text: # Copyright 2020 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # Lint as: python3
  """Utilties for working with sdf and pseudo-sdf functions."""
  
  import tensorflow as tf
  
  
  def apply_class_transfer(sdf, model_config, soft_transfer, offset, dtype=None):
    """Applies a class label transformation to an input sdf.
  
    Args:
      sdf: Tensor of any shape. The sdfs to transform elementwise.
      model_config: A ModelConfig object.
      soft_transfer: Boolean. Whether the input gt should have a smooth
        classification transfer applied, or a hard one. A hard transformation
        destroys gradients.
      offset: The isolevel at which the surface lives.
      dtype: tf.float32 or tf.bool if specified. The output type. A soft transfer
        is always a float32, so this parameter is ignored if soft_transfer is
        true. If soft_transfer is false, a cast from bool to float32 is made if
        necessary. Defaults to tf.float32.
  
    Returns:
      classes: Tensor of the same shape as sdf.
    """
    # If the prediction defines the surface boundary at a location other than
    # zero, we have to offset before we apply the classification transfer:
    if offset:
      sdf -= offset
    if soft_transfer:
      if model_config.hparams.lhdn == 't':
        with tf.variable_scope('lhdn', reuse=tf.AUTO_REUSE):
          tf.logging.info('Getting hdn variable in scope %s',
                          tf.get_variable_scope().name)
          init_value = tf.constant([model_config.hparams.hdn], dtype=tf.float32)
          hdn = tf.get_variable(
              'hdn', dtype=tf.float32, initializer=init_value, trainable=True)
      else:
        hdn = model_config.hparams.hdn
      return tf.sigmoid(hdn * sdf)
    else:
      if dtype is None or dtype == tf.float32:
        return tf.cast(sdf > 0.0, dtype=tf.float32)
      else:
        return sdf > 0.0
  
  
  def apply_density_transfer(sdf):
    """Applies a density transfer function to an input sdf.
  
    The input sdf could either be from a prediction or the ground truth.
  
    Args:
      sdf: Tensor of any shape. The sdfs to transform elementwise.
  
    Returns:
      densities: Tensor of the same shape as sdf. Contains values in the range
      0-1, where it is 1 near the surface. The density is not a pdf, as it does
      not sum to 1 over the tensor.
    """
    # TODO(kgenova) This is one of the simplest possible transfer functions,
    # but is it the right one? Falloff rate should be controlled, and a 'signed'
    # density might even be relevant.
    return tf.exp(-tf.abs(sdf))
  