-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathyololoss.py
149 lines (113 loc) · 5.6 KB
/
yololoss.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
import tensorflow as tf
#生成带序号的网格
def _create_mesh_xy(batch_size,grid_h,grid_w,n_box):
mesh_x = tf.cast(tf.reshape(tf.tile(tf.range(grid_w), [grid_h]), (1, grid_h, grid_w, 1, 1)), tf.float32)
mesh_y = tf.transpose(mesh_x, (0, 2, 1, 3, 4))
mesh_xy = tf.tile(tf.concat([mesh_x, mesh_y], -1), [batch_size, 1, 1, n_box, 1])
return mesh_xy
#将网格信息融入坐标，置信度做sigmoid。并重新组合
def adjust_pred_tensor(y_pred):
grid_offset=_create_mesh_xy(*y_pred.shape[:4])
# 计算该尺度矩阵上的坐标sigma(t_xy) + c_xy
pred_xy=grid_offset+tf.sigmoid(y_pred[...,:2])
# 取出预测物体的尺寸t_wh
pred_wh=y_pred[...,2:4]
# 对分类概率（置信度）做sigmoid转化
pred_conf=tf.sigmoid(y_pred[...,4])
# 取出分类结果
pred_classes=y_pred[...,5:]
#重新组合
preds=tf.concat([pred_xy,pred_wh,tf.expand_dims(pred_conf,axis=-1),pred_classes],axis=-1)
return preds
#生成一个矩阵。每个格子里放有3个候选框
def _create_mesh_anchor(anchors, batch_size, grid_h, grid_w, n_box):
mesh_anchor = tf.tile(anchors, [batch_size * grid_h * grid_w])
mesh_anchor = tf.reshape(mesh_anchor, [batch_size, grid_h, grid_w, n_box, 2]) # 每个候选框有2个值
mesh_anchor = tf.cast(mesh_anchor, tf.float32)
return mesh_anchor
def conf_delta_tensor(y_true, y_pred, anchors, ignore_thresh):
pred_box_xy,pred_box_wh,pred_box_conf=y_pred[...,:2],y_pred[...,2:4],y_pred[...,4]
#带有候选框的格子矩阵
anchors_grid=_create_mesh_anchor(anchors,*y_pred.shape[:4])
true_wh=y_true[:,:,:,:,2:4]
true_wh=anchors_grid*tf.exp(true_wh)
# 还原真实尺寸，高和宽
true_wh=true_wh*tf.expand_dims(y_true[:,:,:,:,4],4)
# y_pred.shape[3]为候选框个数
anchors_=tf.constant(anchors,dtype="float",shape=[1,1,1,y_pred.shape[3],2])
#获取中心点
true_xy=y_true[...,0:2]
true_wh_half=true_wh/2.
true_mins=true_xy-true_wh_half#左上角
true_maxes=true_xy+true_wh_half#右下角
pred_xy=pred_box_xy
pred_wh=tf.exp(pred_box_wh)*anchors_
pred_wh_half = pred_wh / 2.
pred_mins = pred_xy - pred_wh_half # 计算起始坐标
pred_maxes = pred_xy + pred_wh_half # 计算尾部坐标
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
#计算重叠面积
intersect_wh=tf.maximum(intersect_maxes-intersect_mins,0.)
intersect_areas=intersect_wh[...,0]*intersect_wh[...,1]
true_area=true_wh[...,0]*true_wh[...,1]
pred_area=pred_wh[...,0]*pred_wh[...,1]
#计算不重叠面积
union_area=pred_area+true_area-intersect_areas
best_ious=tf.truediv(intersect_areas,union_area)#计算iou
#iou小于阈值作为负向的loss
conf_delta=pred_box_conf*tf.cast(best_ious<ignore_thresh,tf.float32)
return conf_delta
def wh_scale_tensor(true_box_wh, anchors, image_size):
image_size_=tf.reshape(tf.cast(image_size,tf.float32),[1,1,1,1,2])
anchors_=tf.constant(anchors,dtype='float',shape=[1,1,1,3,2])
#计算高和宽缩放范围
wh_scale=tf.exp(true_box_wh)*anchors_/image_size_
#物体尺寸占整个图片的面积比
wh_scale=tf.expand_dims(2-wh_scale[...,0]*wh_scale[...,1],axis=4)
return wh_scale
#位置loss为box之差乘缩放比，所得的结果，再进行平方求和
def loss_coord_tensor(object_mask, pred_box, true_box, wh_scale, xywh_scale):
xy_delta=object_mask*(pred_box-true_box)*wh_scale*xywh_scale
loss_xy=tf.reduce_sum(tf.square(xy_delta),list(range(1,5)))
return loss_xy
def loss_conf_tensor(object_mask, pred_box_conf, true_box_conf, obj_scale, noobj_scale, conf_delta):
object_mask_ = tf.squeeze(object_mask, axis=-1)
conf_delta=object_mask_*(pred_box_conf-true_box_conf) * obj_scale + (1-object_mask_) * conf_delta * noobj_scale
loss_conf=tf.reduce_sum(tf.square(conf_delta),list(range(1,4)))
return loss_conf
def loss_class_tensor(object_mask, pred_box_class, true_box_class, class_scale):
true_box_class_ = tf.cast(true_box_class, tf.int64)
class_delta = object_mask *\
tf.expand_dims(tf.nn.softmax_cross_entropy_with_logits_v2(labels=true_box_class_, logits=pred_box_class), 4) * \
class_scale
loss_class = tf.reduce_sum(class_delta, list(range(1, 5)))
return loss_class
ignore_thresh=0.5
grid_scale=1
obj_scale=5
noobj_scale=1
xywh_scale=1
class_scale=1
def lossCalculator(y_true, y_pred, anchors,image_size):
y_pred=tf.reshape(y_pred,y_true.shape)
object_mask=tf.expand_dims(y_true[...,4],4)
preds=adjust_pred_tensor(y_pred)
conf_delta = conf_delta_tensor(y_true, preds, anchors, ignore_thresh)
wh_scale = wh_scale_tensor(y_true[..., 2:4], anchors, image_size)
loss_box = loss_coord_tensor(object_mask, preds[..., :4], y_true[..., :4], wh_scale, xywh_scale)
loss_conf = loss_conf_tensor(object_mask, preds[..., 4], y_true[..., 4], obj_scale, noobj_scale, conf_delta)
loss_class = loss_class_tensor(object_mask, preds[..., 5:], y_true[..., 5:], class_scale)
loss = loss_box + loss_conf + loss_class
return loss * grid_scale
def loss_fn(list_y_trues, list_y_preds,anchors,image_size):
inputanchors = [anchors[12:], anchors[6:12], anchors[:6]]
losses = [lossCalculator(list_y_trues[i], list_y_preds[i], inputanchors[i], image_size) for i in
range(len(list_y_trues))]
return tf.sqrt(tf.reduce_sum(losses)) # 将三个矩阵的loss相加再开平方
if __name__=="__main__":
xy=_create_mesh_xy(1,8,8,3)
sess=tf.Session()
sess.run(tf.global_variables_initializer())
m_xy=sess.run(xy)
print(m_xy)