Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,233 Bytes
f499d3b |
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 |
from collections import defaultdict
import torch.distributed
import lightning as L
import os
import torch
import numpy as np
from torch import Tensor, FloatTensor, LongTensor
from typing import Dict, Union, List, Literal
from lightning.pytorch.callbacks import BasePredictionWriter
from numpy import ndarray
from scipy.sparse import csr_matrix
from scipy.spatial import cKDTree
from ..data.order import OrderConfig, get_order
from ..data.raw_data import RawSkin, RawData
from ..data.exporter import Exporter
from ..model.spec import ModelSpec
class SkinSystem(L.LightningModule):
def __init__(
self,
steps_per_epoch: int,
model: ModelSpec,
output_path: Union[str, None]=None,
record_res: Union[bool]=False,
val_interval: Union[int, None]=None,
val_start_from: Union[int, None]=None,
):
super().__init__()
self.save_hyperparameters(ignore="model")
self.steps_per_epoch = steps_per_epoch
self.model = model
self.output_path = output_path
self.record_res = record_res
self.val_interval = val_interval
self.val_start_from = val_start_from
if self.record_res:
assert self.output_path is not None, "record_res is True, but output_path in skin is None"
def predict_step(self, batch, batch_idx, dataloader_idx=None):
res = self.model.predict_step(batch)
if isinstance(res, list):
return {
'skin_pred': res,
}
elif isinstance(res, dict):
assert 'skin_pred' in res, f"expect key 'skin_pred' in prediction from {self.model.__class__}, found: {res.keys()}"
return res
else:
assert 0, f"expect type of prediction from {self.model.__class__} to be a list or dict, found: {type(res)}"
class SkinWriter(BasePredictionWriter):
def __init__(
self,
output_dir: Union[str, None],
save_name: str,
order_config: Union[OrderConfig, None]=None,
**kwargs
):
super().__init__('batch')
self.output_dir = output_dir
self.npz_dir = kwargs.get('npz_dir', None)
self.user_mode = kwargs.get('user_mode', False)
self.output_name = kwargs.get('output_name', None) # for a single name
self.save_name = save_name
self.add_num = kwargs.get('add_num', False)
self.export_npz = kwargs.get('export_npz', True)
self.export_fbx = kwargs.get('export_fbx', False)
if order_config is not None:
self.order = get_order(config=order_config)
else:
self.order = None
self._epoch = 0
def write_on_batch_end(self, trainer, pl_module: SkinSystem, prediction: List[Dict], batch_indices, batch, batch_idx, dataloader_idx):
assert 'path' in batch
paths: List[str] = batch['path']
data_names: List[str] = batch['data_name']
joints: FloatTensor = batch['joints']
num_bones: LongTensor = batch['num_bones']
num_faces: LongTensor = batch['num_faces']
num_points: LongTensor = batch['num_points']
tails: FloatTensor = batch['tails']
parents_list: LongTensor = batch['parents'] # -1 represents root
vertices: FloatTensor = batch['origin_vertices']
sampled_vertices: FloatTensor = batch['vertices']
faces: LongTensor = batch['origin_faces']
joints = joints.detach().cpu().numpy()
tails = tails.detach().cpu().numpy()
parents_list = parents_list.detach().cpu().numpy()
num_bones = num_bones.detach().cpu().numpy()
num_faces = num_faces.detach().cpu().numpy()
vertices = vertices.detach().cpu().numpy()
faces = faces.detach().cpu().numpy()
skin_pred_list: List = prediction['skin_pred']
ret_sampled_vertices = prediction.get('sampled_vertices', None)
if ret_sampled_vertices is not None:
assert isinstance(ret_sampled_vertices, Tensor)
sampled_vertices = ret_sampled_vertices
if isinstance(sampled_vertices, Tensor):
sampled_vertices = sampled_vertices.type(torch.float32).detach().cpu().numpy()
for (id, skin_pred) in enumerate(skin_pred_list):
if isinstance(skin_pred, Tensor):
skin_pred = skin_pred.type(torch.float32).detach().cpu().numpy()
# TODO: add custom post-processing here
# resample
N = num_points[id]
J = num_bones[id]
F = num_faces[id]
o_vertices = vertices[id, :N]
_parents = parents_list[id]
parents = []
for i in range(J):
if _parents[i] == -1:
parents.append(None)
else:
parents.append(_parents[i])
skin_resampled = reskin(
sampled_vertices=sampled_vertices[id],
vertices=o_vertices,
parents=parents,
faces=faces[id, :F],
sampled_skin=skin_pred,
sample_method='median',
alpha=2.0,
threshold=0.03,
)
def make_path(save_name: str, suffix: str, trim: bool=False):
if trim:
path = os.path.relpath(paths[id], self.npz_dir)
else:
path = paths[id]
if self.output_dir is not None:
path = os.path.join(self.output_dir, path)
if self.add_num:
path = os.path.join(path, f"{save_name}_{self._epoch}.{suffix}")
else:
path = os.path.join(path, f"{save_name}.{suffix}")
return path
raw_data = RawSkin(skin=skin_pred, vertices=sampled_vertices[id], joints=joints[id, :J])
if self.export_npz is not None:
raw_data.save(path=make_path(self.export_npz, 'npz'))
if self.export_fbx is not None:
try:
exporter = Exporter()
names = RawData.load(path=os.path.join(paths[id], data_names[id])).names
if names is None:
names = [f"bone_{i}" for i in range(J)]
if self.user_mode:
if self.output_name is not None:
path = self.output_name
else:
path = make_path(self.save_name, 'fbx', trim=True)
else:
path = make_path(self.export_fbx, 'fbx')
exporter._export_fbx(
path=path,
vertices=o_vertices,
joints=joints[id, :J],
skin=skin_resampled,
parents=parents,
names=names,
faces=faces[id, :F],
group_per_vertex=4,
tails=tails[id, :J],
use_extrude_bone=False,
use_connect_unique_child=False,
# do_not_normalize=True,
)
except Exception as e:
print(str(e))
def write_on_epoch_end(self, trainer, pl_module, predictions, batch_indices):
self._epoch += 1
def reskin(
sampled_vertices: ndarray,
vertices: ndarray,
parents: List[Union[None, int]],
faces: ndarray,
sampled_skin: ndarray,
sample_method: Literal['mean', 'median']='mean',
**kwargs,
) -> ndarray:
nearest_samples = kwargs.get('nearest_samples', 7)
iter_steps = kwargs.get('iter_steps', 1)
threshold = kwargs.get('threshold', 0.01)
alpha = kwargs.get('alpha', 2)
assert sample_method in ['mean', 'median']
N = vertices.shape[0]
J = sampled_skin.shape[1]
if sample_method == 'mean':
tree = cKDTree(sampled_vertices)
dis, nearest = tree.query(vertices, k=nearest_samples, p=2)
# weighted sum
weights = np.exp(-alpha * dis) # (N, nearest_samples)
weight_sum = weights.sum(axis=1, keepdims=True)
sampled_skin_nearest = sampled_skin[nearest]
skin = (sampled_skin_nearest * weights[..., np.newaxis]).sum(axis=1) / weight_sum
elif sample_method == 'median':
tree = cKDTree(sampled_vertices)
dis, nearest = tree.query(vertices, k=nearest_samples, p=2)
skin = np.median(sampled_skin[nearest], axis=1)
else:
assert 0
# (from, to)
edges = np.concatenate([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [2, 0]]], axis=0)
edges = np.concatenate([edges, edges[:, [1, 0]]], axis=0) # (2*F*3, 2)
# diffusion in neighbours
for _ in range(iter_steps):
sum_skin = skin.copy()
for i in reversed(range(J)):
p = parents[i]
if p is None:
continue
sum_skin[:, p] += sum_skin[:, i]
# (2*F*3, J)
# only transfer from hotter to cooler
mask = sum_skin[edges[:, 1]] < sum_skin[edges[:, 0]]
neighbor_skin = np.zeros_like(sum_skin) # (N, J)
neighbor_co = np.zeros((N, J), dtype=np.float32)
dis = np.sqrt(((vertices[edges[:, 1]] - vertices[edges[:, 0]])**2).sum(axis=1, keepdims=True))
co = np.exp(-dis * alpha)
neighbor_skin[edges[:, 1]] += sum_skin[edges[:, 0]] * co * mask
neighbor_co[edges[:, 1]] += co * mask
sum_skin = (sum_skin + neighbor_skin) / (1. + neighbor_co)
for i in range(J):
p = parents[i]
if p is None:
continue
sum_skin[:, p] -= sum_skin[:, i]
skin = sum_skin / sum_skin.sum(axis=-1, keepdims=True)
# avoid 0-skin
mask = (skin>=threshold).any(axis=-1, keepdims=True)
skin[(skin<threshold)&mask] = 0.
skin = skin / skin.sum(axis=-1, keepdims=True)
return skin |