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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import models.utils as mutils
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from models import register
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class Block(nn.Module):
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def __init__(self, nf, group=1):
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super(Block, self).__init__()
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self.b1 = mutils.EResidualBlock(nf, nf, group=group)
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self.c1 = mutils.BasicBlock(nf*2, nf, 1, 1, 0)
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self.c2 = mutils.BasicBlock(nf*3, nf, 1, 1, 0)
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self.c3 = mutils.BasicBlock(nf*4, nf, 1, 1, 0)
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def forward(self, x):
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c0 = o0 = x
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b1 = self.b1(o0)
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c1 = torch.cat([c0, b1], dim=1)
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o1 = self.c1(c1)
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b2 = self.b1(o1)
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c2 = torch.cat([c1, b2], dim=1)
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o2 = self.c2(c2)
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b3 = self.b1(o2)
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c3 = torch.cat([c2, b3], dim=1)
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o3 = self.c3(c3)
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return o3
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@register('carn')
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class CARN_M(nn.Module):
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def __init__(self, in_nc=3, out_nc=3, nf=64, scale=4, group=4, no_upsampling=False):
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super(CARN_M, self).__init__()
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self.scale = scale
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self.out_dim = nf
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self.entry = nn.Conv2d(in_nc, nf, 3, 1, 1)
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self.b1 = Block(nf, group=group)
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self.b2 = Block(nf, group=group)
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self.b3 = Block(nf, group=group)
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self.c1 = mutils.BasicBlock(nf*2, nf, 1, 1, 0)
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self.c2 = mutils.BasicBlock(nf*3, nf, 1, 1, 0)
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self.c3 = mutils.BasicBlock(nf*4, nf, 1, 1, 0)
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self.no_upsampling = no_upsampling
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if not no_upsampling:
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self.upsample = mutils.UpsampleBlock(nf, scale=scale, multi_scale=False, group=group)
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self.exit = nn.Conv2d(nf, out_nc, 3, 1, 1)
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def forward(self, x):
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x = self.entry(x)
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c0 = o0 = x
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b1 = self.b1(o0)
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c1 = torch.cat([c0, b1], dim=1)
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o1 = self.c1(c1)
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b2 = self.b2(o1)
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c2 = torch.cat([c1, b2], dim=1)
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o2 = self.c2(c2)
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b3 = self.b3(o2)
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c3 = torch.cat([c2, b3], dim=1)
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o3 = self.c3(c3)
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out = o3.clone()
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if not self.no_upsampling:
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out = self.upsample(out, scale=self.scale)
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out = self.exit(out)
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return out
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