feat(dataset): 添加统计数据计算脚本

roboimi/vla/scripts/calculate_stats.py

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+import h5py
+import numpy as np
+import os
+import glob
+import pickle
+
+def get_data_stats(dataset_dir):
+    """
+    计算 action 和 qpos 的 Min, Max, Mean, Std
+    """
+    files = sorted(glob.glob(os.path.join(dataset_dir, 'episode_*.hdf5')))
+    print(f"Found {len(files)} episodes in {dataset_dir}")
+
+    all_actions = []
+    all_qpos = []
+
+    print("Reading data...")
+    for file_path in files:
+        with h5py.File(file_path, 'r') as f:
+            action = f['action'][:] 
+            qpos = f['observations']['qpos'][:] 
+            all_actions.append(action)
+            all_qpos.append(qpos)
+
+    # 拼接所有数据
+    all_actions = np.concatenate(all_actions, axis=0)
+    all_qpos = np.concatenate(all_qpos, axis=0)
+
+    print(f"Total steps: {all_actions.shape[0]}")
+
+    # --- 核心计算部分 ---
+    stats = {
+        'action': {
+            'min': np.min(all_actions, axis=0),
+            'max': np.max(all_actions, axis=0),
+            'mean': np.mean(all_actions, axis=0), # 均值
+            'std': np.std(all_actions, axis=0)    # 标准差
+        },
+        'qpos': {
+            'min': np.min(all_qpos, axis=0),
+            'max': np.max(all_qpos, axis=0),
+            'mean': np.mean(all_qpos, axis=0),    # 均值
+            'std': np.std(all_qpos, axis=0)       # 标准差
+        }
+    }
+
+    # --- 修正标准差 (防止除以 0) ---
+    # 如果某个关节从未移动（例如备用按钮），std 会是 0，导致除零错误。
+    # 策略：将 std 为 0 的地方替换为 1.0 (不缩放) 或一个小的 epsilon
+    for key in stats:
+        # 找到 std 极小的维度
+        std = stats[key]['std']
+        std = np.where(std < 1e-8, 1.0, std) # 如果 std 太小，设为 1.0 避免除零
+        stats[key]['std'] = std
+
+    return stats
+
+if __name__ == "__main__":
+    DATASET_DIR = 'roboimi/demos/dataset/sim_transfer'
+    OUTPUT_PATH = DATASET_DIR + "/data_stats.pkl"
+
+    stats = get_data_stats(DATASET_DIR)
+
+    # 打印检查
+    print("\n--- Stats Computed ---")
+    print(f"Action Mean shape: {stats['action']['mean'].shape}")
+    print(f"Action Std shape:  {stats['action']['std'].shape}")
+    
+    # 保存
+    with open(OUTPUT_PATH, 'wb') as f:
+        pickle.dump(stats, f)
+    print(f"\nStats saved to {OUTPUT_PATH}")