File size: 9,069 Bytes
ec0a9aa | 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 | from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
import einops
import imageio.v2 as imageio
import numpy as np
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from finetuning.config import FinetuneConfig, add_config_arguments, config_from_args
from finetuning.data.dataset_robo_ctrl_world import RoboCtrlWorldDataset
from finetuning.models.ctrl_world_robo import CtrlWorldRobo, load_checkpoint_flexible
def save_checkpoint(accelerator: Accelerator, model: CtrlWorldRobo, output_dir: Path, step: int) -> None:
if not accelerator.is_main_process:
return
output_dir.mkdir(parents=True, exist_ok=True)
unwrapped = accelerator.unwrap_model(model)
ckpt = unwrapped.state_dict()
step_path = output_dir / f"checkpoint-{step}.pt"
latest_path = output_dir / "latest.pt"
torch.save(ckpt, step_path)
latest_path.unlink(missing_ok=True)
try:
os.symlink(step_path.name, latest_path)
except OSError:
torch.save(ckpt, latest_path)
(output_dir / "latest_checkpoint.txt").write_text(str(step_path.name), encoding="utf-8")
@torch.no_grad()
def decode_stacked_latents(vae, latents: torch.Tensor, cfg: FinetuneConfig, max_frames: int | None = None) -> np.ndarray:
# latents: [T, C, V*H, W]
if max_frames is not None:
latents = latents[:max_frames]
slot_h = cfg.latent_slot_height
views = einops.rearrange(latents, "t c (v h) w -> v t c h w", v=cfg.n_views, h=slot_h)
flat = einops.rearrange(views, "v t c h w -> (v t) c h w").to(vae.device)
decoded = []
for i in range(0, flat.shape[0], cfg.decode_chunk_size):
chunk = flat[i : i + cfg.decode_chunk_size] / vae.config.scaling_factor
decoded.append(vae.decode(chunk, num_frames=chunk.shape[0]).sample.detach().cpu())
video = torch.cat(decoded, dim=0)
video = einops.rearrange(video, "(v t) c h w -> v t h w c", v=cfg.n_views)
video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(torch.uint8).numpy()
frames = []
for t in range(video.shape[1]):
view_imgs = [video[v, t] for v in range(cfg.n_views)]
if cfg.n_views == 4:
top = np.concatenate(view_imgs[:2], axis=1)
bottom = np.concatenate(view_imgs[2:4], axis=1)
frames.append(np.concatenate([top, bottom], axis=0))
else:
frames.append(np.concatenate(view_imgs, axis=1))
return np.stack(frames)
@torch.no_grad()
def save_validation_sample(
accelerator: Accelerator,
model: CtrlWorldRobo,
batch: dict,
cfg: FinetuneConfig,
output_dir: Path,
step: int,
) -> None:
if not accelerator.is_main_process:
return
unwrapped = accelerator.unwrap_model(model)
unwrapped.eval()
device = accelerator.device
latents = batch["latent"][:1].to(device)
actions = batch["action"][:1].to(device)
texts = [batch["text"][0]] if cfg.text_cond else None
embodiment_id = batch["embodiment_id"][:1].to(device) if "embodiment_id" in batch else None
history = latents[:, : cfg.num_history]
current = latents[:, cfg.num_history]
try:
_, pred_future = unwrapped.generate_latents(
current,
history,
actions,
texts,
embodiment_id=embodiment_id,
output_type="latent",
)
pred_full = torch.cat([history, pred_future], dim=1)[0].float()
gt_full = latents[0].float()
pred_video = decode_stacked_latents(unwrapped.vae, pred_full, cfg)
gt_video = decode_stacked_latents(unwrapped.vae, gt_full, cfg)
comparison = np.concatenate([gt_video, pred_video], axis=1)
sample_dir = output_dir / "samples"
sample_dir.mkdir(parents=True, exist_ok=True)
imageio.mimsave(sample_dir / f"step_{step:08d}.mp4", comparison, fps=cfg.fps, macro_block_size=1)
except Exception as exc:
sample_dir = output_dir / "samples"
sample_dir.mkdir(parents=True, exist_ok=True)
torch.save({"error": str(exc), "batch": {k: str(v) for k, v in batch.items() if k not in {"latent", "action"}}}, sample_dir / f"step_{step:08d}_failed.pt")
finally:
unwrapped.train()
def main() -> None:
parser = argparse.ArgumentParser(description="Train a RoboCOIN multiview Ctrl-World style world model.")
add_config_arguments(parser)
parser.add_argument("--resume", default=None, help="Checkpoint to resume/load. Defaults to cfg.ckpt_path.")
parser.add_argument("--no-sample-video", action="store_true")
args = parser.parse_args()
cfg = config_from_args(args)
os.environ.setdefault("WANDB_MODE", "offline")
logger = get_logger(__name__, log_level="INFO")
set_seed(cfg.seed)
accelerator = Accelerator(
gradient_accumulation_steps=cfg.gradient_accumulation_steps,
mixed_precision=cfg.mixed_precision,
log_with="wandb",
project_dir=cfg.output_dir,
)
train_dataset = RoboCtrlWorldDataset(cfg, mode="train")
val_dataset = RoboCtrlWorldDataset(cfg, mode="val")
train_loader = DataLoader(
train_dataset,
batch_size=cfg.train_batch_size,
shuffle=cfg.shuffle,
num_workers=cfg.num_workers,
pin_memory=True,
)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0)
model = CtrlWorldRobo(cfg)
resume_path = args.resume or cfg.ckpt_path or cfg.init_ckpt_path
if resume_path:
missing, unexpected = load_checkpoint_flexible(model, resume_path)
logger.info(f"Loaded compatible weights from {resume_path}; missing/skipped={len(missing)} unexpected={len(unexpected)}")
optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate)
model, optimizer, train_loader, val_loader = accelerator.prepare(model, optimizer, train_loader, val_loader)
output_dir = Path(cfg.output_dir)
if accelerator.is_main_process:
output_dir.mkdir(parents=True, exist_ok=True)
(output_dir / "config.json").write_text(json.dumps(cfg.to_dict(), indent=2), encoding="utf-8")
try:
accelerator.init_trackers("ctrl_world_robo", config=cfg.to_dict())
except Exception:
pass
total_batch_size = cfg.train_batch_size * accelerator.num_processes * cfg.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Train windows = {len(train_dataset)}")
logger.info(f" Val windows = {len(val_dataset)}")
logger.info(f" Total batch size = {total_batch_size}")
logger.info(f" Max steps = {cfg.max_train_steps}")
global_step = 0
train_loss = 0.0
val_iter = iter(val_loader)
progress = tqdm(range(cfg.max_train_steps), disable=not accelerator.is_local_main_process)
while global_step < cfg.max_train_steps:
for batch in train_loader:
with accelerator.accumulate(model):
with accelerator.autocast():
loss, _ = model(batch)
avg_loss = accelerator.gather(loss.detach().repeat(cfg.train_batch_size)).mean()
train_loss += avg_loss.item() / cfg.gradient_accumulation_steps
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), cfg.max_grad_norm)
optimizer.step()
optimizer.zero_grad()
if accelerator.sync_gradients:
global_step += 1
progress.update(1)
if global_step % cfg.log_steps == 0:
value = train_loss / cfg.log_steps
progress.set_postfix({"loss": value})
accelerator.log({"train_loss": value}, step=global_step)
train_loss = 0.0
if global_step % cfg.checkpointing_steps == 0:
save_checkpoint(accelerator, model, output_dir, global_step)
if global_step % cfg.validation_steps == 0:
try:
val_batch = next(val_iter)
except StopIteration:
val_iter = iter(val_loader)
val_batch = next(val_iter)
with torch.no_grad(), accelerator.autocast():
val_loss, _ = model(val_batch)
val_loss_mean = accelerator.gather(val_loss.detach().repeat(1)).mean().item()
accelerator.log({"val_loss": val_loss_mean}, step=global_step)
if not args.no_sample_video:
save_validation_sample(accelerator, model, val_batch, cfg, output_dir, global_step)
if global_step >= cfg.max_train_steps:
break
save_checkpoint(accelerator, model, output_dir, global_step)
accelerator.end_training()
if __name__ == "__main__":
main()
|