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