1233 lines
48 KiB
Python
1233 lines
48 KiB
Python
import argparse, os, glob
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import pandas as pd
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import random
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import torch
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import torchvision
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import h5py
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import numpy as np
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import logging
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import einops
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import warnings
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import imageio
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import atexit
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from concurrent.futures import ThreadPoolExecutor
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from contextlib import nullcontext
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from typing import Optional, Dict, List, Any, Mapping
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from pytorch_lightning import seed_everything
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from omegaconf import OmegaConf
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from tqdm import tqdm
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from einops import rearrange, repeat
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from collections import OrderedDict
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from torch import nn
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from eval_utils import populate_queues
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from collections import deque
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from torch import Tensor
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from PIL import Image
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from unifolm_wma.models.samplers.ddim import DDIMSampler
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from unifolm_wma.utils.utils import instantiate_from_config
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# ========== Async I/O ==========
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_io_executor: Optional[ThreadPoolExecutor] = None
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_io_futures: List[Any] = []
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def _get_io_executor() -> ThreadPoolExecutor:
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global _io_executor
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if _io_executor is None:
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_io_executor = ThreadPoolExecutor(max_workers=2)
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return _io_executor
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def _flush_io():
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"""Wait for all pending async I/O to finish."""
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global _io_futures
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for fut in _io_futures:
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try:
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fut.result()
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except Exception as e:
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print(f">>> [async I/O] error: {e}")
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_io_futures.clear()
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atexit.register(_flush_io)
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def _save_results_sync(video_cpu: Tensor, filename: str, fps: int) -> None:
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"""Synchronous save on CPU tensor (runs in background thread)."""
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video = torch.clamp(video_cpu.float(), -1., 1.)
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n = video.shape[0]
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video = video.permute(2, 0, 1, 3, 4)
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frame_grids = [
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torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0)
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for framesheet in video
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]
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grid = torch.stack(frame_grids, dim=0)
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grid = (grid + 1.0) / 2.0
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grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
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torchvision.io.write_video(filename,
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grid,
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fps=fps,
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video_codec='h264',
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options={'crf': '10'})
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def save_results_async(video: Tensor, filename: str, fps: int = 8) -> None:
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"""Submit video saving to background thread pool."""
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video_cpu = video.detach().cpu()
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fut = _get_io_executor().submit(_save_results_sync, video_cpu, filename, fps)
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_io_futures.append(fut)
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# ========== Original Functions ==========
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def get_device_from_parameters(module: nn.Module) -> torch.device:
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"""Get a module's device by checking one of its parameters.
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Args:
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module (nn.Module): The model whose device is to be inferred.
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Returns:
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torch.device: The device of the model's parameters.
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"""
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return next(iter(module.parameters())).device
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def write_video(video_path: str, stacked_frames: list, fps: int) -> None:
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"""Save a list of frames to a video file.
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Args:
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video_path (str): Output path for the video.
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stacked_frames (list): List of image frames.
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fps (int): Frames per second for the video.
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"""
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore",
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"pkg_resources is deprecated as an API",
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category=DeprecationWarning)
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imageio.mimsave(video_path, stacked_frames, fps=fps)
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def get_filelist(data_dir: str, postfixes: list[str]) -> list[str]:
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"""Return sorted list of files in a directory matching specified postfixes.
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Args:
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data_dir (str): Directory path to search in.
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postfixes (list[str]): List of file extensions to match.
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Returns:
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list[str]: Sorted list of file paths.
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"""
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patterns = [
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os.path.join(data_dir, f"*.{postfix}") for postfix in postfixes
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]
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file_list = []
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for pattern in patterns:
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file_list.extend(glob.glob(pattern))
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file_list.sort()
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return file_list
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def _load_state_dict(model: nn.Module,
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state_dict: Mapping[str, torch.Tensor],
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strict: bool = True,
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assign: bool = False) -> None:
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if assign:
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try:
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model.load_state_dict(state_dict, strict=strict, assign=True)
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return
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except TypeError:
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warnings.warn(
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"load_state_dict(assign=True) not supported; "
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"falling back to copy load.")
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model.load_state_dict(state_dict, strict=strict)
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def load_model_checkpoint(model: nn.Module,
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ckpt: str,
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assign: bool | None = None,
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device: str | torch.device = "cpu") -> nn.Module:
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"""Load model weights from checkpoint file.
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Args:
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model (nn.Module): Model instance.
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ckpt (str): Path to the checkpoint file.
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assign (bool | None): Whether to preserve checkpoint tensor dtypes
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via load_state_dict(assign=True). If None, auto-enable when a
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casted checkpoint metadata is detected.
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device (str | torch.device): Target device for loaded tensors.
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Returns:
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nn.Module: Model with loaded weights.
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"""
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ckpt_data = torch.load(ckpt, map_location=device, mmap=True)
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use_assign = False
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if assign is not None:
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use_assign = assign
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elif isinstance(ckpt_data, Mapping) and "precision_metadata" in ckpt_data:
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use_assign = True
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if isinstance(ckpt_data, Mapping) and "state_dict" in ckpt_data:
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state_dict = ckpt_data["state_dict"]
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try:
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_load_state_dict(model, state_dict, strict=True, assign=use_assign)
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except Exception:
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new_pl_sd = OrderedDict()
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for k, v in state_dict.items():
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new_pl_sd[k] = v
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for k in list(new_pl_sd.keys()):
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if "framestride_embed" in k:
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new_key = k.replace("framestride_embed", "fps_embedding")
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new_pl_sd[new_key] = new_pl_sd[k]
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del new_pl_sd[k]
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_load_state_dict(model,
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new_pl_sd,
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strict=True,
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assign=use_assign)
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elif isinstance(ckpt_data, Mapping) and "module" in ckpt_data:
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new_pl_sd = OrderedDict()
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for key in ckpt_data['module'].keys():
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new_pl_sd[key[16:]] = ckpt_data['module'][key]
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_load_state_dict(model, new_pl_sd, strict=True, assign=use_assign)
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else:
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_load_state_dict(model,
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ckpt_data,
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strict=True,
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assign=use_assign)
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print('>>> model checkpoint loaded.')
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return model
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def maybe_cast_module(module: nn.Module | None,
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dtype: torch.dtype,
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label: str) -> None:
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if module is None:
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return
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try:
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param = next(module.parameters())
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except StopIteration:
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print(f">>> {label} has no parameters; skip cast")
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return
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if param.dtype == dtype:
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print(f">>> {label} already {dtype}; skip cast")
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return
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module.to(dtype=dtype)
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print(f">>> {label} cast to {dtype}")
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def save_casted_checkpoint(model: nn.Module,
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save_path: str,
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metadata: Optional[Dict[str, Any]] = None) -> None:
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if not save_path:
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return
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save_dir = os.path.dirname(save_path)
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if save_dir:
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os.makedirs(save_dir, exist_ok=True)
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cpu_state = {}
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for key, value in model.state_dict().items():
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if isinstance(value, torch.Tensor):
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cpu_state[key] = value.detach().to("cpu")
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else:
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cpu_state[key] = value
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payload: Dict[str, Any] = {"state_dict": cpu_state}
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if metadata:
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payload["precision_metadata"] = metadata
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torch.save(payload, save_path)
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print(f">>> Saved casted checkpoint to {save_path}")
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def _module_param_dtype(module: nn.Module | None) -> str:
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if module is None:
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return "None"
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dtype_counts: Dict[str, int] = {}
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for param in module.parameters():
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dtype_key = str(param.dtype)
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dtype_counts[dtype_key] = dtype_counts.get(dtype_key, 0) + param.numel()
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if not dtype_counts:
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return "no_params"
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if len(dtype_counts) == 1:
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return next(iter(dtype_counts))
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total = sum(dtype_counts.values())
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parts = []
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for dtype_key in sorted(dtype_counts.keys()):
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ratio = dtype_counts[dtype_key] / total
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parts.append(f"{dtype_key}={ratio:.1%}")
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return f"mixed({', '.join(parts)})"
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def log_inference_precision(model: nn.Module) -> None:
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device = "unknown"
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for param in model.parameters():
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device = str(param.device)
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break
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model_dtype = _module_param_dtype(model)
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print(f">>> inference precision: model={model_dtype}, device={device}")
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for attr in [
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"model", "first_stage_model", "cond_stage_model", "embedder",
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"image_proj_model"
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]:
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if hasattr(model, attr):
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submodule = getattr(model, attr)
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print(f">>> {attr} param dtype: {_module_param_dtype(submodule)}")
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print(
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">>> autocast gpu dtype default: "
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f"{torch.get_autocast_gpu_dtype()} "
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f"(enabled={torch.is_autocast_enabled()})")
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def is_inferenced(save_dir: str, filename: str) -> bool:
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"""Check if a given filename has already been processed and saved.
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Args:
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save_dir (str): Directory where results are saved.
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filename (str): Name of the file to check.
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Returns:
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bool: True if processed file exists, False otherwise.
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"""
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video_file = os.path.join(save_dir, "samples_separate",
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f"{filename[:-4]}_sample0.mp4")
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return os.path.exists(video_file)
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def save_results(video: Tensor, filename: str, fps: int = 8) -> None:
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"""Save video tensor to file using torchvision.
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Args:
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video (Tensor): Tensor of shape (B, C, T, H, W).
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filename (str): Output file path.
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fps (int, optional): Frames per second. Defaults to 8.
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"""
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video = video.detach().cpu()
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video = torch.clamp(video.float(), -1., 1.)
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n = video.shape[0]
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video = video.permute(2, 0, 1, 3, 4)
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frame_grids = [
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torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0)
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for framesheet in video
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]
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grid = torch.stack(frame_grids, dim=0)
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grid = (grid + 1.0) / 2.0
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grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1)
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torchvision.io.write_video(filename,
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grid,
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fps=fps,
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video_codec='h264',
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options={'crf': '10'})
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def get_init_frame_path(data_dir: str, sample: dict) -> str:
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"""Construct the init_frame path from directory and sample metadata.
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Args:
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data_dir (str): Base directory containing videos.
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sample (dict): Dictionary containing 'data_dir' and 'videoid'.
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Returns:
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str: Full path to the video file.
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"""
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rel_video_fp = os.path.join(sample['data_dir'],
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str(sample['videoid']) + '.png')
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full_image_fp = os.path.join(data_dir, 'images', rel_video_fp)
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return full_image_fp
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def get_transition_path(data_dir: str, sample: dict) -> str:
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"""Construct the full transition file path from directory and sample metadata.
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Args:
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data_dir (str): Base directory containing transition files.
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sample (dict): Dictionary containing 'data_dir' and 'videoid'.
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Returns:
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str: Full path to the HDF5 transition file.
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"""
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rel_transition_fp = os.path.join(sample['data_dir'],
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str(sample['videoid']) + '.h5')
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full_transition_fp = os.path.join(data_dir, 'transitions',
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rel_transition_fp)
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return full_transition_fp
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def prepare_init_input(start_idx: int,
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init_frame_path: str,
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transition_dict: dict[str, torch.Tensor],
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frame_stride: int,
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wma_data,
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video_length: int = 16,
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n_obs_steps: int = 2) -> dict[str, Tensor]:
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"""
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Extracts a structured sample from a video sequence including frames, states, and actions,
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along with properly padded observations and pre-processed tensors for model input.
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Args:
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start_idx (int): Starting frame index for the current clip.
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video: decord video instance.
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transition_dict (Dict[str, Tensor]): Dictionary containing tensors for 'action',
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'observation.state', 'action_type', 'state_type'.
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frame_stride (int): Temporal stride between sampled frames.
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wma_data: Object that holds configuration and utility functions like normalization,
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transformation, and resolution info.
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video_length (int, optional): Number of frames to sample from the video. Default is 16.
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n_obs_steps (int, optional): Number of historical steps for observations. Default is 2.
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"""
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indices = [start_idx + frame_stride * i for i in range(video_length)]
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init_frame = Image.open(init_frame_path).convert('RGB')
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init_frame = torch.tensor(np.array(init_frame)).unsqueeze(0).permute(
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3, 0, 1, 2).float()
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if start_idx < n_obs_steps - 1:
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state_indices = list(range(0, start_idx + 1))
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states = transition_dict['observation.state'][state_indices, :]
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num_padding = n_obs_steps - 1 - start_idx
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first_slice = states[0:1, :] # (t, d)
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padding = first_slice.repeat(num_padding, 1)
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states = torch.cat((padding, states), dim=0)
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else:
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state_indices = list(range(start_idx - n_obs_steps + 1, start_idx + 1))
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states = transition_dict['observation.state'][state_indices, :]
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actions = transition_dict['action'][indices, :]
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ori_state_dim = states.shape[-1]
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ori_action_dim = actions.shape[-1]
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frames_action_state_dict = {
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'action': actions,
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'observation.state': states,
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}
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frames_action_state_dict = wma_data.normalizer(frames_action_state_dict)
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frames_action_state_dict = wma_data.get_uni_vec(
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frames_action_state_dict,
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transition_dict['action_type'],
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transition_dict['state_type'],
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)
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if wma_data.spatial_transform is not None:
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init_frame = wma_data.spatial_transform(init_frame)
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init_frame = (init_frame / 255 - 0.5) * 2
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data = {
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'observation.image': init_frame,
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}
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data.update(frames_action_state_dict)
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return data, ori_state_dim, ori_action_dim
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|
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def get_latent_z(model, videos: Tensor) -> Tensor:
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"""
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Extracts latent features from a video batch using the model's first-stage encoder.
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Args:
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model: the world model.
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videos (Tensor): Input videos of shape [B, C, T, H, W].
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Returns:
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Tensor: Latent video tensor of shape [B, C, T, H, W].
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"""
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b, c, t, h, w = videos.shape
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x = rearrange(videos, 'b c t h w -> (b t) c h w')
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vae_ctx = nullcontext()
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if getattr(model, "vae_bf16", False) and model.device.type == "cuda":
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vae_ctx = torch.autocast("cuda", dtype=torch.bfloat16)
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with vae_ctx:
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z = model.encode_first_stage(x)
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z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t)
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return z
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|
|
|
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def preprocess_observation(
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model, observations: dict[str, np.ndarray]) -> dict[str, Tensor]:
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"""Convert environment observation to LeRobot format observation.
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Args:
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observation: Dictionary of observation batches from a Gym vector environment.
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Returns:
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Dictionary of observation batches with keys renamed to LeRobot format and values as tensors.
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"""
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# Map to expected inputs for the policy
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return_observations = {}
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|
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if isinstance(observations["pixels"], dict):
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imgs = {
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f"observation.images.{key}": img
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for key, img in observations["pixels"].items()
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}
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else:
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imgs = {"observation.images.top": observations["pixels"]}
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for imgkey, img in imgs.items():
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img = torch.from_numpy(img)
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|
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# Sanity check that images are channel last
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_, h, w, c = img.shape
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assert c < h and c < w, f"expect channel first images, but instead {img.shape}"
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|
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# Sanity check that images are uint8
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assert img.dtype == torch.uint8, f"expect torch.uint8, but instead {img.dtype=}"
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# Convert to channel first of type float32 in range [0,1]
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img = einops.rearrange(img, "b h w c -> b c h w").contiguous()
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img = img.type(torch.float32)
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return_observations[imgkey] = img
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|
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return_observations["observation.state"] = torch.from_numpy(
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observations["agent_pos"]).float()
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return_observations['observation.state'] = model.normalize_inputs({
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'observation.state':
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return_observations['observation.state'].to(model.device)
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})['observation.state']
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|
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return return_observations
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|
|
|
|
def _move_to_device(batch: Mapping[str, Any],
|
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device: torch.device) -> dict[str, Any]:
|
|
moved = {}
|
|
for key, value in batch.items():
|
|
if isinstance(value, torch.Tensor) and value.device != device:
|
|
moved[key] = value.to(device, non_blocking=True)
|
|
else:
|
|
moved[key] = value
|
|
return moved
|
|
|
|
|
|
def image_guided_synthesis_sim_mode(
|
|
model: torch.nn.Module,
|
|
prompts: list[str],
|
|
observation: dict,
|
|
noise_shape: tuple[int, int, int, int, int],
|
|
action_cond_step: int = 16,
|
|
n_samples: int = 1,
|
|
ddim_steps: int = 50,
|
|
ddim_eta: float = 1.0,
|
|
unconditional_guidance_scale: float = 1.0,
|
|
fs: int | None = None,
|
|
text_input: bool = True,
|
|
timestep_spacing: str = 'uniform',
|
|
guidance_rescale: float = 0.0,
|
|
sim_mode: bool = True,
|
|
diffusion_autocast_dtype: Optional[torch.dtype] = None,
|
|
**kwargs) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Performs image-guided video generation in a simulation-style mode with optional multimodal guidance (image, state, action, text).
|
|
|
|
Args:
|
|
model (torch.nn.Module): The diffusion-based generative model with multimodal conditioning.
|
|
prompts (list[str]): A list of textual prompts to guide the synthesis process.
|
|
observation (dict): A dictionary containing observed inputs including:
|
|
- 'observation.images.top': Tensor of shape [B, O, C, H, W] (top-down images)
|
|
- 'observation.state': Tensor of shape [B, O, D] (state vector)
|
|
- 'action': Tensor of shape [B, T, D] (action sequence)
|
|
noise_shape (tuple[int, int, int, int, int]): Shape of the latent variable to generate,
|
|
typically (B, C, T, H, W).
|
|
action_cond_step (int): Number of time steps where action conditioning is applied. Default is 16.
|
|
n_samples (int): Number of samples to generate (unused here, always generates 1). Default is 1.
|
|
ddim_steps (int): Number of DDIM sampling steps. Default is 50.
|
|
ddim_eta (float): DDIM eta parameter controlling the stochasticity. Default is 1.0.
|
|
unconditional_guidance_scale (float): Scale for classifier-free guidance. If 1.0, guidance is off.
|
|
fs (int | None): Frame index to condition on, broadcasted across the batch if specified. Default is None.
|
|
text_input (bool): Whether to use text prompt as conditioning. If False, uses empty strings. Default is True.
|
|
timestep_spacing (str): Timestep sampling method in DDIM sampler. Typically "uniform" or "linspace".
|
|
guidance_rescale (float): Guidance rescaling factor to mitigate overexposure from classifier-free guidance.
|
|
sim_mode (bool): Whether to perform world-model interaction or decision-making using the world-model.
|
|
diffusion_autocast_dtype (Optional[torch.dtype]): Autocast dtype for diffusion sampling (e.g., torch.bfloat16).
|
|
**kwargs: Additional arguments passed to the DDIM sampler.
|
|
|
|
Returns:
|
|
batch_variants (torch.Tensor): Predicted pixel-space video frames [B, C, T, H, W].
|
|
actions (torch.Tensor): Predicted action sequences [B, T, D] from diffusion decoding.
|
|
states (torch.Tensor): Predicted state sequences [B, T, D] from diffusion decoding.
|
|
"""
|
|
b, _, t, _, _ = noise_shape
|
|
ddim_sampler = getattr(model, "_ddim_sampler", None)
|
|
if ddim_sampler is None:
|
|
ddim_sampler = DDIMSampler(model)
|
|
model._ddim_sampler = ddim_sampler
|
|
batch_size = noise_shape[0]
|
|
|
|
fs = torch.tensor([fs] * batch_size, dtype=torch.long, device=model.device)
|
|
|
|
img = observation['observation.images.top'].permute(0, 2, 1, 3, 4)
|
|
cond_img = rearrange(img, 'b o c h w -> (b o) c h w')[-1:]
|
|
if getattr(model, "encoder_bf16", False) and model.device.type == "cuda":
|
|
if getattr(model, "encoder_mode", "autocast") == "autocast":
|
|
preprocess_ctx = torch.autocast("cuda", enabled=False)
|
|
with preprocess_ctx:
|
|
cond_img_fp32 = cond_img.float()
|
|
if hasattr(model.embedder, "preprocess"):
|
|
preprocessed = model.embedder.preprocess(cond_img_fp32)
|
|
else:
|
|
preprocessed = cond_img_fp32
|
|
|
|
if hasattr(model.embedder,
|
|
"encode_with_vision_transformer") and hasattr(
|
|
model.embedder, "preprocess"):
|
|
original_preprocess = model.embedder.preprocess
|
|
try:
|
|
model.embedder.preprocess = lambda x: x
|
|
with torch.autocast("cuda", dtype=torch.bfloat16):
|
|
cond_img_emb = model.embedder.encode_with_vision_transformer(
|
|
preprocessed)
|
|
finally:
|
|
model.embedder.preprocess = original_preprocess
|
|
else:
|
|
with torch.autocast("cuda", dtype=torch.bfloat16):
|
|
cond_img_emb = model.embedder(preprocessed)
|
|
else:
|
|
with torch.autocast("cuda", dtype=torch.bfloat16):
|
|
cond_img_emb = model.embedder(cond_img)
|
|
else:
|
|
cond_img_emb = model.embedder(cond_img)
|
|
|
|
if model.model.conditioning_key == 'hybrid':
|
|
z = get_latent_z(model, img.permute(0, 2, 1, 3, 4))
|
|
img_cat_cond = z[:, :, -1:, :, :]
|
|
img_cat_cond = repeat(img_cat_cond,
|
|
'b c t h w -> b c (repeat t) h w',
|
|
repeat=noise_shape[2])
|
|
cond = {"c_concat": [img_cat_cond]}
|
|
|
|
if not text_input:
|
|
prompts = [""] * batch_size
|
|
encoder_ctx = nullcontext()
|
|
if getattr(model, "encoder_bf16", False) and model.device.type == "cuda":
|
|
encoder_ctx = torch.autocast("cuda", dtype=torch.bfloat16)
|
|
with encoder_ctx:
|
|
cond_ins_emb = model.get_learned_conditioning(prompts)
|
|
target_dtype = cond_ins_emb.dtype
|
|
|
|
cond_img_emb = model._projector_forward(model.image_proj_model,
|
|
cond_img_emb, target_dtype)
|
|
|
|
cond_state_emb = model._projector_forward(
|
|
model.state_projector, observation['observation.state'],
|
|
target_dtype)
|
|
cond_state_emb = cond_state_emb + model.agent_state_pos_emb.to(
|
|
dtype=target_dtype)
|
|
|
|
cond_action_emb = model._projector_forward(
|
|
model.action_projector, observation['action'], target_dtype)
|
|
cond_action_emb = cond_action_emb + model.agent_action_pos_emb.to(
|
|
dtype=target_dtype)
|
|
|
|
if not sim_mode:
|
|
cond_action_emb = torch.zeros_like(cond_action_emb)
|
|
|
|
cond["c_crossattn"] = [
|
|
torch.cat(
|
|
[cond_state_emb, cond_action_emb, cond_ins_emb, cond_img_emb],
|
|
dim=1)
|
|
]
|
|
cond["c_crossattn_action"] = [
|
|
observation['observation.images.top'][:, :,
|
|
-model.n_obs_steps_acting:],
|
|
observation['observation.state'][:, -model.n_obs_steps_acting:],
|
|
sim_mode,
|
|
False,
|
|
]
|
|
|
|
uc = None
|
|
kwargs.update({"unconditional_conditioning_img_nonetext": None})
|
|
cond_mask = None
|
|
cond_z0 = None
|
|
|
|
if ddim_sampler is not None:
|
|
autocast_ctx = nullcontext()
|
|
if diffusion_autocast_dtype is not None and model.device.type == "cuda":
|
|
autocast_ctx = torch.autocast("cuda", dtype=diffusion_autocast_dtype)
|
|
with autocast_ctx:
|
|
samples, actions, states, intermedia = ddim_sampler.sample(
|
|
S=ddim_steps,
|
|
conditioning=cond,
|
|
batch_size=batch_size,
|
|
shape=noise_shape[1:],
|
|
verbose=False,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=uc,
|
|
eta=ddim_eta,
|
|
cfg_img=None,
|
|
mask=cond_mask,
|
|
x0=cond_z0,
|
|
fs=fs,
|
|
timestep_spacing=timestep_spacing,
|
|
guidance_rescale=guidance_rescale,
|
|
**kwargs)
|
|
|
|
# Reconstruct from latent to pixel space
|
|
if getattr(model, "vae_bf16", False):
|
|
if samples.dtype != torch.bfloat16:
|
|
samples = samples.to(dtype=torch.bfloat16)
|
|
vae_ctx = nullcontext()
|
|
if model.device.type == "cuda":
|
|
vae_ctx = torch.autocast("cuda", dtype=torch.bfloat16)
|
|
with vae_ctx:
|
|
batch_images = model.decode_first_stage(samples)
|
|
else:
|
|
if samples.dtype != torch.float32:
|
|
samples = samples.float()
|
|
batch_images = model.decode_first_stage(samples)
|
|
batch_variants = batch_images
|
|
|
|
return batch_variants, actions, states
|
|
|
|
|
|
def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
|
|
"""
|
|
Run inference pipeline on prompts and image inputs.
|
|
|
|
Args:
|
|
args (argparse.Namespace): Parsed command-line arguments.
|
|
gpu_num (int): Number of GPUs.
|
|
gpu_no (int): Index of the current GPU.
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
# Create inference dir
|
|
os.makedirs(args.savedir + '/inference', exist_ok=True)
|
|
|
|
# Load prompt
|
|
csv_path = os.path.join(args.prompt_dir, f"{args.dataset}.csv")
|
|
df = pd.read_csv(csv_path)
|
|
|
|
# Load config (always needed for data setup)
|
|
config = OmegaConf.load(args.config)
|
|
|
|
prepared_path = args.ckpt_path + ".prepared.pt"
|
|
if os.path.exists(prepared_path):
|
|
# ---- Fast path: load the fully-prepared model ----
|
|
print(f">>> Loading prepared model from {prepared_path} ...")
|
|
model = torch.load(prepared_path,
|
|
map_location=f"cuda:{gpu_no}",
|
|
weights_only=False,
|
|
mmap=True)
|
|
model.eval()
|
|
diffusion_autocast_dtype = (torch.bfloat16
|
|
if args.diffusion_dtype == "bf16"
|
|
else None)
|
|
print(f">>> Prepared model loaded.")
|
|
else:
|
|
# ---- Normal path: construct + checkpoint + casting ----
|
|
config['model']['params']['wma_config']['params'][
|
|
'use_checkpoint'] = False
|
|
model = instantiate_from_config(config.model)
|
|
model.perframe_ae = args.perframe_ae
|
|
|
|
assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!"
|
|
|
|
model = load_model_checkpoint(model, args.ckpt_path,
|
|
device=f"cuda:{gpu_no}")
|
|
model.eval()
|
|
model = model.cuda(gpu_no) # move residual buffers not in state_dict
|
|
print(f'>>> Load pre-trained model ...')
|
|
|
|
diffusion_autocast_dtype = None
|
|
if args.diffusion_dtype == "bf16":
|
|
maybe_cast_module(
|
|
model.model,
|
|
torch.bfloat16,
|
|
"diffusion backbone",
|
|
)
|
|
diffusion_autocast_dtype = torch.bfloat16
|
|
print(">>> diffusion backbone set to bfloat16")
|
|
|
|
if hasattr(model, "first_stage_model") and model.first_stage_model is not None:
|
|
vae_weight_dtype = torch.bfloat16 if args.vae_dtype == "bf16" else torch.float32
|
|
maybe_cast_module(
|
|
model.first_stage_model,
|
|
vae_weight_dtype,
|
|
"VAE",
|
|
)
|
|
model.vae_bf16 = args.vae_dtype == "bf16"
|
|
print(f">>> VAE dtype set to {args.vae_dtype}")
|
|
|
|
# --- VAE performance optimizations ---
|
|
if hasattr(model, "first_stage_model") and model.first_stage_model is not None:
|
|
vae = model.first_stage_model
|
|
|
|
# torch.compile: fuses GroupNorm+SiLU, conv chains, etc.
|
|
if args.vae_compile:
|
|
vae.decoder = torch.compile(vae.decoder, mode="reduce-overhead")
|
|
vae.encoder = torch.compile(vae.encoder, mode="reduce-overhead")
|
|
print(">>> VAE encoder/decoder compiled with torch.compile (reduce-overhead)")
|
|
|
|
# Batch decode size
|
|
vae_decode_bs = args.vae_decode_bs if args.vae_decode_bs > 0 else 9999
|
|
model.vae_decode_bs = vae_decode_bs
|
|
model.vae_encode_bs = vae_decode_bs
|
|
if args.vae_decode_bs > 0:
|
|
print(f">>> VAE encode/decode batch size set to {args.vae_decode_bs}")
|
|
else:
|
|
print(">>> VAE encode/decode batch size: all frames at once")
|
|
|
|
encoder_mode = args.encoder_mode
|
|
encoder_bf16 = encoder_mode in ("autocast", "bf16_full")
|
|
encoder_weight_dtype = torch.bfloat16 if encoder_mode == "bf16_full" else torch.float32
|
|
if hasattr(model, "cond_stage_model") and model.cond_stage_model is not None:
|
|
maybe_cast_module(
|
|
model.cond_stage_model,
|
|
encoder_weight_dtype,
|
|
"cond_stage_model",
|
|
)
|
|
if hasattr(model, "embedder") and model.embedder is not None:
|
|
maybe_cast_module(
|
|
model.embedder,
|
|
encoder_weight_dtype,
|
|
"embedder",
|
|
)
|
|
model.encoder_bf16 = encoder_bf16
|
|
model.encoder_mode = encoder_mode
|
|
print(
|
|
f">>> encoder mode set to {encoder_mode} (weights={encoder_weight_dtype})"
|
|
)
|
|
|
|
projector_mode = args.projector_mode
|
|
projector_bf16 = projector_mode in ("autocast", "bf16_full")
|
|
projector_weight_dtype = torch.bfloat16 if projector_mode == "bf16_full" else torch.float32
|
|
if hasattr(model, "image_proj_model") and model.image_proj_model is not None:
|
|
maybe_cast_module(
|
|
model.image_proj_model,
|
|
projector_weight_dtype,
|
|
"image_proj_model",
|
|
)
|
|
if hasattr(model, "state_projector") and model.state_projector is not None:
|
|
maybe_cast_module(
|
|
model.state_projector,
|
|
projector_weight_dtype,
|
|
"state_projector",
|
|
)
|
|
if hasattr(model, "action_projector") and model.action_projector is not None:
|
|
maybe_cast_module(
|
|
model.action_projector,
|
|
projector_weight_dtype,
|
|
"action_projector",
|
|
)
|
|
if hasattr(model, "projector_bf16"):
|
|
model.projector_bf16 = projector_bf16
|
|
model.projector_mode = projector_mode
|
|
print(
|
|
f">>> projector mode set to {projector_mode} (weights={projector_weight_dtype})"
|
|
)
|
|
|
|
log_inference_precision(model)
|
|
|
|
if args.export_casted_ckpt:
|
|
metadata = {
|
|
"diffusion_dtype": args.diffusion_dtype,
|
|
"vae_dtype": args.vae_dtype,
|
|
"encoder_mode": args.encoder_mode,
|
|
"projector_mode": args.projector_mode,
|
|
"perframe_ae": args.perframe_ae,
|
|
}
|
|
save_casted_checkpoint(model, args.export_casted_ckpt, metadata)
|
|
if args.export_only:
|
|
print(">>> export_only set; skipping inference.")
|
|
return
|
|
|
|
# Save prepared model for fast loading next time
|
|
if prepared_path:
|
|
print(f">>> Saving prepared model to {prepared_path} ...")
|
|
torch.save(model, prepared_path)
|
|
print(f">>> Prepared model saved ({os.path.getsize(prepared_path) / 1024**3:.1f} GB).")
|
|
|
|
# Build normalizer (always needed, independent of model loading path)
|
|
logging.info("***** Configing Data *****")
|
|
data = instantiate_from_config(config.data)
|
|
data.setup()
|
|
print(">>> Dataset is successfully loaded ...")
|
|
device = get_device_from_parameters(model)
|
|
|
|
# Run over data
|
|
assert (args.height % 16 == 0) and (
|
|
args.width % 16
|
|
== 0), "Error: image size [h,w] should be multiples of 16!"
|
|
assert args.bs == 1, "Current implementation only support [batch size = 1]!"
|
|
|
|
# Get latent noise shape
|
|
h, w = args.height // 8, args.width // 8
|
|
channels = model.model.diffusion_model.out_channels
|
|
n_frames = args.video_length
|
|
print(f'>>> Generate {n_frames} frames under each generation ...')
|
|
noise_shape = [args.bs, channels, n_frames, h, w]
|
|
|
|
# Start inference
|
|
for idx in range(0, len(df)):
|
|
sample = df.iloc[idx]
|
|
|
|
# Got initial frame path
|
|
init_frame_path = get_init_frame_path(args.prompt_dir, sample)
|
|
ori_fps = float(sample['fps'])
|
|
|
|
video_save_dir = args.savedir + f"/inference/sample_{sample['videoid']}"
|
|
os.makedirs(video_save_dir, exist_ok=True)
|
|
os.makedirs(video_save_dir + '/dm', exist_ok=True)
|
|
os.makedirs(video_save_dir + '/wm', exist_ok=True)
|
|
|
|
# Load transitions to get the initial state later
|
|
transition_path = get_transition_path(args.prompt_dir, sample)
|
|
with h5py.File(transition_path, 'r') as h5f:
|
|
transition_dict = {}
|
|
for key in h5f.keys():
|
|
transition_dict[key] = torch.tensor(h5f[key][()])
|
|
for key in h5f.attrs.keys():
|
|
transition_dict[key] = h5f.attrs[key]
|
|
|
|
# If many, test various frequence control and world-model generation
|
|
for fs in args.frame_stride:
|
|
|
|
# For saving imagens in policy
|
|
sample_save_dir = f'{video_save_dir}/dm/{fs}'
|
|
os.makedirs(sample_save_dir, exist_ok=True)
|
|
# For saving environmental changes in world-model
|
|
sample_save_dir = f'{video_save_dir}/wm/{fs}'
|
|
os.makedirs(sample_save_dir, exist_ok=True)
|
|
# For collecting interaction videos
|
|
wm_video = []
|
|
# Initialize observation queues
|
|
cond_obs_queues = {
|
|
"observation.images.top":
|
|
deque(maxlen=model.n_obs_steps_imagen),
|
|
"observation.state": deque(maxlen=model.n_obs_steps_imagen),
|
|
"action": deque(maxlen=args.video_length),
|
|
}
|
|
|
|
# Obtain initial frame and state
|
|
start_idx = 0
|
|
model_input_fs = ori_fps // fs
|
|
batch, ori_state_dim, ori_action_dim = prepare_init_input(
|
|
start_idx,
|
|
init_frame_path,
|
|
transition_dict,
|
|
fs,
|
|
data.test_datasets[args.dataset],
|
|
n_obs_steps=model.n_obs_steps_imagen)
|
|
observation = {
|
|
'observation.images.top':
|
|
batch['observation.image'].permute(1, 0, 2,
|
|
3)[-1].unsqueeze(0),
|
|
'observation.state':
|
|
batch['observation.state'][-1].unsqueeze(0),
|
|
'action':
|
|
torch.zeros_like(batch['action'][-1]).unsqueeze(0)
|
|
}
|
|
observation = _move_to_device(observation, device)
|
|
# Update observation queues
|
|
cond_obs_queues = populate_queues(cond_obs_queues, observation)
|
|
|
|
# Multi-round interaction with the world-model
|
|
for itr in tqdm(range(args.n_iter)):
|
|
log_every = max(1, args.step_log_every)
|
|
log_step = (itr % log_every == 0)
|
|
|
|
# Get observation
|
|
observation = {
|
|
'observation.images.top':
|
|
torch.stack(list(
|
|
cond_obs_queues['observation.images.top']),
|
|
dim=1).permute(0, 2, 1, 3, 4),
|
|
'observation.state':
|
|
torch.stack(list(cond_obs_queues['observation.state']),
|
|
dim=1),
|
|
'action':
|
|
torch.stack(list(cond_obs_queues['action']), dim=1),
|
|
}
|
|
observation = _move_to_device(observation, device)
|
|
|
|
# Use world-model in policy to generate action
|
|
if log_step:
|
|
print(f'>>> Step {itr}: generating actions ...')
|
|
pred_videos_0, pred_actions, _ = image_guided_synthesis_sim_mode(
|
|
model,
|
|
sample['instruction'],
|
|
observation,
|
|
noise_shape,
|
|
action_cond_step=args.exe_steps,
|
|
ddim_steps=args.ddim_steps,
|
|
ddim_eta=args.ddim_eta,
|
|
unconditional_guidance_scale=args.
|
|
unconditional_guidance_scale,
|
|
fs=model_input_fs,
|
|
timestep_spacing=args.timestep_spacing,
|
|
guidance_rescale=args.guidance_rescale,
|
|
sim_mode=False,
|
|
diffusion_autocast_dtype=diffusion_autocast_dtype)
|
|
|
|
# Update future actions in the observation queues
|
|
for act_idx in range(len(pred_actions[0])):
|
|
obs_update = {'action': pred_actions[0][act_idx:act_idx + 1]}
|
|
obs_update['action'][:, ori_action_dim:] = 0.0
|
|
cond_obs_queues = populate_queues(cond_obs_queues,
|
|
obs_update)
|
|
|
|
# Collect data for interacting the world-model using the predicted actions
|
|
observation = {
|
|
'observation.images.top':
|
|
torch.stack(list(
|
|
cond_obs_queues['observation.images.top']),
|
|
dim=1).permute(0, 2, 1, 3, 4),
|
|
'observation.state':
|
|
torch.stack(list(cond_obs_queues['observation.state']),
|
|
dim=1),
|
|
'action':
|
|
torch.stack(list(cond_obs_queues['action']), dim=1),
|
|
}
|
|
observation = _move_to_device(observation, device)
|
|
|
|
# Interaction with the world-model
|
|
if log_step:
|
|
print(f'>>> Step {itr}: interacting with world model ...')
|
|
pred_videos_1, _, pred_states = image_guided_synthesis_sim_mode(
|
|
model,
|
|
"",
|
|
observation,
|
|
noise_shape,
|
|
action_cond_step=args.exe_steps,
|
|
ddim_steps=args.ddim_steps,
|
|
ddim_eta=args.ddim_eta,
|
|
unconditional_guidance_scale=args.
|
|
unconditional_guidance_scale,
|
|
fs=model_input_fs,
|
|
text_input=False,
|
|
timestep_spacing=args.timestep_spacing,
|
|
guidance_rescale=args.guidance_rescale,
|
|
diffusion_autocast_dtype=diffusion_autocast_dtype)
|
|
|
|
for step_idx in range(args.exe_steps):
|
|
obs_update = {
|
|
'observation.images.top':
|
|
pred_videos_1[0][:, step_idx:step_idx + 1].permute(1, 0, 2, 3),
|
|
'observation.state':
|
|
torch.zeros_like(pred_states[0][step_idx:step_idx + 1]) if
|
|
args.zero_pred_state else pred_states[0][step_idx:step_idx + 1],
|
|
'action':
|
|
torch.zeros_like(pred_actions[0][-1:])
|
|
}
|
|
obs_update['observation.state'][:, ori_state_dim:] = 0.0
|
|
cond_obs_queues = populate_queues(cond_obs_queues,
|
|
obs_update)
|
|
|
|
# Save the imagen videos for decision-making (async)
|
|
sample_video_file = f'{video_save_dir}/dm/{fs}/itr-{itr}.mp4'
|
|
save_results_async(pred_videos_0,
|
|
sample_video_file,
|
|
fps=args.save_fps)
|
|
# Save videos environment changes via world-model interaction
|
|
sample_video_file = f'{video_save_dir}/wm/{fs}/itr-{itr}.mp4'
|
|
save_results_async(pred_videos_1,
|
|
sample_video_file,
|
|
fps=args.save_fps)
|
|
|
|
print('>' * 24)
|
|
# Collect the result of world-model interactions
|
|
wm_video.append(pred_videos_1[:, :, :args.exe_steps].cpu())
|
|
|
|
full_video = torch.cat(wm_video, dim=2)
|
|
sample_full_video_file = f"{video_save_dir}/../{sample['videoid']}_full_fs{fs}.mp4"
|
|
save_results_async(full_video, sample_full_video_file, fps=args.save_fps)
|
|
|
|
# Wait for all async I/O to complete
|
|
_flush_io()
|
|
|
|
|
|
def get_parser():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--savedir",
|
|
type=str,
|
|
default=None,
|
|
help="Path to save the results.")
|
|
parser.add_argument("--ckpt_path",
|
|
type=str,
|
|
default=None,
|
|
help="Path to the model checkpoint.")
|
|
parser.add_argument("--config",
|
|
type=str,
|
|
help="Path to the model checkpoint.")
|
|
parser.add_argument(
|
|
"--prompt_dir",
|
|
type=str,
|
|
default=None,
|
|
help="Directory containing videos and corresponding prompts.")
|
|
parser.add_argument("--dataset",
|
|
type=str,
|
|
default=None,
|
|
help="the name of dataset to test")
|
|
parser.add_argument(
|
|
"--ddim_steps",
|
|
type=int,
|
|
default=50,
|
|
help="Number of DDIM steps. If non-positive, DDPM is used instead.")
|
|
parser.add_argument(
|
|
"--ddim_eta",
|
|
type=float,
|
|
default=1.0,
|
|
help="Eta for DDIM sampling. Set to 0.0 for deterministic results.")
|
|
parser.add_argument("--bs",
|
|
type=int,
|
|
default=1,
|
|
help="Batch size for inference. Must be 1.")
|
|
parser.add_argument("--height",
|
|
type=int,
|
|
default=320,
|
|
help="Height of the generated images in pixels.")
|
|
parser.add_argument("--width",
|
|
type=int,
|
|
default=512,
|
|
help="Width of the generated images in pixels.")
|
|
parser.add_argument(
|
|
"--frame_stride",
|
|
type=int,
|
|
nargs='+',
|
|
required=True,
|
|
help=
|
|
"frame stride control for 256 model (larger->larger motion), FPS control for 512 or 1024 model (smaller->larger motion)"
|
|
)
|
|
parser.add_argument(
|
|
"--unconditional_guidance_scale",
|
|
type=float,
|
|
default=1.0,
|
|
help="Scale for classifier-free guidance during sampling.")
|
|
parser.add_argument("--seed",
|
|
type=int,
|
|
default=123,
|
|
help="Random seed for reproducibility.")
|
|
parser.add_argument("--video_length",
|
|
type=int,
|
|
default=16,
|
|
help="Number of frames in the generated video.")
|
|
parser.add_argument("--num_generation",
|
|
type=int,
|
|
default=1,
|
|
help="seed for seed_everything")
|
|
parser.add_argument(
|
|
"--timestep_spacing",
|
|
type=str,
|
|
default="uniform",
|
|
help=
|
|
"Strategy for timestep scaling. See Table 2 in the paper: 'Common Diffusion Noise Schedules and Sample Steps are Flawed' (https://huggingface.co/papers/2305.08891)."
|
|
)
|
|
parser.add_argument(
|
|
"--guidance_rescale",
|
|
type=float,
|
|
default=0.0,
|
|
help=
|
|
"Rescale factor for guidance as discussed in 'Common Diffusion Noise Schedules and Sample Steps are Flawed' (https://huggingface.co/papers/2305.08891)."
|
|
)
|
|
parser.add_argument(
|
|
"--perframe_ae",
|
|
action='store_true',
|
|
default=False,
|
|
help=
|
|
"Use per-frame autoencoder decoding to reduce GPU memory usage. Recommended for models with resolutions like 576x1024."
|
|
)
|
|
parser.add_argument(
|
|
"--diffusion_dtype",
|
|
type=str,
|
|
choices=["fp32", "bf16"],
|
|
default="fp32",
|
|
help="Dtype for diffusion backbone weights and sampling autocast."
|
|
)
|
|
parser.add_argument(
|
|
"--projector_mode",
|
|
type=str,
|
|
choices=["fp32", "autocast", "bf16_full"],
|
|
default="fp32",
|
|
help=
|
|
"Projector precision mode for image/state/action projectors: "
|
|
"fp32=full fp32, autocast=fp32 weights + bf16 autocast in forward, "
|
|
"bf16_full=bf16 weights + bf16 forward."
|
|
)
|
|
parser.add_argument(
|
|
"--encoder_mode",
|
|
type=str,
|
|
choices=["fp32", "autocast", "bf16_full"],
|
|
default="fp32",
|
|
help=
|
|
"Encoder precision mode for cond_stage_model/embedder: "
|
|
"fp32=full fp32, autocast=fp32 weights + bf16 autocast in forward, "
|
|
"bf16_full=bf16 weights + bf16 forward."
|
|
)
|
|
parser.add_argument(
|
|
"--vae_dtype",
|
|
type=str,
|
|
choices=["fp32", "bf16"],
|
|
default="fp32",
|
|
help="Dtype for VAE/first_stage_model weights and forward autocast."
|
|
)
|
|
parser.add_argument(
|
|
"--vae_compile",
|
|
action='store_true',
|
|
default=False,
|
|
help="Apply torch.compile to VAE decoder for kernel fusion."
|
|
)
|
|
parser.add_argument(
|
|
"--vae_decode_bs",
|
|
type=int,
|
|
default=0,
|
|
help="VAE decode batch size (0=all frames at once). Reduces kernel launch overhead."
|
|
)
|
|
parser.add_argument(
|
|
"--export_casted_ckpt",
|
|
type=str,
|
|
default=None,
|
|
help=
|
|
"Save a checkpoint after applying precision settings (mixed dtypes preserved)."
|
|
)
|
|
parser.add_argument(
|
|
"--export_only",
|
|
action='store_true',
|
|
default=False,
|
|
help="Exit after exporting the casted checkpoint."
|
|
)
|
|
parser.add_argument(
|
|
"--step_log_every",
|
|
type=int,
|
|
default=1,
|
|
help="Print per-iteration step logs every N iterations."
|
|
)
|
|
parser.add_argument(
|
|
"--n_action_steps",
|
|
type=int,
|
|
default=16,
|
|
help="num of samples per prompt",
|
|
)
|
|
parser.add_argument(
|
|
"--exe_steps",
|
|
type=int,
|
|
default=16,
|
|
help="num of samples to execute",
|
|
)
|
|
parser.add_argument(
|
|
"--n_iter",
|
|
type=int,
|
|
default=40,
|
|
help="num of iteration to interact with the world model",
|
|
)
|
|
parser.add_argument("--zero_pred_state",
|
|
action='store_true',
|
|
default=False,
|
|
help="not using the predicted states as comparison")
|
|
parser.add_argument("--save_fps",
|
|
type=int,
|
|
default=8,
|
|
help="fps for the saving video")
|
|
return parser
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = get_parser()
|
|
args = parser.parse_args()
|
|
seed = args.seed
|
|
if seed < 0:
|
|
seed = random.randint(0, 2**31)
|
|
seed_everything(seed)
|
|
|
|
rank, gpu_num = 0, 1
|
|
run_inference(args, gpu_num, rank)
|