'Constructor.
Args:
is_training: Indicates whether the BoxPredictor is in training mode.
num_classes: number of classes.  Note that num_classes *does not*
include the background category, so if groundtruth labels take values
in {0, 1, .., K-1}, num_classes=K (and not K+1, even though the
assigned classification targets can range from {0,... K}).
conv_hyperparams: Slim arg_scope with hyperparameters for conolutional
layers.
num_spatial_bins: A list of two integers `[spatial_bins_y,
spatial_bins_x]`.
depth: Target depth to reduce the input feature maps to.
crop_size: A list of two integers `[crop_height, crop_width]`.
box_code_size: Size of encoding for each box.'

def __init__(self, is_training, num_classes, conv_hyperparams, num_spatial_bins, depth, crop_size, box_code_size):

    super(RfcnBoxPredictor, self).__init__(is_training, num_classes)
    self._conv_hyperparams = conv_hyperparams
    self._num_spatial_bins = num_spatial_bins
    self._depth = depth
    self._crop_size = crop_size
    self._box_code_size = box_code_size
