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from typing import * |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import numpy as np |
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from ...modules import sparse as sp |
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from .base import SparseTransformerBase |
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from ...representations import Strivec |
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class SLatRadianceFieldDecoder(SparseTransformerBase): |
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def __init__( |
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self, |
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resolution: int, |
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model_channels: int, |
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latent_channels: int, |
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num_blocks: int, |
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num_heads: Optional[int] = None, |
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num_head_channels: Optional[int] = 64, |
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mlp_ratio: float = 4, |
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attn_mode: Literal[ |
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"full", "shift_window", "shift_sequence", "shift_order", "swin" |
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] = "swin", |
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window_size: int = 8, |
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pe_mode: Literal["ape", "rope"] = "ape", |
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use_fp16: bool = False, |
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use_checkpoint: bool = False, |
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qk_rms_norm: bool = False, |
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representation_config: dict = None, |
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): |
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super().__init__( |
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in_channels=latent_channels, |
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model_channels=model_channels, |
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num_blocks=num_blocks, |
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num_heads=num_heads, |
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num_head_channels=num_head_channels, |
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mlp_ratio=mlp_ratio, |
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attn_mode=attn_mode, |
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window_size=window_size, |
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pe_mode=pe_mode, |
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use_fp16=use_fp16, |
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use_checkpoint=use_checkpoint, |
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qk_rms_norm=qk_rms_norm, |
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) |
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self.resolution = resolution |
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self.rep_config = representation_config |
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self._calc_layout() |
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self.out_layer = sp.SparseLinear(model_channels, self.out_channels) |
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self.initialize_weights() |
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if use_fp16: |
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self.convert_to_fp16() |
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def initialize_weights(self) -> None: |
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super().initialize_weights() |
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nn.init.constant_(self.out_layer.weight, 0) |
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nn.init.constant_(self.out_layer.bias, 0) |
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def _calc_layout(self) -> None: |
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self.layout = { |
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"trivec": { |
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"shape": (self.rep_config["rank"], 3, self.rep_config["dim"]), |
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"size": self.rep_config["rank"] * 3 * self.rep_config["dim"], |
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}, |
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"density": { |
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"shape": (self.rep_config["rank"],), |
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"size": self.rep_config["rank"], |
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}, |
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"features_dc": { |
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"shape": (self.rep_config["rank"], 1, 3), |
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"size": self.rep_config["rank"] * 3, |
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}, |
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} |
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start = 0 |
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for k, v in self.layout.items(): |
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v["range"] = (start, start + v["size"]) |
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start += v["size"] |
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self.out_channels = start |
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def to_representation(self, x: sp.SparseTensor) -> List[Strivec]: |
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""" |
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Convert a batch of network outputs to 3D representations. |
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Args: |
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x: The [N x * x C] sparse tensor output by the network. |
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Returns: |
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list of representations |
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""" |
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ret = [] |
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for i in range(x.shape[0]): |
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representation = Strivec( |
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sh_degree=0, |
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resolution=self.resolution, |
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aabb=[-0.5, -0.5, -0.5, 1, 1, 1], |
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rank=self.rep_config["rank"], |
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dim=self.rep_config["dim"], |
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device="cuda", |
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) |
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representation.density_shift = 0.0 |
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representation.position = ( |
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x.coords[x.layout[i]][:, 1:].float() + 0.5 |
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) / self.resolution |
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representation.depth = torch.full( |
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(representation.position.shape[0], 1), |
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int(np.log2(self.resolution)), |
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dtype=torch.uint8, |
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device="cuda", |
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) |
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for k, v in self.layout.items(): |
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setattr( |
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representation, |
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k, |
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x.feats[x.layout[i]][:, v["range"][0] : v["range"][1]].reshape( |
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-1, *v["shape"] |
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), |
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) |
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representation.trivec = representation.trivec + 1 |
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ret.append(representation) |
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return ret |
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def forward(self, x: sp.SparseTensor) -> List[Strivec]: |
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h = super().forward(x) |
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h = h.type(x.dtype) |
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h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) |
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h = self.out_layer(h) |
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return self.to_representation(h) |
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