File size: 6,413 Bytes
908a1ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
182
183
184
185
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 substraction, 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.

    It has a style of:
        ---Conv-ReLU-Conv-ReLU-Conv-+-ReLU-
         |__________________________|
    """

    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