def test_spike_latency_encode_without_batch_3():
spikes = torch.as_tensor([
[
[1.0, 1.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 1.0],
[1.0, 0.0, 1.0, 0.0, 0.0],
[1.0, 0.0, 1.0, 0.0, 1.0],
[0.0, 1.0, 1.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 1.0, 0.0],
],
[
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0],
],
])
actual = spike_latency_encode(spikes)
expected = spikes.clone()
expected[1] = torch.as_tensor([
[0.0, 0.0, 1.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 0.0, 1.0, 1.0],
[0.0, 1.0, 0.0, 1.0, 0.0],
[1.0, 0.0, 0.0, 1.0, 1.0],
[0.0, 1.0, 1.0, 1.0, 0.0],
[1.0, 1.0, 1.0, 0.0, 1.0],
])
assert torch.equal(actual, expected)
def test_spike_latency_encode_with_batch():
data = torch.as_tensor([[100, 100], [100, 100]])
spikes = constant_current_lif_encode(data, 5)
actual = spike_latency_encode(spikes)
expected = torch.zeros((5, 2, 2))
expected[0] = torch.as_tensor([[1, 1], [1, 1]])
assert torch.equal(actual, expected)
def forward(self, input_spikes):
return encode.spike_latency_encode(input_spikes)
def test_spike_latency_encode_without_batch_2():
spikes = torch.as_tensor([[[0, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1]]])
actual = spike_latency_encode(spikes)
expected = torch.as_tensor([[[0, 1, 1], [1, 1, 1]], [[1, 0, 0], [0, 0,
0]]])
assert torch.equal(actual, expected)
def test_spike_latency_encode_without_batch():
spikes = torch.as_tensor([[0, 1, 1, 0], [1, 1, 1, 0]])
actual = spike_latency_encode(spikes)
assert torch.equal(actual, torch.as_tensor([[0, 1, 1, 0], [1, 0, 0, 0]]))