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Running
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Zero
File size: 4,227 Bytes
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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 |