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import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from .arch_util import LayerNorm2d
from .local_arch import Local_Base
except:
from arch_util import LayerNorm2d
from local_arch import Local_Base
class SimpleGate(nn.Module):
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
return x1 * x2
class NAFBlock(nn.Module):
def __init__(self, c, DW_Expand=2, FFN_Expand=2, drop_out_rate=0.):
super().__init__()
dw_channel = c * DW_Expand
self.conv1 = nn.Conv2d(in_channels=c, 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=c, 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 * 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.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, c, 1, 1)), requires_grad=True)
self.gamma = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True)
def forward(self, inp):
x = inp # size [B, C, H, W]
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 = inp + 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 NAFNet(nn.Module):
def __init__(self, img_channel=3, width=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[]):
super().__init__()
self.intro = nn.Conv2d(in_channels=img_channel, out_channels=width, kernel_size=3, padding=1, stride=1, groups=1,
bias=True)
self.ending = nn.Conv2d(in_channels=width, out_channels=img_channel, kernel_size=3, padding=1, stride=1, groups=1,
bias=True)
self.encoders = nn.ModuleList()
self.decoders = nn.ModuleList()
self.middle_blks = nn.ModuleList()
self.ups = nn.ModuleList()
self.downs = nn.ModuleList()
chan = width
for num in enc_blk_nums:
self.encoders.append(
nn.Sequential(
*[NAFBlock(chan) for _ in range(num)]
)
)
self.downs.append(
nn.Conv2d(chan, 2*chan, 2, 2)
)
chan = chan * 2
self.middle_blks = \
nn.Sequential(
*[NAFBlock(chan) for _ in range(middle_blk_num)]
)
for num in dec_blk_nums:
self.ups.append(
nn.Sequential(
nn.Conv2d(chan, chan * 2, 1, bias=False),
nn.PixelShuffle(2)
)
)
chan = chan // 2
self.decoders.append(
nn.Sequential(
*[NAFBlock(chan) for _ in range(num)]
)
)
self.padder_size = 2 ** len(self.encoders)
def forward(self, inp):
B, C, H, W = inp.shape
inp = self.check_image_size(inp)
x = self.intro(inp)
encs = []
for encoder, down in zip(self.encoders, self.downs):
x = encoder(x)
encs.append(x)
x = down(x)
x = self.middle_blks(x)
for decoder, up, enc_skip in zip(self.decoders, self.ups, encs[::-1]):
x = up(x)
x = x + enc_skip
x = decoder(x)
x = self.ending(x)
x = x + inp
return x[:, :, :H, :W]
def check_image_size(self, x):
_, _, h, w = x.size()
mod_pad_h = (self.padder_size - h % self.padder_size) % self.padder_size
mod_pad_w = (self.padder_size - w % self.padder_size) % self.padder_size
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), value = 0)
return x
class NAFNetLocal(Local_Base, NAFNet):
def __init__(self, *args, train_size=(1, 3, 256, 256), fast_imp=False, **kwargs):
Local_Base.__init__(self)
NAFNet.__init__(self, *args, **kwargs)
N, C, H, W = train_size
base_size = (int(H * 1.5), int(W * 1.5))
self.eval()
with torch.no_grad():
self.convert(base_size=base_size, train_size=train_size, fast_imp=fast_imp)
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 FPA(nn.Module):
# def __init__(self,nc):
# super(FPA, self).__init__()
# self.process_mag = nn.Sequential(
# nn.Conv2d(nc, nc, 1, 1, 0),
# nn.LeakyReLU(0.1, inplace=True),
# nn.Conv2d(nc, nc, 1, 1, 0),
# nn.LeakyReLU(0.1, inplace=True),
# nn.Conv2d(nc, nc, 1, 1, 0))
# self.process_pha = nn.Sequential(
# nn.Conv2d(nc, nc, 1, 1, 0),
# nn.LeakyReLU(0.1, inplace=True),
# nn.Conv2d(nc, nc, 1, 1, 0),
# nn.LeakyReLU(0.1, inplace=True),
# nn.Conv2d(nc, nc, 1, 1, 0))
# def forward(self, input):
# _, _, H, W = input.shape
# x_freq = torch.fft.rfft2(input, norm='backward')
# mag = torch.abs(x_freq)
# pha = torch.angle(x_freq)
# mag = mag + self.process_mag(mag)
# pha = pha + self.process_pha(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
# class FBlock(nn.Module):
# def __init__(self, c, DW_Expand=2, FFN_Expand=2, dilations = [1], extra_depth_wise = False):
# super(FBlock, self).__init__()
# self.branches = nn.ModuleList()
# for dilation in dilations:
# self.branches.append(Branch_v2(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.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)
# self.norm1 = LayerNorm2d(c)
# self.norm2 = LayerNorm2d(c)
# ffn_channel = FFN_Expand * c
# self.conv_fpr_intro = nn.Conv2d(in_channels=c, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1)
# self.fpa = FPA(nc = ffn_channel)
# self.conv_fpr_out = nn.Conv2d(in_channels=ffn_channel, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1)
# 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
#Frequency pixel residue
x = self.conv_fpr_intro(self.norm2(y)) # size [B, C, H, W]
x = self.fpa(x) # size [B, C, H, W]
x = self.conv_fpr_out(x)
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_v2(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) |