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models.py
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models.py
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import os
import numpy as np
import torch
import torch.nn as nn
from batchrenorm import BatchRenorm2d
class UpNet(nn.Module):
def __init__(self):
super(UpNet, self).__init__()
layers = [nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1),
BatchRenorm2d(64),
nn.ReLU()]
for i in range(15):
layers.append(nn.Conv2d(64, 64, 3, 1, 1))
layers.append(BatchRenorm2d(64))
layers.append(nn.ReLU())
layers.append(nn.Conv2d(64, 1, 3, 1, 1))
self.net = nn.Sequential(*layers)
def forward(self, x):
out = self.net(x)
return out
class DownNet(nn.Module):
def __init__(self):
super(DownNet, self).__init__()
layers = [nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1),
BatchRenorm2d(64),
nn.ReLU()]
for i in range(7):
layers.append(nn.Conv2d(64, 64, 3, 1, padding=2, dilation=2))
layers.append(nn.ReLU())
layers.append(nn.Conv2d(64, 64, 3, 1, 1))
layers.append(BatchRenorm2d(64))
layers.append(nn.ReLU())
for i in range(6):
layers.append(nn.Conv2d(64, 64, 3, 1, padding=2, dilation=2))
layers.append(nn.ReLU())
layers.append(nn.Conv2d(64, 64, 3, 1, 1))
layers.append(BatchRenorm2d(64))
layers.append(nn.ReLU())
layers.append(nn.Conv2d(64, 1, 3, 1, 1))
self.net = nn.Sequential(*layers)
def forward(self, x):
out = self.net(x)
return out
class BRDNet(nn.Module):
def __init__(self):
super(BRDNet, self).__init__()
self.upnet = UpNet()
self.dwnet = DownNet()
self.conv = nn.Conv2d(2, 1, 3, 1, 1)
def forward(self, x):
#import pdb;pdb.set_trace()
out1 = self.upnet(x)
out2 = self.dwnet(x)
out1 = x - out1
out2 = x - out2
out = torch.cat((out1, out2), 1)
out = self.conv(out)
out = x - out
return out