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networks.py
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networks.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 9 16:14:29 2022
@author: apramanik
"""
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.autograd as autograd
from layers import convlayer
def complex_to_real(x):
xr = torch.cat((torch.real(x),torch.imag(x)),dim=1)
xr = xr.type(torch.float32)
return xr
def real_to_complex(x):
re,im = torch.split(x,[1,1],dim=1)
xc = re + 1j*im
xc = xc.type(torch.complex64)
return xc
class dwblock(nn.Module):
def __init__(self, input_channels, features, output_channels, number_of_layers, spectral_norm=False):
super(dwblock, self).__init__()
self.num_layers = number_of_layers
layers = []
layers.append(convlayer(input_channels*2, features, False, spectral_norm))
for i in range(1, self.num_layers-1):
layers.append(convlayer(features, features, False, spectral_norm)) # conv layer
layers.append(convlayer(features, output_channels*2, True, spectral_norm))
self.net = nn.Sequential(*layers)
def forward(self, x):
out = real_to_complex(self.net(complex_to_real(x)))
return out
class UNETBlock(nn.Module):
def __init__(self, input_channels, features, output_channels):
super(UNETBlock, self).__init__()
self.cnn = UNet(in_channels=input_channels, out_channels=output_channels, init_features=features)
def forward(self, x):
xcnn = real_to_complex(self.cnn(complex_to_real(x)))
xout = x + xcnn
return xout
class UNet(nn.Module):
def __init__(self, in_channels=2, out_channels=2, init_features=32):
super(UNet, self).__init__()
features = init_features
self.encoder1 = UNet._block(in_channels, features, name="enc1")
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder2 = UNet._block(features, features * 2, name="enc2")
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
#self.encoder3 = UNet._block(features * 2, features * 4, name="enc3")
#self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
#self.encoder4 = UNet._block(features * 4, features * 8, name="enc4")
#self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
#self.bottleneck = UNet._block(features * 8, features * 16, name="bottleneck")
#self.bottleneck = UNet._block(features * 4, features * 8, name="bottleneck")
self.bottleneck = UNet._block(features * 2, features * 4, name="bottleneck")
# self.upconv4 = nn.ConvTranspose2d(
# features * 16, features * 8, kernel_size=2, stride=2
# )
# self.decoder4 = UNet._block((features * 8) * 2, features * 8, name="dec4")
# self.upconv3 = nn.ConvTranspose2d(
# features * 8, features * 4, kernel_size=2, stride=2
# )
# self.decoder3 = UNet._block((features * 4) * 2, features * 4, name="dec3")
self.upconv2 = nn.ConvTranspose2d(
features * 4, features * 2, kernel_size=2, stride=2
)
self.decoder2 = UNet._block((features * 2) * 2, features * 2, name="dec2")
self.upconv1 = nn.ConvTranspose2d(
features * 2, features, kernel_size=2, stride=2
)
self.decoder1 = UNet._block(features * 2, features, name="dec1")
self.conv = nn.Conv2d(
in_channels=features, out_channels=out_channels, kernel_size=1
)
def forward(self, x):
enc1 = self.encoder1(x)
enc2 = self.encoder2(self.pool1(enc1))
#enc3 = self.encoder3(self.pool2(enc2))
#enc4 = self.encoder4(self.pool3(enc3))
#bottleneck = self.bottleneck(self.pool4(enc4))
#bottleneck = self.bottleneck(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool2(enc2))
#dec4 = self.upconv4(bottleneck)
#dec4 = torch.cat((dec4, enc4), dim=1)
#dec4 = self.decoder4(dec4)
#dec3 = self.upconv3(dec4)
#dec3 = self.upconv3(bottleneck)
#dec3 = torch.cat((dec3, enc3), dim=1)
#dec3 = self.decoder3(dec3)
#dec2 = self.upconv2(dec3)
dec2 = self.upconv2(bottleneck)
dec2 = torch.cat((dec2, enc2), dim=1)
dec2 = self.decoder2(dec2)
dec1 = self.upconv1(dec2)
dec1 = torch.cat((dec1, enc1), dim=1)
dec1 = self.decoder1(dec1)
return self.conv(dec1)
@staticmethod
def _block(in_channels, features, name):
return nn.Sequential(
OrderedDict(
[
(
name + "conv1",
nn.Conv2d(
in_channels=in_channels,
out_channels=features,
kernel_size=3,
padding=1,
bias=False,
),
),
#(name + "norm1", nn.BatchNorm2d(num_features=features)),
(name + "relu1", nn.ReLU(inplace=True)),
(
name + "conv2",
nn.Conv2d(
in_channels=features,
out_channels=features,
kernel_size=3,
padding=1,
bias=False,
),
),
#(name + "norm2", nn.BatchNorm2d(num_features=features)),
(name + "relu2", nn.ReLU(inplace=True)),
]
)
)