mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-04 18:50:29 +08:00
1186 lines
45 KiB
Python
1186 lines
45 KiB
Python
import torch
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import torch.nn as nn
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from typing import List, Any, Dict, Optional, overload, Union, Tuple
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from fractions import Fraction
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import torch.nn.functional as F
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from dataclasses import dataclass
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import numpy as np
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from cumesh import TorchHashMap, Mesh, MeshWithVoxel
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# TODO: determine which conv they actually use
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@dataclass
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class config:
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CONV = "none"
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# TODO post processing
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def simplify(self, target_num_faces: int, verbose: bool=False, options: dict={}):
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num_face = self.cu_mesh.num_faces()
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if num_face <= target_num_faces:
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return
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thresh = options.get('thresh', 1e-8)
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lambda_edge_length = options.get('lambda_edge_length', 1e-2)
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lambda_skinny = options.get('lambda_skinny', 1e-3)
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while True:
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new_num_vert, new_num_face = self.cu_mesh.simplify_step(lambda_edge_length, lambda_skinny, thresh, False)
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if new_num_face <= target_num_faces:
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break
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del_num_face = num_face - new_num_face
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if del_num_face / num_face < 1e-2:
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thresh *= 10
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num_face = new_num_face
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class VarLenTensor:
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def __init__(self, feats: torch.Tensor, layout: List[slice]=None):
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self.feats = feats
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self.layout = layout if layout is not None else [slice(0, feats.shape[0])]
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self._cache = {}
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@staticmethod
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def layout_from_seqlen(seqlen: list) -> List[slice]:
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"""
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Create a layout from a tensor of sequence lengths.
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"""
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layout = []
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start = 0
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for l in seqlen:
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layout.append(slice(start, start + l))
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start += l
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return layout
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@staticmethod
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def from_tensor_list(tensor_list: List[torch.Tensor]) -> 'VarLenTensor':
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"""
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Create a VarLenTensor from a list of tensors.
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"""
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feats = torch.cat(tensor_list, dim=0)
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layout = []
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start = 0
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for tensor in tensor_list:
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layout.append(slice(start, start + tensor.shape[0]))
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start += tensor.shape[0]
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return VarLenTensor(feats, layout)
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def __len__(self) -> int:
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return len(self.layout)
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@property
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def shape(self) -> torch.Size:
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return torch.Size([len(self.layout), *self.feats.shape[1:]])
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def dim(self) -> int:
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return len(self.shape)
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@property
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def ndim(self) -> int:
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return self.dim()
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@property
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def dtype(self):
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return self.feats.dtype
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@property
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def device(self):
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return self.feats.device
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@property
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def seqlen(self) -> torch.LongTensor:
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if 'seqlen' not in self._cache:
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self._cache['seqlen'] = torch.tensor([l.stop - l.start for l in self.layout], dtype=torch.long, device=self.device)
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return self._cache['seqlen']
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@property
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def cum_seqlen(self) -> torch.LongTensor:
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if 'cum_seqlen' not in self._cache:
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self._cache['cum_seqlen'] = torch.cat([
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torch.tensor([0], dtype=torch.long, device=self.device),
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self.seqlen.cumsum(dim=0)
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], dim=0)
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return self._cache['cum_seqlen']
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@property
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def batch_boardcast_map(self) -> torch.LongTensor:
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"""
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Get the broadcast map for the varlen tensor.
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"""
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if 'batch_boardcast_map' not in self._cache:
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self._cache['batch_boardcast_map'] = torch.repeat_interleave(
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torch.arange(len(self.layout), device=self.device),
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self.seqlen,
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)
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return self._cache['batch_boardcast_map']
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@overload
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def to(self, dtype: torch.dtype, *, non_blocking: bool = False, copy: bool = False) -> 'VarLenTensor': ...
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@overload
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def to(self, device: Optional[Union[str, torch.device]] = None, dtype: Optional[torch.dtype] = None, *, non_blocking: bool = False, copy: bool = False) -> 'VarLenTensor': ...
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def to(self, *args, **kwargs) -> 'VarLenTensor':
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device = None
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dtype = None
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if len(args) == 2:
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device, dtype = args
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elif len(args) == 1:
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if isinstance(args[0], torch.dtype):
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dtype = args[0]
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else:
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device = args[0]
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if 'dtype' in kwargs:
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assert dtype is None, "to() received multiple values for argument 'dtype'"
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dtype = kwargs['dtype']
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if 'device' in kwargs:
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assert device is None, "to() received multiple values for argument 'device'"
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device = kwargs['device']
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non_blocking = kwargs.get('non_blocking', False)
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copy = kwargs.get('copy', False)
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new_feats = self.feats.to(device=device, dtype=dtype, non_blocking=non_blocking, copy=copy)
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return self.replace(new_feats)
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def type(self, dtype):
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new_feats = self.feats.type(dtype)
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return self.replace(new_feats)
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def cpu(self) -> 'VarLenTensor':
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new_feats = self.feats.cpu()
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return self.replace(new_feats)
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def cuda(self) -> 'VarLenTensor':
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new_feats = self.feats.cuda()
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return self.replace(new_feats)
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def half(self) -> 'VarLenTensor':
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new_feats = self.feats.half()
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return self.replace(new_feats)
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def float(self) -> 'VarLenTensor':
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new_feats = self.feats.float()
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return self.replace(new_feats)
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def detach(self) -> 'VarLenTensor':
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new_feats = self.feats.detach()
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return self.replace(new_feats)
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def reshape(self, *shape) -> 'VarLenTensor':
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new_feats = self.feats.reshape(self.feats.shape[0], *shape)
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return self.replace(new_feats)
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def unbind(self, dim: int) -> List['VarLenTensor']:
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return varlen_unbind(self, dim)
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def replace(self, feats: torch.Tensor) -> 'VarLenTensor':
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new_tensor = VarLenTensor(
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feats=feats,
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layout=self.layout,
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)
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new_tensor._cache = self._cache
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return new_tensor
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def to_dense(self, max_length=None) -> torch.Tensor:
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N = len(self)
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L = max_length or self.seqlen.max().item()
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spatial = self.feats.shape[1:]
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idx = torch.arange(L, device=self.device).unsqueeze(0).expand(N, L)
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mask = (idx < self.seqlen.unsqueeze(1))
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mapping = mask.reshape(-1).cumsum(dim=0) - 1
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dense = self.feats[mapping]
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dense = dense.reshape(N, L, *spatial)
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return dense, mask
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def __neg__(self) -> 'VarLenTensor':
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return self.replace(-self.feats)
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def __elemwise__(self, other: Union[torch.Tensor, 'VarLenTensor'], op: callable) -> 'VarLenTensor':
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if isinstance(other, torch.Tensor):
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try:
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other = torch.broadcast_to(other, self.shape)
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other = other[self.batch_boardcast_map]
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except:
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pass
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if isinstance(other, VarLenTensor):
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other = other.feats
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new_feats = op(self.feats, other)
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new_tensor = self.replace(new_feats)
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return new_tensor
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def __add__(self, other: Union[torch.Tensor, 'VarLenTensor', float]) -> 'VarLenTensor':
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return self.__elemwise__(other, torch.add)
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def __radd__(self, other: Union[torch.Tensor, 'VarLenTensor', float]) -> 'VarLenTensor':
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return self.__elemwise__(other, torch.add)
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def __sub__(self, other: Union[torch.Tensor, 'VarLenTensor', float]) -> 'VarLenTensor':
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return self.__elemwise__(other, torch.sub)
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def __rsub__(self, other: Union[torch.Tensor, 'VarLenTensor', float]) -> 'VarLenTensor':
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return self.__elemwise__(other, lambda x, y: torch.sub(y, x))
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def __mul__(self, other: Union[torch.Tensor, 'VarLenTensor', float]) -> 'VarLenTensor':
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return self.__elemwise__(other, torch.mul)
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def __rmul__(self, other: Union[torch.Tensor, 'VarLenTensor', float]) -> 'VarLenTensor':
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return self.__elemwise__(other, torch.mul)
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def __truediv__(self, other: Union[torch.Tensor, 'VarLenTensor', float]) -> 'VarLenTensor':
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return self.__elemwise__(other, torch.div)
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def __rtruediv__(self, other: Union[torch.Tensor, 'VarLenTensor', float]) -> 'VarLenTensor':
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return self.__elemwise__(other, lambda x, y: torch.div(y, x))
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def __getitem__(self, idx):
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if isinstance(idx, int):
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idx = [idx]
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elif isinstance(idx, slice):
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idx = range(*idx.indices(self.shape[0]))
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elif isinstance(idx, list):
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assert all(isinstance(i, int) for i in idx), f"Only integer indices are supported: {idx}"
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elif isinstance(idx, torch.Tensor):
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if idx.dtype == torch.bool:
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assert idx.shape == (self.shape[0],), f"Invalid index shape: {idx.shape}"
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idx = idx.nonzero().squeeze(1)
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elif idx.dtype in [torch.int32, torch.int64]:
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assert len(idx.shape) == 1, f"Invalid index shape: {idx.shape}"
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else:
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raise ValueError(f"Unknown index type: {idx.dtype}")
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else:
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raise ValueError(f"Unknown index type: {type(idx)}")
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new_feats = []
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new_layout = []
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start = 0
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for new_idx, old_idx in enumerate(idx):
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new_feats.append(self.feats[self.layout[old_idx]])
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new_layout.append(slice(start, start + len(new_feats[-1])))
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start += len(new_feats[-1])
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new_feats = torch.cat(new_feats, dim=0).contiguous()
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new_tensor = VarLenTensor(feats=new_feats, layout=new_layout)
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return new_tensor
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def reduce(self, op: str, dim: Optional[Union[int, Tuple[int,...]]] = None, keepdim: bool = False) -> torch.Tensor:
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if isinstance(dim, int):
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dim = (dim,)
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if op =='mean':
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red = self.feats.mean(dim=dim, keepdim=keepdim)
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elif op =='sum':
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red = self.feats.sum(dim=dim, keepdim=keepdim)
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elif op == 'prod':
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red = self.feats.prod(dim=dim, keepdim=keepdim)
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else:
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raise ValueError(f"Unsupported reduce operation: {op}")
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if dim is None or 0 in dim:
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return red
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red = torch.segment_reduce(red, reduce=op, lengths=self.seqlen)
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return red
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def mean(self, dim: Optional[Union[int, Tuple[int,...]]] = None, keepdim: bool = False) -> torch.Tensor:
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return self.reduce(op='mean', dim=dim, keepdim=keepdim)
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def sum(self, dim: Optional[Union[int, Tuple[int,...]]] = None, keepdim: bool = False) -> torch.Tensor:
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return self.reduce(op='sum', dim=dim, keepdim=keepdim)
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def prod(self, dim: Optional[Union[int, Tuple[int,...]]] = None, keepdim: bool = False) -> torch.Tensor:
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return self.reduce(op='prod', dim=dim, keepdim=keepdim)
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def std(self, dim: Optional[Union[int, Tuple[int,...]]] = None, keepdim: bool = False) -> torch.Tensor:
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mean = self.mean(dim=dim, keepdim=True)
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mean2 = self.replace(self.feats ** 2).mean(dim=dim, keepdim=True)
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std = (mean2 - mean ** 2).sqrt()
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return std
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def __repr__(self) -> str:
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return f"VarLenTensor(shape={self.shape}, dtype={self.dtype}, device={self.device})"
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def varlen_unbind(input: VarLenTensor, dim: int) -> Union[List[VarLenTensor]]:
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if dim == 0:
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return [input[i] for i in range(len(input))]
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else:
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feats = input.feats.unbind(dim)
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return [input.replace(f) for f in feats]
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class SparseTensor(VarLenTensor):
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SparseTensorData = None
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@overload
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def __init__(self, feats: torch.Tensor, coords: torch.Tensor, shape: Optional[torch.Size] = None, **kwargs): ...
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@overload
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def __init__(self, data, shape: Optional[torch.Size] = None, **kwargs): ...
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def __init__(self, *args, **kwargs):
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# Lazy import of sparse tensor backend
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if self.SparseTensorData is None:
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import importlib
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if config.CONV == 'torchsparse':
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self.SparseTensorData = importlib.import_module('torchsparse').SparseTensor
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elif config.CONV == 'spconv':
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self.SparseTensorData = importlib.import_module('spconv.pytorch').SparseConvTensor
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method_id = 0
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if len(args) != 0:
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method_id = 0 if isinstance(args[0], torch.Tensor) else 1
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else:
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method_id = 1 if 'data' in kwargs else 0
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if method_id == 0:
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feats, coords, shape = args + (None,) * (3 - len(args))
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if 'feats' in kwargs:
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feats = kwargs['feats']
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del kwargs['feats']
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if 'coords' in kwargs:
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coords = kwargs['coords']
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del kwargs['coords']
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if 'shape' in kwargs:
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shape = kwargs['shape']
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del kwargs['shape']
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if config.CONV == 'torchsparse':
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self.data = self.SparseTensorData(feats, coords, **kwargs)
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elif config.CONV == 'spconv':
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spatial_shape = list(coords.max(0)[0] + 1)
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self.data = self.SparseTensorData(feats.reshape(feats.shape[0], -1), coords, spatial_shape[1:], spatial_shape[0], **kwargs)
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self.data._features = feats
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else:
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self.data = {
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'feats': feats,
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'coords': coords,
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}
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elif method_id == 1:
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data, shape = args + (None,) * (2 - len(args))
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if 'data' in kwargs:
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data = kwargs['data']
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del kwargs['data']
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if 'shape' in kwargs:
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shape = kwargs['shape']
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del kwargs['shape']
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self.data = data
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self._shape = shape
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self._scale = kwargs.get('scale', (Fraction(1, 1), Fraction(1, 1), Fraction(1, 1)))
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self._spatial_cache = kwargs.get('spatial_cache', {})
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@staticmethod
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def from_tensor_list(feats_list: List[torch.Tensor], coords_list: List[torch.Tensor]) -> 'SparseTensor':
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"""
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Create a SparseTensor from a list of tensors.
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"""
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feats = torch.cat(feats_list, dim=0)
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coords = []
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for i, coord in enumerate(coords_list):
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coord = torch.cat([torch.full_like(coord[:, :1], i), coord[:, 1:]], dim=1)
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coords.append(coord)
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coords = torch.cat(coords, dim=0)
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return SparseTensor(feats, coords)
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def to_tensor_list(self) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
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"""
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Convert a SparseTensor to list of tensors.
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"""
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feats_list = []
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coords_list = []
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for s in self.layout:
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feats_list.append(self.feats[s])
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coords_list.append(self.coords[s])
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return feats_list, coords_list
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def __len__(self) -> int:
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return len(self.layout)
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def __cal_shape(self, feats, coords):
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shape = []
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shape.append(coords[:, 0].max().item() + 1)
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shape.extend([*feats.shape[1:]])
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return torch.Size(shape)
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def __cal_layout(self, coords, batch_size):
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seq_len = torch.bincount(coords[:, 0], minlength=batch_size)
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offset = torch.cumsum(seq_len, dim=0)
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layout = [slice((offset[i] - seq_len[i]).item(), offset[i].item()) for i in range(batch_size)]
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return layout
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def __cal_spatial_shape(self, coords):
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return torch.Size((coords[:, 1:].max(0)[0] + 1).tolist())
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@property
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def shape(self) -> torch.Size:
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if self._shape is None:
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self._shape = self.__cal_shape(self.feats, self.coords)
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return self._shape
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@property
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def layout(self) -> List[slice]:
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layout = self.get_spatial_cache('layout')
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if layout is None:
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layout = self.__cal_layout(self.coords, self.shape[0])
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self.register_spatial_cache('layout', layout)
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return layout
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@property
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def spatial_shape(self) -> torch.Size:
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spatial_shape = self.get_spatial_cache('shape')
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if spatial_shape is None:
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spatial_shape = self.__cal_spatial_shape(self.coords)
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self.register_spatial_cache('shape', spatial_shape)
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return spatial_shape
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@property
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def feats(self) -> torch.Tensor:
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if config.CONV == 'torchsparse':
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return self.data.F
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elif config.CONV == 'spconv':
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return self.data.features
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else:
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return self.data['feats']
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@feats.setter
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def feats(self, value: torch.Tensor):
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if config.CONV == 'torchsparse':
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self.data.F = value
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elif config.CONV == 'spconv':
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self.data.features = value
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else:
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self.data['feats'] = value
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@property
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def coords(self) -> torch.Tensor:
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if config.CONV == 'torchsparse':
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return self.data.C
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elif config.CONV == 'spconv':
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return self.data.indices
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else:
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return self.data['coords']
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@coords.setter
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def coords(self, value: torch.Tensor):
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if config.CONV == 'torchsparse':
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self.data.C = value
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elif config.CONV == 'spconv':
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self.data.indices = value
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else:
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self.data['coords'] = value
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|
@property
|
|
def dtype(self):
|
|
return self.feats.dtype
|
|
|
|
@property
|
|
def device(self):
|
|
return self.feats.device
|
|
|
|
@property
|
|
def seqlen(self) -> torch.LongTensor:
|
|
seqlen = self.get_spatial_cache('seqlen')
|
|
if seqlen is None:
|
|
seqlen = torch.tensor([l.stop - l.start for l in self.layout], dtype=torch.long, device=self.device)
|
|
self.register_spatial_cache('seqlen', seqlen)
|
|
return seqlen
|
|
|
|
@property
|
|
def cum_seqlen(self) -> torch.LongTensor:
|
|
cum_seqlen = self.get_spatial_cache('cum_seqlen')
|
|
if cum_seqlen is None:
|
|
cum_seqlen = torch.cat([
|
|
torch.tensor([0], dtype=torch.long, device=self.device),
|
|
self.seqlen.cumsum(dim=0)
|
|
], dim=0)
|
|
self.register_spatial_cache('cum_seqlen', cum_seqlen)
|
|
return cum_seqlen
|
|
|
|
@property
|
|
def batch_boardcast_map(self) -> torch.LongTensor:
|
|
"""
|
|
Get the broadcast map for the varlen tensor.
|
|
"""
|
|
batch_boardcast_map = self.get_spatial_cache('batch_boardcast_map')
|
|
if batch_boardcast_map is None:
|
|
batch_boardcast_map = torch.repeat_interleave(
|
|
torch.arange(len(self.layout), device=self.device),
|
|
self.seqlen,
|
|
)
|
|
self.register_spatial_cache('batch_boardcast_map', batch_boardcast_map)
|
|
return batch_boardcast_map
|
|
|
|
@overload
|
|
def to(self, dtype: torch.dtype, *, non_blocking: bool = False, copy: bool = False) -> 'SparseTensor': ...
|
|
|
|
@overload
|
|
def to(self, device: Optional[Union[str, torch.device]] = None, dtype: Optional[torch.dtype] = None, *, non_blocking: bool = False, copy: bool = False) -> 'SparseTensor': ...
|
|
|
|
def to(self, *args, **kwargs) -> 'SparseTensor':
|
|
device = None
|
|
dtype = None
|
|
if len(args) == 2:
|
|
device, dtype = args
|
|
elif len(args) == 1:
|
|
if isinstance(args[0], torch.dtype):
|
|
dtype = args[0]
|
|
else:
|
|
device = args[0]
|
|
if 'dtype' in kwargs:
|
|
assert dtype is None, "to() received multiple values for argument 'dtype'"
|
|
dtype = kwargs['dtype']
|
|
if 'device' in kwargs:
|
|
assert device is None, "to() received multiple values for argument 'device'"
|
|
device = kwargs['device']
|
|
non_blocking = kwargs.get('non_blocking', False)
|
|
copy = kwargs.get('copy', False)
|
|
|
|
new_feats = self.feats.to(device=device, dtype=dtype, non_blocking=non_blocking, copy=copy)
|
|
new_coords = self.coords.to(device=device, non_blocking=non_blocking, copy=copy)
|
|
return self.replace(new_feats, new_coords)
|
|
|
|
def type(self, dtype):
|
|
new_feats = self.feats.type(dtype)
|
|
return self.replace(new_feats)
|
|
|
|
def cpu(self) -> 'SparseTensor':
|
|
new_feats = self.feats.cpu()
|
|
new_coords = self.coords.cpu()
|
|
return self.replace(new_feats, new_coords)
|
|
|
|
def cuda(self) -> 'SparseTensor':
|
|
new_feats = self.feats.cuda()
|
|
new_coords = self.coords.cuda()
|
|
return self.replace(new_feats, new_coords)
|
|
|
|
def half(self) -> 'SparseTensor':
|
|
new_feats = self.feats.half()
|
|
return self.replace(new_feats)
|
|
|
|
def float(self) -> 'SparseTensor':
|
|
new_feats = self.feats.float()
|
|
return self.replace(new_feats)
|
|
|
|
def detach(self) -> 'SparseTensor':
|
|
new_coords = self.coords.detach()
|
|
new_feats = self.feats.detach()
|
|
return self.replace(new_feats, new_coords)
|
|
|
|
def reshape(self, *shape) -> 'SparseTensor':
|
|
new_feats = self.feats.reshape(self.feats.shape[0], *shape)
|
|
return self.replace(new_feats)
|
|
|
|
def unbind(self, dim: int) -> List['SparseTensor']:
|
|
return sparse_unbind(self, dim)
|
|
|
|
def replace(self, feats: torch.Tensor, coords: Optional[torch.Tensor] = None) -> 'SparseTensor':
|
|
if config.CONV == 'torchsparse':
|
|
new_data = self.SparseTensorData(
|
|
feats=feats,
|
|
coords=self.data.coords if coords is None else coords,
|
|
stride=self.data.stride,
|
|
spatial_range=self.data.spatial_range,
|
|
)
|
|
new_data._caches = self.data._caches
|
|
elif config.CONV == 'spconv':
|
|
new_data = self.SparseTensorData(
|
|
self.data.features.reshape(self.data.features.shape[0], -1),
|
|
self.data.indices,
|
|
self.data.spatial_shape,
|
|
self.data.batch_size,
|
|
self.data.grid,
|
|
self.data.voxel_num,
|
|
self.data.indice_dict
|
|
)
|
|
new_data._features = feats
|
|
new_data.benchmark = self.data.benchmark
|
|
new_data.benchmark_record = self.data.benchmark_record
|
|
new_data.thrust_allocator = self.data.thrust_allocator
|
|
new_data._timer = self.data._timer
|
|
new_data.force_algo = self.data.force_algo
|
|
new_data.int8_scale = self.data.int8_scale
|
|
if coords is not None:
|
|
new_data.indices = coords
|
|
else:
|
|
new_data = {
|
|
'feats': feats,
|
|
'coords': self.data['coords'] if coords is None else coords,
|
|
}
|
|
new_tensor = SparseTensor(
|
|
new_data,
|
|
shape=torch.Size([self._shape[0]] + list(feats.shape[1:])) if self._shape is not None else None,
|
|
scale=self._scale,
|
|
spatial_cache=self._spatial_cache
|
|
)
|
|
return new_tensor
|
|
|
|
def to_dense(self) -> torch.Tensor:
|
|
if config.CONV == 'torchsparse':
|
|
return self.data.dense()
|
|
elif config.CONV == 'spconv':
|
|
return self.data.dense()
|
|
else:
|
|
spatial_shape = self.spatial_shape
|
|
ret = torch.zeros(*self.shape, *spatial_shape, dtype=self.dtype, device=self.device)
|
|
idx = [self.coords[:, 0], slice(None)] + self.coords[:, 1:].unbind(1)
|
|
ret[tuple(idx)] = self.feats
|
|
return ret
|
|
|
|
@staticmethod
|
|
def full(aabb, dim, value, dtype=torch.float32, device=None) -> 'SparseTensor':
|
|
N, C = dim
|
|
x = torch.arange(aabb[0], aabb[3] + 1)
|
|
y = torch.arange(aabb[1], aabb[4] + 1)
|
|
z = torch.arange(aabb[2], aabb[5] + 1)
|
|
coords = torch.stack(torch.meshgrid(x, y, z, indexing='ij'), dim=-1).reshape(-1, 3)
|
|
coords = torch.cat([
|
|
torch.arange(N).view(-1, 1).repeat(1, coords.shape[0]).view(-1, 1),
|
|
coords.repeat(N, 1),
|
|
], dim=1).to(dtype=torch.int32, device=device)
|
|
feats = torch.full((coords.shape[0], C), value, dtype=dtype, device=device)
|
|
return SparseTensor(feats=feats, coords=coords)
|
|
|
|
def __merge_sparse_cache(self, other: 'SparseTensor') -> dict:
|
|
new_cache = {}
|
|
for k in set(list(self._spatial_cache.keys()) + list(other._spatial_cache.keys())):
|
|
if k in self._spatial_cache:
|
|
new_cache[k] = self._spatial_cache[k]
|
|
if k in other._spatial_cache:
|
|
if k not in new_cache:
|
|
new_cache[k] = other._spatial_cache[k]
|
|
else:
|
|
new_cache[k].update(other._spatial_cache[k])
|
|
return new_cache
|
|
|
|
def __elemwise__(self, other: Union[torch.Tensor, VarLenTensor], op: callable) -> 'SparseTensor':
|
|
if isinstance(other, torch.Tensor):
|
|
try:
|
|
other = torch.broadcast_to(other, self.shape)
|
|
other = other[self.batch_boardcast_map]
|
|
except:
|
|
pass
|
|
if isinstance(other, VarLenTensor):
|
|
other = other.feats
|
|
new_feats = op(self.feats, other)
|
|
new_tensor = self.replace(new_feats)
|
|
if isinstance(other, SparseTensor):
|
|
new_tensor._spatial_cache = self.__merge_sparse_cache(other)
|
|
return new_tensor
|
|
|
|
def __getitem__(self, idx):
|
|
if isinstance(idx, int):
|
|
idx = [idx]
|
|
elif isinstance(idx, slice):
|
|
idx = range(*idx.indices(self.shape[0]))
|
|
elif isinstance(idx, list):
|
|
assert all(isinstance(i, int) for i in idx), f"Only integer indices are supported: {idx}"
|
|
elif isinstance(idx, torch.Tensor):
|
|
if idx.dtype == torch.bool:
|
|
assert idx.shape == (self.shape[0],), f"Invalid index shape: {idx.shape}"
|
|
idx = idx.nonzero().squeeze(1)
|
|
elif idx.dtype in [torch.int32, torch.int64]:
|
|
assert len(idx.shape) == 1, f"Invalid index shape: {idx.shape}"
|
|
else:
|
|
raise ValueError(f"Unknown index type: {idx.dtype}")
|
|
else:
|
|
raise ValueError(f"Unknown index type: {type(idx)}")
|
|
|
|
new_coords = []
|
|
new_feats = []
|
|
new_layout = []
|
|
new_shape = torch.Size([len(idx)] + list(self.shape[1:]))
|
|
start = 0
|
|
for new_idx, old_idx in enumerate(idx):
|
|
new_coords.append(self.coords[self.layout[old_idx]].clone())
|
|
new_coords[-1][:, 0] = new_idx
|
|
new_feats.append(self.feats[self.layout[old_idx]])
|
|
new_layout.append(slice(start, start + len(new_coords[-1])))
|
|
start += len(new_coords[-1])
|
|
new_coords = torch.cat(new_coords, dim=0).contiguous()
|
|
new_feats = torch.cat(new_feats, dim=0).contiguous()
|
|
new_tensor = SparseTensor(feats=new_feats, coords=new_coords, shape=new_shape)
|
|
new_tensor.register_spatial_cache('layout', new_layout)
|
|
return new_tensor
|
|
|
|
def clear_spatial_cache(self) -> None:
|
|
"""
|
|
Clear all spatial caches.
|
|
"""
|
|
self._spatial_cache = {}
|
|
|
|
def register_spatial_cache(self, key, value) -> None:
|
|
"""
|
|
Register a spatial cache.
|
|
The spatial cache can be any thing you want to cache.
|
|
The registery and retrieval of the cache is based on current scale.
|
|
"""
|
|
scale_key = str(self._scale)
|
|
if scale_key not in self._spatial_cache:
|
|
self._spatial_cache[scale_key] = {}
|
|
self._spatial_cache[scale_key][key] = value
|
|
|
|
def get_spatial_cache(self, key=None):
|
|
"""
|
|
Get a spatial cache.
|
|
"""
|
|
scale_key = str(self._scale)
|
|
cur_scale_cache = self._spatial_cache.get(scale_key, {})
|
|
if key is None:
|
|
return cur_scale_cache
|
|
return cur_scale_cache.get(key, None)
|
|
|
|
def __repr__(self) -> str:
|
|
return f"SparseTensor(shape={self.shape}, dtype={self.dtype}, device={self.device})"
|
|
|
|
def sparse_cat(inputs: List[SparseTensor], dim: int = 0) -> SparseTensor:
|
|
if dim == 0:
|
|
start = 0
|
|
coords = []
|
|
for input in inputs:
|
|
coords.append(input.coords.clone())
|
|
coords[-1][:, 0] += start
|
|
start += input.shape[0]
|
|
coords = torch.cat(coords, dim=0)
|
|
feats = torch.cat([input.feats for input in inputs], dim=0)
|
|
output = SparseTensor(
|
|
coords=coords,
|
|
feats=feats,
|
|
)
|
|
else:
|
|
feats = torch.cat([input.feats for input in inputs], dim=dim)
|
|
output = inputs[0].replace(feats)
|
|
|
|
return output
|
|
|
|
|
|
def sparse_unbind(input: SparseTensor, dim: int) -> List[SparseTensor]:
|
|
if dim == 0:
|
|
return [input[i] for i in range(input.shape[0])]
|
|
else:
|
|
feats = input.feats.unbind(dim)
|
|
return [input.replace(f) for f in feats]
|
|
|
|
class SparseLinear(nn.Linear):
|
|
def __init__(self, in_features, out_features, bias=True):
|
|
super(SparseLinear, self).__init__(in_features, out_features, bias)
|
|
|
|
def forward(self, input: VarLenTensor) -> VarLenTensor:
|
|
return input.replace(super().forward(input.feats))
|
|
|
|
|
|
|
|
class SparseUnetVaeEncoder(nn.Module):
|
|
"""
|
|
Sparse Swin Transformer Unet VAE model.
|
|
"""
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
model_channels: List[int],
|
|
latent_channels: int,
|
|
num_blocks: List[int],
|
|
block_type: List[str],
|
|
down_block_type: List[str],
|
|
block_args: List[Dict[str, Any]],
|
|
use_fp16: bool = False,
|
|
):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.model_channels = model_channels
|
|
self.num_blocks = num_blocks
|
|
self.dtype = torch.float16 if use_fp16 else torch.float32
|
|
self.dtype = torch.float16 if use_fp16 else torch.float32
|
|
|
|
self.input_layer = SparseLinear(in_channels, model_channels[0])
|
|
self.to_latent = SparseLinear(model_channels[-1], 2 * latent_channels)
|
|
|
|
self.blocks = nn.ModuleList([])
|
|
for i in range(len(num_blocks)):
|
|
self.blocks.append(nn.ModuleList([]))
|
|
for j in range(num_blocks[i]):
|
|
self.blocks[-1].append(
|
|
globals()[block_type[i]](
|
|
model_channels[i],
|
|
**block_args[i],
|
|
)
|
|
)
|
|
if i < len(num_blocks) - 1:
|
|
self.blocks[-1].append(
|
|
globals()[down_block_type[i]](
|
|
model_channels[i],
|
|
model_channels[i+1],
|
|
**block_args[i],
|
|
)
|
|
)
|
|
|
|
@property
|
|
def device(self) -> torch.device:
|
|
return next(self.parameters()).device
|
|
|
|
def forward(self, x: SparseTensor, sample_posterior=False, return_raw=False):
|
|
h = self.input_layer(x)
|
|
h = h.type(self.dtype)
|
|
for i, res in enumerate(self.blocks):
|
|
for j, block in enumerate(res):
|
|
h = block(h)
|
|
h = h.type(x.dtype)
|
|
h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
|
|
h = self.to_latent(h)
|
|
|
|
# Sample from the posterior distribution
|
|
mean, logvar = h.feats.chunk(2, dim=-1)
|
|
if sample_posterior:
|
|
std = torch.exp(0.5 * logvar)
|
|
z = mean + std * torch.randn_like(std)
|
|
else:
|
|
z = mean
|
|
z = h.replace(z)
|
|
|
|
if return_raw:
|
|
return z, mean, logvar
|
|
else:
|
|
return z
|
|
|
|
|
|
|
|
class FlexiDualGridVaeEncoder(SparseUnetVaeEncoder):
|
|
def __init__(
|
|
self,
|
|
model_channels: List[int],
|
|
latent_channels: int,
|
|
num_blocks: List[int],
|
|
block_type: List[str],
|
|
down_block_type: List[str],
|
|
block_args: List[Dict[str, Any]],
|
|
use_fp16: bool = False,
|
|
):
|
|
super().__init__(
|
|
6,
|
|
model_channels,
|
|
latent_channels,
|
|
num_blocks,
|
|
block_type,
|
|
down_block_type,
|
|
block_args,
|
|
use_fp16,
|
|
)
|
|
|
|
def forward(self, vertices: SparseTensor, intersected: SparseTensor, sample_posterior=False, return_raw=False):
|
|
x = vertices.replace(torch.cat([
|
|
vertices.feats - 0.5,
|
|
intersected.feats.float() - 0.5,
|
|
], dim=1))
|
|
return super().forward(x, sample_posterior, return_raw)
|
|
|
|
class SparseUnetVaeDecoder(nn.Module):
|
|
"""
|
|
Sparse Swin Transformer Unet VAE model.
|
|
"""
|
|
def __init__(
|
|
self,
|
|
out_channels: int,
|
|
model_channels: List[int],
|
|
latent_channels: int,
|
|
num_blocks: List[int],
|
|
block_type: List[str],
|
|
up_block_type: List[str],
|
|
block_args: List[Dict[str, Any]],
|
|
use_fp16: bool = False,
|
|
pred_subdiv: bool = True,
|
|
):
|
|
super().__init__()
|
|
self.out_channels = out_channels
|
|
self.model_channels = model_channels
|
|
self.num_blocks = num_blocks
|
|
self.use_fp16 = use_fp16
|
|
self.pred_subdiv = pred_subdiv
|
|
self.dtype = torch.float16 if use_fp16 else torch.float32
|
|
self.low_vram = False
|
|
|
|
self.output_layer = SparseLinear(model_channels[-1], out_channels)
|
|
self.from_latent = SparseLinear(latent_channels, model_channels[0])
|
|
|
|
self.blocks = nn.ModuleList([])
|
|
for i in range(len(num_blocks)):
|
|
self.blocks.append(nn.ModuleList([]))
|
|
for j in range(num_blocks[i]):
|
|
self.blocks[-1].append(
|
|
globals()[block_type[i]](
|
|
model_channels[i],
|
|
**block_args[i],
|
|
)
|
|
)
|
|
if i < len(num_blocks) - 1:
|
|
self.blocks[-1].append(
|
|
globals()[up_block_type[i]](
|
|
model_channels[i],
|
|
model_channels[i+1],
|
|
pred_subdiv=pred_subdiv,
|
|
**block_args[i],
|
|
)
|
|
)
|
|
@property
|
|
def device(self) -> torch.device:
|
|
return next(self.parameters()).device
|
|
|
|
def forward(self, x: SparseTensor, guide_subs: Optional[List[SparseTensor]] = None, return_subs: bool = False) -> SparseTensor:
|
|
|
|
h = self.from_latent(x)
|
|
h = h.type(self.dtype)
|
|
subs = []
|
|
for i, res in enumerate(self.blocks):
|
|
for j, block in enumerate(res):
|
|
if i < len(self.blocks) - 1 and j == len(res) - 1:
|
|
if self.pred_subdiv:
|
|
h, sub = block(h)
|
|
subs.append(sub)
|
|
else:
|
|
h = block(h, subdiv=guide_subs[i] if guide_subs is not None else None)
|
|
else:
|
|
h = block(h)
|
|
h = h.type(x.dtype)
|
|
h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
|
|
h = self.output_layer(h)
|
|
if return_subs:
|
|
return h, subs
|
|
else:
|
|
return h
|
|
|
|
def upsample(self, x: SparseTensor, upsample_times: int) -> torch.Tensor:
|
|
|
|
h = self.from_latent(x)
|
|
h = h.type(self.dtype)
|
|
for i, res in enumerate(self.blocks):
|
|
if i == upsample_times:
|
|
return h.coords
|
|
for j, block in enumerate(res):
|
|
if i < len(self.blocks) - 1 and j == len(res) - 1:
|
|
h, sub = block(h)
|
|
else:
|
|
h = block(h)
|
|
|
|
class FlexiDualGridVaeDecoder(SparseUnetVaeDecoder):
|
|
def __init__(
|
|
self,
|
|
resolution: int,
|
|
model_channels: List[int],
|
|
latent_channels: int,
|
|
num_blocks: List[int],
|
|
block_type: List[str],
|
|
up_block_type: List[str],
|
|
block_args: List[Dict[str, Any]],
|
|
voxel_margin: float = 0.5,
|
|
use_fp16: bool = False,
|
|
):
|
|
self.resolution = resolution
|
|
self.voxel_margin = voxel_margin
|
|
# cache for a TorchHashMap instance
|
|
self._torch_hashmap_cache = None
|
|
|
|
super().__init__(
|
|
7,
|
|
model_channels,
|
|
latent_channels,
|
|
num_blocks,
|
|
block_type,
|
|
up_block_type,
|
|
block_args,
|
|
use_fp16,
|
|
)
|
|
|
|
def set_resolution(self, resolution: int) -> None:
|
|
self.resolution = resolution
|
|
|
|
def _build_or_get_hashmap(self, coords: torch.Tensor, grid_size: torch.Tensor):
|
|
device = coords.device
|
|
N = coords.shape[0]
|
|
# compute flat keys for all coords (prepend batch 0 same as original code)
|
|
b = torch.zeros((N,), dtype=torch.long, device=device)
|
|
x, y, z = coords[:, 0].long(), coords[:, 1].long(), coords[:, 2].long()
|
|
W, H, D = int(grid_size[0].item()), int(grid_size[1].item()), int(grid_size[2].item())
|
|
flat_keys = b * (W * H * D) + x * (H * D) + y * D + z
|
|
values = torch.arange(N, dtype=torch.long, device=device)
|
|
DEFAULT_VAL = 0xffffffff # sentinel used in original code
|
|
return TorchHashMap(flat_keys, values, DEFAULT_VAL)
|
|
|
|
def forward(self, x: SparseTensor, gt_intersected: SparseTensor = None, **kwargs):
|
|
decoded = super().forward(x, **kwargs)
|
|
out_list = list(decoded) if isinstance(decoded, tuple) else [decoded]
|
|
h = out_list[0]
|
|
vertices = h.replace((1 + 2 * self.voxel_margin) * F.sigmoid(h.feats[..., 0:3]) - self.voxel_margin)
|
|
intersected = h.replace(h.feats[..., 3:6] > 0)
|
|
quad_lerp = h.replace(F.softplus(h.feats[..., 6:7]))
|
|
mesh = [Mesh(*flexible_dual_grid_to_mesh(
|
|
v.coords[:, 1:], v.feats, i.feats, q.feats,
|
|
aabb=[[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]],
|
|
grid_size=self.resolution,
|
|
train=False,
|
|
hashmap_builder=self._build_or_get_hashmap,
|
|
)) for v, i, q in zip(vertices, intersected, quad_lerp)]
|
|
out_list[0] = mesh
|
|
return out_list[0] if len(out_list) == 1 else tuple(out_list)
|
|
|
|
def flexible_dual_grid_to_mesh(
|
|
coords: torch.Tensor,
|
|
dual_vertices: torch.Tensor,
|
|
intersected_flag: torch.Tensor,
|
|
split_weight: Union[torch.Tensor, None],
|
|
aabb: Union[list, tuple, np.ndarray, torch.Tensor],
|
|
voxel_size: Union[float, list, tuple, np.ndarray, torch.Tensor] = None,
|
|
grid_size: Union[int, list, tuple, np.ndarray, torch.Tensor] = None,
|
|
train: bool = False,
|
|
hashmap_builder=None, # optional callable for building/caching a TorchHashMap
|
|
):
|
|
|
|
if not hasattr(flexible_dual_grid_to_mesh, "edge_neighbor_voxel_offset"):
|
|
flexible_dual_grid_to_mesh.edge_neighbor_voxel_offset = torch.tensor([
|
|
[[0, 0, 0], [0, 0, 1], [0, 1, 1], [0, 1, 0]], # x-axis
|
|
[[0, 0, 0], [1, 0, 0], [1, 0, 1], [0, 0, 1]], # y-axis
|
|
[[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]], # z-axis
|
|
], dtype=torch.int, device=coords.device).unsqueeze(0)
|
|
if not hasattr(flexible_dual_grid_to_mesh, "quad_split_1"):
|
|
flexible_dual_grid_to_mesh.quad_split_1 = torch.tensor([0, 1, 2, 0, 2, 3], dtype=torch.long, device=coords.device, requires_grad=False)
|
|
if not hasattr(flexible_dual_grid_to_mesh, "quad_split_2"):
|
|
flexible_dual_grid_to_mesh.quad_split_2 = torch.tensor([0, 1, 3, 3, 1, 2], dtype=torch.long, device=coords.device, requires_grad=False)
|
|
if not hasattr(flexible_dual_grid_to_mesh, "quad_split_train"):
|
|
flexible_dual_grid_to_mesh.quad_split_train = torch.tensor([0, 1, 4, 1, 2, 4, 2, 3, 4, 3, 0, 4], dtype=torch.long, device=coords.device, requires_grad=False)
|
|
|
|
# AABB
|
|
if isinstance(aabb, (list, tuple)):
|
|
aabb = np.array(aabb)
|
|
if isinstance(aabb, np.ndarray):
|
|
aabb = torch.tensor(aabb, dtype=torch.float32, device=coords.device)
|
|
|
|
# Voxel size
|
|
if voxel_size is not None:
|
|
if isinstance(voxel_size, float):
|
|
voxel_size = [voxel_size, voxel_size, voxel_size]
|
|
if isinstance(voxel_size, (list, tuple)):
|
|
voxel_size = np.array(voxel_size)
|
|
if isinstance(voxel_size, np.ndarray):
|
|
voxel_size = torch.tensor(voxel_size, dtype=torch.float32, device=coords.device)
|
|
grid_size = ((aabb[1] - aabb[0]) / voxel_size).round().int()
|
|
else:
|
|
if isinstance(grid_size, int):
|
|
grid_size = [grid_size, grid_size, grid_size]
|
|
if isinstance(grid_size, (list, tuple)):
|
|
grid_size = np.array(grid_size)
|
|
if isinstance(grid_size, np.ndarray):
|
|
grid_size = torch.tensor(grid_size, dtype=torch.int32, device=coords.device)
|
|
voxel_size = (aabb[1] - aabb[0]) / grid_size
|
|
|
|
# Extract mesh
|
|
N = dual_vertices.shape[0]
|
|
mesh_vertices = (coords.float() + dual_vertices) / (2 * N) - 0.5
|
|
|
|
if hashmap_builder is None:
|
|
# build local TorchHashMap
|
|
device = coords.device
|
|
b = torch.zeros((N,), dtype=torch.long, device=device)
|
|
x, y, z = coords[:, 0].long(), coords[:, 1].long(), coords[:, 2].long()
|
|
W, H, D = int(grid_size[0].item()), int(grid_size[1].item()), int(grid_size[2].item())
|
|
flat_keys = b * (W * H * D) + x * (H * D) + y * D + z
|
|
values = torch.arange(N, dtype=torch.long, device=device)
|
|
DEFAULT_VAL = 0xffffffff
|
|
torch_hashmap = TorchHashMap(flat_keys, values, DEFAULT_VAL)
|
|
else:
|
|
torch_hashmap = hashmap_builder(coords, grid_size)
|
|
|
|
# Find connected voxels
|
|
edge_neighbor_voxel = coords.reshape(N, 1, 1, 3) + flexible_dual_grid_to_mesh.edge_neighbor_voxel_offset # (N, 3, 4, 3)
|
|
connected_voxel = edge_neighbor_voxel[intersected_flag] # (M, 4, 3)
|
|
M = connected_voxel.shape[0]
|
|
# flatten connected voxel coords and lookup
|
|
conn_flat_b = torch.zeros((M * 4,), dtype=torch.long, device=coords.device)
|
|
conn_x = connected_voxel.reshape(-1, 3)[:, 0].long()
|
|
conn_y = connected_voxel.reshape(-1, 3)[:, 1].long()
|
|
conn_z = connected_voxel.reshape(-1, 3)[:, 2].long()
|
|
W, H, D = int(grid_size[0].item()), int(grid_size[1].item()), int(grid_size[2].item())
|
|
conn_flat = conn_flat_b * (W * H * D) + conn_x * (H * D) + conn_y * D + conn_z
|
|
|
|
conn_indices = torch_hashmap.lookup_flat(conn_flat).reshape(M, 4).int()
|
|
connected_voxel_valid = (conn_indices != 0xffffffff).all(dim=1)
|
|
quad_indices = conn_indices[connected_voxel_valid].int() # (L, 4)
|
|
|
|
mesh_vertices = (coords.float() + dual_vertices) * voxel_size + aabb[0].reshape(1, 3)
|
|
if split_weight is None:
|
|
# if split 1
|
|
atempt_triangles_0 = quad_indices[:, flexible_dual_grid_to_mesh.quad_split_1]
|
|
normals0 = torch.cross(mesh_vertices[atempt_triangles_0[:, 1]] - mesh_vertices[atempt_triangles_0[:, 0]], mesh_vertices[atempt_triangles_0[:, 2]] - mesh_vertices[atempt_triangles_0[:, 0]])
|
|
normals1 = torch.cross(mesh_vertices[atempt_triangles_0[:, 2]] - mesh_vertices[atempt_triangles_0[:, 1]], mesh_vertices[atempt_triangles_0[:, 3]] - mesh_vertices[atempt_triangles_0[:, 1]])
|
|
align0 = (normals0 * normals1).sum(dim=1, keepdim=True).abs()
|
|
# if split 2
|
|
atempt_triangles_1 = quad_indices[:, flexible_dual_grid_to_mesh.quad_split_2]
|
|
normals0 = torch.cross(mesh_vertices[atempt_triangles_1[:, 1]] - mesh_vertices[atempt_triangles_1[:, 0]], mesh_vertices[atempt_triangles_1[:, 2]] - mesh_vertices[atempt_triangles_1[:, 0]])
|
|
normals1 = torch.cross(mesh_vertices[atempt_triangles_1[:, 2]] - mesh_vertices[atempt_triangles_1[:, 1]], mesh_vertices[atempt_triangles_1[:, 3]] - mesh_vertices[atempt_triangles_1[:, 1]])
|
|
align1 = (normals0 * normals1).sum(dim=1, keepdim=True).abs()
|
|
# select split
|
|
mesh_triangles = torch.where(align0 > align1, atempt_triangles_0, atempt_triangles_1).reshape(-1, 3)
|
|
else:
|
|
split_weight_ws = split_weight[quad_indices]
|
|
split_weight_ws_02 = split_weight_ws[:, 0] * split_weight_ws[:, 2]
|
|
split_weight_ws_13 = split_weight_ws[:, 1] * split_weight_ws[:, 3]
|
|
mesh_triangles = torch.where(
|
|
split_weight_ws_02 > split_weight_ws_13,
|
|
quad_indices[:, flexible_dual_grid_to_mesh.quad_split_1],
|
|
quad_indices[:, flexible_dual_grid_to_mesh.quad_split_2]
|
|
).reshape(-1, 3)
|
|
|
|
return mesh_vertices, mesh_triangles
|
|
|
|
class Vae(nn.Module):
|
|
def __init__(self, config, operations=None):
|
|
operations = operations or torch.nn
|
|
|
|
self.txt_dec = SparseUnetVaeDecoder(
|
|
out_channels=6,
|
|
model_channels=[1024, 512, 256, 128, 64],
|
|
latent_channels=32,
|
|
num_blocks=[4, 16, 8, 4, 0],
|
|
block_type=["SparseConvNeXtBlock3d"] * 5,
|
|
up_block_type=["SparseResBlockS2C3d"] * 4,
|
|
pred_subdiv=False
|
|
)
|
|
|
|
self.shape_dec = FlexiDualGridVaeDecoder(
|
|
resolution=256,
|
|
model_channels=[1024, 512, 256, 128, 64],
|
|
latent_channels=32,
|
|
num_blocks=[4, 16, 8, 4, 0],
|
|
block_type=["SparseConvNeXtBlock3d"] * 5,
|
|
up_block_type=["SparseResBlockS2C3d"] * 4,
|
|
)
|
|
|
|
def decode_shape_slat(self, slat, resolution: int):
|
|
self.shape_dec.set_resolution(resolution)
|
|
return self.shape_dec(slat, return_subs=True)
|
|
|
|
def decode_tex_slat(self, slat, subs):
|
|
return self.txt_dec(slat, guide_subs=subs) * 0.5 + 0.5
|
|
|
|
@torch.no_grad()
|
|
def decode(
|
|
self,
|
|
shape_slat: SparseTensor,
|
|
tex_slat: SparseTensor,
|
|
resolution: int,
|
|
):
|
|
meshes, subs = self.decode_shape_slat(shape_slat, resolution)
|
|
tex_voxels = self.decode_tex_slat(tex_slat, subs)
|
|
out_mesh = []
|
|
for m, v in zip(meshes, tex_voxels):
|
|
m.fill_holes() # TODO
|
|
out_mesh.append(
|
|
MeshWithVoxel(
|
|
m.vertices, m.faces,
|
|
origin = [-0.5, -0.5, -0.5],
|
|
voxel_size = 1 / resolution,
|
|
coords = v.coords[:, 1:],
|
|
attrs = v.feats,
|
|
voxel_shape = torch.Size([*v.shape, *v.spatial_shape]),
|
|
layout=self.pbr_attr_layout
|
|
)
|
|
)
|
|
return out_mesh
|