# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE.md file in the project root
# for full license information.
# ==============================================================================
import numpy as np
import cntk as C
def SmoothL1Loss(sigma, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights):
"""
From https://github.com/smallcorgi/Faster-RCNN_TF/blob/master/lib/fast_rcnn/train.py
ResultLoss = outside_weights * SmoothL1(inside_weights * (bbox_pred - bbox_targets))
SmoothL1(x) = 0.5 * (sigma * x)^2, if |x| < 1 / sigma^2
|x| - 0.5 / sigma^2, otherwise
"""
sigma2 = sigma * sigma
inside_mul_abs = C.abs(C.element_times(bbox_inside_weights, C.minus(bbox_pred, bbox_targets)))
smooth_l1_sign = C.less(inside_mul_abs, 1.0 / sigma2)
smooth_l1_option1 = C.element_times(C.element_times(inside_mul_abs, inside_mul_abs), 0.5 * sigma2)
smooth_l1_option2 = C.minus(inside_mul_abs, 0.5 / sigma2)
smooth_l1_result = C.plus(C.element_times(smooth_l1_option1, smooth_l1_sign),
C.element_times(smooth_l1_option2, C.minus(1.0, smooth_l1_sign)))
return C.element_times(bbox_outside_weights, smooth_l1_result)
