| 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 |
|
|
|
|
| @ARCH_REGISTRY.register() |
| 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 |
|
|
