Spaces:
Running
on
T4
Running
on
T4
File size: 6,306 Bytes
06f26d7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
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
|