post_slice], weights)
# post slice already applied to encoders (either here or in
# `build_decoders`), so don't apply later
post_slice = slice(None)
else:
needs_decode_neurons = True
elif isinstance(conn.pre_obj, (Neurons, ChipReceiveNeurons)) or is_chip_process:
weights = expand_matrix(transform, shape=(conn.post.size_in, conn.pre.size_out))
weights = scale_matrix(weights, 1.0 / model.dt)
neuron_type = (
None
if is_chip_process
else conn.pre_obj.neuron_type
if isinstance(conn.pre_obj, ChipReceiveNeurons)
else conn.pre_obj.ensemble.neuron_type
)
if isinstance(conn.post_obj, Ensemble):
needs_decode_neurons = True
else:
raise NotImplementedError("Connection from type %r" % (type(conn.pre_obj),))
if neuron_type is not None and hasattr(neuron_type, "amplitude"):
weights = scale_matrix(weights, neuron_type.amplitude)
# to proper dtype
transform = transform.astype(nengo.rc.float_dtype)
weights = weights.astype(nengo.rc.float_dtype)
# loihi_weights has shape (in, out), to match the shape by block.Synapses
loihi_weights = weights.T
mid_obj = pre_obj
mid_axon_inds = None
post_tau = tau_s
if needs_decode_neurons and not isinstance(conn.post_obj, Neurons):
# --- add decode neurons
assert weights.ndim == 2
n, d = loihi_weights.shape
if isinstance(post_obj, LoihiProbe):
# use non-spiking decode neurons for voltage probing
assert len(post_obj.target) == 0 or post_obj.target == [None]
assert post_slice == slice(None)
# use the same scaling as the ensemble does, to get good
# decodes. Note that this assumes that the decoded value
# is in the range -radius to radius, which is usually true.
gain = np.array(1.0 / conn.pre_obj.radius, dtype=nengo.rc.float_dtype)
decoder_block = LoihiBlock(2 * d, label="%s" % conn)
decoder_block.compartment.configure_nonspiking(
dt=model.dt, vth=model.vth_nonspiking
)
decoder_block.compartment.bias[:] = 0
dec_syn = Synapse(n, label="probe_decoders")
weights2 = stack_matrices(
[scale_matrix(loihi_weights, gain), scale_matrix(loihi_weights, -gain)],
order="h",
)
dec_syn.set_weights(weights2)
decoder_block.add_synapse(dec_syn)
else:
# use spiking decode neurons for on-chip connection
if isinstance(conn.post_obj, Ensemble):
# loihi encoders don't include radius, so handle scaling here
gain = np.array(1.0 / conn.post_obj.radius, dtype=nengo.rc.float_dtype)
loihi_weights = scale_matrix(loihi_weights, gain)
post_d = conn.post_obj.size_in
post_inds = np.arange(post_d, dtype=np.int32)[post_slice]
assert loihi_weights.shape[1] == len(post_inds) == conn.size_out
mid_axon_inds = model.decode_neurons.get_post_inds(post_inds, post_d)
target_encoders = "decode_neuron_encoders"
decoder_block, dec_syn = model.decode_neurons.get_block(
loihi_weights, block_label="%s" % conn, syn_label="decoders"
)
model.add_block(decoder_block)
model.objs[conn]["decoded"] = decoder_block
model.objs[conn]["decoders"] = dec_syn
model.connection_decode_neurons[conn] = decoder_block
# use tau_s for filter into decode neurons, decode_tau for filter out
decoder_block.compartment.configure_filter(tau_s, dt=model.dt)
post_tau = model.decode_tau
target_axons = -np.ones(pre_obj.n_neurons, dtype=np.int32)
target_axons[pre_slice] = np.arange(target_axons[pre_slice].size)
pre_slice = slice(None)
dec_ax0 = Axon(n, label="decoders")
dec_ax0.target = dec_syn
dec_ax0.set_compartment_axon_map(target_axons)
pre_obj.add_axon(dec_ax0)
model.objs[conn]["decode_axon"] = dec_ax0
loihi_weights = None # weights have now been handled
if conn.learning_rule_type is not None:
rule_type = conn.learning_rule_type
if isinstance(rule_type, nengo.PES):
if not isinstance(rule_type.pre_synapse, nengo.synapses.Lowpass):
raise ValidationError(
"Loihi only supports `Lowpass` pre-synapses for learning rules",
attr="pre_synapse",
obj=rule_type,
)
pre_tau = rule_type.pre_synapse.tau
float_tracing_tau = pre_tau / model.dt
tracing_tau = int(round(float_tracing_tau))
if not np.allclose(float_tracing_tau, tracing_tau):
warnings.warn(
f"PES learning rule `pre_synapse.tau` ({pre_tau}) is not an "
f"integer multiple of `dt` ({model.dt}). Rounding."
)
# Nengo builder scales PES learning rate by `dt / n_neurons`
n_neurons = (
conn.pre_obj.n_neurons
if isinstance(conn.pre_obj, Ensemble)
else conn.pre_obj.size_in
)
learning_rate = rule_type.learning_rate * model.dt / n_neurons
# Account for scaling to put integer error in range [-127, 127]
learning_rate /= model.pes_error_scale
# Tracing mag set so that the magnitude of the pre trace
# is independent of the pre tau. `dt` factor accounts for
# Nengo's `dt` spike scaling. Where is the second `dt` from?
# Maybe the fact that post decode neurons have `vth = 1/dt`?
tracing_mag = -np.expm1(-1.0 / tracing_tau) / model.dt**2
# learning weight exponent controls the maximum weight
# magnitude/weight resolution
wgt_exp = model.pes_wgt_exp
dec_syn.set_learning(
learning_rate=learning_rate,
tracing_mag=tracing_mag,
tracing_tau=tracing_tau,
wgt_exp=wgt_exp,
)
else:
raise NotImplementedError()
mid_obj = decoder_block
if isinstance(post_obj, LoihiProbe):
assert post_obj.target == [None]
assert post_slice == slice(None)
post_obj.target[0] = mid_obj
model.add_probe(post_obj)
elif isinstance(conn.post_obj, Neurons):
assert isinstance(post_obj, LoihiBlock)
assert post_slice == slice(None)
if loihi_weights is None:
raise NotImplementedError("Need weights for connection to neurons")
assert loihi_weights.ndim == 2
n1, n2 = loihi_weights.shape
assert post_obj.n_neurons == n2
syn = Synapse(n1, label="neuron_weights")
gain = model.params[conn.post_obj.ensemble].gain
loihi_weights = scale_matrix(loihi_weights, gain)
syn.set_weights(loihi_weights)
post_obj.add_synapse(syn)
model.objs[conn]["weights"] = syn
target_axons = -np.ones(mid_obj.n_neurons, dtype=np.int32)
target_axons[pre_slice] = np.arange(target_axons[pre_slice].size)
assert target_axons[pre_slice].size == n1
ax = Axon(mid_obj.n_neurons, label="neuron_weights")
ax.target = syn
ax.set_compartment_axon_map(target_axons)
mid_obj.add_axon(ax)
post_obj.compartment.configure_filter(post_tau, dt=model.dt)
if conn.learning_rule_type is not None:
raise NotImplementedError()
elif isinstance(conn.post_obj, Ensemble) and conn.solver.weights:
assert isinstance(post_obj, LoihiBlock)
assert pre_slice == slice(None), "Not implemented"
assert post_slice == slice(None)
assert loihi_weights.ndim == 2
n1, n2 = loihi_weights.shape
assert post_obj.n_neurons == n2
# loihi encoders don't include radius, so handle scaling here
scale = np.array(1.0 / conn.post_obj.radius, dtype=nengo.rc.float_dtype)
loihi_weights = scale_matrix(loihi_weights, scale)
syn = Synapse(n1, label="%s::decoder_weights" % conn)
syn.set_weights(loihi_weights)
post_obj.add_synapse(syn)
model.objs[conn]["weights"] = syn
ax = Axon(n1, label="decoder_weights")
ax.target = syn
mid_obj.add_axon(ax)
post_obj.compartment.configure_filter(post_tau, dt=model.dt)
if conn.learning_rule_type is not None:
raise NotImplementedError()
elif isinstance(conn.post_obj, Ensemble):
assert isinstance(post_obj, LoihiBlock)
assert pre_slice == slice(None), "Not implemented"
assert post_slice == slice(None)
assert target_encoders is not None
if target_encoders not in post_obj.named_synapses:
build_decode_neuron_encoders(model, conn.post_obj, kind=target_encoders)
mid_ax = Axon(mid_obj.n_neurons, label="encoders")
mid_ax.target = post_obj.named_synapses[target_encoders]
mid_ax.set_compartment_axon_map(mid_axon_inds)
mid_obj.add_axon(mid_ax)
model.objs[conn]["mid_axon"] = mid_ax
post_obj.compartment.configure_filter(post_tau, dt=model.dt)
else:
# This includes Node, since nodes can't be targets on-chip
raise NotImplementedError()
model.params[conn] = BuiltConnection(
eval_points=eval_points,
solver_info=solver_info,
transform=transform, # sampled transform
weights=weights, # scaled weights (including decoders)
)
def split_axon(old_axon, old_axon_idxs, old_atoms, new_synapses):
"""Split one old axon into multiple axons, each going to one of the new synapses.
Parameters
----------
old_axon : Axon
The old axon that we want to split.
old_axon_idxs : list
The indices of the target synapse for each axon. If the old block owning the
old axon has been split, these indices should relate to the compartments in
the new block that will own the new axon.
old_atoms : list
The atoms for each of the old axons.
new_synapses : dict {Synapse: list of int}
Map from new synapses to axon ids of the old synapse that the new synapse needs.
Returns
-------
new_axons : list of Axon
A list of the new axons.
"""
if new_synapses is None:
# This indicates that the target synapses have not changed. However, the block
# that owned the old axon could have split, in which case we need to make sure
# the new axon uses just the axon indices and atoms pertaining to the
# compartments in the new block.
new_axon_idxs = old_axon_idxs
assert all(i is not None for i in new_axon_idxs)
new_axon_idxs = np.asarray(new_axon_idxs)
n_axons = np.unique(new_axon_idxs[new_axon_idxs >= 0]).size
new_axon = Axon(n_axons)
new_axon.label = old_axon.label
new_axon.target = old_axon.target
new_axon.compartment_map = new_axon_idxs
new_axon.compartment_atoms = np.asarray(old_atoms)
return [new_axon]
# turn the one old axon going to one old synapse, into one new axon per new synapse
new_axons = []
for k, (new_synapse,
old_synapse_axon_ids) in enumerate(new_synapses.items()):
# `synapse_axon_map` maps old axons to new axons
new_synapse_axon_idxs = range(len(old_synapse_axon_ids))
synapse_axon_map = dict(
zip(old_synapse_axon_ids, new_synapse_axon_idxs))
# `new_axon_idxs` are the new synapse indices that the new axon connects to
new_axon_idxs = [synapse_axon_map.get(i, -1) for i in old_axon_idxs]
assert all(i is not None for i in new_axon_idxs)
new_axon_idxs = np.asarray(new_axon_idxs)
n_axons = np.unique(new_axon_idxs[new_axon_idxs >= 0]).size
if n_axons == 0:
continue
new_axon = Axon(n_axons)
if old_axon.label is not None:
new_axon.label = "%s[%d]" % (old_axon.label, k)
new_axon.target = new_synapse
new_axon.compartment_map = new_axon_idxs
new_axon.compartment_atoms = np.asarray(old_atoms)
new_axons.append(new_axon)
return new_axons