def test_decode_shape_crf(generate_batch):
unary, _, lengths = generate_batch
h = unary.size(2)
crf = CRF(h, batch_first=False)
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_score_sentence_batch_stable(generate_examples_and_batch):
i1, t1, l1, i2, t2, l2, i, t, l = generate_examples_and_batch
h = i1.size(2)
crf = CRF(h, batch_first=False)
crf.transitions_p.data = torch.rand(1, h, h)
score1 = crf.score_sentence(i1, t1, l1)
score2 = crf.score_sentence(i2, t2, l2)
one_x_one = torch.cat([score1, score2], dim=0)
batched = crf.score_sentence(i, t, l)
np.testing.assert_allclose(one_x_one.detach().numpy(), batched.detach().numpy())
def test_forward_batch_stable(generate_examples_and_batch):
i1, _, l1, i2, _, l2, i, _, l = generate_examples_and_batch
h = i1.size(2)
crf = CRF(h, batch_first=False)
crf.transitions_p.data = torch.rand(1, h, h)
fw1 = crf.forward((i1, l1))
fw2 = crf.forward((i2, l2))
one_x_one = torch.cat([fw1, fw2], dim=0)
batched = crf.forward((i, l))
np.testing.assert_allclose(one_x_one.detach().numpy(), batched.detach().numpy())
def test_viterbi_score_equals_sentence_score(generate_batch):
"""Test that the scores from viterbi decoding are the same scores that you get when looking up those returned paths."""
unary, _, lengths = generate_batch
h = unary.size(2)
trans = torch.rand(h, h)
crf = CRF(h)
p, viterbi_scores = Viterbi(Offsets.GO, Offsets.EOS)(unary, crf.transitions, lengths)
gold_scores = crf.score_sentence(unary, p, lengths)
np.testing.assert_allclose(viterbi_scores.detach().numpy(), gold_scores.detach().numpy(), rtol=1e-6)
def test_neg_log_loss_batch_stable(generate_examples_and_batch): i1, t1, l1, i2, t2, l2, i, t, l = generate_examples_and_batch h = i1.size(2) crf = CRF(h, batch_first=False) crf.transitions_p.data = torch.rand(1, h, h) nll1 = crf.neg_log_loss(i1, t1, l1) nll2 = crf.neg_log_loss(i2, t2, l2) one_x_one = (nll1 + nll2) / 2 batched = crf.neg_log_loss(i, t, l) np.testing.assert_allclose(one_x_one.detach().numpy(), batched.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, batch_first=False)
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_forward(generate_batch):
unary, _, lengths = generate_batch
h = unary.size(2)
crf = CRF(h, batch_first=False)
trans = torch.rand(h, h)
crf.transitions_p.data = trans.unsqueeze(0)
forward = crf.forward((unary, lengths))
new_trans = build_trans(trans)
unary = unary.transpose(0, 1)
scores = []
for u, l in zip(unary, lengths):
emiss = build_emission(u[:l])
scores.append(explicit_forward(emiss, new_trans, Offsets.GO, Offsets.EOS))
gold_scores = np.array(scores)
np.testing.assert_allclose(forward.detach().numpy(), gold_scores, rtol=1e-6)
def test_mask_is_applied():
h = np.random.randint(22, 41)
loc = np.random.randint(h)
constraint = torch.zeros(h, h, dtype=torch.uint8)
constraint[Offsets.GO, loc] = 1
crf = CRF(h, constraint_mask=constraint)
t = crf.transitions.detach().numpy()
assert t[0, Offsets.GO, loc] == -1e4
def test_score_sentence(generate_batch):
unary, tags, lengths = generate_batch
h = unary.size(2)
crf = CRF(h, batch_first=False)
trans = torch.rand(h, h)
crf.transitions_p.data = trans.unsqueeze(0)
sentence_score = crf.score_sentence(unary, tags, lengths)
new_trans = build_trans(trans)
unary = unary.transpose(0, 1)
tags = tags.transpose(0, 1)
scores = []
for u, t, l in zip(unary, tags, lengths):
emiss = build_emission(u[:l])
golds = t[:l].tolist()
scores.append(explicit_score_gold(emiss, new_trans, golds, Offsets.GO, Offsets.EOS))
gold_scores = np.array(scores)
np.testing.assert_allclose(sentence_score.detach().numpy(), gold_scores, rtol=1e-6)
def test_mask_same_after_update(generate_batch):
from torch.optim import SGD
unary, tags, lengths = generate_batch
h = unary.size(2)
constraint = torch.rand(h, h) < 0.5
crf = CRF(h, constraint_mask=constraint, batch_first=False)
opt = SGD(crf.parameters(), lr=10)
m1 = crf.constraint_mask.numpy()
t1 = crf.transitions_p.detach().clone().numpy()
l = crf.neg_log_loss(unary, tags, lengths)
l = torch.mean(l)
l.backward()
opt.step()
m2 = crf.constraint_mask.numpy()
t2 = crf.transitions_p.detach().numpy()
np.testing.assert_allclose(m1, m2)
with pytest.raises(AssertionError):
np.testing.assert_allclose(t1, t2)
def test_mask_not_applied():
h = np.random.randint(22, 41)
crf = CRF(h)
t = crf.transitions.detach().numpy()
assert t[0, Offsets.GO, np.random.randint(h)] != -1e4
def test_forward_shape(generate_batch):
unary, _, lengths = generate_batch
h = unary.size(2)
crf = CRF(h, batch_first=False)
fwd = crf.forward((unary, lengths))
assert fwd.shape == torch.Size([unary.size(1)])
def test_score_sentence_shape(generate_batch):
unary, tags, lengths = generate_batch
h = unary.size(2)
crf = CRF(h, batch_first=False)
score = crf.score_sentence(unary, tags, lengths)
assert score.shape == torch.Size([unary.size(1)])
