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from torch import nn as nn
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from torch.nn import functional as F
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from basicsr.utils.registry import ARCH_REGISTRY
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from .arch_util import ResidualBlockNoBN, default_init_weights, make_layer
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@ARCH_REGISTRY.register()
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class MSRResNet(nn.Module):
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"""Modified SRResNet.
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A compacted version modified from SRResNet in
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"Photo-Realistic Single Image Super-Resolution Using a Generative
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Adversarial Network"
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It uses residual blocks without BN, similar to EDSR.
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Currently, it supports x2, x3 and x4 upsampling scale factor.
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Args:
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num_in_ch (int): Channel number of inputs. Default: 3.
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num_out_ch (int): Channel number of outputs. Default: 3.
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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 body network. Default: 16.
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upscale (int): Upsampling factor. Support x2, x3 and x4.
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Default: 4.
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"""
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def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, upscale=4):
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super(MSRResNet, self).__init__()
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self.upscale = upscale
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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)
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if self.upscale in [2, 3]:
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self.upconv1 = nn.Conv2d(num_feat, num_feat * self.upscale * self.upscale, 3, 1, 1)
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self.pixel_shuffle = nn.PixelShuffle(self.upscale)
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elif self.upscale == 4:
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self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
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self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
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self.pixel_shuffle = nn.PixelShuffle(2)
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self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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default_init_weights([self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1)
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if self.upscale == 4:
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default_init_weights(self.upconv2, 0.1)
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def forward(self, x):
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feat = self.lrelu(self.conv_first(x))
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out = self.body(feat)
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if self.upscale == 4:
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out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
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out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
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elif self.upscale in [2, 3]:
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out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
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out = self.conv_last(self.lrelu(self.conv_hr(out)))
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base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False)
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out += base
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return out
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