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models_original.py
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models_original.py
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from my_tools import *
from utils import A, At
import torch
import torch.nn as nn
class ADMM_net(nn.Module):
def __init__(self):
super(ADMM_net, self).__init__()
self.unet1 = Unet(8, 8)
self.unet2 = Unet(8, 8)
self.unet3 = Unet(8, 8)
self.unet4 = Unet(8, 8)
self.unet5 = Unet(8, 8)
self.unet6 = Unet(8, 8)
self.unet7 = Unet(8, 8)
self.unet8 = Unet(8, 8)
self.unet9 = Unet(8, 8)
self.gamma1 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma2 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma3 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma4 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma5 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma6 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma7 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma8 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma9 = torch.nn.Parameter(torch.Tensor([0]))
def forward(self, y, Phi, Phi_s):
x_list = []
theta = At(y,Phi)
b = torch.zeros_like(Phi)
### 1-3
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma1),Phi)
x1 = x-b
theta = self.unet1(x1)
b = b- (x-theta)
x_list.append(theta)
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma2),Phi)
x1 = x-b
theta = self.unet2(x1)
b = b- (x-theta)
x_list.append(theta)
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma3),Phi)
x1 = x-b
theta = self.unet3(x1)
b = b- (x-theta)
x_list.append(theta)
### 4-6
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma4),Phi)
x1 = x-b
theta = self.unet4(x1)
b = b- (x-theta)
x_list.append(theta)
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma5),Phi)
x1 = x-b
theta = self.unet5(x1)
b = b- (x-theta)
x_list.append(theta)
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma6),Phi)
x1 = x-b
theta = self.unet6(x1)
b = b- (x-theta)
x_list.append(theta)
### 7-9
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma7),Phi)
x1 = x-b
theta = self.unet7(x1)
b = b- (x-theta)
x_list.append(theta)
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma8),Phi)
x1 = x-b
theta = self.unet8(x1)
b = b- (x-theta)
x_list.append(theta)
yb = A(theta+b,Phi)
x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma9),Phi)
x1 = x-b
theta = self.unet9(x1)
b = b- (x-theta)
x_list.append(theta)
output_list = x_list[-3:]
return output_list
class ADMM_net_large(nn.Module):
def __init__(self, n_resblocks=8, n_feats=16):
super(ADMM_net_large, self).__init__()
self.unet1 = Unet(8, 8)
self.unet2 = Unet(8, 8)
self.unet3 = Unet(8, 8)
self.unet4 = Unet(8, 8)
self.unet5 = Unet(8, 8)
self.unet6 = Unet(8, 8)
self.unet7 = Unet(8, 8)
self.unet8 = Unet(8, 8)
self.unet9 = Unet(8, 8)
self.unet10 = Unet(8, 8)
self.unet11 = Unet(8, 8)
self.unet12 = Unet(8, 8)
self.gamma1 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma2 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma3 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma4 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma5 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma6 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma7 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma8 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma9 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma10 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma11 = torch.nn.Parameter(torch.Tensor([0]))
self.gamma12 = torch.nn.Parameter(torch.Tensor([0]))
def forward(self, y, Phi, Phi_s):
x_list = []
theta = At(y, Phi)
b = torch.zeros_like(Phi)
### 1-3
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma1), Phi)
x1 = x - b
theta = self.unet1(x1)
b = b - (x - theta)
x_list.append(theta)
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma2), Phi)
x1 = x - b
theta = self.unet2(x1)
b = b - (x - theta)
x_list.append(theta)
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma3), Phi)
x1 = x - b
theta = self.unet3(x1)
b = b - (x - theta)
x_list.append(theta)
### 4-6
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma4), Phi)
x1 = x - b
theta = self.unet4(x1)
b = b - (x - theta)
x_list.append(theta)
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma5), Phi)
x1 = x - b
theta = self.unet5(x1)
b = b - (x - theta)
x_list.append(theta)
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma6), Phi)
x1 = x - b
theta = self.unet6(x1)
b = b - (x - theta)
x_list.append(theta)
### 7-9
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma7), Phi)
x1 = x - b
theta = self.unet7(x1)
b = b - (x - theta)
x_list.append(theta)
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma8), Phi)
x1 = x - b
theta = self.unet8(x1)
b = b - (x - theta)
x_list.append(theta)
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma9), Phi)
x1 = x - b
theta = self.unet9(x1)
b = b - (x - theta)
x_list.append(theta)
### 10-12
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma10), Phi)
x1 = x - b
theta = self.unet10(x1)
b = b - (x - theta)
x_list.append(theta)
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma11), Phi)
x1 = x - b
theta = self.unet11(x1)
b = b - (x - theta)
x_list.append(theta)
yb = A(theta + b, Phi)
x = theta + b + At(torch.div(y - yb, Phi_s + self.gamma12), Phi)
x1 = x - b
theta = self.unet12(x1)
b = b - (x - theta)
x_list.append(theta)
output_list = x_list[-3:]
return output_list