File size: 10,065 Bytes
f02dec9 | 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 | """Precompute frozen SigLIP2 features for Dense UV Parser fixed views.
The direct trainer has no render augmentation, so recomputing the same frozen
vision tower output in every epoch wastes most of the semantic-encoder time.
The optional spatial cache stores full-width FP16 patch features after removing
only deterministic letterbox padding. Trainable projections remain in the
downstream model.
"""
import argparse
import json
import os
import sys
from contextlib import nullcontext
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader
TOOLKIT_ROOT = Path(__file__).resolve().parents[1]
WORKSPACE_ROOT = TOOLKIT_ROOT.parent
if str(WORKSPACE_ROOT) not in sys.path:
sys.path.insert(0, str(WORKSPACE_ROOT))
from SkingToolkit.renderer import DifferentiableRenderer # noqa: E402
from SkingToolkit.dense_uv_parser.semantic_backbone import ( # noqa: E402
SigLIP2VisionBackbone,
)
from SkingToolkit.dense_uv_parser.semantic_cache import ( # noqa: E402
SIGLIP_CACHE_VERSION,
SigLIPGlobalCache,
)
from SkingToolkit.dense_uv_parser.runtime import get_device # noqa: E402
from SkingToolkit.dense_uv_parser.skin_dataset import SkinUVDataset # noqa: E402
from SkingToolkit.dense_uv_parser.utils import parse_views # noqa: E402
try:
from tqdm import tqdm
except ImportError:
tqdm = None
def autocast_context(device, precision):
if precision == "no" or device.type == "cpu":
return nullcontext()
dtype = torch.float16 if precision == "fp16" else torch.bfloat16
return torch.autocast(device_type=device.type, dtype=dtype)
def cache_is_reusable(
cache_dir,
dataset,
views,
siglip_model,
require_spatial=False,
):
try:
cache = SigLIPGlobalCache(
cache_dir,
expected_views=views,
expected_model=siglip_model,
expected_data_dir=dataset.data_dir,
require_spatial=require_spatial,
)
except (FileNotFoundError, ValueError, KeyError, json.JSONDecodeError):
return False
return list(cache.filename_to_index) == [path.name for path in dataset.skin_paths]
def build_arg_parser():
parser = argparse.ArgumentParser(
description="Cache fixed-view SigLIP2 global and optional spatial features."
)
parser.add_argument("--data_dir", default="../skins")
parser.add_argument("--cache_dir", required=True)
parser.add_argument("--mappings_dir", required=True)
parser.add_argument(
"--views",
default="walk_front_both_layer_ortho,walk_back_both_layer_ortho",
)
parser.add_argument("--siglip_model", default="google/siglip2-base-patch16-224")
parser.add_argument("--siglip_local_files_only", action="store_true")
parser.add_argument("--max_samples", type=int, default=None)
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--num_workers", type=int, default=16)
parser.add_argument("--prefetch_factor", type=int, default=4)
parser.add_argument("--device", default="auto")
parser.add_argument("--mixed_precision", choices=["no", "fp16", "bf16"], default="bf16")
parser.add_argument(
"--spatial",
action="store_true",
help="Also cache raw cropped patch features as FP16 mmap data.",
)
parser.add_argument("--force", action="store_true")
return parser
@torch.inference_mode()
def main():
args = build_arg_parser().parse_args()
if args.batch_size < 1:
raise ValueError("--batch_size must be positive.")
views = parse_views(args.views)
if len(views) < 2:
raise ValueError("At least front and back cache views are required.")
dataset = SkinUVDataset(args.data_dir, max_samples=args.max_samples)
cache_dir = Path(args.cache_dir)
if not args.force and cache_is_reusable(
cache_dir,
dataset,
views,
args.siglip_model,
require_spatial=args.spatial,
):
cache_kind = "global + spatial" if args.spatial else "global"
print(f"Reusing complete SigLIP {cache_kind} cache: {cache_dir}")
return
device = get_device(args.device)
renderer = DifferentiableRenderer(mappings_dir=args.mappings_dir).to(device).eval()
missing_views = [view for view in views if view not in renderer.views]
if missing_views:
raise ValueError(f"Renderer cache mappings are missing views {missing_views}.")
backbone = SigLIP2VisionBackbone(
model_name=args.siglip_model,
token_channels=128,
local_files_only=args.siglip_local_files_only,
).to(device).eval()
loader_kwargs = {
"batch_size": args.batch_size,
"shuffle": False,
"num_workers": args.num_workers,
"pin_memory": device.type == "cuda",
}
if args.num_workers > 0:
loader_kwargs["persistent_workers"] = True
loader_kwargs["prefetch_factor"] = args.prefetch_factor
loader = DataLoader(dataset, **loader_kwargs)
cache_dir.mkdir(parents=True, exist_ok=True)
temporary_embeddings = cache_dir / ".embeddings.npy.tmp"
temporary_spatial_embeddings = cache_dir / ".spatial_embeddings.npy.tmp"
temporary_metadata = cache_dir / ".metadata.json.tmp"
embeddings = np.lib.format.open_memmap(
temporary_embeddings,
mode="w+",
dtype=np.float16,
shape=(len(dataset), len(views), backbone.raw_feature_dim),
)
spatial_embeddings = None
spatial_shape = None
iterator = tqdm(loader, desc="cache SigLIP2", leave=False) if tqdm else loader
offset = 0
for batch in iterator:
uv = batch["uv"].to(device, non_blocking=True)
renders = torch.stack(
[renderer.forward_view(uv, view) for view in views], dim=1
)
batch_size, view_count, _, height, width = renders.shape
images = renders[:, :, :3].reshape(batch_size * view_count, 3, height, width)
if images.is_cuda:
images = images.contiguous(memory_format=torch.channels_last)
with autocast_context(device, args.mixed_precision):
encoded = (
backbone.encode_dense(images)
if args.spatial
else backbone.encode_global(images)
)
raw_global = encoded["raw_global"].reshape(
batch_size, view_count, backbone.raw_feature_dim
)
embeddings[offset : offset + batch_size] = (
raw_global.detach().to(device="cpu", dtype=torch.float16).numpy()
)
if args.spatial:
raw_spatial = encoded["raw_spatial"]
current_spatial_shape = tuple(raw_spatial.shape[-2:])
if spatial_embeddings is None:
spatial_shape = current_spatial_shape
spatial_embeddings = np.lib.format.open_memmap(
temporary_spatial_embeddings,
mode="w+",
dtype=np.float16,
shape=(
len(dataset),
len(views),
backbone.raw_spatial_feature_dim,
*spatial_shape,
),
)
spatial_size_gib = (
np.prod(spatial_embeddings.shape)
* np.dtype(np.float16).itemsize
/ (1024**3)
)
print(
"SigLIP2 spatial cache: "
f"shape={spatial_embeddings.shape}, "
f"estimated_size={spatial_size_gib:.1f} GiB."
)
elif current_spatial_shape != spatial_shape:
raise ValueError(
"SigLIP spatial shape changed within a fixed-view cache: "
f"expected {spatial_shape}, got {current_spatial_shape}."
)
raw_spatial = raw_spatial.reshape(
batch_size,
view_count,
backbone.raw_spatial_feature_dim,
*spatial_shape,
)
spatial_embeddings[offset : offset + batch_size] = (
raw_spatial.detach().to(device="cpu", dtype=torch.float16).numpy()
)
offset += batch_size
embeddings.flush()
del embeddings
if spatial_embeddings is not None:
spatial_embeddings.flush()
del spatial_embeddings
if offset != len(dataset):
raise RuntimeError(f"Cached {offset} samples, expected {len(dataset)}.")
metadata = {
"version": SIGLIP_CACHE_VERSION,
"data_dir": str(Path(args.data_dir).resolve()),
"filenames": [path.name for path in dataset.skin_paths],
"views": views,
"siglip_model": args.siglip_model,
"feature_dim": backbone.raw_feature_dim,
"dtype": "float16",
}
if args.spatial:
if spatial_shape is None:
raise RuntimeError("Spatial SigLIP cache was requested but no features were written.")
metadata.update(
{
"spatial_feature_dim": backbone.raw_spatial_feature_dim,
"spatial_height": spatial_shape[0],
"spatial_width": spatial_shape[1],
"spatial_dtype": "float16",
"spatial_layout": "N,V,C,H,W",
"spatial_letterbox_padding_removed": True,
}
)
temporary_metadata.write_text(
json.dumps(metadata, indent=2, ensure_ascii=False), encoding="utf-8"
)
os.replace(temporary_embeddings, cache_dir / "embeddings.npy")
if args.spatial:
os.replace(
temporary_spatial_embeddings,
cache_dir / "spatial_embeddings.npy",
)
os.replace(temporary_metadata, cache_dir / "metadata.json")
cache_kind = "global + spatial" if args.spatial else "global"
print(
f"Cached {cache_kind} features for {len(dataset)} skins x "
f"{len(views)} views to {cache_dir} "
f"({backbone.raw_feature_dim} features/view)."
)
if __name__ == "__main__":
main()
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