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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 .arch_util import LayerNorm2d
except:
from arch_util import LayerNorm2d
class SimpleGate(nn.Module):
def forward(self, x):
x1, x2 = x.chunk(2, dim=1)
return x1 * x2
class Adapter(nn.Module):
def __init__(self, c, ffn_channel = None):
super().__init__()
if ffn_channel:
ffn_channel = 2
else:
ffn_channel = c
self.conv1 = nn.Conv2d(in_channels=c, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.conv2 = nn.Conv2d(in_channels=ffn_channel, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
self.depthwise = nn.Conv2d(in_channels=c, out_channels=ffn_channel, kernel_size=3, padding=1, stride=1, groups=c, bias=True, dilation=1)
def forward(self, input):
x = self.conv1(input) + self.depthwise(input)
x = self.conv2(x)
return x
class FreMLP(nn.Module):
def __init__(self, nc, expand = 2):
super(FreMLP, self).__init__()
self.process1 = nn.Sequential(
nn.Conv2d(nc, expand * nc, 1, 1, 0),
nn.LeakyReLU(0.1, inplace=True),
nn.Conv2d(expand * nc, nc, 1, 1, 0))
def forward(self, x):
_, _, H, W = x.shape
x_freq = torch.fft.rfft2(x, norm='backward')
mag = torch.abs(x_freq)
pha = torch.angle(x_freq)
mag = self.process1(mag)
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 Branch(nn.Module):
'''
Branch that lasts lonly the dilated convolutions
'''
def __init__(self, c, DW_Expand, dilation = 1):
super().__init__()
self.dw_channel = DW_Expand * c
self.branch = nn.Sequential(
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 DBlock(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.dw_channel = DW_Expand * c
self.conv1 = nn.Conv2d(in_channels=c, out_channels=self.dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1)
self.extra_conv = nn.Conv2d(self.dw_channel, self.dw_channel, kernel_size=3, padding=1, stride=1, groups=c, bias=True, dilation=1) if extra_depth_wise else nn.Identity() #optional extra dw
self.branches = nn.ModuleList()
for dilation in dilations:
self.branches.append(Branch(self.dw_channel, DW_Expand = 1, dilation = dilation))
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)
# self.adapter = Adapter(c, ffn_channel=None)
# self.use_adapters = False
# def set_use_adapters(self, use_adapters):
# self.use_adapters = use_adapters
def forward(self, inp, adapter = None):
y = inp
x = self.norm1(inp)
# x = self.conv1(self.extra_conv(x))
x = self.extra_conv(self.conv1(x))
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]
x = y + x * self.gamma
# if self.use_adapters:
# return self.adapter(x)
# else:
return x
class EBlock(nn.Module):
'''
Change this block using Branch
'''
def __init__(self, c, DW_Expand=2, dilations = [1], extra_depth_wise = False):
super().__init__()
#we define the 2 branches
self.dw_channel = DW_Expand * c
self.extra_conv = 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
self.conv1 = nn.Conv2d(in_channels=c, out_channels=self.dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1)
self.branches = nn.ModuleList()
for dilation in dilations:
self.branches.append(Branch(c, DW_Expand, dilation = dilation))
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)
# second step
self.norm1 = LayerNorm2d(c)
self.norm2 = LayerNorm2d(c)
self.freq = FreMLP(nc = c, expand=2)
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)
# self.adapter = Adapter(c, ffn_channel=None)
# self.use_adapters = False
# def set_use_adapters(self, use_adapters):
# self.use_adapters = use_adapters
def forward(self, inp):
y = inp
x = self.norm1(inp)
x = self.conv1(self.extra_conv(x))
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_step2 = self.norm2(y) # size [B, 2*C, H, W]
x_freq = self.freq(x_step2) # size [B, C, H, W]
x = y * x_freq
x = y + x * self.gamma
# if self.use_adapters:
# return self.adapter(x)
# else:
return x
#----------------------------------------------------------------------------------------------
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 = True
# 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=False)
output = net(torch.randn((4, 3, 256, 256)))
# print('Values of EBlock:')
print(macs, params)
channels = 128
resol = 32
ksize = 5
# net = FAC(channels=channels, ksize=ksize)
# inp_shape = (channels, resol, resol)
# macs, params = get_model_complexity_info(net, inp_shape, verbose=False, print_per_layer_stat=True)
# print('Values of FAC:')
# print(macs, params)
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