105 lines
3.0 KiB
Python
105 lines
3.0 KiB
Python
import logging
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import threading
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from pathlib import Path
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import einops
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import hydra
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import imageio
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import torch
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from lerobot.common.datasets.factory import make_dataset
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from lerobot.common.logger import log_output_dir
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from lerobot.common.utils import init_logging
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NUM_EPISODES_TO_RENDER = 50
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MAX_NUM_STEPS = 1000
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FIRST_FRAME = 0
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@hydra.main(version_base=None, config_name="default", config_path="../configs")
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def visualize_dataset_cli(cfg: dict):
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visualize_dataset(cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().runtime.output_dir)
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def cat_and_write_video(video_path, frames, fps):
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# Expects images in [0, 255].
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frames = torch.cat(frames)
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assert frames.dtype == torch.uint8
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frames = einops.rearrange(frames, "b c h w -> b h w c").numpy()
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imageio.mimsave(video_path, frames, fps=fps)
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def visualize_dataset(cfg: dict, out_dir=None):
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if out_dir is None:
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raise NotImplementedError()
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init_logging()
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log_output_dir(out_dir)
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logging.info("make_dataset")
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dataset = make_dataset(
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cfg,
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# remove all transformations such as rescale images from [0,255] to [0,1] or normalization
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normalize=False,
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)
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logging.info("Start rendering episodes from offline buffer")
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video_paths = render_dataset(dataset, out_dir, MAX_NUM_STEPS * NUM_EPISODES_TO_RENDER, cfg.fps)
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for video_path in video_paths:
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logging.info(video_path)
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def render_dataset(dataset, out_dir, max_num_episodes):
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out_dir = Path(out_dir)
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video_paths = []
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threads = []
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dataloader = torch.utils.data.DataLoader(
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dataset,
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num_workers=4,
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batch_size=1,
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shuffle=False,
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)
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dl_iter = iter(dataloader)
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num_episodes = len(dataset.data_ids_per_episode)
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for ep_id in range(min(max_num_episodes, num_episodes)):
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logging.info(f"Rendering episode {ep_id}")
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frames = {}
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for _ in dataset.data_ids_per_episode[ep_id]:
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item = next(dl_iter)
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for im_key in dataset.image_keys:
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# when first frame of episode, initialize frames dict
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if im_key not in frames:
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frames[im_key] = []
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# add current frame to list of frames to render
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frames[im_key].append(item[im_key])
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out_dir.mkdir(parents=True, exist_ok=True)
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for im_key in dataset.image_keys:
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if len(dataset.image_keys) > 1:
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im_name = im_key.replace("observation.images.", "")
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video_path = out_dir / f"episode_{ep_id}_{im_name}.mp4"
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else:
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video_path = out_dir / f"episode_{ep_id}.mp4"
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video_paths.append(video_path)
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thread = threading.Thread(
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target=cat_and_write_video,
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args=(str(video_path), frames[im_key], dataset.fps),
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)
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thread.start()
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threads.append(thread)
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for thread in threads:
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thread.join()
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logging.info("End of visualize_dataset")
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return video_paths
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if __name__ == "__main__":
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visualize_dataset_cli()
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