d95808f4ef
This bad boy is for a workflow where you train a model on disjoint image sets to downsample a "good" set of images like a "bad" set of images looks. You then use that downsampler to generate a training set of paired images for supersampling.
64 lines
2.8 KiB
Python
64 lines
2.8 KiB
Python
import functools
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import torch.nn as nn
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import torch.nn.functional as F
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import models.archs.arch_util as arch_util
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import torch
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class HighToLowResNet(nn.Module):
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''' ResNet that applies a noise channel to the input, then downsamples it. Currently only downscale=4 is supported. '''
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def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16, downscale=4):
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super(HighToLowResNet, self).__init__()
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self.downscale = downscale
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# We will always apply a noise channel to the inputs, account for that here.
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in_nc += 1
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self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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basic_block = functools.partial(arch_util.ResidualBlock_noBN, nf=nf)
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basic_block2 = functools.partial(arch_util.ResidualBlock_noBN, nf=nf*2)
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# To keep the total model size down, the residual trunks will be applied across 3 downsampling stages.
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# The first will be applied against the hi-res inputs and will have only 4 layers.
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# The second will be applied after half of the downscaling and will also have only 6 layers.
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# The final will be applied against the final resolution and will have all of the remaining layers.
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self.trunk_hires = arch_util.make_layer(basic_block, 4)
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self.trunk_medres = arch_util.make_layer(basic_block, 6)
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self.trunk_lores = arch_util.make_layer(basic_block2, nb - 10)
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# downsampling
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if self.downscale == 4:
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self.downconv1 = nn.Conv2d(nf, nf, 3, stride=2, padding=1, bias=True)
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self.downconv2 = nn.Conv2d(nf, nf*2, 3, stride=2, padding=1, bias=True)
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else:
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raise EnvironmentError("Requested downscale not supported: %i" % (downscale,))
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self.HRconv = nn.Conv2d(nf*2, nf*2, 3, stride=1, padding=1, bias=True)
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self.conv_last = nn.Conv2d(nf*2, out_nc, 3, stride=1, padding=1, bias=True)
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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# initialization
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arch_util.initialize_weights([self.conv_first, self.HRconv, self.conv_last, self.downconv1, self.downconv2],
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0.1)
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def forward(self, x):
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# Noise has the same shape as the input with only one channel.
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rand_feature = torch.randn((x.shape[0], 1) + x.shape[2:], device=x.device)
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out = torch.cat([x, rand_feature], dim=1)
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out = self.lrelu(self.conv_first(out))
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out = self.trunk_hires(out)
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if self.downscale == 4:
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out = self.lrelu(self.downconv1(out))
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out = self.trunk_medres(out)
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out = self.lrelu(self.downconv2(out))
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out = self.trunk_lores(out)
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out = self.conv_last(self.lrelu(self.HRconv(out)))
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base = F.interpolate(x, scale_factor=1/self.downscale, mode='bilinear', align_corners=False)
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out += base
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return out
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