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Running
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Zero
| 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 | |
| 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 | |