change env_num to 1

Author: kew6688

scripts/eval/configs/challenge_cfg.py

@@ -18,14 +18,14 @@
         env_type='vln_pe',
         env_settings={
             'use_fabric': False,
-            'headless': True,   # display option: set to False will open isaac-sim interactive window
+            'headless': True,  # display option: set to False will open isaac-sim interactive window
         },
     ),
     task=TaskCfg(
         task_name='rdp_iros_test',
         task_settings={
-            'env_num': 2,
-            'use_distributed': False,   # Ray distributed framework
+            'env_num': 1,
+            'use_distributed': False,  # Ray distributed framework
             'proc_num': 8,
         },
         scene=SceneCfg(
@@ -34,7 +34,7 @@
         ),
         robot_name='h1',
         robot_usd_path='data/Embodiments/vln-pe/h1/h1_vln_pointcloud.usd',
-        camera_resolution=[256,256], # (W,H)
+        camera_resolution=[256, 256],  # (W,H)
         camera_prim_path='torso_link/h1_pano_camera_0',
     ),
     dataset=EvalDatasetCfg(
@@ -45,8 +45,5 @@
             'filter_stairs': False,
         },
     ),
-    eval_settings={
-        'save_to_json': True,
-        'vis_output': True      # save result to video under logs/
-    }
+    eval_settings={'save_to_json': True, 'vis_output': True},  # save result to video under logs/
 )

scripts/eval/configs/challenge_kujiale_cfg.py

@@ -18,14 +18,14 @@
         env_type='vln_pe',
         env_settings={
             'use_fabric': False,
-            'headless': True,   # display option: set to False will open isaac-sim interactive window
+            'headless': True,  # display option: set to False will open isaac-sim interactive window
         },
     ),
     task=TaskCfg(
         task_name='challenge_kujiale_eval',
         task_settings={
-            'env_num': 2,
-            'use_distributed': False,   # Ray distributed framework
+            'env_num': 1,
+            'use_distributed': False,  # Ray distributed framework
             'proc_num': 8,
         },
         scene=SceneCfg(
@@ -34,7 +34,7 @@
         ),
         robot_name='h1',
         robot_usd_path='data/Embodiments/vln-pe/h1/h1_vln_pointcloud.usd',
-        camera_resolution=[256,256], # (W,H)
+        camera_resolution=[256, 256],  # (W,H)
         camera_prim_path='torso_link/h1_pano_camera_0',
     ),
     dataset=EvalDatasetCfg(
@@ -45,8 +45,5 @@
             'filter_stairs': False,
         },
     ),
-    eval_settings={
-        'save_to_json': True,
-        'vis_output': True      # save result to video under logs/
-    }
+    eval_settings={'save_to_json': True, 'vis_output': True},  # save result to video under logs/
 )

scripts/eval/configs/challenge_mp3d_cfg.py

@@ -24,7 +24,7 @@
     task=TaskCfg(
         task_name='challenge_mp3d_eval',
         task_settings={
-            'env_num': 2,
+            'env_num': 1,
             'use_distributed': False,
             'proc_num': 2,
         },
@@ -34,10 +34,10 @@
         ),
         robot_name='h1',
         robot_usd_path='data/Embodiments/vln-pe/h1/h1_vln_pointcloud.usd',
-        camera_resolution=[256,256], # (W,H)
+        camera_resolution=[256, 256],  # (W,H)
         camera_prim_path='torso_link/h1_pano_camera_0',
-        vlnce=False,   # vlnpe by default
-        obstacle_detection=False,   # whether allow flash across obstacle
+        vlnce=False,  # vlnpe by default
+        obstacle_detection=False,  # whether allow flash across obstacle
     ),
     dataset=EvalDatasetCfg(
         dataset_type="mp3d",
@@ -47,8 +47,5 @@
             'filter_stairs': False,
         },
     ),
-    eval_settings={
-        'save_to_json': False,
-        'vis_output': True     
-    }
+    eval_settings={'save_to_json': False, 'vis_output': True},
 )

setup.cfg

@@ -24,7 +24,6 @@ skip =
     *.txt
     *.md
     *.json
-exclude = internnav/model/basemodel/LongCLIP/eval/classification/imagenet/categories.txt
 
 
 [flake8]