DL-Art-School/codes/models/archs/RRDBNetXL_arch.py
James Betker 3b4e54c4c5 Add support for passthrough disc/gen
Add RRDBNetXL, which performs processing at multiple image sizes.
Add DiscResnet_passthrough, which allows passthrough of image at different sizes for discrimination.
Adjust the rest of the repo to allow generators that return more than just a single image.
2020-05-04 14:01:43 -06:00

98 lines
4.2 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_lo, nb_med, nb_hi, gc=32, interpolation_scale_factor=2):
super(RRDBNet, self).__init__()
nfmed = int(nf/2)
nfhi = int(nf/8)
gcmed = int(gc/2)
gchi = int(gc/8)
RRDB_block_f_lo = functools.partial(RRDB, nf=nf, gc=gc)
RRDB_block_f_lo_med = functools.partial(RRDB, nf=nfmed, gc=gcmed)
RRDB_block_f_lo_hi = functools.partial(RRDB, nf=nfhi, gc=gchi)
self.conv_first = nn.Conv2d(in_nc, nf, 7, 1, padding=3, bias=True)
self.RRDB_trunk_lo = arch_util.make_layer(RRDB_block_f_lo, nb_lo)
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.lo_skip_conv1 = nn.Conv2d(nf, nf, 3, 1, padding=1, bias=True)
self.lo_skip_conv2 = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
#### upsampling
self.upconv1 = nn.Conv2d(nf, nfmed, 3, 1, padding=1, bias=True)
self.RRDB_trunk_med = arch_util.make_layer(RRDB_block_f_lo_med, nb_med)
self.trunk_conv_med = nn.Conv2d(nfmed, nfmed, 3, 1, 1, bias=True)
self.med_skip_conv1 = nn.Conv2d(nfmed, nfmed, 3, 1, padding=1, bias=True)
self.med_skip_conv2 = nn.Conv2d(nfmed, out_nc, 3, 1, 1, bias=True)
self.upconv2 = nn.Conv2d(nfmed, nfhi, 3, 1, padding=1, bias=True)
self.RRDB_trunk_hi = arch_util.make_layer(RRDB_block_f_lo_hi, nb_hi)
self.trunk_conv_hi = nn.Conv2d(nfhi, nfhi, 3, 1, 1, bias=True)
self.HRconv = nn.Conv2d(nfhi, nfhi, 5, 1, padding=2, bias=True)
self.conv_last = nn.Conv2d(nfhi, out_nc, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.interpolation_scale_factor = interpolation_scale_factor
def forward(self, x):
fea = self.conv_first(x)
branch = self.trunk_conv(self.RRDB_trunk_lo(fea))
fea = (fea + branch) / 2
lo_skip = self.lo_skip_conv2(self.lrelu(self.lo_skip_conv1(fea)))
fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=self.interpolation_scale_factor, mode='nearest')))
branch = self.trunk_conv_med(self.RRDB_trunk_med(fea))
fea = (fea + branch) / 2
med_skip = self.med_skip_conv2(self.lrelu(self.med_skip_conv1(fea)))
fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=self.interpolation_scale_factor, mode='nearest')))
branch = self.trunk_conv_hi(self.RRDB_trunk_hi(fea))
fea = (fea + branch) / 2
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return out, med_skip, lo_skip