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 four times using strides. Finally, the input is upsampled to the desired downscale. Currently downscale=1,2,4 is supported. ''' def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16, downscale=4): super(HighToLowResNet, self).__init__() assert downscale in [1, 2, 4], "Requested downscale not supported; %i" % (downscale, ) 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) # All sub-modules must be explicit members. Make it so. Then add them to a list. self.trunk1 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf), 4) self.trunk2 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*2), 6) self.trunk3 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*4), 12) self.trunk4 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*8), 12) self.trunks = [self.trunk1, self.trunk2, self.trunk3, self.trunk4] self.trunkshapes = [4, 6, 12, 12] self.r1 = nn.Conv2d(nf, nf*2, 3, stride=2, padding=1, bias=True) self.r2 = nn.Conv2d(nf*2, nf*4, 3, stride=2, padding=1, bias=True) self.r3 = nn.Conv2d(nf*4, nf*8, 3, stride=2, padding=1, bias=True) self.reducers = [self.r1, self.r2, self.r3] self.pixel_shuffle = nn.PixelShuffle(2) self.a1 = nn.Conv2d(nf*2, nf*4, 3, stride=1, padding=1, bias=True) self.a2 = nn.Conv2d(nf, nf*4, 3, stride=1, padding=1, bias=True) self.a3 = nn.Conv2d(nf, nf, 3, stride=1, padding=1, bias=True) self.assemblers = [self.a1, self.a2, self.a3] if self.downscale == 1: nf_last = nf elif self.downscale == 2: nf_last = nf * 4 elif self.downscale == 4: nf_last = nf * 4 self.conv_last = nn.Conv2d(nf_last, 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.conv_last] + self.reducers + self.assemblers, .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, dtype=x.dtype) out = torch.cat([x, rand_feature], dim=1) out = self.lrelu(self.conv_first(out)) skips = [] for i in range(4): skips.append(out) out = self.trunks[i](out) if i < 3: out = self.lrelu(self.reducers[i](out)) target_width = x.shape[-1] / self.downscale i = 0 while out.shape[-1] != target_width: out = self.pixel_shuffle(out) out = self.lrelu(self.assemblers[i](out)) out = out + skips[-i-2] i += 1 # TODO: Figure out where this magic number '12' comes from and fix it. out = 12 * self.conv_last(out) if self.downscale == 1: base = x else: base = F.interpolate(x, scale_factor=1/self.downscale, mode='bilinear', align_corners=False) return out + base