import torch
from norse.torch.functional.lif import LIFState
from norse.torch.module.lif_mc import LIFMCRecurrentCell
[docs]def test_lif_mc_cell():
cell = LIFMCRecurrentCell(2, 4)
data = torch.randn(5, 2)
out, s = cell(data)
assert s.v.shape == (5, 4)
assert s.i.shape == (5, 4)
assert s.z.shape == (5, 4)
assert out.shape == (5, 4)
[docs]def test_lif_mc_cell_state():
cell = LIFMCRecurrentCell(2, 4)
input_tensor = torch.randn(5, 2)
state = LIFState(
z=torch.zeros(
input_tensor.shape[0],
cell.hidden_size,
),
v=cell.p.v_leak
* torch.ones(
input_tensor.shape[0],
cell.hidden_size,
),
i=torch.zeros(
input_tensor.shape[0],
cell.hidden_size,
),
)
out, s = cell(input_tensor, state)
assert s.v.shape == (5, 4)
assert s.i.shape == (5, 4)
assert s.z.shape == (5, 4)
assert out.shape == (5, 4)
[docs]def test_lif_mc_cell_autapses():
cell = LIFMCRecurrentCell(2, 2, autapses=True)
assert not torch.allclose(
torch.zeros(2),
(cell.recurrent_weights * torch.eye(*cell.recurrent_weights.shape)).sum(0),
)
s1 = LIFState(z=torch.ones(1, 2), v=torch.zeros(1, 2), i=torch.zeros(1, 2))
z, s_full = cell(torch.zeros(1, 2), s1)
s2 = LIFState(
z=torch.tensor([[0, 1]], dtype=torch.float32),
v=torch.zeros(1, 2),
i=torch.zeros(1, 2),
)
z, s_part = cell(torch.zeros(1, 2), s2)
assert not s_full.i[0, 0] == s_part.i[0, 0]
[docs]def test_lif_mc_cell_no_autapses():
cell = LIFMCRecurrentCell(2, 2, autapses=False)
assert (
cell.recurrent_weights * torch.eye(*cell.recurrent_weights.shape)
).sum() == 0
s1 = LIFState(z=torch.ones(1, 2), v=torch.zeros(1, 2), i=torch.zeros(1, 2))
z, s_full = cell(torch.zeros(1, 2), s1)
s2 = LIFState(
z=torch.tensor([[0, 1]], dtype=torch.float32),
v=torch.zeros(1, 2),
i=torch.zeros(1, 2),
)
z, s_part = cell(torch.zeros(1, 2), s2)
assert s_full.i[0, 0] == s_part.i[0, 0]
[docs]def test_lif_mc_cell_backward():
cell = LIFMCRecurrentCell(2, 4)
data = torch.randn(5, 2)
out, _ = cell(data)
out.sum().backward()
