Init

scripts/draw_openloop_plot.py

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+import os
+import yaml
+import torch
+from wall_x.model.qwen2_5_based.modeling_qwen2_5_vl_act import Qwen2_5_VLMoEForAction
+from wall_x.data.load_lerobot_dataset import load_test_dataset, get_data_configs
+
+
+model_path = "path/to/model"
+action_tokenizer_path = "path/to/action_tokenizer"
+save_dir = "path/to/plot"
+model = Qwen2_5_VLMoEForAction.from_pretrained(model_path, action_tokenizer_path=action_tokenizer_path)
+model.eval()
+model = model.to("cuda")
+model = model.bfloat16()
+
+def load_config(config_path):
+    """Load configuration from YAML file."""
+    with open(config_path, "r") as f:
+        config = yaml.load(f, Loader=yaml.FullLoader)
+    
+    config["data"]["model_type"] = config.get("model_type")
+    
+    return config
+
+# get test dataloader
+path = "path/to/config"
+config = load_config(path)
+dataload_config = get_data_configs(config["data"])
+lerobot_config = dataload_config.get("lerobot_config", {})
+dataset = load_test_dataset(config, lerobot_config, seed=42)
+dataloader = dataset.get_dataloader()
+
+total_frames = len(dataloader)
+
+pred_horizon = 32
+action_dim = 14
+gt_traj = torch.zeros((total_frames, action_dim))
+pred_traj = torch.zeros((total_frames, action_dim))
+
+for idx, batch in enumerate(dataloader):
+    gt_traj[idx] = batch['action_chunk'][0, 0,:action_dim]
+    if idx % 32 ==0 and idx + 32 < total_frames:
+        batch = batch.to("cuda")
+        with torch.no_grad():
+            outputs = model(
+                **batch,
+                action_dim=action_dim,
+                pred_horizon=pred_horizon,
+                mode="predict",
+                predict_mode="fast"
+            )
+        pred_traj[idx : idx + pred_horizon] = outputs['predict_action'].detach().cpu()
+
+
+gt_traj_np = gt_traj.numpy()
+pred_traj_np = pred_traj.numpy()
+
+timesteps = gt_traj.shape[0]
+
+import matplotlib.pyplot as plt
+
+fig, axs = plt.subplots(action_dim, 1, figsize=(15, 5 * action_dim), sharex=True)
+fig.suptitle(f'Action Comparison for lerobot', fontsize=16)
+
+for i in range(action_dim):
+    axs[i].plot(range(timesteps), gt_traj_np[:, i], label='Ground Truth')
+    axs[i].plot(range(timesteps), pred_traj_np[:, i], label='Prediction')
+    axs[i].set_ylabel(f'Action Dim {i+1}')
+    axs[i].legend()
+    axs[i].grid(True)
+    
+axs[-1].set_xlabel('Timestep')
+plt.tight_layout(rect=[0, 0.03, 1, 0.95])
+os.makedirs(save_dir, exist_ok=True)
+plt.savefig(os.path.join(save_dir, f"lerobot_comparison.png"))
+plt.close()