def test_run_profile(network, train, pytestconfig, monkeypatch, tmpdir):
monkeypatch.chdir(tmpdir)
if network == "integrator":
net = benchmarks.integrator(3, 2, nengo.SpikingRectifiedLinear())
elif network == "cconv":
net = benchmarks.cconv(3, 10, nengo.LIF())
elif network == "test":
with nengo.Network() as net:
ens = nengo.Ensemble(10, 1)
net.p = nengo.Probe(ens)
benchmarks.run_profile(
net,
train=train,
n_steps=10,
do_profile=True,
device=pytestconfig.getoption("--device"),
unroll_simulation=pytestconfig.getoption("--unroll-simulation"),
dtype=pytestconfig.getoption("dtype"),
)
assert net.config[net].inference_only == (not train)
with Simulator(net) as sim:
n_trainable = sum(
np.prod(w.shape.as_list())
for w in sim.keras_model.trainable_weights)
assert n_trainable == 102017
assert net.inp.size_out == 1 if native_nengo else n_steps
assert net.p.size_in == 10
@pytest.mark.performance
@pytest.mark.parametrize(
"net, train, minibatch_size, min, max",
[
(benchmarks.cconv(128, 64,
nengo.RectifiedLinear()), False, 64, 0.95, 1.1),
(benchmarks.cconv(128, 64, nengo.LIF()), False, 64, 2.2, 2.5),
(benchmarks.integrator(128, 32,
nengo.RectifiedLinear()), True, 64, 0.6, 0.9),
(benchmarks.integrator(128, 32, nengo.LIF()), True, 64, 1.25, 1.45),
(
benchmarks.random_network(
64,
32,
nengo.RectifiedLinear(),
n_ensembles=20,
connections_per_ensemble=5,
seed=0,
),
False,
None,
with Simulator(net) as sim:
n_trainable = sum(
np.prod(w.shape.as_list()) for w in sim.keras_model.trainable_weights
)
assert n_trainable == 102017
assert net.inp.size_out == 1 if native_nengo else n_steps
assert net.p.size_in == 10
@pytest.mark.performance
@pytest.mark.parametrize(
"net, train, minibatch_size, eager, min, max",
[
(
benchmarks.cconv(128, 64, nengo.RectifiedLinear()),
False,
64,
True,
1.05,
1.2,
),
(benchmarks.cconv(128, 64, nengo.LIF()), False, 64, True, 2.3, 2.6),
(
benchmarks.integrator(128, 32, nengo.RectifiedLinear()),
True,
64,
True,
0.6,
0.9,
),
with Simulator(net) as sim:
n_trainable = sum(
np.prod(w.shape.as_list())
for w in sim.keras_model.trainable_weights)
assert n_trainable == 102017
assert net.inp.size_out == 1 if native_nengo else n_steps
assert net.p.size_in == 10
@pytest.mark.performance
@pytest.mark.parametrize(
"net, train, minibatch_size, min, max",
[
(benchmarks.cconv(128, 64,
nengo.RectifiedLinear()), False, 64, 0.6, 0.75),
(benchmarks.cconv(128, 64, nengo.LIF()), False, 64, 1.4, 1.6),
(benchmarks.integrator(128, 32,
nengo.RectifiedLinear()), True, 64, 0.55, 0.9),
(benchmarks.integrator(128, 32, nengo.LIF()), True, 64, 0.95, 1.25),
(
benchmarks.random_network(
64,
32,
nengo.RectifiedLinear(),
n_ensembles=20,
connections_per_ensemble=5,
seed=0,
),
False,
None,