models/decoders/mvp.py [473:527]:
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        algo = renderoptions.get("algo")
        chlast = renderoptions.get("chlast")
        half = renderoptions.get("half")

        if self.rgbadec is not None:
            # shared rgb and alpha branch
            scale = torch.tensor([25., 25., 25., 1.], device=encoding.device)
            bias = torch.tensor([100., 100., 100., 0.], device=encoding.device)
            if chlast is not None and bool(chlast):
                scale = scale[None, None, None, None, None, :]
                bias = bias[None, None, None, None, None, :]
            else:
                scale = scale[None, None, :, None, None, None]
                bias = bias[None, None, :, None, None, None]

            templatein = torch.cat([encoding, viewdirs], dim=1)
            if half is not None and bool(half):
                templatein = templatein.half()
            template = self.rgbadec(templatein, trainiter=trainiter, renderoptions=renderoptions)
            template = bias + scale * template
            if not self.notplateact:
                template = F.relu(template)
            if half is not None and bool(half):
                template = template.float()
        else:
            templatein = torch.cat([encoding, viewdirs], dim=1)
            if half is not None and bool(half):
                templatein = templatein.half()
            primrgb = self.rgbdec(templatein, trainiter=trainiter, renderoptions=renderoptions)
            primrgb = primrgb * 25. + 100.
            if not self.notplateact:
                primrgb = F.relu(primrgb)

            templatein = encoding
            if half is not None and bool(half):
                templatein = templatein.half()
            primalpha = self.alphadec(templatein, trainiter=trainiter, renderoptions=renderoptions)
            if not self.notplateact:
                primalpha = F.relu(primalpha)

            if trainiter <= self.alphatrainstart:
                primalpha = primalpha * 0. + 1.
        
            if algo is not None and int(algo) == 4:
                template = torch.cat([primrgb, primalpha], dim=-1)
            elif chlast is not None and bool(chlast):
                template = torch.cat([primrgb, primalpha], dim=-1)
            else:
                template = torch.cat([primrgb, primalpha], dim=2)
            if half is not None and bool(half):
                template = template.float()

        if self.warpdec is not None:
            warp = self.warpdec(encoding, trainiter=trainiter, renderoptions=renderoptions) * 0.01
            warp = warp + torch.stack(torch.meshgrid(
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models/decoders/nv.py [225:279]:
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        algo = renderoptions.get("algo")
        chlast = renderoptions.get("chlast")
        half = renderoptions.get("half")

        if self.rgbadec is not None:
            # shared rgb and alpha branch
            scale = torch.tensor([25., 25., 25., 1.], device=encoding.device)
            bias = torch.tensor([100., 100., 100., 0.], device=encoding.device)
            if chlast is not None and bool(chlast):
                scale = scale[None, None, None, None, None, :]
                bias = bias[None, None, None, None, None, :]
            else:
                scale = scale[None, None, :, None, None, None]
                bias = bias[None, None, :, None, None, None]

            templatein = torch.cat([encoding, viewdirs], dim=1)
            if half is not None and bool(half):
                templatein = templatein.half()
            template = self.rgbadec(templatein, trainiter=trainiter, renderoptions=renderoptions)
            template = bias + scale * template
            if not self.notplateact:
                template = F.relu(template)
            if half is not None and bool(half):
                template = template.float()
        else:
            templatein = torch.cat([encoding, viewdirs], dim=1)
            if half is not None and bool(half):
                templatein = templatein.half()
            primrgb = self.rgbdec(templatein, trainiter=trainiter, renderoptions=renderoptions)
            primrgb = primrgb * 25. + 100.
            if not self.notplateact:
                primrgb = F.relu(primrgb)

            templatein = encoding
            if half is not None and bool(half):
                templatein = templatein.half()
            primalpha = self.alphadec(templatein, trainiter=trainiter, renderoptions=renderoptions)
            if not self.notplateact:
                primalpha = F.relu(primalpha)

            if trainiter <= self.alphatrainstart:
                primalpha = primalpha * 0. + 1.
        
            if algo is not None and int(algo) == 4:
                template = torch.cat([primrgb, primalpha], dim=-1)
            elif chlast is not None and bool(chlast):
                template = torch.cat([primrgb, primalpha], dim=-1)
            else:
                template = torch.cat([primrgb, primalpha], dim=2)
            if half is not None and bool(half):
                template = template.float()

        if self.warpdec is not None:
            warp = self.warpdec(encoding, trainiter=trainiter, renderoptions=renderoptions) * 0.01
            warp = warp + torch.stack(torch.meshgrid(
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