def ctc_label_dense_to_sparse(labels, label_lengths):
# undocumented feature soon to be made public
from tensorflow.python.ops import functional_ops
label_shape = tf.shape(labels)
num_batches_tns = tf.pack([label_shape[0]])
max_num_labels_tns = tf.pack([label_shape[1]])
def range_less_than(previous_state, current_input):
return tf.expand_dims(tf.range(label_shape[1]), 0) < tf.fill(max_num_labels_tns, current_input)
init = tf.cast(tf.fill([1, label_shape[1]], 0), tf.bool)
dense_mask = functional_ops.scan(range_less_than, label_lengths,
initializer=init, parallel_iterations=1)
dense_mask = dense_mask[:, 0, :]
label_array = tf.reshape(tf.tile(tf.range(0, label_shape[1]), num_batches_tns),
label_shape)
label_ind = tf.boolean_mask(label_array, dense_mask)
batch_array = tf.transpose(tf.reshape(tf.tile(tf.range(0, label_shape[0]),
max_num_labels_tns), tf.reverse(label_shape, [True])))
batch_ind = tf.boolean_mask(batch_array, dense_mask)
indices = tf.transpose(tf.reshape(tf.concat(0, [batch_ind, label_ind]), [2, -1]))
vals_sparse = tf.gather_nd(labels, indices)
return tf.SparseTensor(tf.to_int64(indices), vals_sparse, tf.to_int64(label_shape))
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