-
Notifications
You must be signed in to change notification settings - Fork 0
/
loss.py
executable file
·162 lines (136 loc) · 6.5 KB
/
loss.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
150
151
152
153
154
155
156
157
158
159
160
161
162
# Copyright (c) Gorilla-Lab. All rights reserved.
from torch import mul
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
import contextlib
class EstimationLoss(nn.Module):
# for our model
def __init__(self, gamma=0.0):
super(EstimationLoss, self).__init__()
self.weights = torch.from_numpy(np.load('/home/longpham2/tuan.vo/temp/Data/full_shape_weights.npy')).cuda().float()
self.gamma = gamma
self.alpha = 0
# pred & target shape: B x N x M
def forward(self, pred, target):
total_loss = F.nll_loss(pred, target, weight=self.weights)
return total_loss
class SemiLoss(nn.Module):
def __init__(self):
super(SemiLoss, self).__init__()
def MSELoss(self, input1, input2):
return torch.sum(torch.mean((input1-input2)**2, (0, 1)))
def forward(self, labeled_pred, target, unlabeled_pred, validate=False):
if not validate:
bs = int(labeled_pred.size(0)) // 2
pred = labeled_pred[0:bs, :, :]
else:
pred = labeled_pred
temp1 = -torch.mul(1-target, torch.log(1-pred+1e-6))
temp2 = -torch.mul(target, torch.log(pred+1e-6))
temp = temp1+temp2
CELoss = torch.sum(torch.mean(temp, (0, 1)))
intersection_positive = torch.sum(pred*target, 1)
cardinality_positive = torch.sum(torch.abs(pred)+torch.abs(target), 1)
dice_positive = (intersection_positive+1e-6) / \
(cardinality_positive+1e-6)
intersection_negative = torch.sum((1.-pred)*(1.-target), 1)
cardinality_negative = torch.sum(
2-torch.abs(pred)-torch.abs(target), 1)
dice_negative = (intersection_negative+1e-6) / \
(cardinality_negative+1e-6)
temp3 = torch.mean(1.0-dice_positive-dice_negative, 0)
DICELoss = torch.sum(temp3)
if validate:
return CELoss+DICELoss
else:
pred_noise = labeled_pred[bs:, :, :]
ul_pred1 = unlabeled_pred[0:bs, :, :]
ul_pred2 = unlabeled_pred[bs:, :, :]
consistentLoss = self.MSELoss(input1=torch.cat([pred.detach(), ul_pred1.detach(
)], dim=0), input2=torch.cat([pred_noise, ul_pred2], dim=0))
return CELoss+DICELoss+consistentLoss
@contextlib.contextmanager
def _disable_tracking_bn_stats(model):
def switch_attr(m):
if hasattr(m, 'track_running_stats'):
m.track_running_stats ^= True
model.apply(switch_attr)
yield
model.apply(switch_attr)
def _l2_normalize(d):
d_reshaped = d.view(d.shape[0], -1, *(1 for _ in range(d.dim() - 2)))
d /= torch.norm(d_reshaped, dim=1, keepdim=True) + 1e-8
return d
class VATLoss(nn.Module):
def __init__(self, warmup_epoch=0):
super(VATLoss, self).__init__()
self.xi = 1e-6
self.eps = 2.0
self.ip = 1
self.warmup_epoch = warmup_epoch
def MSELoss(self, input1, input2):
return torch.sum(torch.mean((input1-input2)**2, (0, 1)))
def forward(self, model, labeled_data, unlabeled_data, labeled_pred, target, unlabeled_pred, epoch, validate=False):
pred = labeled_pred
temp1 = -torch.mul(1-target, torch.log(1-pred+1e-6))
temp2 = -torch.mul(target, torch.log(pred+1e-6))
temp = temp1+temp2
CELoss = torch.sum(torch.mean(temp, (0, 1)))
intersection_positive = torch.sum(pred*target, 1)
cardinality_positive = torch.sum(torch.abs(pred)+torch.abs(target), 1)
dice_positive = (intersection_positive+1e-6) / \
(cardinality_positive+1e-6)
intersection_negative = torch.sum((1.-pred)*(1.-target), 1)
cardinality_negative = torch.sum(
2-torch.abs(pred)-torch.abs(target), 1)
dice_negative = (intersection_negative+1e-6) / \
(cardinality_negative+1e-6)
temp3 = torch.mean(1.5-dice_positive-dice_negative, 0)
DICELoss = torch.sum(temp3)
if validate:
return CELoss+DICELoss
elif epoch < self.warmup_epoch:
return CELoss+DICELoss
else:
d_labeled = torch.rand(labeled_data.shape).sub(0.5).cuda()
d_labeled = _l2_normalize(d_labeled)
d_unlabeled = torch.rand(unlabeled_data.shape).sub(0.5).cuda()
d_unlabeled = _l2_normalize(d_unlabeled)
with _disable_tracking_bn_stats(model):
for _ in range(self.ip):
d_labeled.requires_grad_()
d_unlabeled.requires_grad_()
train_labeled_data_view2 = labeled_data + self.xi*d_labeled
train_unlabeled_data_view2 = unlabeled_data + self.xi*d_unlabeled
pred_label_view2 = model(
train_labeled_data_view2).permute(0, 2, 1)
pred_unlabel_view2 = model(
train_unlabeled_data_view2).permute(0, 2, 1)
pred_label_view2 = torch.sigmoid(
pred_label_view2).contiguous()
pred_unlabel_view2 = torch.sigmoid(
pred_unlabel_view2).contiguous()
consist_loss = self.MSELoss(input1=torch.cat([labeled_pred.detach(), unlabeled_pred.detach()], dim=0),
input2=torch.cat([pred_label_view2, pred_unlabel_view2], dim=0))
consist_loss.backward()
d_labeled = _l2_normalize(d_labeled.grad)
d_unlabeled = _l2_normalize(d_unlabeled.grad)
model.zero_grad()
r_adv_label = d_labeled * self.eps
r_adv_unlabel = d_unlabeled * self.eps
train_labeled_data_view2 = labeled_data + r_adv_label
train_unlabeled_data_view2 = unlabeled_data + r_adv_unlabel
pred_label_view2 = model(
train_labeled_data_view2).permute(0, 2, 1)
pred_unlabel_view2 = model(
train_unlabeled_data_view2).permute(0, 2, 1)
pred_label_view2 = torch.sigmoid(
pred_label_view2).contiguous()
pred_unlabel_view2 = torch.sigmoid(
pred_unlabel_view2).contiguous()
consist_loss = self.MSELoss(input1=torch.cat([labeled_pred.detach(), unlabeled_pred.detach()], dim=0),
input2=torch.cat([pred_label_view2, pred_unlabel_view2], dim=0))
return CELoss+DICELoss+consist_loss