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import torch
import torch.nn as nn
from WT.transform import DWT, IWT
##---------- Basic Layers ----------
def conv3x3(in_chn, out_chn, bias=True):
layer = nn.Conv2d(in_chn, out_chn, kernel_size=3, stride=1, padding=1, bias=bias)
return layer
def conv(in_channels, out_channels, kernel_size, bias=False, stride=1):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size // 2), bias=bias, stride=stride)
def bili_resize(factor):
return nn.Upsample(scale_factor=factor, mode='bilinear', align_corners=False)
##---------- Basic Blocks ----------
class UNetConvBlock(nn.Module):
def __init__(self, in_size, out_size, downsample):
super(UNetConvBlock, self).__init__()
self.downsample = downsample
self.body = [HWB(n_feat=in_size, o_feat=in_size, kernel_size=3, reduction=16, bias=False, act=nn.PReLU())]# for _ in range(wab)]
self.body = nn.Sequential(*self.body)
if downsample:
self.downsample = PS_down(out_size, out_size, downscale=2)
self.tail = nn.Conv2d(in_size, out_size, kernel_size=1)
def forward(self, x):
out = self.body(x)
out = self.tail(out)
if self.downsample:
out_down = self.downsample(out)
return out_down, out
else:
return out
class UNetUpBlock(nn.Module):
def __init__(self, in_size, out_size):
super(UNetUpBlock, self).__init__()
self.up = PS_up(in_size, out_size, upscale=2)
self.conv_block = UNetConvBlock(in_size, out_size, downsample=False)
def forward(self, x, bridge):
up = self.up(x)
out = torch.cat([up, bridge], dim=1)
out = self.conv_block(out)
return out
##---------- Resizing Modules (Pixel(Un)Shuffle) ----------
class PS_down(nn.Module):
def __init__(self, in_size, out_size, downscale):
super(PS_down, self).__init__()
self.UnPS = nn.PixelUnshuffle(downscale)
self.conv1 = nn.Conv2d((downscale**2) * in_size, out_size, 1, 1, 0)
def forward(self, x):
x = self.UnPS(x) # h/2, w/2, 4*c
x = self.conv1(x)
return x
class PS_up(nn.Module):
def __init__(self, in_size, out_size, upscale):
super(PS_up, self).__init__()
self.PS = nn.PixelShuffle(upscale)
self.conv1 = nn.Conv2d(in_size//(upscale**2), out_size, 1, 1, 0)
def forward(self, x):
x = self.PS(x) # h/2, w/2, 4*c
x = self.conv1(x)
return x
##---------- Selective Kernel Feature Fusion (SKFF) ----------
class SKFF(nn.Module):
def __init__(self, in_channels, height=3, reduction=8, bias=False):
super(SKFF, self).__init__()
self.height = height
d = max(int(in_channels / reduction), 4)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv_du = nn.Sequential(nn.Conv2d(in_channels, d, 1, padding=0, bias=bias), nn.PReLU())
self.fcs = nn.ModuleList([])
for i in range(self.height):
self.fcs.append(nn.Conv2d(d, in_channels, kernel_size=1, stride=1, bias=bias))
self.softmax = nn.Softmax(dim=1)
def forward(self, inp_feats):
batch_size, n_feats, H, W = inp_feats[1].shape
inp_feats = torch.cat(inp_feats, dim=1)
inp_feats = inp_feats.view(batch_size, self.height, n_feats, inp_feats.shape[2], inp_feats.shape[3])
feats_U = torch.sum(inp_feats, dim=1)
feats_S = self.avg_pool(feats_U)
feats_Z = self.conv_du(feats_S)
attention_vectors = [fc(feats_Z) for fc in self.fcs]
attention_vectors = torch.cat(attention_vectors, dim=1)
attention_vectors = attention_vectors.view(batch_size, self.height, n_feats, 1, 1)
attention_vectors = self.softmax(attention_vectors)
feats_V = torch.sum(inp_feats * attention_vectors, dim=1)
return feats_V
##########################################################################
# Spatial Attention Layer
class SALayer(nn.Module):
def __init__(self, kernel_size=5, bias=False):
super(SALayer, self).__init__()
self.conv_du = nn.Sequential(
nn.Conv2d(2, 1, kernel_size=kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias),
nn.Sigmoid()
)
def forward(self, x):
# torch.max will output 2 things, and we want the 1st one
max_pool, _ = torch.max(x, dim=1, keepdim=True)
avg_pool = torch.mean(x, 1, keepdim=True)
channel_pool = torch.cat([max_pool, avg_pool], dim=1) # [N,2,H,W] could add 1x1 conv -> [N,3,H,W]
y = self.conv_du(channel_pool)
return x * y
##########################################################################
# Channel Attention Layer
class CALayer(nn.Module):
def __init__(self, channel, reduction=16, bias=False):
super(CALayer, self).__init__()
# global average pooling: feature --> point
self.avg_pool = nn.AdaptiveAvgPool2d(1)
# feature channel downscale and upscale --> channel weight
self.conv_du = nn.Sequential(
nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=bias),
nn.ReLU(inplace=True),
nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=bias),
nn.Sigmoid()
)
def forward(self, x):
y = self.avg_pool(x)
y = self.conv_du(y)
return x * y
##########################################################################
# Half Wavelet Dual Attention Block (HWB)
class HWB(nn.Module):
def __init__(self, n_feat, o_feat, kernel_size, reduction, bias, act):
super(HWB, self).__init__()
self.dwt = DWT()
self.iwt = IWT()
modules_body = \
[
conv(n_feat*2, n_feat, kernel_size, bias=bias),
act,
conv(n_feat, n_feat*2, kernel_size, bias=bias)
]
self.body = nn.Sequential(*modules_body)
self.WSA = SALayer()
self.WCA = CALayer(n_feat*2, reduction, bias=bias)
self.conv1x1 = nn.Conv2d(n_feat*4, n_feat*2, kernel_size=1, bias=bias)
self.conv3x3 = nn.Conv2d(n_feat, o_feat, kernel_size=3, padding=1, bias=bias)
self.activate = act
self.conv1x1_final = nn.Conv2d(n_feat, o_feat, kernel_size=1, bias=bias)
def forward(self, x):
residual = x
# Split 2 part
wavelet_path_in, identity_path = torch.chunk(x, 2, dim=1)
# Wavelet domain (Dual attention)
x_dwt = self.dwt(wavelet_path_in)
res = self.body(x_dwt)
branch_sa = self.WSA(res)
branch_ca = self.WCA(res)
res = torch.cat([branch_sa, branch_ca], dim=1)
res = self.conv1x1(res) + x_dwt
wavelet_path = self.iwt(res)
out = torch.cat([wavelet_path, identity_path], dim=1)
out = self.activate(self.conv3x3(out))
out += self.conv1x1_final(residual)
return out
##########################################################################
##---------- HWMNet-LOL ----------
class HWMNet(nn.Module):
def __init__(self, in_chn=3, wf=64, depth=4):
super(HWMNet, self).__init__()
self.depth = depth
self.down_path = nn.ModuleList()
self.bili_down = bili_resize(0.5)
self.conv_01 = nn.Conv2d(in_chn, wf, 3, 1, 1)
# encoder of UNet-64
prev_channels = 0
for i in range(depth): # 0,1,2,3
downsample = True if (i + 1) < depth else False
self.down_path.append(UNetConvBlock(prev_channels + wf, (2 ** i) * wf, downsample))
prev_channels = (2 ** i) * wf
# decoder of UNet-64
self.up_path = nn.ModuleList()
self.skip_conv = nn.ModuleList()
self.conv_up = nn.ModuleList()
self.bottom_conv = nn.Conv2d(prev_channels, wf, 3, 1, 1)
self.bottom_up = bili_resize(2 ** (depth-1))
for i in reversed(range(depth - 1)):
self.up_path.append(UNetUpBlock(prev_channels, (2 ** i) * wf))
self.skip_conv.append(nn.Conv2d((2 ** i) * wf, (2 ** i) * wf, 3, 1, 1))
self.conv_up.append(nn.Sequential(*[bili_resize(2 ** i), nn.Conv2d((2 ** i) * wf, wf, 3, 1, 1)]))
prev_channels = (2 ** i) * wf
self.final_ff = SKFF(in_channels=wf, height=depth)
self.last = conv3x3(prev_channels, in_chn, bias=True)
def forward(self, x):
img = x
scale_img = img
##### shallow conv #####
x1 = self.conv_01(img)
encs = []
######## UNet-64 ########
# Down-path (Encoder)
for i, down in enumerate(self.down_path):
if i == 0:
x1, x1_up = down(x1)
encs.append(x1_up)
elif (i + 1) < self.depth:
scale_img = self.bili_down(scale_img)
left_bar = self.conv_01(scale_img)
x1 = torch.cat([x1, left_bar], dim=1)
x1, x1_up = down(x1)
encs.append(x1_up)
else:
scale_img = self.bili_down(scale_img)
left_bar = self.conv_01(scale_img)
x1 = torch.cat([x1, left_bar], dim=1)
x1 = down(x1)
# Up-path (Decoder)
ms_result = [self.bottom_up(self.bottom_conv(x1))]
for i, up in enumerate(self.up_path):
x1 = up(x1, self.skip_conv[i](encs[-i - 1]))
ms_result.append(self.conv_up[i](x1))
# Multi-scale selective feature fusion
msff_result = self.final_ff(ms_result)
##### Reconstruct #####
out_1 = self.last(msff_result) + img
return out_1
if __name__ == "__main__":
input = torch.ones(1, 3, 400, 592, dtype=torch.float, requires_grad=False).cuda()
model = HWMNet(in_chn=3, wf=96, depth=4).cuda()
out = model(input)
flops, params = profile(model, inputs=(input,))
# RDBlayer = SK_RDB(in_channels=64, growth_rate=64, num_layers=3)
# print(RDBlayer)
# out = RDBlayer(input)
# flops, params = profile(RDBlayer, inputs=(input,))
print('input shape:', input.shape)
print('parameters:', params/1e6)
print('flops', flops/1e9)
print('output shape', out.shape)
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