model.py

import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable



class ReconNet(nn.Module):
  def __init__(self,measurement_rate=0.25):
    super(ReconNet,self).__init__()

    self.measurement_rate = measurement_rate
    self.fc1 = nn.Linear(int(self.measurement_rate*1089),1089)
    nn.init.normal_(self.fc1.weight, mean=0, std=0.1)
    self.conv1 = nn.Conv2d(1,64,11,1,padding=5)
    nn.init.normal_(self.conv1.weight, mean=0, std=0.1)
    self.conv2 = nn.Conv2d(64,32,1,1,padding=0)
    nn.init.normal_(self.conv2.weight, mean=0, std=0.1)
    self.conv3 = nn.Conv2d(32,1,7,1,padding=3)
    nn.init.normal_(self.conv3.weight, mean=0, std=0.1)
    self.conv4 = nn.Conv2d(1,64,11,1,padding=5)
    nn.init.normal_(self.conv4.weight, mean=0, std=0.1)
    self.conv5 = nn.Conv2d(64,32,1,1,padding=0)
    nn.init.normal_(self.conv5.weight, mean=0, std=0.1)
    self.conv6 = nn.Conv2d(32,1,7,1,padding=3)
    nn.init.normal_(self.conv6.weight, mean=0, std=0.1)

  def forward(self,x):
    x = F.relu(self.fc1(x))
    x = x.view(-1,33,33)
    x = x.unsqueeze(1)
    x = F.relu(self.conv1(x))
    x = F.relu(self.conv2(x))
    x = F.relu(self.conv3(x))
    x = F.relu(self.conv4(x))
    x = F.relu(self.conv5(x))
    x = self.conv6(x)

    return x