299 lines
9.8 KiB
Python
299 lines
9.8 KiB
Python
#
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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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import cProfile
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import time
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# Third Party
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import torch
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from torch.profiler import ProfilerActivity, profile, record_function
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# CuRobo
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from curobo.cuda_robot_model.cuda_robot_model import CudaRobotModel, CudaRobotModelConfig
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from curobo.geom.sdf.world import CollisionCheckerType
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from curobo.types.base import TensorDeviceType
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from curobo.types.math import Pose
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from curobo.types.robot import JointState, RobotConfig
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from curobo.util.logger import setup_curobo_logger
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from curobo.util_file import get_robot_configs_path, get_robot_path, join_path, load_yaml
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from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
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from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
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def demo_motion_gen(robot_file, motion_gen=None):
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st_time = time.time()
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if motion_gen is None:
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setup_curobo_logger("warn")
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# Standard Library
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tensor_args = TensorDeviceType()
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world_file = "collision_table.yml"
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robot_file = "ur5e.yml"
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motion_gen_config = MotionGenConfig.load_from_robot_config(
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robot_file,
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world_file,
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tensor_args,
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trajopt_tsteps=32,
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collision_checker_type=CollisionCheckerType.PRIMITIVE,
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use_cuda_graph=True,
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num_trajopt_seeds=4,
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num_graph_seeds=4,
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evaluate_interpolated_trajectory=True,
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interpolation_dt=0.02,
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)
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motion_gen = MotionGen(motion_gen_config)
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# st_time = time.time()
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torch.cuda.synchronize()
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print("LOAD TIME: ", time.time() - st_time)
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st_time = time.time()
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motion_gen.warmup(enable_graph=True, warmup_js_trajopt=False)
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torch.cuda.synchronize()
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print("warmup TIME: ", time.time() - st_time)
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return motion_gen
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# print(time.time() - st_time)
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# return
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retract_cfg = motion_gen.get_retract_config()
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state = motion_gen.rollout_fn.compute_kinematics(
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JointState.from_position(retract_cfg.view(1, -1))
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)
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retract_pose = Pose(state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze())
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
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result = motion_gen.plan(
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start_state,
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retract_pose,
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enable_graph=False,
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enable_opt=True,
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max_attempts=1,
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# need_graph_success=True,
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)
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traj = result.get_interpolated_plan() # $.position.view(-1, 7) # optimized plan
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print("Trajectory Generated: ", result.success, time.time() - st_time)
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return motion_gen
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def demo_basic_ik(config_file="ur10e.yml"):
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st_time = time.time()
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tensor_args = TensorDeviceType()
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config_file = load_yaml(join_path(get_robot_configs_path(), config_file))
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urdf_file = config_file["kinematics"]["urdf_path"] # Send global path starting with "/"
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base_link = config_file["kinematics"]["base_link"]
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ee_link = config_file["kinematics"]["ee_link"]
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robot_cfg = RobotConfig.from_basic(urdf_file, base_link, ee_link, tensor_args)
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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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None,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=20,
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self_collision_check=False,
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self_collision_opt=False,
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tensor_args=tensor_args,
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use_cuda_graph=False,
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)
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ik_solver = IKSolver(ik_config)
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torch.cuda.synchronize()
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print("IK load time:", time.time() - st_time)
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st_time = time.time()
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# print(kin_state)
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q_sample = ik_solver.sample_configs(100)
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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torch.cuda.synchronize()
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print("FK time:", time.time() - st_time)
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st_time = time.time()
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result = ik_solver.solve_batch(goal)
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torch.cuda.synchronize()
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print(
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"Cold Start Solve Time(s) ",
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result.solve_time,
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)
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def demo_basic_robot():
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st_time = time.time()
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tensor_args = TensorDeviceType()
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# load a urdf:
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config_file = load_yaml(join_path(get_robot_path(), "franka.yml"))
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urdf_file = config_file["robot_cfg"]["kinematics"][
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"urdf_path"
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] # Send global path starting with "/"
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base_link = config_file["robot_cfg"]["kinematics"]["base_link"]
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ee_link = config_file["robot_cfg"]["kinematics"]["ee_link"]
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robot_cfg = RobotConfig.from_basic(urdf_file, base_link, ee_link, tensor_args)
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kin_model = CudaRobotModel(robot_cfg.kinematics)
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print("base kin time:", time.time() - st_time)
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return
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# compute forward kinematics:
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# q = torch.rand((10, kin_model.get_dof()), **vars(tensor_args))
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# out = kin_model.get_state(q)
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# here is the kinematics state:
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# print(out)
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def demo_full_config_robot(config_file):
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st_time = time.time()
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tensor_args = TensorDeviceType()
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# load a urdf:
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robot_cfg = RobotConfig.from_dict(config_file, tensor_args)
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# kin_model = CudaRobotModel(robot_cfg.kinematics)
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print("full kin time: ", time.time() - st_time)
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# compute forward kinematics:
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# q = torch.rand((10, kin_model.get_dof()), **vars(tensor_args))
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# out = kin_model.get_state(q)
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# here is the kinematics state:
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# print(out)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--save_path",
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type=str,
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default="benchmark/log/trace",
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help="path to save file",
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)
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parser.add_argument(
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"--file_name",
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type=str,
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default="startup_trace",
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help="File name prefix to use to save benchmark results",
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)
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parser.add_argument(
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"--motion_gen",
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action="store_true",
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help="When True, runs startup for motion generation",
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default=False,
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)
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parser.add_argument(
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"--motion_gen_plan",
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action="store_true",
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help="When True, runs startup for motion generation",
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default=False,
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)
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parser.add_argument(
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"--kinematics",
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action="store_true",
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help="When True, runs startup for kinematics",
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default=False,
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)
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parser.add_argument(
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"--inverse_kinematics",
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action="store_true",
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help="When True, runs startup for kinematics",
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default=False,
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)
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parser.add_argument(
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"--motion_gen_once",
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action="store_true",
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help="When True, runs startup for kinematics",
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default=False,
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)
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args = parser.parse_args()
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# cProfile.run('demo_motion_gen()')
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config_file = load_yaml(join_path(get_robot_path(), "franka.yml"))["robot_cfg"]
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# Third Party
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if args.kinematics:
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for _ in range(5):
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demo_full_config_robot(config_file)
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pr = cProfile.Profile()
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pr.enable()
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demo_full_config_robot(config_file)
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pr.disable()
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filename = join_path(args.save_path, args.file_name) + "_kinematics_cprofile.prof"
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pr.dump_stats(filename)
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with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
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demo_full_config_robot(config_file)
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filename = join_path(args.save_path, args.file_name) + "_kinematics_trace.json"
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prof.export_chrome_trace(filename)
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if args.inverse_kinematics:
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with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
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demo_basic_ik(config_file)
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filename = join_path(args.save_path, args.file_name) + "_inverse_kinematics_trace.json"
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prof.export_chrome_trace(filename)
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pr = cProfile.Profile()
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pr.enable()
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demo_basic_ik(config_file)
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pr.disable()
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filename = join_path(args.save_path, args.file_name) + "_inverse_kinematics_cprofile.prof"
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pr.dump_stats(filename)
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if args.motion_gen_once:
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pr = cProfile.Profile()
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pr.enable()
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demo_motion_gen(config_file)
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pr.disable()
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filename = join_path(args.save_path, args.file_name) + "_motion_gen_cprofile.prof"
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pr.dump_stats(filename)
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with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
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demo_motion_gen(config_file)
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filename = join_path(args.save_path, args.file_name) + "_motion_gen_trace.json"
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prof.export_chrome_trace(filename)
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if args.motion_gen_plan:
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motion_gen = demo_motion_gen(config_file)
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pr = cProfile.Profile()
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pr.enable()
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demo_motion_gen(config_file, motion_gen)
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pr.disable()
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filename = join_path(args.save_path, args.file_name) + "_motion_gen_plan_cprofile.prof"
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pr.dump_stats(filename)
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with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
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demo_motion_gen(config_file, motion_gen)
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filename = join_path(args.save_path, args.file_name) + "_motion_gen_plan_trace.json"
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prof.export_chrome_trace(filename)
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if args.motion_gen:
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for _ in range(5):
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demo_motion_gen(config_file)
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pr = cProfile.Profile()
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pr.enable()
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demo_motion_gen(config_file)
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pr.disable()
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filename = join_path(args.save_path, args.file_name) + "_motion_gen_cprofile.prof"
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pr.dump_stats(filename)
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with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
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demo_motion_gen(config_file)
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filename = join_path(args.save_path, args.file_name) + "_motion_gen_trace.json"
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prof.export_chrome_trace(filename)
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