From 64e1bc816d93033f0f46e17378758e6ee05740c8 Mon Sep 17 00:00:00 2001 From: Jeremy Morgan Date: Wed, 8 May 2024 21:19:33 -0700 Subject: [PATCH] clean up, and updating to be compatible with jrl==0.1.0 --- .gitignore | 1 + ikflow/evaluation_utils.py | 21 +- ikflow/ikflow_solver.py | 17 +- ikflow/model_descriptions.yaml | 2 +- ikflow/training/lt_data.py | 4 +- ikflow/training/lt_model.py | 8 +- ikflow/visualizations.py | 8 +- model_performances.md | 58 -- poetry.lock | 1398 ++++++++++++++++---------------- pyproject.toml | 12 +- scripts/benchmark_runtime.py | 117 +-- scripts/evaluate.py | 45 +- scripts/train.py | 2 +- tests/evaluation_utils_test.py | 7 +- tests/ikflow_solver_test.py | 2 +- 15 files changed, 800 insertions(+), 902 deletions(-) delete mode 100644 model_performances.md diff --git a/.gitignore b/.gitignore index eb76446..89f05f4 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ thirdparty/mdn/mdn/__pycache__/models.cpython-38.pyc # Virtual Environments venv/ venvTemp/ +venv-test/ # IDE Config .vscode/ diff --git a/ikflow/evaluation_utils.py b/ikflow/evaluation_utils.py index e6217a1..bf09202 100644 --- a/ikflow/evaluation_utils.py +++ b/ikflow/evaluation_utils.py @@ -70,37 +70,30 @@ def solution_pose_errors( Args: robot (Robot): The Robot which contains the FK function we will use - solutions (Union[torch.Tensor, np.ndarray]): [n x 7] IK solutions for the given target pose + solutions torch.Tensor: [n x 7] IK solutions for the given target pose target_pose (np.ndarray): [7] the target pose the IK solutions were generated for Returns: Tuple[np.ndarray, np.ndarray]: The L2, and angular (rad) errors of IK solutions for the given target_pose """ - assert isinstance( - target_poses, (np.ndarray, torch.Tensor) - ), f"target_poses must be a torch.Tensor or np.ndarray (got {type(target_poses)})" + assert isinstance(target_poses, torch.Tensor), f"target_poses must be a torch.Tensor (got {type(target_poses)})" assert isinstance(solutions, torch.Tensor), f"solutions must be a torch.Tensor (got {type(solutions)})" n_solutions = solutions.shape[0] if n_solutions >= 1000: print("Heads up: It may be faster to run solution_pose_errors() with pytorch directly on the cpu/gpu") - if isinstance(target_poses, torch.Tensor): - target_poses = target_poses.detach().cpu().numpy() target_poses = _get_target_pose_batch(target_poses, solutions.shape[0]) - - ee_pose_ikflow = robot.forward_kinematics(solutions[:, 0 : robot.ndof].detach().cpu().numpy()) + ee_pose_ikflow = robot.forward_kinematics(solutions[:, 0 : robot.ndof]) rot_output = ee_pose_ikflow[:, 3:] # Positional Error - l2_errors = np.linalg.norm(ee_pose_ikflow[:, 0:3] - target_poses[:, 0:3], axis=1) + l2_errors = torch.norm(ee_pose_ikflow[:, 0:3] - target_poses[:, 0:3], dim=1) rot_target = target_poses[:, 3:] assert rot_target.shape == rot_output.shape # Surprisingly, this is almost always faster to calculate on the gpu than on the cpu. I would expect the opposite # for low number of solutions (< 200). - q_target_pt = torch.tensor(rot_target, device=DEVICE, dtype=torch.float32) - q_current_pt = torch.tensor(rot_output, device=DEVICE, dtype=torch.float32) - ang_errors = geodesic_distance_between_quaternions(q_target_pt, q_current_pt).detach().cpu().numpy() + ang_errors = geodesic_distance_between_quaternions(rot_target, rot_output) return l2_errors, ang_errors @@ -145,9 +138,7 @@ def evaluate_solutions( robot, ee_pose_target, samples ) """ - assert isinstance( - target_poses, (np.ndarray, torch.Tensor) - ), f"target_poses must be a torch.Tensor or np.ndarray (got {type(target_poses)})" + assert isinstance(target_poses, torch.Tensor), f"target_poses must be a torch.Tensor (got {type(target_poses)})" assert isinstance(solutions, torch.Tensor), f"solutions must be a torch.Tensor (got {type(solutions)})" target_poses = _get_target_pose_batch(target_poses, solutions.shape[0]) l2_errors, angular_errors = solution_pose_errors(robot, solutions, target_poses) diff --git a/ikflow/ikflow_solver.py b/ikflow/ikflow_solver.py index 7c31189..f0cba7f 100644 --- a/ikflow/ikflow_solver.py +++ b/ikflow/ikflow_solver.py @@ -71,15 +71,6 @@ def conditional_size(self) -> int: """ return self.dim_cond - """Refine a batch of IK solutions using the klampt IK solver - Args: - ikflow_solutions (torch.Tensor): A batch of IK solutions of the form [batch x n_dofs] - target_pose (Union[List[float], np.ndarray]): The target endpose(s). Must either be of the form - [x, y, z, q0, q1, q2, q3] or be a [batch x 7] numpy array - Returns: - torch.Tensor: A batch of IK refined solutions [batch x n_dofs] - """ - def _run_inference( self, latent: torch.Tensor, @@ -109,7 +100,7 @@ def _run_inference( def _calculate_pose_error(self, qs: torch.Tensor, target_poses: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # check error - pose_realized = self.robot.forward_kinematics_batch(qs) + pose_realized = self.robot.forward_kinematics(qs) pos_errors = torch.norm(pose_realized[:, 0:3] - target_poses[:, 0:3], dim=1) rot_errors = geodesic_distance_between_quaternions(target_poses[:, 3:], pose_realized[:, 3:]) return pos_errors, rot_errors @@ -201,10 +192,10 @@ def _generate_exact_ik_solutions( assert len(q) == n_invalid * repeat_count t02 = time() if run_lma_on_cpu and not do_run_entirely_on_cpu: - q = self.robot.inverse_kinematics_single_step_levenburg_marquardt(target_poses_tiled.cpu(), q.cpu()) + q = self.robot.inverse_kinematics_step_levenburg_marquardt(target_poses_tiled.cpu(), q.cpu()) q = q.to(device) else: - q = self.robot.inverse_kinematics_single_step_levenburg_marquardt(target_poses_tiled, q) + q = self.robot.inverse_kinematics_step_levenburg_marquardt(target_poses_tiled, q) t_lma += time() - t02 pos_errors, rot_errors = self._calculate_pose_error(q, target_poses_tiled) valids_i_tiled = torch.logical_and(pos_errors < pos_error_threshold, rot_errors < rot_error_threshold) @@ -253,7 +244,7 @@ def _generate_exact_ik_solutions( def generate_ik_solutions( self, y: torch.Tensor, - n: Optional[int], + n: Optional[int] = None, latent: Optional[torch.Tensor] = None, latent_distribution: str = "gaussian", latent_scale: float = 1.0, diff --git a/ikflow/model_descriptions.yaml b/ikflow/model_descriptions.yaml index 526bf56..ba9da20 100644 --- a/ikflow/model_descriptions.yaml +++ b/ikflow/model_descriptions.yaml @@ -66,7 +66,7 @@ fetch_arm__large__mh186_9.25m: -# TODO: add weights back once Iiwa7 is added to Jrl (currently ommited because missing capsules) +# TODO: add weights back once Iiwa7 is added to jrl (currently ommited because missing capsules) # =========================================================== # === Iiwa7 diff --git a/ikflow/training/lt_data.py b/ikflow/training/lt_data.py index ba4a2e6..cea3402 100644 --- a/ikflow/training/lt_data.py +++ b/ikflow/training/lt_data.py @@ -27,8 +27,8 @@ def __init__( dataset_directory = get_dataset_directory(self._robot_name) assert os.path.isdir(dataset_directory), ( - f"Directory '{dataset_directory}' doesn't exist - have you created the dataset for this robot yet? (try" - f" `python scripts/build_dataset.py --robot_name={robot_name} --training_set_size=10000000`)" + f"Directory '{dataset_directory}' doesn't exist - have you created the dataset for this robot yet? Your probably want to run" + f" `python scripts/build_dataset.py --robot_name={robot_name} --training_set_size=10000000 --only_non_self_colliding`)" ) samples_tr_file_path, poses_tr_file_path, samples_te_file_path, poses_te_file_path, _ = get_dataset_filepaths( dataset_directory, dataset_tags diff --git a/ikflow/training/lt_model.py b/ikflow/training/lt_model.py index db794c2..5a1c352 100644 --- a/ikflow/training/lt_model.py +++ b/ikflow/training/lt_model.py @@ -228,7 +228,9 @@ def on_after_backward(self): def validation_step(self, batch, batch_idx) -> Dict[str, torch.Tensor]: del batch_idx _, y = batch - ee_pose_target = y.cpu().detach().numpy()[0] + ee_pose_target = y[0] + + # Generates m=self.hparams.samples_per_pose solutions for a single pose solutions, model_runtime = self.generate_ik_solutions( ee_pose_target, self.hparams.samples_per_pose, return_runtime=True ) @@ -339,8 +341,8 @@ def generate_ik_solutions( # Note: No code change required here to handle using/not using softflow. conditional = torch.zeros(m, self.ik_solver.dim_cond) - conditional[:, 0:3] = torch.FloatTensor(y[:3]) - conditional[:, 3 : 3 + 4] = torch.FloatTensor(np.array([y[3:]])) + conditional[:, 0:3] = y[:3] + conditional[:, 3 : 3 + 4] = y[3:] conditional = conditional.to(config.device) shape = (m, self.dim_tot) diff --git a/ikflow/visualizations.py b/ikflow/visualizations.py index e95db4f..280cfed 100644 --- a/ikflow/visualizations.py +++ b/ikflow/visualizations.py @@ -129,7 +129,7 @@ def loop_fn(worlds, _demo_state): else: # (B x 3*(n+1) ) x_torch = torch.from_numpy(x_random).float().to(device) - fk = robot.forward_kinematics_batch(x_torch) + fk = robot.forward_kinematics(x_torch) ee_pose = fk[0, 0:3] vis.add("ee", (so3.identity(), ee_pose[0:3]), length=0.15, width=2) @@ -260,8 +260,8 @@ def loop_fn(worlds, _demo_state): ik_solutions = ik_solver.generate_ik_solutions(_demo_state.target_pose, nb_sols, latent=latent) l2_errors, ang_errors = solution_pose_errors(ik_solver.robot, ik_solutions, _demo_state.target_pose) - _demo_state.ave_l2_error = np.mean(l2_errors) * 1000 - _demo_state.ave_ang_error = np.rad2deg(np.mean(ang_errors)) + _demo_state.ave_l2_error = l2_errors.mean().item() * 1000 + _demo_state.ave_ang_error = np.rad2deg(ang_errors.mean().item()) # Update viz with solutions qs = robot._x_to_qs(ik_solutions.detach().cpu().numpy()) @@ -331,7 +331,6 @@ def __init__(self): def setup_fn(worlds): vis.add("robot", worlds[0].robot(0)) - vis.setColor("robot", 1, 0.1, 0.1, 1) assert len(worlds) == 1 def loop_fn(worlds, _demo_state): @@ -369,6 +368,5 @@ def viz_update_fn(worlds, _demo_state): viz_update_fn, time_p_loop=time_p_loop, title=title, - load_terrain=True, demo_state=demo_state, ) diff --git a/model_performances.md b/model_performances.md deleted file mode 100644 index e3cf436..0000000 --- a/model_performances.md +++ /dev/null @@ -1,58 +0,0 @@ -## Model Performances - - - -**01/11/2024, 07:43:13** | Generated with `python scripts/evaluate.py --testset_size=250 --n_solutions_for_runtime=100 --all` - -| | Robot | Model name | Mean positional error (cm) | Mean rotational error (deg) | Joint limits exceeded % | Self-colliding % | Mean runtime for 100 solutions (ms) | Number of coupling layers | -|---:|:----------|:-------------------------------------------|-----------------------------:|------------------------------:|--------------------------:|-------------------:|--------------------------------------:|----------------------------:| -| 0 | panda | panda__full__lp191_5.25m | 0.665803 | 2.02923 | 0 | 1.536 | 10.5915 | 12 | -| 1 | panda | panda_lite_tpm | 1.42935 | 6.4271 | 0 | 3.192 | 5.36361 | 6 | -| 2 | fetch | fetch_full_temp_nsc_tpm | 1.46884 | 2.67093 | 0 | 2.048 | 9.74665 | 12 | -| 3 | fetch | fetch__large__ns183_9.75m | 0.98994 | 1.73842 | 0 | 1.72 | 13.0442 | 16 | -| 4 | fetch_arm | fetch_arm__large__mh186_9.25m | 0.853547 | 1.82127 | 0 | 1.528 | 12.6399 | 16 | -| 5 | rizon4 | rizon4__snowy-brook-208__global_step=2.75M | 2.01684 | 4.77571 | 0 | 0.976 | 9.71355 | 12 | - -**01/11/2024, 07:45:15** | Generated with `python scripts/evaluate.py --testset_size=250 --n_solutions_for_runtime=100 --all` - -| | Robot | Model name | Mean positional error (cm) | Mean rotational error (deg) | Joint limits exceeded % | Self-colliding % | Mean runtime for 100 solutions (ms) | Number of coupling layers | -|---:|:----------|:-------------------------------------------|-----------------------------:|------------------------------:|--------------------------:|-------------------:|--------------------------------------:|----------------------------:| -| 0 | panda | panda__full__lp191_5.25m | 0.665803 | 11.5626 | 0 | 1.536 | 10.137 | 12 | -| 1 | panda | panda_lite_tpm | 1.42935 | 8.73578 | 0 | 3.192 | 5.47514 | 6 | -| 2 | fetch | fetch_full_temp_nsc_tpm | 1.46884 | 2.16771 | 0 | 2.048 | 10.1961 | 12 | -| 3 | fetch | fetch__large__ns183_9.75m | 0.98994 | 7.89807 | 0 | 1.72 | 13.4672 | 16 | -| 4 | fetch_arm | fetch_arm__large__mh186_9.25m | 0.853547 | 0.795852 | 0 | 1.528 | 13.4083 | 16 | -| 5 | rizon4 | rizon4__snowy-brook-208__global_step=2.75M | 2.01684 | 3.68022 | 0 | 0.976 | 10.0469 | 12 | - -**01/11/2024, 07:46:46** | Generated with `python scripts/evaluate.py --testset_size=250 --n_solutions_for_runtime=100 --all` - -| | Robot | Model name | Mean positional error (cm) | Mean rotational error (deg) | Joint limits exceeded % | Self-colliding % | Mean runtime for 100 solutions (ms) | Number of coupling layers | -|---:|:----------|:-------------------------------------------|-----------------------------:|------------------------------:|--------------------------:|-------------------:|--------------------------------------:|----------------------------:| -| 0 | panda | panda__full__lp191_5.25m | 0.665803 | 11.5626 | 0 | 1.536 | 10.1632 | 12 | -| 1 | panda | panda_lite_tpm | 1.42935 | 8.73578 | 0 | 3.192 | 5.32579 | 6 | -| 2 | fetch | fetch_full_temp_nsc_tpm | 1.46884 | 2.16771 | 0 | 2.048 | 10.2276 | 12 | -| 3 | fetch | fetch__large__ns183_9.75m | 0.98994 | 7.89807 | 0 | 1.72 | 13.3203 | 16 | -| 4 | fetch_arm | fetch_arm__large__mh186_9.25m | 0.853547 | 0.795852 | 0 | 1.528 | 13.2277 | 16 | -| 5 | rizon4 | rizon4__snowy-brook-208__global_step=2.75M | 2.01684 | 3.68022 | 0 | 0.976 | 10.0656 | 12 | - -**01/11/2024, 07:47:56** | Generated with `python scripts/evaluate.py --testset_size=250 --n_solutions_for_runtime=100 --all` - -| | Robot | Model name | Mean positional error (cm) | Mean rotational error (deg) | Joint limits exceeded % | Self-colliding % | Mean runtime for 100 solutions (ms) | Number of coupling layers | -|---:|:----------|:-------------------------------------------|-----------------------------:|------------------------------:|--------------------------:|-------------------:|--------------------------------------:|----------------------------:| -| 0 | panda | panda__full__lp191_5.25m | 0.665803 | 2.02923 | 0 | 1.536 | 10.7388 | 12 | -| 1 | panda | panda_lite_tpm | 1.42935 | 6.4271 | 0 | 3.192 | 5.22847 | 6 | -| 2 | fetch | fetch_full_temp_nsc_tpm | 1.46884 | 2.67093 | 0 | 2.048 | 10.1667 | 12 | -| 3 | fetch | fetch__large__ns183_9.75m | 0.98994 | 1.73842 | 0 | 1.72 | 13.2497 | 16 | -| 4 | fetch_arm | fetch_arm__large__mh186_9.25m | 0.853547 | 1.82127 | 0 | 1.528 | 13.224 | 16 | -| 5 | rizon4 | rizon4__snowy-brook-208__global_step=2.75M | 2.01684 | 4.77571 | 0 | 0.976 | 9.89065 | 12 | - -**01/11/2024, 07:59:24** | Generated with `python scripts/evaluate.py --testset_size=500 --n_solutions_for_runtime=100 --all` - -| | Robot | Model name | Mean positional error (cm) | Mean rotational error (deg) | Joint limits exceeded % | Self-colliding % | Mean runtime for 100 solutions (ms) | Number of coupling layers | -|---:|:----------|:-------------------------------------------|-----------------------------:|------------------------------:|--------------------------:|-------------------:|--------------------------------------:|----------------------------:| -| 0 | panda | panda__full__lp191_5.25m | 0.68255 | 2.28165 | 0 | 1.584 | 10.0496 | 12 | -| 1 | panda | panda_lite_tpm | 1.41881 | 6.32345 | 0 | 2.744 | 5.45092 | 6 | -| 2 | fetch | fetch_full_temp_nsc_tpm | 1.48439 | 2.57337 | 0 | 3.004 | 12.3224 | 12 | -| 3 | fetch | fetch__large__ns183_9.75m | 0.977624 | 1.7899 | 0 | 2.112 | 13.475 | 16 | -| 4 | fetch_arm | fetch_arm__large__mh186_9.25m | 0.860086 | 1.86085 | 0 | 1.66 | 15.4206 | 16 | -| 5 | rizon4 | rizon4__snowy-brook-208__global_step=2.75M | 2.02006 | 4.98125 | 0 | 1.068 | 11.0826 | 12 | \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index a12210f..c4632fd 100644 --- a/poetry.lock +++ b/poetry.lock @@ -13,87 +13,87 @@ files = [ [[package]] name = "aiohttp" -version = 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[metadata] lock-version = "2.0" python-versions = "^3.8" -content-hash = "671b0a061b75d87b8137974dbbc4dbec51e712f9e42414507d9b9c449dd17c42" +content-hash = "1ce1ecbd811d0cc2966ca68b19e304da41089758fe3774e655805807b1f72fea" diff --git a/pyproject.toml b/pyproject.toml index 3d1a18f..6ce295f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,20 +1,16 @@ [tool.poetry] name = "ikflow" -version = "0.0.10" +version = "0.1.0" description = "Open source implementation of the 'IKFlow' inverse kinematics solver" authors = ["Jeremy Morgan "] readme = "README.md" [tool.poetry.dependencies] python = "^3.8" -klampt = "0.9.1" -torch = "^2.0.1" FrEIA = "^0.2" -more-itertools = "^9.0.0" -jrl = { git = "https://github.com/jstmn/Jrl.git", branch = "master" } -pynvml = "11.5.0" -numpy = "^1.24.2" -pyyaml = "^6.0.1" +jrl = { git = "https://github.com/jstmn/jrl.git", branch = "master" } +# pynvml = "11.5.0" +# pyyaml = "^6.0.1" [tool.poetry.group.dev.dependencies] diff --git a/scripts/benchmark_runtime.py b/scripts/benchmark_runtime.py index de15ab9..a972e32 100644 --- a/scripts/benchmark_runtime.py +++ b/scripts/benchmark_runtime.py @@ -13,7 +13,6 @@ from ikflow.evaluation_utils import solution_pose_errors from ikflow.ikflow_solver import IKFlowSolver -# torch.set_printoptions(linewidth=200, precision=5, sci_mode=False) torch.set_printoptions(linewidth=200, precision=6, sci_mode=False) set_seed() @@ -33,99 +32,35 @@ def fn_mean_std(fn: Callable, k: int): def solve_ikflow(ikflow_solver: IKFlowSolver, target_poses: torch.Tensor): - ikflow_solver.generate_ik_solutions(target_poses, None) - + ikflow_solver.generate_ik_solutions(target_poses) def solve_klampt(ikflow_solver: IKFlowSolver, target_poses: torch.Tensor): - ikflow_solver.generate_ik_solutions(target_poses, None) + n_failed = 0 for i in range(len(target_poses)): q_i = ikflow_solver.robot.inverse_kinematics_klampt(target_poses[i]) - assert q_i is not None - - -def check_converged(robot, qs, target_poses, i): - # check error - pose_realized = robot.forward_kinematics_batch(qs) - pos_errors = torch.norm(pose_realized[:, 0:3] - target_poses[:, 0:3], dim=1) - rot_errors = geodesic_distance_between_quaternions(target_poses[:, 3:], pose_realized[:, 3:]) - - # - # converged = pos_errors.max().item() < POS_ERROR_THRESHOLD and rot_errors.max().item() < ROT_ERROR_THRESHOLD - # print("solve_lma", i, converged, round(pos_errors.max().item(), 6), "\t", round(rot_errors.max().item(), 6), "\t", pos_errors.data) - # - converged = pos_errors.mean().item() < POS_ERROR_THRESHOLD and rot_errors.mean().item() < ROT_ERROR_THRESHOLD - print( - "solve_lma", - i, - converged, - round(pos_errors.mean().item(), 6), - "\t", - round(rot_errors.mean().item(), 6), - "\t", - pos_errors.data, - rot_errors.data, - ) - - return converged, pos_errors.cpu().numpy(), rot_errors.cpu().numpy() - - -def plot_errors(pos_errors, rot_errors, i): - - fig, (axl, axr) = plt.subplots(1, 2, figsize=(16, 8)) - fig.suptitle(f"Errors at iteration {i}") - axl.set_title(f"Positional errors") - axl.grid(alpha=0.2) - axr.set_title(f"Rotational errors") - axr.grid(alpha=0.2) - axl.scatter(list(range(len(pos_errors))), pos_errors, label="position") - axl.plot([0, len(pos_errors)], [POS_ERROR_THRESHOLD, POS_ERROR_THRESHOLD], label="threshold", color="green") - axr.scatter(list(range(len(pos_errors))), rot_errors, label="rotation") - axr.plot([0, len(pos_errors)], [ROT_ERROR_THRESHOLD, ROT_ERROR_THRESHOLD], label="threshold", color="green") - plt.savefig(f"lma_errors_{i}.pdf", bbox_inches="tight") - plt.show() + if q_i is None: + n_failed += 1 +def solve_klampt_ikflow_seed(ikflow_solver: IKFlowSolver, target_poses: torch.Tensor): + qs = ikflow_solver.generate_ik_solutions(target_poses).cpu().numpy() + target_poses = target_poses.cpu().numpy() + n_failed = 0 + for i in range(len(target_poses)): + q_i = ikflow_solver.robot.inverse_kinematics_klampt(target_poses[i], seed=qs[i, :]) + if q_i is None: + n_failed += 1 def solve_lma(ikflow_solver: IKFlowSolver, target_poses: torch.Tensor): - - qs = ikflow_solver.generate_ik_solutions(target_poses, None) - robot = ikflow_solver.robot - converged = False - i = 0 - print() - _, pos_errors, rot_errors = check_converged(robot, qs, target_poses, "init") - plot_errors(pos_errors, rot_errors, "init") - - while not converged: - - qs = robot.inverse_kinematics_single_step_levenburg_marquardt(target_poses, qs) - converged, _, _ = check_converged(robot, qs, target_poses, i) - - # print(i, converged) - i += 1 - assert i < 20, f"Failed to converge after {i} iterations" - - -def solve_j_pinv(ikflow_solver: IKFlowSolver, target_poses: torch.Tensor): - - qs = ikflow_solver.generate_ik_solutions(target_poses, None) - - converged = False - i = 0 - while not converged: - qs = ikflow_solver.robot.inverse_kinematics_single_step_batch_pt(target_poses, qs, alpha=0.25) - pos_errors, rot_errors = solution_pose_errors(ikflow_solver.robot, qs, target_poses) - converged = np.all(pos_errors < POS_ERROR_THRESHOLD) and np.all(rot_errors < ROT_ERROR_THRESHOLD) - print("solve_j_pinv", i, converged, pos_errors.max().item(), rot_errors.max().item()) - i += 1 + ikflow_solver.generate_exact_ik_solutions(target_poses, run_lma_on_cpu=True) """ Example python scripts/benchmark_runtime.py --model_name=panda__full__lp191_5.25m +TODO: report success pct as well. klampt / lma are going to have a different % exact solutions generated """ - if __name__ == "__main__": parser = argparse.ArgumentParser(prog="evaluate.py - evaluates IK models") @@ -139,29 +74,27 @@ def solve_j_pinv(ikflow_solver: IKFlowSolver, target_poses: torch.Tensor): columns=["method", "number of solutions", "total runtime (ms)", "runtime std", "runtime per solution (ms)"] ) method_names = [ - "ikflow", - "ikflow + klampt ik", - "ikflow + levenberg-marquardt", - # "ikflow + J psuedo-inverse", + "ikflow - NOT EXACT", + "klampt", + "ikflow with levenberg-marquardt", + "klampt with ikflow seeds" ] ikflow_solver, _ = get_ik_solver(args.model_name) - for batch_size in [1, 5, 10, 50, 100]: - # for batch_size in [1, 5, 10, 50, 100, 500, 1000, 5000, 10000]: - # for batch_size in [1, 5, 10]: + for batch_size in [2, 5, 10, 50, 100, 500, 1000, 2500]: print(f"\n---\nBatch size: {batch_size}") _, target_poses = ikflow_solver.robot.sample_joint_angles_and_poses( batch_size, only_non_self_colliding=True, tqdm_enabled=False ) - target_poses_pt = torch.tensor(target_poses, dtype=torch.float32, device="cuda:0") + target_poses = torch.tensor(target_poses, dtype=torch.float32, device="cuda:0") lambdas = [ - lambda: solve_ikflow(ikflow_solver, target_poses_pt), - lambda: solve_klampt(ikflow_solver, target_poses), - lambda: solve_lma(ikflow_solver, target_poses_pt), - # lambda: solve_j_pinv(ikflow_solver, target_poses_pt), + lambda: solve_ikflow(ikflow_solver, target_poses.clone()), + lambda: solve_klampt(ikflow_solver, target_poses.cpu().numpy()), + lambda: solve_lma(ikflow_solver, target_poses.clone()), + lambda: solve_klampt_ikflow_seed(ikflow_solver, target_poses.clone()), ] for lambda_, method_name in zip(lambdas, method_names): mean_runtime_ms, std_runtime = fn_mean_std(lambda_, args.k) @@ -190,5 +123,5 @@ def solve_j_pinv(ikflow_solver: IKFlowSolver, target_poses: torch.Tensor): ax.set_xlabel("Number of solutions") ax.set_ylabel("Total runtime (ms)") ax.legend() - fig.savefig("rotational_distance_runtime_comparison.pdf", bbox_inches="tight") + fig.savefig("ik_benchmark_runtime.pdf", bbox_inches="tight") plt.show() diff --git a/scripts/evaluate.py b/scripts/evaluate.py index a940db7..1e82443 100644 --- a/scripts/evaluate.py +++ b/scripts/evaluate.py @@ -45,7 +45,7 @@ def calculate_error_stats( testset: np.ndarray, latent_distribution: str, latent_scale: float, - samples_per_pose: int, + solutions_per_pose: int, refine_solutions: bool, clamp_to_joint_limits: bool, ) -> ErrorStats: @@ -55,33 +55,36 @@ def calculate_error_stats( """ ik_solver.nn_model.eval() - pos_errs = [] - rot_errs = [] + pos_errs = 1e6*torch.ones(testset.shape[0] * solutions_per_pose, dtype=torch.float32) + rot_errs = 1e6*torch.ones(testset.shape[0] * solutions_per_pose, dtype=torch.float32) jlims_exceeded_count = 0 self_collisions_count = 0 with torch.inference_mode(): for i in range(testset.shape[0]): ee_pose_target = torch.tensor(testset[i], dtype=torch.float32) - samples = ik_solver.generate_ik_solutions( - ee_pose_target, - samples_per_pose, - latent_distribution=latent_distribution, - latent_scale=latent_scale, - clamp_to_joint_limits=clamp_to_joint_limits, - refine_solutions=refine_solutions, - ) + if refine_solutions: + solutions, _ = ik_solver.generate_exact_ik_solutions(ee_pose_target.repeat(solutions_per_pose, 1)) + else: + solutions = ik_solver.generate_ik_solutions( + ee_pose_target, + solutions_per_pose, + latent_distribution=latent_distribution, + latent_scale=latent_scale, + clamp_to_joint_limits=clamp_to_joint_limits, + ) pos_errors_i, rot_errors_i, joint_limits_exceeded, self_collisions = evaluate_solutions( - robot, ee_pose_target, samples + robot, ee_pose_target, solutions ) - pos_errs.extend(pos_errors_i) - rot_errs.extend(rot_errors_i) + pos_errs[i*solutions_per_pose: (i+1)*solutions_per_pose] = pos_errors_i + rot_errs[i*solutions_per_pose: (i+1)*solutions_per_pose] = rot_errors_i jlims_exceeded_count += joint_limits_exceeded.sum().item() self_collisions_count += self_collisions.sum().item() - n_total = testset.shape[0] * samples_per_pose + + n_total = testset.shape[0] * solutions_per_pose return ErrorStats( - float(1000 * np.mean(pos_errs)), - float(np.rad2deg(np.mean(rot_errs))), + 1000 * pos_errs.mean().item(), + float(np.rad2deg(rot_errs.mean().item())), 100 * (jlims_exceeded_count / n_total), 100 * (self_collisions_count / n_total), ) @@ -141,7 +144,7 @@ def evaluate_model( testset, _DEFAULT_LATENT_DISTRIBUTION, _DEFAULT_LATENT_SCALE, - args.samples_per_pose, + args.solutions_per_pose, refine_solutions=args.do_refinement, clamp_to_joint_limits=args.clamp_to_joint_limits, ) @@ -170,8 +173,10 @@ def evaluate_model( python scripts/evaluate.py --testset_size=500 --n_solutions_for_runtime=100 --all +python scripts/evaluate.py --testset_size=500 --model_name=panda__full__lp191_5.25m --solutions_per_pose=20 +python scripts/evaluate.py --testset_size=500 --model_name=panda__full__lp191_5.25m --solutions_per_pose=20 --do_refinement + python scripts/evaluate.py --testset_size=500 --model_name=fetch_arm_full_temp -python scripts/evaluate.py --testset_size=5 --model_name=panda__full__lp191_5.25m --samples_per_pose=3 python scripts/evaluate.py --testset_size=5 --model_name=fetch_full_temp_nsc_tpm python scripts/evaluate.py --testset_size=5 --model_name=fetch_full_temp_nsc_tpm python scripts/evaluate.py --testset_size=500 --model_name=panda__full__lp191_5.25m --do_refinement @@ -180,7 +185,7 @@ def evaluate_model( if __name__ == "__main__": parser = argparse.ArgumentParser(prog="evaluate.py - evaluates IK models") - parser.add_argument("--samples_per_pose", default=50, type=int) + parser.add_argument("--solutions_per_pose", default=50, type=int) parser.add_argument( "--n_solutions_for_runtime", default=100, diff --git a/scripts/train.py b/scripts/train.py index 2437a21..8ab22f1 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -102,7 +102,7 @@ # Test the learning rate scheduler python scripts/train.py \ --robot_name=panda \ - --learning_rate=1.0 \ + --learning_rate=0.01 \ --gamma=0.5 \ --step_lr_every=10 \ --disable_wandb diff --git a/tests/evaluation_utils_test.py b/tests/evaluation_utils_test.py index 38dca71..efee33f 100644 --- a/tests/evaluation_utils_test.py +++ b/tests/evaluation_utils_test.py @@ -15,11 +15,10 @@ def test_solution_pose_errors(self): """Check that solution_pose_errors() matches the expected value""" robot = Panda() - target_pose = np.array([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0]) - + target_pose = torch.tensor([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device="cpu") solutions = torch.zeros((1, 7), dtype=torch.float32, device="cpu") - realized_pose = robot.forward_kinematics(solutions.numpy())[0] - realized_pose_gt = np.array([0.088, 0.0, 0.926, 0.0, 0.92387953, 0.38268343, 0.0]) + realized_pose = robot.forward_kinematics(solutions)[0] + realized_pose_gt = torch.tensor([0.088, 0.0, 0.926, 0.0, 0.92387953, 0.38268343, 0.0], dtype=torch.float32, device="cpu") np.testing.assert_allclose(realized_pose, realized_pose_gt, atol=1e-5) # sanity check values # np.sqrt((1 - 0.088 )**2 + (1 - 0.0 )**2 + (1 - 0.926 )**2 ) = 1.355440887681938 diff --git a/tests/ikflow_solver_test.py b/tests/ikflow_solver_test.py index 6a8370a..7841c39 100644 --- a/tests/ikflow_solver_test.py +++ b/tests/ikflow_solver_test.py @@ -80,7 +80,7 @@ def test_generate_exact_ik_solutions(self): ) # evaluate solutions - pose_realized = robot.forward_kinematics_batch(solutions) + pose_realized = robot.forward_kinematics(solutions) torch.testing.assert_close(pose_realized[:, 0:3], target_poses[:, 0:3], atol=POS_ERROR_THRESHOLD, rtol=1e-2) rot_errors = geodesic_distance_between_quaternions(target_poses[:, 3:], pose_realized[:, 3:]) self.assertLess(rot_errors.max().item(), ROT_ERROR_THRESHOLD)