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5a2e445 76edeec 5a2e445 76edeec 5a2e445 76edeec 5a2e445 | 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 | #!/usr/bin/env python
"""Auto-eval for in-context demo conditioning on held-out LeKiwi.
The key test: give the model K=3 REAL (obs, action) example pairs from LeKiwi's
OWN data at test time — NO gradient update, NO fine-tuning — and see if that beats
zero-shot with no examples, wrong examples, or the numeric/text descriptor alone.
"""
from __future__ import annotations
import numpy as np
import torch
import yaml
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
from transformers import AutoTokenizer
from tinyvla.data.canonical import CanonicalChunkStore, quantile_normalize
from tinyvla.data.eval_utils import StateAdapter
from tinyvla.modeling_tinyvla import TinyVLAPolicy
CKPT = "outputs/tv2_C_incontext/final"
DS = "lekiwi_cleanup"
ROOT = f"/home/alexw/tinyvla_data/lekiwi/{DS}"
K = 3
@torch.no_grad()
def main():
pol = TinyVLAPolicy.from_pretrained(CKPT).cuda().eval()
cfg = pol.config
tok = AutoTokenizer.from_pretrained(cfg.lm_model_name)
prompts = yaml.safe_load(open("configs/morphology/robot_prompts.yaml"))
m = LeRobotDatasetMetadata(DS, root=ROOT)
ds = LeRobotDataset(DS, root=ROOT, delta_timestamps={"action": [t / m.fps for t in range(50)]},
video_backend="torchcodec")
_sa = StateAdapter(ds.meta, cfg.max_state_dim)
store = CanonicalChunkStore(DS, src_fps=m.fps, chunk=50)
st = store.compute_stats()
q01, q99 = np.asarray(st["q01"]), np.asarray(st["q99"])
span = np.maximum(q99 - q01, 0.01 * np.median(np.abs(np.concatenate([q01, q99])) + 1e-6))
mid = 0.5 * (q01 + q99)
imk = sorted(k for k in ds.meta.features if k.startswith("observation.images"))
prim = next((k for k in imk if "front" in k or "base" in k), imk[0])
def integ(d):
return np.cumsum(d[:, :3], 0)
def img_at(idx, ep, ep_start):
it = ds[idx]
im = torch.nn.functional.interpolate(it[prim][None], size=(256, 256), mode="bilinear")[0]
raw = quantile_normalize(store.chunk_for(ep, idx - ep_start), q01, q99)
act = torch.from_numpy(raw[:, :7]).float()
act = torch.nn.functional.pad(act, (0, cfg.max_action_dim - 7))
return im, act
# REAL LeKiwi demo pairs from early episodes (support pool), disjoint from test
support_pool_eps = range(0, min(20, m.total_episodes - 15))
test = range(max(0, m.total_episodes - 15), m.total_episodes)
def get_real_support(k):
imgs, acts = [], []
for _ in range(k):
ep = int(np.random.choice(list(support_pool_eps)))
s = int(m.episodes["dataset_from_index"][ep])
e = int(m.episodes["dataset_to_index"][ep])
idx = int(np.random.randint(s, max(s + 1, e - 1)))
im, act = img_at(idx, ep, s)
imgs.append(im)
acts.append(act)
return torch.stack(imgs), torch.stack(acts)
def get_wrong_support(k):
# random noise images + random actions — a garbage support set control
return torch.rand(k, 3, 256, 256), torch.randn(k, 50, cfg.max_action_dim) * 0.3
def run(support_fn, desc_text):
ids_t = tok([desc_text] if desc_text else [""], padding="max_length", truncation=True,
max_length=cfg.morph_text_max_len, return_tensors="pt")
errs = []
for ep in test:
s = int(m.episodes["dataset_from_index"][ep])
e = int(m.episodes["dataset_to_index"][ep])
for idx in range(s, e - 1, 30):
item = ds[idx]
task = item.get("task") or ""
t = tok([task], padding=True, truncation=True, max_length=48, return_tensors="pt")
img = torch.nn.functional.interpolate(item[prim][None], size=(256, 256), mode="bilinear")[0]
stt = _sa(item["observation.state"])
sup_img, sup_act = support_fn(K)
b = {"observation.images.cam0": img[None].cuda(),
"observation.images.cam1": torch.zeros_like(img)[None].cuda(),
"observation.state": stt[None].cuda(),
"observation.language.tokens": t["input_ids"].cuda(),
"observation.language.attention_mask": t["attention_mask"].bool().cuda(),
"morph_text_ids": ids_t["input_ids"].cuda(),
"morph_text_mask": ids_t["attention_mask"].bool().cuda(),
"support_images": sup_img[None].cuda(), "support_actions": sup_act[None].cuda(),
"embodiment_id": torch.tensor([0]).cuda()}
with torch.autocast("cuda", torch.bfloat16):
pr = pol.predict_action_chunk(b)[0].cpu().float().numpy()
gu = quantile_normalize(store.chunk_for(ep, idx - s), q01, q99)[:, :7] * span / 2 + mid
pu = pr[:, :7] * span / 2 + mid
errs.append(np.linalg.norm(integ(pu)[-1] - integ(gu)[-1]) * 1000)
return np.mean(errs)
print("=== C-incontext (demo conditioning, no FT) on held-out LeKiwi ===")
print(f"REAL LeKiwi demos (K={K}) + desc=lekiwi endpoint {run(get_real_support, prompts['lekiwi']):.1f}mm")
print(f"REAL LeKiwi demos (K={K}) + desc=none endpoint {run(get_real_support, None):.1f}mm")
print(f"WRONG/garbage demos + desc=lekiwi endpoint {run(get_wrong_support, prompts['lekiwi']):.1f}mm")
print(f"WRONG/garbage demos + desc=none endpoint {run(get_wrong_support, None):.1f}mm")
print("\nprior baselines (no demo conditioning): C-diverse 295mm | C-qwen-morph desc=none 294mm")
if __name__ == "__main__":
main()
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