| import torch.nn as nn | |
| from . import common | |
| from lambda_networks import LambdaLayer | |
| def build_model(args): | |
| return ResNet(args) | |
| class ResNet(nn.Module): | |
| def __init__( | |
| self, | |
| args, | |
| in_channels=3, | |
| out_channels=3, | |
| n_feats=None, | |
| kernel_size=None, | |
| n_resblocks=None, | |
| mean_shift=True, | |
| ): | |
| super(ResNet, self).__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.n_feats = args.n_feats if n_feats is None else n_feats | |
| self.kernel_size = args.kernel_size if kernel_size is None else kernel_size | |
| self.n_resblocks = args.n_resblocks if n_resblocks is None else n_resblocks | |
| self.mean_shift = mean_shift | |
| self.rgb_range = args.rgb_range | |
| self.mean = self.rgb_range / 2 | |
| modules = [] | |
| modules.append( | |
| common.default_conv(self.in_channels, self.n_feats, self.kernel_size) | |
| ) | |
| for _ in range(self.n_resblocks // 3): | |
| modules.append(common.ResBlock(self.n_feats, self.kernel_size)) | |
| modules.append( | |
| LambdaLayer( | |
| dim=self.n_feats, dim_out=self.n_feats, r=23, dim_k=16, heads=4, dim_u=1 | |
| ) | |
| ) | |
| for _ in range(self.n_resblocks // 3): | |
| modules.append(common.ResBlock(self.n_feats, self.kernel_size)) | |
| modules.append( | |
| LambdaLayer( | |
| dim=self.n_feats, dim_out=self.n_feats, r=7, dim_k=16, heads=4, dim_u=4 | |
| ) | |
| ) | |
| for _ in range(self.n_resblocks // 3): | |
| modules.append(common.ResBlock(self.n_feats, self.kernel_size)) | |
| modules.append( | |
| common.default_conv(self.n_feats, self.n_feats, self.kernel_size) | |
| ) | |
| modules.append(common.default_conv(self.n_feats, self.out_channels, 1)) | |
| self.body = nn.Sequential(*modules) | |
| def forward(self, input): | |
| if self.mean_shift: | |
| input = input - self.mean | |
| output = self.body(input) | |
| if self.mean_shift: | |
| output = output + self.mean | |
| return output | |