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import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
try:
from .nafnet_utils.arch_util import LayerNorm2d
from .nafnet_utils.arch_model import SimpleGate
except:
from nafnet_utils.arch_util import LayerNorm2d
from nafnet_utils.arch_model import SimpleGate
'''
https://github.com/wangchx67/FourLLIE.git
'''
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 SpaBlock(nn.Module):
def __init__(self, nc):
super(SpaBlock, self).__init__()
self.block = nn.Sequential(
nn.Conv2d(nc,nc,3,1,1),
nn.LeakyReLU(0.1,inplace=True),
nn.Conv2d(nc, nc, 3, 1, 1),
nn.LeakyReLU(0.1, inplace=True))
def forward(self, x):
return x+self.block(x)
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 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 Attention_Light(nn.Module):
def __init__(self, img_channels = 3, width = 16, spatial = False):
super(Attention_Light, self).__init__()
self.block = nn.Sequential(
nn.Conv2d(in_channels = img_channels, out_channels = width//2, kernel_size = 1, padding = 0, stride = 1, groups = 1, bias = True),
ProcessBlock(in_nc = width //2, spatial = spatial),
nn.Conv2d(in_channels = width//2, out_channels = width, kernel_size = 1, padding = 0, stride = 1, groups = 1, bias = True),
ProcessBlock(in_nc = width, spatial = spatial),
nn.Conv2d(in_channels = width, out_channels = width, kernel_size = 1, padding = 0, stride = 1, groups = 1, bias = True),
ProcessBlock(in_nc=width, spatial = spatial),
nn.Sigmoid()
)
def forward(self, input):
return self.block(input)
class Branch(nn.Module):
'''
Branch that lasts lonly the dilated convolutions
'''
def __init__(self, c, DW_Expand, dilation = 1, extra_depth_wise = False):
super().__init__()
self.dw_channel = DW_Expand * c
self.branch = nn.Sequential(
nn.Conv2d(c, c, kernel_size=3, padding=1, stride=1, groups=c, bias=True, dilation=1) if extra_depth_wise else nn.Identity(), #optional extra dw
nn.Conv2d(in_channels=c, out_channels=self.dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1),
nn.Conv2d(in_channels=self.dw_channel, out_channels=self.dw_channel, kernel_size=3, padding=dilation, stride=1, groups=self.dw_channel,
bias=True, dilation = dilation) # the dconv
)
def forward(self, input):
return self.branch(input)
class EBlock(nn.Module):
'''
Change this block using Branch
'''
def __init__(self, c, DW_Expand=2, FFN_Expand=2, dilations = [1], extra_depth_wise = False):
super().__init__()
#we define the 2 branches
self.branches = nn.ModuleList()
for dilation in dilations:
self.branches.append(Branch(c, DW_Expand, dilation = dilation, extra_depth_wise=extra_depth_wise))
assert len(dilations) == len(self.branches)
self.dw_channel = DW_Expand * c
self.sca = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels=self.dw_channel // 2, out_channels=self.dw_channel // 2, kernel_size=1, padding=0, stride=1,
groups=1, bias=True, dilation = 1),
)
self.sg1 = SimpleGate()
self.sg2 = SimpleGate()
self.conv3 = nn.Conv2d(in_channels=self.dw_channel // 2, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1)
ffn_channel = FFN_Expand * c
self.conv4 = nn.Conv2d(in_channels=c, 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=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.norm1 = LayerNorm2d(c)
self.norm2 = LayerNorm2d(c)
self.gamma = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True)
self.beta = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True)
def forward(self, inp):
y = inp
x = self.norm1(inp)
z = 0
for branch in self.branches:
z += branch(x)
z = self.sg1(z)
x = self.sca(z) * z
x = self.conv3(x)
y = inp + self.beta * x
#second step
x = self.conv4(self.norm2(y)) # size [B, 2*C, H, W]
x = self.sg2(x) # size [B, C, H, W]
x = self.conv5(x) # size [B, C, H, W]
return y + x * self.gamma
#----------------------------------------------------------------------------------------------
if __name__ == '__main__':
img_channel = 3
width = 32
enc_blks = [1, 2, 3]
middle_blk_num = 3
dec_blks = [3, 1, 1]
dilations = [1, 4, 9]
extra_depth_wise = False
# net = NAFNet(img_channel=img_channel, width=width, middle_blk_num=middle_blk_num,
# enc_blk_nums=enc_blks, dec_blk_nums=dec_blks)
net = EBlock(c = img_channel,
dilations = dilations,
extra_depth_wise=extra_depth_wise)
inp_shape = (3, 256, 256)
from ptflops import get_model_complexity_info
macs, params = get_model_complexity_info(net, inp_shape, verbose=False, print_per_layer_stat=True)
print(macs, params)
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