forked from mrq/DL-Art-School
74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
|
import functools
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
import models.archs.arch_util as arch_util
|
||
|
|
|
||
|
|
|
||
|
|
class ResidualDenseBlock_5C(nn.Module):
|
||
|
|
def __init__(self, nf=64, gc=32, bias=True):
|
||
|
|
super(ResidualDenseBlock_5C, self).__init__()
|
||
|
|
# gc: growth channel, i.e. intermediate channels
|
||
|
|
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
|
||
|
|
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
|
||
|
|
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
|
||
|
|
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
|
||
|
|
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
|
||
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
||
|
|
|
||
|
|
# initialization
|
||
|
|
arch_util.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5],
|
||
|
|
0.1)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x1 = self.lrelu(self.conv1(x))
|
||
|
|
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
|
||
|
|
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
|
||
|
|
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
|
||
|
|
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
|
||
|
|
return x5 * 0.2 + x
|
||
|
|
|
||
|
|
|
||
|
|
class RRDB(nn.Module):
|
||
|
|
'''Residual in Residual Dense Block'''
|
||
|
|
|
||
|
|
def __init__(self, nf, gc=32):
|
||
|
|
super(RRDB, self).__init__()
|
||
|
|
self.RDB1 = ResidualDenseBlock_5C(nf, gc)
|
||
|
|
self.RDB2 = ResidualDenseBlock_5C(nf, gc)
|
||
|
|
self.RDB3 = ResidualDenseBlock_5C(nf, gc)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
out = self.RDB1(x)
|
||
|
|
out = self.RDB2(out)
|
||
|
|
out = self.RDB3(out)
|
||
|
|
return out * 0.2 + x
|
||
|
|
|
||
|
|
|
||
|
|
class RRDBNet(nn.Module):
|
||
|
|
def __init__(self, in_nc, out_nc, nf, nb, gc=32):
|
||
|
|
super(RRDBNet, self).__init__()
|
||
|
|
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
|
||
|
|
|
||
|
|
self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
|
||
|
|
self.RRDB_trunk = arch_util.make_layer(RRDB_block_f, nb)
|
||
|
|
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
|
||
|
|
#### upsampling
|
||
|
|
self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
|
||
|
|
self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
|
||
|
|
self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
|
||
|
|
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
|
||
|
|
|
||
|
|
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
fea = self.conv_first(x)
|
||
|
|
trunk = self.trunk_conv(self.RRDB_trunk(fea))
|
||
|
|
fea = fea + trunk
|
||
|
|
|
||
|
|
fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
|
||
|
|
fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
|
||
|
|
out = self.conv_last(self.lrelu(self.HRconv(fea)))
|
||
|
|
|
||
|
|
return out
|