import functools import torch import torch.nn as nn import torch.nn.functional as F import models.archs.arch_util as arch_util import torchvision 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, initial_stride=1): 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, initial_stride, 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,) # Variant of RRDBNet that is "assisted" by an external pretrained image classifier whose # intermediate layers have been splayed out, pixel-shuffled, and fed back in. class AssistedRRDBNet(nn.Module): # in_nc=number of input channels. # out_nc=number of output channels. # nf=internal filter count # nb=number of additional blocks after the assistance layers. # gc=growth channel inside of residual blocks # scale=the number of times the output is doubled in size. # initial_stride=the stride on the first conv. can be used to downsample the image for processing. def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1): super(AssistedRRDBNet, self).__init__() self.scale = scale self.conv_first = nn.Conv2d(in_nc, nf, 7, initial_stride, padding=3, bias=True) # Set-up the assist-net, which should do feature extraction for us. self.assistnet = torchvision.models.wide_resnet50_2(pretrained=True) self.set_enable_assistnet_training(False) assist_nf = [2, 4, 8, 16] # Fixed for resnet. Re-evaluate if using other networks. self.assist1 = RRDB(nf + assist_nf[0], gc) self.assist2 = RRDB(nf + sum(assist_nf[:2]), gc) self.assist3 = RRDB(nf + sum(assist_nf[:3]), gc) self.assist4 = RRDB(nf + sum(assist_nf), gc) nf = nf + sum(assist_nf) # After this, it's just a "standard" RRDB net. RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) 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 set_enable_assistnet_training(self, en): for p in self.assistnet.parameters(): p.requires_grad = en def res_extract(self, x): x = self.assistnet.conv1(x) x = self.assistnet.bn1(x) x = self.assistnet.relu(x) x = self.assistnet.maxpool(x) x = self.assistnet.layer1(x) l1 = F.pixel_shuffle(x, 4) x = self.assistnet.layer2(x) l2 = F.pixel_shuffle(x, 8) x = self.assistnet.layer3(x) l3 = F.pixel_shuffle(x, 16) x = self.assistnet.layer4(x) l4 = F.pixel_shuffle(x, 32) return l1, l2, l3, l4 def forward(self, x): # Invoke the assistant net first. l1, l2, l3, l4 = self.res_extract(x) fea = self.conv_first(x) fea = self.assist1(torch.cat([fea, l4], dim=1)) fea = self.assist2(torch.cat([fea, l3], dim=1)) fea = self.assist3(torch.cat([fea, l2], dim=1)) fea = self.assist4(torch.cat([fea, l1], dim=1)) 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,)