def process(b):
# Create emissions graph:
emissions = gtn.linear_graph(T, C, inputs.requires_grad)
cpu_data = inputs[b].cpu().contiguous()
emissions.set_weights(cpu_data.data_ptr())
target = make_chain_graph(targets[b])
target.arc_sort(True)
# Create token to grapheme decomposition graph
tokens_target = gtn.remove(gtn.project_output(gtn.compose(target, lexicon)))
tokens_target.arc_sort()
# Create alignment graph:
alignments = gtn.project_input(
gtn.remove(gtn.compose(tokens, tokens_target))
)
alignments.arc_sort()
# Add transition scores:
if transitions is not None:
alignments = gtn.intersect(transitions, alignments)
alignments.arc_sort()
loss = gtn.forward_score(gtn.intersect(emissions, alignments))
# Normalize if needed:
if transitions is not None:
norm = gtn.forward_score(gtn.intersect(emissions, transitions))
loss = gtn.subtract(loss, norm)
losses[b] = gtn.negate(loss)
# Save for backward:
if emissions.calc_grad:
emissions_graphs[b] = emissions
def test_scalar_ops(self):
g1 = gtn.Graph()
g1.add_node(True)
g1.add_node(False, True)
g1.add_arc(0, 1, 0, 0, 1.0)
# Test negate:
res = gtn.negate(g1)
self.assertEqual(res.item(), -1.0)
gtn.backward(res)
self.assertEqual(g1.grad().item(), -1.0)
g1.zero_grad()
g2 = gtn.Graph()
g2.add_node(True)
g2.add_node(False, True)
g2.add_arc(0, 1, 0, 0, 3.0)
# Test add:
res = gtn.add(g1, g2)
self.assertEqual(res.item(), 4.0)
gtn.backward(res)
self.assertEqual(g1.grad().item(), 1.0)
self.assertEqual(g2.grad().item(), 1.0)
g1.zero_grad()
g2.zero_grad()
# Test subtract:
res = gtn.subtract(g1, g2)
self.assertEqual(res.item(), -2.0)
gtn.backward(res)
self.assertEqual(g1.grad().item(), 1.0)
self.assertEqual(g2.grad().item(), -1.0)
def process(b):
# create emission graph
g_emissions = gtn.linear_graph(T, C, inputs.requires_grad)
cpu_data = inputs[b].cpu().contiguous()
g_emissions.set_weights(cpu_data.data_ptr())
# create transition graph
g_transitions = ASGLossFunction.create_transitions_graph(
transitions, calc_trans_grad)
# create force align criterion graph
g_fal = ASGLossFunction.create_force_align_graph(targets[b])
# compose the graphs
g_fal_fwd = gtn.forward_score(
gtn.intersect(gtn.intersect(g_fal, g_transitions),
g_emissions))
g_fcc_fwd = gtn.forward_score(
gtn.intersect(g_emissions, g_transitions))
g_loss = gtn.subtract(g_fcc_fwd, g_fal_fwd)
scale = 1.0
if reduction == "mean":
L = len(targets[b])
scale = 1.0 / L if L > 0 else scale
elif reduction != "none":
raise ValueError("invalid value for reduction '" +
str(reduction) + "'")
# Save for backward:
losses[b] = g_loss
scales[b] = scale
emissions_graphs[b] = g_emissions
transitions_graphs[b] = g_transitions
def test_simple_decomposition(self):
T = 5
tokens = ["a", "b", "ab", "ba", "aba"]
scores = torch.randn((1, T, len(tokens)), requires_grad=True)
labels = [[0, 1, 0]]
transducer = Transducer(tokens=tokens,
graphemes_to_idx={
"a": 0,
"b": 1
})
# Hand construct the alignment graph with all of the decompositions
alignments = gtn.Graph(False)
alignments.add_node(True)
# Add the path ['a', 'b', 'a']
alignments.add_node()
alignments.add_arc(0, 1, 0)
alignments.add_arc(1, 1, 0)
alignments.add_node()
alignments.add_arc(1, 2, 1)
alignments.add_arc(2, 2, 1)
alignments.add_node(False, True)
alignments.add_arc(2, 3, 0)
alignments.add_arc(3, 3, 0)
# Add the path ['a', 'ba']
alignments.add_node(False, True)
alignments.add_arc(1, 4, 3)
alignments.add_arc(4, 4, 3)
# Add the path ['ab', 'a']
alignments.add_node()
alignments.add_arc(0, 5, 2)
alignments.add_arc(5, 5, 2)
alignments.add_arc(5, 3, 0)
# Add the path ['aba']
alignments.add_node(False, True)
alignments.add_arc(0, 6, 4)
alignments.add_arc(6, 6, 4)
emissions = gtn.linear_graph(T, len(tokens), True)
emissions.set_weights(scores.data_ptr())
expected_loss = gtn.subtract(
gtn.forward_score(emissions),
gtn.forward_score(gtn.intersect(emissions, alignments)),
)
loss = transducer(scores, labels)
self.assertAlmostEqual(loss.item(), expected_loss.item(), places=5)
loss.backward()
gtn.backward(expected_loss)
expected_grad = torch.tensor(emissions.grad().weights_to_numpy())
expected_grad = expected_grad.view((1, T, len(tokens)))
self.assertTrue(
torch.allclose(scores.grad, expected_grad, rtol=1e-4, atol=1e-5))
def crf_loss(X, Y, potentials, transitions):
feature_graph = gtn.compose(X, potentials)
# Compute the unnormalized score of `(X, Y)`
target_graph = gtn.compose(feature_graph, gtn.intersect(Y, transitions))
target_score = gtn.forward_score(target_graph)
# Compute the partition function
norm_graph = gtn.compose(feature_graph, transitions)
norm_score = gtn.forward_score(norm_graph)
return gtn.subtract(norm_score, target_score)
def test_scalar_ops(self):
g1 = gtn.scalar_graph(3.0)
result = gtn.negate(g1)
self.assertEqual(result.item(), -3.0)
g2 = gtn.scalar_graph(4.0)
result = gtn.add(g1, g2)
self.assertEqual(result.item(), 7.0)
result = gtn.subtract(g2, g1)
self.assertEqual(result.item(), 1.0)
def forward_single(b):
emissions = gtn.linear_graph(T, C, inputs.requires_grad)
data = inputs[b].contiguous()
emissions.set_weights(data.data_ptr())
target = GTNLossFunction.make_target_graph(targets[b])
# Score the target:
target_score = gtn.forward_score(gtn.intersect(target, emissions))
# Normalization term:
norm = gtn.forward_score(emissions)
# Compute the loss:
loss = gtn.subtract(norm, target_score)
# Save state for backward:
losses[b] = loss
emissions_graphs[b] = emissions
def seq_loss(batch_index):
obs_fst = linearFstFromArray(arc_scores[batch_index].reshape(
num_samples, -1))
gt_fst = fromSequence(arc_labels[batch_index])
# Compose each sequence fst individually: it seems like composition
# only works for lattices
denom_fst = obs_fst
for seq_fst in seq_fsts:
denom_fst = gtn.compose(denom_fst, seq_fst)
denom_fst = gtn.project_output(denom_fst)
num_fst = gtn.compose(denom_fst, gt_fst)
loss = gtn.subtract(gtn.forward_score(num_fst),
gtn.forward_score(denom_fst))
losses[batch_index] = loss
obs_fsts[batch_index] = obs_fst
def test_scalar_ops_grad(self):
g1 = gtn.scalar_graph(3.0)
result = gtn.negate(g1)
gtn.backward(result)
self.assertEqual(g1.grad().item(), -1.0)
g1.zero_grad()
g2 = gtn.scalar_graph(4.0)
result = gtn.add(g1, g2)
gtn.backward(result)
self.assertEqual(g1.grad().item(), 1.0)
self.assertEqual(g2.grad().item(), 1.0)
g1.zero_grad()
g2.zero_grad()
result = gtn.subtract(g1, g2)
gtn.backward(result)
self.assertEqual(g1.grad().item(), 1.0)
self.assertEqual(g2.grad().item(), -1.0)
g1.zero_grad()
g2.zero_grad()
result = gtn.add(gtn.add(g1, g2), g1)
gtn.backward(result)
self.assertEqual(g1.grad().item(), 2.0)
self.assertEqual(g2.grad().item(), 1.0)
g1.zero_grad()
g2nograd = gtn.scalar_graph(4.0, False)
result = gtn.add(g1, g2nograd)
gtn.backward(result)
self.assertEqual(g1.grad().item(), 1.0)
self.assertRaises(RuntimeError, g2nograd.grad)
def test_ctc_criterion(self):
# These test cases are taken from wav2letter: https:#fburl.com/msom2e4v
# Test case 1
ctc = ctc_graph([0, 0], 1)
emissions = emissions_graph([1.0, 0.0, 0.0, 1.0, 1.0, 0.0], 3, 2)
loss = gtn.forward_score(gtn.compose(ctc, emissions))
self.assertEqual(loss.item(), 0.0)
# Should be 0 since scores are normalized
z = gtn.forward_score(emissions)
self.assertEqual(z.item(), 0.0)
# Test case 2
T = 3
N = 4
ctc = ctc_graph([1, 2], N - 1)
emissions = emissions_graph([1.0] * (T * N), T, N)
expected_loss = -math.log(0.25 * 0.25 * 0.25 * 5)
loss = gtn.subtract(gtn.forward_score(gtn.compose(ctc, emissions)),
gtn.forward_score(emissions))
self.assertAlmostEqual(-loss.item(), expected_loss)
# Test case 3
T = 5
N = 6
target = [0, 1, 2, 1, 0]
# generate CTC graph
ctc = ctc_graph(target, N - 1)
# fmt: off
emissions_vec = [
0.633766,
0.221185,
0.0917319,
0.0129757,
0.0142857,
0.0260553,
0.111121,
0.588392,
0.278779,
0.0055756,
0.00569609,
0.010436,
0.0357786,
0.633813,
0.321418,
0.00249248,
0.00272882,
0.0037688,
0.0663296,
0.643849,
0.280111,
0.00283995,
0.0035545,
0.00331533,
0.458235,
0.396634,
0.123377,
0.00648837,
0.00903441,
0.00623107,
]
# fmt: on
emissions = emissions_graph(emissions_vec, T, N)
# The log probabilities are already normalized,
# so this should be close to 0
z = gtn.forward_score(emissions)
self.assertTrue(abs(z.item()) < 1e-5)
loss = gtn.subtract(z, gtn.forward_score(gtn.compose(ctc, emissions)))
expected_loss = 3.34211
self.assertAlmostEqual(loss.item(), expected_loss, places=5)
# Check the gradients
gtn.backward(loss)
# fmt: off
expected_grad = [
-0.366234, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553,
0.111121, -0.411608, 0.278779, 0.0055756, 0.00569609, 0.010436,
0.0357786, 0.633813, -0.678582, 0.00249248, 0.00272882, 0.0037688,
0.0663296, -0.356151, 0.280111, 0.00283995, 0.0035545, 0.00331533,
-0.541765, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107
]
# fmt: on
all_close = True
grad = emissions.grad()
grad_weights = grad.weights_to_list()
for i in range(T * N):
g = grad_weights[i]
all_close = all_close and (abs(expected_grad[i] - g) < 1e-5)
self.assertTrue(all_close)
# Test case 4
# This test case is taken from Tensor Flow CTC implementation
# tinyurl.com/y9du5v5a
T = 5
N = 6
target = [0, 1, 1, 0]
# generate CTC graph
ctc = ctc_graph(target, N - 1)
# fmt: off
emissions_vec = [
0.30176,
0.28562,
0.0831517,
0.0862751,
0.0816851,
0.161508,
0.24082,
0.397533,
0.0557226,
0.0546814,
0.0557528,
0.19549,
0.230246,
0.450868,
0.0389607,
0.038309,
0.0391602,
0.202456,
0.280884,
0.429522,
0.0326593,
0.0339046,
0.0326856,
0.190345,
0.423286,
0.315517,
0.0338439,
0.0393744,
0.0339315,
0.154046,
]
# fmt: on
emissions = emissions_graph(emissions_vec, T, N)
# The log probabilities are already normalized,
# so this should be close to 0
z = gtn.forward_score(emissions)
self.assertTrue(abs(z.item()) < 1e-5)
loss = gtn.subtract(z, gtn.forward_score(gtn.compose(ctc, emissions)))
expected_loss = 5.42262
self.assertAlmostEqual(loss.item(), expected_loss, places=4)
# Check the gradients
gtn.backward(loss)
# fmt: off
expected_grad = [
-0.69824,
0.28562,
0.0831517,
0.0862751,
0.0816851,
0.161508,
0.24082,
-0.602467,
0.0557226,
0.0546814,
0.0557528,
0.19549,
0.230246,
0.450868,
0.0389607,
0.038309,
0.0391602,
-0.797544,
0.280884,
-0.570478,
0.0326593,
0.0339046,
0.0326856,
0.190345,
-0.576714,
0.315517,
0.0338439,
0.0393744,
0.0339315,
0.154046,
]
# fmt: on
all_close = True
grad = emissions.grad()
grad_weights = grad.weights_to_list()
for i in range(T * N):
g = grad_weights[i]
all_close = all_close and (abs(expected_grad[i] - g) < 1e-5)
self.assertTrue(all_close)
def test_asg_criterion(self):
# This test cases is taken from wav2letter: https://fburl.com/msom2e4v
T = 5
N = 6
# fmt: off
targets = [
[2, 1, 5, 1, 3],
[4, 3, 5],
[3, 2, 2, 1],
]
expected_loss = [
7.7417464256287,
6.4200420379639,
8.2780694961548,
]
emissions_vecs = [
[
-0.4340, -0.0254, 0.3667, 0.4180, -0.3805, -0.1707, 0.1060,
0.3631, -0.1122, -0.3825, -0.0031, -0.3801, 0.0443, -0.3795,
0.3194, -0.3130, 0.0094, 0.1560, 0.1252, 0.2877, 0.1997,
-0.4554, 0.2774, -0.2526, -0.4001, -0.2402, 0.1295, 0.0172,
0.1805, -0.3299
],
[
0.3298,
-0.2259,
-0.0959,
0.4909,
0.2996,
-0.2543,
-0.2863,
0.3239,
-0.3988,
0.0732,
-0.2107,
-0.4739,
-0.0906,
0.0480,
-0.1301,
0.3975,
-0.3317,
-0.1967,
0.4372,
-0.2006,
0.0094,
0.3281,
0.1873,
-0.2945,
0.2399,
0.0320,
-0.3768,
-0.2849,
-0.2248,
0.3186,
],
[
0.0225,
-0.3867,
-0.1929,
-0.2904,
-0.4958,
-0.2533,
0.4001,
-0.1517,
-0.2799,
-0.2915,
0.4198,
0.4506,
0.1446,
-0.4753,
-0.0711,
0.2876,
-0.1851,
-0.1066,
0.2081,
-0.1190,
-0.3902,
-0.1668,
0.1911,
-0.2848,
-0.3846,
0.1175,
0.1052,
0.2172,
-0.0362,
0.3055,
],
]
emissions_grads = [
[
0.1060,
0.1595,
-0.7639,
0.2485,
0.1118,
0.1380,
0.1915,
-0.7524,
0.1539,
0.1175,
0.1717,
0.1178,
0.1738,
0.1137,
0.2288,
0.1216,
0.1678,
-0.8057,
0.1766,
-0.7923,
0.1902,
0.0988,
0.2056,
0.1210,
0.1212,
0.1422,
0.2059,
-0.8160,
0.2166,
0.1300,
],
[
0.2029,
0.1164,
0.1325,
0.2383,
-0.8032,
0.1131,
0.1414,
0.2602,
0.1263,
-0.3441,
-0.3009,
0.1172,
0.1557,
0.1788,
0.1496,
-0.5498,
0.0140,
0.0516,
0.2306,
0.1219,
0.1503,
-0.4244,
0.1796,
-0.2579,
0.2149,
0.1745,
0.1160,
0.1271,
0.1350,
-0.7675,
],
[
0.2195,
0.1458,
0.1770,
-0.8395,
0.1307,
0.1666,
0.2148,
0.1237,
-0.6613,
-0.1223,
0.2191,
0.2259,
0.2002,
0.1077,
-0.8386,
0.2310,
0.1440,
0.1557,
0.2197,
-0.1466,
-0.5742,
0.1510,
0.2160,
0.1342,
0.1050,
-0.8265,
0.1714,
0.1917,
0.1488,
0.2094,
],
]
# fmt: on
transitions = gtn.Graph()
transitions.add_node(True)
for i in range(1, N + 1):
transitions.add_node(False, True)
transitions.add_arc(0, i, i - 1) # p(i | <s>)
for i in range(N):
for j in range(N):
transitions.add_arc(j + 1, i + 1, i) # p(i | j)
for b in range(len(targets)):
target = targets[b]
emissions_vec = emissions_vecs[b]
emissions_grad = emissions_grads[b]
fal = gtn.Graph()
fal.add_node(True)
for l in range(1, len(target) + 1):
fal.add_node(False, l == len(target))
fal.add_arc(l - 1, l, target[l - 1])
fal.add_arc(l, l, target[l - 1])
emissions = emissions_graph(emissions_vec, T, N, True)
loss = gtn.subtract(
gtn.forward_score(gtn.compose(emissions, transitions)),
gtn.forward_score(
gtn.compose(gtn.compose(fal, transitions), emissions)),
)
self.assertAlmostEqual(loss.item(), expected_loss[b], places=3)
# Check the gradients
gtn.backward(loss)
all_close = True
grad = emissions.grad()
grad_weights = grad.weights_to_list()
for i in range(T * N):
g = grad_weights[i]
all_close = all_close and (abs(emissions_grad[i] - g) < 1e-4)
self.assertTrue(all_close)
all_close = True
# fmt: off
trans_grad = [
0.3990,
0.3396,
0.3486,
0.3922,
0.3504,
0.3155,
0.3666,
0.0116,
-1.6678,
0.3737,
0.3361,
-0.7152,
0.3468,
0.3163,
-1.1583,
-0.6803,
0.3216,
0.2722,
0.3694,
-0.6688,
0.3047,
-0.8531,
-0.6571,
0.2870,
0.3866,
0.3321,
0.3447,
0.3664,
-0.2163,
0.3039,
0.3640,
-0.6943,
0.2988,
-0.6722,
0.3215,
-0.1860,
]
# fmt: on
grad = transitions.grad()
grad_weights = grad.weights_to_list()
for i in range(N * N):
g = grad_weights[i + N]
all_close = all_close and (abs(trans_grad[i] - g) < 1e-4)
self.assertTrue(all_close)
def main(out_dir=None,
gpu_dev_id=None,
num_samples=10,
random_seed=None,
learning_rate=1e-3,
num_epochs=500,
dataset_kwargs={},
dataloader_kwargs={},
model_kwargs={}):
if out_dir is None:
out_dir = os.path.join('~', 'data', 'output', 'seqtools', 'test_gtn')
out_dir = os.path.expanduser(out_dir)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
fig_dir = os.path.join(out_dir, 'figures')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
vocabulary = ['a', 'b', 'c', 'd', 'e']
transition = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 1, 0], [0, 0, 0, 0, 1],
[0, 1, 0, 0, 1], [0, 0, 0, 0, 0]],
dtype=float)
initial = np.array([1, 0, 1, 0, 0], dtype=float)
final = np.array([0, 1, 0, 0, 1], dtype=float) / 10
seq_params = (transition, initial, final)
simulated_dataset = simulate(num_samples, *seq_params)
label_seqs, obsv_seqs = tuple(zip(*simulated_dataset))
seq_params = tuple(map(lambda x: -np.log(x), seq_params))
dataset = torchutils.SequenceDataset(obsv_seqs, label_seqs,
**dataset_kwargs)
data_loader = torch.utils.data.DataLoader(dataset, **dataloader_kwargs)
train_loader = data_loader
val_loader = data_loader
transition_weights = torch.tensor(transition, dtype=torch.float).log()
initial_weights = torch.tensor(initial, dtype=torch.float).log()
final_weights = torch.tensor(final, dtype=torch.float).log()
model = libfst.LatticeCrf(vocabulary,
transition_weights=transition_weights,
initial_weights=initial_weights,
final_weights=final_weights,
debug_output_dir=fig_dir,
**model_kwargs)
gtn.draw(model._transition_fst,
os.path.join(fig_dir, 'transitions-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(model._duration_fst,
os.path.join(fig_dir, 'durations-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
if True:
for i, (inputs, targets, seq_id) in enumerate(train_loader):
arc_scores = model.scores_to_arc(inputs)
arc_labels = model.labels_to_arc(targets)
batch_size, num_samples, num_classes = arc_scores.shape
obs_fst = libfst.linearFstFromArray(arc_scores[0].reshape(
num_samples, -1))
gt_fst = libfst.fromSequence(arc_labels[0])
d1_fst = gtn.compose(obs_fst, model._duration_fst)
d1_fst = gtn.project_output(d1_fst)
denom_fst = gtn.compose(d1_fst, model._transition_fst)
# denom_fst = gtn.project_output(denom_fst)
num_fst = gtn.compose(denom_fst, gt_fst)
viterbi_fst = gtn.viterbi_path(denom_fst)
pred_fst = gtn.remove(gtn.project_output(viterbi_fst))
loss = gtn.subtract(gtn.forward_score(num_fst),
gtn.forward_score(denom_fst))
loss = torch.tensor(loss.item())
if torch.isinf(loss).any():
denom_alt = gtn.compose(obs_fst, model._transition_fst)
d1_min = gtn.remove(gtn.project_output(d1_fst))
denom_alt = gtn.compose(d1_min, model._transition_fst)
num_alt = gtn.compose(denom_alt, gt_fst)
gtn.draw(obs_fst,
os.path.join(fig_dir, 'observations-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(gt_fst,
os.path.join(fig_dir, 'labels-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(d1_fst,
os.path.join(fig_dir, 'd1-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(d1_min,
os.path.join(fig_dir, 'd1-min-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(denom_fst,
os.path.join(fig_dir, 'denominator-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(denom_alt,
os.path.join(fig_dir, 'denominator-alt-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(num_fst,
os.path.join(fig_dir, 'numerator-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(num_alt,
os.path.join(fig_dir, 'numerator-alt-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(viterbi_fst,
os.path.join(fig_dir, 'viterbi-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(pred_fst,
os.path.join(fig_dir, 'pred-init.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
import pdb
pdb.set_trace()
# Train the model
train_epoch_log = collections.defaultdict(list)
val_epoch_log = collections.defaultdict(list)
metric_dict = {
'Avg Loss': metrics.AverageLoss(),
'Accuracy': metrics.Accuracy()
}
criterion = model.nllLoss
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=1.00)
model, last_model_wts = torchutils.trainModel(
model,
criterion,
optimizer,
scheduler,
train_loader,
val_loader,
metrics=metric_dict,
test_metric='Avg Loss',
train_epoch_log=train_epoch_log,
val_epoch_log=val_epoch_log,
num_epochs=num_epochs)
gtn.draw(model._transition_fst,
os.path.join(fig_dir, 'transitions-trained.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
gtn.draw(model._duration_fst,
os.path.join(fig_dir, 'durations-trained.png'),
isymbols=model._arc_symbols,
osymbols=model._arc_symbols)
torchutils.plotEpochLog(train_epoch_log,
title="Train Epoch Log",
fn=os.path.join(fig_dir, "train-log.png"))
tokens = token_graph(word_pieces)
gtn.draw(tokens, "tokens.pdf", idx_to_wp, idx_to_wp)
# Recognizes "abc":
abc = gtn.Graph(False)
abc.add_node(True)
abc.add_node()
abc.add_node()
abc.add_node(False, True)
abc.add_arc(0, 1, let_to_idx["a"])
abc.add_arc(1, 2, let_to_idx["b"])
abc.add_arc(2, 3, let_to_idx["c"])
gtn.draw(abc, "abc.pdf", idx_to_let)
# Compute the decomposition graph for "abc":
abc_decomps = gtn.remove(gtn.project_output(gtn.compose(abc, lex)))
gtn.draw(abc_decomps, "abc_decomps.pdf", idx_to_wp, idx_to_wp)
# Compute the alignment graph for "abc":
abc_alignments = gtn.project_input(
gtn.remove(gtn.compose(tokens, abc_decomps)))
gtn.draw(abc_alignments, "abc_alignments.pdf", idx_to_wp)
# From here we can use the alignment graph with an emissions graph and
# transitions graphs to compute the sequence level criterion:
emissions = gtn.linear_graph(10, len(word_pieces), True)
loss = gtn.subtract(
gtn.forward_score(emissions),
gtn.forward_score(gtn.intersect(emissions, abc_alignments)))
print(f"Loss is {loss.item():.2f}")