__init__.py

from typing import Tuple
import torch
import torch.nn as nn
from torch.nn.modules.linear import Identity
from utils.constants import *
from .generic import *
from .sampler import *
from .input_encoder import *
from .renderer import *


class NerfCore(nn.Module):

    def __init__(self, *, coord_chns, density_chns, color_chns, core_nf, core_layers,
                 dir_chns=0, dir_nf=0, activation='relu', skips=[]):
        super().__init__()
        self.core = FcNet(in_chns=coord_chns, out_chns=0, nf=core_nf, n_layers=core_layers,
                          skips=skips, activation=activation)
        self.density_out = FcLayer(core_nf, density_chns) if density_chns > 0 else None
        if color_chns == 0:
            self.feature_out = None
            self.color_out = None
        elif dir_chns > 0:
            self.feature_out = FcLayer(core_nf, core_nf)
            self.color_out = nn.Sequential(
                FcLayer(core_nf + dir_chns, dir_nf, activation),
                FcLayer(dir_nf, color_chns)
            )
        else:
            self.feature_out = Identity()
            self.color_out = FcLayer(core_nf, color_chns)

    def forward(self, coord: torch.Tensor, dir: torch.Tensor = None) -> Tuple[torch.Tensor, torch.Tensor]:
        core_output = self.core(coord)
        density = self.density_out(core_output) if self.density_out is not None else None
        if self.color_out is None:
            color = None
        else:
            feature = self.feature_out(core_output)
            if dir is not None:
                feature = torch.cat([feature, dir], dim=-1)
            color = torch.sigmoid(self.color_out(feature))
        return color, density