-
Notifications
You must be signed in to change notification settings - Fork 2
/
connect_loss_ty.py
139 lines (107 loc) · 5.55 KB
/
connect_loss_ty.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
"""
BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection
Ziyun Yang, Somayyeh Soltanian-Zadeh and Sina Farsiu
Codes from: https://github.com/Zyun-Y/BiconNets
Paper: https://arxiv.org/abs/2103.00334
"""
import numpy as np
from torch.nn.modules.loss import _Loss
from torch.autograd import Function, Variable
import torch.nn as nn
import torch
import numpy as np
from torch.nn.modules.loss import _Loss
from torch.autograd import Function, Variable
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from skimage.io import imread, imsave
import scipy.io as scio
#Directable={'upper_left':[-1,-1],'up':[0,-1],'upper_right':[1,-1],'left':[-1,0],'right':[1,0],'lower_left':[-1,1],'down':[0,1],'lower_right':[1,1]}
#TL_table = ['lower_right','down','lower_left','right','left','upper_right','up','upper_left']
Directable={'up':[0,-1],'upper':[0,-2],'left':[-1,0],'lefter':[-2,0],'right':[1,0],'righter':[2,0],'down':[0,1],'downer':[0,2]}
TL_table = ['downer','down','righter','right','lefter','left','upper','up']
class dice_loss(nn.Module):
def __init__(self):
super(dice_loss, self).__init__()
def soft_dice_coeff(self, y_pred,y_true):
smooth = 0.001 # may change
i = torch.sum(y_true,dim=(1,2))
j = torch.sum(y_pred,dim=(1,2))
intersection = torch.sum(y_true * y_pred,dim=(1,2))
score = (2. * intersection + smooth) / (i + j + smooth)
return (1-score)
def soft_dice_loss(self, y_pred,y_true):
loss = self.soft_dice_coeff(y_true, y_pred)
return loss.mean()
def __call__(self, y_pred,y_true):
b = self.soft_dice_loss(y_true, y_pred)
return b
def edge_loss(vote_out,con_target):
# print(vote_out.shape,con_target.shape)
sum_conn = torch.sum(con_target.clone(),dim=1)
edge = torch.where((sum_conn<8) & (sum_conn>0),torch.full_like(sum_conn, 1),torch.full_like(sum_conn, 0))
pred_mask_min, _ = torch.min(vote_out.cuda(), dim=1)
pred_mask_min = pred_mask_min*edge
minloss = F.binary_cross_entropy(pred_mask_min,torch.full_like(pred_mask_min, 0))
# print(minloss)
return minloss#+maxloss
class bicon_loss(nn.Module):
def __init__(self, size):
super(bicon_loss, self).__init__()
self.cross_entropy_loss = nn.BCELoss()
self.dice_loss = dice_loss()
self.num_class = 1
hori_translation = torch.zeros([1,self.num_class, size[1],size[1]])
for i in range(size[1]-1):
hori_translation[:,:,i,i+1] = torch.tensor(1.0)
verti_translation = torch.zeros([1,self.num_class,size[0],size[0]])
for j in range(size[0]-1):
verti_translation[:,:,j,j+1] = torch.tensor(1.0)
self.hori_trans = hori_translation.float().cuda()
self.verti_trans = verti_translation.float().cuda()
def forward(self, c_map, target, con_target):
batch_num = c_map.shape[0]
con_target = con_target.type(torch.FloatTensor).cuda()
#find edge ground truth
#sum_conn = torch.sum(con_target,dim=1)
#edge = torch.where(sum_conn<8,torch.full_like(sum_conn, 1),torch.full_like(sum_conn, 0))
#edge0 = torch.where(sum_conn>0,torch.full_like(sum_conn, 1),torch.full_like(sum_conn, 0))
#edge = edge*edge0
target = target.type(torch.FloatTensor).cuda()
c_map = F.sigmoid(c_map)
# construct the translation matrix
self.hori_translation = self.hori_trans.repeat(batch_num,1,1,1).cuda()
self.verti_translation = self.verti_trans.repeat(batch_num,1,1,1).cuda()
#final_pred, bimap = self.Bilater_voting(c_map)
# apply any loss you want below using final_pred, e.g., dice loss.
#loss_dice = self.dice_loss(final_pred, target)
# vote_out = vote_out.squeeze(1)
#decouple loss
# print(c_map.max(),bimap.max(),final_pred.max())
#decouple_loss = edge_loss(bimap.squeeze(1),con_target)
#bce_loss = self.cross_entropy_loss(final_pred,target)
conmap_l = self.cross_entropy_loss(c_map,con_target)
#bimap_l = self.cross_entropy_loss(bimap.squeeze(1),con_target)
loss = conmap_l #+ bce_loss# + loss_dice ## add dice loss if needed for biomedical data loss_dice = self.dice_loss(final_pred, target) +0.2*bimap_l+0.2*bimap_l+
# print(loss)
return loss
def shift_diag(self,img,shift):
## shift = [1,1] moving right and down
# print(img.shape,self.hori_translation.shape)
batch,class_num, row, column = img.size()
if shift[0]: ###horizontal
img = torch.bmm(img.view(-1,row,column),self.hori_translation.view(-1,column,column)) if shift[0]==1 else torch.bmm(img.view(-1,row,column),self.hori_translation.transpose(3,2).view(-1,column,column))
if shift[1]: ###vertical
img = torch.bmm(self.verti_translation.transpose(3,2).view(-1,row,row),img.view(-1,row,column)) if shift[1]==1 else torch.bmm(self.verti_translation.view(-1,row,row),img.view(-1,row,column))
return img.view(batch,class_num, row, column)
def Bilater_voting(self,c_map):
c_map = c_map.view(c_map.shape[0],-1,8,c_map.shape[2],c_map.shape[3])
batch,class_num,channel, row, column = c_map.size()
shifted_c_map = torch.zeros(c_map.size()).cuda()
for i in range(8):
shifted_c_map[:,:,i] = self.shift_diag(c_map[:,:,7-i].clone(),Directable[TL_table[i]])
vote_out = c_map*shifted_c_map
pred_mask,_ = torch.max(vote_out,dim=2)
# print(pred_mask)
return pred_mask, vote_out#, bimap