def test_conv_split(Simulator, rng, plt, allclose):
channels_last = False
# load data
with open(os.path.join(test_dir, 'mnist10.pkl'), 'rb') as f:
test10 = pickle.load(f)
input_shape = nengo_transforms.ChannelShape((1, 28, 28),
channels_last=channels_last)
n_filters = 8
kernel_size = (7, 7)
kernel = Gabor(freq=Uniform(0.5, 1)).generate(n_filters,
kernel_size,
rng=rng)
kernel = kernel[None, :, :, :] # single channel
kernel = np.transpose(kernel, (2, 3, 0, 1))
strides = (2, 2)
seed = 3 # fix seed to do the same computation for both channel positions
with nengo.Network(seed=seed) as net:
nengo_loihi.add_params(net)
a = nengo.Node(test10[0][0].ravel())
# --- make population to turn image into spikes
nc = 1
in_kernel = np.array([1.]).reshape((1, 1, 1, nc))
transform = nengo_transforms.Convolution(1,
input_shape,
kernel_size=(1, 1),
init=in_kernel,
channels_last=channels_last)
b = nengo.Ensemble(transform.output_shape.size,
1,
neuron_type=nengo.SpikingRectifiedLinear(),
max_rates=nengo.dists.Choice([50]),
intercepts=nengo.dists.Choice([0]))
net.config[b].on_chip = False
nengo.Connection(a, b.neurons, transform=transform)
in_shape = transform.output_shape
transform = nengo_transforms.Convolution(n_filters,
in_shape,
kernel_size=kernel_size,
strides=strides,
init=kernel,
channels_last=channels_last)
out_shape = transform.output_shape
split_slices = conv.split_channels(out_shape,
max_size=1024,
max_channels=4)
# --- make convolution population, split across ensembles
cc = []
cp = []
out_shapes = []
xslice = conv.ImageSlice(in_shape)
for yslice in split_slices:
transform_xy = conv.split_transform(transform, xslice, yslice)
out_shapes.append(transform_xy.output_shape)
c = nengo.Ensemble(transform_xy.output_shape.size,
1,
neuron_type=nengo.LIF(),
max_rates=nengo.dists.Choice([15]),
intercepts=nengo.dists.Choice([0]))
nengo.Connection(b.neurons, c.neurons, transform=transform_xy)
cc.append(c)
cp.append(nengo.Probe(c.neurons))
simtime = 0.3
with nengo.Simulator(net, optimize=False) as sim_nengo:
sim_nengo.run(simtime)
hw_opts = dict(snip_max_spikes_per_step=100)
with Simulator(net, seed=seed, hardware_options=hw_opts) as sim_loihi:
sim_loihi.run(simtime)
nengo_out = []
loihi_out = []
for p, out_shape_i in zip(cp, out_shapes):
nengo_out.append(
(sim_nengo.data[p] > 0).sum(axis=0).reshape(out_shape_i.shape))
loihi_out.append(
(sim_loihi.data[p] > 0).sum(axis=0).reshape(out_shape_i.shape))
if channels_last:
nengo_out = np.concatenate(nengo_out, axis=2)
loihi_out = np.concatenate(loihi_out, axis=2)
# put channels first to display them separately
nengo_out = np.transpose(nengo_out, (2, 0, 1))
loihi_out = np.transpose(loihi_out, (2, 0, 1))
else:
nengo_out = np.concatenate(nengo_out, axis=0)
loihi_out = np.concatenate(loihi_out, axis=0)
out_max = np.maximum(nengo_out.max(), loihi_out.max())
# --- plot results
rows = 2
cols = 3
ax = plt.subplot(rows, cols, 1)
imshow(test10[0][0].reshape((28, 28)), vmin=0, vmax=1, ax=ax)
ax = plt.subplot(rows, cols, 2)
tile(np.transpose(kernel[0], (2, 0, 1)), cols=8, ax=ax)
ax = plt.subplot(rows, cols, 3)
plt.hist(nengo_out.ravel(), bins=31)
plt.hist(loihi_out.ravel(), bins=31)
ax = plt.subplot(rows, cols, 4)
tile(nengo_out, vmin=0, vmax=out_max, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 6)
tile(loihi_out, vmin=0, vmax=out_max, cols=8, ax=ax)
assert allclose(loihi_out, nengo_out, atol=0.05 * out_max, rtol=0.15)
def test_imshow_color_vlim(plt):
image = wave_image((31, 32), 0.6, color=(1, 1, 0))
image = 2*image - 1
ne_plt.imshow(image, vmin=-1, vmax=1)
def test_conv_connection(channels, channels_last, Simulator, seed, rng, plt,
allclose):
if channels_last:
plt.saveas = None
pytest.xfail("Blocked by CxBase cannot be > 256 bug")
# 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 = 1.999 * test_x - 0.999 # range (-1, 1)
input_shape = nengo_transforms.ChannelShape(
(test_x.shape + (channels, )) if channels_last else
((channels, ) + test_x.shape),
channels_last=channels_last)
filters = Gabor(freq=Uniform(0.5, 1)).generate(8, (7, 7), rng=rng)
filters = filters[None, :, :, :] # single channel
filters = np.transpose(filters, (2, 3, 0, 1))
strides = (2, 2)
tau_rc = 0.02
tau_ref = 0.002
tau_s = 0.005
dt = 0.001
neuron_type = LoihiLIF(tau_rc=tau_rc, tau_ref=tau_ref)
pres_time = 0.1
with nengo.Network(seed=seed) as model:
nengo_loihi.add_params(model)
u = nengo.Node(test_x.ravel(), label="u")
a = nengo.Ensemble(input_shape.size,
1,
neuron_type=LoihiSpikingRectifiedLinear(),
max_rates=nengo.dists.Choice([40 / channels]),
intercepts=nengo.dists.Choice([0]),
label='a')
model.config[a].on_chip = False
if channels == 1:
nengo.Connection(u, a.neurons, transform=1, synapse=None)
elif channels == 2:
# encode image into spikes using two channels (on/off)
if input_shape.channels_last:
nengo.Connection(u, a.neurons[0::2], transform=1, synapse=None)
nengo.Connection(u,
a.neurons[1::2],
transform=-1,
synapse=None)
else:
k = input_shape.spatial_shape[0] * input_shape.spatial_shape[1]
nengo.Connection(u, a.neurons[:k], transform=1, synapse=None)
nengo.Connection(u, a.neurons[k:], transform=-1, synapse=None)
filters = np.concatenate([filters, -filters], axis=2)
else:
raise ValueError("Test not configured for more than two channels")
conv2d_transform = nengo_transforms.Convolution(
8,
input_shape,
strides=strides,
kernel_size=(7, 7),
channels_last=channels_last,
init=filters)
output_shape = conv2d_transform.output_shape
gain, bias = neuron_type.gain_bias(max_rates=100, intercepts=0)
gain = gain * 0.01 # account for `a` max_rates
b = nengo.Ensemble(output_shape.size,
1,
neuron_type=neuron_type,
gain=nengo.dists.Choice([gain[0]]),
bias=nengo.dists.Choice([bias[0]]),
label='b')
nengo.Connection(a.neurons,
b.neurons,
synapse=tau_s,
transform=conv2d_transform)
bp = nengo.Probe(b.neurons)
with nengo.Simulator(model, dt=dt, optimize=False) as sim:
sim.run(pres_time)
ref_out = sim.data[bp].mean(axis=0).reshape(output_shape.shape)
# Currently, non-gpu TensorFlow does not support channels first in conv
use_nengo_dl = HAS_DL and channels_last
ndl_out = np.zeros_like(ref_out)
if use_nengo_dl:
with nengo_dl.Simulator(model, dt=dt) as sim_dl:
sim_dl.run(pres_time)
ndl_out = sim_dl.data[bp].mean(axis=0).reshape(output_shape.shape)
with nengo_loihi.Simulator(model, dt=dt, target='simreal') as sim_real:
sim_real.run(pres_time)
real_out = sim_real.data[bp].mean(axis=0).reshape(output_shape.shape)
with Simulator(model, dt=dt) as sim_loihi:
if "loihi" in sim_loihi.sims:
sim_loihi.sims["loihi"].snip_max_spikes_per_step = 800
sim_loihi.run(pres_time)
sim_out = sim_loihi.data[bp].mean(axis=0).reshape(output_shape.shape)
if not output_shape.channels_last:
ref_out = np.transpose(ref_out, (1, 2, 0))
ndl_out = np.transpose(ndl_out, (1, 2, 0))
real_out = np.transpose(real_out, (1, 2, 0))
sim_out = np.transpose(sim_out, (1, 2, 0))
out_max = max(ref_out.max(), sim_out.max())
# --- plot results
rows = 2
cols = 3
ax = plt.subplot(rows, cols, 1)
imshow(test_x, vmin=0, vmax=1, ax=ax)
ax = plt.subplot(rows, cols, 2)
tile(np.transpose(filters[0], (2, 0, 1)), 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(np.transpose(ref_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 5)
tile(np.transpose(ndl_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 6)
tile(np.transpose(sim_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
if use_nengo_dl:
assert allclose(ndl_out, ref_out, atol=1e-5, rtol=1e-5)
assert allclose(real_out, ref_out, atol=1, rtol=1e-3)
assert allclose(sim_out, ref_out, atol=10, rtol=1e-3)
def test_imshow_gray(plt):
image = wave_image((31, 32), 0.2)
ne_plt.imshow(image)
def test_imshow_color(plt):
image = wave_image((31, 32), 0.6, color=(1, 1, 0))
ne_plt.imshow(image)
def test_conv_connection(channels, channels_last, Simulator, seed, rng, plt,
allclose):
# 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 = 1.999 * test_x - 0.999 # range (-1, 1)
input_shape = make_channel_shape(test_x.shape, channels, channels_last)
filters = Gabor(freq=Uniform(0.5, 1)).generate(8, (7, 7), rng=rng)
filters = filters[None, :, :, :] # single channel
filters = np.transpose(filters, (2, 3, 0, 1))
strides = (2, 2)
tau_rc = 0.02
tau_ref = 0.002
tau_s = 0.005
neuron_type = LoihiLIF(tau_rc=tau_rc, tau_ref=tau_ref)
pres_time = 0.1
with nengo.Network(seed=seed) as model:
nengo_loihi.add_params(model)
u = nengo.Node(test_x.ravel(), label="u")
a = nengo.Ensemble(
input_shape.size,
1,
neuron_type=LoihiSpikingRectifiedLinear(),
max_rates=nengo.dists.Choice([40 / channels]),
intercepts=nengo.dists.Choice([0]),
label="a",
)
model.config[a].on_chip = False
if channels == 1:
nengo.Connection(u, a.neurons, transform=1, synapse=None)
elif channels == 2:
# encode image into spikes using two channels (on/off)
if input_shape.channels_last:
nengo.Connection(u, a.neurons[0::2], transform=1, synapse=None)
nengo.Connection(u,
a.neurons[1::2],
transform=-1,
synapse=None)
else:
k = input_shape.spatial_shape[0] * input_shape.spatial_shape[1]
nengo.Connection(u, a.neurons[:k], transform=1, synapse=None)
nengo.Connection(u, a.neurons[k:], transform=-1, synapse=None)
filters = np.concatenate([filters, -filters], axis=2)
else:
raise ValueError("Test not configured for more than two channels")
conv2d_transform = nengo_transforms.Convolution(
8,
input_shape,
strides=strides,
kernel_size=(7, 7),
channels_last=channels_last,
init=filters,
)
output_shape = conv2d_transform.output_shape
gain, bias = neuron_type.gain_bias(max_rates=100, intercepts=0)
gain = gain * 0.01 # account for `a` max_rates
b = nengo.Ensemble(
output_shape.size,
1,
neuron_type=neuron_type,
gain=nengo.dists.Choice([gain[0]]),
bias=nengo.dists.Choice([bias[0]]),
label="b",
)
nengo.Connection(a.neurons,
b.neurons,
synapse=tau_s,
transform=conv2d_transform)
bp = nengo.Probe(b.neurons)
with nengo.Simulator(model, optimize=False) as sim_nengo:
sim_nengo.run(pres_time)
ref_out = sim_nengo.data[bp].mean(axis=0).reshape(output_shape.shape)
with Simulator(model, target="simreal") as sim_emu:
sim_emu.run(pres_time)
emu_out = sim_emu.data[bp].mean(axis=0).reshape(output_shape.shape)
with Simulator(model, hardware_options={"snip_max_spikes_per_step":
800}) as sim_loihi:
sim_loihi.run(pres_time)
sim_out = sim_loihi.data[bp].mean(axis=0).reshape(output_shape.shape)
if not output_shape.channels_last:
ref_out = np.transpose(ref_out, (1, 2, 0))
emu_out = np.transpose(emu_out, (1, 2, 0))
sim_out = np.transpose(sim_out, (1, 2, 0))
out_max = max(ref_out.max(), emu_out.max(), sim_out.max())
# --- plot results
rows = 2
cols = 3
ax = plt.subplot(rows, cols, 1)
imshow(test_x, vmin=0, vmax=1, ax=ax)
ax = plt.subplot(rows, cols, 2)
tile(np.transpose(filters[0], (2, 0, 1)), 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(np.transpose(ref_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 5)
tile(np.transpose(emu_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 6)
tile(np.transpose(sim_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
assert allclose(emu_out, ref_out, atol=10, rtol=1e-3)
assert allclose(sim_out, ref_out, atol=10, rtol=1e-3)
def test_conv_deepnet(
channels_last,
pop_type,
precompute,
Simulator,
request,
rng,
seed,
plt,
allclose,
):
"""Run a convolutional network with two layers on the chip.
Checks that network with block splitting on the target matches one without
on the emulator.
"""
# TODO: This case fails in NxSDK 0.9.0 but will be fixed in the next version.
# Remove this check once the next version is released.
if pop_type == 32:
pytest.skip("Pop32 multichip test requires latest NxSDK")
def set_partition(partition):
os.environ["PARTITION"] = partition
request.addfinalizer(lambda: set_partition(""))
# multichip pop_type = 16 works only on nahuku32 board currently
if pop_type == 16:
set_partition("nahuku32")
def conv_layer(x,
input_shape,
array_init=None,
label=None,
conn_args=None,
**conv_args):
conn_args = {} if conn_args is None else conn_args
if array_init is not None:
assert all(a not in conv_args
for a in ("init", "kernel_size", "n_filters"))
assert array_init.ndim == 4
conv_args["init"] = array_init
conv_args["kernel_size"] = array_init.shape[:2]
assert array_init.shape[2] == input_shape.n_channels
conv_args["n_filters"] = array_init.shape[3]
conv = nengo.Convolution(input_shape=input_shape, **conv_args)
# add an ensemble to implement the activation function
layer = nengo.Ensemble(conv.output_shape.size, 1, label=label)
# connect up the input object to the new layer
conn = nengo.Connection(x, layer.neurons, transform=conv)
return layer, conv, conn
channels = 1
n_filters0 = 1
n_filters1 = 4
n_filters2 = 4
# 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) # range (0, 1)
input_shape = make_channel_shape(test_x.shape, channels, channels_last)
filters0 = np.ones((1, 1, channels, n_filters0))
# use Gabor filters for first layer
filters1 = Gabor(freq=Uniform(0.5, 1),
sigma_x=Choice([0.9]),
sigma_y=Choice([0.9])).generate(n_filters1, (7, 7),
rng=rng)
assert n_filters0 == 1
filters1 = filters1[None, :, :, :] # single channel
filters1 = np.transpose(filters1,
(2, 3, 0, 1)) # rows, cols, in_chan, out_chan
# use random combinations of first-layer channels in 1x1 convolution
filters2 = rng.uniform(-0.2, 1,
size=(n_filters1, n_filters2)).clip(0, None)
filters2 *= 2 / filters2.sum(axis=0,
keepdims=True) # each filter sums to 2
filters2 = filters2[None, None, :, :] # rows, cols, in_chan, out_chan
tau_s = 0.001
max_rate = 100
amp = 1 / max_rate
f_split = 2 if pop_type == 32 else 4
# use Loihi neuron type so Nengo sim mimics Loihi neuron effects
neuron_type = LoihiSpikingRectifiedLinear(amplitude=amp)
pres_time = 0.2
with nengo.Network(seed=seed) as net:
nengo_loihi.add_params(net)
net.config[nengo.Ensemble].neuron_type = neuron_type
net.config[nengo.Ensemble].max_rates = Choice([max_rate])
net.config[nengo.Ensemble].intercepts = Choice([0])
net.config[nengo.Connection].synapse = tau_s
u = nengo.Node(test_x.ravel(), label="u")
layer0, conv0, conn0 = conv_layer(
u,
input_shape=input_shape,
array_init=filters0,
strides=(1, 1),
channels_last=channels_last,
label="layer0",
conn_args=dict(synapse=None),
)
net.config[layer0].on_chip = False
layer1, conv1, conn1 = conv_layer(
layer0.neurons,
input_shape=conv0.output_shape,
array_init=filters1,
strides=(2, 2),
channels_last=channels_last,
label="layer1",
)
net.config[layer1].block_shape = nengo_loihi.BlockShape(
make_shape((4, 4), f_split, channels_last), conv1)
net.config[conn1].pop_type = pop_type
layer2, conv2, conn2 = conv_layer(
layer1.neurons,
input_shape=conv1.output_shape,
array_init=filters2,
strides=(1, 1),
channels_last=channels_last,
label="layer2",
)
net.config[layer2].block_shape = nengo_loihi.BlockShape(
make_shape((4, 4), f_split, channels_last), conv2)
net.config[conn2].pop_type = pop_type
output_p = nengo.Probe(layer2.neurons)
output_shape = conv2.output_shape
with nengo.Simulator(net, optimize=False) as sim_nengo:
sim_nengo.run(pres_time)
ref_out = (sim_nengo.data[output_p] > 0).sum(axis=0).reshape(
output_shape.shape)
with Simulator(net, target="sim") as sim_emu:
sim_emu.run(pres_time)
emu_out = (sim_emu.data[output_p] > 0).sum(axis=0).reshape(
output_shape.shape)
# TODO: Remove the if condition when configurable timeout parameter
# is available in nxsdk
if (pop_type == 32 or
os.popen("sinfo -h --partition=nahuku32").read().find("idle") > 0):
with Simulator(
net,
precompute=precompute,
hardware_options={
"allocator": RoundRobin(),
"snip_max_spikes_per_step": 800,
},
) as sim_loihi:
sim_loihi.run(pres_time)
sim_out = ((sim_loihi.data[output_p] > 0).sum(axis=0).reshape(
output_shape.shape))
elif nengo_loihi.version.dev is None:
pytest.fail(
"Pop16 multichip test failed since Nahuku32 is unavailable")
else:
pytest.skip(
"Pop16 multichip test skipped since Nahuku32 is unavailable")
out_max = ref_out.max()
ref_out = ref_out / out_max
emu_out = emu_out / out_max
sim_out = sim_out / out_max
if channels_last:
# channels first, to display channels in separate plots
ref_out = np.transpose(ref_out, (2, 0, 1))
emu_out = np.transpose(emu_out, (2, 0, 1))
sim_out = np.transpose(sim_out, (2, 0, 1))
# --- plot results
rows = 2
cols = 3
ax = plt.subplot(rows, cols, 1)
imshow(test_x, vmin=0, vmax=1, ax=ax)
ax = plt.subplot(rows, cols, 2)
tile(np.transpose(filters1, (2, 3, 0, 1))[0],
rows=2,
cols=2,
grid=True,
ax=ax)
ax = plt.subplot(rows, cols, 3)
plt.hist((ref_out.ravel(), emu_out.ravel(), sim_out.ravel()), bins=21)
ax = plt.subplot(rows, cols, 4)
tile(ref_out, rows=2, cols=2, grid=True, ax=ax)
ax = plt.subplot(rows, cols, 5)
tile(emu_out, rows=2, cols=2, grid=True, ax=ax)
ax = plt.subplot(rows, cols, 6)
tile(sim_out, rows=2, cols=2, grid=True, ax=ax)
assert allclose(sim_out, ref_out, atol=0.15, rtol=1e-3)
assert allclose(sim_out, emu_out, atol=1e-3, rtol=1e-3)
plt.figure(figsize=(6.4, 7))
rows = 2 + len(spike_ps)
cols = n_presentations
neuron_inds = [rng.permutation(block.shape[-1])[:40] for block in spike_blocks]
for col in range(cols):
label0 = label_names[Ytest[col]]
tj = sim.dt * np.arange(nt) + col * presentation_time
# plot image
ax = plt.subplot(rows, cols, 0 * cols + col + 1)
neplt.imshow(np.transpose(Xtest[col], (1, 2, 0)),
vmin=-128,
vmax=128,
ax=ax,
axes=True)
ax.set_title(label0)
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
if col == 0:
ax.set_ylabel('input')
# plot spike rasters
for i, (spike_block,
neuron_ind) in enumerate(zip(spike_blocks, neuron_inds)):
ax = plt.subplot(rows, cols, (i + 1) * cols + col + 1)
rasterplot(tj, spike_block[col][:, neuron_ind], ax=ax)
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
def test_imshow_color_vlim(plt):
image = wave_image((31, 32), 0.6, color=(1, 1, 0))
image = 2 * image - 1
ne_plt.imshow(image, vmin=-1, vmax=1)
def test_imshow_color(plt):
image = wave_image((31, 32), 0.6, color=(1, 1, 0))
ne_plt.imshow(image)
def test_imshow_gray(plt):
image = wave_image((31, 32), 0.2)
ne_plt.imshow(image)
def test_conv_connection(channels, Simulator, seed, rng, plt, allclose):
# channels_last = True
channels_last = False
if channels > 1:
pytest.xfail("Cannot send population spikes to chip")
# 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 = 1.999 * test_x - 0.999 # range (-1, 1)
test_x = test_x[:, :, None] # single channel
input_shape = ImageShape(test_x.shape[0],
test_x.shape[1],
channels,
channels_last=channels_last)
filters = Gabor(freq=Uniform(0.5, 1)).generate(8, (7, 7), rng=rng)
filters = filters[None, :, :, :] # single channel
filters = np.transpose(filters, (0, 2, 3, 1)) # filters last
strides = (2, 2)
tau_rc = 0.02
tau_ref = 0.002
tau_s = 0.005
dt = 0.001
neuron_type = LoihiLIF(tau_rc=tau_rc, tau_ref=tau_ref)
pres_time = 0.1
with nengo.Network(seed=seed) as model:
nengo_loihi.add_params(model)
u = nengo.Node(nengo.processes.PresentInput([test_x.ravel()],
pres_time),
label='u')
a = nengo.Ensemble(input_shape.size,
1,
neuron_type=LoihiSpikingRectifiedLinear(),
max_rates=nengo.dists.Choice([40 / channels]),
intercepts=nengo.dists.Choice([0]),
label='a')
model.config[a].on_chip = False
if channels == 1:
nengo.Connection(u, a.neurons, transform=1, synapse=None)
elif channels == 2:
# encode image into spikes using two channels (on/off)
if input_shape.channels_last:
nengo.Connection(u, a.neurons[0::2], transform=1, synapse=None)
nengo.Connection(u,
a.neurons[1::2],
transform=-1,
synapse=None)
else:
k = input_shape.rows * input_shape.cols
nengo.Connection(u, a.neurons[:k], transform=1, synapse=None)
nengo.Connection(u, a.neurons[k:], transform=-1, synapse=None)
filters = np.vstack([filters, -filters])
else:
raise ValueError("Test not configured for more than two channels")
conv2d_transform = Conv2D.from_kernel(filters,
input_shape,
strides=strides)
output_shape = conv2d_transform.output_shape
gain, bias = neuron_type.gain_bias(max_rates=100, intercepts=0)
gain = gain * 0.01 # account for `a` max_rates
b = nengo.Ensemble(output_shape.size,
1,
neuron_type=neuron_type,
gain=nengo.dists.Choice([gain[0]]),
bias=nengo.dists.Choice([bias[0]]),
label='b')
nengo.Connection(a.neurons,
b.neurons,
synapse=tau_s,
transform=conv2d_transform)
bp = nengo.Probe(b.neurons)
with nengo.Simulator(model, dt=dt, optimize=False) as sim:
sim.run(pres_time)
ref_out = sim.data[bp].mean(axis=0).reshape(output_shape.shape())
# Currently, default TensorFlow does not support channels first in conv
use_nengo_dl = nengo_dl is not None and channels_last
ndl_out = np.zeros_like(ref_out)
if use_nengo_dl:
with nengo_dl.Simulator(model, dt=dt) as sim:
sim.run(pres_time)
ndl_out = sim.data[bp].mean(axis=0).reshape(output_shape.shape())
with nengo_loihi.Simulator(model, dt=dt, target='simreal') as sim:
sim.run(pres_time)
real_out = sim.data[bp].mean(axis=0).reshape(output_shape.shape())
with Simulator(model, dt=dt) as sim:
sim.run(pres_time)
sim_out = sim.data[bp].mean(axis=0).reshape(output_shape.shape())
if not output_shape.channels_last:
ref_out = np.transpose(ref_out, (1, 2, 0))
ndl_out = np.transpose(ndl_out, (1, 2, 0))
real_out = np.transpose(real_out, (1, 2, 0))
sim_out = np.transpose(sim_out, (1, 2, 0))
out_max = max(ref_out.max(), sim_out.max())
# --- plot results
rows = 2
cols = 3
ax = plt.subplot(rows, cols, 1)
imshow(test_x, vmin=0, vmax=1, ax=ax)
ax = plt.subplot(rows, cols, 2)
tile(np.transpose(filters[0], (2, 0, 1)), 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(np.transpose(ref_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 5)
tile(np.transpose(ndl_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
ax = plt.subplot(rows, cols, 6)
tile(np.transpose(sim_out, (2, 0, 1)), vmin=0, vmax=out_max, cols=8, ax=ax)
if use_nengo_dl:
assert allclose(ndl_out, ref_out, atol=1e-5, rtol=1e-5)
assert allclose(real_out, ref_out, atol=1, rtol=1e-3)
assert allclose(sim_out, ref_out, atol=10, rtol=1e-3)