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| import torch | |
| from torch import nn as nn | |
| from basicsr.archs.arch_util import ResidualBlockNoBN, Upsample, make_layer | |
| from basicsr.utils.registry import ARCH_REGISTRY | |
| class EDSR(nn.Module): | |
| """EDSR network structure. | |
| Paper: Enhanced Deep Residual Networks for Single Image Super-Resolution. | |
| Ref git repo: https://github.com/thstkdgus35/EDSR-PyTorch | |
| Args: | |
| num_in_ch (int): Channel number of inputs. | |
| num_out_ch (int): Channel number of outputs. | |
| num_feat (int): Channel number of intermediate features. | |
| Default: 64. | |
| num_block (int): Block number in the trunk network. Default: 16. | |
| upscale (int): Upsampling factor. Support 2^n and 3. | |
| Default: 4. | |
| res_scale (float): Used to scale the residual in residual block. | |
| Default: 1. | |
| 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, | |
| num_in_ch, | |
| num_out_ch, | |
| num_feat=64, | |
| num_block=16, | |
| upscale=4, | |
| res_scale=1, | |
| img_range=255., | |
| rgb_mean=(0.4488, 0.4371, 0.4040)): | |
| super(EDSR, self).__init__() | |
| self.img_range = img_range | |
| self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) | |
| self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) | |
| self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat, res_scale=res_scale, pytorch_init=True) | |
| self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) | |
| self.upsample = Upsample(upscale, num_feat) | |
| self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) | |
| def forward(self, x): | |
| self.mean = self.mean.type_as(x) | |
| x = (x - self.mean) * self.img_range | |
| x = self.conv_first(x) | |
| res = self.conv_after_body(self.body(x)) | |
| res += x | |
| x = self.conv_last(self.upsample(res)) | |
| x = x / self.img_range + self.mean | |
| return x | |