File size: 10,301 Bytes
a791811 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from utils.arch_utils import LayerNorm2d
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale # for residual block
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
def make_layer(block, n_layers):
layers = []
for _ in range(n_layers):
layers.append(block())
return nn.Sequential(*layers)
class ResidualBlock_noBN(nn.Module):
'''Residual block w/o BN
---Conv-ReLU-Conv-+-
|________________|
'''
def __init__(self, nf=64):
super(ResidualBlock_noBN, self).__init__()
self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
# initialization
initialize_weights([self.conv1, self.conv2], 0.1)
def forward(self, x):
identity = x
out = F.relu(self.conv1(x), inplace=True)
out = self.conv2(out)
return identity + out
class ResidualBlock(nn.Module):
'''Residual block w/o BN
---Conv-ReLU-Conv-+-
|________________|
'''
def __init__(self, nf=64):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.bn = nn.BatchNorm2d(nf)
self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
# initialization
initialize_weights([self.conv1, self.conv2], 0.1)
def forward(self, x):
identity = x
out = F.relu(self.bn(self.conv1(x)), inplace=True)
out = self.conv2(out)
return identity + out
###########################################################################################################
class SimpleGate(nn.Module):
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
return x1 * x2
class SGE(nn.Module):
def __init__(self, dw_channel):
super().__init__()
self.dwc = nn.Conv2d(in_channels=dw_channel //2, out_channels=dw_channel//2, kernel_size=3, padding=1, stride=1, groups=dw_channel//2, bias=True)
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
x1 = self.dwc(x1)
return x1 * x2
class SpaBlock(nn.Module):
def __init__(self, nc, DW_Expand = 2, FFN_Expand=2, drop_out_rate=0.):
super(SpaBlock, self).__init__()
dw_channel = nc * DW_Expand
self.conv1 = nn.Conv2d(in_channels=nc, out_channels=dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.conv2 = nn.Conv2d(in_channels=dw_channel, out_channels=dw_channel, kernel_size=3, padding=1, stride=1, groups=dw_channel,
bias=True) # the dconv
self.conv3 = nn.Conv2d(in_channels=dw_channel // 2, out_channels=nc, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
# Simplified Channel Attention
self.sca = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels=dw_channel // 2, out_channels=dw_channel // 2, kernel_size=1, padding=0, stride=1,
groups=1, bias=True),
)
# SimpleGate
self.sg = SimpleGate()
ffn_channel = FFN_Expand * nc
self.conv4 = nn.Conv2d(in_channels=nc, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.conv5 = nn.Conv2d(in_channels=ffn_channel // 2, out_channels=nc, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.norm1 = LayerNorm2d(nc)
self.norm2 = LayerNorm2d(nc)
self.dropout1 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity()
self.dropout2 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity()
self.beta = nn.Parameter(torch.zeros((1, nc, 1, 1)), requires_grad=True)
self.gamma = nn.Parameter(torch.zeros((1, nc, 1, 1)), requires_grad=True)
def forward(self, x):
x = self.norm1(x) # size [B, C, H, W]
x = self.conv1(x) # size [B, 2*C, H, W]
x = self.conv2(x) # size [B, 2*C, H, W]
x = self.sg(x) # size [B, C, H, W]
x = x * self.sca(x) # size [B, C, H, W]
x = self.conv3(x) # size [B, C, H, W]
x = self.dropout1(x)
y = x + x * self.beta # size [B, C, H, W]
x = self.conv4(self.norm2(y)) # size [B, 2*C, H, W]
x = self.sg(x) # size [B, C, H, W]
x = self.conv5(x) # size [B, C, H, W]
x = self.dropout2(x)
return y + x * self.gamma
class FreBlock(nn.Module):
def __init__(self, nc):
super(FreBlock, self).__init__()
self.fpre = nn.Conv2d(nc, nc, 1, 1, 0)
self.process1 = nn.Sequential(
nn.Conv2d(nc, nc, 1, 1, 0),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(nc, nc, 1, 1, 0))
self.process2 = nn.Sequential(
nn.Conv2d(nc, nc, 1, 1, 0),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(nc, nc, 1, 1, 0))
def forward(self, x):
_, _, H, W = x.shape
x_freq = torch.fft.rfft2(self.fpre(x), norm='backward')
mag = torch.abs(x_freq)
pha = torch.angle(x_freq)
mag = self.process1(mag)
pha = self.process2(pha)
real = mag * torch.cos(pha)
imag = mag * torch.sin(pha)
x_out = torch.complex(real, imag)
x_out = torch.fft.irfft2(x_out, s=(H, W), norm='backward')
return x_out+x
class SFBlock(nn.Module):
def __init__(self, nc, DW_Expand = 2, FFN_Expand=2):
super(SFBlock, self).__init__()
dw_channel = nc * DW_Expand
self.conv1 = nn.Conv2d(in_channels=nc, out_channels=dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.conv2 = nn.Conv2d(in_channels=dw_channel, out_channels=dw_channel, kernel_size=3, padding=1, stride=1, groups=dw_channel,
bias=True) # the dconv
self.conv3 = nn.Conv2d(in_channels=dw_channel // 2, out_channels=nc, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.fatt = FreBlock(dw_channel // 2)
self.sge = SGE(dw_channel)
# SimpleGate
self.sg = SimpleGate()
ffn_channel = FFN_Expand * nc
self.conv4 = nn.Conv2d(in_channels=nc, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.conv5 = nn.Conv2d(in_channels=ffn_channel // 2, out_channels=nc, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.norm1 = LayerNorm2d(nc)
self.norm2 = LayerNorm2d(nc)
self.beta = nn.Parameter(torch.zeros((1, nc, 1, 1)), requires_grad=True)
self.gamma = nn.Parameter(torch.zeros((1, nc, 1, 1)), requires_grad=True)
def forward(self, x):
x = self.norm1(x) # size [B, C, H, W]
x = self.conv1(x) # size [B, 2*C, H, W]
x = self.conv2(x) # size [B, 2*C, H, W]
x = self.sge(x) # size [B, C, H, W]
x = self.fatt(x)
x = self.conv3(x) # size [B, C, H, W]
y = x + x * self.beta # size [B, C, H, W]
x = self.conv4(self.norm2(y)) # size [B, 2*C, H, W]
x = self.sg(x) # size [B, C, H, W]
x = self.conv5(x) # size [B, C, H, W]
return y + x * self.gamma
class ProcessBlock(nn.Module):
def __init__(self, in_nc, spatial = True):
super(ProcessBlock,self).__init__()
self.spatial = spatial
self.spatial_process = SpaBlock(in_nc) if spatial else nn.Identity()
self.frequency_process = FreBlock(in_nc)
self.cat = nn.Conv2d(2*in_nc,in_nc,1,1,0) if spatial else nn.Conv2d(in_nc,in_nc,1,1,0)
def forward(self, x):
xori = x
x_freq = self.frequency_process(x)
x_spatial = self.spatial_process(x)
xcat = torch.cat([x_spatial,x_freq],1)
x_out = self.cat(xcat) if self.spatial else self.cat(x_freq)
return x_out+xori
class SFNet(nn.Module):
def __init__(self, nc,n=5):
super(SFNet,self).__init__()
self.list_block = list()
for index in range(n):
self.list_block.append(ProcessBlock(nc,spatial=False))
self.block = nn.Sequential(*self.list_block)
def forward(self, x):
x_ori = x
x_out = self.block(x_ori)
xout = x_ori + x_out
return xout
class AmplitudeNet_skip(nn.Module):
def __init__(self, nc,n=1):
super(AmplitudeNet_skip,self).__init__()
self.conv_init = nn.Conv2d(3, nc, 1, 1, 0)
self.conv1 = SFBlock (nc)
self.conv2 = SFBlock (nc)
self.conv3 = SFBlock (nc)
self.conv_out = nn.Conv2d(nc, 3, 1, 1, 0)
def forward(self, x):
x_lr = F.interpolate(x, scale_factor=0.5, mode='bilinear') # Resize and Normalize SNR map
x_lr = self.conv_init(x_lr)
x_lr = self.conv1(x_lr)
x_lr = self.conv2(x_lr)
x_lr = self.conv3(x_lr)
x_lr = self.conv_out(x_lr)
xout = F.interpolate(x_lr, scale_factor=2, mode='bilinear') # Resize and Normalize SNR map
return xout
###########################################################################################################
class SG(nn.Module):
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
return x1 * x2
class SGE(nn.Module):
def __init__(self, dw_channel):
super().__init__()
self.dwc = nn.Conv2d(in_channels=dw_channel //2, out_channels=dw_channel//2, kernel_size=3, padding=1, stride=1, groups=dw_channel//2, bias=True)
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
x1 = self.dwc(x1)
return x1 * x2 |