Semantics aren't loading correctly (Hm3D, Instance Image Nav)

[FlightVin]

Habitat-Lab and Habitat-Sim versions

Habitat-Lab: master

Habitat-Sim: master

Master branch contains 'bleeding edge' code and should be used at your own risk.

Docs and Tutorials

Did you read the docs? https://aihabitat.org/docs/habitat-lab/ -> Yes

Did you check out the tutorials? https://aihabitat.org/tutorial/2020/ -> Yes

Perhaps your question is answered there. If not, carry on!

Semantics aren't loading correctly

For the sake of brevity, consider the following image showing the data present in the minival set of HM3D:

The relevant files that have been listed in README for semantics are present.

However, when I try to access the unique semantic labels here (permalink), I always get either 0 or 3. This is despite me setting the scene dataset correctly here.

I get the following warnings on InstanceImageNav-v1

[20:32:03:831051]:[Warning]:[Metadata] SceneDatasetAttributes.cpp(107)::addNewSceneInstanceToDataset 
: Dataset : 'hm3d_annotated_basis' : Lighting Layout Attributes 'no_lights' specified in Scene Attrib
utes but does not exist in dataset, so creating default.                                             
[20:32:03:831186]:[Warning]:[Scene] SemanticScene.h(328)::checkFileExists : ::loadSemanticSceneDescri
ptor: File/scratch/vineeth.bhat/sg_habitat/data/scene_datasets/hm3d_v0.2/minival/00802-wcojb4TFT35/wc
ojb4TFT35.basis.scndoes not exist.  Aborting load.                                                   
[20:32:03:831202]:[Warning]:[Scene] SemanticScene.cpp(121)::loadSemanticSceneDescriptor : SSD File Na
ming Issue! Neither SemanticAttributes-provided name : `/scratch/vineeth.bhat/sg_habitat/data/scene_d
atasets/hm3d_v0.2/minival/00802-wcojb4TFT35/wcojb4TFT35.basis.scn` nor constructed filename : `/scrat
ch/vineeth.bhat/sg_habitat/data/scene_datasets/hm3d_v0.2/minival/00802-wcojb4TFT35/info_semantic.json
` exist on disk.                                                                                     
[20:32:03:831210]:[Error]:[Scene] SemanticScene.cpp(137)::loadSemanticSceneDescriptor : SSD Load Fail
ure! File with SemanticAttributes-provided name `/scratch/vineeth.bhat/sg_habitat/data/scene_datasets
/hm3d_v0.2/minival/00802-wcojb4TFT35/wcojb4TFT35.basis.scn` exists but failed to load.               
[20:32:04:928962]:[Warning]:[Sim] Simulator.cpp(594)::instanceStageForSceneAttributes : The active sc
ene does not contain semantic annotations : activeSemanticSceneID_ = 0                               
2024-10-16 20:32:05,059 Initializing task InstanceImageNav-v1                                        

Any help would be appreciated. Thanks!

[FlightVin]

Adding code here (same as the links given above):

Data formation:

"""
Code used to create dataset for pixel goals. Uses latest habitat lab version.
"""

import os
import argparse
import numpy as np
import habitat
from habitat.tasks.nav.shortest_path_follower import ShortestPathFollower
import time
from pathlib import Path
import h5py
from habitat.utils.geometry_utils import quaternion_from_coeff, quaternion_to_list
from utils import *

seed_everything(42)


def main(args):
    config_path = args.config_path
    if not os.path.exists(config_path):
        raise RuntimeError(f"{config_path} does not exist!")

    habitat_config = create_habitat_config(config_path, args)

    env = habitat.Env(habitat_config)
    env.seed(42)
    follower = ShortestPathFollower(env.sim, goal_radius=0.5, return_one_hot=False)

    episode_counter = 0
    time_before_loop = time.time()

    hdf5_file_path = args.save_path

    # create parent directories
    directory_path = Path(hdf5_file_path).parent
    directory_path.mkdir(parents=True, exist_ok=True)

    # Creation loop
    with h5py.File(hdf5_file_path, "w") as hdf:
        while episode_counter < args.num_saved_episodes:
            start_time = time.time()

            try:
                obs = env.reset()
            except Exception as e:
                print(f"An error occurred while resetting environment: {e}")
                continue

            rgb_data, depth_data, pose_data, action_data, normed_target_point_data = (
                [],
                [],
                [],
                [],
                [],
            )
            timesteps = 0

            (
                goal_flag,
                goal_image,
                goal_mask,
                goal_point,
                height_uncorrected_goal_point,
            ) = random_pixel_goal(habitat_config, env, args.mask_shape)
            if not goal_flag:
                print(f"Rejected the current goal with goal-flag {goal_flag}")
                continue

            best_action = follower.get_next_action(goal_point)

            if best_action == 0:
                print(
                    f"Rejected the current goal with goal-flag {goal_flag} and best-action {best_action}"
                )
                continue

            start_rgb_image = obs["rgb"]
            start_semantic_image = np.squeeze(obs["semantic"])
            print(np.unique(start_semantic_image))
            start_depth_image = obs["depth"]
            start_pose = np.concatenate(
                [
                    np.array(env.sim.get_agent_state().position),
                    np.array(quaternion_to_list(env.sim.get_agent_state().rotation)),
                ]
            )

            last_best_action = None

            while True:
                assert best_action is not None and last_best_action != 0  # sanity check

                action_data.append(best_action)
                obs = env.step(best_action)

                rgb_data.append(obs["rgb"])
                depth_data.append(obs["depth"])

                pose = np.concatenate(
                    [
                        np.array(env.sim.get_agent_state().position),
                        np.array(
                            quaternion_to_list(env.sim.get_agent_state().rotation)
                        ),
                    ]
                )

                semantic_image = np.squeeze(obs["semantic"])
                print(np.unique(semantic_image))

                pose_data.append(pose)
                normed_target_point_data.append(
                    get_normalized_goal_point_location_in_current_obs(
                        habitat_config, env, height_uncorrected_goal_point
                    )
                )

                # gen_observations = env.sim.get_observations_at(
                #     position=pose[:3],
                #     rotation=quaternion_from_coeff(pose[3:]),
                # )
                # print(
                #     "Images are equal",
                #     np.array_equal(obs["rgb"], gen_observations["rgb"]),
                # )

                timesteps += 1

                if not (env.episode_over or timesteps >= args.max_timesteps):
                    last_best_action = best_action
                    best_action = follower.get_next_action(goal_point)
                else:
                    print(f"Goal reached in {timesteps} steps")
                    if timesteps < args.min_timesteps:
                        print(
                            f"{timesteps} less than min. timesteps of {args.min_timesteps}"
                        )
                        break

                    episode_group = hdf.create_group(f"episode_{episode_counter}")

                    episode_group.create_dataset(
                        "start_rgb_image", data=start_rgb_image
                    )
                    # episode_group.create_dataset("start_depth_image", data=start_depth_image)
                    episode_group.create_dataset("goal_mask", data=goal_mask)
                    episode_group.create_dataset("start_pose", data=start_pose)
                    episode_group.create_dataset("poses", data=np.array(pose_data))
                    episode_group.create_dataset("actions", data=np.array(action_data))
                    episode_group.create_dataset(
                        "normalized_target_points",
                        data=np.array(normed_target_point_data),
                    )
                    episode_group.create_dataset("rgb_images", data=np.array(rgb_data))
                    episode_group.create_dataset(
                        "height_uncorrected_goal_point",
                        data=np.array(height_uncorrected_goal_point),
                    )
                    episode_group.create_dataset(
                        "goal_point",
                        data=np.array(goal_point),
                    )
                    # episode_group.create_dataset("depth_images", data=np.array(depth_data))

                    episode_counter += 1
                    end_time = time.time()
                    time_taken = end_time - start_time
                    total_time_taken = end_time - time_before_loop
                    total_hours, rem = divmod(total_time_taken, 3600)
                    total_minutes, total_seconds = divmod(rem, 60)

                    print(
                        f"Done with episode {episode_counter} out of {args.num_saved_episodes} total"
                    )
                    print(f"Time taken for this episode: {time_taken:.2f} seconds")
                    print(
                        f"Total time taken: {int(total_hours)} hours, {int(total_minutes)} minutes, {int(total_seconds)} seconds"
                    )
                    print()
                    break

    print(f"All episodes saved to {hdf5_file_path}")
    env.close()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Habitat Data Collection Script")
    parser.add_argument(
        "--stage", type=str, default="train", help="Stage (train/val/minival)"
    )
    parser.add_argument(
        "--split", type=str, default="train", help="Split (train/val/valmini)"
    )
    parser.add_argument(
        "--num_sampled_episodes",
        "-nse",
        type=int,
        default=int(1e4),
        help="Number of sampled episodes",
    )
    parser.add_argument(
        "--max_timesteps", type=int, default=64, help="Maximum timesteps per episode"
    )
    parser.add_argument(
        "--min_timesteps", type=int, default=5, help="Minimum timesteps per episode"
    )
    parser.add_argument(
        "--mask_shape", type=int, default=3, help="Shape of the goal mask"
    )
    parser.add_argument(
        "--config_path",
        type=str,
        default="hm3d_config_instance_image_nav_mod.yaml",
        help="Path to Habitat config file",
    )
    parser.add_argument("--robot_height", type=float, default=0.88, help="Robot height")
    parser.add_argument("--robot_radius", type=float, default=0.25, help="Robot radius")
    parser.add_argument(
        "--sensor_height", type=float, default=0.88, help="Sensor height"
    )
    parser.add_argument(
        "--image_width", type=int, default=224, help="Image width"
    )  # don't change
    parser.add_argument("--image_height", type=int, default=224, help="Image height")
    parser.add_argument(
        "--image_hfov", type=float, default=79, help="Image horizontal field of view"
    )
    parser.add_argument("--step_size", type=float, default=0.25, help="Step size")
    parser.add_argument(
        "--turn_angle", type=float, default=30, help="Turn angle in degrees"
    )

    parser.add_argument(
        "--data_dir",
        type=str,
        default="/scratch/vineeth.bhat/sg_habitat/data/datasets/instance_imagenav/hm3d/v3",
        help="Path to data directory",
    )
    parser.add_argument(
        "--scene_dataset_dir",
        type=str,
        default="/scratch/vineeth.bhat/sg_habitat/data/scene_datasets/hm3d",
        help="Path to scene dataset directory",
    )
    parser.add_argument(
        "--scenes_dir",
        type=str,
        default="/scratch/vineeth.bhat/sg_habitat/data/scene_datasets",
        help="Path to scenes directory",
    )
    parser.add_argument(
        "--save_path",
        "-s",
        type=str,
        default="/scratch/vineeth.bhat/pix_nav_point_based_data/training_100.h5",
        help="Path to save data",
    )
    parser.add_argument(
        "--num_saved_episodes",
        "-n",
        type=int,
        default=int(1),
        help="Number of saved episodes",
    )

    args = parser.parse_args()
    main(args)

The code used to create the config object:

def create_habitat_config(config_path, args):

    habitat_config = habitat.get_config(config_path)

    data_path = f"{args.data_dir}/{args.stage}/{args.stage}.json.gz"
    scene_dataset = (
        f"{args.scene_dataset_dir}/hm3d_annotated_basis.scene_dataset_config.json"
    )
    print(f"Using data path: {data_path}")
    print(f"Using scene dataset: {scene_dataset}")

    if not os.path.exists(data_path):
        raise RuntimeError(f"Data path path does not exist: {data_path}")
    if not os.path.exists(scene_dataset):
        raise RuntimeError(f"Scene dataset path does not exist: {scene_dataset}")

    with read_write(habitat_config):
        habitat_config.habitat.dataset.split = args.split
        habitat_config.habitat.dataset.scenes_dir = args.scenes_dir
        habitat_config.habitat.dataset.data_path = data_path
        habitat_config.habitat.simulator.scene_dataset = scene_dataset
        habitat_config.habitat.environment.iterator_options.num_episode_sample = (
            args.num_sampled_episodes
        )
        habitat_config.habitat.simulator.agents.main_agent.height = args.robot_height
        habitat_config.habitat.simulator.agents.main_agent.radius = args.robot_radius
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.rgb_sensor.height = (
            args.image_height
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.rgb_sensor.width = (
            args.image_width
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.rgb_sensor.hfov = (
            args.image_hfov
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.rgb_sensor.position = [
            0,
            args.sensor_height,
            0,
        ]
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.depth_sensor.height = (
            args.image_height
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.depth_sensor.width = (
            args.image_width
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.depth_sensor.hfov = (
            args.image_hfov
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.depth_sensor.position = [
            0,
            args.sensor_height,
            0,
        ]
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.depth_sensor.max_depth = (
            500.0
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.depth_sensor.min_depth = (
            0.0
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.depth_sensor.normalize_depth = (
            False
        )
        habitat_config.habitat.simulator.forward_step_size = args.step_size
        habitat_config.habitat.simulator.turn_angle = args.turn_angle
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.semantic_sensor.height = (
            args.image_height
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.semantic_sensor.width = (
            args.image_width
        )
        habitat_config.habitat.simulator.agents.main_agent.sim_sensors.semantic_sensor.position = [
            0,
            args.sensor_height,
            0,
        ]
    return habitat_config

The YAML file:

defaults:
  - /habitat: habitat_config_base
  - /habitat/task: instance_imagenav
  - /habitat/simulator/sensor_setups@habitat.simulator.agents.main_agent: rgbd_agent
  - /habitat/dataset/instance_imagenav: hm3d_v2
  - _self_

habitat:
  environment:
    max_episode_steps: 100
  simulator:
    turn_angle: 15
    agents:
      main_agent:
        sim_sensors:
          rgb_sensor:
            width: 360
            height: 640
            hfov: 42
            position: [0, 1.31, 0]
          depth_sensor:
            width: 360
            height: 640
            hfov: 58
            min_depth: 0.05
            max_depth: 50.0
            position: [0, 1.31, 0]
          semantic_sensor:
            type: HabitatSimSemanticSensor
            width: 360
            height: 640
            position: [0, 1.31, 0]
        height: 1.41
        radius: 0.17
    habitat_sim_v0:
      gpu_device_id: 0
      allow_sliding: False

[zhouak47]

I also meet the same problem home/zhoulei/dataset_total/habitat/scenes/hm3d_v0.2/val/00829-QaLdnwvtxbs/QaLdnwvtxbs.basis.scn exists but failed to load . Have you solved this?

[FlightVin]

@zhouak47 Sorry, I haven't found a solution yet.

[zhouak47]

@FlightVin I have found a solution . You can comment these codes out in .../habitat-lab/habitat-lab/habitat/core/env.py ,check your path of habitat.simulator.scene_dataset and try again:

If it does not work . Try to use habitat==0.2.5 instead of 0.3.Hope this will help you. Best wishes.

[FlightVin]

@zhouak47 That worked. Thank you so much!

[aclegg3]

Interesting, thanks for the report on this issue, folks. Logging it as a bug for someone to look into.

[ByZ0e]

Habitat-Lab and Habitat-Sim versions

Habitat-Lab: master

Habitat-Sim: master

Master branch contains 'bleeding edge' code and should be used at your own risk.

Docs and Tutorials

Did you read the docs? https://aihabitat.org/docs/habitat-lab/ -> Yes

Did you check out the tutorials? https://aihabitat.org/tutorial/2020/ -> Yes

Perhaps your question is answered there. If not, carry on!

Semantics aren't loading correctly

For the sake of brevity, consider the following image showing the data present in the minival set of HM3D:

The relevant files that have been listed in README for semantics are present.

However, when I try to access the unique semantic labels here (permalink), I always get either 0 or 3. This is despite me setting the scene dataset correctly here.

I get the following warnings on InstanceImageNav-v1

[20:32:03:831051]:[Warning]:[Metadata] SceneDatasetAttributes.cpp(107)::addNewSceneInstanceToDataset 
: Dataset : 'hm3d_annotated_basis' : Lighting Layout Attributes 'no_lights' specified in Scene Attrib
utes but does not exist in dataset, so creating default.                                             
[20:32:03:831186]:[Warning]:[Scene] SemanticScene.h(328)::checkFileExists : ::loadSemanticSceneDescri
ptor: File/scratch/vineeth.bhat/sg_habitat/data/scene_datasets/hm3d_v0.2/minival/00802-wcojb4TFT35/wc
ojb4TFT35.basis.scndoes not exist.  Aborting load.                                                   
[20:32:03:831202]:[Warning]:[Scene] SemanticScene.cpp(121)::loadSemanticSceneDescriptor : SSD File Na
ming Issue! Neither SemanticAttributes-provided name : `/scratch/vineeth.bhat/sg_habitat/data/scene_d
atasets/hm3d_v0.2/minival/00802-wcojb4TFT35/wcojb4TFT35.basis.scn` nor constructed filename : `/scrat
ch/vineeth.bhat/sg_habitat/data/scene_datasets/hm3d_v0.2/minival/00802-wcojb4TFT35/info_semantic.json
` exist on disk.                                                                                     
[20:32:03:831210]:[Error]:[Scene] SemanticScene.cpp(137)::loadSemanticSceneDescriptor : SSD Load Fail
ure! File with SemanticAttributes-provided name `/scratch/vineeth.bhat/sg_habitat/data/scene_datasets
/hm3d_v0.2/minival/00802-wcojb4TFT35/wcojb4TFT35.basis.scn` exists but failed to load.               
[20:32:04:928962]:[Warning]:[Sim] Simulator.cpp(594)::instanceStageForSceneAttributes : The active sc
ene does not contain semantic annotations : activeSemanticSceneID_ = 0                               
2024-10-16 20:32:05,059 Initializing task InstanceImageNav-v1                                        

Any help would be appreciated. Thanks!

sorry, i meet the same problem here. I use the habitat==0.2.5. And i have tried the solution in #2090 (comment). But it did not work for me.

I got the error like this:
ESP_CHECK failed: No Stage Attributes exists for requested scene 'data/scene_datasets/hm3d_v0.2/minival/00802-wcojb4TFT35/wcojb4TFT35.basis.glb' in currently specified Scene Dataset ./data/scene_datasets/hm3d_v0.2/hm3d_annotated_basis.scene_dataset_config.json. Likely the Scene Dataset Configuration requested was not found and so a new, empty Scene Dataset was created. Verify the Scene Dataset Configuration file name used.

Any suggestions, please.

[francescotaioli]

a few notes that I found here and there that solve my issues:

1. if you have a custom dataset, inside the from_json method:
   episode.scene_dataset_config_file = "data/scene_datasets/hm3d/hm3d_annotated_basis.scene_dataset_config.json"
2. in your main, override the config:

    with read_write(config):
        config.habitat.seed = args.seed
        config.habitat.simulator.scene_dataset = "data/scene_datasets/hm3d/hm3d_annotated_basis.scene_dataset_config.json" # !important, otherwise is "default"
        config.habitat.simulator.scene_dataset_config_file = "data/scene_datasets/hm3d/hm3d_annotated_basis.scene_dataset_config.json"

3. once you have the semantic sensor working,
   instance_id_to_label_id = {int(obj.id.split("_")[-1]): obj.category.index() for obj in sim.semantic_scene.objects}

from habitat_sim.utils.common import d3_40_colors_rgb
def display_semantic( semantic_obs: ndarray, instance_id_to_label_id):
        # Map instance IDs to label IDs
        label_map = np.vectorize(instance_id_to_label_id.get)(semantic_obs)

        # Prepare the palette image
        semantic_img = Image.new("P", (label_map.shape[1], label_map.shape[0]))
        semantic_img.putpalette(d3_40_colors_rgb.flatten())
        semantic_img.putdata((label_map.flatten() % 40).astype(np.uint8))
        semantic_img = semantic_img.convert("RGB")
        semantic_img.save("semantic.jpg")

display_semantic(semantic_obs, instance_id_to_label_id)

[shipshipliucixin1]

Thanks a lot for raising this important question(for me)

[shipshipliucixin1]

@FlightVin I have found a solution . You can comment these codes out in .../habitat-lab/habitat-lab/habitat/core/env.py ,check your path of habitat.simulator.scene_dataset and try again: If it does not work . Try to use habitat==0.2.5 instead of 0.3.Hope this will help you. Best wishes.

For why this will help, I asked Gemini.Here is its answer:
您好！这是一个非常棒的调试发现，您的分析完全正确。您发现的这个解决方案之所以有效，其核心原理在于解决了配置覆盖（Configuration Override）的问题。

让我为您详细解释一下这个过程：

核心原理：语义配置被基础配置覆盖

简单来说，您遇到的问题可以概括为：
一个带有完整语义信息路径的正确配置，被一个只包含基础信息、缺少语义路径的配置给覆盖了，导致模拟器无法加载语义场景。

下面是详细的步骤分解：

1. 初始状态：正确的“语义感知”配置

1. 当您在主程序 objnav_benchmark.py 中加载一个任务配置（比如一个YAML文件）时，这个配置通常会指定一个包含语义信息的 scene_dataset。例如，它可能会指向 hm3d_semantic.scene_dataset_config.json。
2. 这个 “语义配置文件” (semantic config) 是关键，它不仅告诉模拟器场景的视觉模型（.glb文件）在哪里，还包含了指向语义数据（如.semantic.json或.house文件）的路径。

2. 问题所在：env.py 中的配置覆盖

1. 在 habitat.Env 的 __init__ 方法中，程序会加载第一个 episode 来确定要使用的初始场景。
2. 然后，执行了这行您注意到的关键代码：

   self._config.simulator.scene_dataset = (
       self.current_episode.scene_dataset_config
   )

3. 这里的 self.current_episode.scene_dataset_config 通常指向一个更基础的配置文件，比如 hm3d_basis.scene_dataset_config.json。这个 “基础配置文件” 只包含了运行基本导航任务所需的最少信息，但它没有包含加载语义场景所需的路径。
4. 结果：这行代码将您原来正确、完整的“语义配置”用一个不完整的“基础配置”给覆盖了。

3. 产生错误：模拟器加载失败

1. 当模拟器（HabitatSim）根据这个被覆盖后的“基础配置”进行初始化或重置时，它依然会尝试加载语义信息（因为您的任务需要它）。
2. 但由于“基础配置”中没有提供正确的语义文件路径，模拟器只能尝试根据默认规则或不完整的信息去猜测路径。
3. 这就导致了您看到的 SSD Load Failure! ... .basis.scn 错误。模拟器尝试加载一个它认为应该存在的语义文件，但该文件要么不存在，要么格式不正确，最终导致加载失败。
4. 加载失败的直接后果就是 self._sim.semantic_annotations().objects 返回一个空列表，因为没有任何语义对象被成功加载。

您的解决方案为何有效？

通过注释掉这行代码：

# self._config.simulator.scene_dataset = (
#     self.current_episode.scene_dataset_config
# )

您阻止了配置覆盖的发生。

1. 保留了原始配置：模拟器现在会一直使用您在任务级YAML文件中指定的、完整且正确的“语义配置”。
2. 成功加载语义：因为这个配置包含了正确的语义文件路径，HabitatSim 在初始化时可以成功找到并加载 *.semantic.json 文件。
3. 问题解决：self._sim.semantic_annotations().objects 因此能被正确填充，后续代码（如 _draw_goals_aabb）在访问 sem_scene.objects[object_id] 时就不会再出错。

总结与类比

您可以把这个过程想象成准备一次旅行：

• 您的主配置文件：是一份完整的旅行指南，上面有地图、景点介绍（语义信息）和酒店地址。
• Episode里的配置：是一张只包含地图的简易传单。
• 原始代码的行为：强制您扔掉完整的旅行指南，只拿着那张简易传单出发。当您想找某个景点（语义对象）的详细介绍时，自然就找不到了。
• 您的解决方案：就是决定不理会那张简易传单，一直使用您最初那本完整的旅行指南。

因此，对于需要场景语义的任务（如ObjectGoal Navigation），您采取的这个修改是完全正确且必要的。这行代码的本意可能是为了支持那些不需要语义，或者每个episode都自带完整语义路径的简单任务。

[mikle55]

Hi,@shipshipliucixin1 ,请问你的意思是注释掉：
self._config.simulator.scene_dataset = (
self.current_episode.scene_dataset_config
)
这部分内容就可以吗，需不需要进行类似如下改动呢：
with read_write(config):
config.habitat.seed = args.seed
config.habitat.simulator.scene_dataset = "data/scene_datasets/hm3d/hm3d_annotated_basis.scene_dataset_config.json"
config.habitat.simulator.scene_dataset_config_file ="data/scene_datasets/hm3d/hm3d_annotated_basis.scene_dataset_config.json"