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, scale=2): super(RRDBNet, self).__init__() RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) self.scale = scale self.conv_first = nn.Conv2d(in_nc, nf, 7, 1, padding=3, 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, 5, 1, padding=2, bias=True) self.upconv2 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True) self.HRconv = nn.Conv2d(nf, nf, 5, 1, padding=2, 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 if self.scale >= 2: fea = F.interpolate(fea, scale_factor=2, mode='nearest') fea = self.lrelu(self.upconv1(fea)) if self.scale >= 4: fea = F.interpolate(fea, scale_factor=2, mode='nearest') fea = self.lrelu(self.upconv2(fea)) out = self.conv_last(self.lrelu(self.HRconv(fea))) return (out,)