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9d7cf7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 | from abc import ABC, abstractmethod
from copy import deepcopy
from dataclasses import dataclass
from numpy import ndarray
from omegaconf import OmegaConf
from typing import Dict, List, Optional, final
from torch import Tensor
import numpy as np
import lightning.pytorch as pl
import torch
from ..data.transform import Transform
from ..rig_package.info.asset import Asset
from ..tokenizer.spec import DetokenizeOutput
@dataclass
class ModelInput():
asset: Asset
tokens: Optional[ndarray]=None
class ModelSpec(pl.LightningModule, ABC):
model_config: Dict
transform_config: Dict
tokenizer_config: Dict|None
@abstractmethod
def __init__(self, model_config, transform_config, tokenizer_config=None):
super().__init__()
if not isinstance(model_config, dict):
model_cfg = OmegaConf.to_container(model_config, resolve=True)
else:
model_cfg = model_config
if not isinstance(transform_config, dict):
transform_cfg = OmegaConf.to_container(transform_config, resolve=True)
else:
transform_cfg = transform_config
if tokenizer_config is not None and not isinstance(tokenizer_config, dict):
tokenizer_cfg = OmegaConf.to_container(tokenizer_config, resolve=True)
else:
tokenizer_cfg = tokenizer_config
self.model_config = model_cfg # type: ignore
self.transform_config = transform_cfg # type: ignore
self.tokenizer_config = tokenizer_cfg # type: ignore
self.save_hyperparameters(model_cfg)
self.save_hyperparameters(transform_cfg)
self.save_hyperparameters(tokenizer_cfg)
@final
def _process_fn(self, batch: List[ModelInput]) -> List[Dict]:
n_batch = self.process_fn(batch)
if self._trainer is None or not self.trainer.training:
for k in n_batch[0].keys():
if not isinstance(n_batch[0][k], ndarray) and not isinstance(n_batch[0][k], Tensor):
continue
s = n_batch[0][k].shape
for i in range(1, len(n_batch)):
assert n_batch[i][k].shape == s, f"{k} has different shape in batch"
for (i, b) in enumerate(batch):
non = n_batch[i].get('non', {})
non['model_input'] = deepcopy(b)
n_batch[i]['non'] = non
else:
for b in batch:
del b.asset
return n_batch
@abstractmethod
def process_fn(self, batch: List[ModelInput]) -> List[Dict]:
"""
Fetch data from dataloader and turn it into Tensor objects.
"""
raise NotImplementedError()
def compile_model(self):
"""
Compile the model. Do this before training and after loading state dicts.
"""
pass
@classmethod
def load_from_system_checkpoint(cls, checkpoint_path: str, strict: bool=True, **kwargs):
ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
state_dict = ckpt['state_dict']
model_config = kwargs.get('model_config', None)
transform_config = kwargs.get('transform_config', None)
tokenizer_config = kwargs.get('tokenizer_config', None)
if model_config is None:
model_config = ckpt['hyper_parameters']['model_config']
if transform_config is None:
transform_config = ckpt['hyper_parameters']['transform_config']
if tokenizer_config is None:
tokenizer_config = ckpt['hyper_parameters']['tokenizer_config']
new_state_dict = {}
for k, v in state_dict.items():
k = k.replace("_orig_mod.", "")
if k.startswith("model."):
k = k[len("model.") :]
new_state_dict[k] = v
model = cls(
model_config=model_config,
transform_config=transform_config,
tokenizer_config=tokenizer_config,
)
missing, unexpected = model.load_state_dict(new_state_dict, strict=strict)
if missing or unexpected:
print(f"[Warning] Missing keys: {missing}")
print(f"[Warning] Unexpected keys: {unexpected}")
model.on_load_checkpoint(ckpt)
return model
def get_train_transform(self) -> Transform|None:
cfg = self.transform_config.get('train_transform', None)
if cfg is None:
return None
return Transform.parse(**cfg)
def get_validate_transform(self) -> Transform|None:
cfg = self.transform_config.get('validate_transform', None)
if cfg is None:
return None
return Transform.parse(**cfg)
def get_predict_transform(self) -> Transform|None:
cfg = self.transform_config.get('predict_transform', None)
if cfg is None:
return None
return Transform.parse(**cfg)
def predict_step(self, batch: Dict, no_cls: bool=False, skeleton_tokens=None) -> Dict:
raise NotImplementedError()
@dataclass
class VaeInput():
dense_cond: List[Tensor] # [(J, skin_samples, 6)]
dense_skin: List[Tensor] # [(J, skin_samples)]
dense_indices: List[List[int]] # [List[J]], corresponding indices of gt
uniform_cond: Tensor # (B, N, 6)
uniform_skin: List[Tensor] # [(N, J)]
@property
def B(self):
return self.uniform_cond.shape[0]
@property
def max_J(self):
return max([len(s) for s in self.dense_indices])
def get_len(self, i) -> int:
return len(self.dense_indices[i])
def _clamp_j(self, i: int, j: int) -> int:
return min(j, len(self.dense_indices[i])-1)
def get_dense_cond(self, j: int) -> Tensor:
"""return (B, skin_samples, 6)"""
return torch.stack([self.dense_cond[i][self._clamp_j(i=i, j=j)] for i in range(self.B)])
def get_dense_skin(self, j: int) -> Tensor:
"""return (B, skin_samples)"""
return torch.stack([self.dense_skin[i][self._clamp_j(i=i, j=j)] for i in range(self.B)])
def get_full_cond(self, j: int) -> Tensor:
"""return (B, N+skin_samples, 6)"""
return torch.cat([self.uniform_cond, self.get_dense_cond(j=j)], dim=1)
def get_uniform_skin(self, j: int) -> Tensor:
"""return (B, N)"""
return torch.stack([self.uniform_skin[i][:, self._clamp_j(i=i, j=j)] for i in range(self.B)])
def get_full_skin(self, j: int) -> Tensor:
"""return (B, N+skin_samples)"""
return torch.cat([self.get_uniform_skin(j=j), self.get_dense_skin(j=j)], dim=1)
def get_flatten_uniform_cond(self) -> Tensor:
"""return (sum_J, N, 6)"""
return self.uniform_cond[self.get_flatten_indices()]
def get_flatten_dense_cond(self) -> Tensor:
"""return (sum_J, skin_samples, 6)"""
return torch.cat(self.dense_cond, dim=0)
def get_flatten_dense_skin(self) -> Tensor:
"""return (sum_J, skin_samples)"""
return torch.cat(self.dense_skin, dim=0)
def get_flatten_full_skin(self) -> Tensor:
"""return (sum_J, N+skin_samples)"""
# (sum_J, N)
s = torch.cat(self.uniform_skin, dim=-1).permute(1, 0)
return torch.cat([s, self.get_flatten_dense_skin()], dim=1)
def get_flatten_full_cond(self) -> Tensor:
"""return (sum_J, N+skin_samples, 6)"""
return torch.cat([self.get_flatten_uniform_cond(), self.get_flatten_dense_cond()], dim=1)
def get_flatten_indices(self) -> List[int]:
"""return (sum_J)"""
return [i for i in range(self.B) for _ in range(self.get_len(i=i))]
def true_j(self, i: int, j: int) -> int:
"""return (clamped) corresponding indice in the skeleton"""
return self.dense_indices[i][self._clamp_j(i=i, j=j)]
@dataclass
class TokenRigResult():
cond: Optional[Tensor]=None # [vertices, normals]
cond_latents: Optional[Tensor]=None # (len, dim)
input_ids: Optional[Tensor]=None # (l,)
output_ids: Optional[Tensor]=None # (l,)
skin_pred: Optional[Tensor]=None # (N, J)
detokenize_output: Optional[DetokenizeOutput]=None
asset: Optional[Asset]=None
@dataclass
class BoneVaeInput():
dense_cond: List[Tensor] # [(J, skin_samples, 6)]
dense_skin: List[Tensor] # [(J, skin_samples)]
dense_indices: List[List[int]] # [List[J]], corresponding indices of gt
bones: List[Tensor] # [(J, 6)]
uniform_cond: Tensor # (B, N, 6)
uniform_skin: List[Tensor] # [(N, J)]
@property
def total_samples(self) -> int:
return self.dense_cond[0].shape[1] + self.uniform_cond.shape[1]
@property
def B(self) -> int:
return self.uniform_cond.shape[0]
@property
def max_J(self) -> int:
return max([len(s) for s in self.dense_indices])
def get_len(self, i) -> int:
return len(self.dense_indices[i])
def _clamp_j(self, i: int, j: int) -> int:
return min(j, len(self.dense_indices[i])-1)
def get_dense_cond(self, j: int) -> Tensor:
"""return (B, skin_samples, 6)"""
return torch.stack([self.dense_cond[i][self._clamp_j(i=i, j=j)] for i in range(self.B)])
def get_dense_skin(self, j: int) -> Tensor:
"""return (B, skin_samples)"""
return torch.stack([self.dense_skin[i][self._clamp_j(i=i, j=j)] for i in range(self.B)])
def get_full_cond(self, j: int) -> Tensor:
"""return (B, N+skin_samples, 6)"""
return torch.cat([self.uniform_cond, self.get_dense_cond(j=j)], dim=1)
def get_uniform_skin(self, j: int) -> Tensor:
"""return (B, N)"""
return torch.stack([self.uniform_skin[i][:, self._clamp_j(i=i, j=j)] for i in range(self.B)])
def get_full_skin(self, j: int) -> Tensor:
"""return (B, N+skin_samples)"""
return torch.cat([self.get_uniform_skin(j=j), self.get_dense_skin(j=j)], dim=1)
def get_bones(self, j: int) -> Tensor:
"""return (B, 3)"""
return torch.stack([self.bones[i][self._clamp_j(i=i, j=j)] for i in range(self.B)])
def get_flatten_bones(self) -> Tensor:
"""return (sum_J, 3)"""
return torch.cat([self.bones[i] for i in range(self.B)])
def get_flatten_uniform_cond(self) -> Tensor:
"""return (sum_J, N, 6)"""
return self.uniform_cond[self.get_flatten_indices()]
def get_flatten_dense_cond(self) -> Tensor:
"""return (sum_J, skin_samples, 6)"""
return torch.cat(self.dense_cond, dim=0)
def get_flatten_dense_skin(self) -> Tensor:
"""return (sum_J, skin_samples)"""
return torch.cat(self.dense_skin, dim=0)
def get_flatten_full_skin(self) -> Tensor:
"""return (sum_J, N+skin_samples)"""
# (sum_J, N)
s = torch.cat(self.uniform_skin, dim=-1).permute(1, 0)
return torch.cat([s, self.get_flatten_dense_skin()], dim=1)
def get_flatten_full_cond(self) -> Tensor:
"""return (sum_J, N+skin_samples, 6)"""
return torch.cat([self.get_flatten_uniform_cond(), self.get_flatten_dense_cond()], dim=1)
def get_flatten_indices(self) -> List[int]:
"""return (sum_J)"""
return [i for i in range(self.B) for _ in range(self.get_len(i=i))]
def true_j(self, i: int, j: int) -> int:
"""return (clamped) corresponding indice in the skeleton"""
return self.dense_indices[i][self._clamp_j(i=i, j=j)] |