d533214900cea56a4ec0be87577e97d3807b6bc5,keras_ssd_loss.py,SSD_Loss,smooth_L1_loss,#SSD_Loss#Any#Any#,41

Before Change

        // In order to normalize the localization loss, we perform element-wise division by the default box widths and heights.
        // Deviations in xmin and xmax are divided by their respective default box widths, deviations in ymin and ymax are divided
        // by their respective default box heights.
        absolute_loss = tf.abs(y_true - y_pred) / self.loc_norm
        square_loss = 0.5 * (y_true - y_pred)**2 / self.loc_norm
        l1_loss = tf.where(tf.less(absolute_loss, 1.0), square_loss, absolute_loss - 0.5)
        return tf.reduce_sum(l1_loss, axis=-1)

After Change

        References:
            https://arxiv.org/abs/1504.08083
        """
        absolute_loss = tf.abs(y_true - y_pred)
        square_loss = 0.5 * (y_true - y_pred)**2
        l1_loss = tf.where(tf.less(absolute_loss, 1.0), square_loss, absolute_loss - 0.5)
        return tf.reduce_sum(l1_loss, axis=-1)

Instances