def sample_model(
num_features, num_classes,
potentials, transitions,
num_samples, max_len=20):
"""
Sample `num_samples` from a linear-chain CRF specified
by a `potentials` graph and a `transitions` graph. The
samples will have a random length in `[1, max_len]`.
"""
model = gtn.compose(potentials, transitions)
# Draw a random X with length randomly from [1, max_len] and find the
# most likely Y under the model:
samples = []
while len(samples) < num_samples:
# Sample X:
T = np.random.randint(1, max_len + 1)
X = np.random.randint(0, num_features, size=(T,))
X = make_chain_graph(X)
# Find the most likely Y given X:
Y = gtn.viterbi_path(gtn.compose(X, model))
# Clean up Y:
Y = gtn.project_output(Y)
Y.set_weights(np.zeros(Y.num_arcs()))
samples.append((X, Y))
return samples
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 pred_seq(batch_index):
obs_fst = linearFstFromArray(arc_scores[batch_index].reshape(
num_samples, -1))
# 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)
viterbi_path = gtn.viterbi_path(denom_fst)
best_paths[batch_index] = gtn.remove(
gtn.project_output(viterbi_path))
def test_project_clone(self):
g_str = [
"0 1",
"3 4",
"0 1 0 0 2",
"0 2 1 1 1",
"1 2 0 0 2",
"2 3 0 0 1",
"2 3 1 1 1",
"1 4 0 0 2",
"2 4 1 1 3",
"3 4 0 0 2",
]
graph = create_graph_from_text(g_str)
# Test clone
cloned = gtn.clone(graph)
self.assertTrue(gtn.equal(graph, cloned))
# Test projecting input
g_str = [
"0 1",
"3 4",
"0 1 0 0 2",
"0 2 1 1 1",
"1 2 0 0 2",
"2 3 0 0 1",
"2 3 1 1 1",
"1 4 0 0 2",
"2 4 1 1 3",
"3 4 0 0 2",
]
inputExpected = create_graph_from_text(g_str)
self.assertTrue(gtn.equal(gtn.project_input(graph), inputExpected))
# Test projecting output
g_str = [
"0 1",
"3 4",
"0 1 0 0 2",
"0 2 1 1 1",
"1 2 0 0 2",
"2 3 0 0 1",
"2 3 1 1 1",
"1 4 0 0 2",
"2 4 1 1 3",
"3 4 0 0 2",
]
outputExpected = create_graph_from_text(g_str)
self.assertTrue(gtn.equal(gtn.project_output(graph), outputExpected))
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 process(b):
emissions = gtn.linear_graph(T, C, False)
cpu_data = outputs[b].cpu().contiguous()
emissions.set_weights(cpu_data.data_ptr())
if self.transitions is not None:
full_graph = gtn.intersect(emissions, self.transitions)
else:
full_graph = emissions
# Find the best path and remove back-off arcs:
path = gtn.remove(gtn.viterbi_path(full_graph))
# Left compose the viterbi path with the "alignment to token"
# transducer to get the outputs:
path = gtn.compose(path, self.tokens)
# When there are ambiguous paths (allow_repeats is true), we take
# the shortest:
path = gtn.viterbi_path(path)
path = gtn.remove(gtn.project_output(path))
paths[b] = path.labels_to_list()
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}")
