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import torch
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from torch import nn as nn
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from basicsr.archs.arch_util import ResidualBlockNoBN, Upsample, make_layer
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from basicsr.utils.registry import ARCH_REGISTRY
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@ARCH_REGISTRY.register()
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class EDSR(nn.Module):
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"""EDSR network structure.
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Paper: Enhanced Deep Residual Networks for Single Image Super-Resolution.
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Ref git repo: https://github.com/thstkdgus35/EDSR-PyTorch
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Args:
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num_in_ch (int): Channel number of inputs.
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num_out_ch (int): Channel number of outputs.
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num_feat (int): Channel number of intermediate features.
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Default: 64.
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num_block (int): Block number in the trunk network. Default: 16.
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upscale (int): Upsampling factor. Support 2^n and 3.
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Default: 4.
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res_scale (float): Used to scale the residual in residual block.
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Default: 1.
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img_range (float): Image range. Default: 255.
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rgb_mean (tuple[float]): Image mean in RGB orders.
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Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset.
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"""
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def __init__(self,
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num_in_ch,
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num_out_ch,
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num_feat=64,
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num_block=16,
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upscale=4,
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res_scale=1,
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img_range=255.,
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rgb_mean=(0.4488, 0.4371, 0.4040)):
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super(EDSR, self).__init__()
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self.img_range = img_range
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self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
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self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
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self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat, res_scale=res_scale, pytorch_init=True)
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self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.upsample = Upsample(upscale, num_feat)
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self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
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def forward(self, x):
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self.mean = self.mean.type_as(x)
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x = (x - self.mean) * self.img_range
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x = self.conv_first(x)
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res = self.conv_after_body(self.body(x))
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res += x
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x = self.conv_last(self.upsample(res))
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x = x / self.img_range + self.mean
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return x
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