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merge=lfs -text +artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/visualizations/frames/visibility_focus/frame_0170.png filter=lfs diff=lfs merge=lfs -text +artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/visualizations/frames/visibility_focus/frame_0171.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index fe8672fb13b36387682f4492ee5b71d4260c3ece..8b5f770ff699dabc8f5bae2b04db68fc7e43af46 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,15 @@ This is still a label-validation repository, not a policy repository. No `pi0.5` ## Current Status -The latest work in this upload is the `metric_iter24_*_door_contact_geom` rerun for episodes 0 and 1. +The latest work in this upload is the `metric_iter29_ep0_single_pass_full_logging_fixed_templates` validation pass for episode 0, plus the updated debug-aware GIF renderer. + +That run keeps the trusted `iter24` template bundle fixed, adds the fuller dense/debug logging in a single pass, and regenerates the episode-0 visualization suite from the richer artifact. It is the current reference for: + +- the `episode0.debug.jsonl` sidecar with per-frame `p_pre` and `p_ext` internals +- the single-pass dense CSV with fuller logged sub-metrics +- the updated `path_quality_focus` GIF that now exposes the `p_ext` milestone search, milestone scores, and planner outcomes directly in the visualization + +The earlier `metric_iter24_*_door_contact_geom` reruns for episodes 0 and 1 remain the trusted baseline for the repaired oven metrics. That rerun fixes the main simulator-state bugs that were still contaminating the oven metrics: @@ -44,6 +52,7 @@ The old `iter4_*`, `iter6_*`, `iter19_*`, and `iter22_*` outputs are still usefu - `artifacts/results/metric_iter24_ep0_door_contact_geom/` - `artifacts/results/metric_iter24_ep1_door_contact_geom/` +- `artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/` The main new fix in `iter24` is the assisted-door contact scoring inside `p_pre`: diff --git a/artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/episode0.debug.jsonl b/artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/episode0.debug.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..fa7b6d8e240dc9435d07a541dbc6d98723f7db9e --- /dev/null +++ b/artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/episode0.debug.jsonl @@ -0,0 +1,329 @@ +{"frame_index": 0, "state": {"door_angle": 1.5707963705062866, "tray_height": 1.047481894493103, "tray_grasped_left": false, "left_arm_pose": [0.30253246426582336, 0.06815281510353088, 1.4700140953063965, -0.043302085250616074, 0.9918959736824036, 0.0052024610340595245, 0.11933258920907974], "right_arm_pose": [0.2451794296503067, -0.24187642335891724, 1.4707512855529785, -0.9927899241447449, -1.3553760254580993e-05, 0.11986755579710007, 1.4868519429001026e-05], "left_arm_joint_positions": [2.149345164070837e-06, 0.17598441243171692, -2.8279719117563218e-06, -0.8735098242759705, -2.6340137992519885e-05, 1.221383810043335, 0.785420298576355], "right_arm_joint_positions": [3.5779521567746997e-09, 0.17574456334114075, -1.3795226550428197e-05, -0.8734995722770691, -2.1333349650376476e-05, 1.221384048461914, 0.7853978872299194], "pregrasp_goal_pose": [0.48597909064479167, 0.20878563625006075, 1.0409298321402598, 0.36842674152361066, 0.6665119158348012, 0.565251166888581, 0.31704056603471875], "pregrasp_position_error": 0.48738440681897394, "pregrasp_orientation_error_deg": 93.38229341044853}, "visibility": {"best_grasp_visibility_camera": "front", "best_whole_tray_visibility_camera": "front", "camera_values": {"front": {"grasp_visibility": 0.0, "grasp_projected_count": 90, "grasp_visible_count": 0, "tray_visibility": 0.0, "tray_projected_count": 254, "tray_visible_count": 0}, "overhead": {"grasp_visibility": 0.0, "grasp_projected_count": 90, "grasp_visible_count": 0, "tray_visibility": 0.0, "tray_projected_count": 296, "tray_visible_count": 0}, "wrist_right": {"grasp_visibility": 0.0, "grasp_projected_count": 0, "grasp_visible_count": 0, "tray_visibility": 0.0, "tray_projected_count": 0, "tray_visible_count": 0}, "wrist_left": {"grasp_visibility": 0.0, "grasp_projected_count": 90, "grasp_visible_count": 0, "tray_visibility": 0.0, "tray_projected_count": 250, "tray_visible_count": 0}, "over_shoulder_left": {"grasp_visibility": 0.0, "grasp_projected_count": 90, "grasp_visible_count": 0, "tray_visibility": 0.0, 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b/artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/episode0.dense.csv new file mode 100644 index 0000000000000000000000000000000000000000..ca130543987fb9c6ecc7ed1d1bfc0e3b3d0845e2 --- /dev/null +++ b/artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/episode0.dense.csv @@ -0,0 +1,330 @@ 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diff --git a/artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/episode0.keyframes.csv b/artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/episode0.keyframes.csv new file mode 100644 index 0000000000000000000000000000000000000000..1425ef60890423da1c614bb9b0c79f6c1ab187d9 --- /dev/null +++ b/artifacts/results/metric_iter29_ep0_single_pass_full_logging_fixed_templates/episode0.keyframes.csv @@ -0,0 +1,15 @@ 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dense frames 127-300. +- `episode0_all_metrics.gif`: full episode overlay GIF over dense frames 0-328, sampled every 2 frames. +- `frames/visibility_focus/`: per-frame PNGs with scene overlays, x-ray projections, and tray-mask views. +- `frames/path_quality_focus/`: per-frame PNGs with demo wrist trails, milestone pose overlays, and p_ext planner-search tables from the debug JSONL sidecar. +- `frames/all_metrics/`: per-frame PNGs with the episode-wide metric bars and phase banner. + +Legend highlights: +- Visibility: blue = sampled tray surface, magenta = sampled grasp region, green = depth-consistent visible grasp samples. +- Path quality: cyan/purple = recent left/right demo wrist trails, yellow = pregrasp, orange = grasp, green = retreat / extraction path. +- All metrics: red banner = reveal phase, green banner = retrieve phase. \ No newline at end of file diff --git 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a/code/rr_label_study/oven_study.py b/code/rr_label_study/oven_study.py index fa6202315b9beb102b6781355b7bad499456dc75..1d86e82d549f03b6b3ac46588601d2e482b3ba6f 100644 --- a/code/rr_label_study/oven_study.py +++ b/code/rr_label_study/oven_study.py @@ -5,7 +5,7 @@ import os import pickle from dataclasses import dataclass, field from pathlib import Path -from typing import Dict, Iterable, List, Optional, Sequence, Tuple +from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple import numpy as np import pandas as pd @@ -167,6 +167,7 @@ class EpisodeArtifacts: keyframes: pd.DataFrame metrics: Dict[str, object] template_frames: Dict[str, int] + debug_rows: Optional[List[Dict[str, object]]] = None def _configure_runtime() -> None: @@ -564,6 +565,19 @@ def _apply_relative_pose(reference_pose: Sequence[float], relative_pose: Sequenc return _matrix_to_pose(world) +def _orientation_error_degrees(current_quat: Sequence[float], target_quat: Sequence[float]) -> float: + current = np.asarray(current_quat, dtype=np.float64) + target = np.asarray(target_quat, dtype=np.float64) + current_norm = float(np.linalg.norm(current)) + target_norm = float(np.linalg.norm(target)) + if current_norm <= 1e-9 or target_norm <= 1e-9: + return 180.0 + current = current / current_norm + target = target / target_norm + dot = float(np.clip(abs(current @ target), -1.0, 1.0)) + return float(np.degrees(2.0 * np.arccos(dot))) + + def _world_to_local(reference_pose: Sequence[float], point_world: Sequence[float]) -> np.ndarray: reference = _pose_to_matrix(reference_pose) point = np.concatenate([np.asarray(point_world, dtype=np.float64), [1.0]]) @@ -996,6 +1010,25 @@ def _point_visibility_ratio_from_point_cloud( return float(len(visible) / len(projected)) if projected else 0.0 +def _point_visibility_stats_from_point_cloud( + points_world: np.ndarray, + point_cloud_world: np.ndarray, + extrinsics: np.ndarray, + intrinsics: np.ndarray, +) -> Dict[str, float]: + projected, visible = _visibility_projection_details( + points_world, point_cloud_world, extrinsics, intrinsics + ) + projected_count = int(len(projected)) + visible_count = int(len(visible)) + ratio = float(visible_count / projected_count) if projected_count else 0.0 + return { + "ratio": ratio, + "projected_count": projected_count, + "visible_count": visible_count, + } + + def _mask_visibility_ratio( points_world: np.ndarray, mask: np.ndarray, @@ -1032,6 +1065,39 @@ def _union_visibility(values: Iterable[float]) -> float: return 1.0 - product +def _sequence_summary(values: Sequence[float]) -> Dict[str, float]: + if not values: + return { + "count": 0.0, + "mean": 0.0, + "median": 0.0, + "min": 0.0, + "max": 0.0, + } + array = np.asarray(values, dtype=np.float64) + return { + "count": float(array.size), + "mean": float(np.mean(array)), + "median": float(np.median(array)), + "min": float(np.min(array)), + "max": float(np.max(array)), + } + + +def _json_safe(value: Any) -> Any: + if isinstance(value, dict): + return {str(key): _json_safe(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [_json_safe(item) for item in value] + if isinstance(value, np.generic): + return _json_safe(value.item()) + if isinstance(value, np.ndarray): + return [_json_safe(item) for item in value.tolist()] + if isinstance(value, float): + return None if not math.isfinite(value) else value + return value + + def _keypoint_discovery(demo: Demo, stopping_delta: float = 0.1) -> List[int]: keypoints: List[int] = [] right_prev = demo[0].right.gripper_open @@ -1390,9 +1456,10 @@ def _door_contact_path_quality(step_qualities: Sequence[float], door_open_delta: return float(np.clip(0.7 * mean_quality + 0.3 * open_bonus, 0.0, 1.0)) -def _execute_path_with_collision_trace(task, path, arm_name: str) -> Tuple[List[str], List[float]]: +def _execute_path_with_collision_trace(task, path, arm_name: str) -> Dict[str, object]: collisions = set() step_qualities: List[float] = [] + alignments_all: List[float] = [] door_shapes = _door_collision_shapes(task._scene) robot_shape_handles = _robot_collision_shape_handles(task._scene, arm_name) arm = task._scene.robot.left_arm if arm_name == "left" else task._scene.robot.right_arm @@ -1419,6 +1486,7 @@ def _execute_path_with_collision_trace(task, path, arm_name: str) -> Tuple[List[ current_collisions.add(shape.get_name()) alignments = _door_contact_alignments(shape, contacts, tip_delta) if alignments: + alignments_all.extend(float(value) for value in alignments) step_qualities.append( _door_contact_step_quality(alignments, step_open_delta) ) @@ -1426,7 +1494,13 @@ def _execute_path_with_collision_trace(task, path, arm_name: str) -> Tuple[List[ steps += 1 if done or steps > 400: break - return sorted(collisions), step_qualities + return { + "collision_names": sorted(collisions), + "step_qualities": [float(value) for value in step_qualities], + "alignments": [float(value) for value in alignments_all], + "door_contact_steps": int(len(step_qualities)), + "path_steps": int(steps), + } def _single_door_assisted_pregrasp_score( @@ -1435,8 +1509,30 @@ def _single_door_assisted_pregrasp_score( base_snapshot: Optional[SimulatorSnapshot] = None, before_angle: Optional[float] = None, allowed_collisions: Optional[set] = None, -) -> Tuple[float, bool]: +) -> Tuple[float, bool, Dict[str, object]]: snapshot = base_snapshot if base_snapshot is not None else _capture_snapshot(task) + debug: Dict[str, object] = { + "path_found": False, + "path_length": math.inf, + "base_score": 0.0, + "final_score": 0.0, + "bump_applied": False, + "post_assist_strict_feasible": False, + "post_assist_strict_length": math.inf, + "door_open_delta": 0.0, + "door_quality": 0.0, + "collision_names": [], + "bad_collision_names": [], + "step_quality_mean": 0.0, + "step_quality_min": 0.0, + "step_quality_max": 0.0, + "alignment_signed_mean": 0.0, + "alignment_abs_mean": 0.0, + "alignment_min": 0.0, + "alignment_max": 0.0, + "door_contact_steps": 0, + "path_steps": 0, + } try: if before_angle is None: before_angle = float(Joint("oven_door_joint").get_joint_position()) @@ -1448,9 +1544,11 @@ def _single_door_assisted_pregrasp_score( ) length = _path_length(path) if path is None or not np.isfinite(length): - return 0.0, False + return 0.0, False, debug + debug["path_found"] = True + debug["path_length"] = float(length) - collision_names, step_qualities = _execute_path_with_collision_trace( + trace = _execute_path_with_collision_trace( task, path, "left" ) allowed = ( @@ -1458,20 +1556,47 @@ def _single_door_assisted_pregrasp_score( if allowed_collisions is None else allowed_collisions ) + collision_names = [str(name) for name in trace["collision_names"]] bad_collisions = [name for name in collision_names if name not in allowed] door_open_delta = before_angle - float(Joint("oven_door_joint").get_joint_position()) + step_qualities = [float(value) for value in trace["step_qualities"]] + alignments = [float(value) for value in trace["alignments"]] door_quality = _door_contact_path_quality(step_qualities, door_open_delta) + step_summary = _sequence_summary(step_qualities) + alignment_summary = _sequence_summary(alignments) + debug.update( + { + "collision_names": collision_names, + "bad_collision_names": bad_collisions, + "door_open_delta": float(door_open_delta), + "door_quality": float(door_quality), + "step_quality_mean": float(step_summary["mean"]), + "step_quality_min": float(step_summary["min"]), + "step_quality_max": float(step_summary["max"]), + "alignment_signed_mean": float(alignment_summary["mean"]), + "alignment_abs_mean": float( + np.mean(np.abs(np.asarray(alignments, dtype=np.float64))) + ) + if alignments + else 0.0, + "alignment_min": float(alignment_summary["min"]), + "alignment_max": float(alignment_summary["max"]), + "door_contact_steps": int(trace["door_contact_steps"]), + "path_steps": int(trace["path_steps"]), + } + ) if ( bad_collisions or door_open_delta <= DEFAULT_DOOR_ASSIST_MIN_OPEN_DELTA or door_quality < DEFAULT_DOOR_ASSIST_MIN_QUALITY ): - return 0.0, False + return 0.0, False, debug base_score = float( math.exp(-float(length) / DEFAULT_PREGRASP_ASSIST_PATH_SCALE) ) base_score *= door_quality + debug["base_score"] = float(base_score) strict_path = _plan_path( task._scene, "left", @@ -1479,10 +1604,16 @@ def _single_door_assisted_pregrasp_score( ignore_collisions=False, ) strict_length = _path_length(strict_path) + debug["post_assist_strict_length"] = float(strict_length) if strict_path is not None and np.isfinite(strict_length): - return float(0.5 + 0.5 * base_score), False - - return base_score, False + final_score = float(0.5 + 0.5 * base_score) + debug["bump_applied"] = True + debug["post_assist_strict_feasible"] = True + debug["final_score"] = final_score + return final_score, False, debug + + debug["final_score"] = float(base_score) + return base_score, False, debug finally: _restore_snapshot(task, snapshot) @@ -1493,48 +1624,169 @@ def _door_assisted_pregrasp_score( base_snapshot: Optional[SimulatorSnapshot] = None, before_angle: Optional[float] = None, allowed_collisions: Optional[set] = None, -) -> Tuple[float, bool]: +) -> Tuple[float, bool, Dict[str, object]]: attempts = max(1, DEFAULT_PLAN_ATTEMPTS) successful_scores: List[float] = [] + successful_base_scores: List[float] = [] + successful_lengths: List[float] = [] + successful_door_qualities: List[float] = [] + successful_open_deltas: List[float] = [] + successful_step_means: List[float] = [] + successful_align_abs_means: List[float] = [] + successful_align_signed_means: List[float] = [] + successful_collision_counts: List[float] = [] + successful_bad_collision_counts: List[float] = [] + post_assist_strict_count = 0 + bump_count = 0 assisted_success = False + attempt_debugs: List[Dict[str, object]] = [] for _ in range(attempts): - score, success = _single_door_assisted_pregrasp_score( + score, success, attempt_debug = _single_door_assisted_pregrasp_score( task, target_pose, base_snapshot=base_snapshot, before_angle=before_angle, allowed_collisions=allowed_collisions, ) + attempt_debugs.append(attempt_debug) if score > 0.0: successful_scores.append(float(score)) + successful_base_scores.append(float(attempt_debug["base_score"])) + successful_lengths.append(float(attempt_debug["path_length"])) + successful_door_qualities.append(float(attempt_debug["door_quality"])) + successful_open_deltas.append(float(attempt_debug["door_open_delta"])) + successful_step_means.append(float(attempt_debug["step_quality_mean"])) + successful_align_abs_means.append(float(attempt_debug["alignment_abs_mean"])) + successful_align_signed_means.append(float(attempt_debug["alignment_signed_mean"])) + successful_collision_counts.append(float(len(attempt_debug["collision_names"]))) + successful_bad_collision_counts.append( + float(len(attempt_debug["bad_collision_names"])) + ) + post_assist_strict_count += int(bool(attempt_debug["post_assist_strict_feasible"])) + bump_count += int(bool(attempt_debug["bump_applied"])) assisted_success = assisted_success or success + debug: Dict[str, object] = { + "attempts": attempt_debugs, + "attempt_count": int(attempts), + "successful_attempt_count": int(len(successful_scores)), + "reliability": 0.0, + "base_score": 0.0, + "score_before_reliability": 0.0, + "median_path_length": 0.0, + "door_quality": 0.0, + "door_open_delta": 0.0, + "step_quality_mean": 0.0, + "alignment_abs_mean": 0.0, + "alignment_signed_mean": 0.0, + "collision_count": 0.0, + "bad_collision_count": 0.0, + "post_assist_strict_feasible": False, + "bump_applied": False, + } if not successful_scores: - return 0.0, assisted_success + return 0.0, assisted_success, debug reliability = float(len(successful_scores) / attempts) + debug.update( + { + "reliability": reliability, + "base_score": float(np.median(np.asarray(successful_base_scores, dtype=np.float64))), + "score_before_reliability": float( + np.median(np.asarray(successful_scores, dtype=np.float64)) + ), + "median_path_length": float( + np.median(np.asarray(successful_lengths, dtype=np.float64)) + ), + "door_quality": float( + np.median(np.asarray(successful_door_qualities, dtype=np.float64)) + ), + "door_open_delta": float( + np.median(np.asarray(successful_open_deltas, dtype=np.float64)) + ), + "step_quality_mean": float( + np.median(np.asarray(successful_step_means, dtype=np.float64)) + ), + "alignment_abs_mean": float( + np.median(np.asarray(successful_align_abs_means, dtype=np.float64)) + ), + "alignment_signed_mean": float( + np.median(np.asarray(successful_align_signed_means, dtype=np.float64)) + ), + "collision_count": float( + np.median(np.asarray(successful_collision_counts, dtype=np.float64)) + ), + "bad_collision_count": float( + np.median(np.asarray(successful_bad_collision_counts, dtype=np.float64)) + ), + "post_assist_strict_feasible": bool(post_assist_strict_count > 0), + "bump_applied": bool(bump_count > 0), + } + ) if not _plan_is_reliable(reliability): - return 0.0, assisted_success + return 0.0, assisted_success, debug stable_score = float(np.median(np.asarray(successful_scores, dtype=np.float64))) - return stable_score * reliability, assisted_success + return stable_score * reliability, assisted_success, debug -def _pregrasp_score_and_success(task, templates: MotionTemplates) -> Tuple[float, bool]: +def _pregrasp_score_and_success( + task, templates: MotionTemplates +) -> Tuple[float, bool, Dict[str, object]]: tray = Shape("tray") if any( grasped.get_name() == tray.get_name() for grasped in task._scene.robot.left_gripper.get_grasped_objects() ): - return 1.0, True + return ( + 1.0, + True, + { + "tray_grasped_left": True, + "source": "grasped", + "best_goal_index": -1, + "num_goal_poses": 0, + "source_strict": False, + "source_assisted": False, + "bump_applied": False, + "base_score": 1.0, + "strict_length": 0.0, + "assisted_length": 0.0, + "assisted_reliability": 1.0, + "post_assist_strict_feasible": False, + "door_open_delta": 0.0, + "door_quality": 0.0, + "step_quality_mean": 0.0, + "step_quality_min": 0.0, + "step_quality_max": 0.0, + "alignment_signed_mean": 0.0, + "alignment_abs_mean": 0.0, + "collision_count": 0, + "bad_collision_count": 0, + "best_goal_pose": [], + "goal_evaluations": [], + }, + ) tray_pose = tray.get_pose() goal_poses = _late_pregrasp_goal_poses(tray_pose, templates) best_score = 0.0 best_success = False + best_goal_index = -1 + best_goal_debug: Optional[Dict[str, object]] = None base_snapshot: Optional[SimulatorSnapshot] = None before_angle = float(Joint("oven_door_joint").get_joint_position()) allowed_collisions: Optional[set] = None - for target_pose in goal_poses: + goal_debugs: List[Dict[str, object]] = [] + for goal_index, target_pose in enumerate(goal_poses): + goal_debug: Dict[str, object] = { + "goal_index": int(goal_index), + "target_pose": [float(value) for value in np.asarray(target_pose, dtype=np.float64)], + "strict_path_found": False, + "strict_length": 0.0, + "assisted": None, + "candidate_score": 0.0, + "source": "none", + } path = _plan_path( task._scene, "left", @@ -1543,24 +1795,133 @@ def _pregrasp_score_and_success(task, templates: MotionTemplates) -> Tuple[float ) length = _path_length(path) if path is not None and np.isfinite(length): - return 1.0, True + goal_debug["strict_path_found"] = True + goal_debug["strict_length"] = float(length) + goal_debug["candidate_score"] = 1.0 + goal_debug["source"] = "strict" + goal_debugs.append(goal_debug) + return ( + 1.0, + True, + { + "tray_grasped_left": False, + "source": "strict", + "best_goal_index": int(goal_index), + "num_goal_poses": int(len(goal_poses)), + "source_strict": True, + "source_assisted": False, + "bump_applied": False, + "base_score": 1.0, + "strict_length": float(length), + "assisted_length": 0.0, + "assisted_reliability": 0.0, + "post_assist_strict_feasible": False, + "door_open_delta": 0.0, + "door_quality": 0.0, + "step_quality_mean": 0.0, + "step_quality_min": 0.0, + "step_quality_max": 0.0, + "alignment_signed_mean": 0.0, + "alignment_abs_mean": 0.0, + "collision_count": 0, + "bad_collision_count": 0, + "best_goal_pose": [ + float(value) for value in np.asarray(target_pose, dtype=np.float64) + ], + "goal_evaluations": goal_debugs, + }, + ) if base_snapshot is None: base_snapshot = _capture_snapshot(task) allowed_collisions = _allowed_door_collision_names(task._scene) - assisted_score, assisted_success = _door_assisted_pregrasp_score( + assisted_score, assisted_success, assisted_debug = _door_assisted_pregrasp_score( task, target_pose, base_snapshot=base_snapshot, before_angle=before_angle, allowed_collisions=allowed_collisions, ) + goal_debug["assisted"] = assisted_debug + goal_debug["candidate_score"] = float(assisted_score) + goal_debug["source"] = "assisted" if assisted_score > 0.0 else "none" + goal_debugs.append(goal_debug) if assisted_score > best_score: best_score = assisted_score + best_goal_index = goal_index + best_goal_debug = goal_debug best_success = best_success or assisted_success - return float(best_score), bool(best_success) + if best_goal_debug is None: + best_goal_debug = { + "goal_index": -1, + "strict_length": 0.0, + "assisted": None, + } + assisted_debug = best_goal_debug.get("assisted") or {} + return ( + float(best_score), + bool(best_success), + { + "tray_grasped_left": False, + "source": "assisted" if best_score > 0.0 else "none", + "best_goal_index": int(best_goal_index), + "num_goal_poses": int(len(goal_poses)), + "source_strict": False, + "source_assisted": bool(best_score > 0.0), + "bump_applied": bool(assisted_debug.get("bump_applied", False)), + "base_score": float(assisted_debug.get("base_score", 0.0)), + "strict_length": float(best_goal_debug.get("strict_length", 0.0)), + "assisted_length": float(assisted_debug.get("median_path_length", 0.0)), + "assisted_reliability": float(assisted_debug.get("reliability", 0.0)), + "post_assist_strict_feasible": bool( + assisted_debug.get("post_assist_strict_feasible", False) + ), + "door_open_delta": float(assisted_debug.get("door_open_delta", 0.0)), + "door_quality": float(assisted_debug.get("door_quality", 0.0)), + "step_quality_mean": float(assisted_debug.get("step_quality_mean", 0.0)), + "step_quality_min": float( + min( + [ + float(attempt.get("step_quality_min", 0.0)) + for attempt in assisted_debug.get("attempts", []) + if attempt.get("path_found", False) + ] + or [0.0] + ) + ), + "step_quality_max": float( + max( + [ + float(attempt.get("step_quality_max", 0.0)) + for attempt in assisted_debug.get("attempts", []) + if attempt.get("path_found", False) + ] + or [0.0] + ) + ), + "alignment_signed_mean": float( + assisted_debug.get("alignment_signed_mean", 0.0) + ), + "alignment_abs_mean": float(assisted_debug.get("alignment_abs_mean", 0.0)), + "collision_count": int(round(float(assisted_debug.get("collision_count", 0.0)))), + "bad_collision_count": int( + round(float(assisted_debug.get("bad_collision_count", 0.0))) + ), + "best_goal_pose": [ + float(value) + for value in np.asarray( + goal_poses[best_goal_index], dtype=np.float64 + ) + ] + if best_goal_index >= 0 + else [], + "goal_evaluations": goal_debugs, + }, + ) -def _extract_score_and_success(task, templates: MotionTemplates) -> Tuple[float, bool]: +def _extract_score_and_success( + task, templates: MotionTemplates +) -> Tuple[float, bool, Dict[str, object]]: tray = Shape("tray") robot = task._scene.robot snapshot = _capture_snapshot(task) @@ -1571,8 +1932,25 @@ def _extract_score_and_success(task, templates: MotionTemplates) -> Tuple[float, grasped.get_name() == tray.get_name() for grasped in robot.left_gripper.get_grasped_objects() ) + debug: Dict[str, object] = { + "initial_height": float(current_height), + "final_height": float(current_height), + "already_grasped_start": bool(already_grasped), + "already_grasped_end": bool(already_grasped), + "total_length": 0.0, + "num_milestones": 0, + "first_failed_milestone": -1, + "min_reliability": 0.0, + "mean_reliability": 0.0, + "approach_score": 0.0, + "retreat_score": 0.0, + "milestones": [], + "score": 0.0, + } if already_grasped and current_height >= _extract_height_threshold(templates): - return _extraction_progress_score(current_height, templates), True + score = _extraction_progress_score(current_height, templates) + debug["score"] = float(score) + return score, True, debug poses = _extract_sequence_poses( tray.get_pose(), task._task.get_base().get_pose(), templates ) @@ -1592,6 +1970,8 @@ def _extract_score_and_success(task, templates: MotionTemplates) -> Tuple[float, milestone_collision = ([False] * len(approach_poses)) + ([True] * len(retreat_poses)) progress = _extraction_progress_score(current_height, templates) * 0.25 milestone_weight = 0.75 / max(1, len(milestone_poses)) + milestone_reliabilities: List[float] = [] + debug["num_milestones"] = int(len(milestone_poses)) for milestone_index, (pose, ignore_collisions) in enumerate( zip(milestone_poses, milestone_collision) @@ -1599,17 +1979,48 @@ def _extract_score_and_success(task, templates: MotionTemplates) -> Tuple[float, path, length, reliability = _stable_plan( task._scene, "left", pose, ignore_collisions=ignore_collisions ) + planner_score = ( + reliability * math.exp(-length / DEFAULT_PATH_SCALE) + if path is not None and np.isfinite(length) + else 0.0 + ) + milestone_debug = { + "milestone_index": int(milestone_index), + "kind": "approach" if milestone_index < len(approach_poses) else "retreat", + "ignore_collisions": bool(ignore_collisions), + "path_found": bool(path is not None and np.isfinite(length)), + "path_length": float(length) if np.isfinite(length) else 0.0, + "reliability": float(reliability), + "planner_score": float(planner_score), + "height_before": float(current_height), + } + debug["milestones"].append(milestone_debug) if path is None or not np.isfinite(length): - return progress, False - planner_score = reliability * math.exp(-length / DEFAULT_PATH_SCALE) + debug["first_failed_milestone"] = int(milestone_index) + debug["final_height"] = float(current_height) + debug["total_length"] = float(total_length) + debug["score"] = float(progress) + return progress, False, debug + milestone_reliabilities.append(float(reliability)) progress += milestone_weight * planner_score + if milestone_debug["kind"] == "approach": + debug["approach_score"] = float(debug["approach_score"] + milestone_weight * planner_score) + else: + debug["retreat_score"] = float(debug["retreat_score"] + milestone_weight * planner_score) if not _plan_is_reliable(reliability): - return progress, False + debug["first_failed_milestone"] = int(milestone_index) + debug["final_height"] = float(current_height) + debug["total_length"] = float(total_length + length) + debug["score"] = float(progress) + debug["min_reliability"] = float(min(milestone_reliabilities)) + debug["mean_reliability"] = float(np.mean(np.asarray(milestone_reliabilities))) + return progress, False, debug total_length += length path.set_to_end(disable_dynamics=True) task._pyrep.step() current_height = float(tray.get_position()[2]) progress = max(progress, _extraction_progress_score(current_height, templates) * 0.25) + milestone_debug["height_after"] = float(current_height) if (not already_grasped) and milestone_index == (len(approach_poses) - 1): robot.left_gripper.grasp(tray) already_grasped = True @@ -1622,7 +2033,14 @@ def _extract_score_and_success(task, templates: MotionTemplates) -> Tuple[float, math.exp(-total_length / (DEFAULT_PATH_SCALE * 2.5)), _extraction_progress_score(final_height, templates) if success else 0.0, ) - return score, bool(success) + debug["final_height"] = float(final_height) + debug["already_grasped_end"] = bool(already_grasped) + debug["total_length"] = float(total_length) + debug["score"] = float(score) + if milestone_reliabilities: + debug["min_reliability"] = float(min(milestone_reliabilities)) + debug["mean_reliability"] = float(np.mean(np.asarray(milestone_reliabilities))) + return score, bool(success), debug finally: _restore_snapshot(task, snapshot) @@ -1653,7 +2071,7 @@ def _frame_metrics( demo: Demo, frame_state: ReplayState, templates: MotionTemplates, -) -> Dict[str, float]: +) -> Tuple[Dict[str, float], Dict[str, object]]: grasp_points = _sample_grasp_points(templates, frame_state.tray_pose) full_tray_points = _sample_full_tray_points(frame_state.tray_pose) camera_values: Dict[str, Dict[str, float]] = {} @@ -1661,13 +2079,19 @@ def _frame_metrics( point_cloud, extrinsics, intrinsics = _camera_point_cloud( episode_dir, demo, frame_state.frame_index, camera_name ) + grasp_stats = _point_visibility_stats_from_point_cloud( + grasp_points, point_cloud, extrinsics, intrinsics + ) + tray_stats = _point_visibility_stats_from_point_cloud( + full_tray_points, point_cloud, extrinsics, intrinsics + ) camera_values[camera_name] = { - "grasp_visibility": _point_visibility_ratio_from_point_cloud( - grasp_points, point_cloud, extrinsics, intrinsics - ), - "tray_visibility": _point_visibility_ratio_from_point_cloud( - full_tray_points, point_cloud, extrinsics, intrinsics - ), + "grasp_visibility": float(grasp_stats["ratio"]), + "grasp_projected_count": int(grasp_stats["projected_count"]), + "grasp_visible_count": int(grasp_stats["visible_count"]), + "tray_visibility": float(tray_stats["ratio"]), + "tray_projected_count": int(tray_stats["projected_count"]), + "tray_visible_count": int(tray_stats["visible_count"]), } values: Dict[str, float] = {} @@ -1678,7 +2102,172 @@ def _frame_metrics( values[f"{name}_whole_tray_visibility"] = _union_visibility( camera_values[camera_name]["tray_visibility"] for camera_name in camera_set ) - return values + for camera_name in FULL_CAMERA_SET: + camera_value = camera_values[camera_name] + values[f"{camera_name}_grasp_visibility"] = float(camera_value["grasp_visibility"]) + values[f"{camera_name}_tray_visibility"] = float(camera_value["tray_visibility"]) + values[f"{camera_name}_grasp_projected_count"] = float( + camera_value["grasp_projected_count"] + ) + values[f"{camera_name}_grasp_visible_count"] = float(camera_value["grasp_visible_count"]) + values[f"{camera_name}_tray_projected_count"] = float(camera_value["tray_projected_count"]) + values[f"{camera_name}_tray_visible_count"] = float(camera_value["tray_visible_count"]) + + best_grasp_camera = max( + FULL_CAMERA_SET, + key=lambda camera_name: float(camera_values[camera_name]["grasp_visibility"]), + ) + best_tray_camera = max( + FULL_CAMERA_SET, + key=lambda camera_name: float(camera_values[camera_name]["tray_visibility"]), + ) + debug = { + "best_grasp_visibility_camera": best_grasp_camera, + "best_whole_tray_visibility_camera": best_tray_camera, + "camera_values": camera_values, + } + return values, debug + + +def _build_frame_artifacts( + episode_dir: Path, + demo: Demo, + templates: MotionTemplates, + task, + state: ReplayState, +) -> Tuple[Dict[str, float], Dict[str, object]]: + visibility_values, visibility_debug = _frame_metrics(episode_dir, demo, state, templates) + tray_pose = np.asarray(state.tray_pose, dtype=np.float64) + current_pose = np.asarray(state.left_gripper_pose, dtype=np.float64) + right_pose = np.asarray(state.right_gripper_pose, dtype=np.float64) + robot = task._scene.robot + left_joints = np.asarray(robot.left_arm.get_joint_positions(), dtype=np.float64) + right_joints = np.asarray(robot.right_arm.get_joint_positions(), dtype=np.float64) + pregrasp_goal_pose = _apply_relative_pose(tray_pose, templates.pregrasp_rel_pose) + pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance( + current_pose, + tray_pose, + templates, + ) + pregrasp_position_error = float(np.linalg.norm(current_pose[:3] - pregrasp_goal_pose[:3])) + pregrasp_orientation_error_deg = _orientation_error_degrees( + current_pose[3:], pregrasp_goal_pose[3:] + ) + tray = Shape("tray") + tray_grasped_left = any( + grasped.get_name() == tray.get_name() + for grasped in task._scene.robot.left_gripper.get_grasped_objects() + ) + tray_height = float(tray.get_position()[2]) + p_pre, y_pre, p_pre_debug = _pregrasp_score_and_success(task, templates) + p_ext, y_ext, p_ext_debug = _extract_score_and_success(task, templates) + + row = { + "frame_index": int(state.frame_index), + "time_norm": float(state.frame_index / max(1, len(demo) - 1)), + "door_angle": float(state.door_angle), + "right_gripper_open": float(state.right_gripper_open), + "left_gripper_open": float(state.left_gripper_open), + "right_arm_pose_x": float(right_pose[0]), + "right_arm_pose_y": float(right_pose[1]), + "right_arm_pose_z": float(right_pose[2]), + "right_arm_pose_qx": float(right_pose[3]), + "right_arm_pose_qy": float(right_pose[4]), + "right_arm_pose_qz": float(right_pose[5]), + "right_arm_pose_qw": float(right_pose[6]), + "left_arm_pose_x": float(current_pose[0]), + "left_arm_pose_y": float(current_pose[1]), + "left_arm_pose_z": float(current_pose[2]), + "left_arm_pose_qx": float(current_pose[3]), + "left_arm_pose_qy": float(current_pose[4]), + "left_arm_pose_qz": float(current_pose[5]), + "left_arm_pose_qw": float(current_pose[6]), + "tray_height": float(tray_height), + "tray_grasped_left": float(bool(tray_grasped_left)), + "pregrasp_progress": float(pregrasp_progress), + "pregrasp_distance": float(pregrasp_distance), + "pregrasp_position_error": float(pregrasp_position_error), + "pregrasp_orientation_error_deg": float(pregrasp_orientation_error_deg), + "p_pre": float(p_pre), + "p_ext": float(p_ext), + "y_pre_raw": float(bool(y_pre)), + "y_ext_raw": float(bool(y_ext)), + "y_pre": float(bool(y_pre)), + "y_ext": float(bool(y_ext)), + "p_pre_source_strict": float(bool(p_pre_debug["source_strict"])), + "p_pre_source_assisted": float(bool(p_pre_debug["source_assisted"])), + "p_pre_bump_applied": float(bool(p_pre_debug["bump_applied"])), + "p_pre_base_score": float(p_pre_debug["base_score"]), + "p_pre_strict_length": float(p_pre_debug["strict_length"]), + "p_pre_assisted_length": float(p_pre_debug["assisted_length"]), + "p_pre_assisted_reliability": float(p_pre_debug["assisted_reliability"]), + "p_pre_post_assist_strict_feasible": float( + bool(p_pre_debug["post_assist_strict_feasible"]) + ), + "p_pre_door_open_delta": float(p_pre_debug["door_open_delta"]), + "p_pre_door_quality": float(p_pre_debug["door_quality"]), + "p_pre_step_quality_mean": float(p_pre_debug["step_quality_mean"]), + "p_pre_step_quality_min": float(p_pre_debug["step_quality_min"]), + "p_pre_step_quality_max": float(p_pre_debug["step_quality_max"]), + "p_pre_alignment_signed_mean": float(p_pre_debug["alignment_signed_mean"]), + "p_pre_alignment_abs_mean": float(p_pre_debug["alignment_abs_mean"]), + "p_pre_collision_count": float(p_pre_debug["collision_count"]), + "p_pre_bad_collision_count": float(p_pre_debug["bad_collision_count"]), + "p_pre_best_goal_index": float(p_pre_debug["best_goal_index"]), + "p_pre_num_goal_poses": float(p_pre_debug["num_goal_poses"]), + "p_pre_best_target_pose_x": float(p_pre_debug["best_goal_pose"][0]) + if p_pre_debug["best_goal_pose"] + else float("nan"), + "p_pre_best_target_pose_y": float(p_pre_debug["best_goal_pose"][1]) + if p_pre_debug["best_goal_pose"] + else float("nan"), + "p_pre_best_target_pose_z": float(p_pre_debug["best_goal_pose"][2]) + if p_pre_debug["best_goal_pose"] + else float("nan"), + "p_pre_best_target_pose_qx": float(p_pre_debug["best_goal_pose"][3]) + if p_pre_debug["best_goal_pose"] + else float("nan"), + "p_pre_best_target_pose_qy": float(p_pre_debug["best_goal_pose"][4]) + if p_pre_debug["best_goal_pose"] + else float("nan"), + "p_pre_best_target_pose_qz": float(p_pre_debug["best_goal_pose"][5]) + if p_pre_debug["best_goal_pose"] + else float("nan"), + "p_pre_best_target_pose_qw": float(p_pre_debug["best_goal_pose"][6]) + if p_pre_debug["best_goal_pose"] + else float("nan"), + "p_ext_initial_height": float(p_ext_debug["initial_height"]), + "p_ext_final_height": float(p_ext_debug["final_height"]), + "p_ext_total_length": float(p_ext_debug["total_length"]), + "p_ext_already_grasped_start": float(bool(p_ext_debug["already_grasped_start"])), + "p_ext_already_grasped_end": float(bool(p_ext_debug["already_grasped_end"])), + "p_ext_num_milestones": float(p_ext_debug["num_milestones"]), + "p_ext_first_failed_milestone": float(p_ext_debug["first_failed_milestone"]), + "p_ext_min_reliability": float(p_ext_debug["min_reliability"]), + "p_ext_mean_reliability": float(p_ext_debug["mean_reliability"]), + "p_ext_approach_score": float(p_ext_debug["approach_score"]), + "p_ext_retreat_score": float(p_ext_debug["retreat_score"]), + **visibility_values, + } + debug = { + "frame_index": int(state.frame_index), + "state": { + "door_angle": float(state.door_angle), + "tray_height": float(tray_height), + "tray_grasped_left": bool(tray_grasped_left), + "left_arm_pose": [float(value) for value in current_pose], + "right_arm_pose": [float(value) for value in right_pose], + "left_arm_joint_positions": [float(value) for value in left_joints], + "right_arm_joint_positions": [float(value) for value in right_joints], + "pregrasp_goal_pose": [float(value) for value in pregrasp_goal_pose], + "pregrasp_position_error": float(pregrasp_position_error), + "pregrasp_orientation_error_deg": float(pregrasp_orientation_error_deg), + }, + "visibility": visibility_debug, + "p_pre": p_pre_debug, + "p_ext": p_ext_debug, + } + return row, debug def _compute_frame_row_isolated( @@ -1707,30 +2296,8 @@ def _build_frame_row( task, state: ReplayState, ) -> Dict[str, float]: - visibility = _frame_metrics(episode_dir, demo, state, templates) - pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance( - np.asarray(state.left_gripper_pose, dtype=np.float64), - np.asarray(state.tray_pose, dtype=np.float64), - templates, - ) - p_pre, y_pre = _pregrasp_score_and_success(task, templates) - p_ext, y_ext = _extract_score_and_success(task, templates) - return { - "frame_index": int(state.frame_index), - "time_norm": float(state.frame_index / max(1, len(demo) - 1)), - "door_angle": float(state.door_angle), - "right_gripper_open": float(state.right_gripper_open), - "left_gripper_open": float(state.left_gripper_open), - "pregrasp_progress": float(pregrasp_progress), - "pregrasp_distance": float(pregrasp_distance), - "p_pre": float(p_pre), - "p_ext": float(p_ext), - "y_pre_raw": float(bool(y_pre)), - "y_ext_raw": float(bool(y_ext)), - "y_pre": float(bool(y_pre)), - "y_ext": float(bool(y_ext)), - **visibility, - } + row, _ = _build_frame_artifacts(episode_dir, demo, templates, task, state) + return row def _compute_frame_rows_sequential( @@ -1833,6 +2400,7 @@ def _monotone_after_first(binary_values: np.ndarray) -> np.ndarray: def _annotate_phase_columns(frame_df: pd.DataFrame) -> pd.DataFrame: door_speed = np.gradient(frame_df["door_angle"].to_numpy(dtype=float), DEMO_DT) + frame_df["door_speed_signed"] = door_speed frame_df["door_speed_abs"] = np.abs(door_speed) y_ext_raw = ( @@ -1865,9 +2433,11 @@ def _annotate_phase_columns(frame_df: pd.DataFrame) -> pd.DataFrame: y_ext_binary = _monotone_after_first( _persistent_rise_mask(y_ext_raw, DEFAULT_READY_PERSISTENCE) ) + frame_df["y_pre_seed_raw"] = y_pre_seed.astype(float) frame_df["y_pre_progress_seed"] = pregrasp_progress_seed.astype(float) frame_df["y_pre"] = y_pre_binary.astype(float) frame_df["y_ext"] = y_ext_binary.astype(float) + frame_df["y_ext_seed_raw"] = y_ext_raw.astype(float) frame_df["phase_score"] = np.clip(frame_df["p_pre"].to_numpy(dtype=float), 0.0, 1.0) approach_active = ( @@ -1879,6 +2449,7 @@ def _annotate_phase_columns(frame_df: pd.DataFrame) -> pd.DataFrame: retrieve_onset = _persistent_rise_mask( approach_active & y_pre_binary, DEFAULT_RETRIEVE_PERSISTENCE ) + frame_df["retrieve_onset_raw"] = retrieve_onset.astype(float) frame_df["y_retrieve"] = _monotone_after_first(retrieve_onset).astype(float) ready_seed = np.zeros(len(frame_df), dtype=bool) @@ -1890,13 +2461,22 @@ def _annotate_phase_columns(frame_df: pd.DataFrame) -> pd.DataFrame: and frame_df.iloc[index]["door_speed_abs"] <= DEFAULT_DOOR_SPEED_TAU ): ready_seed[index] = True + frame_df["ready_seed_raw"] = ready_seed.astype(float) frame_df["y_ready"] = _monotone_after_first(ready_seed).astype(float) phase_seed = _persistent_rise_mask( frame_df["p_pre"].to_numpy(dtype=float) >= DEFAULT_PHASE_SCORE_TAU, DEFAULT_RETRIEVE_PERSISTENCE, ) + frame_df["phase_seed_raw"] = phase_seed.astype(float) frame_df["phase_switch"] = _monotone_after_first(phase_seed).astype(float) + frame_df["p_pre_minus_tau"] = ( + frame_df["p_pre"].to_numpy(dtype=float) - DEFAULT_PHASE_SCORE_TAU + ) + frame_df["p_ext_minus_tau"] = ( + frame_df["p_ext"].to_numpy(dtype=float) - DEFAULT_PEXT_TAU + ) + frame_df["door_speed_margin"] = DEFAULT_DOOR_SPEED_TAU - frame_df["door_speed_abs"] return frame_df @@ -1999,19 +2579,23 @@ def _isolated_intervention_outcomes( cache.step_to(frame_index) snapshot = cache.snapshot() - base = _extract_score_and_success(task, templates) + base_score, base_success, _ = _extract_score_and_success(task, templates) + base = (base_score, base_success) _restore_snapshot(task, snapshot) _open_more_branch(task, templates) - open_more = _extract_score_and_success(task, templates) + open_score, open_success, _ = _extract_score_and_success(task, templates) + open_more = (open_score, open_success) _restore_snapshot(task, snapshot) _hold_open_branch(task, templates) - hold_open = _extract_score_and_success(task, templates) + hold_score, hold_success, _ = _extract_score_and_success(task, templates) + hold_open = (hold_score, hold_success) _restore_snapshot(task, snapshot) _wait_branch(task) - wait = _extract_score_and_success(task, templates) + wait_score, wait_success, _ = _extract_score_and_success(task, templates) + wait = (wait_score, wait_success) return { "base": base, @@ -2120,6 +2704,7 @@ def _analyze_episode( num_frames = len(demo) if max_frames is None else min(len(demo), max_frames) rows: List[Dict[str, float]] = [] + debug_rows: List[Dict[str, object]] = [] initial_snapshot = cache.checkpoints[0] if independent_replay else None for frame_index in range(num_frames): if independent_replay: @@ -2127,32 +2712,11 @@ def _analyze_episode( cache.step_to(frame_index) frame_snapshot = cache.snapshot() if not independent_replay else None state = cache.current_state() - visibility = _frame_metrics(episode_dir, demo, state, templates) - pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance( - np.asarray(state.left_gripper_pose, dtype=np.float64), - np.asarray(state.tray_pose, dtype=np.float64), - templates, - ) - p_pre, y_pre = _pregrasp_score_and_success(task, templates) - p_ext, y_ext = _extract_score_and_success(task, templates) - rows.append( - { - "frame_index": frame_index, - "time_norm": frame_index / max(1, num_frames - 1), - "door_angle": state.door_angle, - "right_gripper_open": state.right_gripper_open, - "left_gripper_open": state.left_gripper_open, - "pregrasp_progress": pregrasp_progress, - "pregrasp_distance": pregrasp_distance, - "p_pre": p_pre, - "p_ext": p_ext, - "y_pre_raw": float(bool(y_pre)), - "y_ext_raw": float(bool(y_ext)), - "y_pre": float(bool(y_pre)), - "y_ext": float(bool(y_ext)), - **visibility, - } + row, debug_row = _build_frame_artifacts( + episode_dir, demo, templates, task, state ) + rows.append(row) + debug_rows.append(debug_row) if frame_snapshot is not None: cache.restore(frame_snapshot) if (frame_index + 1) % 25 == 0 or (frame_index + 1) == num_frames: @@ -2188,6 +2752,7 @@ def _analyze_episode( keyframes=key_df, metrics=metrics, template_frames=template_frames, + debug_rows=debug_rows, ) @@ -2321,6 +2886,13 @@ def run_study( artifacts.keyframes.to_csv( result_path.joinpath(f"{episode_dir.name}.keyframes.csv"), index=False ) + if artifacts.debug_rows is not None: + with result_path.joinpath(f"{episode_dir.name}.debug.jsonl").open( + "w", encoding="utf-8" + ) as handle: + for debug_row in artifacts.debug_rows: + handle.write(json.dumps(_json_safe(debug_row))) + handle.write("\n") with result_path.joinpath(f"{episode_dir.name}.metrics.json").open( "w", encoding="utf-8" ) as handle: diff --git a/code/scripts/augment_oven_logging_parallel.py b/code/scripts/augment_oven_logging_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..f551cd23ce26a99861727b5ed13a9291a35eb747 --- /dev/null +++ b/code/scripts/augment_oven_logging_parallel.py @@ -0,0 +1,337 @@ +from pathlib import Path +import argparse +import json +import pickle +import sys +from typing import Dict, List, Optional, Sequence, Tuple + +import pandas as pd + + +PROJECT_ROOT = Path(__file__).resolve().parents[1] +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) + +from rr_label_study.oven_study import ( + _aggregate_summary, + _annotate_phase_columns, + _derive_templates, + _json_safe, + _load_demo, +) +from scripts.recompute_oven_episode_parallel import ( + _chunk_frame_indices, + _collect_debug_rows, + _collect_rows, + _launch_xvfb, + _spawn_frame_batch_job, + _stop_process, +) + + +def _merge_new_columns( + base_df: pd.DataFrame, probe_df: pd.DataFrame +) -> Tuple[pd.DataFrame, List[str]]: + new_columns = [ + column for column in probe_df.columns if column != "frame_index" and column not in base_df.columns + ] + if not new_columns: + return base_df.copy(), [] + merged = base_df.merge( + probe_df[["frame_index", *new_columns]], + on="frame_index", + how="left", + sort=False, + ) + return merged, new_columns + + +def _verification_record( + base_df: pd.DataFrame, + merged_df: pd.DataFrame, + base_key_df: Optional[pd.DataFrame], + merged_key_df: Optional[pd.DataFrame], + base_metrics: Optional[Dict[str, object]], + output_metrics: Optional[Dict[str, object]], + debug_rows: List[Dict[str, object]], +) -> Dict[str, object]: + dense_equal = base_df.equals(merged_df[base_df.columns]) + key_equal = True + if base_key_df is not None and merged_key_df is not None: + key_equal = base_key_df.equals(merged_key_df[base_key_df.columns]) + + metrics_equal = True + if base_metrics is not None and output_metrics is not None: + metrics_equal = base_metrics == output_metrics + + dense_pose_consistent = True + best_pose_consistent = True + num_goal_consistent = True + debug_by_frame = { + int(row["frame_index"]): row for row in debug_rows + } + for _, row in merged_df.iterrows(): + frame_index = int(row["frame_index"]) + debug = debug_by_frame.get(frame_index) + if debug is None: + dense_pose_consistent = False + best_pose_consistent = False + num_goal_consistent = False + continue + state = debug["state"] + if "left_arm_pose_x" in merged_df.columns: + expected_pose = state.get("left_arm_pose", []) + actual_pose = [ + float(row["left_arm_pose_x"]), + float(row["left_arm_pose_y"]), + float(row["left_arm_pose_z"]), + float(row["left_arm_pose_qx"]), + float(row["left_arm_pose_qy"]), + float(row["left_arm_pose_qz"]), + float(row["left_arm_pose_qw"]), + ] + if any(abs(a - b) > 1e-9 for a, b in zip(actual_pose, expected_pose)): + dense_pose_consistent = False + if "p_pre_num_goal_poses" in merged_df.columns: + if int(round(float(row["p_pre_num_goal_poses"]))) != int( + debug["p_pre"].get("num_goal_poses", 0) + ): + num_goal_consistent = False + if "p_pre_best_target_pose_x" in merged_df.columns: + expected_best = debug["p_pre"].get("best_goal_pose", []) + if expected_best: + actual_best = [ + float(row["p_pre_best_target_pose_x"]), + float(row["p_pre_best_target_pose_y"]), + float(row["p_pre_best_target_pose_z"]), + float(row["p_pre_best_target_pose_qx"]), + float(row["p_pre_best_target_pose_qy"]), + float(row["p_pre_best_target_pose_qz"]), + float(row["p_pre_best_target_pose_qw"]), + ] + if any(abs(a - b) > 1e-9 for a, b in zip(actual_best, expected_best)): + best_pose_consistent = False + else: + actual_best = [ + row["p_pre_best_target_pose_x"], + row["p_pre_best_target_pose_y"], + row["p_pre_best_target_pose_z"], + row["p_pre_best_target_pose_qx"], + row["p_pre_best_target_pose_qy"], + row["p_pre_best_target_pose_qz"], + row["p_pre_best_target_pose_qw"], + ] + if any(pd.notna(value) for value in actual_best): + best_pose_consistent = False + + return { + "dense_existing_columns_unchanged": bool(dense_equal), + "keyframe_existing_columns_unchanged": bool(key_equal), + "metrics_json_preserved": bool(metrics_equal), + "debug_row_count": int(len(debug_rows)), + "dense_row_count": int(len(merged_df)), + "dense_pose_columns_match_debug_state": bool(dense_pose_consistent), + "best_target_pose_columns_match_debug": bool(best_pose_consistent), + "num_goal_pose_columns_match_debug": bool(num_goal_consistent), + } + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--dataset-root", required=True) + parser.add_argument("--episode-dir", required=True) + parser.add_argument("--base-dense-csv", required=True) + parser.add_argument("--output-dir", required=True) + parser.add_argument("--checkpoint-stride", type=int, default=16) + parser.add_argument("--num-workers", type=int, default=8) + parser.add_argument("--base-display", type=int, default=700) + parser.add_argument("--template-episode-dir") + parser.add_argument("--stagger-seconds", type=float, default=0.15) + parser.add_argument("--base-keyframes-csv") + parser.add_argument("--base-metrics-json") + parser.add_argument("--base-summary-json") + parser.add_argument("--keep-frame-json", action="store_true") + args = parser.parse_args() + + dataset_root = Path(args.dataset_root) + episode_dir = Path(args.episode_dir) + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + base_dense_csv = Path(args.base_dense_csv) + base_df = pd.read_csv(base_dense_csv) + + base_key_df = None + if args.base_keyframes_csv: + base_key_df = pd.read_csv(args.base_keyframes_csv) + + base_metrics = None + if args.base_metrics_json: + base_metrics = json.loads(Path(args.base_metrics_json).read_text()) + + base_summary = None + if args.base_summary_json: + base_summary = json.loads(Path(args.base_summary_json).read_text()) + + demo = _load_demo(episode_dir) + num_frames = len(demo) + if len(base_df) != num_frames: + raise ValueError( + f"base dense rows {len(base_df)} do not match demo length {num_frames} for {episode_dir}" + ) + + template_episode_dir = ( + Path(args.template_episode_dir) if args.template_episode_dir else episode_dir + ) + templates, template_frames = _derive_templates(dataset_root, template_episode_dir) + + templates_pkl = output_dir.joinpath("templates.pkl") + with templates_pkl.open("wb") as handle: + pickle.dump(templates, handle) + with output_dir.joinpath("templates.json").open("w", encoding="utf-8") as handle: + json.dump( + { + "template_mode": "per_episode", + "template_episode": template_episode_dir.name, + "template_frames": template_frames, + "templates": templates.to_json(), + "preserve_base_dense_csv": str(base_dense_csv), + }, + handle, + indent=2, + ) + + frame_json_dir = output_dir.joinpath("frame_rows") + frame_json_dir.mkdir(parents=True, exist_ok=True) + frame_indices = list(range(num_frames)) + frame_chunks = _chunk_frame_indices(frame_indices, args.num_workers) + displays = [args.base_display + index for index in range(len(frame_chunks))] + xvfb_procs = [] + active: Dict[int, Tuple[Sequence[int], object]] = {} + + try: + for display_num in displays: + xvfb_procs.append( + _launch_xvfb(display_num, output_dir.joinpath(f"xvfb_{display_num}.log")) + ) + for display_num, frame_chunk in zip(displays, frame_chunks): + process = _spawn_frame_batch_job( + display_num=display_num, + episode_dir=episode_dir, + templates_pkl=templates_pkl, + frame_indices=frame_chunk, + checkpoint_stride=args.checkpoint_stride, + output_dir=frame_json_dir, + ) + active[display_num] = (frame_chunk, process) + if args.stagger_seconds > 0: + import time + + time.sleep(args.stagger_seconds) + + while active: + import time + + time.sleep(1.0) + finished: List[int] = [] + for display_num, (frame_chunk, process) in active.items(): + return_code = process.poll() + if return_code is None: + continue + missing = [ + frame_index + for frame_index in frame_chunk + if not frame_json_dir.joinpath(f"frame_{frame_index:04d}.json").exists() + or not frame_json_dir.joinpath(f"frame_{frame_index:04d}.debug.json").exists() + ] + if return_code != 0 or missing: + raise RuntimeError( + f"display :{display_num} failed for frames {list(frame_chunk)[:3]}...; missing={missing[:8]}" + ) + finished.append(display_num) + for display_num in finished: + active.pop(display_num) + finally: + for _, process in list(active.values()): + _stop_process(process) + for xvfb in xvfb_procs: + _stop_process(xvfb) + + probe_df = _collect_rows(frame_json_dir, num_frames) + debug_rows = _collect_debug_rows(frame_json_dir, num_frames) + merged_df, new_columns = _merge_new_columns(base_df, probe_df) + annotated_df = _annotate_phase_columns(merged_df.copy()) + phase_new_columns = [ + column + for column in annotated_df.columns + if column not in merged_df.columns + ] + if phase_new_columns: + merged_df = merged_df.merge( + annotated_df[["frame_index", *phase_new_columns]], + on="frame_index", + how="left", + sort=False, + ) + new_columns.extend(phase_new_columns) + + merged_key_df = None + if base_key_df is not None: + merged_key_df, _ = _merge_new_columns(base_key_df, probe_df) + if phase_new_columns: + merged_key_df = merged_key_df.merge( + annotated_df[["frame_index", *phase_new_columns]], + on="frame_index", + how="left", + sort=False, + ) + + output_metrics = base_metrics if base_metrics is not None else None + output_summary = base_summary if base_summary is not None else ( + _aggregate_summary([output_metrics]) if output_metrics is not None else None + ) + + merged_df.to_csv(output_dir.joinpath(f"{episode_dir.name}.dense.csv"), index=False) + if merged_key_df is not None: + merged_key_df.to_csv(output_dir.joinpath(f"{episode_dir.name}.keyframes.csv"), index=False) + elif args.base_keyframes_csv: + raise RuntimeError("base keyframes csv was provided but merged keyframes are missing") + + if output_metrics is not None: + with output_dir.joinpath(f"{episode_dir.name}.metrics.json").open("w", encoding="utf-8") as handle: + json.dump(output_metrics, handle, indent=2) + if output_summary is not None: + with output_dir.joinpath("summary.json").open("w", encoding="utf-8") as handle: + json.dump(output_summary, handle, indent=2) + + with output_dir.joinpath(f"{episode_dir.name}.debug.jsonl").open("w", encoding="utf-8") as handle: + for row in debug_rows: + handle.write(json.dumps(_json_safe(row))) + handle.write("\n") + + verification = _verification_record( + base_df=base_df, + merged_df=merged_df, + base_key_df=base_key_df, + merged_key_df=merged_key_df, + base_metrics=base_metrics, + output_metrics=output_metrics, + debug_rows=debug_rows, + ) + verification["new_columns_added"] = new_columns + verification["phase_new_columns_added"] = phase_new_columns + verification["probe_mode"] = "preserve_base_metrics" + with output_dir.joinpath("verification.json").open("w", encoding="utf-8") as handle: + json.dump(_json_safe(verification), handle, indent=2) + + if not args.keep_frame_json: + for row_path in frame_json_dir.glob("frame_*.json*"): + row_path.unlink() + frame_json_dir.rmdir() + + print(json.dumps(_json_safe(verification), indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/code/scripts/recompute_oven_episode_parallel.py b/code/scripts/recompute_oven_episode_parallel.py index 6b741c263077df77ec51a37afcf96ce5ddb05662..ec25d01a3fb534a3759a7b3fb738cf765f6fa107 100644 --- a/code/scripts/recompute_oven_episode_parallel.py +++ b/code/scripts/recompute_oven_episode_parallel.py @@ -19,6 +19,7 @@ if str(PROJECT_ROOT) not in sys.path: sys.path.insert(0, str(PROJECT_ROOT)) from rr_label_study.oven_study import ( + MotionTemplates, _aggregate_summary, _annotate_phase_columns, _derive_templates, @@ -134,6 +135,17 @@ def _collect_rows(frame_json_dir: Path, num_frames: int) -> pd.DataFrame: return frame_df +def _collect_debug_rows(frame_json_dir: Path, num_frames: int) -> List[Dict[str, object]]: + rows: List[Dict[str, object]] = [] + for frame_index in range(num_frames): + row_path = frame_json_dir.joinpath(f"frame_{frame_index:04d}.debug.json") + if not row_path.exists(): + raise RuntimeError(f"missing recomputed frame debug output: {row_path}") + with row_path.open("r", encoding="utf-8") as handle: + rows.append(json.load(handle)) + return rows + + def main() -> int: parser = argparse.ArgumentParser() parser.add_argument("--dataset-root", required=True) @@ -143,6 +155,7 @@ def main() -> int: parser.add_argument("--num-workers", type=int, default=8) parser.add_argument("--base-display", type=int, default=380) parser.add_argument("--template-episode-dir") + parser.add_argument("--templates-json") parser.add_argument("--stagger-seconds", type=float, default=0.15) parser.add_argument("--keep-frame-json", action="store_true") args = parser.parse_args() @@ -155,25 +168,41 @@ def main() -> int: demo = _load_demo(episode_dir) descriptions = _load_descriptions(episode_dir) num_frames = len(demo) - template_episode_dir = ( - Path(args.template_episode_dir) if args.template_episode_dir else episode_dir - ) - templates, template_frames = _derive_templates(dataset_root, template_episode_dir) + if args.templates_json: + templates_payload = json.loads(Path(args.templates_json).read_text(encoding="utf-8")) + templates = MotionTemplates.from_json(templates_payload["templates"]) + template_frames = dict(templates_payload.get("template_frames", {})) + template_episode_dir = ( + Path(args.template_episode_dir) + if args.template_episode_dir + else episode_dir + ) + template_metadata = { + "template_mode": templates_payload.get("template_mode", "external"), + "template_episode": templates_payload.get( + "template_episode", template_episode_dir.name + ), + "template_frames": template_frames, + "templates": templates.to_json(), + "template_source_json": str(Path(args.templates_json).resolve()), + } + else: + template_episode_dir = ( + Path(args.template_episode_dir) if args.template_episode_dir else episode_dir + ) + templates, template_frames = _derive_templates(dataset_root, template_episode_dir) + template_metadata = { + "template_mode": "per_episode", + "template_episode": template_episode_dir.name, + "template_frames": template_frames, + "templates": templates.to_json(), + } templates_pkl = output_dir.joinpath("templates.pkl") with templates_pkl.open("wb") as handle: pickle.dump(templates, handle) with output_dir.joinpath("templates.json").open("w", encoding="utf-8") as handle: - json.dump( - { - "template_mode": "per_episode", - "template_episode": template_episode_dir.name, - "template_frames": template_frames, - "templates": templates.to_json(), - }, - handle, - indent=2, - ) + json.dump(template_metadata, handle, indent=2) frame_json_dir = output_dir.joinpath("frame_rows") frame_json_dir.mkdir(parents=True, exist_ok=True) @@ -229,6 +258,7 @@ def main() -> int: _stop_process(xvfb) frame_df = _collect_rows(frame_json_dir, num_frames) + debug_rows = _collect_debug_rows(frame_json_dir, num_frames) frame_df = _annotate_phase_columns(frame_df) keyframes = [index for index in _keypoint_discovery(demo) if index < len(frame_df)] key_df = _keyframe_subset(frame_df, keyframes) @@ -248,6 +278,12 @@ def main() -> int: frame_df.to_csv(output_dir.joinpath(f"{episode_dir.name}.dense.csv"), index=False) key_df.to_csv(output_dir.joinpath(f"{episode_dir.name}.keyframes.csv"), index=False) + with output_dir.joinpath(f"{episode_dir.name}.debug.jsonl").open( + "w", encoding="utf-8" + ) as handle: + for row in debug_rows: + handle.write(json.dumps(row)) + handle.write("\n") with output_dir.joinpath(f"{episode_dir.name}.metrics.json").open("w", encoding="utf-8") as handle: json.dump(metrics, handle, indent=2) summary = _aggregate_summary([metrics]) @@ -255,7 +291,7 @@ def main() -> int: json.dump(summary, handle, indent=2) if not args.keep_frame_json: - for row_path in frame_json_dir.glob("frame_*.json"): + for row_path in frame_json_dir.glob("frame_*.json*"): row_path.unlink() frame_json_dir.rmdir() diff --git a/code/scripts/recompute_oven_visibility_pregrasp.py b/code/scripts/recompute_oven_visibility_pregrasp.py index d7b0e0d82506f64fbbc1be5ba8c66616e06045f5..426d166587f524e5759f9758fda9953c8dea3348 100644 --- a/code/scripts/recompute_oven_visibility_pregrasp.py +++ b/code/scripts/recompute_oven_visibility_pregrasp.py @@ -49,13 +49,13 @@ def _recompute_columns( for frame_index in range(num_frames): cache.step_to(frame_index) state = cache.current_state() - visibility = _frame_metrics(episode_dir, demo, state, templates) + visibility, _ = _frame_metrics(episode_dir, demo, state, templates) pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance( state.left_gripper_pose, state.tray_pose, templates, ) - p_pre, y_pre = _pregrasp_score_and_success(task, templates) + p_pre, y_pre, _ = _pregrasp_score_and_success(task, templates) frame_df.at[frame_index, "frame_index"] = frame_index frame_df.at[frame_index, "time_norm"] = frame_index / max(1, num_frames - 1) diff --git a/code/scripts/render_oven_metric_frame.py b/code/scripts/render_oven_metric_frame.py index 8b1780f6aa695f5c22eb9e309b169a5d8be95c7b..31011e23a24415cbab538ed017bd575eed3e45b6 100644 --- a/code/scripts/render_oven_metric_frame.py +++ b/code/scripts/render_oven_metric_frame.py @@ -27,6 +27,7 @@ from rr_label_study.oven_study import ( _apply_relative_pose, _camera_point_cloud, _camera_file, + _extract_height_threshold, _extract_sequence_poses, _frame_metrics, _launch_replay_env, @@ -119,6 +120,19 @@ def _draw_segmented_polyline( draw.line((coords[idx], coords[idx + 1]), fill=color, width=width) +def _draw_text_lines( + draw: ImageDraw.ImageDraw, + x: int, + y: int, + lines: Sequence[str], + fill: Tuple[int, int, int] = (255, 255, 255), + font: ImageFont.FreeTypeFont = FONT_SM, + line_height: int = 18, +) -> None: + for line_index, line in enumerate(lines): + draw.text((x, y + line_index * line_height), line, fill=fill, font=font) + + def _bar(draw: ImageDraw.ImageDraw, x: int, y: int, w: int, h: int, value: float, label: str, color: Tuple[int, int, int], threshold: Optional[float] = None) -> None: draw.text((x, y - 18), f"{label}: {value:.3f}", fill=(255, 255, 255), font=FONT_SM) draw.rounded_rectangle((x, y, x + w, y + h), radius=5, outline=(120, 120, 120), fill=(20, 20, 20)) @@ -324,8 +338,9 @@ def _compose_path_quality( templates: MotionTemplates, frame_row: pd.Series, left_grasped: bool, + debug_row: Optional[Dict[str, object]], ) -> Image.Image: - canvas = Image.new("RGB", (PANEL_SIZE * 2, HEADER_HEIGHT + PANEL_SIZE * 2), (12, 12, 12)) + canvas = Image.new("RGB", (PANEL_SIZE * 3, HEADER_HEIGHT + PANEL_SIZE * 2), (12, 12, 12)) draw = ImageDraw.Draw(canvas) draw.rectangle((0, 0, canvas.size[0], HEADER_HEIGHT), fill=(0, 0, 0)) draw.text((12, 10), f"Path Quality Debug | frame {frame_index}", fill=(255, 255, 255), font=FONT_LG) @@ -348,6 +363,9 @@ def _compose_path_quality( (80, 255, 120), (80, 255, 120), ] + milestone_poses = _milestone_poses(tray_pose, task_base_pose, templates, frame_row) + pext_debug = debug_row.get("p_ext", {}) if debug_row else {} + milestone_debugs = list(pext_debug.get("milestones", [])) demo_left_trail = np.asarray( [_demo_gripper_pose(demo[i], "left")[:3] for i in range(max(0, frame_index - 12), frame_index + 1)], @@ -385,8 +403,22 @@ def _compose_path_quality( xy = _world_pose_to_coords(pose[:3], extrinsics, intrinsics) if xy is not None: _draw_point(draw, _scaled_coords([xy], PANEL_SIZE / RGB_SIZE)[0], color, radius=4) - x = (camera_slot % 2) * PANEL_SIZE - y = HEADER_HEIGHT + (camera_slot // 2) * PANEL_SIZE + for milestone_pose, milestone_debug in zip(milestone_poses, milestone_debugs): + xy = _world_pose_to_coords(milestone_pose[:3], extrinsics, intrinsics) + if xy is None: + continue + scaled_xy = _scaled_coords([xy], PANEL_SIZE / RGB_SIZE)[0] + marker_color = _milestone_color(milestone_debug) + _draw_point(draw, scaled_xy, marker_color, radius=5) + label = _milestone_label(milestone_debug) + draw.text( + (scaled_xy[0] + 6, scaled_xy[1] - 10), + label, + fill=marker_color, + font=FONT_SM, + ) + x = camera_slot * PANEL_SIZE + y = HEADER_HEIGHT canvas.paste(panel, (x, y)) text_panel = Image.new("RGB", (PANEL_SIZE, PANEL_SIZE), (18, 18, 18)) @@ -398,7 +430,93 @@ def _compose_path_quality( draw.text((12, 172), f"pre_prog={float(frame_row.get('pregrasp_progress', 0.0)):.3f}", fill=(255, 255, 255), font=FONT_MD) draw.text((12, 198), f"left_grasped={int(left_grasped)} door={float(frame_row['door_angle']):.3f}", fill=(255, 255, 255), font=FONT_MD) draw.text((12, 224), f"y_retrieve={int(frame_row.get('y_retrieve', 0))} phase={int(frame_row['phase_switch'])} score={float(frame_row.get('phase_score', frame_row['phase_switch'])):.3f}", fill=(255, 255, 255), font=FONT_MD) - canvas.paste(text_panel, (PANEL_SIZE, HEADER_HEIGHT + PANEL_SIZE)) + _draw_badge(draw, 12, 36, "yellow=approach", (130, 110, 30)) + _draw_badge(draw, 138, 36, "green=retreat", (32, 130, 64)) + _draw_badge(draw, 250, 36, "red=failed", (140, 40, 40)) + canvas.paste(text_panel, (0, HEADER_HEIGHT + PANEL_SIZE)) + + search_panel = Image.new("RGB", (PANEL_SIZE * 2, PANEL_SIZE), (18, 18, 18)) + draw = ImageDraw.Draw(search_panel) + draw.text((12, 12), "p_ext Planner Search", fill=(255, 255, 255), font=FONT_LG) + if pext_debug: + initial_height = float(pext_debug.get("initial_height", frame_row.get("tray_height", 0.0))) + final_height = float(pext_debug.get("final_height", initial_height)) + total_length = float(pext_debug.get("total_length", 0.0)) + num_milestones = int(pext_debug.get("num_milestones", len(milestone_debugs))) + first_failed = int(pext_debug.get("first_failed_milestone", -1)) + mean_rel = float(pext_debug.get("mean_reliability", 0.0)) + min_rel = float(pext_debug.get("min_reliability", 0.0)) + approach_score = float(pext_debug.get("approach_score", 0.0)) + retreat_score = float(pext_debug.get("retreat_score", 0.0)) + _bar(draw, 12, 50, 220, 16, float(frame_row["p_ext"]), "p_ext", (80, 255, 120), threshold=0.45) + _bar(draw, 260, 50, 220, 16, approach_score, "approach_score", (255, 210, 80)) + _bar(draw, 508, 50, 220, 16, retreat_score, "retreat_score", (80, 255, 120)) + _bar(draw, 12, 94, 220, 16, mean_rel, "mean_rel", (120, 180, 255)) + _bar(draw, 260, 94, 220, 16, min_rel, "min_rel", (120, 180, 255)) + draw.text( + (12, 124), + f"height {initial_height:.3f} -> {final_height:.3f} | total_len={total_length:.3f} | milestones={num_milestones} | first_failed={first_failed}", + fill=(235, 235, 235), + font=FONT_SM, + ) + draw.text( + (12, 146), + f"start_grasped={int(bool(pext_debug.get('already_grasped_start', False)))} end_grasped={int(bool(pext_debug.get('already_grasped_end', False)))}", + fill=(235, 235, 235), + font=FONT_SM, + ) + start_y = 172 + row_h = 16 + bar_x = 550 + for row_index, milestone_debug in enumerate(milestone_debugs[:5]): + y = start_y + row_index * row_h + color = _milestone_color(milestone_debug) + label = _milestone_label(milestone_debug) + status = "OK" if bool(milestone_debug.get("path_found", False)) else "FAIL" + planner_score = float(milestone_debug.get("planner_score", 0.0)) + reliability = float(milestone_debug.get("reliability", 0.0)) + path_length = float(milestone_debug.get("path_length", 0.0)) + ignore_collisions = bool(milestone_debug.get("ignore_collisions", False)) + height_before = float(milestone_debug.get("height_before", initial_height)) + height_after = float(milestone_debug.get("height_after", height_before)) + line = ( + f"{label:<3} {status:<4} rel={reliability:.2f} score={planner_score:.3f} " + f"len={path_length:.3f} {'IGN' if ignore_collisions else 'STRICT':<6} " + f"h={height_before:.3f}->{height_after:.3f}" + ) + draw.text((12, y), line, fill=color, font=FONT_SM) + draw.rounded_rectangle((bar_x, y + 2, bar_x + 160, y + 12), radius=3, outline=(80, 80, 80), fill=(25, 25, 25)) + fill_w = int(max(0.0, min(1.0, planner_score)) * 160) + if fill_w > 0: + draw.rounded_rectangle((bar_x, y + 2, bar_x + fill_w, y + 12), radius=3, fill=color) + if not milestone_debugs: + _draw_text_lines( + draw, + 12, + 176, + [ + "No milestone planning was needed at this frame.", + "This usually means the tray was already safely extractable", + "or the planner returned immediate success without staged search.", + ], + fill=(220, 220, 220), + font=FONT_MD, + line_height=22, + ) + else: + _draw_text_lines( + draw, + 12, + 60, + [ + "No debug sidecar was provided for this frame.", + "The richer p_ext milestone search diagnostics are unavailable.", + ], + fill=(220, 220, 220), + font=FONT_MD, + line_height=22, + ) + canvas.paste(search_panel, (PANEL_SIZE, HEADER_HEIGHT + PANEL_SIZE)) return canvas @@ -414,6 +532,60 @@ def _project_world_polyline(points_world: np.ndarray, extrinsics: np.ndarray, in return coords +def _load_debug_row(path: Optional[Path], frame_index: int) -> Optional[Dict[str, object]]: + if path is None or not path.exists(): + return None + with path.open("r", encoding="utf-8") as handle: + for line in handle: + row = json.loads(line) + if int(row.get("frame_index", -1)) == int(frame_index): + return row + return None + + +def _milestone_poses( + tray_pose: np.ndarray, + task_base_pose: np.ndarray, + templates: MotionTemplates, + frame_row: pd.Series, +) -> List[np.ndarray]: + poses = _extract_sequence_poses(tray_pose, task_base_pose, templates) + current_height = float(frame_row.get("p_ext_initial_height", tray_pose[2])) + already_grasped = bool(frame_row.get("p_ext_already_grasped_start", 0.0)) + approach_poses = [] if already_grasped else poses[:2] + retreat_poses = poses[2:] + if already_grasped and retreat_poses: + future_retreat_poses = [ + pose for pose in retreat_poses if float(pose[2]) > current_height + 0.01 + ] + if future_retreat_poses: + retreat_poses = future_retreat_poses + elif current_height < _extract_height_threshold(templates): + retreat_poses = [retreat_poses[-1]] + else: + retreat_poses = [] + return [*approach_poses, *retreat_poses] + + +def _milestone_color(milestone_debug: Dict[str, object]) -> Tuple[int, int, int]: + kind = str(milestone_debug.get("kind", "approach")) + path_found = bool(milestone_debug.get("path_found", False)) + reliability = float(milestone_debug.get("reliability", 0.0)) + if not path_found: + return (230, 70, 70) + if reliability < 1.0: + return (255, 170, 60) + if kind == "retreat": + return (80, 255, 120) + return (255, 220, 0) + + +def _milestone_label(milestone_debug: Dict[str, object]) -> str: + kind = str(milestone_debug.get("kind", "approach")) + prefix = "A" if kind == "approach" else "R" + return f"{prefix}{int(milestone_debug.get('milestone_index', 0))}" + + def _compose_all_metrics( episode_name: str, episode_dir: Path, @@ -472,6 +644,7 @@ def main() -> int: parser.add_argument("--dense-csv", required=True) parser.add_argument("--frame-index", type=int, required=True) parser.add_argument("--checkpoint-stride", type=int, default=16) + parser.add_argument("--debug-jsonl") parser.add_argument("--visibility-out") parser.add_argument("--path-out") parser.add_argument("--all-out") @@ -488,6 +661,8 @@ def main() -> int: else: frame_row = frame_df.iloc[int(args.frame_index)] demo = _load_demo(episode_dir) + debug_jsonl = Path(args.debug_jsonl) if args.debug_jsonl else None + debug_row = _load_debug_row(debug_jsonl, int(args.frame_index)) env = _launch_replay_env() try: @@ -530,6 +705,7 @@ def main() -> int: templates=templates, frame_row=frame_row, left_grasped=left_grasped, + debug_row=debug_row, ) _save_if_requested(path_image, path_out) diff --git a/code/scripts/render_oven_metric_gifs.py b/code/scripts/render_oven_metric_gifs.py index d05c465fe7b7ed67589c25c5a68093c57993cf04..281d75e59f0da1258206dedeb33bf6523487ee08 100644 --- a/code/scripts/render_oven_metric_gifs.py +++ b/code/scripts/render_oven_metric_gifs.py @@ -58,6 +58,7 @@ def _spawn_worker( episode_dir: Path, templates_pkl: Path, dense_csv: Path, + debug_jsonl: Optional[Path], frame_index: int, checkpoint_stride: int, visibility_out: Optional[Path], @@ -86,6 +87,8 @@ def _spawn_worker( "--checkpoint-stride", str(checkpoint_stride), ] + if debug_jsonl is not None: + command.extend(["--debug-jsonl", str(debug_jsonl)]) if visibility_out is not None: command.extend(["--visibility-out", str(visibility_out)]) if path_out is not None: @@ -139,6 +142,7 @@ def main() -> int: "--output-dir", default="/workspace/reveal_retrieve_label_study/results/oven_episode0_repaired_v1/visualizations", ) + parser.add_argument("--debug-jsonl") parser.add_argument("--checkpoint-stride", type=int, default=16) parser.add_argument("--num-workers", type=int, default=6) parser.add_argument("--base-display", type=int, default=190) @@ -148,6 +152,11 @@ def main() -> int: dense_csv = Path(args.dense_csv) templates_pkl = Path(args.templates_pkl) output_dir = Path(args.output_dir) + debug_jsonl = Path(args.debug_jsonl) if args.debug_jsonl else dense_csv.with_name( + dense_csv.name.replace(".dense.csv", ".debug.jsonl") + ) + if not debug_jsonl.exists(): + debug_jsonl = None frame_df = pd.read_csv(dense_csv) episode_name = episode_dir.name frame_indices = ( @@ -162,6 +171,12 @@ def main() -> int: else: ppre_candidates = frame_df.loc[frame_df["p_pre"].to_numpy(dtype=float) >= 0.45] phase_cross = int(ppre_candidates.iloc[0]["frame_index"]) if len(ppre_candidates) else max_frame // 2 + pext_candidates = frame_df.loc[frame_df["p_ext"].to_numpy(dtype=float) >= 0.45] + pext_cross = int(pext_candidates.iloc[0]["frame_index"]) if len(pext_candidates) else phase_cross + ready_candidates = frame_df.loc[frame_df["y_ready"].to_numpy(dtype=float) >= 0.5] + ready_cross = int(ready_candidates.iloc[0]["frame_index"]) if len(ready_candidates) else pext_cross + retrieve_candidates = frame_df.loc[frame_df["y_retrieve"].to_numpy(dtype=float) >= 0.5] + retrieve_cross = int(retrieve_candidates.iloc[0]["frame_index"]) if len(retrieve_candidates) else max(phase_cross, pext_cross) frames_dir = output_dir.joinpath("frames") visibility_dir = frames_dir.joinpath("visibility_focus") @@ -171,7 +186,14 @@ def main() -> int: directory.mkdir(parents=True, exist_ok=True) visibility_frames = _range_inclusive(max(0, phase_cross - 12), min(max_frame, phase_cross + 28), 1) - path_frames = _range_inclusive(max(0, phase_cross - 8), min(max_frame, phase_cross + 80), 2) + path_start = max(0, min(phase_cross, pext_cross) - 16) + path_end = min(max_frame, max(pext_cross, ready_cross, retrieve_cross) + 24) + path_frames = sorted( + set( + _range_inclusive(path_start, min(path_end, max(path_start, pext_cross - 18)), 2) + + _range_inclusive(max(path_start, pext_cross - 18), path_end, 1) + ) + ) all_frames = _range_inclusive(0, max_frame, 2) unique_frames = sorted(set(visibility_frames) | set(path_frames) | set(all_frames)) @@ -194,6 +216,7 @@ def main() -> int: episode_dir=episode_dir, templates_pkl=templates_pkl, dense_csv=dense_csv, + debug_jsonl=debug_jsonl, frame_index=frame_index, checkpoint_stride=args.checkpoint_stride, visibility_out=visibility_dir.joinpath(f"frame_{frame_index:04d}.png") @@ -239,7 +262,7 @@ def main() -> int: _assemble_gif( path_pngs, output_dir.joinpath(f"{episode_name}_path_quality_focus.gif"), - duration_ms=120, + duration_ms=160, ) _assemble_gif( all_pngs, @@ -254,10 +277,10 @@ def main() -> int: "# Visualizations", "", f"- `{episode_name}_visibility_focus.gif`: three-view visibility montage over dense frames {visibility_frames[0]}-{visibility_frames[-1]}.", - f"- `{episode_name}_path_quality_focus.gif`: path-quality montage over dense frames {path_frames[0]}-{path_frames[-1]}.", + f"- `{episode_name}_path_quality_focus.gif`: debug-aware p_ext planner montage over dense frames {path_frames[0]}-{path_frames[-1]}.", f"- `{episode_name}_all_metrics.gif`: full episode overlay GIF over dense frames 0-{max_frame}, sampled every 2 frames.", "- `frames/visibility_focus/`: per-frame PNGs with scene overlays, x-ray projections, and tray-mask views.", - "- `frames/path_quality_focus/`: per-frame PNGs with demo wrist trails plus planned pregrasp/grasp/retreat path overlays.", + "- `frames/path_quality_focus/`: per-frame PNGs with demo wrist trails, milestone pose overlays, and p_ext planner-search tables from the debug JSONL sidecar.", "- `frames/all_metrics/`: per-frame PNGs with the episode-wide metric bars and phase banner.", "", "Legend highlights:", diff --git a/code/scripts/run_oven_frame_batch.py b/code/scripts/run_oven_frame_batch.py index a37e3d259c8181c880439e913b6ee226aa75ada9..a3300ef7f19d54aab048a6233dddc2270a15faae 100644 --- a/code/scripts/run_oven_frame_batch.py +++ b/code/scripts/run_oven_frame_batch.py @@ -10,8 +10,10 @@ if str(PROJECT_ROOT) not in sys.path: sys.path.insert(0, str(PROJECT_ROOT)) from rr_label_study.oven_study import ( - _compute_frame_rows_independent, - _compute_frame_rows_sequential, + BimanualTakeTrayOutOfOven, + ReplayCache, + _build_frame_artifacts, + _launch_replay_env, _load_demo, ) @@ -30,27 +32,39 @@ def main() -> int: with Path(args.templates_pkl).open("rb") as handle: templates = pickle.load(handle) demo = _load_demo(episode_dir) - compute_rows = ( - _compute_frame_rows_independent - if args.independent_replay - else _compute_frame_rows_sequential - ) - rows = compute_rows( - episode_dir=episode_dir, - demo=demo, - templates=templates, - checkpoint_stride=args.checkpoint_stride, - frame_indices=args.frame_indices, - ) - output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) - for row in rows: - frame_index = int(row["frame_index"]) - with output_dir.joinpath(f"frame_{frame_index:04d}.json").open( - "w", encoding="utf-8" - ) as handle: - json.dump(row, handle) + env = _launch_replay_env() + try: + task = env.get_task(BimanualTakeTrayOutOfOven) + cache = ReplayCache(task, demo, checkpoint_stride=args.checkpoint_stride) + cache.reset() + initial_snapshot = cache.snapshot() if args.independent_replay else None + for frame_index in sorted({int(index) for index in args.frame_indices}): + if args.independent_replay: + cache.restore_to_index(initial_snapshot, 0) + cache.step_to(frame_index) + frame_snapshot = cache.snapshot() if not args.independent_replay else None + state = cache.current_state() + row, debug = _build_frame_artifacts( + episode_dir=episode_dir, + demo=demo, + templates=templates, + task=task, + state=state, + ) + with output_dir.joinpath(f"frame_{frame_index:04d}.json").open( + "w", encoding="utf-8" + ) as handle: + json.dump(row, handle) + with output_dir.joinpath(f"frame_{frame_index:04d}.debug.json").open( + "w", encoding="utf-8" + ) as handle: + json.dump(debug, handle) + if frame_snapshot is not None: + cache.restore(frame_snapshot) + finally: + env.shutdown() return 0 diff --git a/code/scripts/run_oven_pregrasp_batch.py b/code/scripts/run_oven_pregrasp_batch.py index 1cd672bf4e71de70031cedbc393d93e79553d5a9..1c9026b6664486913bd1c73ea84e7ea0033770da 100644 --- a/code/scripts/run_oven_pregrasp_batch.py +++ b/code/scripts/run_oven_pregrasp_batch.py @@ -60,7 +60,7 @@ def main() -> int: np.asarray(state.tray_pose, dtype=np.float64), templates, ) - p_pre, y_pre = _pregrasp_score_and_success(task, templates) + p_pre, y_pre, _ = _pregrasp_score_and_success(task, templates) row = { "frame_index": int(frame_index), "pregrasp_progress": float(pregrasp_progress),