'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 convolution ops.
min_depth: Minumum feature depth prior to predicting box encodings
and class predictions.
max_depth: Maximum feature depth prior to predicting box encodings
and class predictions. If max_depth is set to 0, no additional
feature map will be inserted before location and class predictions.
num_layers_before_predictor: Number of the additional conv layers before
the predictor.
use_dropout: Option to use dropout for class prediction or not.
dropout_keep_prob: Keep probability for dropout.
This is only used if use_dropout is True.
kernel_size: Size of final convolution kernel.  If the
spatial resolution of the feature map is smaller than the kernel size,
then the kernel size is automatically set to be
min(feature_width, feature_height).
box_code_size: Size of encoding for each box.
apply_sigmoid_to_scores: if True, apply the sigmoid on the output
class_predictions.
Raises:
ValueError: if min_depth > max_depth.'

def __init__(self, is_training, num_classes, conv_hyperparams, min_depth, max_depth, num_layers_before_predictor, use_dropout, dropout_keep_prob, kernel_size, box_code_size, apply_sigmoid_to_scores=False):

    super(ConvolutionalBoxPredictor, self).__init__(is_training, num_classes)
    if (min_depth > max_depth):
        raise ValueError('min_depth    should    be    less    than    or    equal    to    max_depth')
    self._conv_hyperparams = conv_hyperparams
    self._min_depth = min_depth
    self._max_depth = max_depth
    self._num_layers_before_predictor = num_layers_before_predictor
    self._use_dropout = use_dropout
    self._kernel_size = kernel_size
    self._box_code_size = box_code_size
    self._dropout_keep_prob = dropout_keep_prob
    self._apply_sigmoid_to_scores = apply_sigmoid_to_scores
