1 # -*- coding: utf-8 -*-
4 @contact: zengarden2009@gmail.com
6 from __future__ import absolute_import
7 from __future__ import division
8 from __future__ import print_function
10 import tensorflow as tf
13 def _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=1.0):
16 :param bbox_pred: [-1, 4] in RPN. [-1, cls_num+1, 4] in Fast-rcnn
17 :param bbox_targets: shape is same as bbox_pred
23 box_diff = bbox_pred - bbox_targets
25 abs_box_diff = tf.abs(box_diff)
27 smoothL1_sign = tf.stop_gradient(
28 tf.to_float(tf.less(abs_box_diff, 1. / sigma_2)))
29 loss_box = tf.pow(box_diff, 2) * (sigma_2 / 2.0) * smoothL1_sign \
30 + (abs_box_diff - (0.5 / sigma_2)) * (1.0 - smoothL1_sign)
33 def smooth_l1_loss_rpn(bbox_pred, bbox_targets, label, sigma=1.0):
36 :param bbox_pred: [-1, 4]
37 :param bbox_targets: [-1, 4]
42 value = _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=sigma)
43 value = tf.reduce_sum(value, axis=1) # to sum in axis 1
44 rpn_positive = tf.where(tf.greater(label, 0))
46 # rpn_select = tf.stop_gradient(rpn_select) # to avoid
47 selected_value = tf.gather(value, rpn_positive)
48 non_ignored_mask = tf.stop_gradient(
49 1.0 - tf.to_float(tf.equal(label, -1))) # positve is 1.0 others is 0.0
51 bbox_loss = tf.reduce_sum(selected_value) / tf.maximum(1.0, tf.reduce_sum(non_ignored_mask))
55 def smooth_l1_loss_rcnn(bbox_pred, bbox_targets, label, num_classes, sigma=1.0):
58 :param bbox_pred: [-1, (cfgs.CLS_NUM +1) * 4]
59 :param bbox_targets:[-1, (cfgs.CLS_NUM +1) * 4]
66 outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0)))
68 bbox_pred = tf.reshape(bbox_pred, [-1, num_classes, 4])
69 bbox_targets = tf.reshape(bbox_targets, [-1, num_classes, 4])
71 value = _smooth_l1_loss_base(bbox_pred,
74 value = tf.reduce_sum(value, 2)
75 value = tf.reshape(value, [-1, num_classes])
77 inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]),
78 depth=num_classes, axis=1)
80 inside_mask = tf.stop_gradient(
81 tf.to_float(tf.reshape(inside_mask, [-1, num_classes])))
83 normalizer = tf.to_float(tf.shape(bbox_pred)[0])
84 bbox_loss = tf.reduce_sum(
85 tf.reduce_sum(value * inside_mask, 1)*outside_mask) / normalizer
89 def sum_ohem_loss(cls_score, label, bbox_pred, bbox_targets,
90 num_classes, num_ohem_samples=256, sigma=1.0):
93 :param cls_score: [-1, cls_num+1]
95 :param bbox_pred: [-1, 4*(cls_num+1)]
96 :param bbox_targets: [-1, 4*(cls_num+1)]
97 :param num_ohem_samples: 256 by default
98 :param num_classes: cls_num+1
102 cls_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label) # [-1, ]
103 # cls_loss = tf.Print(cls_loss, [tf.shape(cls_loss)], summarize=10, message='CLS losss shape ****')
105 outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0)))
106 bbox_pred = tf.reshape(bbox_pred, [-1, num_classes, 4])
107 bbox_targets = tf.reshape(bbox_targets, [-1, num_classes, 4])
109 value = _smooth_l1_loss_base(bbox_pred,
112 value = tf.reduce_sum(value, 2)
113 value = tf.reshape(value, [-1, num_classes])
115 inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]),
116 depth=num_classes, axis=1)
118 inside_mask = tf.stop_gradient(
119 tf.to_float(tf.reshape(inside_mask, [-1, num_classes])))
120 loc_loss = tf.reduce_sum(value * inside_mask, 1)*outside_mask
121 # loc_loss = tf.Print(loc_loss, [tf.shape(loc_loss)], summarize=10, message='loc_loss shape***')
123 sum_loss = cls_loss + loc_loss
125 num_ohem_samples = tf.stop_gradient(tf.minimum(num_ohem_samples, tf.shape(sum_loss)[0]))
126 _, top_k_indices = tf.nn.top_k(sum_loss, k=num_ohem_samples)
128 cls_loss_ohem = tf.gather(cls_loss, top_k_indices)
129 cls_loss_ohem = tf.reduce_mean(cls_loss_ohem)
131 loc_loss_ohem = tf.gather(loc_loss, top_k_indices)
132 normalizer = tf.to_float(num_ohem_samples)
133 loc_loss_ohem = tf.reduce_sum(loc_loss_ohem) / normalizer
135 return cls_loss_ohem, loc_loss_ohem