def test_acceptor_from_tensor(self):
fsa_tensor = torch.tensor([[0, 1, 2, _k2.float_as_int(-1.2)],
[0, 2, 10, _k2.float_as_int(-2.2)],
[1, 6, -1, _k2.float_as_int(-3.2)],
[1, 3, 3, _k2.float_as_int(-4.2)],
[2, 6, -1, _k2.float_as_int(-5.2)],
[2, 4, 2, _k2.float_as_int(-6.2)],
[3, 6, -1, _k2.float_as_int(-7.2)],
[5, 0, 1, _k2.float_as_int(-8.2)]],
dtype=torch.int32)
fsa = k2.Fsa(fsa_tensor)
expected_str = '''
0 1 2 -1.2
0 2 10 -2.2
1 6 -1 -3.2
1 3 3 -4.2
2 6 -1 -5.2
2 4 2 -6.2
3 6 -1 -7.2
5 0 1 -8.2
6
'''
assert _remove_leading_spaces(expected_str) == \
_remove_leading_spaces(k2.to_str(fsa))
arcs = fsa.arcs.values()[:, :-1]
assert isinstance(arcs, torch.Tensor)
assert arcs.dtype == torch.int32
assert arcs.device.type == 'cpu'
assert arcs.shape == (8, 3), 'there should be 8 arcs'
assert torch.all(
torch.eq(arcs[0], torch.tensor([0, 1, 2], dtype=torch.int32)))
assert torch.allclose(
fsa.scores,
torch.tensor([-1.2, -2.2, -3.2, -4.2, -5.2, -6.2, -7.2, -8.2],
dtype=torch.float32))
fsa.scores *= -1
assert torch.allclose(
fsa.scores,
torch.tensor([1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2],
dtype=torch.float32))
def test_transducer_from_tensor(self):
for device in self.devices:
fsa_tensor = torch.tensor(
[[0, 1, 2, _k2.float_as_int(-1.2)],
[0, 2, 10, _k2.float_as_int(-2.2)],
[1, 6, -1, _k2.float_as_int(-4.2)],
[1, 3, 3, _k2.float_as_int(-3.2)],
[2, 6, -1, _k2.float_as_int(-5.2)],
[2, 4, 2, _k2.float_as_int(-6.2)],
[3, 6, -1, _k2.float_as_int(-7.2)],
[5, 0, 1, _k2.float_as_int(-8.2)]],
dtype=torch.int32).to(device)
aux_labels_tensor = torch.tensor([22, 100, 16, 33, 26, 22, 36, 50],
dtype=torch.int32).to(device)
fsa = k2.Fsa(fsa_tensor, aux_labels_tensor)
assert fsa.aux_labels.dtype == torch.int32
assert fsa.aux_labels.device.type == device.type
assert torch.all(
torch.eq(
fsa.aux_labels,
torch.tensor([22, 100, 16, 33, 26, 22, 36, 50],
dtype=torch.int32).to(device)))
assert torch.allclose(
fsa.scores,
torch.tensor([-1.2, -2.2, -4.2, -3.2, -5.2, -6.2, -7.2, -8.2],
dtype=torch.float32,
device=device))
expected_str = '''
0 1 2 22 -1.2
0 2 10 100 -2.2
1 6 -1 16 -4.2
1 3 3 33 -3.2
2 6 -1 26 -5.2
2 4 2 22 -6.2
3 6 -1 36 -7.2
5 0 1 50 -8.2
6
'''
assert _remove_leading_spaces(expected_str) == \
_remove_leading_spaces(k2.to_str(fsa))
def test_transducer_from_tensor(self):
devices = [torch.device('cpu')]
if torch.cuda.is_available():
devices.append(torch.device('cuda', 0))
for device in devices:
fsa_tensor = torch.tensor(
[[0, 1, 2, _k2.float_as_int(-1.2)],
[0, 2, 10, _k2.float_as_int(-2.2)],
[1, 6, -1, _k2.float_as_int(-4.2)],
[1, 3, 3, _k2.float_as_int(-3.2)],
[2, 6, -1, _k2.float_as_int(-5.2)],
[2, 4, 2, _k2.float_as_int(-6.2)],
[3, 6, -1, _k2.float_as_int(-7.2)],
[5, 0, 1, _k2.float_as_int(-8.2)]],
dtype=torch.int32).to(device)
aux_labels_tensor = torch.tensor([22, 100, 16, 33, 26, 22, 36, 50],
dtype=torch.int32).to(device)
fsa = k2.Fsa(fsa_tensor, aux_labels_tensor)
assert fsa.aux_labels.dtype == torch.int32
assert fsa.aux_labels.device.type == device.type
assert torch.allclose(
fsa.aux_labels,
torch.tensor([22, 100, 16, 33, 26, 22, 36, 50],
dtype=torch.int32).to(device))
expected_str = '''
0 1 2 22 -1.2
0 2 10 100 -2.2
1 6 -1 16 -4.2
1 3 3 33 -3.2
2 6 -1 26 -5.2
2 4 2 22 -6.2
3 6 -1 36 -7.2
5 0 1 50 -8.2
6
'''
assert _remove_leading_spaces(
expected_str) == _remove_leading_spaces(k2.to_str(fsa))