找出你应该用多少/什么相机训练

目前在Isaac Lab，有几种摄像头类型; USD摄像头（标准）、平铺摄像头和光线投射摄像头。这些摄像头类型在功能和性能上有所不同。benchmark_cameras.py 脚本可用于了解摄像头类型的差异，以及表征它们在不同参数（如摄像头数量、图像尺寸和数据类型）下的相对性能。

这个实用程序的目的是让用户能够轻松找到在满足用户场景要求的情况下性能最优的摄像头类型/参数。该实用程序还可以帮助估计用户可以实际运行的摄像头最大数量，假设用户想要最大化环境数量同时最小化步骤时间。

这个实用程序可以将摄像头注入到来自健身房注册表的现有任务中，这对于在特定场景中对摄像头进行基准测试可能很有用。此外，如果您安装了 pynvml ，则可以让此实用程序自动查找可以在您的任务环境中运行的摄像头的最大数量，直到达到特定指定的系统资源利用阈值为止（不进行训练，在每个时间步骤上不采取任何行动）。

这个指南配套使用 source/standalone/benchmarks 目录中的 benchmark_cameras.py 脚本。

代码 for benchmark_cameras.py

# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

"""
This script might help you determine how many cameras your system can realistically run
at different desired settings.

You can supply different task environments to inject cameras into, or just test a sample scene.
Additionally, you can automatically find the maximum amount of cameras you can run a task with
through the auto-tune functionality.

.. code-block:: bash

    # Usage with GUI
    ./isaaclab.sh -p source/standalone/benchmarks/benchmark_cameras.py -h

    # Usage with headless
    ./isaaclab.sh -p source/standalone/benchmarks/benchmark_cameras.py -h --headless

"""

"""Launch Isaac Sim Simulator first."""

import argparse
from collections.abc import Callable

from omni.isaac.lab.app import AppLauncher

# parse the arguments
args_cli = argparse.Namespace()

parser = argparse.ArgumentParser(description="This script can help you benchmark how many cameras you could run.")

"""
The following arguments only need to be supplied for when one wishes
to try injecting cameras into their environment, and automatically determining
the maximum camera count.
"""
parser.add_argument(
    "--task",
    type=str,
    default=None,
    required=False,
    help="Supply this argument to spawn cameras within an known manager-based task environment.",
)

parser.add_argument(
    "--autotune",
    default=False,
    action="store_true",
    help=(
        "Autotuning is only supported for provided task environments."
        " Supply this argument to increase the number of environments until a desired threshold is reached."
        "Install pynvml in your environment; ./isaaclab.sh -m pip install pynvml"
    ),
)

parser.add_argument(
    "--task_num_cameras_per_env",
    type=int,
    default=1,
    help="The number of cameras per environment to use when using a known task.",
)

parser.add_argument(
    "--use_fabric", action="store_true", default=False, help="Enable fabric and use USD I/O operations."
)

parser.add_argument(
    "--autotune_max_percentage_util",
    nargs="+",
    type=float,
    default=[100.0, 80.0, 80.0, 80.0],
    required=False,
    help=(
        "The system utilization percentage thresholds to reach before an autotune is finished. "
        "If any one of these limits are hit, the autotune stops."
        "Thresholds are, in order, maximum CPU percentage utilization,"
        "maximum RAM percentage utilization, maximum GPU compute percent utilization, "
        "amd maximum GPU memory utilization."
    ),
)

parser.add_argument(
    "--autotune_max_camera_count", type=int, default=4096, help="The maximum amount of cameras allowed in an autotune."
)

parser.add_argument(
    "--autotune_camera_count_interval",
    type=int,
    default=25,
    help=(
        "The number of cameras to try to add to the environment if the current camera count"
        " falls within permitted system resource utilization limits."
    ),
)

"""
The following arguments are shared for when injecting cameras into a task environment,
as well as when creating cameras independent of a task environment.
"""

parser.add_argument(
    "--num_tiled_cameras",
    type=int,
    default=0,
    required=False,
    help="Number of tiled cameras to create. For autotuning, this is how many cameras to start with.",
)

parser.add_argument(
    "--num_standard_cameras",
    type=int,
    default=0,
    required=False,
    help="Number of standard cameras to create. For autotuning, this is how many cameras to start with.",
)

parser.add_argument(
    "--num_ray_caster_cameras",
    type=int,
    default=0,
    required=False,
    help="Number of ray caster cameras to create. For autotuning, this is how many cameras to start with.",
)

parser.add_argument(
    "--tiled_camera_data_types",
    nargs="+",
    type=str,
    default=["rgb", "depth"],
    help="The data types rendered by the tiled camera",
)

parser.add_argument(
    "--standard_camera_data_types",
    nargs="+",
    type=str,
    default=["rgb", "distance_to_image_plane", "distance_to_camera"],
    help="The data types rendered by the standard camera",
)

parser.add_argument(
    "--ray_caster_camera_data_types",
    nargs="+",
    type=str,
    default=["distance_to_image_plane"],
    help="The data types rendered by the ray caster camera.",
)

parser.add_argument(
    "--ray_caster_visible_mesh_prim_paths",
    nargs="+",
    type=str,
    default=["/World/ground"],
    help="WARNING: Ray Caster can currently only cast against a single, static, object",
)

parser.add_argument(
    "--convert_depth_to_camera_to_image_plane",
    action="store_true",
    default=True,
    help=(
        "Enable undistorting from perspective view (distance to camera data_type)"
        "to orthogonal view (distance to plane data_type) for depth."
        "This is currently needed to create undisorted depth images/point cloud."
    ),
)

parser.add_argument(
    "--keep_raw_depth",
    dest="convert_depth_to_camera_to_image_plane",
    action="store_false",
    help=(
        "Disable undistorting from perspective view (distance to camera)"
        "to orthogonal view (distance to plane data_type) for depth."
    ),
)

parser.add_argument(
    "--height",
    type=int,
    default=120,
    required=False,
    help="Height in pixels of cameras",
)

parser.add_argument(
    "--width",
    type=int,
    default=140,
    required=False,
    help="Width in pixels of cameras",
)

parser.add_argument(
    "--warm_start_length",
    type=int,
    default=3,
    required=False,
    help=(
        "Number of steps to run the sim before starting benchmark."
        "Needed to avoid blank images at the start of the simulation."
    ),
)

parser.add_argument(
    "--experiment_length",
    type=int,
    default=15,
    required=False,
    help="Number of steps to average over",
)

# This argument is only used when a task is not provided.
parser.add_argument(
    "--num_objects",
    type=int,
    default=10,
    required=False,
    help="Number of objects to spawn into the scene when not using a known task.",
)


AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
args_cli.enable_cameras = True

if args_cli.autotune:
    import pynvml

if len(args_cli.ray_caster_visible_mesh_prim_paths) > 1:
    print("[WARNING]: Ray Casting is only currently supported for a single, static object")
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app

"""Rest everything follows."""

import gymnasium as gym
import numpy as np
import random
import time
import torch

import omni.isaac.core.utils.prims as prim_utils
import psutil
from omni.isaac.core.utils.stage import create_new_stage

import omni.isaac.lab.sim as sim_utils
from omni.isaac.lab.assets import RigidObject, RigidObjectCfg
from omni.isaac.lab.scene.interactive_scene import InteractiveScene
from omni.isaac.lab.sensors import (
    Camera,
    CameraCfg,
    RayCasterCamera,
    RayCasterCameraCfg,
    TiledCamera,
    TiledCameraCfg,
    patterns,
)
from omni.isaac.lab.utils.math import orthogonalize_perspective_depth, unproject_depth

from omni.isaac.lab_tasks.utils import load_cfg_from_registry

"""
Camera Creation
"""


def create_camera_base(
    camera_cfg: type[CameraCfg | TiledCameraCfg],
    num_cams: int,
    data_types: list[str],
    height: int,
    width: int,
    prim_path: str | None = None,
    instantiate: bool = True,
) -> Camera | TiledCamera | CameraCfg | TiledCameraCfg | None:
    """Generalized function to create a camera or tiled camera sensor."""
    # Determine prim prefix based on the camera class
    name = camera_cfg.class_type.__name__

    if instantiate:
        # Create the necessary prims
        for idx in range(num_cams):
            prim_utils.create_prim(f"/World/{name}_{idx:02d}", "Xform")
    if prim_path is None:
        prim_path = f"/World/{name}_.*/{name}"
    # If valid camera settings are provided, create the camera
    if num_cams > 0 and len(data_types) > 0 and height > 0 and width > 0:
        cfg = camera_cfg(
            prim_path=prim_path,
            update_period=0,
            height=height,
            width=width,
            data_types=data_types,
            spawn=sim_utils.PinholeCameraCfg(
                focal_length=24, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1e4)
            ),
        )
        if instantiate:
            return camera_cfg.class_type(cfg=cfg)
        else:
            return cfg
    else:
        return None


def create_tiled_cameras(
    num_cams: int = 2, data_types: list[str] | None = None, height: int = 100, width: int = 120
) -> TiledCamera | None:
    if data_types is None:
        data_types = ["rgb", "depth"]
    """Defines the tiled camera sensor to add to the scene."""
    return create_camera_base(
        camera_cfg=TiledCameraCfg,
        num_cams=num_cams,
        data_types=data_types,
        height=height,
        width=width,
    )


def create_cameras(
    num_cams: int = 2, data_types: list[str] | None = None, height: int = 100, width: int = 120
) -> Camera | None:
    """Defines the Standard cameras."""
    if data_types is None:
        data_types = ["rgb", "depth"]
    return create_camera_base(
        camera_cfg=CameraCfg, num_cams=num_cams, data_types=data_types, height=height, width=width
    )


def create_ray_caster_cameras(
    num_cams: int = 2,
    data_types: list[str] = ["distance_to_image_plane"],
    mesh_prim_paths: list[str] = ["/World/ground"],
    height: int = 100,
    width: int = 120,
    prim_path: str = "/World/RayCasterCamera_.*/RayCaster",
    instantiate: bool = True,
) -> RayCasterCamera | RayCasterCameraCfg | None:
    """Create the raycaster cameras; different configuration than Standard/Tiled camera"""
    for idx in range(num_cams):
        prim_utils.create_prim(f"/World/RayCasterCamera_{idx:02d}/RayCaster", "Xform")

    if num_cams > 0 and len(data_types) > 0 and height > 0 and width > 0:
        cam_cfg = RayCasterCameraCfg(
            prim_path=prim_path,
            mesh_prim_paths=mesh_prim_paths,
            update_period=0,
            offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)),
            data_types=data_types,
            debug_vis=False,
            pattern_cfg=patterns.PinholeCameraPatternCfg(
                focal_length=24.0,
                horizontal_aperture=20.955,
                height=480,
                width=640,
            ),
        )
        if instantiate:
            return RayCasterCamera(cfg=cam_cfg)
        else:
            return cam_cfg

    else:
        return None


def create_tiled_camera_cfg(prim_path: str) -> TiledCameraCfg:
    """Grab a simple tiled camera config for injecting into task environments."""
    return create_camera_base(
        TiledCameraCfg,
        num_cams=args_cli.num_tiled_cameras,
        data_types=args_cli.tiled_camera_data_types,
        width=args_cli.width,
        height=args_cli.height,
        prim_path="{ENV_REGEX_NS}/" + prim_path,
        instantiate=False,
    )


def create_standard_camera_cfg(prim_path: str) -> CameraCfg:
    """Grab a simple standard camera config for injecting into task environments."""
    return create_camera_base(
        CameraCfg,
        num_cams=args_cli.num_standard_cameras,
        data_types=args_cli.standard_camera_data_types,
        width=args_cli.width,
        height=args_cli.height,
        prim_path="{ENV_REGEX_NS}/" + prim_path,
        instantiate=False,
    )


def create_ray_caster_camera_cfg(prim_path: str) -> RayCasterCameraCfg:
    """Grab a simple ray caster config for injecting into task environments."""
    return create_ray_caster_cameras(
        num_cams=args_cli.num_ray_caster_cameras,
        data_types=args_cli.ray_caster_camera_data_types,
        width=args_cli.width,
        height=args_cli.height,
        prim_path="{ENV_REGEX_NS}/" + prim_path,
    )


"""
Scene Creation
"""


def design_scene(
    num_tiled_cams: int = 2,
    num_standard_cams: int = 0,
    num_ray_caster_cams: int = 0,
    tiled_camera_data_types: list[str] | None = None,
    standard_camera_data_types: list[str] | None = None,
    ray_caster_camera_data_types: list[str] | None = None,
    height: int = 100,
    width: int = 200,
    num_objects: int = 20,
    mesh_prim_paths: list[str] = ["/World/ground"],
) -> dict:
    """Design the scene."""
    if tiled_camera_data_types is None:
        tiled_camera_data_types = ["rgb"]
    if standard_camera_data_types is None:
        standard_camera_data_types = ["rgb"]
    if ray_caster_camera_data_types is None:
        ray_caster_camera_data_types = ["distance_to_image_plane"]

    # Populate scene
    # -- Ground-plane
    cfg = sim_utils.GroundPlaneCfg()
    cfg.func("/World/ground", cfg)
    # -- Lights
    cfg = sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
    cfg.func("/World/Light", cfg)

    # Create a dictionary for the scene entities
    scene_entities = {}

    # Xform to hold objects
    prim_utils.create_prim("/World/Objects", "Xform")
    # Random objects
    for i in range(num_objects):
        # sample random position
        position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0])
        position *= np.asarray([1.5, 1.5, 0.5])
        # sample random color
        color = (random.random(), random.random(), random.random())
        # choose random prim type
        prim_type = random.choice(["Cube", "Cone", "Cylinder"])
        common_properties = {
            "rigid_props": sim_utils.RigidBodyPropertiesCfg(),
            "mass_props": sim_utils.MassPropertiesCfg(mass=5.0),
            "collision_props": sim_utils.CollisionPropertiesCfg(),
            "visual_material": sim_utils.PreviewSurfaceCfg(diffuse_color=color, metallic=0.5),
            "semantic_tags": [("class", prim_type)],
        }
        if prim_type == "Cube":
            shape_cfg = sim_utils.CuboidCfg(size=(0.25, 0.25, 0.25), **common_properties)
        elif prim_type == "Cone":
            shape_cfg = sim_utils.ConeCfg(radius=0.1, height=0.25, **common_properties)
        elif prim_type == "Cylinder":
            shape_cfg = sim_utils.CylinderCfg(radius=0.25, height=0.25, **common_properties)
        # Rigid Object
        obj_cfg = RigidObjectCfg(
            prim_path=f"/World/Objects/Obj_{i:02d}",
            spawn=shape_cfg,
            init_state=RigidObjectCfg.InitialStateCfg(pos=position),
        )
        scene_entities[f"rigid_object{i}"] = RigidObject(cfg=obj_cfg)

    # Sensors
    standard_camera = create_cameras(
        num_cams=num_standard_cams, data_types=standard_camera_data_types, height=height, width=width
    )
    tiled_camera = create_tiled_cameras(
        num_cams=num_tiled_cams, data_types=tiled_camera_data_types, height=height, width=width
    )
    ray_caster_camera = create_ray_caster_cameras(
        num_cams=num_ray_caster_cams,
        data_types=ray_caster_camera_data_types,
        mesh_prim_paths=mesh_prim_paths,
        height=height,
        width=width,
    )
    # return the scene information
    if tiled_camera is not None:
        scene_entities["tiled_camera"] = tiled_camera
    if standard_camera is not None:
        scene_entities["standard_camera"] = standard_camera
    if ray_caster_camera is not None:
        scene_entities["ray_caster_camera"] = ray_caster_camera
    return scene_entities


def inject_cameras_into_task(
    task: str,
    num_cams: int,
    camera_name_prefix: str,
    camera_creation_callable: Callable,
    num_cameras_per_env: int = 1,
) -> gym.Env:
    """Loads the task, sticks cameras into the config, and creates the environment."""
    cfg = load_cfg_from_registry(task, "env_cfg_entry_point")
    cfg.sim.device = args_cli.device
    cfg.sim.use_fabric = args_cli.use_fabric
    scene_cfg = cfg.scene

    num_envs = int(num_cams / num_cameras_per_env)
    scene_cfg.num_envs = num_envs

    for idx in range(num_cameras_per_env):
        suffix = "" if idx == 0 else str(idx)
        name = camera_name_prefix + suffix
        setattr(scene_cfg, name, camera_creation_callable(name))
    cfg.scene = scene_cfg
    env = gym.make(task, cfg=cfg)
    return env


"""
System diagnosis
"""


def get_utilization_percentages(reset: bool = False, max_values: list[float] = [0.0, 0.0, 0.0, 0.0]) -> list[float]:
    """Get the maximum CPU, RAM, GPU utilization (processing), and
    GPU memory usage percentages since the last time reset was true."""
    if reset:
        max_values[:] = [0, 0, 0, 0]  # Reset the max values

    # CPU utilization
    cpu_usage = psutil.cpu_percent(interval=0.1)
    max_values[0] = max(max_values[0], cpu_usage)

    # RAM utilization
    memory_info = psutil.virtual_memory()
    ram_usage = memory_info.percent
    max_values[1] = max(max_values[1], ram_usage)

    # GPU utilization using pynvml
    if torch.cuda.is_available():

        if args_cli.autotune:
            pynvml.nvmlInit()  # Initialize NVML
            for i in range(torch.cuda.device_count()):
                handle = pynvml.nvmlDeviceGetHandleByIndex(i)

                # GPU Utilization
                gpu_utilization = pynvml.nvmlDeviceGetUtilizationRates(handle)
                gpu_processing_utilization_percent = gpu_utilization.gpu  # GPU core utilization
                max_values[2] = max(max_values[2], gpu_processing_utilization_percent)

                # GPU Memory Usage
                memory_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
                gpu_memory_total = memory_info.total
                gpu_memory_used = memory_info.used
                gpu_memory_utilization_percent = (gpu_memory_used / gpu_memory_total) * 100
                max_values[3] = max(max_values[3], gpu_memory_utilization_percent)

            pynvml.nvmlShutdown()  # Shutdown NVML after usage
    else:
        gpu_processing_utilization_percent = None
        gpu_memory_utilization_percent = None
    return max_values


"""
Experiment
"""


def run_simulator(
    sim: sim_utils.SimulationContext | None,
    scene_entities: dict | InteractiveScene,
    warm_start_length: int = 10,
    experiment_length: int = 100,
    tiled_camera_data_types: list[str] | None = None,
    standard_camera_data_types: list[str] | None = None,
    ray_caster_camera_data_types: list[str] | None = None,
    depth_predicate: Callable = lambda x: "to" in x or x == "depth",
    perspective_depth_predicate: Callable = lambda x: x == "distance_to_camera",
    convert_depth_to_camera_to_image_plane: bool = True,
    max_cameras_per_env: int = 1,
    env: gym.Env | None = None,
) -> dict:
    """Run the simulator with all cameras, and return timing analytics. Visualize if desired."""

    if tiled_camera_data_types is None:
        tiled_camera_data_types = ["rgb"]
    if standard_camera_data_types is None:
        standard_camera_data_types = ["rgb"]
    if ray_caster_camera_data_types is None:
        ray_caster_camera_data_types = ["distance_to_image_plane"]

    # Initialize camera lists
    tiled_cameras = []
    standard_cameras = []
    ray_caster_cameras = []

    # Dynamically extract cameras from the scene entities up to max_cameras_per_env
    for i in range(max_cameras_per_env):
        # Extract tiled cameras
        tiled_camera_key = f"tiled_camera{i}" if i > 0 else "tiled_camera"
        standard_camera_key = f"standard_camera{i}" if i > 0 else "standard_camera"
        ray_caster_camera_key = f"ray_caster_camera{i}" if i > 0 else "ray_caster_camera"

        try:  # if instead you checked ... if key is in scene_entities... # errors out always even if key present
            tiled_cameras.append(scene_entities[tiled_camera_key])
            standard_cameras.append(scene_entities[standard_camera_key])
            ray_caster_cameras.append(scene_entities[ray_caster_camera_key])
        except KeyError:
            break

    # Initialize camera counts
    camera_lists = [tiled_cameras, standard_cameras, ray_caster_cameras]
    camera_data_types = [tiled_camera_data_types, standard_camera_data_types, ray_caster_camera_data_types]
    labels = ["tiled", "standard", "ray_caster"]

    if sim is not None:
        # Set camera world poses
        for camera_list in camera_lists:
            for camera in camera_list:
                num_cameras = camera.data.intrinsic_matrices.size(0)
                positions = torch.tensor([[2.5, 2.5, 2.5]], device=sim.device).repeat(num_cameras, 1)
                targets = torch.tensor([[0.0, 0.0, 0.0]], device=sim.device).repeat(num_cameras, 1)
                camera.set_world_poses_from_view(positions, targets)

    # Initialize timing variables
    timestep = 0
    total_time = 0.0
    valid_timesteps = 0
    sim_step_time = 0.0

    while simulation_app.is_running() and timestep < experiment_length:
        print(f"On timestep {timestep} of {experiment_length}, with warm start of {warm_start_length}")
        get_utilization_percentages()

        # Measure the total simulation step time
        step_start_time = time.time()

        if sim is not None:
            sim.step()

        if env is not None:
            with torch.inference_mode():
                # compute zero actions
                actions = torch.zeros(env.action_space.shape, device=env.unwrapped.device)
                # apply actions
                env.step(actions)

        # Update cameras and process vision data within the simulation step
        clouds = {}
        images = {}
        depth_images = {}

        # Loop through all camera lists and their data_types
        for camera_list, data_types, label in zip(camera_lists, camera_data_types, labels):
            for cam_idx, camera in enumerate(camera_list):

                if env is None:  # No env, need to step cams manually
                    # Only update the camera if it hasn't been updated as part of scene_entities.update ...
                    camera.update(dt=sim.get_physics_dt())

                for data_type in data_types:
                    data_label = f"{label}_{cam_idx}_{data_type}"

                    if depth_predicate(data_type):  # is a depth image, want to create cloud
                        depth = camera.data.output[data_type]
                        depth_images[data_label + "_raw"] = depth
                        if perspective_depth_predicate(data_type) and convert_depth_to_camera_to_image_plane:
                            depth = orthogonalize_perspective_depth(
                                camera.data.output[data_type], camera.data.intrinsic_matrices
                            )
                            depth_images[data_label + "_undistorted"] = depth

                        pointcloud = unproject_depth(depth=depth, intrinsics=camera.data.intrinsic_matrices)
                        clouds[data_label] = pointcloud
                    else:  # rgb image, just save it
                        image = camera.data.output[data_type]
                        images[data_label] = image

        # End timing for the step
        step_end_time = time.time()
        sim_step_time += step_end_time - step_start_time

        if timestep > warm_start_length:
            get_utilization_percentages(reset=True)
            total_time += step_end_time - step_start_time
            valid_timesteps += 1

        timestep += 1

    # Calculate average timings
    if valid_timesteps > 0:
        avg_timestep_duration = total_time / valid_timesteps
        avg_sim_step_duration = sim_step_time / experiment_length
    else:
        avg_timestep_duration = 0.0
        avg_sim_step_duration = 0.0

    # Package timing analytics in a dictionary
    timing_analytics = {
        "average_timestep_duration": avg_timestep_duration,
        "average_sim_step_duration": avg_sim_step_duration,
        "total_simulation_time": sim_step_time,
        "total_experiment_duration": sim_step_time,
    }

    system_utilization_analytics = get_utilization_percentages()

    print("--- Benchmark Results ---")
    print(f"Average timestep duration: {avg_timestep_duration:.6f} seconds")
    print(f"Average simulation step duration: {avg_sim_step_duration:.6f} seconds")
    print(f"Total simulation time: {sim_step_time:.6f} seconds")
    print("\nSystem Utilization Statistics:")
    print(
        f"| CPU:{system_utilization_analytics[0]}% | "
        f"RAM:{system_utilization_analytics[1]}% | "
        f"GPU Compute:{system_utilization_analytics[2]}% | "
        f" GPU Memory: {system_utilization_analytics[3]:.2f}% |"
    )

    return {"timing_analytics": timing_analytics, "system_utilization_analytics": system_utilization_analytics}


def main():
    """Main function."""
    # Load simulation context
    if args_cli.num_tiled_cameras + args_cli.num_standard_cameras + args_cli.num_ray_caster_cameras <= 0:
        raise ValueError("You must select at least one camera.")
    if (
        (args_cli.num_tiled_cameras > 0 and args_cli.num_standard_cameras > 0)
        or (args_cli.num_ray_caster_cameras > 0 and args_cli.num_standard_cameras > 0)
        or (args_cli.num_ray_caster_cameras > 0 and args_cli.num_tiled_cameras > 0)
    ):
        print("[WARNING]: You have elected to use more than one camera type.")
        print("[WARNING]: For a benchmark to be meaningful, use ONLY ONE camera type at a time.")
        print(
            "[WARNING]: For example, if num_tiled_cameras=100, for a meaningful benchmark,"
            "num_standard_cameras should be 0, and num_ray_caster_cameras should be 0"
        )
        raise ValueError("Benchmark one camera at a time.")

    print("[INFO]: Designing the scene")
    if args_cli.task is None:
        print("[INFO]: No task environment provided, creating random scene.")
        sim_cfg = sim_utils.SimulationCfg(device=args_cli.device)
        sim = sim_utils.SimulationContext(sim_cfg)
        # Set main camera
        sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0])
        scene_entities = design_scene(
            num_tiled_cams=args_cli.num_tiled_cameras,
            num_standard_cams=args_cli.num_standard_cameras,
            num_ray_caster_cams=args_cli.num_ray_caster_cameras,
            tiled_camera_data_types=args_cli.tiled_camera_data_types,
            standard_camera_data_types=args_cli.standard_camera_data_types,
            ray_caster_camera_data_types=args_cli.ray_caster_camera_data_types,
            height=args_cli.height,
            width=args_cli.width,
            num_objects=args_cli.num_objects,
            mesh_prim_paths=args_cli.ray_caster_visible_mesh_prim_paths,
        )
        # Play simulator
        sim.reset()
        # Now we are ready!
        print("[INFO]: Setup complete...")
        # Run simulator
        run_simulator(
            sim=sim,
            scene_entities=scene_entities,
            warm_start_length=args_cli.warm_start_length,
            experiment_length=args_cli.experiment_length,
            tiled_camera_data_types=args_cli.tiled_camera_data_types,
            standard_camera_data_types=args_cli.standard_camera_data_types,
            ray_caster_camera_data_types=args_cli.ray_caster_camera_data_types,
            convert_depth_to_camera_to_image_plane=args_cli.convert_depth_to_camera_to_image_plane,
        )
    else:
        print("[INFO]: Using known task environment, injecting cameras.")
        autotune_iter = 0
        max_sys_util_thresh = [0.0, 0.0, 0.0]
        max_num_cams = max(args_cli.num_tiled_cameras, args_cli.num_standard_cameras, args_cli.num_ray_caster_cameras)
        cur_num_cams = max_num_cams
        cur_sys_util = max_sys_util_thresh
        interval = args_cli.autotune_camera_count_interval

        if args_cli.autotune:
            max_sys_util_thresh = args_cli.autotune_max_percentage_util
            max_num_cams = args_cli.autotune_max_camera_count
            print("[INFO]: Auto tuning until any of the following threshold are met")
            print(f"|CPU: {max_sys_util_thresh[0]}% | RAM {max_sys_util_thresh[1]}% | GPU: {max_sys_util_thresh[2]}% |")
            print(f"[INFO]: Maximum number of cameras allowed: {max_num_cams}")
        # Determine which camera is being tested...
        tiled_camera_cfg = create_tiled_camera_cfg("tiled_camera")
        standard_camera_cfg = create_standard_camera_cfg("standard_camera")
        ray_caster_camera_cfg = create_ray_caster_camera_cfg("ray_caster_camera")
        camera_name_prefix = ""
        camera_creation_callable = None
        num_cams = 0
        if tiled_camera_cfg is not None:
            camera_name_prefix = "tiled_camera"
            camera_creation_callable = create_tiled_camera_cfg
            num_cams = args_cli.num_tiled_cameras
        elif standard_camera_cfg is not None:
            camera_name_prefix = "standard_camera"
            camera_creation_callable = create_standard_camera_cfg
            num_cams = args_cli.num_standard_cameras
        elif ray_caster_camera_cfg is not None:
            camera_name_prefix = "ray_caster_camera"
            camera_creation_callable = create_ray_caster_camera_cfg
            num_cams = args_cli.num_ray_caster_cameras

        while (
            all(cur <= max_thresh for cur, max_thresh in zip(cur_sys_util, max_sys_util_thresh))
            and cur_num_cams <= max_num_cams
        ):
            cur_num_cams = num_cams + interval * autotune_iter
            autotune_iter += 1

            env = inject_cameras_into_task(
                task=args_cli.task,
                num_cams=cur_num_cams,
                camera_name_prefix=camera_name_prefix,
                camera_creation_callable=camera_creation_callable,
                num_cameras_per_env=args_cli.task_num_cameras_per_env,
            )
            env.reset()
            print(f"Testing with {cur_num_cams} {camera_name_prefix}")
            analysis = run_simulator(
                sim=None,
                scene_entities=env.unwrapped.scene,
                warm_start_length=args_cli.warm_start_length,
                experiment_length=args_cli.experiment_length,
                tiled_camera_data_types=args_cli.tiled_camera_data_types,
                standard_camera_data_types=args_cli.standard_camera_data_types,
                ray_caster_camera_data_types=args_cli.ray_caster_camera_data_types,
                convert_depth_to_camera_to_image_plane=args_cli.convert_depth_to_camera_to_image_plane,
                max_cameras_per_env=args_cli.task_num_cameras_per_env,
                env=env,
            )

            cur_sys_util = analysis["system_utilization_analytics"]
            print("Triggering reset...")
            env.close()
            create_new_stage()
        print("[INFO]: DONE! Feel free to CTRL + C Me ")
        print(f"[INFO]: If you've made it this far, you can likely simulate {cur_num_cams} {camera_name_prefix}")
        print("Keep in mind, this is without any training running on the GPU.")
        print("Set lower utilization thresholds to account for training.")

        if not args_cli.autotune:
            print("[WARNING]: GPU Util Statistics only correct while autotuning, ignore above.")


if __name__ == "__main__":
    # run the main function
    main()
    # close sim app
    simulation_app.close()

可能的参数

首先，运行

./isaaclab.sh -p source/standalone/benchmarks/benchmark_cameras.py -h

要查看您可以使用此实用程序变化的所有可能参数。

请参阅与 autotune 相关的命令行参数，了解有关自动确定最大摄像头数量的更多信息。

比较任务环境中的性能并自动确定任务最大摄像头数量

目前，平铺摄像头是能够处理多个动态对象并且具有最佳性能的摄像头。

例如，要查看您的系统如何在cartpole环境中处理100个平铺摄像头，每个环境中有2个摄像头（总共50个环境），只在RGB模式下运行。

./isaaclab.sh -p source/standalone/benchmarks/benchmark_cameras.py \
--task Isaac-Cartpole-v0 --num_tiled_cameras 100 \
--task_num_cameras_per_env 2 \
--tiled_camera_data_types rgb

如果您已安装pynvml（./isaaclab.sh -p -m pip install pynvml），您还可以找到在指定环境中运行的摄像头的最大数量，直到达到某个性能阈值（由最大CPU利用率百分比、最大RAM利用率百分比、最大GPU计算百分比和最大GPU内存百分比指定）。例如，要找出您可以用cartpole运行的摄像头的最大数量，您可以运行:

./isaaclab.sh -p source/standalone/benchmarks/benchmark_cameras.py \
--task Isaac-Cartpole-v0 --num_tiled_cameras 100 \
--task_num_cameras_per_env 2 \
--tiled_camera_data_types rgb --autotune \
--autotune_max_percentage_util 100 80 50 50

自动调谐可能会导致程序崩溃，这意味着它试图一次运行太多摄像头。然而，最大百分比利用参数旨在阻止这种情况发生。

基准测试的输出不包括训练网络的开销，因此考虑减少最大利用率百分比以考虑这种开销。最终输出的摄像头数量是针对所有摄像头的，因此要获取总环境数量，将输出的摄像头数量除以每个环境的摄像头数量。

比较相机类型和性能（未指定任务情形下）

这个工具还可以在没有任务环境的情况下评估性能。例如，要查看通过两个标准摄像头查看100个随机物体，可以运行

./isaaclab.sh -p source/standalone/benchmarks/benchmark_cameras.py \
--height 100 --width 100 --num_standard_cameras 2 \
--standard_camera_data_types instance_segmentation_fast normals --num_objects 100 \
--experiment_length 100

如果由于性能原因而无法处理此项，则该进程将被终止。建议在运行此脚本时监视 CPU/RAM 利用率和 GPU 利用率，以了解渲染所需的摄像机需要多少资源。在 Ubuntu 中，您可以使用 htop 和 nvtop 等工具在运行此脚本时实时监视资源，而在 Windows 中，您可以使用任务管理器。

如果您的系统无法处理所需的摄像头，您可以尝试以下操作

• 切换到无头模式（提供 --headless）

• 确保您使用的是GPU pipeline，而不是CPU！

• 如果您没有使用平铺摄像头，请切换到平铺摄像头。

• 减少摄像头分辨率

• 减少每个摄像头的数据类型数量。

• 减少摄像头数量

• 减少场景中的物体数量

如果您的系统能够处理相机的数量，那么时间统计将被打印到终端。在模拟停止后，可以使用 CTRL+C 关闭它。