fix fabric camera delay issue

Author: kew6688

scripts/eval/configs/h1_internvla_n1_async_cfg.py

@@ -14,61 +14,61 @@
         model_name='internvla_n1',
         ckpt_path='',
         model_settings={
-            'env_num': 1, 'sim_num': 1,
+            'env_num': 1,
+            'sim_num': 1,
             'model_path': "checkpoints/InternVLA-N1",
-            'camera_intrinsic': [
-                [585.0, 0.0, 320.0], [0.0, 585.0, 240.0], [0.0, 0.0, 1.0]
-            ],
-            
-            'width': 640, 'height': 480, 'hfov': 79,
-            'resize_w': 384, 'resize_h': 384,
+            'camera_intrinsic': [[585.0, 0.0, 320.0], [0.0, 585.0, 240.0], [0.0, 0.0, 1.0]],
+            'width': 640,
+            'height': 480,
+            'hfov': 79,
+            'resize_w': 384,
+            'resize_h': 384,
             'max_new_tokens': 1024,
             'num_frames': 32,
             'num_history': 8,
             'num_future_steps': 4,
-            
             'device': 'cuda:1',
             'predict_step_nums': 32,
-            'continuous_traj': True, 
-            
-            'infer_mode': 'partial_async', # You can choose "sync" or "partial_async", but for this model, "partial_async" is better.
+            'continuous_traj': True,
+            'infer_mode': 'partial_async',  # You can choose "sync" or "partial_async", but for this model, "partial_async" is better.
             # debug
-            'vis_debug': True, # If vis_debug=True, you can get visualization results
-            'vis_debug_path': './logs/test/vis_debug' 
+            'vis_debug': True,  # If vis_debug=True, you can get visualization results
+            'vis_debug_path': './logs/test/vis_debug',
         },
     ),
     env=EnvCfg(
         env_type='vln_multi',
         env_settings={
-            'use_fabric': False, # Please set use_fabric=False due to the render delay;
+            'rendering_interval': 0,  # You can set rendering_interval=0 to avoid the render delay.
+            'use_fabric': True,
             'headless': True,
         },
     ),
     task=TaskCfg(
         task_name='test',
         task_settings={
             'env_num': 1,
-            'use_distributed': False, # If the others setting in task_settings, please set use_distributed = False.
+            'use_distributed': False,  # If the others setting in task_settings, please set use_distributed = False.
             'proc_num': 1,
-            # 'max_step': 1000, #If use flash mode，default 1000; descrete mode, set 50000 
+            # 'max_step': 1000, #If use flash mode，default 1000; descrete mode, set 50000
         },
         scene=SceneCfg(
             scene_type='mp3d',
             scene_data_dir='data/scene_data/mp3d_pe',
         ),
         robot_name='h1',
-        robot_flash=True, # If robot_flash is True, the mode is flash (set world_pose directly); else you choose physical mode.
+        robot_flash=True,  # If robot_flash is True, the mode is flash (set world_pose directly); else you choose physical mode.
         robot_usd_path='data/Embodiments/vln-pe/h1/h1_internvla.usd',
-        camera_resolution=[640, 480], # (W,H)
+        camera_resolution=[640, 480],  # (W,H)
         camera_prim_path='torso_link/h1_1_25_down_30',
-        one_step_stand_still = True, #For dual-system, please keep this param True.
+        one_step_stand_still=True,  # For dual-system, please keep this param True.
     ),
     dataset=EvalDatasetCfg(
         dataset_type="mp3d",
         dataset_settings={
             'base_data_dir': 'data/vln_pe/raw_data/r2r',
             'split_data_types': ['val_seen', 'val_unseen'],  # 'val_seen'
-            'filter_stairs': False, # For iros challenge, this is False; For results in the paper, this is True.
+            'filter_stairs': False,  # For iros challenge, this is False; For results in the paper, this is True.
             # 'selected_scans': ['zsNo4HB9uLZ'],
             # 'selected_scans': ['8194nk5LbLH', 'pLe4wQe7qrG'],
         },

scripts/eval/configs/h1_internvla_n1_cfg.py

@@ -14,58 +14,59 @@
         model_name='internvla_n1',
         ckpt_path='',
         model_settings={
-            'env_num': 1, 'sim_num': 1,
+            'env_num': 1,
+            'sim_num': 1,
             'model_path': "checkpoints/InternVLA-N1-Preview",
-            'camera_intrinsic': [
-                [585.0, 0.0, 320.0], [0.0, 585.0, 240.0], [0.0, 0.0, 1.0]
-            ],
-            
-            'width': 640, 'height': 480, 'hfov': 79,
-            'resize_w': 384, 'resize_h': 384,
+            'camera_intrinsic': [[585.0, 0.0, 320.0], [0.0, 585.0, 240.0], [0.0, 0.0, 1.0]],
+            'width': 640,
+            'height': 480,
+            'hfov': 79,
+            'resize_w': 384,
+            'resize_h': 384,
             'max_new_tokens': 1024,
             'num_frames': 32,
             'num_history': 8,
             'num_future_steps': 4,
-            
             'device': 'cuda:1',
             'predict_step_nums': 32,
-            'continuous_traj': True, 
+            'continuous_traj': True,
             # debug
-            'vis_debug': True, # If vis_debug=True, you can get visualization results
-            'vis_debug_path': './logs/test/vis_debug' 
+            'vis_debug': True,  # If vis_debug=True, you can get visualization results
+            'vis_debug_path': './logs/test/vis_debug',
         },
     ),
     env=EnvCfg(
         env_type='vln_multi',
         env_settings={
-            'use_fabric': False, # Please set use_fabric=False due to the render delay;
+            'rendering_interval': 0,  # You can set rendering_interval=0 to avoid the render delay.
+            'use_fabric': True,
             'headless': True,
         },
     ),
     task=TaskCfg(
         task_name='test',
         task_settings={
             'env_num': 1,
-            'use_distributed': False, # If the others setting in task_settings, please set use_distributed = False.
+            'use_distributed': False,  # If the others setting in task_settings, please set use_distributed = False.
             'proc_num': 1,
         },
         scene=SceneCfg(
             scene_type='mp3d',
             scene_data_dir='data/scene_data/mp3d_pe',
         ),
         robot_name='h1',
-        robot_flash=True, # If robot_flash is True, the mode is flash (set world_pose directly); else you choose physical mode.
+        robot_flash=True,  # If robot_flash is True, the mode is flash (set world_pose directly); else you choose physical mode.
         robot_usd_path='data/Embodiments/vln-pe/h1/h1_internvla.usd',
-        camera_resolution=[640, 480], # (W,H)
+        camera_resolution=[640, 480],  # (W,H)
         camera_prim_path='torso_link/h1_1_25_down_30',
-        one_step_stand_still = True, #For dual-system, please keep this param True.
+        one_step_stand_still=True,  # For dual-system, please keep this param True.
     ),
     dataset=EvalDatasetCfg(
         dataset_type="mp3d",
         dataset_settings={
             'base_data_dir': 'data/vln_pe/raw_data/r2r',
             'split_data_types': ['val_unseen'],  # 'val_seen'
-            'filter_stairs': False,      
+            'filter_stairs': False,
             # 'selected_scans': ['zsNo4HB9uLZ'],
             # 'selected_scans': ['8194nk5LbLH', 'pLe4wQe7qrG'],
         },