-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathlosses.py
156 lines (132 loc) · 5.07 KB
/
losses.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
import torch
from torch import nn
from torch.nn import functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class SSIMLoss2D(nn.Module):
"""
2D SSIM loss module.
"""
def __init__(self, win_size: int = 7, k1: float = 0.01, k2: float = 0.03):
"""
Args:
win_size: Window size for SSIM calculation.
k1: k1 parameter for SSIM calculation.
k2: k2 parameter for SSIM calculation.
"""
super().__init__()
self.win_size = win_size
self.k1, self.k2 = k1, k2
self.register_buffer("w", torch.ones(1, 1, win_size, win_size) / win_size ** 2)
NP = win_size ** 2
self.cov_norm = NP / (NP - 1)
def forward(self, X: torch.Tensor, Y: torch.Tensor, data_range: torch.Tensor):
#assert isinstance(self.w, torch.Tensor)
C1 = (self.k1 * data_range) ** 2
C2 = (self.k2 * data_range) ** 2
ux = F.conv2d(X, self.w) # typing: ignore
uy = F.conv2d(Y, self.w) #
uxx = F.conv2d(X * X, self.w)
uyy = F.conv2d(Y * Y, self.w)
uxy = F.conv2d(X * Y, self.w)
vx = self.cov_norm * (uxx - ux * ux)
vy = self.cov_norm * (uyy - uy * uy)
vxy = self.cov_norm * (uxy - ux * uy)
A1, A2, B1, B2 = (
2 * ux * uy + C1,
2 * vxy + C2,
ux ** 2 + uy ** 2 + C1,
vx + vy + C2,
)
D = B1 * B2
S = (A1 * A2) / (D + 1e-8)
return 1 - S.mean()
class SSIMLoss2D_MC(nn.Module):
"""
2D multichannel SSIM loss module.
"""
def __init__(self, win_size: int=7, k1: float=0.01, k2: float=0.03, in_chan: int=1):
"""
Args:
win_size: Window size for SSIM calculation.
k1: k1 parameter for SSIM calculation.
k2: k2 parameter for SSIM calculation.
in_chan: number of input channels
"""
super().__init__()
self.win_size = win_size
self.k1, self.k2 = k1, k2
self.in_chan = in_chan
self.register_buffer("w", torch.ones(in_chan, 1, win_size, win_size) / win_size ** 2)
NP = win_size ** 2
self.cov_norm = NP / (NP - 1)
def forward(self, X: torch.Tensor, Y: torch.Tensor, data_range: torch.Tensor):
#assert isinstance(self.w, torch.Tensor)
C1 = (self.k1 * data_range) ** 2
C2 = (self.k2 * data_range) ** 2
ux = F.conv2d(X, self.w.cuda(), groups=self.in_chan) # per-channel convolution
uy = F.conv2d(Y, self.w.cuda(), groups=self.in_chan) #
uxx = F.conv2d(X * X, self.w.cuda(), groups=self.in_chan)
uyy = F.conv2d(Y * Y, self.w.cuda(), groups=self.in_chan)
uxy = F.conv2d(X * Y, self.w.cuda(), groups=self.in_chan)
vx = self.cov_norm * (uxx - ux * ux)
vy = self.cov_norm * (uyy - uy * uy)
vxy = self.cov_norm * (uxy - ux * uy)
A1, A2, B1, B2 = (
2 * ux * uy + C1,
2 * vxy + C2,
ux ** 2 + uy ** 2 + C1,
vx + vy + C2,
)
D = B1 * B2
S = (A1 * A2) / (D + 1e-8)
return 1 - S.mean()
class SSIMLoss3D(nn.Module):
"""
3D SSIM loss module
Square window with uniform weights (non-Gaussian) following the implementation
from Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004).
Image quality assessment: From error visibility to structural similarity.
IEEE Transactions on Image Processing, 13, 600-612.
https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,
:DOI:`10.1109/TIP.2003.819861` with slight modification for efficiency
by Vick Lau 2021
"""
def __init__(self, win_size: int=7, k1: float=0.01, k2: float=0.03):
"""Initialise 3D SSIM loss
Args
-------
win_size (int): Window size
k1 (float): k1 parameter
k2 (float): k2 parameter
Returns
-------
torch.Tensor: 3D SSIM Loss
"""
super().__init__()
self.win_size = win_size
self.k1, self.k2 = k1, k2
self.register_buffer("w", torch.ones(1, 1, win_size, win_size, win_size) / win_size**3)
NP = win_size ** 3
self.cov_norm = NP / (NP - 1) # sample covariance instead of population
def forward(self, X: torch.Tensor, Y: torch.Tensor, data_range: torch.Tensor):
#assert isinstance(self.w, torch.Tensor)
C1 = (self.k1 * data_range) ** 2
C2 = (self.k2 * data_range) ** 2
# compute variances and covariances
ux = F.conv3d(X, self.w)
uy = F.conv3d(Y, self.w)
uxx = F.conv3d(X * X, self.w)
uyy = F.conv3d(Y * Y, self.w)
uxy = F.conv3d(X * Y, self.w)
vx = self.cov_norm * (uxx - ux*ux)
vy = self.cov_norm * (uyy - uy*uy)
vxy = self.cov_norm * (uxy - ux*uy)
A1, A2, B1, B2 = (
2*ux*uy + C1,
2*vxy + C2,
ux**2 + uy**2 + C1,
vx + vy + C2,
)
D = B1*B2
S = (A1*A2) / (D + 1e-8) # eps for floating point stability
return 1 - S.mean() # compute 1 - mean of SSIM