release repository
This commit is contained in:
Balakumar Sundaralingam
558
benchmark/curobo_nvblox_benchmark.py
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558
benchmark/curobo_nvblox_benchmark.py
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#
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# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
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# property and proprietary rights in and to this material, related
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# documentation and any modifications thereto. Any use, reproduction,
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# disclosure or distribution of this material and related documentation
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# without an express license agreement from NVIDIA CORPORATION or
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# its affiliates is strictly prohibited.
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#
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# Standard Library
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import argparse
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from copy import deepcopy
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from typing import Optional
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# Third Party
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from metrics import CuroboGroupMetrics, CuroboMetrics
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from nvblox_torch.datasets.mesh_dataset import MeshDataset
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from robometrics.datasets import demo_raw, motion_benchmaker_raw, mpinets_raw
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from tqdm import tqdm
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# CuRobo
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from curobo.geom.sdf.world import CollisionCheckerType, WorldConfig
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from curobo.geom.types import Mesh
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from curobo.types.base import TensorDeviceType
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from curobo.types.camera import CameraObservation
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from curobo.types.math import Pose
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from curobo.types.robot import RobotConfig
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from curobo.types.state import JointState
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from curobo.util.logger import setup_curobo_logger
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from curobo.util_file import (
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get_assets_path,
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get_robot_configs_path,
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get_world_configs_path,
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join_path,
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load_yaml,
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write_yaml,
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)
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from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
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torch.manual_seed(0)
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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np.random.seed(0)
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def plot_cost_iteration(cost: torch.Tensor, save_path="cost", title="", log_scale=False):
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fig = plt.figure(figsize=(5, 4))
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cost = cost.cpu().numpy()
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# save to csv:
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np.savetxt(save_path + ".csv", cost, delimiter=",")
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# if cost.shape[0] > 1:
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colormap = plt.cm.winter
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plt.gca().set_prop_cycle(plt.cycler("color", colormap(np.linspace(0, 1, cost.shape[0]))))
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x = [i for i in range(cost.shape[-1])]
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for i in range(cost.shape[0]):
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plt.plot(x, cost[i], label="seed_" + str(i))
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plt.tight_layout()
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# plt.title(title)
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plt.xlabel("iteration")
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plt.ylabel("cost")
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if log_scale:
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plt.yscale("log")
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plt.grid()
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# plt.legend()
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plt.tight_layout()
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plt.savefig(save_path + ".pdf")
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plt.close()
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def plot_traj(act_seq: JointState, dt=0.25, title="", save_path="plot.png", sma_filter=False):
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fig, ax = plt.subplots(4, 1, figsize=(5, 8), sharex=True)
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t_steps = np.linspace(0, act_seq.position.shape[0] * dt, act_seq.position.shape[0])
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if sma_filter:
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kernel = 5
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sma = torch.nn.AvgPool1d(kernel_size=kernel, stride=1, padding=2, ceil_mode=False).cuda()
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for i in range(act_seq.position.shape[-1]):
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ax[0].plot(t_steps, act_seq.position[:, i].cpu(), "-", label=str(i))
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ax[1].plot(t_steps[: act_seq.velocity.shape[0]], act_seq.velocity[:, i].cpu(), "-")
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if sma_filter:
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act_seq.acceleration[:, i] = sma(
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act_seq.acceleration[:, i].view(1, -1)
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).squeeze() # @[1:-2]
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ax[2].plot(t_steps[: act_seq.acceleration.shape[0]], act_seq.acceleration[:, i].cpu(), "-")
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if sma_filter:
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act_seq.jerk[:, i] = sma(act_seq.jerk[:, i].view(1, -1)).squeeze() # @[1:-2]\
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ax[3].plot(t_steps[: act_seq.jerk.shape[0]], act_seq.jerk[:, i].cpu(), "-")
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ax[0].set_title(title + " dt=" + "{:.3f}".format(dt))
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ax[3].set_xlabel("Time(s)")
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ax[3].set_ylabel("Jerk rad. s$^{-3}$")
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ax[0].set_ylabel("Position rad.")
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ax[1].set_ylabel("Velocity rad. s$^{-1}$")
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ax[2].set_ylabel("Acceleration rad. s$^{-2}$")
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ax[0].grid()
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ax[1].grid()
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ax[2].grid()
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ax[3].grid()
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ax[0].legend(bbox_to_anchor=(0.5, 1.6), loc="upper center", ncol=4)
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plt.tight_layout()
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plt.savefig(save_path)
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plt.close()
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def load_curobo(
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n_cubes: int,
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enable_debug: bool = False,
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tsteps: int = 30,
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trajopt_seeds: int = 4,
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mpinets: bool = False,
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graph_mode: bool = False,
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cuda_graph: bool = True,
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):
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robot_cfg = load_yaml(join_path(get_robot_configs_path(), "franka.yml"))["robot_cfg"]
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robot_cfg["kinematics"]["collision_sphere_buffer"] = -0.015
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ik_seeds = 30 # 500
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if graph_mode:
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trajopt_seeds = 4
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if trajopt_seeds >= 14:
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ik_seeds = max(100, trajopt_seeds * 4)
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if mpinets:
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robot_cfg["kinematics"]["lock_joints"] = {
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"panda_finger_joint1": 0.025,
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"panda_finger_joint2": -0.025,
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}
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world_cfg = WorldConfig.from_dict(
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load_yaml(join_path(get_world_configs_path(), "collision_nvblox_online.yml"))
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)
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interpolation_steps = 2000
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if graph_mode:
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interpolation_steps = 100
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robot_cfg_instance = RobotConfig.from_dict(robot_cfg, tensor_args=TensorDeviceType())
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K = robot_cfg_instance.kinematics.kinematics_config.joint_limits
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K.position[0, :] -= 0.1
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K.position[1, :] += 0.1
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motion_gen_config = MotionGenConfig.load_from_robot_config(
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robot_cfg_instance,
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world_cfg,
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trajopt_tsteps=tsteps,
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collision_checker_type=CollisionCheckerType.BLOX,
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use_cuda_graph=cuda_graph,
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position_threshold=0.005, # 0.5 cm
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rotation_threshold=0.05,
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num_ik_seeds=ik_seeds,
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num_graph_seeds=trajopt_seeds,
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num_trajopt_seeds=trajopt_seeds,
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interpolation_dt=0.025,
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store_ik_debug=enable_debug,
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store_trajopt_debug=enable_debug,
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interpolation_steps=interpolation_steps,
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collision_activation_distance=0.025,
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state_finite_difference_mode="CENTRAL",
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trajopt_dt=0.25,
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minimize_jerk=True,
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finetune_dt_scale=1.05,
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maximum_trajectory_dt=0.1,
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)
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mg = MotionGen(motion_gen_config)
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mg.warmup(enable_graph=True, warmup_js_trajopt=False)
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return mg, robot_cfg
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def benchmark_mb(
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write_usd=False,
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save_log=False,
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write_plot=False,
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write_benchmark=False,
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plot_cost=False,
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override_tsteps: Optional[int] = None,
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args=None,
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):
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# load dataset:
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graph_mode = args.graph
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interpolation_dt = 0.02
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file_paths = [demo_raw, motion_benchmaker_raw, mpinets_raw][2:]
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enable_debug = save_log or plot_cost
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all_files = []
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og_tsteps = 32
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if override_tsteps is not None:
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og_tsteps = override_tsteps
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og_trajopt_seeds = 12
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if args.graph:
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og_trajopt_seeds = 4
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for file_path in file_paths:
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all_groups = []
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mpinets_data = False
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problems = file_path()
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if "dresser_task_oriented" in list(problems.keys()):
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mpinets_data = True
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mg, robot_cfg = load_curobo(
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1,
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enable_debug,
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og_tsteps,
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og_trajopt_seeds,
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mpinets_data,
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graph_mode,
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not args.disable_cuda_graph,
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)
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for key, v in tqdm(problems.items()):
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scene_problems = problems[key][:] # [:1] # [:20] # [0:10]
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m_list = []
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i = 0
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ik_fail = 0
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for problem in tqdm(scene_problems, leave=False):
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i += 1
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plan_config = MotionGenPlanConfig(
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max_attempts=10, # 00, # 00, # 100, # 00, # 000,#,00,#00, # 5000,
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enable_graph_attempt=3,
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enable_finetune_trajopt=True,
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partial_ik_opt=False,
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enable_graph=graph_mode,
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timeout=60,
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enable_opt=not graph_mode,
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)
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q_start = problem["start"]
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pose = (
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problem["goal_pose"]["position_xyz"] + problem["goal_pose"]["quaternion_wxyz"]
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)
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problem_name = "d_" + key + "_" + str(i)
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# reset planner
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mg.reset(reset_seed=False)
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world = WorldConfig.from_dict(deepcopy(problem["obstacles"])).get_mesh_world(
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merge_meshes=True
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)
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mesh = world.mesh[0].get_trimesh_mesh()
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# world.save_world_as_mesh(problem_name + ".stl")
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mg.world_coll_checker.clear_cache()
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m_dataset = MeshDataset(
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None, n_frames=200, image_size=640, save_data_dir=None, trimesh_mesh=mesh
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)
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tensor_args = mg.tensor_args
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for j in range(len(m_dataset)):
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data = m_dataset[j]
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cam_obs = CameraObservation(
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rgb_image=tensor_args.to_device(data["rgba"]),
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depth_image=tensor_args.to_device(data["depth"]),
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intrinsics=data["intrinsics"],
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pose=Pose.from_matrix(data["pose"].to(device=mg.tensor_args.device)),
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)
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cam_obs = cam_obs
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mg.add_camera_frame(cam_obs, "world")
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mg.process_camera_frames("world", False)
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torch.cuda.synchronize()
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mg.world_coll_checker.update_blox_hashes()
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torch.cuda.synchronize()
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if save_log or write_usd:
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# nvblox_obs = mg.world_coll_checker.get_mesh_from_blox_layer("world", mode="nvblox")
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# nvblox_obs.save_as_mesh("debug_tsdf.obj")
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nvblox_obs = mg.world_coll_checker.get_mesh_from_blox_layer(
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"world", mode="voxel"
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)
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nvblox_obs.color = [0.0, 0.0, 0.8, 0.8]
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# nvblox_obs.save_as_mesh("debug_voxel_occ.obj")
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# exit()
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nvblox_obs.name = "nvblox_world"
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world.add_obstacle(nvblox_obs)
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# run planner
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start_state = JointState.from_position(mg.tensor_args.to_device([q_start]))
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result = mg.plan_single(
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start_state,
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Pose.from_list(pose),
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plan_config,
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)
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if result.status == "IK Fail":
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ik_fail += 1
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problem["solution"] = None
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if plan_config.enable_finetune_trajopt:
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problem_name = key + "_" + str(i)
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else:
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problem_name = "noft_" + key + "_" + str(i)
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problem_name = "nvblox_" + problem_name
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if write_usd or save_log:
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# CuRobo
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from curobo.util.usd_helper import UsdHelper
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world.randomize_color(r=[0.5, 0.9], g=[0.2, 0.5], b=[0.0, 0.2])
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if len(world.mesh) > 1:
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world.mesh[1].color = [125 / 255, 255 / 255, 70.0 / 255, 1.0]
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gripper_mesh = Mesh(
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name="target_gripper",
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file_path=join_path(
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get_assets_path(),
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"robot/franka_description/meshes/visual/hand.dae",
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),
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color=[0.0, 0.8, 0.1, 1.0],
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pose=pose,
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)
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world.add_obstacle(gripper_mesh)
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# get costs:
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if plot_cost:
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dt = 0.5
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problem_name = "approx_wolfe_p" + problem_name
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if result.optimized_dt is not None:
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dt = result.optimized_dt.item()
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if "trajopt_result" in result.debug_info:
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success = result.success.item()
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traj_cost = result.debug_info["trajopt_result"].debug_info["solver"][
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"cost"
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][-1]
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traj_cost = torch.cat(traj_cost, dim=-1)
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plot_cost_iteration(
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traj_cost,
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title=problem_name + "_" + str(success) + "_" + str(dt),
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save_path=join_path("log/plot/", problem_name + "_cost"),
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log_scale=False,
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)
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if "finetune_trajopt_result" in result.debug_info:
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traj_cost = result.debug_info["finetune_trajopt_result"].debug_info[
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"solver"
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]["cost"][0]
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traj_cost = torch.cat(traj_cost, dim=-1)
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plot_cost_iteration(
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traj_cost,
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title=problem_name + "_" + str(success) + "_" + str(dt),
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save_path=join_path("log/plot/", problem_name + "_ft_cost"),
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log_scale=False,
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)
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if result.success.item():
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q_traj = result.get_interpolated_plan()
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problem["goal_ik"] = q_traj.position.cpu().squeeze().numpy()[-1, :].tolist()
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problem["solution"] = {
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"position": result.get_interpolated_plan()
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.position.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"velocity": result.get_interpolated_plan()
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.velocity.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"acceleration": result.get_interpolated_plan()
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.acceleration.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"jerk": result.get_interpolated_plan()
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.jerk.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"dt": interpolation_dt,
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}
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debug = {
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"used_graph": result.used_graph,
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"attempts": result.attempts,
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"ik_time": result.ik_time,
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"graph_time": result.graph_time,
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"trajopt_time": result.trajopt_time,
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"total_time": result.total_time,
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"solve_time": result.solve_time,
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"opt_traj": {
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"position": result.optimized_plan.position.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"velocity": result.optimized_plan.velocity.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"acceleration": result.optimized_plan.acceleration.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"jerk": result.optimized_plan.jerk.cpu().squeeze().numpy().tolist(),
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"dt": result.optimized_dt.item(),
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},
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"valid_query": result.valid_query,
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}
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problem["solution_debug"] = debug
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current_metrics = CuroboMetrics(
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skip=False,
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success=True,
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time=result.total_time,
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collision=False,
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joint_limit_violation=False,
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self_collision=False,
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position_error=result.position_error.item() * 100.0,
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orientation_error=result.rotation_error.item() * 100.0,
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eef_position_path_length=10,
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eef_orientation_path_length=10,
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attempts=result.attempts,
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motion_time=result.motion_time.item(),
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solve_time=result.solve_time,
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)
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if write_usd:
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# CuRobo
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q_traj = result.get_interpolated_plan()
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UsdHelper.write_trajectory_animation_with_robot_usd(
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robot_cfg,
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world,
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start_state,
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q_traj,
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dt=result.interpolation_dt,
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save_path=join_path("log/usd/", problem_name) + ".usd",
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interpolation_steps=1,
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write_robot_usd_path="log/usd/assets/",
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robot_usd_local_reference="assets/",
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base_frame="/world_" + problem_name,
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visualize_robot_spheres=True,
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)
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if write_plot:
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problem_name = problem_name
|
||||
plot_traj(
|
||||
result.optimized_plan,
|
||||
result.optimized_dt.item(),
|
||||
# result.get_interpolated_plan(),
|
||||
# result.interpolation_dt,
|
||||
title=problem_name,
|
||||
save_path=join_path("log/plot/", problem_name + ".pdf"),
|
||||
)
|
||||
plot_traj(
|
||||
# result.optimized_plan,
|
||||
# result.optimized_dt.item(),
|
||||
result.get_interpolated_plan(),
|
||||
result.interpolation_dt,
|
||||
title=problem_name,
|
||||
save_path=join_path("log/plot/", problem_name + "_int.pdf"),
|
||||
)
|
||||
|
||||
m_list.append(current_metrics)
|
||||
all_groups.append(current_metrics)
|
||||
elif result.valid_query:
|
||||
current_metrics = CuroboMetrics()
|
||||
debug = {
|
||||
"used_graph": result.used_graph,
|
||||
"attempts": result.attempts,
|
||||
"ik_time": result.ik_time,
|
||||
"graph_time": result.graph_time,
|
||||
"trajopt_time": result.trajopt_time,
|
||||
"total_time": result.total_time,
|
||||
"solve_time": result.solve_time,
|
||||
"status": result.status,
|
||||
"valid_query": result.valid_query,
|
||||
}
|
||||
problem["solution_debug"] = debug
|
||||
|
||||
m_list.append(current_metrics)
|
||||
all_groups.append(current_metrics)
|
||||
else:
|
||||
print("invalid: " + problem_name)
|
||||
debug = {
|
||||
"used_graph": result.used_graph,
|
||||
"attempts": result.attempts,
|
||||
"ik_time": result.ik_time,
|
||||
"graph_time": result.graph_time,
|
||||
"trajopt_time": result.trajopt_time,
|
||||
"total_time": result.total_time,
|
||||
"solve_time": result.solve_time,
|
||||
"status": result.status,
|
||||
"valid_query": result.valid_query,
|
||||
}
|
||||
problem["solution_debug"] = debug
|
||||
if save_log:
|
||||
UsdHelper.write_motion_gen_log(
|
||||
result,
|
||||
robot_cfg,
|
||||
world,
|
||||
start_state,
|
||||
Pose.from_list(pose),
|
||||
join_path("log/usd/", problem_name) + "_debug",
|
||||
write_ik=not result.success.item(),
|
||||
write_trajopt=True,
|
||||
visualize_robot_spheres=True,
|
||||
grid_space=2,
|
||||
)
|
||||
g_m = CuroboGroupMetrics.from_list(m_list)
|
||||
print(
|
||||
key,
|
||||
f"{g_m.success:2.2f}",
|
||||
g_m.time.mean,
|
||||
g_m.time.percent_98,
|
||||
g_m.position_error.percent_98,
|
||||
g_m.orientation_error.percent_98,
|
||||
)
|
||||
g_m = CuroboGroupMetrics.from_list(all_groups)
|
||||
print(
|
||||
"All: ",
|
||||
f"{g_m.success:2.2f}",
|
||||
g_m.motion_time.percent_98,
|
||||
g_m.time.mean,
|
||||
g_m.time.percent_75,
|
||||
g_m.position_error.percent_75,
|
||||
g_m.orientation_error.percent_75,
|
||||
)
|
||||
print(g_m.attempts)
|
||||
if write_benchmark:
|
||||
if not mpinets_data:
|
||||
write_yaml(problems, "mb_curobo_solution_nvblox.yaml")
|
||||
else:
|
||||
write_yaml(problems, "mpinets_curobo_solution_nvblox.yaml")
|
||||
all_files += all_groups
|
||||
g_m = CuroboGroupMetrics.from_list(all_files)
|
||||
print("######## FULL SET ############")
|
||||
print("All: ", f"{g_m.success:2.2f}")
|
||||
print("MT: ", g_m.motion_time)
|
||||
print("PT:", g_m.time)
|
||||
print("ST: ", g_m.solve_time)
|
||||
print("accuracy: ", g_m.position_error, g_m.orientation_error)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
setup_curobo_logger("error")
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument(
|
||||
"--graph",
|
||||
action="store_true",
|
||||
help="When True, runs only geometric planner",
|
||||
default=False,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--disable_cuda_graph",
|
||||
action="store_true",
|
||||
help="When True, disable cuda graph during benchmarking",
|
||||
default=False,
|
||||
)
|
||||
args = parser.parse_args()
|
||||
benchmark_mb(
|
||||
save_log=False,
|
||||
write_usd=False,
|
||||
write_plot=False,
|
||||
write_benchmark=False,
|
||||
plot_cost=False,
|
||||
args=args,
|
||||
)
|
||||
Reference in New Issue
Block a user