import functools import torch.nn as nn import torch.nn.functional as F import models.archs.arch_util as arch_util import torch class HighToLowResNet(nn.Module): ''' ResNet that applies a noise channel to the input, then downsamples it. Currently only downscale=4 is supported. ''' def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16, downscale=4): super(HighToLowResNet, self).__init__() self.downscale = downscale # We will always apply a noise channel to the inputs, account for that here. in_nc += 1 self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) basic_block = functools.partial(arch_util.ResidualBlock_noBN, nf=nf) basic_block2 = functools.partial(arch_util.ResidualBlock_noBN, nf=nf*2) # To keep the total model size down, the residual trunks will be applied across 3 downsampling stages. # The first will be applied against the hi-res inputs and will have only 4 layers. # The second will be applied after half of the downscaling and will also have only 6 layers. # The final will be applied against the final resolution and will have all of the remaining layers. self.trunk_hires = arch_util.make_layer(basic_block, 5) self.trunk_medres = arch_util.make_layer(basic_block, 10) self.trunk_lores = arch_util.make_layer(basic_block2, nb - 15) # downsampling if self.downscale == 4 or self.downscale == 1: self.downconv1 = nn.Conv2d(nf, nf, 3, stride=2, padding=1, bias=True) self.downconv2 = nn.Conv2d(nf, nf*2, 3, stride=2, padding=1, bias=True) else: raise EnvironmentError("Requested downscale not supported: %i" % (downscale,)) self.HRconv = nn.Conv2d(nf*2, nf*2, 3, stride=1, padding=1, bias=True) if self.downscale == 4: self.conv_last = nn.Conv2d(nf*2, out_nc, 3, stride=1, padding=1, bias=True) else: self.pixel_shuffle = nn.PixelShuffle(4) self.conv_last = nn.Conv2d(int(nf/8), out_nc, 3, stride=1, padding=1, bias=True) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) # initialization arch_util.initialize_weights([self.conv_first, self.HRconv, self.conv_last, self.downconv1, self.downconv2], 0.1) def forward(self, x): # Noise has the same shape as the input with only one channel. rand_feature = torch.randn((x.shape[0], 1) + x.shape[2:], device=x.device) out = torch.cat([x, rand_feature], dim=1) out = self.lrelu(self.conv_first(out)) out = self.trunk_hires(out) if self.downscale == 4 or self.downscale == 1: out = self.lrelu(self.downconv1(out)) out = self.trunk_medres(out) out = self.lrelu(self.downconv2(out)) out = self.trunk_lores(out) if self.downscale == 1: out = self.lrelu(self.pixel_shuffle(self.HRconv(out))) out = self.conv_last(out) else: out = self.conv_last(self.lrelu(self.HRconv(out))) if self.downscale == 1: base = x else: base = F.interpolate(x, scale_factor=1/self.downscale, mode='bilinear', align_corners=False) out += base return out