data/compute_dataset_stat_hdf5.py

"""
This file will compute the min, max, mean, and standard deviation of each datasets 
in `pretrain_datasets.json` or `pretrain_datasets.json`.
"""

import json
import argparse

import numpy as np
from tqdm import tqdm

# from data.hdf5_vla_dataset import TabletopHDF5VLADataset as HDF5VLADataset
from data.hdf5_vla_dataset import AnubisHDF5VLADataset as HDF5VLADataset


def process_hdf5_dataset(vla_dataset):
    EPS = 1e-8
    episode_cnt = 0
    state_sum = 0
    state_sum_sq = 0
    z_state_sum = 0
    z_state_sum_sq = 0
    state_cnt = 0
    nz_state_cnt = None
    state_max = None
    state_min = None
    for i in tqdm(range(len(vla_dataset))):
        # print(i)
        episode = vla_dataset.get_item(i, state_only=True)
        episode_cnt += 1
        
        states = episode['state']
        
        # Zero the values that are close to zero
        z_states = states.copy()
        z_states[np.abs(states) <= EPS] = 0
        # Compute the non-zero count
        if nz_state_cnt is None:
            nz_state_cnt = np.zeros(states.shape[1])
        nz_state_cnt += np.sum(np.abs(states) > EPS, axis=0)
        
        # Update statistics
        state_sum += np.sum(states, axis=0)
        state_sum_sq += np.sum(states**2, axis=0)
        z_state_sum += np.sum(z_states, axis=0)
        z_state_sum_sq += np.sum(z_states**2, axis=0)
        state_cnt += states.shape[0]
        if state_max is None:
            state_max = np.max(states, axis=0)
            state_min = np.min(states, axis=0)
        else:
            state_max = np.maximum(state_max, np.max(states, axis=0))
            state_min = np.minimum(state_min, np.min(states, axis=0))
    
    # Add one to avoid division by zero
    nz_state_cnt = np.maximum(nz_state_cnt, np.ones_like(nz_state_cnt))
    
    result = {
        "dataset_name": vla_dataset.get_dataset_name(),
        "state_mean": (state_sum / state_cnt).tolist(),
        "state_std": np.sqrt(
            np.maximum(
                (z_state_sum_sq / nz_state_cnt) - (z_state_sum / state_cnt)**2 * (state_cnt / nz_state_cnt),
                np.zeros_like(state_sum_sq)
            )
        ).tolist(),
        "state_min": state_min.tolist(),
        "state_max": state_max.tolist(),
    }

    return result


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--save_path', type=str, 
                        default="configs/dataset_stat.json", 
                        help="JSON file path to save the dataset statistics.")
    parser.add_argument('--skip_exist', action='store_true', 
                        help="Whether to skip the existing dataset statistics.")
    parser.add_argument('--dataset', type=str)
    args = parser.parse_args()
    
    vla_dataset = HDF5VLADataset(args.dataset)
    dataset_name = vla_dataset.get_dataset_name()
    
    try:
        with open(args.save_path, 'r') as f:
            results = json.load(f)
    except FileNotFoundError:
        results = {}
    if args.skip_exist and dataset_name in results:
        print(f"Skipping existed {dataset_name} dataset statistics")
    else:
        print(f"Processing {dataset_name} dataset")
        result = process_hdf5_dataset(vla_dataset)
        results[result["dataset_name"]] = result
        with open(args.save_path, 'w') as f:
            json.dump(results, f, indent=4)
    print("All datasets have been processed.")