Python functional_ops.scan方法代码示例

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import scan [as 别名]
def ctc_label_dense_to_sparse( self, labels, label_lengths ):
    """Mike Henry's implementation, with some minor modifications."""
    with self.G.as_default():
      label_shape = tf.shape( labels )
      num_batches_tns = tf.stack( [label_shape[0]] )
      max_num_labels_tns = tf.stack( [label_shape[1]] )

      def range_less_than(previous_state, current_input):
        return tf.expand_dims( tf.range( label_shape[1] ), 0 ) < current_input

      init = tf.cast( tf.fill( max_num_labels_tns, 0 ), tf.bool )
      init = tf.expand_dims( init, 0 )
      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,[0]) ) )
      batch_ind = tf.boolean_mask( batch_array, dense_mask )

      indices = tf.transpose( tf.reshape( tf.concat( axis=0, values=[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 ) ) 

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import scan [as 别名]
def ctc_label_dense_to_sparse(labels, label_lengths):
    """Converts CTC labels from dense to sparse.

    # Arguments
        labels: dense CTC labels.
        label_lengths: length of the labels.

    # Returns
        A sparse tensor representation of the labels.
    """
    label_shape = tf.shape(labels)
    num_batches_tns = tf.stack([label_shape[0]])
    max_num_labels_tns = tf.stack([label_shape[1]])

    def range_less_than(_, 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(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(label_shape[0]),
                                                  max_num_labels_tns), reverse(label_shape, 0)))
    batch_ind = tf.boolean_mask(batch_array, dense_mask)
    indices = tf.transpose(tf.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))

    vals_sparse = tf.gather_nd(labels, indices)

    return tf.SparseTensor(tf.to_int64(indices), vals_sparse, tf.to_int64(label_shape)) 

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import scan [as 别名]
def ctc_label_dense_to_sparse(labels, label_lengths):
    """Converts CTC labels from dense to sparse.

    # Arguments
        labels: dense CTC labels.
        label_lengths: length of the labels.

    # Returns
        A sparse tensor representation of the labels.
    """
    label_shape = tf.shape(labels)
    num_batches_tns = tf.stack([label_shape[0]])
    max_num_labels_tns = tf.stack([label_shape[1]])

    def range_less_than(_, 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), reverse(label_shape, 0)))
    batch_ind = tf.boolean_mask(batch_array, dense_mask)
    indices = tf.transpose(tf.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))

    vals_sparse = tf.gather_nd(labels, indices)

    return tf.SparseTensor(tf.to_int64(indices), vals_sparse, tf.to_int64(label_shape)) 

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import scan [as 别名]
def ctc_label_dense_to_sparse(labels, label_lengths):
    """Converts CTC labels from dense to sparse.

    # Arguments
        labels: dense CTC labels.
        label_lengths: length of the labels.

    # Returns
        A sparse tensor representation of the lablels.
    """
    label_shape = tf.shape(labels)
    num_batches_tns = tf.stack([label_shape[0]])
    max_num_labels_tns = tf.stack([label_shape[1]])

    def range_less_than(_, 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), reverse(label_shape, 0)))
    batch_ind = tf.boolean_mask(batch_array, dense_mask)
    indices = tf.transpose(tf.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))

    vals_sparse = tf.gather_nd(labels, indices)

    return tf.SparseTensor(tf.to_int64(indices), vals_sparse, tf.to_int64(label_shape)) 

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import scan [as 别名]
def ctc_label_dense_to_sparse(labels, label_lengths):
  """Converts CTC labels from dense to sparse.

  Arguments:
      labels: dense CTC labels.
      label_lengths: length of the labels.

  Returns:
      A sparse tensor representation of the lablels.
  """
  label_shape = array_ops.shape(labels)
  num_batches_tns = array_ops.stack([label_shape[0]])
  max_num_labels_tns = array_ops.stack([label_shape[1]])

  def range_less_than(_, current_input):
    return array_ops.expand_dims(
        math_ops.range(label_shape[1]), 0) < array_ops.fill(
            max_num_labels_tns, current_input)

  init = math_ops.cast(
      array_ops.fill([1, label_shape[1]], 0), dtypes_module.bool)
  dense_mask = functional_ops.scan(
      range_less_than, label_lengths, initializer=init, parallel_iterations=1)
  dense_mask = dense_mask[:, 0, :]

  label_array = array_ops.reshape(
      array_ops.tile(math_ops.range(0, label_shape[1]), num_batches_tns),
      label_shape)
  label_ind = array_ops.boolean_mask(label_array, dense_mask)

  batch_array = array_ops.transpose(
      array_ops.reshape(
          array_ops.tile(math_ops.range(0, label_shape[0]), max_num_labels_tns),
          reverse(label_shape, 0)))
  batch_ind = array_ops.boolean_mask(batch_array, dense_mask)
  indices = array_ops.transpose(
      array_ops.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))

  vals_sparse = array_ops.gather_nd(labels, indices)

  return sparse_tensor.SparseTensor(
      math_ops.to_int64(indices), vals_sparse, math_ops.to_int64(label_shape)) 

# 需要导入模块: from tensorflow.python.ops import functional_ops [as 别名]
# 或者: from tensorflow.python.ops.functional_ops import scan [as 别名]
def ctc_label_dense_to_sparse(labels, label_lengths):
  """Converts CTC labels from dense to sparse.

  Arguments:
      labels: dense CTC labels.
      label_lengths: length of the labels.

  Returns:
      A sparse tensor representation of the labels.
  """
  label_shape = array_ops.shape(labels)
  num_batches_tns = array_ops.stack([label_shape[0]])
  max_num_labels_tns = array_ops.stack([label_shape[1]])

  def range_less_than(_, current_input):
    return array_ops.expand_dims(
        math_ops.range(label_shape[1]), 0) < array_ops.fill(
            max_num_labels_tns, current_input)

  init = math_ops.cast(
      array_ops.fill([1, label_shape[1]], 0), dtypes_module.bool)
  dense_mask = functional_ops.scan(
      range_less_than, label_lengths, initializer=init, parallel_iterations=1)
  dense_mask = dense_mask[:, 0, :]

  label_array = array_ops.reshape(
      array_ops.tile(math_ops.range(0, label_shape[1]), num_batches_tns),
      label_shape)
  label_ind = array_ops.boolean_mask(label_array, dense_mask)

  batch_array = array_ops.transpose(
      array_ops.reshape(
          array_ops.tile(math_ops.range(0, label_shape[0]), max_num_labels_tns),
          reverse(label_shape, 0)))
  batch_ind = array_ops.boolean_mask(batch_array, dense_mask)
  indices = array_ops.transpose(
      array_ops.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))

  vals_sparse = array_ops.gather_nd(labels, indices)

  return sparse_tensor.SparseTensor(
      math_ops.to_int64(indices), vals_sparse, math_ops.to_int64(label_shape))