def _compute_track_losses(self, input_height, input_width, prediction_dict):
    'Computes all the losses associated with tracking.\n\n    Args:\n      input_height: An integer scalar tensor representing input image height.\n      input_width: An integer scalar tensor representing input image width.\n      prediction_dict: The dictionary returned from the predict() method.\n\n    Returns:\n      A dictionary with tracking losses.\n    '
    object_reid_predictions = prediction_dict[TRACK_REID]
    embedding_loss = self._compute_track_embedding_loss(input_height=input_height, input_width=input_width, object_reid_predictions=object_reid_predictions)
    losses = {TRACK_REID: embedding_loss}
    return losses
Computes all the losses associated with tracking.

Args:
  input_height: An integer scalar tensor representing input image height.
  input_width: An integer scalar tensor representing input image width.
  prediction_dict: The dictionary returned from the predict() method.

Returns:
  A dictionary with tracking losses.
def _compute_track_losses(self, input_height, input_width, prediction_dict):
    'Computes all the losses associated with tracking.\n\n    Args:\n      input_height: An integer scalar tensor representing input image height.\n      input_width: An integer scalar tensor representing input image width.\n      prediction_dict: The dictionary returned from the predict() method.\n\n    Returns:\n      A dictionary with tracking losses.\n    '
    object_reid_predictions = prediction_dict[TRACK_REID]
    embedding_loss = self._compute_track_embedding_loss(input_height=input_height, input_width=input_width, object_reid_predictions=object_reid_predictions)
    losses = {TRACK_REID: embedding_loss}
    return losses