environments/gym-avd/gym_avd/envs/avd_nav_env.py [649:703]:
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    def reset(self, scene_idx: Optional[int] = None):
        # Spawn agent in a new scene at a random image.
        if self.episodes is None:
            print("Sampling random episodes!")
            if scene_idx is None:
                scenes = getattr(self, "{}_scenes".format(self.split))
                self.scene_idx = self._rng.choice(scenes)
            else:
                self.scene_idx = scene_idx
        else:
            if self.episodes_idx is None:
                self.episodes_idx = 0
            else:
                self.episodes_idx = (self.episodes_idx + 1) % len(self.episodes)
            self.scene_idx = self.episodes[self.episodes_idx]["scene_idx"]
            print(
                "Sampling from pre-defined episodes! Scene id: "
                f"{self.scene_idx}, Episode id: {self.episodes_idx}"
            )
        self.scene_id = self.all_scenes[self.scene_idx]
        scene_conn = self.data_conn[self.scene_idx]
        self.images_to_idx = scene_conn["images_to_idx"]
        self.images_to_camera = scene_conn["images_to_camera"]
        self.images_to_nodes = scene_conn["images_to_nodes"]
        self.scale = scene_conn["scale"]  # Converts positions to mm.
        if self.episodes is None:
            self.agent_image = self._rng.choice(list(self.images_to_idx.keys()))
            while not self.is_valid_image(self.agent_image):
                self.agent_image = self._rng.choice(list(self.images_to_idx.keys()))
        else:
            self.agent_image = self.episodes[self.episodes_idx]["start_image"]
        # Initialize the environment variables.
        self.start_position = self._get_position(self.agent_image)
        self.start_pose = self._get_pose(self.agent_image)
        self.exp_start_image = self.agent_image
        self.exp_start_position = copy.deepcopy(self.start_position)
        self.exp_start_pose = self.start_pose
        self.agent_pose = self.start_pose
        self.agent_position = copy.deepcopy(self.start_position)
        self.scene_images = np.array(self.data_h5[f"{self.scene_id}/rgb"])
        self.scene_depth = np.array(self.data_h5[f"{self.scene_id}/depth"])
        self.delta = (0.0, 0.0, 0.0, 0.0)
        self.collision_occurred = False
        self.steps = 0
        self.graph = create_nav_graph(self.data_conn[self.scene_idx])
        self._load_nav_graph()
        # Get scene annotations.
        annotations_path = f"{ROOT_DIR}/{self.scene_id}/annotations.json"
        self.annotations = json.load(open(annotations_path, "r"))
        self.visited_instances = set([])
        instances_path = (
            f"{VALID_INSTANCES_ROOT_DIR}/{self.scene_id}/instances.json.gzip"
        )
        with gzip.open(instances_path, "rt") as fp:
            self.valid_instances = json.load(fp)
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environments/gym-avd/gym_avd/envs/avd_nav_env.py [914:968]:
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    def reset(self, scene_idx: Optional[int] = None):
        # Spawn agent in a new scene at a random image.
        if self.episodes is None:
            print("Sampling random episodes!")
            if scene_idx is None:
                scenes = getattr(self, "{}_scenes".format(self.split))
                self.scene_idx = self._rng.choice(scenes)
            else:
                self.scene_idx = scene_idx
        else:
            if self.episodes_idx is None:
                self.episodes_idx = 0
            else:
                self.episodes_idx = (self.episodes_idx + 1) % len(self.episodes)
            self.scene_idx = self.episodes[self.episodes_idx]["scene_idx"]
            print(
                "Sampling from pre-defined episodes! Scene id: "
                f"{self.scene_idx}, Episode id: {self.episodes_idx}"
            )
        self.scene_id = self.all_scenes[self.scene_idx]
        scene_conn = self.data_conn[self.scene_idx]
        self.images_to_idx = scene_conn["images_to_idx"]
        self.images_to_camera = scene_conn["images_to_camera"]
        self.images_to_nodes = scene_conn["images_to_nodes"]
        self.scale = scene_conn["scale"]  # Converts positions to mm.
        if self.episodes is None:
            self.agent_image = self._rng.choice(list(self.images_to_idx.keys()))
            while not self.is_valid_image(self.agent_image):
                self.agent_image = self._rng.choice(list(self.images_to_idx.keys()))
        else:
            self.agent_image = self.episodes[self.episodes_idx]["start_image"]
        # Initialize the environment variables.
        self.start_position = self._get_position(self.agent_image)
        self.start_pose = self._get_pose(self.agent_image)
        self.exp_start_image = self.agent_image
        self.exp_start_position = copy.deepcopy(self.start_position)
        self.exp_start_pose = self.start_pose
        self.agent_pose = self.start_pose
        self.agent_position = copy.deepcopy(self.start_position)
        self.scene_images = np.array(self.data_h5[f"{self.scene_id}/rgb"])
        self.scene_depth = np.array(self.data_h5[f"{self.scene_id}/depth"])
        self.delta = (0.0, 0.0, 0.0, 0.0)
        self.collision_occurred = False
        self.steps = 0
        self.graph = create_nav_graph(self.data_conn[self.scene_idx])
        self._load_nav_graph()
        # Get scene annotations.
        annotations_path = f"{ROOT_DIR}/{self.scene_id}/annotations.json"
        self.annotations = json.load(open(annotations_path, "r"))
        self.visited_instances = set([])
        instances_path = (
            f"{VALID_INSTANCES_ROOT_DIR}/{self.scene_id}/instances.json.gzip"
        )
        with gzip.open(instances_path, "rt") as fp:
            self.valid_instances = json.load(fp)
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