def test_decode_batch_stable(generate_examples_and_batch):
i1, _, l1, i2, _, l2, i, _, l = generate_examples_and_batch
h = i1.size(2)
crf = CRF(h)
crf.transitions_p.data = torch.rand(1, h, h)
p1, s1 = crf.decode(i1, l1)
p2, s2 = crf.decode(i2, l2)
pad = torch.zeros(p1.size(0) - p2.size(0), 1, dtype=torch.long)
one_x_one_p = torch.cat([p1, torch.cat([p2, pad], dim=0)], dim=1)
one_x_one_s = torch.cat([s1, s2], dim=0)
batched_p, batched_s = crf.decode(i, l)
np.testing.assert_allclose(one_x_one_s.detach().numpy(), batched_s.detach().numpy())
for p1, p2 in zip(one_x_one_p, batched_p):
np.testing.assert_allclose(p1.detach().numpy(), p2.detach().numpy())
def test_decode_batch_stable(generate_examples_and_batch):
i1, _, l1, i2, _, l2, i, _, l = generate_examples_and_batch
h = i1.size(2)
crf = CRF(h)
crf.transitions_p.data = torch.rand(1, h, h)
p1, s1 = crf.decode(i1, l1)
p2, s2 = crf.decode(i2, l2)
pad = torch.zeros(p1.size(0) - p2.size(0), 1, dtype=torch.long)
one_x_one_p = torch.cat([p1, torch.cat([p2, pad], dim=0)], dim=1)
one_x_one_s = torch.cat([s1, s2], dim=0)
batched_p, batched_s = crf.decode(i, l)
np.testing.assert_allclose(one_x_one_s.detach().numpy(),
batched_s.detach().numpy())
for p1, p2 in zip(one_x_one_p, batched_p):
np.testing.assert_allclose(p1.detach().numpy(), p2.detach().numpy())
def test_decode_shape(generate_batch):
unary, _, lengths = generate_batch
h = unary.size(2)
crf = CRF(h)
paths, scores = crf.decode(unary, lengths)
assert scores.shape == torch.Size([unary.size(1)])
assert paths.shape == torch.Size([unary.size(0), unary.size(1)])
def test_decode_shape(generate_batch):
unary, _, lengths = generate_batch
h = unary.size(2)
crf = CRF(h)
paths, scores = crf.decode(unary, lengths)
assert scores.shape == torch.Size([unary.size(1)])
assert paths.shape == torch.Size([unary.size(0), unary.size(1)])
return torch.stack(new_path[1:]), path_score
if __name__ == '__main__':
from baseline.pytorch.crf import CRF, transition_mask
vocab = ["<GO>", "<EOS>", "B-X", "I-X", "E-X", "S-X", "O", "B-Y", "I-Y", "E-Y", "S-Y"]
vocab = {k: i for i, k in enumerate(vocab)}
mask = transition_mask(vocab, "IOBES", 0, 1)
crf = CRF(10, (0, 1), batch_first=False)
trans = crf.transitions
icrf = InferenceCRF(torch.nn.Parameter(trans.squeeze(0)), 0, 1, False)
u = torch.rand(20, 1, 10)
l = torch.full((1,), 20, dtype=torch.long)
print(crf.decode(u, l))
print(icrf.decode(u, l))
u = torch.rand(15, 1, 10)
traced_model = torch.jit.trace(icrf.decode, (u, l))
traced_model.save('crf.pt')
traced_model = torch.jit.load('crf.pt')
u = torch.rand(8, 1, 10)
l = torch.full((1,), 8, dtype=torch.long)
print(crf.decode(u, l))
print(traced_model(u, l))
u = torch.rand(22, 1, 10)
l = torch.full((1,), 22, dtype=torch.long)
if __name__ == '__main__':
from baseline.pytorch.crf import CRF, transition_mask
vocab = [
"<GO>", "<EOS>", "B-X", "I-X", "E-X", "S-X", "O", "B-Y", "I-Y", "E-Y",
"S-Y"
]
vocab = {k: i for i, k in enumerate(vocab)}
mask = transition_mask(vocab, "IOBES", 0, 1)
crf = CRF(10, (0, 1), batch_first=False)
trans = crf.transitions
icrf = InferenceCRF(torch.nn.Parameter(trans.squeeze(0)), 0, 1, False)
u = torch.rand(20, 1, 10)
l = torch.full((1, ), 20, dtype=torch.long)
print(crf.decode(u, l))
print(icrf.decode(u, l))
u = torch.rand(15, 1, 10)
traced_model = torch.jit.trace(icrf.decode, (u, l))
traced_model.save('crf.pt')
traced_model = torch.jit.load('crf.pt')
u = torch.rand(8, 1, 10)
l = torch.full((1, ), 8, dtype=torch.long)
print(crf.decode(u, l))
print(traced_model(u, l))
u = torch.rand(22, 1, 10)
l = torch.full((1, ), 22, dtype=torch.long)