snerf_fast.py

from .__common__ import *
from .nerf import NeRF


class SnerfFast(NeRF):

    def infer(self, *outputs: str, samples: Samples, inputs: NetInput = None, chunk_id: int, **kwargs) -> NetOutput:
        inputs = inputs or self.input(samples)
        return {
            key: value.reshape(*samples.size, -1)
            for key, value in self.cores[chunk_id](inputs, *outputs).items()
        }

    def _preprocess_args(self):
        self.args0["spherical"] = True
        super()._preprocess_args()
        self.samples_per_part = self.args['n_samples'] // self.multi_nets

    def _init_chns(self):
        super()._init_chns(x=2)

    def _create_core_unit(self):
        return super()._create_core_unit(
            x_chns=self.x_encoder.out_dim * self.samples_per_part,
            density_chns=self.chns('density') * self.samples_per_part,
            color_chns=self.chns('color') * self.samples_per_part)

    def _input(self, samples: Samples, what: str) -> torch.Tensor | None:
        if what == "x":
            return self._encode("x", samples.pts[..., -self.chns("x"):]).flatten(1, 2)
        elif what == "d":
            return self._encode("d", samples.dirs[:, 0])\
                if self.d_encoder and samples.dirs is not None else None
        else:
            return super()._input(samples, what)

    def _encode(self, what: str, val: torch.Tensor) -> torch.Tensor:
        if what == "x":
            # Normalize x according to the encoder's range requirement using space's bounding box
            bbox = self.space.bbox[:, -self.chns("x"):]
            val = (val - bbox[0]) / (bbox[1] - bbox[0])
            val = val * (self.x_encoder.in_range[1] - self.x_encoder.in_range[0])\
                + self.x_encoder.in_range[0]
            return self.x_encoder(val)
        return super()._encode(what, val)

    def _render(self, samples: Samples, *outputs: str, **extra_args) -> ReturnData:
        return super()._render(samples, *outputs,
                               **extra_args |
                               {"raymarching_chunk_size_or_sections", [self.samples_per_part]})

    def _sample(self, data: InputData, **extra_args) -> Samples:
        samples = super()._sample(data, **extra_args)
        samples.voxel_indices = 0
        return samples


class SnerfFastExport(torch.nn.Module):

    def __init__(self, net: SnerfFast):
        super().__init__()
        self.net = net

    def forward(self, coords_encoded, z_vals):
        colors = []
        densities = []
        for i in range(self.net.multi_nets):
            s = slice(i * self.net.samples_per_part, (i + 1) * self.net.samples_per_part)
            mlp = self.net.nets[i] if self.net.nets is not None else self.net.net
            c, d = mlp(coords_encoded[:, s].flatten(1, 2))
            colors.append(c.view(-1, self.net.samples_per_part, self.net.color_chns))
            densities.append(d)
        colors = torch.cat(colors, 1)
        densities = torch.cat(densities, 1)
        alphas = self.net.rendering.density2alpha(densities, z_vals)
        return torch.cat([colors, alphas[..., None]], -1)