#!/usr/bin/env python3 from __future__ import annotations import rerun as rr from datasets import load_dataset from PIL import Image from tqdm import tqdm def log_dataset_to_rerun(dataset) -> None: # Special time-like columns TIME_LIKE = {"index", "frame_id", "timestamp"} # Ignore these columns IGNORE = {"episode_data_index_from", "episode_data_index_to", "episode_id"} num_rows = len(dataset) for row_nr in tqdm(range(num_rows)): row = dataset[row_nr] # Handle time-like columns first, since they set a state (time is an index in Rerun): for column_name in TIME_LIKE: if column_name in row: cell = row[column_name] if isinstance(cell, int): rr.set_time_sequence(column_name, cell) elif isinstance(cell, float): rr.set_time_seconds(column_name, cell) # assume seconds else: print(f"Unknown time-like column {column_name} with value {cell}") # Now log actual data columns for column_name in dataset.column_names: if column_name in TIME_LIKE or column_name in IGNORE: continue cell = row[column_name] if isinstance(cell, Image.Image): rr.log(column_name, rr.Image(cell)) elif isinstance(cell, list): rr.log(column_name, rr.BarChart(cell)) elif isinstance(cell, float) or isinstance(cell, int): rr.log(column_name, rr.Scalar(cell)) else: rr.log(column_name, rr.TextDocument(str(cell))) def main(): print("Loading dataset…") # dataset = load_dataset("lerobot/pusht", split="train") dataset = load_dataset("lerobot/aloha_sim_transfer_cube_human", split="train") print("Selecting specific episode…") ds_subset = dataset.filter(lambda frame: frame["episode_id"] == 3) print("Starting Rerun…") rr.init("rerun_example_lerobot", spawn=True) print("Logging to Rerun…") log_dataset_to_rerun(ds_subset) if __name__ == "__main__": main()