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coefDiffuse = coefDiffuse.unsqueeze(-1)
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coefSpecular = coefSpecular.unsqueeze(-1)
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coefDiffuse = torch.clamp(coefDiffuse, min=0, max=1000 )
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coefSpecular = torch.clamp(coefSpecular, min=0, max=1000 )
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diffScaled = coefDiffuse.expand_as(diffOrig ) * diffOrig
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specScaled = coefSpecular.expand_as(specOrig ) * specOrig
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# Do the regression twice to avoid clamping
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renderedImg = torch.clamp(diffScaled + specScaled, 0, 1)
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renderedImg = renderedImg.view(nb, -1)
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imOrig = imOrig.view(nb, -1)
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coefIm = (torch.sum(renderedImg * imOrig, dim = 1) \
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/ torch.clamp(torch.sum(renderedImg * renderedImg, dim=1), min=1e-5) ).detach()
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coefIm = torch.clamp(coefIm, 0.001, 1000 )
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coefIm = coefIm.view(nb, 1, 1, 1)
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diffScaled = coefIm * diffScaled
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specScaled = coefIm * specScaled
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return diffScaled, specScaled
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class encoder0(nn.Module ):
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def __init__(self, cascadeLevel = 0, isSeg = False):
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super(encoder0, self).__init__()
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self.isSeg = isSeg
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self.cascadeLevel = cascadeLevel
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self.pad1 = nn.ReplicationPad2d(1)
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if self.cascadeLevel == 0:
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=4, stride=2, bias =True )
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else:
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self.conv1 = nn.Conv2d(in_channels=17, out_channels = 64, kernel_size =4, stride =2, bias = True )
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self.gn1 = nn.GroupNorm(num_groups=4, num_channels=64)
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self.pad2 = nn.ZeroPad2d(1)
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self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2, bias=True)
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self.gn2 = nn.GroupNorm(num_groups=8, num_channels=128)
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self.pad3 = nn.ZeroPad2d(1)
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self.conv3 = nn.Conv2d(in_channels = 128, out_channels=256, kernel_size=4, stride=2, bias=True)
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self.gn3 = nn.GroupNorm(num_groups=16, num_channels=256)
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self.pad4 = nn.ZeroPad2d(1)
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self.conv4 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=4, stride=2, bias=True)
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self.gn4 = nn.GroupNorm(num_groups=16, num_channels=256)
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self.pad5 = nn.ZeroPad2d(1)
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self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=2, bias=True)
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self.gn5 = nn.GroupNorm(num_groups=32, num_channels=512)
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self.pad6 = nn.ZeroPad2d(1)
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self.conv6 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, bias=True)
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self.gn6 = nn.GroupNorm(num_groups=64, num_channels=1024)
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def forward(self, x):
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x1 = F.relu(self.gn1(self.conv1(self.pad1(x) ) ), True)
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x2 = F.relu(self.gn2(self.conv2(self.pad2(x1) ) ), True)
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x3 = F.relu(self.gn3(self.conv3(self.pad3(x2) ) ), True)
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x4 = F.relu(self.gn4(self.conv4(self.pad4(x3) ) ), True)
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x5 = F.relu(self.gn5(self.conv5(self.pad5(x4) ) ), True)
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x6 = F.relu(self.gn6(self.conv6(self.pad6(x5) ) ), True)
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return x1, x2, x3, x4, x5, x6
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class decoder0(nn.Module ):
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def __init__(self, mode=0):
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super(decoder0, self).__init__()
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self.mode = mode
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self.dconv1 = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=3, stride=1, padding = 1, bias=True)
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self.dgn1 = nn.GroupNorm(num_groups=32, num_channels=512 )
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self.dconv2 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=3, stride=1, padding = 1, bias=True)
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self.dgn2 = nn.GroupNorm(num_groups=16, num_channels=256 )
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self.dconv3 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=3, stride=1, padding=1, bias=True)
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self.dgn3 = nn.GroupNorm(num_groups=16, num_channels=256 )
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self.dconv4 = nn.Conv2d(in_channels=512, out_channels=128, kernel_size=3, stride=1, padding = 1, bias=True)
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self.dgn4 = nn.GroupNorm(num_groups=8, num_channels=128 )
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self.dconv5 = nn.Conv2d(in_channels=256, out_channels=64, kernel_size=3, stride=1, padding = 1, bias=True)
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self.dgn5 = nn.GroupNorm(num_groups=4, num_channels=64 )
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self.dconv6 = nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, stride=1, padding = 1, bias=True)
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self.dgn6 = nn.GroupNorm(num_groups=4, num_channels=64 )
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self.dpadFinal = nn.ReplicationPad2d(1)
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self.dconvFinal = nn.Conv2d(in_channels=64, out_channels=3, kernel_size = 3, stride=1, bias=True)
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def forward(self, im, x1, x2, x3, x4, x5, x6 ):
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dx1 = F.relu(self.dgn1(self.dconv1(x6 ) ) )
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