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#!/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() | |