Fix normalizer (#57)
* 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
This commit is contained in:
Lufang Chen
@@ -53,6 +53,7 @@ MAX_JOBS=4 pip install flash-attn==2.7.4.post1 --no-build-isolation
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Install lerobot:
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```bash
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git clone https://github.com/huggingface/lerobot.git
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git checkout c66cd401767e60baece16e1cf68da2824227e076
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cd lerobot
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pip install -e .
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```
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@@ -0,0 +1,76 @@
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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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@@ -1,22 +1,13 @@
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import os
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import yaml
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import torch
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import argparse
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from tqdm import tqdm
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import matplotlib.pyplot as plt
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from wall_x.model.qwen2_5_based.modeling_qwen2_5_vl_act import Qwen2_5_VLMoEForAction
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from wall_x.data.load_lerobot_dataset import load_test_dataset, get_data_configs
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model_path = "path/to/model"
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action_tokenizer_path = "path/to/action_tokenizer"
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save_dir = "path/to/plot"
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model = Qwen2_5_VLMoEForAction.from_pretrained(
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model_path, action_tokenizer_path=action_tokenizer_path
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)
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model.eval()
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model = model.to("cuda")
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model = model.bfloat16()
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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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@@ -27,9 +18,31 @@ def load_config(config_path):
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return config
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# get test dataloader
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path = "path/to/config"
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--pred_horizon", type=int, default=32)
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parser.add_argument("--origin_action_dim", type=int, default=7)
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args = parser.parse_args()
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origin_action_dim = args.origin_action_dim
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pred_horizon = args.pred_horizon
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# get train config
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model_path = "/path/to/model"
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action_tokenizer_path = "/path/to/action/tokenizer"
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save_dir = "/path/to/save/dir"
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path = "/path/to/train/config"
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config = load_config(path)
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# load model with customized robot config
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model = Qwen2_5_VLMoEForAction.from_pretrained(
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model_path, train_config=config, action_tokenizer_path=action_tokenizer_path
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)
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model.eval()
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model = model.to("cuda")
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model = model.bfloat16()
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# get test dataloader
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dataload_config = get_data_configs(config["data"])
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lerobot_config = dataload_config.get("lerobot_config", {})
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dataset = load_test_dataset(config, lerobot_config, seed=42)
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@@ -37,12 +50,15 @@ dataloader = dataset.get_dataloader()
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total_frames = len(dataloader)
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pred_horizon = 32
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action_dim = 14
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gt_traj = torch.zeros((total_frames, action_dim))
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pred_traj = torch.zeros((total_frames, action_dim))
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predict_mode = "fast" if config.get("use_fast_tokenizer", False) else "diffusion"
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action_dim = 20 if predict_mode == "diffusion" else origin_action_dim
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gt_traj = torch.zeros((total_frames, origin_action_dim))
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pred_traj = torch.zeros((total_frames, origin_action_dim))
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for idx, batch in enumerate(dataloader):
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# use tqdm to show the progress
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for idx, batch in tqdm(
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enumerate(dataloader), total=total_frames, desc="predicting"
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):
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if idx % pred_horizon == 0 and idx + pred_horizon < total_frames:
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batch = batch.to("cuda")
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with torch.no_grad():
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@@ -51,28 +67,38 @@ for idx, batch in enumerate(dataloader):
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action_dim=action_dim,
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pred_horizon=pred_horizon,
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mode="predict",
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predict_mode="fast",
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predict_mode=predict_mode,
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)
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pred_traj[idx : idx + pred_horizon] = (
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outputs["predict_action"][:, :, :origin_action_dim]
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.detach()
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.cpu()
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.squeeze(0)
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)
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pred_traj[idx : idx + pred_horizon] = outputs["predict_action"].detach().cpu()
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# Denormalize ground truth actions
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gt_action_chunk = batch["action_chunk"][:, :, :action_dim]
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gt_action_chunk = batch["action_chunk"][:, :, :origin_action_dim]
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dof_mask = batch["dof_mask"].to(gt_action_chunk.dtype)
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denormalized_gt = model.action_preprocessor.normalizer_action.unnormalize_data(
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gt_action_chunk, ["x2_normal"], dof_mask
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denormalized_gt = (
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model.action_preprocessor.normalizer_action.unnormalize_data(
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gt_action_chunk,
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[lerobot_config.get("repo_id", "physical-intelligence/libero")],
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dof_mask,
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).squeeze(0)
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)
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gt_traj[idx : idx + pred_horizon] = denormalized_gt.detach().cpu()
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gt_traj_np = gt_traj.numpy()
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pred_traj_np = pred_traj.numpy()
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timesteps = gt_traj.shape[0]
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fig, axs = plt.subplots(action_dim, 1, figsize=(15, 5 * action_dim), sharex=True)
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fig, axs = plt.subplots(
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origin_action_dim, 1, figsize=(15, 5 * origin_action_dim), sharex=True
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)
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fig.suptitle("Action Comparison for lerobot", fontsize=16)
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for i in range(action_dim):
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for i in range(origin_action_dim):
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axs[i].plot(range(timesteps), gt_traj_np[:, i], label="Ground Truth")
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axs[i].plot(range(timesteps), pred_traj_np[:, i], label="Prediction")
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axs[i].set_ylabel(f"Action Dim {i+1}")
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@@ -82,5 +108,7 @@ for i in range(action_dim):
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axs[-1].set_xlabel("Timestep")
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plt.tight_layout(rect=[0, 0.03, 1, 0.95])
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os.makedirs(save_dir, exist_ok=True)
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plt.savefig(os.path.join(save_dir, "lerobot_comparison.png"))
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save_path = os.path.join(save_dir, "lerobot_comparison.png")
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plt.savefig(save_path)
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print(f"Saved plot to {save_path}")
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plt.close()
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@@ -0,0 +1,161 @@
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# This file is copied from openpi
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import json
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import pathlib
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import numpy as np
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import numpydantic
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import pydantic
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@pydantic.dataclasses.dataclass
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class NormStats:
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mean: numpydantic.NDArray
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std: numpydantic.NDArray
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q01: numpydantic.NDArray | None = None # 1st quantile
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q99: numpydantic.NDArray | None = None # 99th quantile
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class RunningStats:
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"""Compute running statistics of a batch of vectors."""
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def __init__(self):
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self._count = 0
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self._mean = None
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self._mean_of_squares = None
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self._min = None
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self._max = None
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self._histograms = None
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self._bin_edges = None
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self._num_quantile_bins = 5000 # for computing quantiles on the fly
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def update(self, batch: np.ndarray) -> None:
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"""
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Update the running statistics with a batch of vectors.
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Args:
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vectors (np.ndarray): An array where all dimensions except the last are batch dimensions.
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"""
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batch = batch.reshape(-1, batch.shape[-1])
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num_elements, vector_length = batch.shape
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if self._count == 0:
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self._mean = np.mean(batch, axis=0)
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self._mean_of_squares = np.mean(batch**2, axis=0)
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self._min = np.min(batch, axis=0)
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self._max = np.max(batch, axis=0)
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self._histograms = [
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np.zeros(self._num_quantile_bins) for _ in range(vector_length)
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]
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self._bin_edges = [
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np.linspace(
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self._min[i] - 1e-10,
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self._max[i] + 1e-10,
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self._num_quantile_bins + 1,
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)
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for i in range(vector_length)
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]
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else:
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if vector_length != self._mean.size:
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raise ValueError(
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"The length of new vectors does not match the initialized vector length."
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)
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new_max = np.max(batch, axis=0)
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new_min = np.min(batch, axis=0)
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max_changed = np.any(new_max > self._max)
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min_changed = np.any(new_min < self._min)
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self._max = np.maximum(self._max, new_max)
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self._min = np.minimum(self._min, new_min)
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if max_changed or min_changed:
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self._adjust_histograms()
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self._count += num_elements
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batch_mean = np.mean(batch, axis=0)
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batch_mean_of_squares = np.mean(batch**2, axis=0)
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# Update running mean and mean of squares.
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self._mean += (batch_mean - self._mean) * (num_elements / self._count)
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self._mean_of_squares += (batch_mean_of_squares - self._mean_of_squares) * (
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num_elements / self._count
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)
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self._update_histograms(batch)
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def get_statistics(self) -> NormStats:
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"""
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Compute and return the statistics of the vectors processed so far.
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Returns:
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dict: A dictionary containing the computed statistics.
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"""
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if self._count < 2:
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raise ValueError("Cannot compute statistics for less than 2 vectors.")
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variance = self._mean_of_squares - self._mean**2
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stddev = np.sqrt(np.maximum(0, variance))
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q01, q99 = self._compute_quantiles([0.01, 0.99])
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return NormStats(mean=self._mean, std=stddev, q01=q01, q99=q99)
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def _adjust_histograms(self):
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"""Adjust histograms when min or max changes."""
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for i in range(len(self._histograms)):
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old_edges = self._bin_edges[i]
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new_edges = np.linspace(
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self._min[i], self._max[i], self._num_quantile_bins + 1
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)
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# Redistribute the existing histogram counts to the new bins
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new_hist, _ = np.histogram(
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old_edges[:-1], bins=new_edges, weights=self._histograms[i]
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)
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self._histograms[i] = new_hist
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self._bin_edges[i] = new_edges
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def _update_histograms(self, batch: np.ndarray) -> None:
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"""Update histograms with new vectors."""
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for i in range(batch.shape[1]):
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hist, _ = np.histogram(batch[:, i], bins=self._bin_edges[i])
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self._histograms[i] += hist
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def _compute_quantiles(self, quantiles):
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"""Compute quantiles based on histograms."""
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results = []
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for q in quantiles:
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target_count = q * self._count
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q_values = []
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for hist, edges in zip(self._histograms, self._bin_edges, strict=True):
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cumsum = np.cumsum(hist)
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idx = np.searchsorted(cumsum, target_count)
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q_values.append(edges[idx])
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results.append(np.array(q_values))
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return results
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class _NormStatsDict(pydantic.BaseModel):
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norm_stats: dict[str, NormStats]
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def serialize_json(norm_stats: dict[str, NormStats]) -> str:
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"""Serialize the running statistics to a JSON string."""
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return _NormStatsDict(norm_stats=norm_stats).model_dump_json(indent=2)
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def deserialize_json(data: str) -> dict[str, NormStats]:
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"""Deserialize the running statistics from a JSON string."""
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return _NormStatsDict(**json.loads(data)).norm_stats
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def save(directory: pathlib.Path | str, norm_stats: dict[str, NormStats]) -> None:
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"""Save the normalization stats to a directory."""
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path = pathlib.Path(directory) / "norm_stats.json"
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(serialize_json(norm_stats))
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def load(directory: pathlib.Path | str) -> dict[str, NormStats]:
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"""Load the normalization stats from a directory."""
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path = pathlib.Path(directory) / "norm_stats.json"
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if not path.exists():
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raise FileNotFoundError(f"Norm stats file not found at: {path}")
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return deserialize_json(path.read_text())
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@@ -36,6 +36,8 @@ ACTION_DATASET_NAMES = [
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"taco_play",
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"utaustin_mutex",
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"viola",
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"physical-intelligence/libero",
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"lerobot/aloha_mobile_cabinet",
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]
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# Supported multimodal datasets
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@@ -17,17 +17,10 @@ from wall_x.data.utils import (
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)
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from transformers import AutoProcessor
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from .utils import load_norm_stats, KEY_MAPPINGS
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T_co = TypeVar("T_co", covariant=True)
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CAMERA_KEY_MAPPINGS = {
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"lerobot/aloha_mobile_cabinet": {
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"observation.images.cam_high": "face_view",
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"observation.images.cam_left_wrist": "left_wrist_view",
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"observation.images.cam_right_wrist": "right_wrist_view",
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},
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}
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# Abstract class for dataset
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class Dataset(Protocol[T_co]):
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@@ -46,6 +39,8 @@ class PreprocessedDataset(Dataset[T_co]):
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dataset,
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config,
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dataload_config,
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norm_stats,
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lerobot_config,
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seed=42,
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rank=0,
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world_size=1,
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@@ -72,6 +67,8 @@ class PreprocessedDataset(Dataset[T_co]):
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self.config = config
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self.use_fast_tokenizer = self.config.get("use_fast_tokenizer", False)
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self.dataload_config = dataload_config
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self.norm_stats = norm_stats
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self.lerobot_config = lerobot_config
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self.data_config = X2RDataProcessingConfig().update(
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train_test_split=self.dataload_config["train_test_split"],
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@@ -82,7 +79,9 @@ class PreprocessedDataset(Dataset[T_co]):
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priority_order=self.dataload_config.get("priority_order", None),
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)
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self._cam_key_mapping = CAMERA_KEY_MAPPINGS[self.hf_dataset.meta.repo_id]
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self._cam_key_mapping = KEY_MAPPINGS[self.hf_dataset.meta.repo_id]["camera"]
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self._state_key_mapping = KEY_MAPPINGS[self.hf_dataset.meta.repo_id]
|
||||
self._action_key_mapping = KEY_MAPPINGS[self.hf_dataset.meta.repo_id]
|
||||
|
||||
def _vision_preprocess(self, frames):
|
||||
processed_frames = []
|
||||
@@ -124,14 +123,14 @@ class PreprocessedDataset(Dataset[T_co]):
|
||||
def __getitem__(self, index):
|
||||
data = self._dataset[index]
|
||||
image_inputs, h, w, resize_h, resize_w = self._vision_preprocess(data)
|
||||
agent_pos = data["observation.state"]
|
||||
action = data["action"]
|
||||
agent_pos = data[self._state_key_mapping["state"]]
|
||||
action = data[self._action_key_mapping["action"]]
|
||||
frame_index = data["frame_index"]
|
||||
instruction_info = {"instruction": data["task"]}
|
||||
generate_subtask_ratio = self.data_config.generate_subtask_ratio
|
||||
complete_text, generate_subtask = get_wallx_normal_text(
|
||||
instruction_info,
|
||||
33 - 1,
|
||||
self.dataload_config.get("action_horizon", 33) - 1,
|
||||
frame_index,
|
||||
self.data_config.priority_order,
|
||||
self._cam_key_mapping,
|
||||
@@ -189,7 +188,7 @@ class PreprocessedDataset(Dataset[T_co]):
|
||||
sampler=sampler, # Use distributed sampler instead of shuffle=True
|
||||
num_workers=num_workers,
|
||||
collate_fn=DataCollator(
|
||||
self.config, self.dataload_config, self.hf_dataset.meta.stats
|
||||
self.config, self.dataload_config, self.norm_stats, self.lerobot_config
|
||||
),
|
||||
pin_memory=True, # Enable for GPU training
|
||||
persistent_workers=num_workers > 0, # Only if num_workers > 0
|
||||
@@ -225,7 +224,7 @@ class PreprocessedDataset(Dataset[T_co]):
|
||||
sampler=sampler,
|
||||
num_workers=num_workers,
|
||||
collate_fn=DataCollator(
|
||||
self.config, self.dataload_config, self.hf_dataset.meta.stats
|
||||
self.config, self.dataload_config, self.norm_stats, self.lerobot_config
|
||||
),
|
||||
pin_memory=True,
|
||||
persistent_workers=num_workers > 0,
|
||||
@@ -241,13 +240,16 @@ class DataCollator:
|
||||
_processor_cache = {}
|
||||
_action_tokenizer_cache = {}
|
||||
|
||||
def __init__(self, config, dataload_config, stats):
|
||||
def __init__(self, config, dataload_config, stats, lerobot_config):
|
||||
self.config = config
|
||||
self.dataload_config = dataload_config
|
||||
self.stats = stats
|
||||
self.min_stat = stats["action"]["min"]
|
||||
self.max_stat = stats["action"]["max"]
|
||||
self.delta = self.max_stat - self.min_stat
|
||||
self.action_min_stat = stats["action"].min
|
||||
self.action_delta = stats["action"].delta
|
||||
self.state_min_stat = stats["state"].min
|
||||
self.state_delta = stats["state"].delta
|
||||
self.lerobot_config = lerobot_config
|
||||
|
||||
self.use_fast_tokenizer = self.config.get("use_fast_tokenizer", False)
|
||||
self.load_processor()
|
||||
|
||||
@@ -271,10 +273,11 @@ class DataCollator:
|
||||
if self.config.get("padding_side", "left") == "left":
|
||||
processor.tokenizer.padding_side = "left"
|
||||
|
||||
new_tokens = ["<|propri|>", "<|action|>"]
|
||||
processor.tokenizer.add_tokens(new_tokens)
|
||||
if self.use_fast_tokenizer and self.config.get("model_type") == "qwen2_5":
|
||||
action_tokenizer = self._action_tokenizer_cache[action_tokenizer_path]
|
||||
new_tokens = ["<|propri|>", "<|action|>"]
|
||||
new_tokens += [
|
||||
new_tokens = [
|
||||
f"<|action_token_{i}|>" for i in range(action_tokenizer.vocab_size)
|
||||
]
|
||||
processor.tokenizer.add_tokens(new_tokens)
|
||||
@@ -301,7 +304,6 @@ class DataCollator:
|
||||
"""
|
||||
Normalize action data using min-max normalization.
|
||||
"""
|
||||
delta = torch.from_numpy(delta)
|
||||
delta = torch.where(delta == 0, torch.ones_like(delta), delta)
|
||||
x = (action - min_stat) / delta
|
||||
x = x * 2 - 1
|
||||
@@ -318,7 +320,9 @@ class DataCollator:
|
||||
agent_pos = agent_pos.unsqueeze(1)
|
||||
agent_pos_mask = (~torch.isnan(agent_pos)).float()
|
||||
agent_pos.nan_to_num_(nan=0.0)
|
||||
agent_pos = self._normalize(agent_pos, self.min_stat, self.delta)
|
||||
agent_pos = self._normalize(
|
||||
agent_pos, self.state_min_stat, self.state_delta
|
||||
)
|
||||
if agent_pos.shape[-1] != 20:
|
||||
agent_pos = torch.cat(
|
||||
[
|
||||
@@ -350,7 +354,9 @@ class DataCollator:
|
||||
action = action.unsqueeze(1)
|
||||
dof_mask = (~torch.isnan(action)).float()
|
||||
action.nan_to_num_(nan=0.0)
|
||||
action = self._normalize(action, self.min_stat, self.delta)
|
||||
action = self._normalize(
|
||||
action, self.action_min_stat, self.action_delta
|
||||
)
|
||||
if action.shape[-1] != 20:
|
||||
action = torch.cat(
|
||||
[
|
||||
@@ -393,7 +399,7 @@ class DataCollator:
|
||||
additional_inputs["text"],
|
||||
additional_inputs["action_chunk"],
|
||||
self.train_action_tokenizer if self.use_fast_tokenizer else None,
|
||||
["x2_normal"] * additional_inputs["text"].__len__(),
|
||||
[self.lerobot_config["repo_id"]] * additional_inputs["text"].__len__(),
|
||||
additional_inputs["dof_mask"],
|
||||
)
|
||||
|
||||
@@ -415,7 +421,9 @@ class DataCollator:
|
||||
|
||||
inputs.update(additional_inputs)
|
||||
|
||||
inputs["dataset_names"] = ["x2_normal"] * inputs["action_chunk"].shape[0]
|
||||
inputs["dataset_names"] = [self.lerobot_config["repo_id"]] * inputs[
|
||||
"action_chunk"
|
||||
].shape[0]
|
||||
|
||||
return inputs
|
||||
|
||||
@@ -447,18 +455,24 @@ def load_lerobot_data(
|
||||
|
||||
dataload_config = get_data_configs(config["data"])
|
||||
|
||||
# repo_id = "lerobot/aloha_mobile_cabinet"
|
||||
repo_id = lerobot_config.get("repo_id", "lerobot/aloha_mobile_cabinet")
|
||||
repo_id = lerobot_config.get("repo_id", None)
|
||||
assert repo_id is not None, "repo id is required"
|
||||
root = lerobot_config.get("root", None)
|
||||
meta_info = LeRobotDatasetMetadata(repo_id)
|
||||
meta_info = LeRobotDatasetMetadata(repo_id, root=root)
|
||||
dataset_fps = meta_info.fps
|
||||
episodes_num = meta_info.total_episodes
|
||||
|
||||
norm_stats_path = config.get("norm_stats_path", None)
|
||||
assert (
|
||||
norm_stats_path is not None
|
||||
), "norm stats is required, please refer to 'wall-x/scripts/compute_norm_stats.py' to compute stats"
|
||||
norm_stats = load_norm_stats(norm_stats_path, repo_id)
|
||||
|
||||
delta_timestamps = {
|
||||
# action chunk
|
||||
"action": [
|
||||
KEY_MAPPINGS[repo_id]["action"]: [
|
||||
t / dataset_fps
|
||||
for t in range(dataload_config.get("action_horizon", 32) - 1)
|
||||
for t in range(dataload_config.get("action_horizon", 33) - 1)
|
||||
],
|
||||
}
|
||||
batch_size = config.get("batch_size_per_gpu", 8)
|
||||
@@ -486,6 +500,8 @@ def load_lerobot_data(
|
||||
train_dataset,
|
||||
config,
|
||||
dataload_config,
|
||||
norm_stats,
|
||||
lerobot_config,
|
||||
seed=seed,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
@@ -567,11 +583,15 @@ def get_data_configs(config):
|
||||
|
||||
|
||||
class TestDataset(PreprocessedDataset):
|
||||
def __init__(self, dataset, config, dataload_config, seed=42):
|
||||
def __init__(
|
||||
self, dataset, config, dataload_config, norm_stats, lerobot_config, seed=42
|
||||
):
|
||||
super().__init__(
|
||||
dataset,
|
||||
config,
|
||||
dataload_config,
|
||||
norm_stats,
|
||||
lerobot_config,
|
||||
seed=seed,
|
||||
rank=0,
|
||||
world_size=1,
|
||||
@@ -587,7 +607,7 @@ class TestDataset(PreprocessedDataset):
|
||||
self,
|
||||
batch_size=1,
|
||||
collate_fn=DataCollator(
|
||||
self.config, self.dataload_config, self.hf_dataset.meta.stats
|
||||
self.config, self.dataload_config, self.norm_stats, self.lerobot_config
|
||||
),
|
||||
)
|
||||
|
||||
@@ -614,29 +634,41 @@ def load_test_dataset(
|
||||
# Set seed for reproducibility
|
||||
torch.manual_seed(seed)
|
||||
|
||||
dataset_fps = 50
|
||||
repo_id = lerobot_config.get("repo_id", None)
|
||||
assert repo_id is not None, "repo id is required"
|
||||
root = lerobot_config.get("root", None)
|
||||
meta_info = LeRobotDatasetMetadata(repo_id, root=root)
|
||||
dataset_fps = meta_info.fps
|
||||
dataload_config = get_data_configs(config["data"])
|
||||
|
||||
norm_stats_path = config.get("norm_stats_path", None)
|
||||
assert (
|
||||
norm_stats_path is not None
|
||||
), "norm stats is required, please refer to 'wall-x/scripts/compute_norm_stats.py' to compute stats"
|
||||
norm_stats = load_norm_stats(norm_stats_path, repo_id)
|
||||
|
||||
delta_timestamps = {
|
||||
# action chunk
|
||||
"action": [
|
||||
KEY_MAPPINGS[repo_id]["action"]: [
|
||||
t / dataset_fps
|
||||
for t in range(dataload_config.get("action_horizon", 32) - 1)
|
||||
for t in range(dataload_config.get("action_horizon", 33) - 1)
|
||||
],
|
||||
}
|
||||
|
||||
repo_id = lerobot_config.get("repo_id", "lerobot/aloha_mobile_cabinet")
|
||||
dataset = LeRobotDataset(
|
||||
repo_id,
|
||||
episodes=[episode],
|
||||
delta_timestamps=delta_timestamps,
|
||||
video_backend="pyav",
|
||||
root=root,
|
||||
)
|
||||
|
||||
print(f"Selected episodes: {dataset.episodes}")
|
||||
print(f"Number of episodes selected: {dataset.num_episodes}")
|
||||
print(f"Number of frames selected: {dataset.num_frames}")
|
||||
|
||||
dataset = TestDataset(dataset, config, dataload_config, seed=seed)
|
||||
dataset = TestDataset(
|
||||
dataset, config, dataload_config, norm_stats, lerobot_config, seed=seed
|
||||
)
|
||||
|
||||
return dataset
|
||||
|
||||
@@ -11,7 +11,28 @@ import random
|
||||
from collections import OrderedDict
|
||||
from typing import List, Dict, Any, Optional, Union, Tuple
|
||||
from transformers import BatchFeature
|
||||
from dataclasses import dataclass
|
||||
import json
|
||||
|
||||
KEY_MAPPINGS = {
|
||||
"lerobot/aloha_mobile_cabinet": {
|
||||
"camera": {
|
||||
"observation.images.cam_high": "face_view",
|
||||
"observation.images.cam_left_wrist": "left_wrist_view",
|
||||
"observation.images.cam_right_wrist": "right_wrist_view",
|
||||
},
|
||||
"state": "observation.state",
|
||||
"action": "action",
|
||||
},
|
||||
"physical-intelligence/libero": {
|
||||
"camera": {
|
||||
"image": "face_view",
|
||||
"wrist_image": "left_wrist_view",
|
||||
},
|
||||
"state": "state",
|
||||
"action": "actions",
|
||||
},
|
||||
}
|
||||
|
||||
CAMERA_NAME_MAPPING = {
|
||||
"face_view": "front view",
|
||||
@@ -609,3 +630,35 @@ def replace_action_token(
|
||||
text = [t.replace("<|action_fast|><|im_end|>\n", "") for t in text]
|
||||
|
||||
return text
|
||||
|
||||
|
||||
@dataclass
|
||||
class NormStats:
|
||||
min: torch.Tensor
|
||||
max: torch.Tensor
|
||||
delta: torch.Tensor
|
||||
|
||||
|
||||
def load_norm_stats(norm_stats_path, dataset_name):
|
||||
with open(norm_stats_path, "r") as f:
|
||||
norm_stats = json.load(f)
|
||||
action_key = KEY_MAPPINGS[dataset_name]["action"]
|
||||
state_key = KEY_MAPPINGS[dataset_name]["state"]
|
||||
q01 = torch.tensor(norm_stats["norm_stats"][action_key]["q01"])
|
||||
q99 = torch.tensor(norm_stats["norm_stats"][action_key]["q99"])
|
||||
delta = q99 - q01
|
||||
action_norm_stats = NormStats(
|
||||
min=q01,
|
||||
max=q99,
|
||||
delta=delta,
|
||||
)
|
||||
q01 = torch.tensor(norm_stats["norm_stats"][state_key]["q01"])
|
||||
q99 = torch.tensor(norm_stats["norm_stats"][state_key]["q99"])
|
||||
delta = q99 - q01
|
||||
state_norm_stats = NormStats(
|
||||
min=q01,
|
||||
max=q99,
|
||||
delta=delta,
|
||||
)
|
||||
|
||||
return {"action": action_norm_stats, "state": state_norm_stats}
|
||||
|
||||
@@ -3,6 +3,7 @@ import torch
|
||||
import torch.nn as nn
|
||||
from torch.distributions import Beta
|
||||
from wall_x.utils.constant import action_statistic_dof
|
||||
import logging
|
||||
|
||||
|
||||
class Normalizer(nn.Module):
|
||||
@@ -14,6 +15,16 @@ class Normalizer(nn.Module):
|
||||
normalization to map actions to the [-1, 1] range.
|
||||
"""
|
||||
|
||||
def _pad_to_action_dim(self, xs, action_dim):
|
||||
"""
|
||||
Pad the action data to the action dimension.
|
||||
"""
|
||||
if xs.shape[-1] < action_dim:
|
||||
padding_shape = list(xs.shape)
|
||||
padding_shape[-1] = action_dim - padding_shape[-1]
|
||||
xs = torch.cat([xs, torch.zeros(padding_shape).to(xs.device)], dim=-1)
|
||||
return xs
|
||||
|
||||
def __init__(self, action_statistic_dof, dof_config):
|
||||
"""
|
||||
Initialize the normalizer with robot-specific action statistics.
|
||||
@@ -25,6 +36,8 @@ class Normalizer(nn.Module):
|
||||
super(Normalizer, self).__init__()
|
||||
|
||||
action_statistic = {}
|
||||
# hard code the action dimension to 20
|
||||
action_dim = 20
|
||||
|
||||
# Process statistics for each robot
|
||||
for robot_name in action_statistic_dof.keys():
|
||||
@@ -39,11 +52,17 @@ class Normalizer(nn.Module):
|
||||
all_dof_delta.extend(action_statistic_dof[robot_name][k]["delta"])
|
||||
else:
|
||||
# Use default values if statistics not available
|
||||
# raise ValueError(f"Statistics not available for {k} of {robot_name}")
|
||||
logging.warning(
|
||||
f"Statistics not available for {k} of {robot_name}, using default values"
|
||||
)
|
||||
all_dof_min.extend([0.0] * dof_config[k])
|
||||
all_dof_delta.extend([1.0] * dof_config[k])
|
||||
|
||||
all_dof_min = torch.tensor(all_dof_min)
|
||||
all_dof_delta = torch.tensor(all_dof_delta)
|
||||
all_dof_min = self._pad_to_action_dim(torch.tensor(all_dof_min), action_dim)
|
||||
all_dof_delta = self._pad_to_action_dim(
|
||||
torch.tensor(all_dof_delta), action_dim
|
||||
)
|
||||
action_statistic[robot_name]["min"] = all_dof_min
|
||||
action_statistic[robot_name]["delta"] = all_dof_delta
|
||||
|
||||
@@ -61,7 +80,7 @@ class Normalizer(nn.Module):
|
||||
}
|
||||
)
|
||||
|
||||
def normalize_data(self, xs, dataset_names):
|
||||
def normalize_data(self, xs, dataset_names, dof_mask=None):
|
||||
"""
|
||||
Normalize action data to [-1, 1] range using robot-specific statistics.
|
||||
|
||||
@@ -75,10 +94,19 @@ class Normalizer(nn.Module):
|
||||
new_xs = []
|
||||
# Filter out multimodal dataset entries
|
||||
dataset_names = [name for name in dataset_names if name != "x2_multimodal"]
|
||||
dof_mask = dof_mask if dof_mask is not None else [None] * len(xs)
|
||||
|
||||
for x, dataset_name in zip(xs, dataset_names):
|
||||
for x, dataset_name, mask in zip(xs, dataset_names, dof_mask):
|
||||
# Apply DOF mask if provided
|
||||
if mask is not None:
|
||||
mask = mask[0].bool()
|
||||
action_space_delta = self.delta[dataset_name][mask]
|
||||
action_space_min = self.min[dataset_name][mask]
|
||||
else:
|
||||
action_space_delta = self.delta[dataset_name]
|
||||
action_space_min = self.min[dataset_name]
|
||||
# Apply min-max normalization
|
||||
x = (x - self.min[dataset_name]) / (self.delta[dataset_name])
|
||||
x = (x - action_space_min) / (action_space_delta)
|
||||
# Scale to [-1, 1] range
|
||||
x = x * 2 - 1
|
||||
# Clamp to ensure bounds
|
||||
@@ -210,9 +238,21 @@ class ActionProcessor(nn.Module):
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
# Initialize data normalizers for actions and proprioception
|
||||
self.normalizer_action = Normalizer(action_statistic_dof, config.dof_config)
|
||||
self.normalizer_action = Normalizer(
|
||||
action_statistic_dof,
|
||||
(
|
||||
config.customized_dof_config
|
||||
if hasattr(config, "customized_dof_config")
|
||||
else config.dof_config
|
||||
),
|
||||
)
|
||||
self.normalizer_propri = Normalizer(
|
||||
action_statistic_dof, config.agent_pos_config
|
||||
action_statistic_dof,
|
||||
(
|
||||
config.customized_agent_pos_config
|
||||
if hasattr(config, "customized_agent_pos_config")
|
||||
else config.agent_pos_config
|
||||
),
|
||||
)
|
||||
|
||||
# Proprioception projection layer (includes history/current state)
|
||||
|
||||
@@ -44,7 +44,9 @@ from wall_x.model.qwen2_5_based.modeling_qwen2_5_vl import (
|
||||
Qwen2_5_VLSdpaAttention,
|
||||
)
|
||||
from wall_x.data.config import ACTION_DATASET_NAMES, MULTIMODAL_DATASET_NAMES
|
||||
|
||||
from wall_x.utils.constant import action_statistic_dof
|
||||
from wall_x.data.utils import load_norm_stats
|
||||
from pprint import pprint
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
@@ -744,10 +746,77 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
|
||||
config_class = Qwen2_5_VLConfig
|
||||
_no_split_modules = ["Qwen2_5_VLDecoderLayer_with_MoE", "Qwen2_5_VLVisionBlock"]
|
||||
|
||||
@classmethod
|
||||
def _set_customized_config(cls, config):
|
||||
"""
|
||||
Processing norm_stats.json and reconstruct the DoF mapping
|
||||
"""
|
||||
dataload_config = config["data"]
|
||||
if not dataload_config.get("use_lerobot", False):
|
||||
raise NotImplementedError(
|
||||
"Not implemented for non-lerobot dataset currently"
|
||||
)
|
||||
|
||||
enable_customized_robot_config = config.get(
|
||||
"enable_customized_robot_config", False
|
||||
)
|
||||
assert (
|
||||
enable_customized_robot_config
|
||||
), "enable_customized_robot_config must be true when use lerobot dataset"
|
||||
|
||||
customized_dof_config = config["customized_robot_config"][
|
||||
"customized_dof_config"
|
||||
]
|
||||
customized_agent_pos_config = config["customized_robot_config"][
|
||||
"customized_agent_pos_config"
|
||||
]
|
||||
norm_stats_path = config["norm_stats_path"]
|
||||
norm_stats = load_norm_stats(
|
||||
norm_stats_path, config["data"]["lerobot_config"]["repo_id"]
|
||||
)
|
||||
action_min = norm_stats["action"].min.numpy().tolist()
|
||||
action_delta = norm_stats["action"].delta.numpy().tolist()
|
||||
state_min = norm_stats["state"].min.numpy().tolist()
|
||||
state_delta = norm_stats["state"].delta.numpy().tolist()
|
||||
|
||||
name = config["customized_robot_config"]["name"]
|
||||
|
||||
dof_key = []
|
||||
agent_pos_key = []
|
||||
dof_value = []
|
||||
agent_pos_value = []
|
||||
stats_dict = {}
|
||||
for k, v in customized_dof_config.items():
|
||||
dof_key.append(k)
|
||||
dof_value.append(v)
|
||||
for k, v in customized_agent_pos_config.items():
|
||||
agent_pos_key.append(k)
|
||||
agent_pos_value.append(v)
|
||||
|
||||
dof_idx = np.array([0] + dof_value).cumsum()
|
||||
for i in range(len(dof_idx) - 1):
|
||||
stats_dict[dof_key[i]] = {
|
||||
"min": action_min[dof_idx[i] : dof_idx[i + 1]],
|
||||
"delta": action_delta[dof_idx[i] : dof_idx[i + 1]],
|
||||
}
|
||||
|
||||
agent_pos_idx = np.array([0] + agent_pos_value).cumsum()
|
||||
for i in range(len(agent_pos_idx) - 1):
|
||||
stats_dict[agent_pos_key[i]] = {
|
||||
"min": state_min[agent_pos_idx[i] : agent_pos_idx[i + 1]],
|
||||
"delta": state_delta[agent_pos_idx[i] : agent_pos_idx[i + 1]],
|
||||
}
|
||||
|
||||
action_statistic_dof[name] = stats_dict
|
||||
|
||||
print("Customized robot config added")
|
||||
pprint(action_statistic_dof)
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(
|
||||
cls,
|
||||
pretrained_model_path,
|
||||
train_config,
|
||||
config_path=None,
|
||||
processor_path=None,
|
||||
action_tokenizer_path=None,
|
||||
@@ -766,7 +835,6 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
|
||||
Returns:
|
||||
Qwen2_5_VLMoEForAction: Loaded model instance
|
||||
"""
|
||||
|
||||
# Load model components from pretrained path
|
||||
config_path = os.path.join(pretrained_model_path, "config.json")
|
||||
config = cls.config_class.from_pretrained(config_path)
|
||||
@@ -776,6 +844,18 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
|
||||
action_tokenizer_path, trust_remote_code=True
|
||||
)
|
||||
|
||||
# Set the customized robot configuration to ensure consistency between cross-embodiment
|
||||
# representations and the Wall-X action dimensionality.
|
||||
cls._set_customized_config(train_config)
|
||||
customized_dof_config = train_config["customized_robot_config"][
|
||||
"customized_dof_config"
|
||||
]
|
||||
customized_agent_pos_config = train_config["customized_robot_config"][
|
||||
"customized_agent_pos_config"
|
||||
]
|
||||
setattr(config, "customized_dof_config", customized_dof_config)
|
||||
setattr(config, "customized_agent_pos_config", customized_agent_pos_config)
|
||||
|
||||
# Initialize model with configuration and processor
|
||||
model = cls(config, processor=processor, **kwargs)
|
||||
|
||||
@@ -789,6 +869,14 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
|
||||
state_dict = {}
|
||||
for file in safetensor_files:
|
||||
sd = load_file(file, device="cpu")
|
||||
# filter normalizer statistic params
|
||||
del_keys = []
|
||||
for key in sd.keys():
|
||||
if "action_preprocessor.normalizer" in key:
|
||||
print(f"filter load model weight {key}")
|
||||
del_keys.append(key)
|
||||
for key in del_keys:
|
||||
del sd[key]
|
||||
state_dict.update(sd)
|
||||
|
||||
model.load_state_dict(state_dict, strict=False)
|
||||
@@ -860,7 +948,10 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
|
||||
"""
|
||||
# Create list of fast action token IDs
|
||||
fast_action_token_list = []
|
||||
for i in range(self.processor.tokenizer.init_kwargs["action_token_vocab_size"]):
|
||||
if self.use_fast_tokenizer:
|
||||
for i in range(
|
||||
self.processor.tokenizer.init_kwargs["action_token_vocab_size"]
|
||||
):
|
||||
action_token_id = self.processor.tokenizer.convert_tokens_to_ids(
|
||||
f"<|action_token_{i}|>"
|
||||
)
|
||||
|
||||
@@ -263,7 +263,6 @@ class QwenVlAct_Trainer:
|
||||
self.train_dataloader = self.dataset.get_train_dataloader()
|
||||
|
||||
self.model.train()
|
||||
grad_accum_steps = self.config.get("gradient_accumulation_steps", 1)
|
||||
total = len(self.train_dataloader)
|
||||
t0 = time.time()
|
||||
enable_profiling = self.config["profile"]
|
||||
@@ -341,7 +340,7 @@ class QwenVlAct_Trainer:
|
||||
self.timers("optimizer").stop()
|
||||
|
||||
# Update global step and learning rate after gradient accumulation
|
||||
if (i + 1) % grad_accum_steps == 0:
|
||||
if self.accelerator.sync_gradients:
|
||||
self.lr_scheduler.step()
|
||||
self.global_step += 1
|
||||
lr = self.lr_scheduler.get_last_lr()[0]
|
||||
@@ -522,7 +521,12 @@ class QwenVlAct_Trainer:
|
||||
if model_type == "wall-oss":
|
||||
model = Qwen2_5_VLMoEForAction.from_pretrained(
|
||||
self.config["pretrained_wallx_path"],
|
||||
**{"use_fast_tokenizer": self.use_fast_tokenizer},
|
||||
train_config=self.config,
|
||||
action_tokenizer_path=(
|
||||
self.config["action_tokenizer_path"]
|
||||
if self.use_fast_tokenizer
|
||||
else None
|
||||
),
|
||||
)
|
||||
self.processor = model.processor
|
||||
model = model.to(torch.bfloat16)
|
||||
@@ -535,14 +539,15 @@ class QwenVlAct_Trainer:
|
||||
self.processor = AutoProcessor.from_pretrained(
|
||||
self.config["pretrained_wallx_path"], use_fast=True
|
||||
)
|
||||
new_tokens = ["<|propri|>", "<|action|>"]
|
||||
self.processor.tokenizer.add_tokens(new_tokens)
|
||||
if self.config.get("use_fast_tokenizer", False):
|
||||
action_tokenizer_path = self.config["action_tokenizer_path"]
|
||||
action_tokenizer = AutoProcessor.from_pretrained(
|
||||
action_tokenizer_path, trust_remote_code=True
|
||||
)
|
||||
# process for use fast
|
||||
new_tokens = ["<|propri|>", "<|action|>"]
|
||||
new_tokens += [
|
||||
new_tokens = [
|
||||
f"<|action_token_{i}|>" for i in range(action_tokenizer.vocab_size)
|
||||
]
|
||||
self.processor.tokenizer.add_tokens(new_tokens)
|
||||
@@ -557,6 +562,19 @@ class QwenVlAct_Trainer:
|
||||
action_tokenizer.vocab_size
|
||||
)
|
||||
self.processor.action_processor = action_tokenizer
|
||||
|
||||
# Set the customized robot configuration to ensure consistency between cross-embodiment
|
||||
# representations and the Wall-X action dimensionality.
|
||||
Qwen2_5_VLMoEForAction._set_customized_config(self.config)
|
||||
customized_dof_config = self.config["customized_robot_config"][
|
||||
"customized_dof_config"
|
||||
]
|
||||
customized_agent_pos_config = self.config["customized_robot_config"][
|
||||
"customized_agent_pos_config"
|
||||
]
|
||||
setattr(config, "customized_dof_config", customized_dof_config)
|
||||
setattr(config, "customized_agent_pos_config", customized_agent_pos_config)
|
||||
|
||||
model = Qwen2_5_VLMoEForAction(
|
||||
config,
|
||||
self.use_fast_tokenizer,
|
||||
@@ -803,6 +821,7 @@ class QwenVlAct_Trainer:
|
||||
# merge checkpoint section to merge the weights into a single safetensors if needed.
|
||||
self.accelerator.save_state(ckpt_path)
|
||||
|
||||
if self.accelerator.is_main_process:
|
||||
self.processor.save_pretrained(os.path.join(ckpt_path, "processor"))
|
||||
|
||||
# Save current iteration steps for dataset resuming
|
||||
@@ -845,7 +864,7 @@ class QwenVlAct_Trainer:
|
||||
# Load full checkpoint including optimizer and scheduler states
|
||||
self.accelerator.load_state(checkpoint_path)
|
||||
|
||||
self.print_rank0(f"Resumed from checkpoint: {checkpoint_path}")
|
||||
self.print_rank0(f"\033[32mResumed from checkpoint: {checkpoint_path}\033[0m")
|
||||
|
||||
def _load_fsdp_state_dict_with_distribute_tensor(self):
|
||||
|
||||
|
||||
+22
-1
@@ -27,7 +27,7 @@ bash ./workspace/lerobot_example/run.sh
|
||||
```
|
||||
|
||||
## Enable FAST tokenizer
|
||||
To fine-tune using the FAST tokenizer, please download the repository and update the `action_tokenizer_path`. Make sure to set `use_fast_tokenizer` to `true`:
|
||||
To fine-tune using the FAST tokenizer, please download the repository and update the `action_tokenizer_path`. Make sure to set `use_fast_tokenizer` to `true` and q01 and q99 to normalize the dataset, refer to `wall-x/scripts/compute_norm_stats.py`:
|
||||
```bash
|
||||
git clone https://huggingface.co/physical-intelligence/fast
|
||||
```
|
||||
@@ -38,7 +38,26 @@ pretrained_wallx_path: "/path/to/wallx_model/" # Path to pretrained wallx m
|
||||
save_path: "/path/to/workspace/" # Path to save training outputs
|
||||
use_fast_tokenizer: False # True: train FAST, False: train Flow
|
||||
action_tokenizer_path: "/path/to/fast/" # Must set if use_fast_tokenizer is True
|
||||
norm_stats_path: "/path/to/stats/" # Must set for normalize dataset
|
||||
```
|
||||
## Customize your robot configuration
|
||||
Ensure that the sum of the configuration dimensions corresponds to the values specified in norm_stats.json, and that each key is unique. The maximum dimensionality is set to 20, consistent with our robot configuration.
|
||||
```yaml
|
||||
customized_dof_config:
|
||||
"action_eef": 6
|
||||
"action_gripper": 1
|
||||
|
||||
customized_agent_pos_config:
|
||||
"state_eef_with_gripper": 7
|
||||
```
|
||||
|
||||
## Compute stats
|
||||
```bash
|
||||
python wall-x/scripts/compute_norm_stats.py
|
||||
```
|
||||
|
||||
## Configuration Explain
|
||||
- `agent_pos_config` corresponds to `obs_action_keys` and subsequently to state, while `dof_config` corresponds to `predict_action_keys` and subsequently to action. Note that the state and action may not necessarily share the same set of DoF.
|
||||
|
||||
## Training Parameters (Commonly Modified)
|
||||
|
||||
@@ -96,6 +115,8 @@ Keep `agent_pos_config` consistent with `dof_config`.
|
||||
```bash
|
||||
# refer to accelerate/commands/merge.py
|
||||
accelerate merge-weights /path/to/sharded_tensors /path/to/model.safetensors
|
||||
# copy the saved processor files
|
||||
cp /path/to/saved_processor_dir/* /path/to/model.safetensors
|
||||
```
|
||||
|
||||
## Memory Usage
|
||||
|
||||
@@ -20,7 +20,7 @@ profile_active_iters: 2
|
||||
# Training hyperparameters
|
||||
num_warmup_steps: 100
|
||||
num_training_steps: 64000000
|
||||
learning_rate: 0.00009
|
||||
learning_rate: 0.00005
|
||||
min_lr: 0.00005
|
||||
num_epoch: 100
|
||||
gradient_accumulation_steps: 32
|
||||
@@ -61,6 +61,41 @@ agent_pos_config:
|
||||
# ckpt: "/path/to/resume_model/"
|
||||
# load_ckpt_only: true
|
||||
|
||||
norm_stats_path: "/path/to/norm_stats.json"
|
||||
|
||||
enable_customized_robot_config: true
|
||||
customized_robot_config:
|
||||
name: "lerobot/aloha_mobile_cabinet"
|
||||
customized_dof_config:
|
||||
"action_left_shoulder" : 1
|
||||
"action_left_elbow" : 1
|
||||
"action_left_forearm_roll" : 1
|
||||
"action_left_wrist_angle" : 1
|
||||
"action_left_wrist_rotate" : 1
|
||||
"action_left_gripper" : 1
|
||||
"action_right_waist" : 1
|
||||
"action_right_shoulder" : 1
|
||||
"action_right_elbow" : 1
|
||||
"action_right_forearm_roll" : 1
|
||||
"action_right_wrist_angle" : 1
|
||||
"action_right_wrist_rotate" : 1
|
||||
"action_right_gripper" : 1
|
||||
|
||||
customized_agent_pos_config:
|
||||
"state_left_shoulder" : 1
|
||||
"state_left_elbow" : 1
|
||||
"state_left_forearm_roll" : 1
|
||||
"state_left_wrist_angle" : 1
|
||||
"state_left_wrist_rotate" : 1
|
||||
"state_left_gripper" : 1
|
||||
"state_right_waist" : 1
|
||||
"state_right_shoulder" : 1
|
||||
"state_right_elbow" : 1
|
||||
"state_right_forearm_roll" : 1
|
||||
"state_right_wrist_angle" : 1
|
||||
"state_right_wrist_rotate" : 1
|
||||
"state_right_gripper" : 1
|
||||
|
||||
# Data configuration
|
||||
data:
|
||||
use_lerobot: true
|
||||
|
||||
@@ -62,6 +62,41 @@ agent_pos_config:
|
||||
# ckpt: "/path/to/resume_model/"
|
||||
# load_ckpt_only: true
|
||||
|
||||
norm_stats_path: "/path/to/norm_stats.json"
|
||||
|
||||
enable_customized_robot_config: true
|
||||
customized_robot_config:
|
||||
name: "physical-intelligence/libero"
|
||||
customized_dof_config:
|
||||
"action_left_shoulder" : 1
|
||||
"action_left_elbow" : 1
|
||||
"action_left_forearm_roll" : 1
|
||||
"action_left_wrist_angle" : 1
|
||||
"action_left_wrist_rotate" : 1
|
||||
"action_left_gripper" : 1
|
||||
"action_right_waist" : 1
|
||||
"action_right_shoulder" : 1
|
||||
"action_right_elbow" : 1
|
||||
"action_right_forearm_roll" : 1
|
||||
"action_right_wrist_angle" : 1
|
||||
"action_right_wrist_rotate" : 1
|
||||
"action_right_gripper" : 1
|
||||
|
||||
customized_agent_pos_config:
|
||||
"state_left_shoulder" : 1
|
||||
"state_left_elbow" : 1
|
||||
"state_left_forearm_roll" : 1
|
||||
"state_left_wrist_angle" : 1
|
||||
"state_left_wrist_rotate" : 1
|
||||
"state_left_gripper" : 1
|
||||
"state_right_waist" : 1
|
||||
"state_right_shoulder" : 1
|
||||
"state_right_elbow" : 1
|
||||
"state_right_forearm_roll" : 1
|
||||
"state_right_wrist_angle" : 1
|
||||
"state_right_wrist_rotate" : 1
|
||||
"state_right_gripper" : 1
|
||||
|
||||
# Data configuration
|
||||
data:
|
||||
use_lerobot: true
|
||||
|
||||
@@ -0,0 +1,124 @@
|
||||
# Training Configuration for Wall-X Robotic Multi-Modal Learning
|
||||
# This configuration supports multi-modal learning with vision, language, and action data
|
||||
|
||||
# Model and paths configuration
|
||||
log_name: "opensource_training"
|
||||
log_project: "libero"
|
||||
model_type: qwen2_5
|
||||
use_fast_tokenizer: true
|
||||
pretrained_wallx_path: "/path/to/qwen/"
|
||||
action_tokenizer_path: "/path/to/fast/"
|
||||
qwen_vl_act_config_path: "/path/to/qwen25_config.json"
|
||||
|
||||
save_path: "/path/to/save"
|
||||
# Torch Profile
|
||||
profile: False
|
||||
profile_save_path: /path/to/profile/
|
||||
profile_wait_iters: 10
|
||||
profile_warmup_iters: 5
|
||||
profile_active_iters: 2
|
||||
|
||||
# Training hyperparameters
|
||||
num_warmup_steps: 100
|
||||
num_training_steps: 64000000
|
||||
learning_rate: 0.00005
|
||||
min_lr: 0.00005
|
||||
num_epoch: 100
|
||||
gradient_accumulation_steps: 1
|
||||
batch_size_per_gpu: 8
|
||||
padding_side: left
|
||||
epoch_save_interval: 1
|
||||
|
||||
# Robot configuration - Define degrees of freedom for each component
|
||||
dof_config:
|
||||
follow_left_ee_cartesian_pos: 3 # Left end-effector Cartesian position
|
||||
follow_left_ee_rotation: 3 # Left end-effector rotation
|
||||
follow_left_gripper: 1 # Left gripper control
|
||||
follow_right_ee_cartesian_pos: 3 # Right end-effector Cartesian position
|
||||
follow_right_ee_rotation: 3 # Right end-effector rotation
|
||||
follow_right_gripper: 1 # Right gripper control
|
||||
head_actions: 2 # Head/camera movement
|
||||
height: 1 # Mobile base height control
|
||||
car_pose: 3 # Mobile base pose (x, y, theta)
|
||||
|
||||
# Agent proprioception configuration (typically matches DOF config)
|
||||
agent_pos_config:
|
||||
follow_left_ee_cartesian_pos: 3
|
||||
follow_left_ee_rotation: 3
|
||||
follow_left_gripper: 1
|
||||
follow_right_ee_cartesian_pos: 3
|
||||
follow_right_ee_rotation: 3
|
||||
follow_right_gripper: 1
|
||||
head_actions: 2
|
||||
height: 1
|
||||
car_pose: 3
|
||||
|
||||
norm_stats_path: "wall-x/workspace/lerobot_example/libero/libero_norm_stats.json"
|
||||
|
||||
enable_customized_robot_config: true
|
||||
customized_robot_config:
|
||||
name: "physical-intelligence/libero"
|
||||
customized_dof_config:
|
||||
"panda_action_eef_with_gripper": 7
|
||||
|
||||
customized_agent_pos_config:
|
||||
"panda_state_eef_with_gripper": 8
|
||||
|
||||
# Checkpoint resuming configuration
|
||||
# resume:
|
||||
# ckpt: "/path/to/ckpt"
|
||||
# load_ckpt_only: false
|
||||
|
||||
# Data configuration
|
||||
data:
|
||||
use_lerobot: true
|
||||
|
||||
# LeRobot dataset configuration
|
||||
lerobot_config:
|
||||
repo_id: "physical-intelligence/libero"
|
||||
root: null
|
||||
episodes: null
|
||||
image_transforms: null
|
||||
delta_timestamps: null
|
||||
tolerance_s: 1e-4
|
||||
revision: null
|
||||
force_cache_sync: false
|
||||
download_videos: true
|
||||
video_backend: null
|
||||
|
||||
action_horizon: 32
|
||||
train_test_split: 0.95
|
||||
|
||||
# Action keys for observation and prediction
|
||||
obs_action_keys:
|
||||
- follow_left_ee_cartesian_pos
|
||||
- follow_left_ee_rotation
|
||||
- follow_left_gripper
|
||||
- follow_right_ee_cartesian_pos
|
||||
- follow_right_ee_rotation
|
||||
- follow_right_gripper
|
||||
- head_actions
|
||||
- height
|
||||
- car_pose
|
||||
|
||||
predict_action_keys:
|
||||
- follow_left_ee_cartesian_pos
|
||||
- follow_left_ee_rotation
|
||||
- follow_left_gripper
|
||||
- follow_right_ee_cartesian_pos
|
||||
- follow_right_ee_rotation
|
||||
- follow_right_gripper
|
||||
- head_actions
|
||||
- height
|
||||
- car_pose
|
||||
|
||||
# Image resolution configuration for different camera views
|
||||
resolution:
|
||||
face_view: 256
|
||||
left_wrist_view: 256
|
||||
right_wrist_view: 256
|
||||
move1_view: 256
|
||||
move2_view: 256
|
||||
top_view: 256
|
||||
wall_view: 256
|
||||
multi_modal: 256
|
||||
Reference in New Issue
Block a user