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## Learning Enriched Features for Real Image Restoration and Enhancement
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao
## ECCV 2020
## https://arxiv.org/abs/2003.06792
"""
# --- Imports --- #
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
# from pdb import set_trace as stx
from Deraining.utils.antialias import Downsample as downsamp
##########################################################################
def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias, stride = stride)
##########################################################################
##---------- 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 = inp_feats[0].shape[0]
n_feats = inp_feats[0].shape[1]
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)
# stx()
attention_vectors = self.softmax(attention_vectors)
feats_V = torch.sum(inp_feats*attention_vectors, dim=1)
return feats_V
##########################################################################
##---------- Spatial Attention ----------
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=False, bias=False):
super(BasicConv, self).__init__()
self.out_channels = out_planes
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
self.bn = nn.BatchNorm2d(out_planes,eps=1e-5, momentum=0.01, affine=True) if bn else None
self.relu = nn.ReLU() if relu else None
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
class ChannelPool(nn.Module):
def forward(self, x):
return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )
class spatial_attn_layer(nn.Module):
def __init__(self, kernel_size=5):
super(spatial_attn_layer, self).__init__()
self.compress = ChannelPool()
self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False)
def forward(self, x):
# import pdb;pdb.set_trace()
x_compress = self.compress(x)
x_out = self.spatial(x_compress)
scale = torch.sigmoid(x_out) # broadcasting
return x * scale
##########################################################################
## ------ Channel Attention --------------
class ca_layer(nn.Module):
def __init__(self, channel, reduction=8, bias=True):
super(ca_layer, 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
##########################################################################
##---------- Dual Attention Unit (DAU) ----------
class DAU(nn.Module):
def __init__(
self, n_feat, kernel_size=3, reduction=8,
bias=False, bn=False, act=nn.PReLU(), res_scale=1):
super(DAU, self).__init__()
modules_body = [conv(n_feat, n_feat, kernel_size, bias=bias), act, conv(n_feat, n_feat, kernel_size, bias=bias)]
self.body = nn.Sequential(*modules_body)
## Spatial Attention
self.SA = spatial_attn_layer()
## Channel Attention
self.CA = ca_layer(n_feat,reduction, bias=bias)
self.conv1x1 = nn.Conv2d(n_feat*2, n_feat, kernel_size=1, bias=bias)
def forward(self, x):
res = self.body(x)
sa_branch = self.SA(res)
ca_branch = self.CA(res)
res = torch.cat([sa_branch, ca_branch], dim=1)
res = self.conv1x1(res)
res += x
return res
##########################################################################
##---------- Resizing Modules ----------
class ResidualDownSample(nn.Module):
def __init__(self, in_channels, bias=False):
super(ResidualDownSample, self).__init__()
self.top = nn.Sequential(nn.Conv2d(in_channels, in_channels, 1, stride=1, padding=0, bias=bias),
nn.PReLU(),
nn.Conv2d(in_channels, in_channels, 3, stride=1, padding=1, bias=bias),
nn.PReLU(),
downsamp(channels=in_channels,filt_size=3,stride=2),
nn.Conv2d(in_channels, in_channels*2, 1, stride=1, padding=0, bias=bias))
self.bot = nn.Sequential(downsamp(channels=in_channels,filt_size=3,stride=2),
nn.Conv2d(in_channels, in_channels*2, 1, stride=1, padding=0, bias=bias))
def forward(self, x):
top = self.top(x)
bot = self.bot(x)
out = top+bot
return out
class DownSample(nn.Module):
def __init__(self, in_channels, scale_factor, stride=2, kernel_size=3):
super(DownSample, self).__init__()
self.scale_factor = int(np.log2(scale_factor))
modules_body = []
for i in range(self.scale_factor):
modules_body.append(ResidualDownSample(in_channels))
in_channels = int(in_channels * stride)
self.body = nn.Sequential(*modules_body)
def forward(self, x):
x = self.body(x)
return x
class ResidualUpSample(nn.Module):
def __init__(self, in_channels, bias=False):
super(ResidualUpSample, self).__init__()
self.top = nn.Sequential(nn.Conv2d(in_channels, in_channels, 1, stride=1, padding=0, bias=bias),
nn.PReLU(),
nn.ConvTranspose2d(in_channels, in_channels, 3, stride=2, padding=1, output_padding=1,bias=bias),
nn.PReLU(),
nn.Conv2d(in_channels, in_channels//2, 1, stride=1, padding=0, bias=bias))
self.bot = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=bias),
nn.Conv2d(in_channels, in_channels//2, 1, stride=1, padding=0, bias=bias))
def forward(self, x):
top = self.top(x)
bot = self.bot(x)
out = top+bot
return out
class UpSample(nn.Module):
def __init__(self, in_channels, scale_factor, stride=2, kernel_size=3):
super(UpSample, self).__init__()
self.scale_factor = int(np.log2(scale_factor))
modules_body = []
for i in range(self.scale_factor):
modules_body.append(ResidualUpSample(in_channels))
in_channels = int(in_channels // stride)
self.body = nn.Sequential(*modules_body)
def forward(self, x):
x = self.body(x)
return x
##########################################################################
##---------- Multi-Scale Resiudal Block (MSRB) ----------
class MSRB(nn.Module):
def __init__(self, n_feat, height, width, stride, bias):
super(MSRB, self).__init__()
self.n_feat, self.height, self.width = n_feat, height, width
self.blocks = nn.ModuleList([nn.ModuleList([DAU(int(n_feat*stride**i))]*width) for i in range(height)])
INDEX = np.arange(0,width, 2)
FEATS = [int((stride**i)*n_feat) for i in range(height)]
SCALE = [2**i for i in range(1,height)]
self.last_up = nn.ModuleDict()
for i in range(1,height):
self.last_up.update({f'{i}': UpSample(int(n_feat*stride**i),2**i,stride)})
self.down = nn.ModuleDict()
self.up = nn.ModuleDict()
i=0
SCALE.reverse()
for feat in FEATS:
for scale in SCALE[i:]:
self.down.update({f'{feat}_{scale}': DownSample(feat,scale,stride)})
i+=1
i=0
FEATS.reverse()
for feat in FEATS:
for scale in SCALE[i:]:
self.up.update({f'{feat}_{scale}': UpSample(feat,scale,stride)})
i+=1
self.conv_out = nn.Conv2d(n_feat, n_feat, kernel_size=3, padding=1, bias=bias)
self.selective_kernel = nn.ModuleList([SKFF(n_feat*stride**i, height) for i in range(height)])
def forward(self, x):
inp = x.clone()
#col 1 only
blocks_out = []
for j in range(self.height):
if j==0:
inp = self.blocks[j][0](inp)
else:
inp = self.blocks[j][0](self.down[f'{inp.size(1)}_{2}'](inp))
blocks_out.append(inp)
#rest of grid
for i in range(1,self.width):
#Mesh
# Replace condition(i%2!=0) with True(Mesh) or False(Plain)
# if i%2!=0:
if True:
tmp=[]
for j in range(self.height):
TENSOR = []
nfeats = (2**j)*self.n_feat
for k in range(self.height):
TENSOR.append(self.select_up_down(blocks_out[k], j, k))
selective_kernel_fusion = self.selective_kernel[j](TENSOR)
tmp.append(selective_kernel_fusion)
#Plain
else:
tmp = blocks_out
#Forward through either mesh or plain
for j in range(self.height):
blocks_out[j] = self.blocks[j][i](tmp[j])
#Sum after grid
out=[]
for k in range(self.height):
out.append(self.select_last_up(blocks_out[k], k))
out = self.selective_kernel[0](out)
out = self.conv_out(out)
out = out + x
return out
def select_up_down(self, tensor, j, k):
if j==k:
return tensor
else:
diff = 2 ** np.abs(j-k)
if j<k:
return self.up[f'{tensor.size(1)}_{diff}'](tensor)
else:
return self.down[f'{tensor.size(1)}_{diff}'](tensor)
def select_last_up(self, tensor, k):
if k==0:
return tensor
else:
return self.last_up[f'{k}'](tensor)
##########################################################################
##---------- Recursive Residual Group (RRG) ----------
class RRG(nn.Module):
def __init__(self, n_feat, n_MSRB, height, width, stride, bias=False):
super(RRG, self).__init__()
modules_body = [MSRB(n_feat, height, width, stride, bias) for _ in range(n_MSRB)]
modules_body.append(conv(n_feat, n_feat, kernel_size=3))
self.body = nn.Sequential(*modules_body)
def forward(self, x):
res = self.body(x)
res += x
return res
##########################################################################
##---------- MIRNet -----------------------
class MIRNet(nn.Module):
def __init__(self, in_channels=3, out_channels=3, n_feat=64, kernel_size=3, stride=2, n_RRG=3, n_MSRB=2, height=3, width=2, bias=False):
super(MIRNet, self).__init__()
self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=bias)
modules_body = [RRG(n_feat, n_MSRB, height, width, stride, bias) for _ in range(n_RRG)]
self.body = nn.Sequential(*modules_body)
self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=bias)
def forward(self, x):
h = self.conv_in(x)
h = self.body(h)
h = self.conv_out(h)
h += x
return h
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