3b4e54c4c5
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.
98 lines
4.2 KiB
Python
98 lines
4.2 KiB
Python
import functools
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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.archs.arch_util as arch_util
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class ResidualDenseBlock_5C(nn.Module):
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def __init__(self, nf=64, gc=32, bias=True):
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super(ResidualDenseBlock_5C, self).__init__()
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# gc: growth channel, i.e. intermediate channels
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self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
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self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
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self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
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self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
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self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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# initialization
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arch_util.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5],
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0.1)
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def forward(self, x):
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x1 = self.lrelu(self.conv1(x))
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x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
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x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
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x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
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x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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return x5 * 0.2 + x
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class RRDB(nn.Module):
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'''Residual in Residual Dense Block'''
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def __init__(self, nf, gc=32):
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super(RRDB, self).__init__()
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self.RDB1 = ResidualDenseBlock_5C(nf, gc)
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self.RDB2 = ResidualDenseBlock_5C(nf, gc)
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self.RDB3 = ResidualDenseBlock_5C(nf, gc)
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def forward(self, x):
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out = self.RDB1(x)
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out = self.RDB2(out)
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out = self.RDB3(out)
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return out * 0.2 + x
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class RRDBNet(nn.Module):
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def __init__(self, in_nc, out_nc, nf, nb_lo, nb_med, nb_hi, gc=32, interpolation_scale_factor=2):
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super(RRDBNet, self).__init__()
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nfmed = int(nf/2)
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nfhi = int(nf/8)
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gcmed = int(gc/2)
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gchi = int(gc/8)
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RRDB_block_f_lo = functools.partial(RRDB, nf=nf, gc=gc)
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RRDB_block_f_lo_med = functools.partial(RRDB, nf=nfmed, gc=gcmed)
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RRDB_block_f_lo_hi = functools.partial(RRDB, nf=nfhi, gc=gchi)
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self.conv_first = nn.Conv2d(in_nc, nf, 7, 1, padding=3, bias=True)
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self.RRDB_trunk_lo = arch_util.make_layer(RRDB_block_f_lo, nb_lo)
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self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.lo_skip_conv1 = nn.Conv2d(nf, nf, 3, 1, padding=1, bias=True)
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self.lo_skip_conv2 = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
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#### upsampling
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self.upconv1 = nn.Conv2d(nf, nfmed, 3, 1, padding=1, bias=True)
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self.RRDB_trunk_med = arch_util.make_layer(RRDB_block_f_lo_med, nb_med)
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self.trunk_conv_med = nn.Conv2d(nfmed, nfmed, 3, 1, 1, bias=True)
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self.med_skip_conv1 = nn.Conv2d(nfmed, nfmed, 3, 1, padding=1, bias=True)
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self.med_skip_conv2 = nn.Conv2d(nfmed, out_nc, 3, 1, 1, bias=True)
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self.upconv2 = nn.Conv2d(nfmed, nfhi, 3, 1, padding=1, bias=True)
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self.RRDB_trunk_hi = arch_util.make_layer(RRDB_block_f_lo_hi, nb_hi)
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self.trunk_conv_hi = nn.Conv2d(nfhi, nfhi, 3, 1, 1, bias=True)
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self.HRconv = nn.Conv2d(nfhi, nfhi, 5, 1, padding=2, bias=True)
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self.conv_last = nn.Conv2d(nfhi, out_nc, 3, 1, 1, bias=True)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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self.interpolation_scale_factor = interpolation_scale_factor
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def forward(self, x):
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fea = self.conv_first(x)
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branch = self.trunk_conv(self.RRDB_trunk_lo(fea))
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fea = (fea + branch) / 2
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lo_skip = self.lo_skip_conv2(self.lrelu(self.lo_skip_conv1(fea)))
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fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=self.interpolation_scale_factor, mode='nearest')))
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branch = self.trunk_conv_med(self.RRDB_trunk_med(fea))
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fea = (fea + branch) / 2
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med_skip = self.med_skip_conv2(self.lrelu(self.med_skip_conv1(fea)))
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fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=self.interpolation_scale_factor, mode='nearest')))
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branch = self.trunk_conv_hi(self.RRDB_trunk_hi(fea))
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fea = (fea + branch) / 2
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out = self.conv_last(self.lrelu(self.HRconv(fea)))
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return out, med_skip, lo_skip |