release repository

benchmark/curobo_python_profile.py

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+#
+# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+#
+
+# Standard Library
+import argparse
+import cProfile
+import time
+
+# Third Party
+import torch
+from torch.profiler import ProfilerActivity, profile, record_function
+
+# CuRobo
+from curobo.cuda_robot_model.cuda_robot_model import CudaRobotModel, CudaRobotModelConfig
+from curobo.geom.sdf.world import CollisionCheckerType
+from curobo.types.base import TensorDeviceType
+from curobo.types.math import Pose
+from curobo.types.robot import JointState, RobotConfig
+from curobo.util_file import get_robot_configs_path, get_robot_path, join_path, load_yaml
+from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
+
+
+def demo_motion_gen():
+    # Standard Library
+
+    st_time = time.time()
+    tensor_args = TensorDeviceType()
+    world_file = "collision_table.yml"
+    robot_file = "franka.yml"
+    motion_gen_config = MotionGenConfig.load_from_robot_config(
+        robot_file,
+        world_file,
+        tensor_args,
+        trajopt_tsteps=32,
+        collision_checker_type=CollisionCheckerType.PRIMITIVE,
+        use_cuda_graph=False,
+        num_trajopt_seeds=4,
+        num_graph_seeds=1,
+        evaluate_interpolated_trajectory=True,
+        interpolation_dt=0.01,
+    )
+    motion_gen = MotionGen(motion_gen_config)
+
+    # st_time = time.time()
+    motion_gen.warmup(batch=50, enable_graph=False, warmup_js_trajopt=False)
+    print("motion gen time:", time.time() - st_time)
+
+    # print(time.time() - st_time)
+    return
+    robot_cfg = load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
+    robot_cfg = RobotConfig.from_dict(robot_cfg, tensor_args)
+    retract_cfg = motion_gen.get_retract_config()
+    print(retract_cfg)
+    state = motion_gen.rollout_fn.compute_kinematics(
+        JointState.from_position(retract_cfg.view(1, -1))
+    )
+
+    retract_pose = Pose(state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze())
+    start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
+    result = motion_gen.plan(
+        start_state, retract_pose, enable_graph=True, enable_opt=False, max_attempts=1
+    )
+    print(result.optimized_plan.position.shape)
+    traj = result.get_interpolated_plan()  # $.position.view(-1, 7)  # optimized plan
+    print("Trajectory Generated: ", result.success, result.optimized_dt.item())
+
+
+def demo_basic_robot():
+    st_time = time.time()
+    tensor_args = TensorDeviceType()
+    # load a urdf:
+    config_file = load_yaml(join_path(get_robot_path(), "franka.yml"))
+
+    urdf_file = config_file["robot_cfg"]["kinematics"][
+        "urdf_path"
+    ]  # Send global path starting with "/"
+    base_link = config_file["robot_cfg"]["kinematics"]["base_link"]
+    ee_link = config_file["robot_cfg"]["kinematics"]["ee_link"]
+    robot_cfg = RobotConfig.from_basic(urdf_file, base_link, ee_link, tensor_args)
+
+    kin_model = CudaRobotModel(robot_cfg.kinematics)
+    print("base kin time:", time.time() - st_time)
+    return
+    # compute forward kinematics:
+
+    # q = torch.rand((10, kin_model.get_dof()), **vars(tensor_args))
+    # out = kin_model.get_state(q)
+    # here is the kinematics state:
+    # print(out)
+
+
+def demo_full_config_robot(config_file):
+    st_time = time.time()
+    tensor_args = TensorDeviceType()
+    # load a urdf:
+
+    robot_cfg = RobotConfig.from_dict(config_file, tensor_args)
+
+    # kin_model = CudaRobotModel(robot_cfg.kinematics)
+    print("full kin time: ", time.time() - st_time)
+    # compute forward kinematics:
+    # q = torch.rand((10, kin_model.get_dof()), **vars(tensor_args))
+    # out = kin_model.get_state(q)
+    # here is the kinematics state:
+    # print(out)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="log/trace",
+        help="path to save file",
+    )
+    parser.add_argument(
+        "--file_name",
+        type=str,
+        default="startup_trace",
+        help="File name prefix to use to save benchmark results",
+    )
+    parser.add_argument(
+        "--motion_gen",
+        action="store_true",
+        help="When True, runs startup for motion generation",
+        default=False,
+    )
+    parser.add_argument(
+        "--kinematics",
+        action="store_true",
+        help="When True, runs startup for kinematics",
+        default=True,
+    )
+
+    args = parser.parse_args()
+
+    # cProfile.run('demo_motion_gen()')
+    config_file = load_yaml(join_path(get_robot_path(), "franka.yml"))["robot_cfg"]
+
+    # Third Party
+
+    if args.kinematics:
+        for _ in range(5):
+            demo_full_config_robot(config_file)
+
+        pr = cProfile.Profile()
+        pr.enable()
+        demo_full_config_robot(config_file)
+        pr.disable()
+        filename = join_path(args.save_path, args.file_name) + "_kinematics_cprofile.prof"
+        pr.dump_stats(filename)
+
+        with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
+            demo_full_config_robot(config_file)
+        filename = join_path(args.save_path, args.file_name) + "_kinematics_trace.json"
+        prof.export_chrome_trace(filename)
+
+    if args.motion_gen:
+        for _ in range(5):
+            demo_motion_gen()
+
+        pr = cProfile.Profile()
+        pr.enable()
+        demo_motion_gen()
+        pr.disable()
+        filename = join_path(args.save_path, args.file_name) + "_motion_gen_cprofile.prof"
+        pr.dump_stats(filename)
+
+        with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
+            demo_motion_gen()
+        filename = join_path(args.save_path, args.file_name) + "_motion_gen_trace.json"
+        prof.export_chrome_trace(filename)