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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
@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
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