code: """CostCalculator that computes network cost or regularization loss."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  
  CONV2D_OPS = ('Conv2D', 'Conv2DBackpropInput', 'DepthwiseConv2dNative')
  FLOP_OPS = CONV2D_OPS + ('MatMul',)
  SUPPORTED_OPS = FLOP_OPS + (
      'Add', 'AddN', 'ConcatV2', 'FusedBatchNorm', 'Mul', 'Relu', 'Relu6', 'Sum')
  
  
  class CostCalculator(object):
    """CostCalculator that calculates resource cost/loss for a network."""
  
    def __init__(self, op_regularizer_manager, resource_function):
      """Creates an instance.
  
      Args:
        op_regularizer_manager: OpRegularizerManager that contains the
          OpRegularizer for each op in the network.
        resource_function: Callable that returns the resource (e.g. FLOP) cost or
          loss for an op.  The function signature is:
            op; A tf.Operation.
            is_regularization; Boolean indicating whether to calculate
              regularization loss.  If False, calculate cost instead.
            num_alive_inputs; Scalar Tensor indicating how many input channels are
              considered alive.
            num_alive_outputs; Scalar Tensor indicating how many output channels
              are considered alive.
            reg_inputs; Scalar Tensor which is the sum over the input
              regularization vector.
            reg_outputs; Scalar Tensor which is the sum over the output
              regularization vector.
            batch_size; Integer batch size to calculate cost/loss for.
      """
      self._manager = op_regularizer_manager
      self._resource_function = resource_function
  
    def _get_cost_or_regularization_term(self, is_regularization, ops=None):
      """Returns cost or regularization term for ops.
  
      Args:
        is_regularization: Boolean indicating whether to calculate regularization
          loss.  If False, calculate cost instead.
        ops: List of tf.Operation.  If None, calculates cost/regularization for
          all ops found by OpRegularizerManager.
  
      Returns:
        Cost or regularization term for ops as a tensor or float.
      """
      total = 0.0
      if not ops:
        ops = self._manager.ops
      for op in ops:
        if op.type not in SUPPORTED_OPS:
          continue
  
        # Get regularization and alive terms for input and output.
        input_tensor = _get_input(op)
        if op.type == 'ConcatV2':
          # For concat, the input and output regularization are identical but the
          # input is composed of multiple concatenated regularizers.  Thus, just
          # use the output regularizer as the input regularizer for simpler cost
          # calculation.
          input_tensor = op.outputs[0]
        input_op_reg = self._manager.get_regularizer(input_tensor.op)
        output_op_reg = self._manager.get_regularizer(op)
        num_alive_inputs = _count_alive(input_tensor, input_op_reg)
        num_alive_outputs = _count_alive(op.outputs[0], output_op_reg)
        reg_inputs = _sum_of_reg_vector(input_op_reg)
        reg_outputs = _sum_of_reg_vector(output_op_reg)
  
        total += self._resource_function(
            op, is_regularization, num_alive_inputs, num_alive_outputs,
            reg_inputs, reg_outputs)
  
      # If at least one supported op is present, type would be tensor, not float.
      if isinstance(total, float):
        # Tests rely on this function not raising exception in this case.
        tf.logging.warning('No supported ops found.')
      return total
  
    def get_cost(self, ops=None):
      """Returns cost for ops.
  
      Args:
        ops: List of tf.Operation.  If None, calculates cost/regularization for
          all ops found by OpRegularizerManager.
  
      Returns:
        Cost of ops as a tensor for float.
      """
  
      return self._get_cost_or_regularization_term(False, ops)
  
    def get_regularization_term(self, ops=None):
      """Returns regularization for ops.
  
      Args:
        ops: List of tf.Operation.  If None, calculates cost/regularization for
          all ops found by OpRegularizerManager.
  
      Returns:
        Regularization term of ops as a tensor or float.
      """
      return self._get_cost_or_regularization_term(True, ops)
  
  
  def _get_input(op):
    """Returns the input to that op that represents the activations.
  
    (as opposed to e.g. weights.)
  
    Args:
      op: A tf.Operation object with type in SUPPORTED_OPS.
  
    Returns:
      A tf.Tensor representing the input activations.
  
    Raises:
      ValueError: MatMul is used with transposition (unsupported).
    """
    assert op.type in SUPPORTED_OPS, 'Op type %s is not supported.' % op.type
    if op.type == 'Conv2D' or op.type == 'DepthwiseConv2dNative':
      return op.inputs[0]
    if op.type == 'Conv2DBackpropInput':
      return op.inputs[2]
    if op.type == 'MatMul':
      if op.get_attr('transpose_a') or op.get_attr('transpose_b'):
        raise ValueError('MatMul with transposition is not yet supported.')
      return op.inputs[0]
    return op.inputs[0]
  
  
  def _count_alive(tensor, opreg):
    if opreg:
      return tf.reduce_sum(tf.cast(opreg.alive_vector, tf.float32))
    shape = tensor.shape.as_list()
    if shape:
      num_outputs = tensor.shape.as_list()[-1]
      if num_outputs is not None:
        return tf.constant(num_outputs, tf.float32)
    tf.logging.info('Unknown channel count in tensor %s', tensor)
    return tf.constant(0, tf.float32)
  
  
  def _sum_of_reg_vector(opreg):
    if opreg:
      return tf.reduce_sum(opreg.regularization_vector)
    else:
      return tf.constant(0.0, tf.float32)
  
apis: tensorflow.logging.info tensorflow.cast tensorflow.logging.warning tensorflow.constant tensorflow.reduce_sum