-
Notifications
You must be signed in to change notification settings - Fork 0
/
solver_blocks.py
executable file
·131 lines (75 loc) · 3.85 KB
/
solver_blocks.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 9 16:10:37 2022
@author: apramanik
"""
import torch
import torch.nn as nn
from networks import dwblock
class modlBlock(nn.Module):
def __init__(self, A, lam, input_channels, features, output_channels, number_of_layers, spectral_norm=False):
super(modlBlock, self).__init__()
self.dw = dwblock(input_channels, features, output_channels, number_of_layers, spectral_norm)
self.lam = nn.Parameter(torch.tensor(lam,dtype=torch.float32))
self.A = A
self.alpha = torch.tensor(1.0,dtype=torch.float32)
def forward(self, x, Atb, csm, mask):
z = x - self.dw(x)
rhs = z + self.lam*Atb
x = self.A.inv(x, rhs, self.lam, csm, mask)
return x
class fwdbwdBlock(nn.Module):
def __init__(self, A, lam, input_channels, features, output_channels, number_of_layers, spectral_norm=False):
super(fwdbwdBlock, self).__init__()
self.dw = dwblock(input_channels, features, output_channels, number_of_layers, spectral_norm)
self.lam = nn.Parameter(torch.tensor(lam,dtype=torch.float32))
self.A = A
self.alpha = torch.tensor(0.1,dtype=torch.float32)
def forward(self, x, Atb, csm, mask):
z = self.dw(x)
rhs = (1 - self.alpha)*x + self.alpha*z + self.alpha*self.lam*Atb
x = self.A.inv(x, rhs, self.alpha*self.lam, csm, mask)
return x
class gradBlock(nn.Module):
def __init__(self, A, lam, input_channels, features, output_channels, number_of_layers, spectral_norm=False):
super(gradBlock, self).__init__()
self.dw = dwblock(input_channels, features, output_channels, number_of_layers, spectral_norm)
#self.lam = nn.Parameter(torch.tensor(lam,dtype=torch.float32))
self.lam = torch.tensor(lam,dtype=torch.float32)
self.A = A
self.alpha = torch.tensor(1.0,dtype=torch.float32)
def forward(self, x, Atb, csm, mask):
#z = x - self.dw(x)
z = self.dw(x)
#x = x + 1e-5*self.lam*((Atb - self.A.adjoint(self.A.forward(x, csm, mask), csm)) - z)
x = x - self.lam*((self.A.adjoint(self.A.forward(x, csm, mask), csm) - Atb) + z)
return x
#return z
class molgradBlock(nn.Module):
def __init__(self, A, lam, input_channels, features, output_channels, number_of_layers, spectral_norm=False):
super(molgradBlock, self).__init__()
self.dw = dwblock(input_channels, features, output_channels, number_of_layers, spectral_norm)
self.lam = nn.Parameter(torch.tensor(lam,dtype=torch.float32))
#self.lam = torch.tensor(lam,dtype=torch.float32)
self.A = A
self.alpha = torch.tensor(1.0,dtype=torch.float32)
def forward(self, x, Atb, csm, mask):
z = self.dw(x)
dcg = self.lam*(self.A.ATA(x, csm, mask) - Atb)
x = (1 - self.alpha)*x + self.alpha*(z - dcg)
return x
class admmBlock(nn.Module):
def __init__(self, A, lam, input_channels, features, output_channels, number_of_layers, spectral_norm=False):
super(admmBlock, self).__init__()
self.dw = dwblock(input_channels, features, output_channels, number_of_layers, spectral_norm)
self.lam = nn.Parameter(torch.tensor(lam,dtype=torch.float32))
#self.lam = torch.tensor(lam,dtype=torch.float32)
self.A = A
self.alpha = torch.tensor(1.0,dtype=torch.float32)
def forward(self, x, u, Atb, csm, mask):
z = x - u - self.dw(x - u)
rhs = z + self.lam*Atb + u
x = self.A.inv(x, rhs, self.lam, csm, mask)
u = u + z - x
return x, u