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af8c481 | 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 | """Build the FACT offline caches for a whole LeRobot-v2 T-Rex dataset.
* ``--what latents``: Wan2.2 VAE latents of every frame of every episode
(``<out>/latents/episode_XXXXXX.npy``, float16 [N,48,10,20]). Shardable
across devices with ``--shard i --num-shards n``; existing files are skipped.
* ``--what text``: umT5-XXL token embeddings of every task string
(``<out>/text_cache.npz``).
Observation preprocessing is identical to the baseline path (per-view resize
to 160x320, DreamZero 2x2 grid [head | right ; left | black], resize back to
160x320, single-frame VAE encode).
"""
from __future__ import annotations
import argparse
import json
import time
from pathlib import Path
import numpy as np
import torch
from fact.data import latent_cache_path, load_episode_tasks
VIEW_DIRS = (
"observation.images.head_left",
"observation.images.left_wrist",
"observation.images.right_wrist",
)
def _load_component(module: torch.nn.Module, path: str, min_coverage: float) -> None:
state = torch.load(path, map_location="cpu")
if isinstance(state, dict):
for key in ("state_dict", "model", "module"):
if isinstance(state.get(key), dict):
state = state[key]
break
state = {k.removeprefix("module."): v for k, v in state.items() if torch.is_tensor(v)}
current = module.state_dict()
compatible = {k: v for k, v in state.items() if k in current and current[k].shape == v.shape}
coverage = sum(v.numel() for v in compatible.values()) / max(1, sum(v.numel() for v in current.values()))
if coverage < min_coverage:
raise ValueError(f"checkpoint coverage {coverage:.3f} < {min_coverage} for {path}")
module.load_state_dict(compatible, strict=False)
def load_vae(path: str, device: torch.device):
from fact.perception.wan_video_vae import WanVideoVAE38
vae = WanVideoVAE38(z_dim=48, dim=160, vae_pretrained_path=path)
_load_component(vae.model, path, 0.99)
vae = vae.to(device).eval()
vae.mean = vae.mean.to(device)
vae.std = vae.std.to(device)
vae.scale = [vae.mean, vae.std.reciprocal()]
return vae
def read_all_frames(path: Path) -> np.ndarray:
import decord
reader = decord.VideoReader(str(path), num_threads=2)
return reader.get_batch(list(range(len(reader)))).asnumpy().astype(np.uint8)
def composite(views: list[np.ndarray], height: int, width: int) -> torch.Tensor:
from torchvision.transforms import InterpolationMode
from torchvision.transforms import functional as TF
resized = []
for frames in views:
t = torch.from_numpy(frames).permute(0, 3, 1, 2)
t = TF.resize(t, [height, width], interpolation=InterpolationMode.BILINEAR, antialias=True)
resized.append(t)
head, left, right = resized
n = head.shape[0]
grid = torch.zeros(n, 3, 2 * height, 2 * width, dtype=torch.uint8)
grid[:, :, :height, :width] = head
grid[:, :, height:, :width] = left
grid[:, :, :height, width:] = right
grid = grid.float() / 255.0 * 2 - 1
return torch.nn.functional.interpolate(grid, size=(height, width), mode="bilinear", align_corners=False)
def encode_episode(vae, root: Path, episode: int, device, height: int, width: int, batch: int) -> np.ndarray:
views = []
for view in VIEW_DIRS:
path = root / "videos" / f"chunk-{episode // 1000:03d}" / view / f"episode_{episode:06d}.mp4"
views.append(read_all_frames(path))
n = min(v.shape[0] for v in views)
images = composite([v[:n] for v in views], height, width)
latents = []
with torch.no_grad():
for i in range(0, n, batch):
chunk = images[i : i + batch].to(device).unsqueeze(2) # B,3,1,H,W
z = vae.encode(chunk, tiled=False)
latents.append(z[:, :, 0].float().cpu())
return torch.cat(latents).numpy().astype(np.float16)
def build_latents(args, root: Path, out: Path) -> None:
device = torch.device("cuda")
tasks = load_episode_tasks(root)
episodes = sorted(tasks) if not args.episodes else [int(e) for e in args.episodes.split(",")]
episodes = [e for i, e in enumerate(episodes) if i % args.num_shards == args.shard]
(out / "latents").mkdir(parents=True, exist_ok=True)
todo = [e for e in episodes if not latent_cache_path(out, e).exists()]
print(f"shard {args.shard}/{args.num_shards}: {len(episodes)} episodes, {len(todo)} to encode", flush=True)
if not todo:
return
vae = load_vae(args.vae, device)
start = time.time()
for n, episode in enumerate(todo):
latents = encode_episode(vae, root, episode, device, args.height, args.width, args.batch)
if not np.isfinite(latents.astype(np.float32)).all():
raise ValueError(f"non-finite latents for episode {episode}")
tmp = latent_cache_path(out, episode).with_suffix(".tmp.npy")
np.save(tmp, latents)
tmp.rename(latent_cache_path(out, episode))
if n % 5 == 0:
print(f" [{n + 1}/{len(todo)}] episode {episode}: {latents.shape} ({time.time() - start:.0f}s)", flush=True)
print(f"shard {args.shard} done in {(time.time() - start) / 60:.1f} min", flush=True)
def build_text(args, root: Path, out: Path) -> None:
from fact.perception.wan_video_text_encoder import WanTextEncoder
from fact.perception.tokenizer import HuggingfaceTokenizer
device = torch.device("cuda")
tasks: dict[int, str] = {}
with (root / "meta" / "tasks.jsonl").open() as handle:
for line in handle:
if line.strip():
rec = json.loads(line)
tasks[int(rec["task_index"])] = rec["task"]
tokenizer = HuggingfaceTokenizer(name=args.tokenizer, seq_len=args.max_text_tokens, clean="whitespace")
encoder = WanTextEncoder(text_encoder_pretrained_path=args.text_encoder)
_load_component(encoder, args.text_encoder, 0.99)
encoder = encoder.to(device=device, dtype=torch.bfloat16).eval()
indices = sorted(tasks)
tokens = np.zeros((len(indices), args.max_text_tokens, 4096), dtype=np.float16)
masks = np.zeros((len(indices), args.max_text_tokens), dtype=bool)
with torch.no_grad():
for i in range(0, len(indices), args.batch):
chunk = indices[i : i + args.batch]
ids, mask = tokenizer([tasks[t] for t in chunk], return_mask=True)
emb = encoder(ids.to(device), mask.to(device)).float().cpu().numpy()
tokens[i : i + len(chunk)] = emb.astype(np.float16)
masks[i : i + len(chunk)] = mask.numpy().astype(bool)
out.mkdir(parents=True, exist_ok=True)
np.savez_compressed(out / "text_cache.npz", tokens=tokens, mask=masks, task_index=np.array(indices))
print(f"text cache: {len(indices)} tasks x {args.max_text_tokens} tokens -> {out / 'text_cache.npz'}", flush=True)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--dataset-root", required=True)
parser.add_argument("--out", required=True, help="cache directory")
parser.add_argument("--what", choices=["latents", "text"], required=True)
parser.add_argument("--vae", default=None)
parser.add_argument("--text-encoder", default=None)
parser.add_argument("--tokenizer", default=None)
parser.add_argument("--max-text-tokens", type=int, default=64)
parser.add_argument("--episodes", default="", help="comma list; default all")
parser.add_argument("--shard", type=int, default=0)
parser.add_argument("--num-shards", type=int, default=1)
parser.add_argument("--height", type=int, default=160)
parser.add_argument("--width", type=int, default=320)
parser.add_argument("--batch", type=int, default=16)
args = parser.parse_args()
root, out = Path(args.dataset_root), Path(args.out)
if args.what == "latents":
if not args.vae:
parser.error("--vae is required for latents")
build_latents(args, root, out)
else:
if not (args.text_encoder and args.tokenizer):
parser.error("--text-encoder and --tokenizer are required for text")
build_text(args, root, out)
if __name__ == "__main__":
main()
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