def _ragged_as_leaf_node(
ragged_tensor: tf.RaggedTensor, is_repeated: bool,
reference_ragged_tensor: tf.RaggedTensor,
options: calculate_options.Options) -> prensor.LeafNodeTensor:
"""Creates a ragged tensor as a leaf node."""
assertions = []
size_dim = tf.compat.dimension_at_index(ragged_tensor.shape, 0).value
reference_size_dim = tf.compat.dimension_at_index(
reference_ragged_tensor.shape, 0).value
if (size_dim is not None and reference_size_dim is not None):
if size_dim != reference_size_dim:
raise ValueError("Returned ragged tensor is not the right size.")
elif options.ragged_checks:
assertions.append(
tf.assert_equal(ragged_tensor.nrows(),
reference_ragged_tensor.nrows()))
if not is_repeated:
rowids = ragged_tensor.value_rowids()
if options.ragged_checks:
assertions.append(
tf.compat.v1.assert_positive(rowids[1:] - rowids[:-1]))
if assertions:
with tf.control_dependencies(assertions):
parent_index = ragged_tensor.value_rowids()
return prensor.LeafNodeTensor(parent_index, ragged_tensor.values,
is_repeated)
else:
parent_index = ragged_tensor.value_rowids()
return prensor.LeafNodeTensor(parent_index, ragged_tensor.values,
is_repeated)
def pad_sequence_left(sequences_batch: tf.RaggedTensor, mask: bool):
""" Pad sequences with zeros on left side """
# Truncate rows to have at most `settings.SEQUENCE_LENGTH` items
sequences_batch = sequences_batch[:,-settings.settings.sequence_length:]
if mask:
# Add one to indices, to reserve 0 index for padding
sequences_batch += 1
pad_row_lengths = settings.settings.sequence_length - sequences_batch.row_lengths()
pad_values = tf.zeros( [(settings.settings.sequence_length * sequences_batch.nrows()) - tf.size(sequences_batch, tf.int64)],
sequences_batch.dtype)
padding = tf.RaggedTensor.from_row_lengths(pad_values, pad_row_lengths)
return tf.concat([padding, sequences_batch], axis=1).to_tensor()
