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_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_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)])