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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 | """Open-loop evaluation of FACT with the baseline ablation protocol.
Same anchors, seeds and metric as ``the T-Rex single-episode ablation protocol (results/ablation_baseline)``:
* fixed-tau forward losses on 6 K=4 anchors x 4 noise repeats
(seeds 100000 + repeat*1000 + anchor*10);
* open-loop chunk reconstruction on 16 K=1 anchors (seed 500+i): normalized
62-D MSE of the sampled chunk vs. the ground-truth delta-base chunk, for
``cascade`` (6 coarse + 4 tactile steps), ``coarse_only`` (coarse action
read out at tau=0.4, exactly the baseline's coarse_only), and the extra
``coarse_to_zero`` (coarse model carried to tau=0), with arm/hand splits.
* open-loop future-state MSE (same anchors).
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import torch
from fact import FactEpisode, FactPolicy
from fact.data import AR_BLOCKS
from fact.force import ACTION_HORIZON
ARM_DIMS = list(range(0, 9)) + list(range(31, 40))
HAND_DIMS = list(range(9, 31)) + list(range(40, 62))
def to_batch(sample: dict[str, np.ndarray], device: torch.device) -> dict[str, torch.Tensor]:
batch = {}
for key, value in sample.items():
if key in ("action_delta_raw", "reference_state", "obs_frame_index"):
continue
batch[key] = torch.as_tensor(value)[None].to(device)
return batch
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--dataset-root", required=True)
parser.add_argument("--episode", type=int, default=0)
parser.add_argument("--cache", required=True)
parser.add_argument("--out", required=True)
parser.add_argument("--forward-anchors", type=int, default=6)
parser.add_argument("--openloop-anchors", type=int, default=16)
parser.add_argument("--tau-grid", default="0.8,0.6,0.4,0.2,0.05")
parser.add_argument("--tau-repeats", type=int, default=4)
parser.add_argument("--baseline-json", default=None)
parser.add_argument("--bf16", action="store_true")
parser.add_argument("--heldout-fraction", type=float, default=None,
help="evaluate only on anchors starting after (1-f) of the episode (unseen by a --train-fraction (1-f) run)")
args = parser.parse_args()
device = torch.device("cuda")
model = FactPolicy.load(args.checkpoint).to(device).eval()
episode = FactEpisode(args.dataset_root, args.episode, latent_cache=args.cache)
t_min = None if args.heldout_fraction is None else episode.split_time(1.0 - args.heldout_fraction)
anchors_k4 = episode.valid_anchor_times(AR_BLOCKS, t_min=t_min)
forward_anchors = [anchors_k4[int(i)] for i in np.linspace(0, len(anchors_k4) - 1, args.forward_anchors)]
anchors_k1 = episode.valid_anchor_times(1, t_min=t_min)
openloop_anchors = [anchors_k1[int(i)] for i in np.linspace(0, len(anchors_k1) - 1, args.openloop_anchors)]
tau_grid = [float(v) for v in args.tau_grid.split(",")]
record: dict[str, object] = {
"checkpoint": args.checkpoint,
"heldout_fraction": args.heldout_fraction,
"num_k1_anchors_available": len(anchors_k1),
"config": json.loads(model.cfg.to_json()),
"params_total": sum(p.numel() for p in model.parameters()),
"params_coarse": sum(p.numel() for p in model.coarse.parameters()),
"params_force": 0 if model.force is None else sum(p.numel() for p in model.force.parameters()),
}
# ---------------- fixed-tau forward losses ----------------
forward_batches = [to_batch(episode.build(a, blocks=AR_BLOCKS), device) for a in forward_anchors]
tau_table = {}
for tau_value in tau_grid:
metrics: dict[str, list[float]] = {}
for repeat in range(args.tau_repeats):
for index, batch in enumerate(forward_batches):
torch.manual_seed(100000 + repeat * 1000 + index * 10)
tau = torch.full((1, AR_BLOCKS), tau_value, device=device)
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16, enabled=args.bf16):
out = model.forward_core(batch, tau=tau)
for key in ("action_loss", "state_loss", "force_loss"):
metrics.setdefault(key, []).append(float(out[key]))
tau_table[f"{tau_value:g}"] = {k: [float(np.mean(v)), float(np.std(v))] for k, v in metrics.items()}
record["forward_tau_losses"] = tau_table
print("forward losses done", flush=True)
# ---------------- open-loop reconstruction ----------------
modes = ["coarse_only", "coarse_to_zero"]
if model.force is not None:
modes.insert(0, "cascade")
openloop: dict[str, list[float]] = {}
split: dict[str, list[float]] = {}
fstate_mse: dict[str, list[float]] = {}
for index, anchor in enumerate(openloop_anchors):
sample = episode.build(anchor, blocks=1)
gt = torch.as_tensor(sample["action"][0, :, :62])
gt_fstate = torch.as_tensor(sample["future_state"][0, :, :62])
batch = to_batch(sample, device)
for mode in modes:
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16, enabled=args.bf16):
result = model.sample(batch, seed=500 + index, mode=mode)
pred = result["action_pred"][0, :, :62].float().cpu()
openloop.setdefault(mode, []).append(float(((pred - gt) ** 2).mean()))
openloop.setdefault(f"{mode}_first11", []).append(float(((pred[:11] - gt[:11]) ** 2).mean()))
split.setdefault(f"{mode}_arm", []).append(float(((pred[:, ARM_DIMS] - gt[:, ARM_DIMS]) ** 2).mean()))
split.setdefault(f"{mode}_hand", []).append(float(((pred[:, HAND_DIMS] - gt[:, HAND_DIMS]) ** 2).mean()))
if "future_state_pred" in result:
fp = result["future_state_pred"][0, :, :62].float().cpu()
fstate_mse.setdefault(mode, []).append(float(((fp - gt_fstate) ** 2).mean()))
record["openloop_action_mse"] = {k: [float(np.mean(v)), float(np.std(v)), len(v)] for k, v in openloop.items()}
record["openloop_action_mse_raw"] = openloop
record["openloop_action_mse_split"] = {k: float(np.mean(v)) for k, v in split.items()}
record["openloop_future_state_mse"] = {k: float(np.mean(v)) for k, v in fstate_mse.items()}
print("open-loop done", flush=True)
# ---------------- comparison table ----------------
lines = []
lines.append(f"FACT params: total {record['params_total']/1e6:.1f}M (coarse {record['params_coarse']/1e6:.1f}M, force {record['params_force']/1e6:.1f}M)")
for mode in modes:
m, s, n = record["openloop_action_mse"][mode]
lines.append(f" open-loop {mode:15s} first 11 steps (0.55 s, T-Rex-horizon) mse {record['openloop_action_mse'][mode + '_first11'][0]:.4f}")
lines.append(
f" open-loop {mode:15s} mse {m:.4f} ± {s:.4f} (n={n}) arm {record['openloop_action_mse_split'][mode+'_arm']:.4f} hand {record['openloop_action_mse_split'][mode+'_hand']:.4f}"
)
for tau_key, vals in tau_table.items():
lines.append(f" tau={tau_key:>4s} action {vals['action_loss'][0]:.4f} state {vals['state_loss'][0]:.4f} force {vals['force_loss'][0]:.4f}")
if args.baseline_json:
base = json.load(open(args.baseline_json))
lines.append("baseline (trex_track_force ablation, same protocol):")
for name, rec in base.items():
ol = rec.get("openloop_action_mse", {})
parts = [f"{k} {v[0]:.4f}" for k, v in ol.items()]
sp = rec.get("openloop_action_mse_split", {})
parts += [f"{k} {v:.4f}" for k, v in sp.items()]
lines.append(f" {name:8s} " + " ".join(parts))
record["baseline"] = {
name: {"openloop_action_mse": rec.get("openloop_action_mse"), "split": rec.get("openloop_action_mse_split")}
for name, rec in base.items()
}
summary = "\n".join(lines)
print(summary, flush=True)
record["summary"] = summary
Path(args.out).write_text(json.dumps(record, indent=1))
print("wrote", args.out)
if __name__ == "__main__":
main()
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