def test_pruned_max(self):
forward_max_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
backward_max_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
wfsa = k2host.WfsaWithFbWeights(self.fsa,
k2host.FbWeightType.kMaxWeight,
forward_max_weights,
backward_max_weights)
beam = 8.0
remover = k2host.EpsilonsRemoverPrunedMax(wfsa, beam)
fsa_size = k2host.IntArray2Size()
arc_derivs_size = k2host.IntArray2Size()
remover.get_sizes(fsa_size, arc_derivs_size)
fsa_out = k2host.Fsa.create_fsa_with_size(fsa_size)
arc_derivs = k2host.IntArray2.create_array_with_size(arc_derivs_size)
arc_weights_out = k2host.FloatArray1.create_array_with_size(
fsa_size.size2)
remover.get_output(fsa_out, arc_derivs)
self.assertTrue(k2host.is_epsilon_free(fsa_out))
self.assertEqual(fsa_out.size1, 6)
self.assertEqual(fsa_out.size2, 11) # TODO: fix this
self.assertEqual(arc_derivs.size1, 11) # TODO: fix this
self.assertEqual(arc_derivs.size2, 18) # TODO: fix this
self.assertTrue(
k2host.is_rand_equivalent_max_weight(self.fsa, fsa_out, beam))
def test_pruned_logsum(self):
forward_logsum_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
backward_logsum_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
wfsa = k2host.WfsaWithFbWeights(self.fsa,
k2host.FbWeightType.kLogSumWeight,
forward_logsum_weights,
backward_logsum_weights)
beam = 10.0
determinizer = k2host.DeterminizerPrunedLogSum(
wfsa, beam, 100, k2host.FbWeightType.kNoWeight)
fsa_size = k2host.IntArray2Size()
arc_derivs_size = k2host.IntArray2Size()
determinizer.get_sizes(fsa_size, arc_derivs_size)
fsa_out = k2host.Fsa.create_fsa_with_size(fsa_size)
arc_derivs = k2host.LogSumArcDerivs.create_arc_derivs_with_size(
arc_derivs_size)
arc_weights_out = k2host.FloatArray1.create_array_with_size(
fsa_size.size2)
determinizer.get_output(fsa_out, arc_derivs)
self.assertTrue(k2host.is_deterministic(fsa_out))
self.assertEqual(fsa_out.size1, 7)
self.assertEqual(fsa_out.size2, 9)
self.assertEqual(arc_derivs.size1, 9)
self.assertEqual(arc_derivs.size2, 15)
self.assertTrue(
k2host.is_rand_equivalent_logsum_weight(self.fsa, fsa_out, beam))
# cast float to int
arc_ids = k2host.StridedIntArray1.from_float_tensor(arc_derivs.data[:,
0])
def test_pruned_max(self):
forward_max_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
backward_max_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
wfsa = k2host.WfsaWithFbWeights(self.fsa,
k2host.FbWeightType.kMaxWeight,
forward_max_weights,
backward_max_weights)
beam = 10.0
determinizer = k2host.DeterminizerPrunedMax(
wfsa, beam, 100, k2host.FbWeightType.kNoWeight)
fsa_size = k2host.IntArray2Size()
arc_derivs_size = k2host.IntArray2Size()
determinizer.get_sizes(fsa_size, arc_derivs_size)
fsa_out = k2host.Fsa.create_fsa_with_size(fsa_size)
arc_derivs = k2host.IntArray2.create_array_with_size(arc_derivs_size)
arc_weights_out = k2host.FloatArray1.create_array_with_size(
fsa_size.size2)
determinizer.get_output(fsa_out, arc_derivs)
self.assertTrue(k2host.is_deterministic(fsa_out))
self.assertEqual(fsa_out.size1, 7)
self.assertEqual(fsa_out.size2, 9)
self.assertEqual(arc_derivs.size1, 9)
self.assertEqual(arc_derivs.size2, 12)
self.assertTrue(
k2host.is_rand_equivalent_max_weight(self.fsa, fsa_out, beam))
def test_logsum_weight(self):
forward_logsum_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
backward_logsum_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
wfsa = k2host.WfsaWithFbWeights(self.fsa,
k2host.FbWeightType.kLogSumWeight,
forward_logsum_weights,
backward_logsum_weights)
expected_forward_logsum_weights = torch.DoubleTensor(
[0, 1, 3, 4, 1,
float('-inf'), 3, 9.126928, 4, 14.143222])
expected_backward_logsum_weights = torch.DoubleTensor(
[14.143222, 13.126928, 9, 10, 9.018150, 4, 3, 5, 6, 0])
self.assertTrue(
torch.allclose(forward_logsum_weights.data,
expected_forward_logsum_weights))
self.assertTrue(
torch.allclose(backward_logsum_weights.data,
expected_backward_logsum_weights))
def test_max_weight(self):
forward_max_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
backward_max_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
wfsa = k2host.WfsaWithFbWeights(self.fsa,
k2host.FbWeightType.kMaxWeight,
forward_max_weights,
backward_max_weights)
expected_forward_max_weights = torch.DoubleTensor(
[0, 1, 3, 4, 1, float('-inf'), 3, 9, 4, 14])
expected_backward_max_weights = torch.DoubleTensor(
[14, 13, 9, 10, 9, 4, 3, 5, 6, 0])
self.assertTrue(
torch.equal(forward_max_weights.data,
expected_forward_max_weights))
self.assertTrue(
torch.allclose(forward_max_weights.data,
expected_forward_max_weights))
self.assertTrue(
torch.allclose(backward_max_weights.data,
expected_backward_max_weights))
def test_pruned_logsum(self):
forward_logsum_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
backward_logsum_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
wfsa = k2host.WfsaWithFbWeights(self.fsa,
k2host.FbWeightType.kLogSumWeight,
forward_logsum_weights,
backward_logsum_weights)
beam = 8.0
remover = k2host.EpsilonsRemoverPrunedLogSum(wfsa, beam)
fsa_size = k2host.IntArray2Size()
arc_derivs_size = k2host.IntArray2Size()
remover.get_sizes(fsa_size, arc_derivs_size)
fsa_out = k2host.Fsa.create_fsa_with_size(fsa_size)
arc_derivs = k2host.LogSumArcDerivs.create_arc_derivs_with_size(
arc_derivs_size)
arc_weights_out = k2host.FloatArray1.create_array_with_size(
fsa_size.size2)
remover.get_output(fsa_out, arc_derivs)
self.assertTrue(k2host.is_epsilon_free(fsa_out))
self.assertEqual(fsa_out.size1, 6)
self.assertEqual(fsa_out.size2, 11) # TODO: fix this
self.assertEqual(arc_derivs.size1, 11) # TODO: fix this
self.assertEqual(arc_derivs.size2, 20) # TODO: fix this
# TODO(haowen): uncomment this after re-implementing
# IsRandEquivalentAfterRmEpsPrunedLogSum
#self.assertTrue(
# k2host.is_rand_equivalent_after_rmeps_pruned_logsum(
# self.fsa, fsa_out, beam))
# cast float to int
arc_ids = k2host.StridedIntArray1.from_float_tensor(arc_derivs.data[:,
0])
# we may get different value of `arc_ids.get_data(1)`
# with different STL implementations as we use
# `std::unordered_map` in implementation of rmepsilon,
# thus below assertion may fail on some platforms.
self.assertEqual(arc_ids.get_data(1), 1)
