def testCtcLossV2(self):
random_seed.set_random_seed(5)
batch_size = 8
num_labels = 6
max_label_length = 5
num_frames = 12
labels = random_ops.random_uniform([batch_size, max_label_length],
minval=1,
maxval=num_labels,
dtype=dtypes.int64)
logits = random_ops.random_uniform(
[num_frames, batch_size, num_labels])
label_length = random_ops.random_uniform([batch_size],
minval=2,
maxval=max_label_length,
dtype=dtypes.int64)
label_mask = array_ops.sequence_mask(label_length,
maxlen=max_label_length,
dtype=label_length.dtype)
labels *= label_mask
logit_length = [num_frames] * batch_size
with backprop.GradientTape() as t:
t.watch(logits)
ref_loss = ctc_ops.ctc_loss_v2(labels=labels,
logits=logits,
label_length=label_length,
logit_length=logit_length)
ref_grad = t.gradient(ref_loss, [logits])
sparse_labels = ctc_ops.dense_labels_to_sparse(labels, label_length)
def assert_same_loss_and_grads(loss):
if context.executing_eagerly():
return
with self.cached_session():
self.assertAllClose(*self.evaluate([loss, ref_loss]))
grad = gradients_impl.gradients(loss, [logits])
self.assertAllClose(*self.evaluate([grad, ref_grad]),
rtol=2e-06,
atol=2e-06)
assert_same_loss_and_grads(
ctc_ops.ctc_loss_v2(labels=sparse_labels,
logits=logits,
label_length=label_length,
logit_length=logit_length,
blank_index=0))
def testCtcLossV2(self):
random_seed.set_random_seed(5)
batch_size = 8
num_labels = 6
max_label_length = 5
num_frames = 12
labels = random_ops.random_uniform(
[batch_size, max_label_length], minval=1, maxval=num_labels,
dtype=dtypes.int64)
logits = random_ops.random_uniform([num_frames, batch_size, num_labels])
label_length = random_ops.random_uniform(
[batch_size], minval=2, maxval=max_label_length, dtype=dtypes.int64)
label_mask = array_ops.sequence_mask(
label_length, maxlen=max_label_length, dtype=label_length.dtype)
labels *= label_mask
logit_length = [num_frames] * batch_size
with backprop.GradientTape() as t:
t.watch(logits)
ref_loss = ctc_ops.ctc_loss_v2(
labels=labels,
logits=logits,
label_length=label_length,
logit_length=logit_length)
ref_grad = t.gradient(ref_loss, [logits])
sparse_labels = ctc_ops.dense_labels_to_sparse(labels, label_length)
def assert_same_loss_and_grads(loss):
if context.executing_eagerly():
return
with self.cached_session():
self.assertAllClose(*self.evaluate([loss, ref_loss]))
grad = gradients_impl.gradients(loss, [logits])
self.assertAllClose(
*self.evaluate([grad, ref_grad]), rtol=2e-06, atol=2e-06)
assert_same_loss_and_grads(
ctc_ops.ctc_loss_v2(
labels=sparse_labels,
logits=logits,
label_length=label_length,
logit_length=logit_length,
blank_index=0))
def _ctc_loss_v2(labels, inputs, sequence_length,
preprocess_collapse_repeated=False,
ctc_merge_repeated=True,
ignore_longer_outputs_than_inputs=False,
time_major=True):
"""Call ctc_loss_v2 with v1 args."""
assert not preprocess_collapse_repeated
assert ctc_merge_repeated
assert not ignore_longer_outputs_than_inputs
return ctc_ops.ctc_loss_v2(
labels=labels,
logits=inputs,
logit_length=sequence_length,
label_length=None,
blank_index=-1,
logits_time_major=time_major)
def _ctc_loss_v2(labels, inputs, sequence_length,
preprocess_collapse_repeated=False,
ctc_merge_repeated=True,
ignore_longer_outputs_than_inputs=False,
time_major=True):
"""Call ctc_loss_v2 with v1 args."""
assert not preprocess_collapse_repeated
assert ctc_merge_repeated
assert not ignore_longer_outputs_than_inputs
return ctc_ops.ctc_loss_v2(
labels=labels,
logits=inputs,
logit_length=sequence_length,
label_length=None,
blank_index=-1,
logits_time_major=time_major)
