def test_pop_tiny(pop_type, out_channels_last, request, plt, seed, rng,
allclose):
nc = 2
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)
filters = np.transpose(filters, (0, 2, 3, 1))
test_x = np.array([[1, 5, 1], [2, 1, 2]])
test_x = test_x[:, :, 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)
filters = np.transpose(filters, (0, 2, 3, 1))
test_x = np.array([[[1, 5, 1], [2, 1, 2]], [[0, 3, 1], [4, 2, 1]]])
test_x = np.transpose(test_x, (1, 2, 0))
test_x = test_x / (test_x.max() + 0.001)
# --- compute nengo_loihi outputs
inp_biases = test_x
inp_shape = ImageShape.from_shape(inp_biases.shape, channels_last=True)
ni, nj, nk = inp_shape.shape(channels_last=True)
nc, si, sj, 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 = loihi_cx.CxModel()
# input group
inp = loihi_cx.CxGroup(ni * nj * nk, label='inp')
assert inp.n <= 1024
inp.configure_relu()
inp.bias[:] = inp_biases.ravel()
inp_ax = loihi_cx.CxAxons(nij, label='inp_ax')
inp_ax.set_axon_map(inp_shape.pixel_idxs(), inp_shape.channel_idxs())
inp.add_axons(inp_ax)
model.add_group(inp)
# conv group
neurons = loihi_cx.CxGroup(out_size, label='neurons')
assert neurons.n <= 1024
neurons.configure_lif(tau_rc=tau_rc, tau_ref=tau_ref, dt=dt)
neurons.configure_filter(tau_s, dt=dt)
neurons.bias[:] = neuron_bias
synapses = loihi_cx.CxSynapses(inp_shape.n_pixels, label='synapses')
conv2d_transform = Conv2D.from_kernel(
filters,
inp_shape,
strides=(sti, stj),
output_channels_last=out_channels_last)
weights, indices, axon_to_weight_map, cx_bases = conv2d_loihi_weights(
conv2d_transform)
synapses.set_population_weights(weights,
indices,
axon_to_weight_map,
cx_bases,
pop_type=pop_type)
neurons.add_synapses(synapses)
out_probe = loihi_cx.CxProbe(target=neurons, key='s')
neurons.add_probe(out_probe)
inp_ax.target = synapses
model.add_group(neurons)
# simulation
model.discretize()
n_steps = int(pres_time / dt)
target = request.config.getoption("--target")
if target == 'loihi':
with LoihiSimulator(model, use_snips=False, seed=seed) as sim:
sim.run_steps(n_steps)
sim_out = sim.get_probe_output(out_probe)
else:
with CxSimulator(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 out_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)
print("sim_out:\n%r" % (sim_out[:, :, 0], ))
# 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(request, plt, seed, rng, allclose):
pop_type = 32
out_channels_last = False
# 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(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(neuron_type, ref_out, neuron_gain, neuron_bias, dt)
# --- compute nengo_loihi outputs
inp_biases = np.array([test_x, -test_x])
inp_shape = ImageShape.from_shape(inp_biases.shape, channels_last=False)
kernel = np.array([filters, -filters]) # two channels, pos and neg
kernel = np.transpose(kernel, (0, 2, 3, 1))
conv2d_transform = Conv2D.from_kernel(
kernel,
inp_shape,
strides=(sti, stj),
output_channels_last=out_channels_last)
ni, nj, nk = inp_shape.shape(channels_last=True)
out_size = ref_out.size
nf, nyi, nyj = ref_out.shape
assert out_size <= 1024
model = loihi_cx.CxModel()
# input group
inp = loihi_cx.CxGroup(inp_shape.size, label='inp')
assert inp.n <= 1024
inp.configure_relu()
inp.bias[:] = inp_biases.ravel()
inp_ax = loihi_cx.CxAxons(inp_shape.n_pixels, label='inp_ax')
inp_ax.set_axon_map(inp_shape.pixel_idxs(), inp_shape.channel_idxs())
inp.add_axons(inp_ax)
model.add_group(inp)
# conv group
neurons = loihi_cx.CxGroup(out_size, label='neurons')
assert neurons.n <= 1024
neurons.configure_lif(tau_rc=tau_rc, tau_ref=tau_ref, dt=dt)
neurons.configure_filter(tau_s, dt=dt)
neurons.bias[:] = neuron_bias
synapses = loihi_cx.CxSynapses(inp_shape.n_pixels, label='synapses')
weights, indices, axon_to_weight_map, cx_bases = conv2d_loihi_weights(
conv2d_transform)
synapses.set_population_weights(weights,
indices,
axon_to_weight_map,
cx_bases,
pop_type=pop_type)
neurons.add_synapses(synapses)
out_probe = loihi_cx.CxProbe(target=neurons, key='s')
neurons.add_probe(out_probe)
inp_ax.target = synapses
model.add_group(neurons)
# simulation
model.discretize()
n_steps = int(pres_time / dt)
target = request.config.getoption("--target")
if target == 'loihi':
with LoihiSimulator(model, use_snips=False, seed=seed) as sim:
sim.run_steps(n_steps)
sim_out = sim.get_probe_output(out_probe)
else:
with CxSimulator(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 out_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)