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