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4cde5e8 | 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 | """Wrist-only RGB-D Stereo-ACT-cross_relbias trainer for RoboFactory.
One shared policy sees only one local Panda wrist RGB-D stream at a time. RGB
uses frozen DINOv3-B/16; native ManiSkill depth (stored in millimetres) uses
frozen DeFM-S/14. Both become 30x40 patches before two region-aligned
cross_relbias fusion blocks and the standard ACT 4/7 CVAE policy.
"""
from __future__ import annotations
import argparse
import json
import os
from collections import Counter, OrderedDict, defaultdict
from pathlib import Path
import h5py
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from rgbd_patch_fusion import RGBDPatchFusion
from train_act import ACT, EpisodeBlockBatchSampler, _stats, _trajectories, seed_everything
from five_task_contract import hierarchical_item_weights
DEPTH_MM_TO_M = 0.001
class WristRGBDACTDataset(Dataset):
def __init__(self, trajectories, horizon, stats, train, *, preload=True, cache_limit=0):
self.horizon, self.stats, self.cache_limit = horizon, stats, int(cache_limit)
kept = [item for index, item in enumerate(trajectories) if (index % 10 != 0) == train]
self.items = [(path, key, t, arm, task) for path, key, n, present, task in kept
for arm in present for t in range(n)]
self.item_tasks = [task for _, _, _, _, task in self.items]
self.item_weights = hierarchical_item_weights(kept, self.items)
self.stream_indices = defaultdict(list)
for index, (path, key, _t, arm, task) in enumerate(self.items):
self.stream_indices[(path, key, arm, task)].append(index)
self.cache = OrderedDict()
if preload:
for path, key, _, present, _ in kept:
for arm in present:
self._episode(path, key, arm)
def __len__(self):
return len(self.items)
def _episode(self, path, key, arm):
tag = (path, key, arm)
if tag not in self.cache:
with h5py.File(path, "r") as h5:
tr = h5[key]
sensor = tr["obs"]["sensor_data"][f"head_camera_agent{arm}"]
if "depth" not in sensor:
raise ValueError(f"Stereo-ACT needs rgbd corpus; missing depth in {path}:{key}")
rgb, depth = sensor["rgb"][:], sensor["depth"][:]
if tuple(rgb.shape[1:]) != (480, 640, 3) or tuple(depth.shape[1:]) != (480, 640, 1):
raise ValueError(
f"strict 640x480 RGB-D required; {path}:{key}:panda-{arm} has "
f"rgb={tuple(rgb.shape[1:])}, depth={tuple(depth.shape[1:])}"
)
self.cache[tag] = (
rgb, depth,
tr["obs"]["agent"][f"panda-{arm}"]["qpos"][:].astype(np.float32),
tr["actions"][f"panda-{arm}"][:].astype(np.float32),
)
if self.cache_limit > 0:
while len(self.cache) > self.cache_limit:
self.cache.popitem(last=False)
else:
self.cache.move_to_end(tag)
return self.cache[tag]
def __getitem__(self, index):
path, key, t, arm, _ = self.items[index]
rgb, depth, qpos, actions = self._episode(path, key, arm)
future = actions[t:t + self.horizon]
valid = len(future)
padded = np.empty((self.horizon, actions.shape[1]), np.float32)
padded[:valid], padded[valid:] = future, future[-1]
mask = np.zeros(self.horizon, np.bool_); mask[:valid] = True
return (
torch.from_numpy(rgb[t]).permute(2, 0, 1).contiguous(),
torch.from_numpy(depth[t]).permute(2, 0, 1).contiguous(),
torch.from_numpy((qpos[t] - self.stats["q_mean"]) / self.stats["q_std"]),
torch.from_numpy((padded - self.stats["a_mean"]) / self.stats["a_std"]),
torch.from_numpy(mask),
)
class StereoACT(ACT):
def __init__(self, state_dim, action_dim, horizon=100, d_model=384, enc_layers=4, dec_layers=7,
dino_model="facebook/dinov3-vitb16-pretrain-lvd1689m", defm_model="defm_vit_s14"):
super().__init__(state_dim, action_dim, horizon, d_model, enc_layers, dec_layers,
vision_backbone="dinov3_vitb16_frozen", dino_model=dino_model)
from defm.model_factory import create_defm_model
self.defm_model_name = defm_model
# Use an explicitly versioned local checkpoint when supplied. This
# prevents an interrupted Hub download from silently changing the
# depth encoder during a formal run.
checkpoint = os.environ.get("DEFM_CHECKPOINT")
self.defm = create_defm_model(
defm_model, pretrained=True, pretrained_path=checkpoint
).eval()
self.defm.requires_grad_(False)
self.depth_proj = nn.Linear(384, d_model)
self.fusion = RGBDPatchFusion(d_model=d_model, heads=8, grid_h=30, grid_w=40,
layers=2, ffn_dim=d_model * 4)
self.fusion_pos = nn.Parameter(torch.randn(1, 30 * 40, d_model) * 0.02)
def train(self, mode=True):
super().train(mode)
self.vision.eval(); self.defm.eval()
return self
def _rgbd_tokens(self, rgb, depth_mm):
# Parent RGB method preserves native 640x480 -> 30x40 DINO patches.
rgb_tokens = super()._vision_tokens(rgb)
from defm.utils.utils import preprocess_depth_batch
depth_m = depth_mm.float().squeeze(1).mul(DEPTH_MM_TO_M)
prepared = preprocess_depth_batch(depth_m, target_size=(420, 560), patch_size=14, device=rgb.device)
self.defm.eval()
with torch.no_grad():
spatial, _ = self.defm.to(rgb.device).get_intermediate_layers(
prepared.float(), n=1, reshape=True, return_class_token=True
)[0]
depth_tokens = self.depth_proj(spatial.flatten(2).transpose(1, 2).to(dtype=rgb_tokens.dtype))
if rgb_tokens.shape[1] != 30 * 40 or depth_tokens.shape[1] != 30 * 40:
raise ValueError(
f"strict aligned 30x40 tokens required, got RGB={rgb_tokens.shape[1]} "
f"and depth={depth_tokens.shape[1]}"
)
return self.fusion(rgb_tokens, depth_tokens, self.fusion_pos.to(dtype=rgb_tokens.dtype))
def forward(self, image, depth_mm, qpos, actions=None):
x = self._rgbd_tokens(image, depth_mm)
state = self.state(qpos).unsqueeze(1)
if actions is not None:
h = self.posterior(self.action(actions) + self.pos)
mu, logvar = self.latent(h.mean(1)).chunk(2, -1)
z = mu + torch.randn_like(mu) * torch.exp(.5 * logvar)
else:
mu = logvar = None
z = torch.zeros((image.shape[0], self.z_proj.in_features), device=image.device)
memory = torch.cat((state, self.z_proj(z).unsqueeze(1), x), dim=1)
return self.out(self.decoder(self.query.expand(image.shape[0], -1, -1), memory)), mu, logvar
def loss(model, rgb, depth, qpos, actions, mask, beta):
rgb = rgb.float().div_(255)
with torch.autocast("cuda", dtype=torch.bfloat16):
pred, mu, logvar = model(rgb, depth, qpos, actions)
mse = ((pred - actions).square().mean(-1) * mask).sum() / mask.sum().clamp_min(1)
kl = -.5 * (1 + logvar - mu.square() - logvar.exp()).sum(-1).mean()
return mse + beta * kl, mse, kl
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data", required=True)
parser.add_argument("--shared-arms", default="0,1")
parser.add_argument("--output", required=True)
# Verified on the target 32 GB RTX 5090 with a real 640×480 RGB-D canary:
# batch=32 completes DINO+DeFM plus two 30×40 fusion blocks' backward pass.
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--updates", type=int, default=60000)
parser.add_argument("--save-updates", default="20000,40000,60000")
parser.add_argument("--lr", type=float, default=2e-4)
parser.add_argument("--beta", type=float, default=1e-3)
parser.add_argument("--workers", type=int, default=0)
parser.add_argument("--cache-episodes", type=int, default=0,
help="Bound decoded episode cache; 0 preloads all trajectories.")
parser.add_argument("--episode-block-updates", type=int, default=64)
parser.add_argument("--task-balanced", action="store_true")
parser.add_argument("--seed", type=int, default=20260724)
args = parser.parse_args()
import glob
arms = tuple(int(item) for item in args.shared_arms.split(","))
paths = sorted({path for item in args.data.split(",") for path in glob.glob(item)})
trajectories = _trajectories(paths, arms)
if len(trajectories) < 10:
raise ValueError("need at least 10 successful RGB-D demonstrations")
seed_everything(args.seed); torch.backends.cudnn.benchmark = True
stats = _stats(trajectories, arms)
lazy_cache = args.cache_episodes > 0
if lazy_cache and args.workers:
raise ValueError("bounded RGB-D cache requires --workers 0 to keep a single cache")
train = WristRGBDACTDataset(trajectories, 100, stats, True, preload=not lazy_cache,
cache_limit=args.cache_episodes)
valid = WristRGBDACTDataset(trajectories, 100, stats, False, preload=False,
cache_limit=args.cache_episodes)
counts = Counter(train.item_tasks)
sampler = None
if lazy_cache:
sampler = EpisodeBlockBatchSampler(train, args.batch_size, args.updates,
args.episode_block_updates, args.seed, args.task_balanced)
loader = DataLoader(train, batch_sampler=sampler, num_workers=0, pin_memory=True)
elif args.task_balanced and len(counts) > 1:
weights = torch.as_tensor(train.item_weights, dtype=torch.double)
sampler = torch.utils.data.WeightedRandomSampler(weights, num_samples=len(weights), replacement=True)
if not lazy_cache:
loader = DataLoader(train, batch_size=args.batch_size, shuffle=sampler is None, sampler=sampler,
drop_last=True, num_workers=args.workers, pin_memory=True,
persistent_workers=args.workers > 0)
device = torch.device("cuda:0")
sample = train[0]
model = StereoACT(len(sample[2]), len(sample[3][0])).to(device)
optimizer = torch.optim.AdamW((p for p in model.parameters() if p.requires_grad), lr=args.lr, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.updates)
output = Path(args.output); output.mkdir(parents=True, exist_ok=True)
config = vars(args) | {"horizon": 100, "enc_layers": 4, "dec_layers": 7, "d_model": 384,
"vision_backbone": "stereo_act_cross_relbias",
"dino_model": "facebook/dinov3-vitb16-pretrain-lvd1689m", "defm_model": model.defm_model_name,
"defm_checkpoint": os.environ.get("DEFM_CHECKPOINT"),
"camera_width": 640, "camera_height": 480, "patch_grid": [30, 40], "fusion_layers": 2,
"depth_storage_unit": "millimeters", "depth_to_meters_scale": DEPTH_MM_TO_M,
"arms": arms, "state_dim": len(sample[2]), "action_dim": len(sample[3][0]),
"files": paths, "episodes": len(trajectories), "train_task_item_counts": dict(counts)}
(output / "config.json").write_text(json.dumps(config, indent=2))
np.savez(output / "normalization.npz", **stats)
milestones, updates = {int(x) for x in args.save_updates.split(",") if x}, 0
while updates < args.updates:
model.train(); totals = {"loss": 0.0, "mse": 0.0, "kl": 0.0, "n": 0}
for rgb, depth, qpos, actions, mask in loader:
rgb, depth, qpos, actions, mask = (item.to(device, non_blocking=True) for item in (rgb, depth, qpos, actions, mask))
optimizer.zero_grad(set_to_none=True)
total, mse, kl = loss(model, rgb, depth, qpos, actions, mask, args.beta)
total.backward(); torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step(); scheduler.step(); updates += 1
batch = len(rgb)
for name, value in (("loss", total), ("mse", mse), ("kl", kl)):
totals[name] += float(value.detach()) * batch
totals["n"] += batch
if updates in milestones:
torch.save({"model": model.state_dict(), "stats": stats, "config": config, "update": updates},
output / f"checkpoint_{updates:06d}.pt")
if updates % 100 == 0:
print(json.dumps({"update": updates, **{key: value / totals["n"] for key, value in totals.items() if key != "n"}}), flush=True)
if updates >= args.updates:
break
if __name__ == "__main__":
main()
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