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#!/usr/bin/env python3
import argparse
import os
import sys
import time
from pathlib import Path
import torch
import yaml
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
os.environ.setdefault("LEROBOT_VIDEO_BACKEND", "pyav")
from models.hrdt_runner import HRDTRunner
from models.encoder.dinosiglip_vit import DinoSigLIPViTBackbone
from hrdt_datasets.dataset import VLAConsumerDataset, DataCollatorForVLAConsumerDataset
from torch.utils.data import DataLoader
def main() -> None:
parser = argparse.ArgumentParser(description="Overfit a single batch to sanity check loss.")
parser.add_argument("--data_root", default="/hfm/data/pick_box")
parser.add_argument("--config_path", default="configs/hrdt_finetune_lerobot.yaml")
parser.add_argument("--pretrained_backbone_path", required=True)
parser.add_argument("--vision_encoder", default="dino-siglip")
parser.add_argument("--device", default="cuda:0")
parser.add_argument("--steps", type=int, default=20)
parser.add_argument("--lr", type=float, default=1e-4)
parser.add_argument("--batch_size", type=int, default=2)
parser.add_argument("--num_workers", type=int, default=1)
parser.add_argument("--use_precomp_lang_embed", action="store_true")
args = parser.parse_args()
with open(args.config_path, "r") as f:
config = yaml.safe_load(f)
device = torch.device(args.device)
vision_encoder = DinoSigLIPViTBackbone(
vision_backbone_id=args.vision_encoder,
image_resize_strategy="letterbox"
if config["dataset"]["image_aspect_ratio"] == "pad"
else "resize-naive",
default_image_size=384,
).to(device)
vision_encoder.eval()
image_transform = vision_encoder.get_image_transform()
dataset = VLAConsumerDataset(
config=config,
image_transform=image_transform,
num_cameras=config["common"]["num_cameras"],
image_aug=False,
dataset_type="finetune",
dataset_name="lerobot",
dataset_root=args.data_root,
use_precomp_lang_embed=args.use_precomp_lang_embed,
upsample_rate=1,
)
collator = DataCollatorForVLAConsumerDataset(use_precomp_lang_embed=args.use_precomp_lang_embed)
loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, collate_fn=collator)
loader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=collator,
num_workers=args.num_workers,
pin_memory=True,
persistent_workers=args.num_workers > 0,
)
batch = next(iter(loader))
if not Path(args.pretrained_backbone_path).exists():
alt_path = Path("./checkpoints/pretrain-0618/checkpoint-500000/pytorch_model.bin")
if alt_path.exists():
print(f"[WARN] Using fallback pretrained backbone at {alt_path}")
args.pretrained_backbone_path = str(alt_path)
else:
raise FileNotFoundError(
f"Pretrained backbone not found: {args.pretrained_backbone_path}"
)
hrdt = HRDTRunner(
state_dim=config["common"]["state_dim"],
action_dim=config["common"]["action_dim"],
pred_horizon=config["common"]["action_chunk_size"],
config=config["model"],
act_pos_emb_config=[("state", 1), ("action", config["common"]["action_chunk_size"])],
img_pos_emb_config=[
("image", (config["common"]["img_history_size"], config["common"]["num_cameras"], -vision_encoder.num_patches)),
],
lang_pos_emb_config=[
("language", -config["dataset"]["tokenizer_max_length"]),
],
max_img_len=config["common"]["img_history_size"]
* config["common"]["num_cameras"]
* vision_encoder.num_patches,
max_lang_len=config["dataset"]["tokenizer_max_length"],
training_mode="lang",
mode="finetune",
pretrained_backbone_path=args.pretrained_backbone_path,
dtype=torch.float32,
).to(device)
optimizer = torch.optim.AdamW(hrdt.parameters(), lr=args.lr)
images = batch["images"]
with torch.no_grad():
k = next(iter(images))
batch_size, _, C, H, W = images[k].shape
for key in images:
images[key] = images[key].to(device).view(-1, C, H, W)
image_features = vision_encoder(images).detach()
image_features = image_features.view((batch_size, -1, vision_encoder.embed_dim))
states = batch["states"].to(device)
actions = batch["actions"].to(device)
lang_embeds = batch.get("lang_embeds")
lang_attn_mask = batch.get("lang_attn_mask")
if lang_embeds is not None:
lang_embeds = lang_embeds.to(device)
if lang_attn_mask is not None:
lang_attn_mask = lang_attn_mask.to(device)
for step in range(args.steps):
t0 = time.time()
loss_dict = hrdt.compute_loss(
state_tokens=states,
action_gt=actions,
image_tokens=image_features,
lang_tokens=lang_embeds,
lang_attn_mask=lang_attn_mask,
)
loss = loss_dict["loss"]
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
dt = time.time() - t0
print(f"step={step:03d} loss={loss.item():.6f} dt={dt:.3f}s")
if __name__ == "__main__":
main()
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