import functools import torch import torch.nn as nn import torch.nn.functional as F import models.archs.srflow.module_util as mutil from utils.util import opt_get, checkpoint 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 mutil.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=4, block_outputs=[], fea_up0=True, fea_up1=False): super(RRDBNet, self).__init__() RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) self.scale = scale self.block_outputs = block_outputs self.fea_up0 = fea_up0 self.fea_up1 = fea_up1 self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) self.RRDB_trunk = mutil.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) if self.scale >= 8: self.upconv3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) if self.scale >= 16: self.upconv4 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) if self.scale >= 32: self.upconv5 = 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, get_steps=False): fea = self.conv_first(x) block_idxs = self.block_outputs or [] block_results = {} for idx, m in enumerate(self.RRDB_trunk.children()): fea = checkpoint(m, fea) for b in block_idxs: if b == idx: block_results["block_{}".format(idx)] = fea trunk = self.trunk_conv(fea) last_lr_fea = fea + trunk fea_up2 = self.upconv1(F.interpolate(last_lr_fea, scale_factor=2, mode='nearest')) fea = self.lrelu(fea_up2) fea_up4 = self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')) fea = self.lrelu(fea_up4) fea_up8 = None fea_up16 = None fea_up32 = None if self.scale >= 8: fea_up8 = self.upconv3(F.interpolate(fea, scale_factor=2, mode='nearest')) fea = self.lrelu(fea_up8) if self.scale >= 16: fea_up16 = self.upconv4(F.interpolate(fea, scale_factor=2, mode='nearest')) fea = self.lrelu(fea_up16) if self.scale >= 32: fea_up32 = self.upconv5(F.interpolate(fea, scale_factor=2, mode='nearest')) fea = self.lrelu(fea_up32) out = self.conv_last(self.lrelu(self.HRconv(fea))) results = {'last_lr_fea': last_lr_fea, 'fea_up1': last_lr_fea, 'fea_up2': fea_up2, 'fea_up4': fea_up4, 'fea_up8': fea_up8, 'fea_up16': fea_up16, 'fea_up32': fea_up32, 'out': out} if self.fea_up0: results['fea_up0'] = F.interpolate(last_lr_fea, scale_factor=1/2, mode='bilinear', align_corners=False, recompute_scale_factor=True) if self.fea_up1: results['fea_up-1'] = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True) if get_steps: for k, v in block_results.items(): results[k] = v return results else: return out