Implement ResGenv2

Implements a ResGenv2 architecture which slightly increases the complexity
of the final output layer but causes it to be shared across all skip outputs.

codes/models/archs/ResGen_arch.py

@@ -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.
+        if layers[1] > 0:
-        self.layer2 = self._make_layer(block, int(nf2), layers[1], stride=1, conv_type=conv5x5)
+            # This is the repeated part.
-        self.skip2 = nn.Conv2d(nf2, 3, kernel_size=5, stride=1, padding=2, bias=False)
+            self.layer2 = self._make_layer(block, int(self.nf2), layers[1], stride=1, conv_type=conv5x5)
-        self.skip2_bias = nn.Parameter(torch.zeros(1))
+            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')
+        if self.upscale_applications > 0:
-        x = self.layer2(x)
+            x = F.interpolate(x, scale_factor=2.0, mode='nearest')
-        skip_med = self.skip2(x) + self.skip2_bias
+            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']

codes/models/networks.py

@@ -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':