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Implement ResGenv2

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Implements a ResGenv2 architecture which slightly increases the complexity
of the final output layer but causes it to be shared across all skip outputs.
This commit is contained in:
James Betker 2020-05-12 10:09:02 -06:00
parent 1596a98493
commit f217216c81
2 changed files with 67 additions and 12 deletions
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76
codes/models/archs/ResGen_arch.py
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@ -78,19 +78,20 @@ class FixupResNet(nn.Module):
# convs which are intended to repair artifacts caused by 2x interpolation.
# This core layer should by itself accomplish 2x super-resolution. We use it in repeat to do the
# requested SR.
nf2 = int(num_filters/4)
self.nf2 = int(num_filters/4)
# This part isn't repeated. It de-filters the output from the previous step to fit the filter size used in the
# upsampler-conv.
self.upsampler_conv = nn.Conv2d(num_filters, nf2, kernel_size=3, stride=1, padding=1, bias=False)
self.upsampler_conv = nn.Conv2d(num_filters, self.nf2, kernel_size=3, stride=1, padding=1, bias=False)
self.uc_bias = nn.Parameter(torch.zeros(1))
self.inplanes = nf2
self.inplanes = self.nf2
# This is the repeated part.
self.layer2 = self._make_layer(block, int(nf2), layers[1], stride=1, conv_type=conv5x5)
self.skip2 = nn.Conv2d(nf2, 3, kernel_size=5, stride=1, padding=2, bias=False)
self.skip2_bias = nn.Parameter(torch.zeros(1))
if layers[1] > 0:
# This is the repeated part.
self.layer2 = self._make_layer(block, int(self.nf2), layers[1], stride=1, conv_type=conv5x5)
self.skip2 = nn.Conv2d(self.nf2, 3, kernel_size=5, stride=1, padding=2, bias=False)
self.skip2_bias = nn.Parameter(torch.zeros(1))
self.final_defilter = nn.Conv2d(nf2, 3, kernel_size=5, stride=1, padding=2, bias=True)
self.final_defilter = nn.Conv2d(self.nf2, 3, kernel_size=5, stride=1, padding=2, bias=True)
self.bias2 = nn.Parameter(torch.zeros(1))
for m in self.modules():
@ -124,9 +125,12 @@ class FixupResNet(nn.Module):
skip_lo = self.skip1(x) + self.skip1_bias
x = self.lrelu(self.upsampler_conv(x) + self.uc_bias)
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.layer2(x)
skip_med = self.skip2(x) + self.skip2_bias
if self.upscale_applications > 0:
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.layer2(x)
skip_med = self.skip2(x) + self.skip2_bias
else:
skip_med = skip_lo
if self.upscale_applications > 1:
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
@ -135,11 +139,59 @@ class FixupResNet(nn.Module):
x = self.final_defilter(x) + self.bias2
return x, skip_med, skip_lo
class FixupResNetV2(FixupResNet):
def __init__(self, **kwargs):
super(FixupResNetV2, self).__init__(**kwargs)
# Use one unified filter-to-image stack, not the previous skip stacks.
self.skip1 = None
self.skip1_bias = None
self.skip2 = None
self.skip2_bias = None
# The new filter-to-image stack will be 2 conv layers deep, not 1.
self.final_process = nn.Conv2d(self.nf2, self.nf2, kernel_size=5, stride=1, padding=2, bias=True)
self.bias2 = nn.Parameter(torch.zeros(1))
self.fp_bn = nn.BatchNorm2d(self.nf2)
self.final_defilter = nn.Conv2d(self.nf2, 3, kernel_size=3, stride=1, padding=1, bias=True)
self.bias3 = nn.Parameter(torch.zeros(1))
def filter_to_image(self, filter):
x = self.final_process(filter) + self.bias2
x = self.lrelu(self.fp_bn(x))
x = self.final_defilter(x) + self.bias3
return x
def forward(self, x):
x = self.conv1(x)
x = self.lrelu(x + self.bias1)
x = self.layer1(x)
x = self.lrelu(self.upsampler_conv(x) + self.uc_bias)
skip_lo = self.filter_to_image(x)
if self.upscale_applications > 0:
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.layer2(x)
skip_med = self.filter_to_image(x)
if self.upscale_applications > 1:
x = F.interpolate(x, scale_factor=2.0, mode='nearest')
x = self.layer2(x)
x = self.filter_to_image(x)
return x, skip_med, skip_lo
def fixup_resnet34(nb_denoiser=20, nb_upsampler=10, **kwargs):
"""Constructs a Fixup-ResNet-34 model.
"""
model = FixupResNet(FixupBasicBlock, [nb_denoiser, nb_upsampler], **kwargs)
return model
def fixup_resnet34_v2(nb_denoiser=20, nb_upsampler=10, **kwargs):
"""Constructs a Fixup-ResNet-34 model.
"""
kwargs['block'] = FixupBasicBlock
kwargs['layers'] = [nb_denoiser, nb_upsampler]
model = FixupResNetV2(**kwargs)
return model
__all__ = ['FixupResNet', 'fixup_resnet34']
__all__ = ['FixupResNet', 'fixup_resnet34', 'fixup_resnet34_v2']

3
codes/models/networks.py
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@ -36,6 +36,9 @@ def define_G(opt):
elif which_model == 'ResGen':
netG = ResGen_arch.fixup_resnet34(nb_denoiser=opt_net['nb_denoiser'], nb_upsampler=opt_net['nb_upsampler'],
upscale_applications=opt_net['upscale_applications'], num_filters=opt_net['nf'])
elif which_model == 'ResGenV2':
netG = ResGen_arch.fixup_resnet34_v2(nb_denoiser=opt_net['nb_denoiser'], nb_upsampler=opt_net['nb_upsampler'],
upscale_applications=opt_net['upscale_applications'], num_filters=opt_net['nf'])
# image corruption
elif which_model == 'HighToLowResNet':