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Running
on
Zero
| from typing import * | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from ...modules import sparse as sp | |
| from .base import SparseTransformerBase | |
| class SparseDownBlock3d(nn.Module): | |
| def __init__( | |
| self, | |
| channels: int, | |
| out_channels: Optional[int] = None, | |
| num_groups: int = 32, | |
| use_checkpoint: bool = False, | |
| ): | |
| super().__init__() | |
| self.channels = channels | |
| self.out_channels = out_channels or channels | |
| self.act_layers = nn.Sequential( | |
| sp.SparseGroupNorm32(num_groups, channels), | |
| sp.SparseSiLU() | |
| ) | |
| self.down = sp.SparseDownsample(2) | |
| self.out_layers = nn.Sequential( | |
| sp.SparseConv3d(channels, self.out_channels, 3, padding=1), | |
| sp.SparseGroupNorm32(num_groups, self.out_channels), | |
| sp.SparseSiLU(), | |
| sp.SparseConv3d(self.out_channels, self.out_channels, 3, padding=1), | |
| ) | |
| if self.out_channels == channels: | |
| self.skip_connection = nn.Identity() | |
| else: | |
| self.skip_connection = sp.SparseConv3d(channels, self.out_channels, 1) | |
| self.use_checkpoint = use_checkpoint | |
| def _forward(self, x: sp.SparseTensor) -> sp.SparseTensor: | |
| h = self.act_layers(x) | |
| h = self.down(h) | |
| x = self.down(x) | |
| h = self.out_layers(h) | |
| h = h + self.skip_connection(x) | |
| return h | |
| def forward(self, x: torch.Tensor): | |
| if self.use_checkpoint: | |
| return torch.utils.checkpoint.checkpoint(self._forward, x, use_reentrant=False) | |
| else: | |
| return self._forward(x) | |
| class SparseSDFEncoder(SparseTransformerBase): | |
| def __init__( | |
| self, | |
| resolution: int, | |
| in_channels: 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, | |
| ): | |
| super().__init__( | |
| in_channels=in_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.input_layer1 = sp.SparseLinear(1, model_channels // 16) | |
| self.downsample = nn.ModuleList([ | |
| SparseDownBlock3d( | |
| channels=model_channels//16, | |
| out_channels=model_channels // 8, | |
| use_checkpoint=use_checkpoint, | |
| ), | |
| SparseDownBlock3d( | |
| channels=model_channels // 8, | |
| out_channels=model_channels // 4, | |
| use_checkpoint=use_checkpoint, | |
| ), | |
| SparseDownBlock3d( | |
| channels=model_channels // 4, | |
| out_channels=model_channels, | |
| use_checkpoint=use_checkpoint, | |
| ) | |
| ]) | |
| self.resolution = resolution | |
| self.out_layer = sp.SparseLinear(model_channels, 2 * latent_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 forward(self, x: sp.SparseTensor, factor: float = None): | |
| x = self.input_layer1(x) | |
| for block in self.downsample: | |
| x = block(x) | |
| h = super().forward(x, factor) | |
| h = h.type(x.dtype) | |
| h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) | |
| h = self.out_layer(h) | |
| return h |