import torch
import pytest
from norse.torch.functional.leaky_integrator import li_step, LIState
from norse.torch.functional.lif import (
lif_step,
lif_feed_forward_step,
LIFState,
LIFFeedForwardState,
LIFParameters,
)
from norse.torch.functional.lift import lift
[docs]def test_lift_without_state_or_parameters():
data = torch.ones(3, 2, 1)
lifted = lift(lif_feed_forward_step)
with pytest.raises(TypeError): # No state given
lifted(data)
[docs]def test_lift_with_state_without_parameters():
data = torch.ones(3, 2, 1)
lifted = lift(lif_feed_forward_step)
z, s = lifted(
data,
state=LIFFeedForwardState(torch.zeros_like(data[0]), torch.zeros_like(data[0])),
)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
assert s.i.shape == (2, 1)
[docs]def test_lift_with_state_and_parameters():
data = torch.ones(3, 2, 1)
lifted = lift(
lif_feed_forward_step, p=LIFParameters(v_th=torch.as_tensor(0.3), method="tanh")
)
z, s = lifted(
data,
state=LIFFeedForwardState(torch.zeros_like(data[0]), torch.zeros_like(data[0])),
)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
assert s.i.shape == (2, 1)
[docs]def test_lift_with_lift_step():
data = torch.ones(3, 2, 1)
lifted = lift(lif_step)
z, s = lifted(
data,
state=LIFState(
v=torch.zeros(2, 1),
i=torch.zeros(2, 1),
z=torch.zeros(2, 1),
),
input_weights=torch.ones(1, 1),
recurrent_weights=torch.ones(1, 1),
)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
[docs]def test_lift_with_leaky_integrator():
data = torch.ones(3, 2, 1)
lifted = lift(li_step)
z, s = lifted(
data,
state=LIState(
v=torch.zeros(2, 1),
i=torch.zeros(2, 1),
),
input_weights=torch.ones(1, 1),
)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
