Lufang Chen
d821b0cb26
* fix normalizer * fix val * update compute stats * delete norm * update readme * minor fix * fix action normalizer * fix * fix * update * update * update * update * update * lint * lint * lint * lint
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import yaml
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import torch
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import tqdm
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from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
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from wall_x.data.load_lerobot_dataset import KEY_MAPPINGS
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import normalize
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import numpy as np
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import argparse
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def load_config(config_path):
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"""Load configuration from YAML file."""
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with open(config_path, "r") as f:
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config = yaml.load(f, Loader=yaml.FullLoader)
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config["data"]["model_type"] = config.get("model_type")
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return config
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def load_lerobot_dataset(repo_id, action_horizon, args):
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dataset_meta = LeRobotDatasetMetadata(repo_id)
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dataset = LeRobotDataset(
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repo_id,
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delta_timestamps={
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key: [t / dataset_meta.fps for t in range(action_horizon)]
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for key in [KEY_MAPPINGS[repo_id]["action"]]
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},
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)
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num_batches = len(dataset) // args.batch_size
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generator = torch.Generator()
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generator.manual_seed(args.seed)
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data_loader = torch.utils.data.DataLoader(
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dataset,
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batch_size=args.batch_size,
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shuffle=False,
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drop_last=True,
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generator=generator,
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num_workers=args.num_workers,
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persistent_workers=True if args.num_workers > 0 else False,
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)
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return data_loader, num_batches
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if __name__ == "__main__":
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# set args
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parser = argparse.ArgumentParser()
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parser.add_argument("--batch_size", type=int, default=256)
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parser.add_argument("--num_workers", type=int, default=2)
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parser.add_argument("--seed", type=int, default=0)
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args = parser.parse_args()
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# Configs
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path = "/path/to/config.yml"
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output_path = "/path/to/output"
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config = load_config(path)
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lerobot_config = config["data"]["lerobot_config"]
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repo_id = lerobot_config.get("repo_id", None)
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assert repo_id is not None, "repo id is required"
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action_horizon = config["data"].get("action_horizon", 32)
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data_loader, num_batches = load_lerobot_dataset(repo_id, action_horizon, args)
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keys = ["state", "action"]
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stats = {key: normalize.RunningStats() for key in keys}
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for batch in tqdm.tqdm(data_loader, total=num_batches, desc="Computing stats"):
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for key in keys:
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stats[key].update(np.asarray(batch[KEY_MAPPINGS[repo_id][key]]))
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norm_stats = {
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KEY_MAPPINGS[repo_id][key]: stats.get_statistics()
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for key, stats in stats.items()
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}
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output_path = output_path + "/" + repo_id
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print(f"Writing stats to: {output_path}")
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normalize.save(output_path, norm_stats)
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