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import torch
import torch.nn as nn
from basicsr.utils.registry import ARCH_REGISTRY
from .arch_util import ResidualBlockNoBN, make_layer
class MeanShift(nn.Conv2d):
""" Data normalization with mean and std.
Args:
rgb_range (int): Maximum value of RGB.
rgb_mean (list[float]): Mean for RGB channels.
rgb_std (list[float]): Std for RGB channels.
sign (int): For subtraction, sign is -1, for addition, sign is 1.
Default: -1.
requires_grad (bool): Whether to update the self.weight and self.bias.
Default: True.
"""
def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1, requires_grad=True):
super(MeanShift, self).__init__(3, 3, kernel_size=1)
std = torch.Tensor(rgb_std)
self.weight.data = torch.eye(3).view(3, 3, 1, 1)
self.weight.data.div_(std.view(3, 1, 1, 1))
self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean)
self.bias.data.div_(std)
self.requires_grad = requires_grad
class EResidualBlockNoBN(nn.Module):
"""Enhanced Residual block without BN.
There are three convolution layers in residual branch.
"""
def __init__(self, in_channels, out_channels):
super(EResidualBlockNoBN, self).__init__()
self.body = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 1, 1, 0),
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
out = self.body(x)
out = self.relu(out + x)
return out
class MergeRun(nn.Module):
""" Merge-and-run unit.
This unit contains two branches with different dilated convolutions,
followed by a convolution to process the concatenated features.
Paper: Real Image Denoising with Feature Attention
Ref git repo: https://github.com/saeed-anwar/RIDNet
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
super(MergeRun, self).__init__()
self.dilation1 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size, stride, 2, 2), nn.ReLU(inplace=True))
self.dilation2 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride, 3, 3), nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size, stride, 4, 4), nn.ReLU(inplace=True))
self.aggregation = nn.Sequential(
nn.Conv2d(out_channels * 2, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True))
def forward(self, x):
dilation1 = self.dilation1(x)
dilation2 = self.dilation2(x)
out = torch.cat([dilation1, dilation2], dim=1)
out = self.aggregation(out)
out = out + x
return out
class ChannelAttention(nn.Module):
"""Channel attention.
Args:
num_feat (int): Channel number of intermediate features.
squeeze_factor (int): Channel squeeze factor. Default:
"""
def __init__(self, mid_channels, squeeze_factor=16):
super(ChannelAttention, self).__init__()
self.attention = nn.Sequential(
nn.AdaptiveAvgPool2d(1), nn.Conv2d(mid_channels, mid_channels // squeeze_factor, 1, padding=0),
nn.ReLU(inplace=True), nn.Conv2d(mid_channels // squeeze_factor, mid_channels, 1, padding=0), nn.Sigmoid())
def forward(self, x):
y = self.attention(x)
return x * y
class EAM(nn.Module):
"""Enhancement attention modules (EAM) in RIDNet.
This module contains a merge-and-run unit, a residual block,
an enhanced residual block and a feature attention unit.
Attributes:
merge: The merge-and-run unit.
block1: The residual block.
block2: The enhanced residual block.
ca: The feature/channel attention unit.
"""
def __init__(self, in_channels, mid_channels, out_channels):
super(EAM, self).__init__()
self.merge = MergeRun(in_channels, mid_channels)
self.block1 = ResidualBlockNoBN(mid_channels)
self.block2 = EResidualBlockNoBN(mid_channels, out_channels)
self.ca = ChannelAttention(out_channels)
# The residual block in the paper contains a relu after addition.
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
out = self.merge(x)
out = self.relu(self.block1(out))
out = self.block2(out)
out = self.ca(out)
return out
@ARCH_REGISTRY.register()
class RIDNet(nn.Module):
"""RIDNet: Real Image Denoising with Feature Attention.
Ref git repo: https://github.com/saeed-anwar/RIDNet
Args:
in_channels (int): Channel number of inputs.
mid_channels (int): Channel number of EAM modules.
Default: 64.
out_channels (int): Channel number of outputs.
num_block (int): Number of EAM. Default: 4.
img_range (float): Image range. Default: 255.
rgb_mean (tuple[float]): Image mean in RGB orders.
Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.
"""
def __init__(self,
in_channels,
mid_channels,
out_channels,
num_block=4,
img_range=255.,
rgb_mean=(0.4488, 0.4371, 0.4040),
rgb_std=(1.0, 1.0, 1.0)):
super(RIDNet, self).__init__()
self.sub_mean = MeanShift(img_range, rgb_mean, rgb_std)
self.add_mean = MeanShift(img_range, rgb_mean, rgb_std, 1)
self.head = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.body = make_layer(
EAM, num_block, in_channels=mid_channels, mid_channels=mid_channels, out_channels=mid_channels)
self.tail = nn.Conv2d(mid_channels, out_channels, 3, 1, 1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
res = self.sub_mean(x)
res = self.tail(self.body(self.relu(self.head(res))))
res = self.add_mean(res)
out = x + res
return out
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