212 lines
7.2 KiB
Python
212 lines
7.2 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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# Third Party
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import torch
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# CuRobo
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from curobo.geom.sdf.world import (
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CollisionCheckerType,
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WorldCollisionConfig,
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WorldPrimitiveCollision,
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)
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from curobo.geom.sdf.world_mesh import WorldMeshCollision
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from curobo.geom.types import WorldConfig
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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 RobotConfig
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from curobo.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml
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from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
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def test_basic_ik():
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tensor_args = TensorDeviceType()
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config_file = load_yaml(join_path(get_robot_configs_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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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=30,
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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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)
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ik_solver = IKSolver(ik_config)
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b_size = 10
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q_sample = ik_solver.sample_configs(b_size)
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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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result = ik_solver.solve_batch(goal)
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success = result.success
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assert torch.count_nonzero(success).item() >= 1.0 # we check if atleast 1 is successful
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def test_full_config_collision_free_ik():
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tensor_args = TensorDeviceType()
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world_file = "collision_cubby.yml"
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=30,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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)
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ik_solver = IKSolver(ik_config)
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b_size = 10
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q_sample = ik_solver.sample_configs(b_size)
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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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result = ik_solver.solve(goal)
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success = result.success
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assert torch.count_nonzero(success).item() >= 9.0 # we check if atleast 90% are successful
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def test_attach_object_full_config_collision_free_ik():
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tensor_args = TensorDeviceType()
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world_file = "collision_cubby.yml"
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=30,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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)
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ik_solver = IKSolver(ik_config)
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b_size = 10
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q_sample = ik_solver.sample_configs(b_size)
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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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result = ik_solver.solve(goal)
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success = result.success
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assert torch.count_nonzero(success).item() >= 9.0 # we check if atleast 90% are successful
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q_sample = ik_solver.sample_configs(b_size)
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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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# ik_solver.attach_object_to_robot(sphere_radius=0.02)
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result = ik_solver.solve(goal)
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success = result.success
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assert torch.count_nonzero(success).item() >= 9.0 # we check if atleast 90% are successful
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def test_batch_env_ik():
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tensor_args = TensorDeviceType()
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world_files = ["collision_cubby.yml", "collision_test.yml"]
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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w_list = [
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WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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for world_file in world_files
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]
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world_ccheck = WorldPrimitiveCollision(WorldCollisionConfig(tensor_args, n_envs=2))
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# create a batched world collision checker:
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world_ccheck.load_batch_collision_model(w_list)
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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_coll_checker=world_ccheck,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=100,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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use_cuda_graph=True,
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)
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ik_solver = IKSolver(ik_config)
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b_size = 2
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q_sample = ik_solver.sample_configs(b_size)
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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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result = ik_solver.solve_batch_env(goal)
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success = result.success
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assert torch.count_nonzero(success).item() >= 1.0 # we check if atleast 90% are successful
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def test_batch_env_mesh_ik():
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tensor_args = TensorDeviceType()
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world_files = ["collision_table.yml", "collision_table.yml"]
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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w_list = [
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WorldConfig.from_dict(
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load_yaml(join_path(get_world_configs_path(), world_file))
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).get_mesh_world()
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for world_file in world_files
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]
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world_ccheck = WorldMeshCollision(
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WorldCollisionConfig(tensor_args, checker_type=CollisionCheckerType.MESH, n_envs=2)
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)
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# create a batched world collision checker:
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# print(w_list)
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world_ccheck.load_batch_collision_model(w_list)
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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_coll_checker=world_ccheck,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=100,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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use_cuda_graph=True,
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)
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ik_solver = IKSolver(ik_config)
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b_size = 2
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q_sample = ik_solver.sample_configs(b_size)
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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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result = ik_solver.solve_batch_env(goal)
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success = result.success
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assert torch.count_nonzero(success).item() >= 1.0 # we check if atleast 90% are successful
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