Update

eval.py

@@ -1,12 +1,14 @@
 import re
+from pathlib import Path
+
 import cv2
 import hydra
-from omegaconf import OmegaConf
 import ray
+from omegaconf import OmegaConf
 from ray.rllib.agents import ppo
 from ray.tune.registry import register_env
-from pathlib import Path
 
+import ig_navigation
 from ig_navigation.callbacks import MetricsCallback
 
 
@@ -20,7 +22,8 @@ def igibson_env_creator(env_config):
         physics_timestep=1 / 120.0,
     )
 
-@hydra.main(config_path=ssg.CONFIG_PATH, config_name="config")
+
+@hydra.main(config_path=ig_navigation.CONFIG_PATH, config_name="config")
 def main(cfg):
     ray.init()
     env_config = OmegaConf.to_object(cfg)
@@ -68,14 +71,11 @@ def main(cfg):
     successes = 0
     frames.append(env.render())
 
-    video_folder = Path('eval', cfg.experiment_name, 'videos')
-    video_folder.mkdir(parents = True, exist_ok = True)
-    video_path = f'eval_episodes.mp4'
+    video_folder = Path("eval", cfg.experiment_name, "videos")
+    video_folder.mkdir(parents=True, exist_ok=True)
+    video_path = f"eval_episodes.mp4"
     video = cv2.VideoWriter(
-        video_path,
-        cv2.VideoWriter_fourcc(*"mp4v"),
-        15,
-        frames[0].shape[:2]
+        video_path, cv2.VideoWriter_fourcc(*"mp4v"), 15, frames[0].shape[:2]
     )
 
     for _ in range(100):
@@ -89,15 +89,15 @@ def main(cfg):
             obs, reward, done, info = env.step(action)
             episode_reward += reward
             frames.append(env.render())
-            for reward, value in info['reward_breakdown'].items():
+            for reward, value in info["reward_breakdown"].items():
                 reward_breakdown[reward] += value
-            if info['success']:
+            if info["success"]:
                 assert done
                 success = True
-                successes +=1
+                successes += 1
         trials += 1
-        print('Success: ', success)
-        print('episode reward: ', episode_reward)
+        print("Success: ", success)
+        print("episode reward: ", episode_reward)
         for key, value in reward_breakdown.items():
             print(f"{key}: {value}")
         print()
@@ -108,5 +108,6 @@ def main(cfg):
         video.write(screen)
     video.release()
 
+
 if __name__ == "__main__":
     main()

ig_navigation/__init__.py

@@ -1,8 +1,10 @@
 import os
-from ig_navigation.turtlebot import Turtlebot
+
 from igibson.robots.robot_base import REGISTERED_ROBOTS
 
-ROOT_PATH= os.path.dirname(__file__)
-CONFIG_PATH=os.path.join(ROOT_PATH, '..', 'configs')
+from ig_navigation.turtlebot import Turtlebot
+
+ROOT_PATH = os.path.dirname(__file__)
+CONFIG_PATH = os.path.join(ROOT_PATH, "..", "configs")
 
 REGISTERED_ROBOTS["Turtlefast"] = Turtlebot

ig_navigation/callbacks.py

@@ -1,7 +1,8 @@
-from ray.rllib.agents.callbacks import DefaultCallbacks
-import cv2
 from pathlib import Path
 
+import cv2
+from ray.rllib.agents.callbacks import DefaultCallbacks
+
 
 class MetricsCallback(DefaultCallbacks):
     def on_episode_step(self, *, worker, base_env, policies, episode, **kwargs):

ig_navigation/floor_sampler.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pybullet as p
-
 from igibson.external.pybullet_tools.utils import (
     get_aabb_center,
     get_aabb_extent,

ig_navigation/igibson_env.py

@@ -1,17 +1,16 @@
 import argparse
 
-from igibson.envs.igibson_env import iGibsonEnv
-import numpy as np
-
-import numpy as np
-
 import gym
+import numpy as np
+from igibson.envs.igibson_env import iGibsonEnv
+from igibson.sensors.bump_sensor import BumpSensor
 from igibson.sensors.scan_sensor import ScanSensor
 from igibson.sensors.vision_sensor import VisionSensor
-from igibson.sensors.bump_sensor import BumpSensor
 from igibson.utils.constants import MAX_CLASS_COUNT, MAX_INSTANCE_COUNT
+
 from ig_navigation.search_task import SearchTask
 
+
 class SearchEnv(iGibsonEnv):
     metadata = {"render.modes": ["rgb_array"]}
 
@@ -113,12 +112,16 @@ def load_observation_space(self):
             vision_modalities.append("normal")
         if "seg" in self.output:
             observation_space["seg"] = self.build_obs_space(
-                shape=(self.image_height, self.image_width, 1), low=0.0, high=MAX_CLASS_COUNT
+                shape=(self.image_height, self.image_width, 1),
+                low=0.0,
+                high=MAX_CLASS_COUNT,
             )
             vision_modalities.append("seg")
         if "ins_seg" in self.output:
             observation_space["ins_seg"] = self.build_obs_space(
-                shape=(self.image_height, self.image_width, 1), low=0.0, high=MAX_INSTANCE_COUNT
+                shape=(self.image_height, self.image_width, 1),
+                low=0.0,
+                high=MAX_INSTANCE_COUNT,
             )
             vision_modalities.append("ins_seg")
         if "rgb_filled" in self.output:  # use filler
@@ -134,9 +137,13 @@ def load_observation_space(self):
         if "scan" in self.output:
             self.n_horizontal_rays = self.config.get("n_horizontal_rays", 128)
             self.n_vertical_beams = self.config.get("n_vertical_beams", 1)
-            assert self.n_vertical_beams == 1, "scan can only handle one vertical beam for now"
+            assert (
+                self.n_vertical_beams == 1
+            ), "scan can only handle one vertical beam for now"
             observation_space["scan"] = self.build_obs_space(
-                shape=(self.n_horizontal_rays * self.n_vertical_beams, 1), low=0.0, high=1.0
+                shape=(self.n_horizontal_rays * self.n_vertical_beams, 1),
+                low=0.0,
+                high=1.0,
             )
             scan_modalities.append("scan")
         if "occupancy_grid" in self.output:
@@ -188,7 +195,6 @@ def get_state(self):
         return state
 
 
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(

ig_navigation/search_reward.py

@@ -1,8 +1,7 @@
-from igibson.reward_functions.reward_function_base import BaseRewardFunction
-from igibson.utils.utils import l2_distance
 import numpy as np
-
 from igibson.object_states.robot_related_states import ObjectsInFOVOfRobot
+from igibson.reward_functions.reward_function_base import BaseRewardFunction
+from igibson.utils.utils import l2_distance
 
 
 class SearchReward(BaseRewardFunction):

ig_navigation/search_task.py

@@ -1,41 +1,24 @@
-import numpy as np
+import os
 
+import numpy as np
 from bddl.object_taxonomy import ObjectTaxonomy
-from igibson.utils.assets_utils import (
-    get_ig_avg_category_specs,
-    get_ig_category_path,
-    get_ig_model_path,
-)
-
+from igibson.object_states.robot_related_states import ObjectsInFOVOfRobot
 from igibson.objects.articulated_object import URDFObject
 from igibson.tasks.task_base import BaseTask
-
-from ig_navigation.floor_sampler import sample_on_floor
-from ig_navigation.search_reward import SearchReward, PotentialReward, PotentialReward
-from igibson.termination_conditions.timeout import Timeout
-
 from igibson.termination_conditions.termination_condition_base import (
     BaseTerminationCondition,
 )
-from igibson.utils.utils import l2_distance
-import numpy as np
-import os
-
-from igibson.object_states.robot_related_states import ObjectsInFOVOfRobot
-
-from bddl.object_taxonomy import ObjectTaxonomy
+from igibson.termination_conditions.timeout import Timeout
 from igibson.utils.assets_utils import (
     get_ig_avg_category_specs,
     get_ig_category_path,
     get_ig_model_path,
 )
+from igibson.utils.utils import l2_distance
 
-from igibson.objects.articulated_object import URDFObject
-from igibson.tasks.task_base import BaseTask
-
-from igibson.termination_conditions.timeout import Timeout
+from ig_navigation.floor_sampler import sample_on_floor
+from ig_navigation.search_reward import PotentialReward, SearchReward
 
-import os
 
 class SearchTermination(BaseTerminationCondition):
     """
@@ -62,7 +45,10 @@ def get_termination(self, task, env):
             )
             < self.dist_tol
         )
-        in_view = (task.target_obj.main_body in env.robots[0].states[ObjectsInFOVOfRobot].get_value())
+        in_view = (
+            task.target_obj.main_body
+            in env.robots[0].states[ObjectsInFOVOfRobot].get_value()
+        )
         done = done and in_view
         success = done
         return done, success
@@ -100,9 +86,7 @@ def get_sampleable_categories(self):
 
     def choose_task(self):
         # obj_pro = self.import_object(wordnet_category = 'microwave.n.02' , model='7128')
-        obj_pro = self.import_object(
-            igibson_category="microwave", model="7320"
-        )
+        obj_pro = self.import_object(igibson_category="microwave", model="7320")
         self.target_obj = obj_pro
         room = np.random.choice(np.array(list(self.scene.room_ins_name_to_ins_id)))
         sample_on_floor(obj_pro, self.scene, room=room)

ig_navigation/turtlebot.py

@@ -1,13 +1,11 @@
 import os
 
-import numpy as np
-
-import igibson
-from igibson.robots.two_wheel_robot import TwoWheelRobot
 import gym
-from scipy.spatial.transform import Rotation as R
 import igibson
+import numpy as np
 import pybullet as p
+from igibson.robots.two_wheel_robot import TwoWheelRobot
+from scipy.spatial.transform import Rotation as R
 
 
 class Turtlebot(TwoWheelRobot):

keyboard_demo.py

@@ -2,12 +2,13 @@
 
 import cv2
 import hydra
+import matplotlib.pyplot as plt
 from igibson.render.mesh_renderer.mesh_renderer_settings import MeshRendererSettings
 from omegaconf import OmegaConf
 
 import ig_navigation
 from ig_navigation.igibson_env import SearchEnv
-import matplotlib.pyplot as plt
+
 
 @hydra.main(config_path=ig_navigation.CONFIG_PATH, config_name="config")
 def main(cfg):

setup.py

@@ -4,5 +4,5 @@
     name="ig_navigation",
     version="0.0.1",
     author="Stanford University",
-    packages=['ig_navigation']
+    packages=["ig_navigation"],
 )

train.py

@@ -1,15 +1,16 @@
 import re
-import numpy as np
+from pathlib import Path
+
 import hydra
-from omegaconf import OmegaConf
+import numpy as np
 import ray
+from omegaconf import OmegaConf
 from ray.rllib.agents import ppo
-from ray.tune.registry import register_env
-from pathlib import Path
 from ray.tune.logger import UnifiedLogger
+from ray.tune.registry import register_env
 
 import ig_navigation
-from ig_navigation.callbacks import MetricsCallback, DummyCallback
+from ig_navigation.callbacks import DummyCallback, MetricsCallback
 
 
 def igibson_env_creator(env_config):
@@ -78,7 +79,7 @@ def main(cfg):
     Path(log_path).mkdir(parents=True, exist_ok=True)
     trainer = ppo.PPOTrainer(
         config,
-        logger_creator=lambda x: UnifiedLogger(x, log_path),
+        logger_creator=lambda x: UnifiedLogger(x, log_path),  # type: ignore
     )
 
     if Path(checkpoint_path).exists():