"""
Test for the encoder module
"""
import torch
from norse.torch.functional.encode import (
population_encode,
constant_current_lif_encode,
spike_latency_lif_encode,
spike_latency_encode,
)
# Fixes a linting error:
# pylint: disable=E1102
[docs]def test_encode_population():
data = torch.as_tensor([0, 0.5, 1])
out_features = 3
actual = population_encode(data, out_features)
expected = torch.as_tensor(
[
[1.0000, 0.8824969, 0.6065307],
[0.8824969, 1.0000, 0.8824969],
[0.6065307, 0.8824969, 1.0000],
]
)
assert torch.allclose(actual, expected)
[docs]def test_encode_population_scale():
data = torch.as_tensor([0, 0.5, 1])
out_features = 3
scale = data.max()
actual = population_encode(data, out_features, scale=scale)
expected = torch.as_tensor(
[
[1.0000, 0.8824969, 0.6065307],
[0.8824969, 1.0000, 0.8824969],
[0.6065307, 0.8824969, 1.0000],
]
)
assert torch.allclose(actual, expected)
[docs]def test_encode_population_batch():
data = torch.as_tensor([[0, 0, 0], [0.5, 0.5, 0.5], [1, 1, 1]])
out_features = 3
actual = population_encode(data, out_features)
assert actual.shape == (3, 3, 3)
[docs]def test_constant_current_lif_encode():
data = torch.as_tensor([0, 0, 0, 0])
z = constant_current_lif_encode(data, 2)
assert torch.equal(z, torch.zeros((2, 4)))
data = torch.as_tensor([[16, 16, 16], [32, 32, 32], [64, 64, 64], [128, 128, 128]])
z = constant_current_lif_encode(data, 10)
assert torch.equal(z[-1], torch.ones((4, 3)))
[docs]def test_spike_latency_lif_encode():
spikes = spike_latency_lif_encode(1.1 * torch.ones(10), seq_length=128)
assert torch.sum(spikes).data == 10
[docs]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)
[docs]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]]))
[docs]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)
[docs]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)
