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