release repository

benchmark/curobo_nvblox_profile.py

@@ -0,0 +1,193 @@
+#
+# 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 time
+from typing import Any, Dict, List
+
+# Third Party
+import numpy as np
+import torch
+import torch.autograd.profiler as profiler
+from robometrics.datasets import demo_raw
+from torch.profiler import ProfilerActivity, profile, record_function
+from tqdm import tqdm
+
+# CuRobo
+from curobo.geom.sdf.world import CollisionCheckerType, WorldConfig
+from curobo.geom.types import Mesh
+from curobo.types.math import Pose
+from curobo.types.state import JointState
+from curobo.util.logger import setup_curobo_logger
+from curobo.util_file import (
+    get_assets_path,
+    get_robot_configs_path,
+    get_task_configs_path,
+    get_world_configs_path,
+    join_path,
+    load_yaml,
+)
+from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
+
+# torch.set_num_threads(8)
+# ttorch.use_deterministic_algorithms(True)
+torch.manual_seed(0)
+
+torch.backends.cudnn.benchmark = True
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+np.random.seed(10)
+# Third Party
+from nvblox_torch.datasets.mesh_dataset import MeshDataset
+
+# CuRobo
+from curobo.types.camera import CameraObservation
+
+
+def load_curobo(n_cubes: int, enable_log: bool = False):
+    robot_cfg = load_yaml(join_path(get_robot_configs_path(), "franka.yml"))["robot_cfg"]
+    robot_cfg["kinematics"]["collision_sphere_buffer"] = -0.015
+    motion_gen_config = MotionGenConfig.load_from_robot_config(
+        robot_cfg,
+        "collision_nvblox_online.yml",
+        trajopt_tsteps=32,
+        collision_checker_type=CollisionCheckerType.BLOX,
+        use_cuda_graph=False,
+        position_threshold=0.005,
+        rotation_threshold=0.05,
+        num_ik_seeds=30,
+        num_trajopt_seeds=12,
+        interpolation_dt=0.02,
+        # grad_trajopt_iters=200,
+        store_ik_debug=enable_log,
+        store_trajopt_debug=enable_log,
+    )
+    mg = MotionGen(motion_gen_config)
+    mg.warmup(enable_graph=False)
+    # print("warmed up")
+    # exit()
+    return mg
+
+
+def benchmark_mb(write_usd=False, save_log=False):
+    robot_cfg = load_yaml(join_path(get_robot_configs_path(), "franka.yml"))["robot_cfg"]
+    spheres = robot_cfg["kinematics"]["collision_spheres"]
+    if isinstance(spheres, str):
+        spheres = load_yaml(join_path(get_robot_configs_path(), spheres))["collision_spheres"]
+
+    plan_config = MotionGenPlanConfig(
+        max_attempts=2,
+        enable_graph_attempt=3,
+        enable_finetune_trajopt=True,
+        partial_ik_opt=False,
+        enable_graph=False,
+    )
+    # load dataset:
+
+    file_paths = [demo_raw]
+    all_files = []
+    for file_path in file_paths:
+        all_groups = []
+
+        problems = file_path()
+
+        for key, v in tqdm(problems.items()):
+            # if key not in ["table_under_pick_panda"]:
+            #    continue
+            scene_problems = problems[key]  # [:2]
+            n_cubes = check_problems(scene_problems)
+            mg = load_curobo(n_cubes, save_log)
+            m_list = []
+            i = 0
+            for problem in tqdm(scene_problems, leave=False):
+                q_start = problem["start"]
+                pose = (
+                    problem["goal_pose"]["position_xyz"] + problem["goal_pose"]["quaternion_wxyz"]
+                )
+
+                # reset planner
+                mg.reset(reset_seed=False)
+                world = WorldConfig.from_dict(problem["obstacles"]).get_mesh_world(
+                    merge_meshes=True
+                )
+                # clear cache:
+                mesh = world.mesh[0].get_trimesh_mesh()
+                mg.clear_world_cache()
+                obs = []
+                # get camera_observations:
+                m_dataset = MeshDataset(
+                    None, n_frames=200, image_size=640, save_data_dir=None, trimesh_mesh=mesh
+                )
+                obs = []
+                tensor_args = mg.tensor_args
+                for j in range(len(m_dataset)):
+                    with profiler.record_function("nvblox/create_camera_images"):
+                        data = m_dataset[j]
+                        cam_obs = CameraObservation(
+                            rgb_image=tensor_args.to_device(data["rgba"]),
+                            depth_image=tensor_args.to_device(data["depth"]),
+                            intrinsics=data["intrinsics"],
+                            pose=Pose.from_matrix(data["pose"].to(device=mg.tensor_args.device)),
+                        )
+                        obs.append(cam_obs)
+                with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
+                    for j in range(len(obs)):
+                        cam_obs = obs[j]
+                        cam_obs.rgb_image = None
+                        with profiler.record_function("nvblox/add_camera_images"):
+                            mg.add_camera_frame(cam_obs, "world")
+
+                        with profiler.record_function("nvblox/process_camera_images"):
+                            mg.process_camera_frames("world", False)
+                    mg.world_coll_checker.update_blox_hashes()
+
+                    # run planner
+                    start_state = JointState.from_position(mg.tensor_args.to_device([q_start]))
+                    result = mg.plan_single(
+                        start_state,
+                        Pose.from_list(pose),
+                        plan_config,
+                    )
+                print("Exporting the trace..")
+                prof.export_chrome_trace("log/trace/trajopt_global_nvblox.json")
+                print(result.success, result.status)
+                exit()
+
+
+def get_metrics_obstacles(obs: Dict[str, List[Any]]):
+    obs_list = []
+    if "cylinder" in obs and len(obs["cylinder"].items()) > 0:
+        for _, vi in enumerate(obs["cylinder"].values()):
+            obs_list.append(
+                Cylinder(
+                    np.ravel(vi["pose"][:3]), vi["radius"], vi["height"], np.ravel(vi["pose"][3:])
+                )
+            )
+    if "cuboid" in obs and len(obs["cuboid"].items()) > 0:
+        for _, vi in enumerate(obs["cuboid"].values()):
+            obs_list.append(
+                Cuboid(np.ravel(vi["pose"][:3]), np.ravel(vi["dims"]), np.ravel(vi["pose"][3:]))
+            )
+    return obs_list
+
+
+def check_problems(all_problems):
+    n_cube = 0
+    for problem in all_problems:
+        cache = WorldConfig.from_dict(problem["obstacles"]).get_obb_world().get_cache_dict()
+        n_cube = max(n_cube, cache["obb"])
+    return n_cube
+
+
+if __name__ == "__main__":
+    setup_curobo_logger("error")
+    benchmark_mb()