diff --git a/README.md b/README.md index a58310b40e..41fcf3f614 100644 --- a/README.md +++ b/README.md @@ -247,6 +247,10 @@ To add a metric to the report: add its key to `TREND_METRICS` / | `collision_behavior` | `1` | `0` ignore, `1` stop, `2` remove | | `offroad_behavior` | `1` | Same options | | `traffic_light_behavior` | `1` | Same options | +| `stop_sign_behavior` | `1` | Same options | +| `traffic_lights_enabled` | `True` | Observe and enforce traffic lights | +| `stop_signs_enabled` | `False` | Observe and enforce stop signs (CaRL RunStopSign2 semantics, penalised with `reward_stop_line`) | +| `yield_signs_enabled` | `False` | Observe yield signs | | `control_mode` | `"control_vehicles"` | `"control_vehicles"`, `"control_agents"`, `"control_sdc_only"` | | `reward_conditioning` | `False` | Condition policy on reward weights | | `reward_randomization` | `False` | Randomize reward weights each episode | diff --git a/data_utils/mirror_map_bin.py b/data_utils/mirror_map_bin.py index be9fa3db81..efbe2fddd0 100644 --- a/data_utils/mirror_map_bin.py +++ b/data_utils/mirror_map_bin.py @@ -29,6 +29,7 @@ TRAFFIC_PHASE_SECTION_TAG = b"TLPHASE1" LANE_WIDTH_SECTION_TAG = b"LANEWID1" +SPEED_ZONE_SECTION_TAG = b"SPDZONE1" def _is_lane(road_type: int) -> bool: @@ -155,6 +156,13 @@ def read_bin(path: Path) -> dict: if _is_lane(r["type"]): r["widths"] = _read_f_array(f, r["S"]) + zone_tag = f.read(len(SPEED_ZONE_SECTION_TAG)) + has_zone_section = zone_tag == SPEED_ZONE_SECTION_TAG + assert has_zone_section or zone_tag == b"", f"unexpected bytes after width section in {path}" + for r in roads: + if _is_lane(r["type"]): + (r["speed_zone_idx"],) = _read(" dict: "tracks_to_predict": tracks_to_predict, "has_phase_section": has_phase_section, "has_width_section": has_width_section, + "has_zone_section": has_zone_section, } @@ -292,6 +301,11 @@ def write_bin(data: dict, path: Path): for r in roads: if _is_lane(r["type"]) and r["S"]: f.write(struct.pack(f"<{r['S']}f", *r["widths"])) + if data["has_zone_section"]: + f.write(SPEED_ZONE_SECTION_TAG) + for r in roads: + if _is_lane(r["type"]): + f.write(struct.pack(" [13.3, 30] m/s) + # Speed cap when reward_randomization is off (eval); null = base_max_speed_mps. Sets C_vel = max_speed_mps / base_max_speed_mps (training range [1/a, a] below) max_speed_mps: null + # Training C_vel ~ X(a) = 0.5 U(1/a, 1) + 0.5 U(1, a): speed cap = base_max_speed_mps * [1/a, a]; 1.5 = paper (13.3-30 m/s), 2.0 = 10-40 m/s + conditioning_speed_scale: 1.5 # Optional nonzero launch speed for gigaflow random spawns spawn_initial_speed: 0.0 # Gigaflow spawn pose noise: lateral offset as a fraction of half the lane width, heading offset in degrees @@ -64,6 +66,13 @@ env: # Gigaflow dynamics randomization: per-step brownian pose buffeting (std in meters / degrees); 0 disables pose_noise_xy_m: 0.0 pose_noise_yaw_deg: 0.0 + # Speed-limit randomization (needs SPDZONE1 maps): per episode, with this probability every speed zone shifts its + # posted limit by one uniform offset in +-delta, clipped to [min, max]; junction lanes inherit the min over entries. + # m/s: 9.72 = 35 km/h, 1.39 = 5 km/h, 36.11 = 130 km/h. 0 disables. + speed_limit_random_prob: 0.0 + speed_limit_random_delta_mps: 9.72 + speed_limit_random_min_mps: 1.39 + speed_limit_random_max_mps: 36.11 # Collision behavior - options: "ignore", "stop", "remove" collision_behavior: stop # Offroad behavior - options: "ignore", "stop", "remove" @@ -72,11 +81,14 @@ env: traffic_light_behavior: stop # Stop sign behavior - options: "ignore", "stop", "remove" stop_sign_behavior: stop + # Traffic controls observed and enforced; stop-sign runs are detected as in CaRL's RunStopSign2 (reward_stop_line) traffic_lights_enabled: true stop_signs_enabled: false yield_signs_enabled: false # Skip red light violation detection (metrics, reward, behavior) disable_red_light_infractions: false + # Skip stop sign violation detection (metrics, reward, behavior); the sign obs still turn red/green + disable_stop_sign_infractions: false # Cycle the lights of a junction phase-by-phase (TLPHASE1 bins); false keeps independent lights (legacy) traffic_light_junction_phases: false # Share static map geometry (roads/grid/lane-graph) across envs using the same map @@ -102,7 +114,11 @@ env: init_step_spread: false # sets an upper bound on the random init step # the sampled init step can be at most this close to the end of the episode; + # also the shortest first episode when stagger_first_episode is on init_step_min_horizon: 20 + # training only: the first episode after every env (re)creation ends at a random step in + # [init_step_min_horizon, scenario_length], so sub-envs do not run their episodes in lockstep + stagger_first_episode: true # options: "control_vehicles", "control_agents", "control_tracks_to_predict", "control_sdc_only" control_mode: control_vehicles # Controller used by agent 0, the canonical SDC/target. @@ -126,7 +142,8 @@ env: # --- Goal / Target --- # Goal regeneration policy - options: "finite" (regen full set when all reached), "rolling" (sliding window) goal_regen_mode: "finite" - # How the goal set is seeded - options: "route" (agent forward route), "map" (uniform free-roam) + # How the goal set is seeded - options: "route" (agent forward route), "map" (uniform free-roam), + # replay only: "gt" (raw logged trajectory points), "gt_map" (logged points snapped to the nearest lane center) goal_source: map # Append normalized lane-graph distance to the current goal lane on each lane obs row obs_goal_lane_distance: True @@ -163,6 +180,8 @@ env: reward_reverse: 0.005 reward_timestep: 2.5e-05 reward_overspeed: 0.05 + # m/s above the lane limit before the overspeed penalty fires + overspeed_tolerance_mps: 0.0 reward_ade: 0.0 # --- Map --- # Path to map used for training @@ -177,6 +196,10 @@ env: obs_slots_partners_n: 20 # Append the partner's velocity relative to ego (ego frame, 2 floats) to every partner row (GIGAFLOW observes velocity) obs_partner_relative_velocity: false + # Ego lane-angle slot: true = signed heading error theta_f / pi (GIGAFLOW B.1), false = cos(theta_f) + obs_lane_heading_signed: false + # Append each lane segment's speed limit (/ obs_norm_speed_mps) to its lane row so limit changes ahead are visible + obs_lane_speed_limit: false obs_slots_traffic_controls_n: 4 # Fraction of segment observation slots to drop (reduces obs size) obs_dropout_lane: 0.5 @@ -265,6 +288,8 @@ train: evaluation_interval_epochs: null # Select multiple benchmarks with a comma-separated value. eg: carla_fast,womd_single evaluation_benchmarks: carla_fast + # Submit the carla_cosim SLURM debug hook at start/end of training. + cosim_debug_evals: False torch_deterministic: false cpu_offload: false device: cuda @@ -354,7 +379,7 @@ eval: base_max_speed_mps: null # Overrides env.goal_regen_mode ("finite" or "rolling") of every selected benchmark; null keeps the benchmark's value. goal_regen_mode: null - # Overrides env.goal_source ("route", "map" or "gt") of every selected benchmark; null keeps the benchmark's value. + # Overrides env.goal_source ("route", "map", "gt" or "gt_map") of every selected benchmark; null keeps the benchmark's value. goal_source: null # Overrides env.goal_speed of every selected benchmark; null keeps the benchmark's value. goal_speed: null @@ -367,6 +392,8 @@ eval: obs_slots_partners_n: null # Overrides env.disable_red_light_infractions of every selected benchmark; null keeps the benchmark's value. disable_red_light_infractions: null + # Overrides env.disable_stop_sign_infractions of every selected benchmark; null keeps the benchmark's value. + disable_stop_sign_infractions: null output_name: null # Results folder inside the run dir; also names the wandb key prefix final__. output_dir_name: eval diff --git a/pufferlib/config_schema.py b/pufferlib/config_schema.py index 34b5361441..72a2c13e11 100644 --- a/pufferlib/config_schema.py +++ b/pufferlib/config_schema.py @@ -161,6 +161,8 @@ class GoalSource(Enum): route = 0 map = 1 gt = 2 + external = 3 + gt_map = 4 class PackageName(Enum): @@ -251,16 +253,22 @@ class DriveEnvConfig: dt: float = _constrained_field(POSITIVE_NUMBER_CONSTRAINT) base_max_speed_mps: float = _constrained_field(POSITIVE_NUMBER_CONSTRAINT) max_speed_mps: float | None = _constrained_field(POSITIVE_NUMBER_CONSTRAINT, default=None) + conditioning_speed_scale: float = _constrained_field(POSITIVE_NUMBER_CONSTRAINT) spawn_initial_speed: float = _constrained_field(NONNEGATIVE_NUMBER_CONSTRAINT) spawn_lateral_offset_max_frac: float = _constrained_field(PROBABILITY_CONSTRAINT) spawn_heading_max_deg: float = _constrained_field(NONNEGATIVE_NUMBER_CONSTRAINT) pose_noise_xy_m: float = _constrained_field(NONNEGATIVE_NUMBER_CONSTRAINT) pose_noise_yaw_deg: float = _constrained_field(NONNEGATIVE_NUMBER_CONSTRAINT) + speed_limit_random_prob: float = _constrained_field(PROBABILITY_CONSTRAINT) + speed_limit_random_delta_mps: float = _constrained_field(NONNEGATIVE_NUMBER_CONSTRAINT) + speed_limit_random_min_mps: float = _constrained_field(NONNEGATIVE_NUMBER_CONSTRAINT) + speed_limit_random_max_mps: float = _constrained_field(NONNEGATIVE_NUMBER_CONSTRAINT) collision_behavior: InfractionBehavior = MISSING offroad_behavior: InfractionBehavior = MISSING traffic_light_behavior: InfractionBehavior = MISSING stop_sign_behavior: InfractionBehavior = MISSING disable_red_light_infractions: bool = MISSING + disable_stop_sign_infractions: bool = MISSING traffic_light_junction_phases: bool = MISSING traffic_lights_enabled: bool = MISSING stop_signs_enabled: bool = MISSING @@ -276,6 +284,7 @@ class DriveEnvConfig: init_step: int = _constrained_field(NONNEGATIVE_INT_CONSTRAINT) init_step_spread: bool = MISSING init_step_min_horizon: int = _constrained_field(POSITIVE_INT_CONSTRAINT) + stagger_first_episode: bool = MISSING control_mode: ControlMode = MISSING sdc_controller: Controller = MISSING non_sdc_controller: Controller = MISSING @@ -311,6 +320,7 @@ class DriveEnvConfig: reward_reverse: float = _constrained_field(FINITE_NUMBER_CONSTRAINT) reward_timestep: float = _constrained_field(FINITE_NUMBER_CONSTRAINT) reward_overspeed: float = _constrained_field(FINITE_NUMBER_CONSTRAINT) + overspeed_tolerance_mps: float = _constrained_field(NONNEGATIVE_NUMBER_CONSTRAINT) reward_ade: float = _constrained_field(FINITE_NUMBER_CONSTRAINT) map_dir: str = MISSING num_maps: int = _constrained_field(POSITIVE_INT_CONSTRAINT) @@ -319,6 +329,8 @@ class DriveEnvConfig: obs_slots_boundary_n: int = _constrained_field(NONNEGATIVE_INT_CONSTRAINT) obs_slots_partners_n: int = _constrained_field(NONNEGATIVE_INT_CONSTRAINT) obs_partner_relative_velocity: bool = MISSING + obs_lane_heading_signed: bool = MISSING + obs_lane_speed_limit: bool = MISSING obs_slots_traffic_controls_n: int = _constrained_field(NONNEGATIVE_INT_CONSTRAINT) obs_dropout_lane: float = _constrained_field(PROBABILITY_CONSTRAINT) obs_dropout_boundary: float = _constrained_field(PROBABILITY_CONSTRAINT) @@ -392,6 +404,7 @@ class TrainingConfig: final_model_name: str = _constrained_field(NONEMPTY_STRING_CONSTRAINT) evaluation_interval_epochs: int | None = _constrained_field(POSITIVE_INT_CONSTRAINT) evaluation_benchmarks: str | None = MISSING + cosim_debug_evals: bool = MISSING torch_deterministic: bool = MISSING cpu_offload: bool = MISSING device: str | int = MISSING @@ -466,6 +479,7 @@ class EvaluationConfig: max_goal_spacing: float | None = _constrained_field(POSITIVE_NUMBER_CONSTRAINT) obs_slots_partners_n: int | None = _constrained_field(POSITIVE_INT_CONSTRAINT) disable_red_light_infractions: bool | None = MISSING + disable_stop_sign_infractions: bool | None = MISSING output_name: str | None = MISSING output_dir_name: str = _constrained_field(NONEMPTY_STRING_CONSTRAINT) scenario_offset: int = _constrained_field(NONNEGATIVE_INT_CONSTRAINT) @@ -605,8 +619,14 @@ def _validate_cross_field_constraints(config, context): _raise_config_error(context, "env.init_step_spread", "is only supported in replay mode") if env["init_step_spread"] and env["init_step_min_horizon"] >= env["scenario_length"]: _raise_config_error(context, "env.init_step_min_horizon", "must be smaller than env.scenario_length") - if env["goal_source"] == "gt" and env["simulation_mode"] != "replay": - _raise_config_error(context, "env.goal_source", "'gt' is only supported in replay mode") + if env["goal_source"] in ("gt", "gt_map") and env["simulation_mode"] != "replay": + _raise_config_error(context, "env.goal_source", f"'{env['goal_source']}' is only supported in replay mode") + if env["stagger_first_episode"] and env["init_step"] + env["init_step_min_horizon"] > env["scenario_length"]: + _raise_config_error( + context, + "env.stagger_first_episode", + "requires env.init_step + env.init_step_min_horizon <= env.scenario_length", + ) if env["terminate_on_goal"] and (env["simulation_mode"] != "replay" or env["control_mode"] != "control_sdc_only"): _raise_config_error(context, "env.terminate_on_goal", "requires replay mode with control_sdc_only") if env.get("eval_mode") is not None and ( diff --git a/pufferlib/ocean/cosim/__init__.py b/pufferlib/ocean/cosim/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/pufferlib/ocean/cosim/arch.py b/pufferlib/ocean/cosim/arch.py new file mode 100644 index 0000000000..9e21009bc9 --- /dev/null +++ b/pufferlib/ocean/cosim/arch.py @@ -0,0 +1,94 @@ +"""Shadow-env Drive kwargs from a checkpoint config (CARLA + nuPlan co-sim). + +The shadow env adopts EVERY Drive-accepted key from the checkpoint's +config.yaml env section -- full parity with the training env, no allowlist to +fall out of date -- then applies CLEAN_EVAL_OVERRIDES (the repo's clean-eval +profile), then the caller's structural co-sim keys (map_dir, num_agents, +scenario_length, ...) win. +""" + +import inspect +from pathlib import Path + +from pufferlib.ocean.drive.drive import Drive + +# Mirror of the noise/light/conditioning keys in +# pufferlib/config/evaluation/benchmark.yaml, duplicated so the co-sim venvs +# (CARLA cp310 / nuPlan) never import the training stack; +# tests/unit_tests/test_cosim_config_contract.py pins the two equal. +# The reward_*/goal_* keys pin the conditioning obs to the GigaFlow paper's +# eval values (Table A2) instead of adopting the training config's +# reward_randomization=true, which would sample a random command profile. +CLEAN_EVAL_OVERRIDES = { + "obs_dropout_lane": 0.0, + "obs_dropout_boundary": 0.0, + "partner_blindness_prob": 0.0, + "partner_blindness_trigger_prob": 0.0, + "phantom_braking_prob": 0.0, + "phantom_braking_trigger_prob": 0.0, + "traffic_light_behavior": "stop", + "reward_randomization": False, + "goal_speed": 3.0, + "goal_radius": 10.0, + "reward_collision": 3.0, + "reward_offroad": 3.0, + "reward_stop_line": 1.0, + "reward_ade": 0.0, + "reward_goal": 1.0, + "reward_overspeed": 0.05, + "reward_comfort": 0.05, + "reward_velocity": 0.0025, + "reward_lane_align": 0.025, + "reward_lane_center": 0.0075, # training-range max (REWARD_BOUNDS), not the paper's 0.0038 + "reward_timestep": 0.000025, + "reward_reverse": 0.005, + "spawn_lateral_offset_max_frac": 0.0, + "spawn_heading_max_deg": 0.0, + "pose_noise_xy_m": 0.0, + "pose_noise_yaw_deg": 0.0, + "speed_limit_random_prob": 0.0, + "stagger_first_episode": False, +} + + +# Some saved checkpoint configs store the infraction behaviors as resolved +# binding enums (0/1/2) instead of the strings Drive.__init__ accepts (e.g. +# weights/mimolette: collision_behavior: 1); normalize either spelling. +_INFRACTION_BEHAVIOR_NAMES = {0: "ignore", 1: "stop", 2: "remove"} +_INFRACTION_BEHAVIOR_KEYS = ("collision_behavior", "offroad_behavior", "traffic_light_behavior") + + +def checkpoint_config_path(checkpoint): + """config.yaml saved with a checkpoint: next to a run-dir `final_model.pt`, or one level up for + `models/model_*.pt`. Raises when neither exists (a shadow env built from Drive defaults would be + silently wrong).""" + checkpoint = Path(checkpoint).resolve() + for candidate in (checkpoint.parent / "config.yaml", checkpoint.parents[1] / "config.yaml"): + if candidate.exists(): + return candidate + raise FileNotFoundError(f"no config.yaml next to or one level above {checkpoint}") + + +# Applied to every co-sim shadow env after CLEAN_EVAL_OVERRIDES: the eval-only semantics the native +# benchmark gets from eval_mode (scripts/kesai/7_eval_8node_slurm.sh passes the 0.2 m margin and the +# 1.5 m/s^3 standstill deadband), which the co-sim env cannot enter because eval_mode's scenario +# batching needs a full agent pool. +COSIM_EVAL_OVERRIDES = { + "cosim_eval_semantics": True, + "eval_perceived_size_margin_m": 0.2, + "eval_standstill_jerk_deadband_mps3": 1.5, +} + + +def shadow_env_kwargs(cfg, defaults=None, overrides=None): + """Drive kwargs for a co-sim shadow env. + + Precedence: `defaults` (no-checkpoint fallback arch) < checkpoint env + config (every Drive-accepted key) < CLEAN_EVAL_OVERRIDES < COSIM_EVAL_OVERRIDES < `overrides` + (the co-sim's structural keys).""" + accepted = set(inspect.signature(Drive.__init__).parameters) + adopted = {k: v for k, v in ((cfg or {}).get("env") or {}).items() if k in accepted} + for key in _INFRACTION_BEHAVIOR_KEYS: + if key in adopted and isinstance(adopted[key], int): + adopted[key] = _INFRACTION_BEHAVIOR_NAMES[adopted[key]] + return {**(defaults or {}), **adopted, **CLEAN_EVAL_OVERRIDES, **COSIM_EVAL_OVERRIDES, **(overrides or {})} diff --git a/pufferlib/ocean/cosim/carla/README.md b/pufferlib/ocean/cosim/carla/README.md new file mode 100644 index 0000000000..d80b7137df --- /dev/null +++ b/pufferlib/ocean/cosim/carla/README.md @@ -0,0 +1,179 @@ +# PufferDrive policy under CaRL's CARLA leaderboard + +## Goal + +Evaluate a PufferDrive-trained policy with **CaRL's unmodified `original_leaderboard`** pipeline — the same evaluator, scenarios, scoring, and statistics that produced the CaRL longest6-v2 numbers + +## Design + +Zero changes to CaRL. The leaderboard imports any agent file via `--agent`, so the whole integration is this package: + +- `leaderboard_agent.py` — `AutonomousAgent` subclass (entry point `PufferAgent`). + Loads a PufferDrive checkpoint, builds the shadow `Drive` env from the checkpoint's config, and every CARLA tick (the shadow env runs at the 0.05 s tick, no action repeat): overwrites ALL shadow agents (ego included) from CARLA ground truth (ego pose/velocity, nearest vehicles+walkers with true bounding boxes, traffic-light states, route goals from the leaderboard's dense global plan), recomputes observations, runs the policy, integrates one dt to produce a target (speed, yaw), and returns a `carla.VehicleControl` every tick. Get `sensor.camera.rgb` chase cam via `sensors()` — the leaderboard's own standard sensor mechanism, so this doesn't touch the read-only-w.r.t.-CARLA contract; frames stream straight to an mp4 writer. `COSIM_OBS_HTML` records the interactive observation replay (exact policy input/outputs). +- `controller.py` — `TrackingController`: + convert that shadow-env kinematic target into actual throttle/brake/steer at CARLA's native tick rate + +All shadow-env assumptions about the model (observation layout, dynamics model, dt, goal +count/radius, reward conditioning) are adopted from the checkpoint's sibling `config.yaml` +(`cosim/arch.py`'s `resolve_arch`); the config keys that contract depends on are pinned by +`tests/unit_tests/test_cosim_config_contract.py`. + +## How to run + +CARLA needs the GPU/Vulkan container +`carl` conda env (cp310 — CARLA 0.9.15 ships no cp312 wheel) + - look into compatability with our env + +`run_leaderboard.sh` exports `SCENARIO_RUNNER_ROOT` itself. When invoking the +evaluator manually you MUST export it (route scenarios silently fail to load +without it; `leaderboard_agent.py` refuses to start when it is unset): + +```bash +JOBID=14401249 +export CKPT=/scratch/yw4142/PufferDrive_nightly/weights/mimolette/models/model_puffer_drive_003815.pt +export SCENARIO_RUNNER_ROOT=/scratch/yw4142/CaRL/CARLA/original_leaderboard/scenario_runner + +srun --jobid=$JOBID --overlap \ + bash pufferlib/ocean/cosim/carla/run_leaderboard.sh +``` + +Optional env vars (see `leaderboard_agent.py` docstring): `COSIM_DEVICE`, +`COSIM_DYNAMICS_SOURCE`, `COSIM_NUM_AGENTS`, `COSIM_MAX_SPEED_MPS` (ego speed cap via +C_vel; default = the checkpoint's `base_max_speed_mps`, must stay inside the trained range), +`COSIM_ZERO_PARTNER_STOPPED_TIME` (1 = partners' `seconds_stopped` obs feature held at 0, ego +untouched; ablation for the parked-car / red-light-queue avoidance), `COSIM_OBS_HTML`, +`COSIM_DEBUG_CARLA_VIEW`, `COSIM_RECORD_INFRACTIONS`. + +### Ego dynamics source + +`COSIM_DYNAMICS_SOURCE=carla` (default) converts the shadow env's target +(speed, yaw) into `carla.VehicleControl` via `controller.py`'s +`TrackingController`, and CARLA's own vehicle physics moves the ego — subject +to real tracking lag against a jerk-model target the policy never trained +against (measured: `yaw_err_max` up to 90° on a real route, and even a +zero-CARLA-physics bicycle+PI proxy of the same controller shows a real +lane_dist/lane_angle drift within ~1s of a sustained turn). + +`COSIM_DYNAMICS_SOURCE=pufferdrive` skips the controller entirely: PufferDrive's +own dynamics (the ones the policy was trained on) move the ego, and the CARLA +actor is teleported to the post-step pose every tick, where the next tick's +observation reads it. The teleport keeps the body flat on CARLA's road plane +(pitch/roll from the waypoint's up vector rotated into the policy's heading, +velocity along the pitched forward vector): a yaw-only pose on Town03/04 +grades of 5-15 deg buried the wheels in the mesh and logged 20-30 +`static.road` collisions per route. Physics is left ON — disabling +it on this already-active, leaderboard-managed hero actor segfaults the UE4 +engine (tried at several points in the agent's init sequence, same crash every +time) — but `set_transform()` overrides it every step regardless, so the net +effect matches the standalone `carla_cosim.py` loop's design (which disables +physics, but only because it spawns its own ego before any tick happens). +Target linear/angular velocity is also pushed each step so CARLA's own +`get_velocity()` (read by CaRL's own criteria, e.g. `MinSpeedTest`) reflects +the actual motion. This removes the dynamics-mismatch tracking lag at the cost +of the ego no longer being a physically-simulated CARLA vehicle; the +leaderboard's route/collision/infraction criteria still see it correctly since +they only read its pose. + +### Bin frame calibration + +The exported bins sit a constant fraction of a metre off the CARLA frame (2026-09-23, stored +offsets: Town01 0.28 m, Town02 0.62 m, Town06 1.2 m along x), so the ego and every streamed +actor drove that far right of CARLA's lane centre and clipped curbs the shadow env could not +see. `carla_bridge.calibrate_town_offset` fits the translation against CARLA's driving +waypoints at init (median lane residual -> 0.00 m on Town01-06) and refuses a bin whose lanes do +not match the loaded map; the agent logs the applied shift. + +### HTML report (nuPlan-style) + +`COSIM_WORLD_LOG=/dir` makes the agent write one `.npz` per route with the bin-frame world +state at every policy step (ego pose/speed, every streamed partner, light states, route goals, +dense route). `scripts/eval/analyze_carla_cosim.py [...] ` then builds +the same report as `analyze_nuplan_cosim.py`: score table worst first, failure categories, +per-route diagnosis (standstills with the ego's own light state, infractions located in time), +six-frame top-down strips over the bin map, speed plots and the chase-cam / top-down videos. +`` is the directory holding `result.json`, `world_log/`, `telemetry/` and `carla_view/`. + +### Infraction clips + +`COSIM_RECORD_INFRACTIONS=/dir` keeps a rolling ~5 s chase-cam buffer and dumps +one short mp4 whenever the shadow env's own detectors flag an ego +collision/offroad/red-light (at most one clip per 10 m of ego travel) — the +co-sim analog of CaRL `eval_agent.py`'s `RECORD=1` infraction videos. The +trigger is the shadow env's proxy detection, not the leaderboard's official +infraction ledger; use it for debugging, not scoring. + +### Route goals and the GPS lane-distance feature + +Goals are the leaderboard's own target points (`set_global_plan`'s sparse +`_global_plan_world_coord`: junction entries/exits, lane changes and one point +every 200 m, the same list every leaderboard sensor agent gets). The ego always +sees the next `num_goals` of them as a sliding window, refilled after every +consumed point, so the window shrinks to 2 and then 1 only at the route end +(the shadow env's speed-gated window-final goal is then the true end). The +route's first point sits under the ego and is skipped. Each goal's direction +is the dense plan's local direction there; on the C side `set_agent_goals` +snaps each goal to its nearest direction-matched drivable lane +(`find_goal_lane` in drive.h), so the GPS lane-distance observation columns +(`obs_goal_lane_distance`) are live, matching how `goal_source=map` training +goals always carry a lane. Requires the town bin to carry a lane graph (all +`carla/opendrive__Town*.bin` do). + +## Slurm launch (verified 2026-08-06) + +One self-contained sbatch per route: the job gets its own GPU, starts its own +CARLA server, runs the evaluator, and writes `result.json` + videos under +`$RUNDIR`. Single route (e.g. longest6 route 5, the shortest at ~686 m): + +```bash +PD=/scratch/yw4142/PufferDrive_cosim +CKPT=$PD/weights/mimolette/models/model_puffer_drive_003815.pt +RUNDIR=/scratch/yw4142/runs/cosim_lb_$(date +%Y%m%d_%H%M%S) +mkdir -p "$RUNDIR" +sbatch -A torch_pr_355_tandon_advanced -p l40s_public --gres=gpu:1 -c 8 --mem=64G -t 02:00:00 \ + --job-name=cosim_r5 --output="$RUNDIR/slurm.out" --error="$RUNDIR/slurm.out" \ + --wrap "CKPT=$CKPT ROUTES_SUBSET=5 OUT=$RUNDIR/result.json \ +bash $PD/pufferlib/ocean/cosim/carla/run_leaderboard.sh" +``` + +### All 36 longest6 routes in parallel + +Same pattern as CaRL's `evaluate_routes_slurm.py` (one job per split route, +one CARLA server per job, aggregate at the end). Distinct `CARLA_PORT`s guard +against two jobs landing on the same node: + +```bash +PD=/scratch/yw4142/PufferDrive_cosim +CKPT=$PD/weights/mimolette/models/model_puffer_drive_003815.pt +SPLIT=/scratch/yw4142/CaRL/CARLA/custom_leaderboard/leaderboard/data/longest6_split +BASE=/scratch/yw4142/runs/cosim_lb_parallel_$(date +%Y%m%d_%H%M%S) +for i in $(seq -w 0 35); do + RUNDIR=$BASE/route_$i; mkdir -p "$RUNDIR" + sbatch -A torch_pr_355_tandon_advanced -p l40s_public --gres=gpu:1 -c 8 --mem=64G -t 04:00:00 \ + --job-name=cosim_r$i --output="$RUNDIR/slurm.out" --error="$RUNDIR/slurm.out" \ + --wrap "CKPT=$CKPT ROUTES=$SPLIT/longest6_$i.xml ROUTES_SUBSET= \ +CARLA_PORT=$((2000 + 10#$i * 4)) OUT=$RUNDIR/result.json \ +bash $PD/pufferlib/ocean/cosim/carla/run_leaderboard.sh" +done +# aggregate: CaRL's tools/result_parser.py over the per-route result.json files +``` + +CaRL's script additionally monitors and resubmits crashed routes — worth +porting if crash rates matter. + +## Features not obtainable from CARLA + +- Partner (background-agent) `seconds_stopped` is approximated: it accumulates + from the shadow env's own step cadence (one update per policy dt from the + synced speeds), not from CARLA's per-tick velocities. Stopped CARLA vehicles + read as stopped, but the duration resolution is one policy dt. +- Background yaw-rate / longitudinal acceleration come from CARLA's physics + (`get_angular_velocity`/`get_acceleration`); they feed the ego's steering + and acceleration observation features only for the ego itself — partner + observations never read them, so nothing is lost there. + +## Remaining TODOs + +- [ ] Double check Town 6 offset +- [ ] handle 2 kinds of goal, different kind of goals +- [ ] per tick smoothness +- [ ] some town might have routing issue, off road error diff --git a/pufferlib/ocean/cosim/carla/__init__.py b/pufferlib/ocean/cosim/carla/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/pufferlib/ocean/cosim/carla/controller.py b/pufferlib/ocean/cosim/carla/controller.py new file mode 100644 index 0000000000..d3b8ee7d7e --- /dev/null +++ b/pufferlib/ocean/cosim/carla/controller.py @@ -0,0 +1,121 @@ +"""Kinematic target -> carla.VehicleControl tracking controller. + +The policy's jerk action is integrated by the shadow PufferDrive env into a +target (speed, yaw) one policy-dt ahead; this controller makes CARLA's physics +chase that target. CARLA (not the shadow env) moves the ego, so all leaderboard +collision/infraction machinery sees a normally-driven vehicle. + +Longitudinal: PI on speed error -> throttle/brake. +Lateral: desired yaw rate over the policy horizon -> inverse kinematic bicycle +-> front wheel angle, normalized by the vehicle's max steer angle. + +Tracking error is accumulated per route (see stats()) — it is the number that +disambiguates "bad policy" from "bad controller" when scores are low. +""" + +import math + +import carla +import numpy as np + +from pufferlib.ocean.cosim.carla_bridge import wrap_deg_180 + + +def read_vehicle_geometry(vehicle): + """(wheelbase_m, max_steer_rad) from the vehicle's physics parameters + (read-only). Falls back to Lincoln MKZ-ish defaults on any surprise.""" + try: + phys = vehicle.get_physics_control() + wheels = phys.wheels + max_steer_deg = max(w.max_steer_angle for w in wheels) + steering = [w for w in wheels if w.max_steer_angle > 0.0] + fixed = [w for w in wheels if w.max_steer_angle <= 0.0] + if steering and fixed: + front = np.mean([[w.position.x, w.position.y, w.position.z] for w in steering], axis=0) + rear = np.mean([[w.position.x, w.position.y, w.position.z] for w in fixed], axis=0) + wheelbase = float(np.linalg.norm(front - rear)) / 100.0 # positions are in cm + else: + wheelbase = 2.85 + if not (1.5 <= wheelbase <= 5.0): + wheelbase = 2.85 + if not (10.0 <= max_steer_deg <= 90.0): + max_steer_deg = 70.0 + return wheelbase, math.radians(max_steer_deg) + except Exception: + return 2.85, math.radians(70.0) + + +class TrackingController: + def __init__( + self, + wheelbase_m=2.85, + max_steer_rad=math.radians(70.0), + horizon_s=0.1, + kp_speed=0.7, + ki_speed=0.15, + kp_brake=0.5, + max_throttle=0.75, + stop_speed=0.1, + ): + # stop_speed must sit BELOW the first integrated target speed of a + # jerk-dynamics launch from rest (~j*dt^2, ~0.36 m/s at dt=0.3), + # otherwise the brake-hold swallows the ramp-up: ego never moves, the + # shadow env re-syncs to zero speed, and the target never grows. + self.wheelbase = wheelbase_m + self.max_steer = max_steer_rad + self.horizon = horizon_s # time in which the target yaw should be reached (policy dt) + self.kp_speed = kp_speed + self.ki_speed = ki_speed + self.kp_brake = kp_brake + self.max_throttle = max_throttle + self.stop_speed = stop_speed + self.reset() + + def reset(self): + self._integral = 0.0 + self._speed_errors = [] + self._yaw_errors = [] + + def step(self, current_speed, current_yaw_deg, target_speed, target_yaw_deg, tick_dt): + """One control tick. current_* from CARLA ground truth, target_* from the + shadow env's integrated policy action (held between policy steps).""" + # --- longitudinal --- + err = target_speed - current_speed + self._speed_errors.append(err) + throttle, brake = 0.0, 0.0 + if target_speed < self.stop_speed and current_speed < 2.0 * self.stop_speed: + # Policy wants a stop and we are (almost) there: hold the brake so + # the car doesn't creep into intersections/red lights. + brake = 1.0 + self._integral = 0.0 + elif err >= 0.0: + self._integral = float(np.clip(self._integral + err * tick_dt, 0.0, 2.0)) + throttle = float(np.clip(self.kp_speed * err + self.ki_speed * self._integral, 0.0, self.max_throttle)) + else: + self._integral = 0.0 + brake = float(np.clip(-self.kp_brake * err, 0.0, 1.0)) + + # --- lateral --- + yaw_err_rad = math.radians(wrap_deg_180(target_yaw_deg - current_yaw_deg)) + self._yaw_errors.append(yaw_err_rad) + desired_yaw_rate = yaw_err_rad / self.horizon + # Kinematic bicycle: yaw_rate = v / L * tan(steer_angle) + steer_angle = math.atan2(self.wheelbase * desired_yaw_rate, max(current_speed, 1.0)) + steer = float(np.clip(steer_angle / self.max_steer, -1.0, 1.0)) + + return carla.VehicleControl(steer=steer, throttle=throttle, brake=brake) + + def stats(self): + """Per-route tracking fidelity: mean/max absolute speed error (m/s) and + yaw error (deg) between the policy's target and what CARLA achieved.""" + if not self._speed_errors: + return {} + se = np.abs(np.array(self._speed_errors)) + ye = np.degrees(np.abs(np.array(self._yaw_errors))) + return { + "speed_err_mean": float(se.mean()), + "speed_err_max": float(se.max()), + "yaw_err_mean": float(ye.mean()), + "yaw_err_max": float(ye.max()), + "ticks": len(se), + } diff --git a/pufferlib/ocean/cosim/carla/leaderboard_agent.py b/pufferlib/ocean/cosim/carla/leaderboard_agent.py new file mode 100644 index 0000000000..fda865955c --- /dev/null +++ b/pufferlib/ocean/cosim/carla/leaderboard_agent.py @@ -0,0 +1,1065 @@ +"""PufferDrive policy as a CARLA leaderboard agent (CaRL original_leaderboard). + +Usage (CaRL env vars/paths as in CaRL/CARLA/README.md, plus PufferDrive repo +root on PYTHONPATH for `pufferlib` and `data_utils`): + + python ${CARL_WORK_DIR}/original_leaderboard/leaderboard/leaderboard/leaderboard_evaluator.py \ + --routes ${CARL_WORK_DIR}/custom_leaderboard/leaderboard/data/longest6_split/longest6_00.xml \ + --agent /path/to/pufferlib/ocean/cosim/carla/leaderboard_agent.py \ + --agent-config /path/to/experiments/puffer_drive_xxx/models/model_xxx.pt \ + --checkpoint /path/to/results/result.json --track MAP + +Everything about the shadow env (obs layout, dynamics, dt, goal count, agent +pool size, ...) comes from the checkpoint's sibling config.yaml, with the +clean-eval profile applied on top (cosim/arch.py CLEAN_EVAL_OVERRIDES); only +the structural co-sim keys (map, pool wiring) are set here. + +Environment variables: + SCENARIO_RUNNER_ROOT REQUIRED: path to the scenario_runner checkout + (route scenarios silently fail to load without it) + CARL_WORK_DIR REQUIRED if COSIM_TELEMETRY or + COSIM_RECORD_INFRACTIONS is set: path to the + CaRL/CARLA checkout, to import its + reward.criteria collision/offroad detectors + COSIM_DEVICE=cpu torch device for the policy + COSIM_DYNAMICS_SOURCE=pufferdrive + "pufferdrive" (default): PufferDrive's own + dynamics (the ones the policy trained on) move + the ego; the CARLA actor is teleported to match + every policy step. + "carla": TrackingController converts the shadow + env's target (speed, yaw) into throttle/brake/ + steer and CARLA's own vehicle physics moves the + ego (subject to dynamics-mismatch tracking lag). + COSIM_MAX_SPEED_MPS=20 ego speed cap; sets the shadow env's C_vel = + cap / base_max_speed_mps. Default: the + checkpoint's base_max_speed_mps (C_vel = 1). + Must lie in the trained range + base * [1/conditioning_speed_scale, scale] + (0036: 13.3-30 m/s, 0038+: 10-40 m/s). + COSIM_ZERO_PARTNER_STOPPED_TIME=0 + 1: every partner's seconds_stopped observation + feature is held at 0 (the ego's own counter is + untouched). Ablates the stopped-time cue behind + parked-car / red-light-queue avoidance. + COSIM_PEDESTRIAN_MIN_SIZE_M=0.0 + walker partner boxes below this on either axis + grow to it (nuPlan's pedestrian_min_size_m; the + training spawn floor is 0.8 m, CARLA walkers + are 0.4-0.5 m) + COSIM_DEBUG_CARLA_VIEW=/dir write a CARLA chase-camera mp4 per route (native + tick rate, streamed to disk frame-by-frame) + COSIM_RECORD_INFRACTIONS=/dir write a short chase-cam clip (last ~5 s) per + ego infraction (collision/offroad/red-light/stop-sign), + from either the shadow pufferdrive env's own + model (_ego_infractions) or real CARLA ground + truth (_carla_infractions) + COSIM_TELEMETRY=/dir write a per-policy-step CSV per route, with + both infraction sources above as columns + COSIM_OBS_HTML=/dir write an interactive pufferlib.viz replay per + route (the exact obs + policy outputs the ego saw) + COSIM_OBS_HTML_RENDER=1 0: save only the compact .replay.zlib per route; + scripts/eval/render_carla_obs_html.py renders the + pages of a whole run into one gallery afterwards + COSIM_DUMP_OBS=/dir write the raw ego obs + action per policy step + per route (.npz: step, obs, action) + COSIM_WORLD_LOG=/dir write the bin-frame world state per policy step + per route (ego, partners, light states, route); + input of scripts/eval/analyze_carla_cosim.py +""" + +import json +import math +import os +import re +import sys +from datetime import datetime +from pathlib import Path + +import numpy as np +import carla + +from leaderboard.autoagents import autonomous_agent +from srunner.scenariomanager.carla_data_provider import CarlaDataProvider + +from pufferlib.ocean.drive.drive import Drive +from pufferlib.ocean.cosim.goals import RouteGoalWindow, route_goals_from_target_points +from pufferlib.ocean.cosim import carla_bridge as cb +from pufferlib.ocean.cosim.arch import checkpoint_config_path, shadow_env_kwargs +from pufferlib.ocean.cosim.carla.controller import TrackingController, read_vehicle_geometry + +# Rolling chase-cam window kept for COSIM_RECORD_INFRACTIONS clips, and the +# minimum ego travel between two logged infractions (suppresses re-triggering +INFRACTION_CLIP_SECONDS = 5.0 +INFRACTION_MIN_SEPARATION_M = 10.0 +RED_LIGHT_CHECK_DISTANCE_M = 30.0 # matches CaRL RunRedLight's own distance_light default + +FAR_AWAY = 1.0e6 # park unused shadow-env agent slots out of observation range +PARTNER_MAX_ABS_DZ_M = 20.0 # CARLA actors farther above/below the ego are hidden scenario props, not traffic +DEFAULT_BASE_MAX_SPEED_MPS = 20.0 # Drive() defaults, for checkpoints whose config predates the keys +DEFAULT_CONDITIONING_SPEED_SCALE = 1.5 +ROAD_RAY_HALF_SPAN_M = ( + 5.0 # vertical ray around the waypoint z; the ego roof and underpass roads are filtered by label/nearest +) +ROAD_RAY_LABELS = (carla.CityObjectLabel.Roads, carla.CityObjectLabel.RoadLines, carla.CityObjectLabel.Bridge) +MAX_POLICY_STEPS = 100_000 # shadow-env episode cap (~1.4 h at the 0.05 s CARLA tick); sizes the light state buffers +SCENARIO_LENGTH_MARGIN_STEPS = 2 +# Shadow-env metrics_array indices (datatypes.h): collision/offroad/red-light flags. +EGO_INFRACTION_METRICS = {"collision": 0, "offroad": 1, "red_light": 2, "stop_sign": 3} + +CARLA_VIEW_SENSOR_ID = "puffer_chase_cam" +CARLA_VIEW_WIDTH, CARLA_VIEW_HEIGHT, CARLA_VIEW_FOV = 960, 540, 90 +# Behind + above the ego, looking forward and down. Closer than +# carla_cosim.py's standalone chase cam (x=-6.5, z=3.2): the leaderboard +# enforces sqrt(x^2+y^2+z^2) <= agent_wrapper.MAX_ALLOWED_RADIUS_SENSOR (3.0 m) +CARLA_VIEW_TRANSFORM = dict(x=-2.0, y=0.0, z=2.1, roll=0.0, pitch=-15.0, yaw=0.0) + + +def get_entry_point(): + return "PufferAgent" + + +def clean_policy_state_dict(state_dict): + """Strip torch.compile / DDP prefixes (wandb, neptune, the _C kernel, ...) + from the leaderboard's evaluation environment.""" + + def clean(key): + while key.startswith(("module.", "_orig_mod.")): + key = key.split(".", 1)[1] + return key + + return {clean(k): v for k, v in state_dict.items()} + + +def resolve_checkpoint(path_to_conf_file): + """(checkpoint_path, config_dict) from a .pt file or an experiment dir.""" + import yaml + + p = Path(path_to_conf_file).resolve() + if p.is_file(): + ckpt, cfg_path = p, checkpoint_config_path(p) + else: + models = sorted((p / "models").glob("*.pt")) or sorted(p.glob("*.pt")) + if not models: + raise FileNotFoundError(f"no .pt checkpoint under {p}") + ckpt, cfg_path = models[-1], p / "config.yaml" + with open(cfg_path) as f: + cfg = yaml.safe_load(f) + return str(ckpt), cfg + + +def road_mesh_z(world, x, y, reference_z): + """Height of the road mesh at (x, y) from a vertical ray, the road hit nearest reference_z; None when none is hit.""" + top = carla.Location(x=x, y=y, z=reference_z + ROAD_RAY_HALF_SPAN_M) + bottom = carla.Location(x=x, y=y, z=reference_z - ROAD_RAY_HALF_SPAN_M) + road_z = [hit.location.z for hit in world.cast_ray(top, bottom) if hit.label in ROAD_RAY_LABELS] + if not road_z: + return None + return min(road_z, key=lambda hit_z: abs(hit_z - reference_z)) + + +def road_aligned_attitude(road_up, yaw_deg): + """(pitch_deg, roll_deg, unit forward) of a body heading yaw_deg that lies flat on the road plane with normal road_up.""" + normal = np.array([road_up.x, road_up.y, road_up.z], dtype=np.float64) + yaw_rad = math.radians(yaw_deg) + forward = np.array([math.cos(yaw_rad), math.sin(yaw_rad), 0.0]) + forward -= normal * float(forward @ normal) + forward /= np.linalg.norm(forward) + right = np.cross(normal, forward) # CARLA is left-handed: right = up x forward + pitch_deg = math.degrees(math.asin(forward[2])) + roll_deg = math.degrees(math.asin(-right[2])) # positive roll = right side down + return pitch_deg, roll_deg, forward + + +def cosim_max_speed_mps(env_cfg): + """COSIM_MAX_SPEED_MPS as a float inside the checkpoint's trained C_vel range, or None when unset.""" + raw = os.environ.get("COSIM_MAX_SPEED_MPS") + if raw is None: + return None + max_speed_mps = float(raw) + base_max_speed_mps = float(env_cfg.get("base_max_speed_mps") or DEFAULT_BASE_MAX_SPEED_MPS) + scale = float(env_cfg.get("conditioning_speed_scale") or DEFAULT_CONDITIONING_SPEED_SCALE) + low_mps, high_mps = base_max_speed_mps / scale, base_max_speed_mps * scale + if not (low_mps <= max_speed_mps <= high_mps): + raise ValueError( + f"COSIM_MAX_SPEED_MPS={max_speed_mps} is outside the trained C_vel range " + f"[{low_mps:.1f}, {high_mps:.1f}] m/s (base_max_speed_mps={base_max_speed_mps}, " + f"conditioning_speed_scale={scale})" + ) + return max_speed_mps + + +def plan_xyz(plan): + """[(carla.Transform, RoadOption)] -> (N, 3) CARLA-frame positions.""" + return np.array([[t.location.x, t.location.y, t.location.z] for t, _ in plan], dtype=np.float64).reshape(-1, 3) + + +class PufferAgent(autonomous_agent.AutonomousAgent): + def setup(self, path_to_conf_file, route_index=None): + if "SCENARIO_RUNNER_ROOT" not in os.environ: + raise RuntimeError( + "SCENARIO_RUNNER_ROOT is not set. Export it to the scenario_runner checkout " + "(see README.md / run_leaderboard.sh); without it route scenarios silently fail to load." + ) + self.track = autonomous_agent.Track.MAP + self.route_index = re.sub(r"[^\w.-]", "_", str(route_index)) if route_index else "route" + # CaRL's `route_index` is route_date_string = Path(ROUTES).stem, fixed + # once per evaluator process unless --collect-dataset is passed. + self.video_tag = f"{self.route_index}_{datetime.now().strftime('%Y%m%d_%H%M%S_%f')}" + self.checkpoint, self.cfg = resolve_checkpoint(path_to_conf_file) + + self.device = os.environ.get("COSIM_DEVICE", "cpu") + self.dynamics_source = os.environ.get("COSIM_DYNAMICS_SOURCE", "pufferdrive") + if self.dynamics_source not in ("carla", "pufferdrive"): + raise ValueError(f"COSIM_DYNAMICS_SOURCE must be 'carla' or 'pufferdrive', got {self.dynamics_source!r}") + env_cfg = self.cfg["env"] + self.max_speed_mps = cosim_max_speed_mps(env_cfg) + zero_stopped_raw = os.environ.get("COSIM_ZERO_PARTNER_STOPPED_TIME", "0") + if zero_stopped_raw not in ("0", "1"): + raise ValueError(f"COSIM_ZERO_PARTNER_STOPPED_TIME must be '0' or '1', got {zero_stopped_raw!r}") + self.zero_partner_stopped_time = zero_stopped_raw == "1" + self.pedestrian_min_size_m = float(os.environ.get("COSIM_PEDESTRIAN_MIN_SIZE_M", "0.0")) + if not (self.pedestrian_min_size_m >= 0.0): + raise ValueError(f"COSIM_PEDESTRIAN_MIN_SIZE_M must be >= 0, got {self.pedestrian_min_size_m}") + # Shadow agent pool == the training per-env cap + self.num_agents = int(env_cfg["max_agents_per_env"]) + + self.debug_carla_view_dir = os.environ.get("COSIM_DEBUG_CARLA_VIEW", None) + self.record_infractions_dir = os.environ.get("COSIM_RECORD_INFRACTIONS", None) + self.telemetry_dir = os.environ.get("COSIM_TELEMETRY", None) + self.telemetry_file = None + self.obs_html_dir = os.environ.get("COSIM_OBS_HTML", None) + self.obs_dump_dir = os.environ.get("COSIM_DUMP_OBS", None) # raw ego obs per policy step (.npz) + self._obs_dump = [] + self.world_log_dir = os.environ.get("COSIM_WORLD_LOG", None) # bin-frame world state per policy step (.npz) + self._world_log = {"ego": [], "partners": [], "lights": []} + self._last_partners = None + self._partner_stopped_s = {} # CARLA actor id -> seconds at standstill + from pufferlib.ocean.drive import binding + + self._partner_stopped_speed_threshold = float(binding.AGENT_STOPPED_SPEED_THRESHOLD) + self.obs_html_max_steps = int(os.environ.get("COSIM_OBS_HTML_MAX_STEPS", "12000")) + obs_html_render_raw = os.environ.get("COSIM_OBS_HTML_RENDER", "1") + if obs_html_render_raw not in ("0", "1"): + raise ValueError(f"COSIM_OBS_HTML_RENDER must be '0' or '1', got {obs_html_render_raw!r}") + self.obs_html_render = obs_html_render_raw == "1" + self._obs_html = None + # Per-policy-step CSV of the ego's current goal vs the lane + # find_goal_lane snapped it to (goal_lane_idx from env.get_state()): + # lane direction, lane-to-goal distance, dot vs the route direction + # used at snap time -- diagnoses whether the goal feature is guiding + # toward a real, correctly-directed lane. Off unless set. + self.debug_goal_lane_dir = os.environ.get("COSIM_DEBUG_GOAL_LANE", None) + self._goal_lane_debug_file = None + self._road_data = None + + self.step = -1 + self.initialized = False + self.policy = None + self.carla_view_writer = None + self.goal_window = None + self.target = (0.0, 0.0) # (target_speed, target_yaw_deg), held between policy steps + # (x, y, yaw_deg, speed) of the shadow ego before and after the current policy step, CARLA frame + self._motion = None + self._carla_collision = None # set by _init_carla_infraction_detectors when needed + self._carla_stop_sign = None + self._stop_sign_lines = np.zeros((0, 6), np.float32) # CARLA trigger volumes as bin-frame stop lines + + def sensors(self): + if not self.debug_carla_view_dir and not self.record_infractions_dir: + return [] + return [ + { + "type": "sensor.camera.rgb", + "id": CARLA_VIEW_SENSOR_ID, + "width": CARLA_VIEW_WIDTH, + "height": CARLA_VIEW_HEIGHT, + "fov": CARLA_VIEW_FOV, + **CARLA_VIEW_TRANSFORM, + } + ] + + def _load_policy_and_env(self, town_bin): + arch = shadow_env_kwargs( + self.cfg, + overrides=dict( + map_dir=town_bin, + num_maps=1, + # One policy agent (the ego); every other slot is a static partner streamed from CARLA. + num_agents=1, + min_agents_per_env=1, + cosim_partner_slots=self.num_agents - 1, + goal_source="external", + dt=self.dt, + scenario_length=MAX_POLICY_STEPS + SCENARIO_LENGTH_MARGIN_STEPS, + resample_frequency=0, + termination_mode=0, + # Enforcement off (detection flags still fire): in one endless + # episode a "stop" latch is permanent and fires only AFTER + # CARLA scored the infraction (route 5 froze 320 s -> DNF). + collision_behavior="ignore", + offroad_behavior="ignore", + traffic_light_behavior="ignore", + **({} if self.max_speed_mps is None else {"max_speed_mps": self.max_speed_mps}), + ), + ) + self.dynamics_model = arch.get("dynamics_model", "classic") + # jerk dynamics build accel across steps while sync() re-seeds accel + self.speed_intent_extension_s = 2.0 * self.dt if self.dynamics_model == "jerk" else 0.0 + + self.env = Drive(**arch) + self.env.reset() + + import torch + import pufferlib.ocean.torch as drive_torch + + policy_state_dict = clean_policy_state_dict( + torch.load(self.checkpoint, map_location=self.device, weights_only=False) + ) + env_for_policy = self.env + self._boundary_feat_ckpt = None + bw = policy_state_dict.get("actor_backbone.boundary_encoder.0.weight") + if bw is not None and bw.shape[1] < env_for_policy.boundary_features: + # Older checkpoints emit fewer boundary features than this drive.h + # (e.g. 7 vs 9 -- two zero-padded GPS columns were added later). + import copy + + self._boundary_feat_ckpt = int(bw.shape[1]) + env_for_policy = copy.copy(self.env) + env_for_policy.boundary_features = self._boundary_feat_ckpt + print( + f"[puffer_agent] checkpoint uses {self._boundary_feat_ckpt} boundary features " + f"(env emits {self.env.boundary_features}); stripping zero-padded columns" + ) + + policy_cls = getattr(drive_torch, self.cfg.get("policy_name", "Drive")) + # pre-3.0 checkpoints keep action_type only in the env section + self.cfg["policy"].setdefault("action_type", self.cfg["env"]["action_type"]) + self.policy = policy_cls(env_for_policy, **self.cfg["policy"]).to(self.device) + self.policy.load_state_dict(policy_state_dict) + self.policy.eval() + print(f"[puffer_agent] loaded policy from {self.checkpoint}") + + def _init_on_first_step(self): + """Deferred init (the ego and world only exist once the route runs) — + same pattern as CaRL's eval_agent.agent_init. Everything here is + read-only with respect to CARLA.""" + self.vehicle = CarlaDataProvider.get_hero_actor() + self.world = self.vehicle.get_world() + self.cmap = self.world.get_map() + town = CarlaDataProvider.get_map().name.split("/")[-1] + self.tick_dt = float(self.world.get_settings().fixed_delta_seconds) # 0.05 @ 20 Hz + self.dt = self.tick_dt # the policy runs every CARLA tick, like the leaderboard's sensor agents + + self.town_bin = cb.bin_path_for_town(town) + self._load_policy_and_env(self.town_bin) + + self.transform = cb.CarlaTransform(town, offset=cb.town_offset(self.town_bin)) + offset, z_offset, residual_before, residual_after = cb.calibrate_town_offset( + self.cmap, self.transform, self.town_bin + ) + print( + f"[puffer_agent] bin offset calibrated against CARLA lanes: shift " + f"({offset[0] - self.transform.tx:+.2f}, {offset[1] - self.transform.ty:+.2f}) m, z {z_offset:+.2f} m, " + f"median lane residual {residual_before:.2f} -> {residual_after:.2f} m" + ) + self.transform = cb.CarlaTransform(town, offset=offset, z_offset=z_offset) + if self.dynamics_source == "pufferdrive": + self._sync_ego_from_carla(zero_velocity=True) + ego_loc = self.vehicle.get_location() + target_points = plan_xyz(self._global_plan_world_coord) + dense_points = plan_xyz(self.dense_global_plan_world_coord) + target_points[:, 2] = self.transform.z_to_bin(target_points[:, 2]) + dense_points[:, 2] = self.transform.z_to_bin(dense_points[:, 2]) + self.route_goals = route_goals_from_target_points( + target_points, + dense_points, + self.transform.loc_to_bin, + self.transform.loc_to_bin(ego_loc.x, ego_loc.y), + self.env.goal_radius, + ) + # the leaderboard's target points, next num_goals of them at all times (fewer only at the route end) + self.goal_window = RouteGoalWindow(self.env, self.route_goals, sliding=True) + + import data_utils.mirror_map_bin as mbin + + bin_data = mbin.read_bin(Path(self.town_bin)) + bin_traffic = bin_data["traffic"] + self.stop_line_centers = np.array( + [ + [0.5 * (t["stop_line"][0] + t["stop_line"][3]), 0.5 * (t["stop_line"][1] + t["stop_line"][4])] + for t in bin_traffic + ] + ).reshape(-1, 2) + + if self.debug_goal_lane_dir: + # goal_lane_idx comes from road_elements[] in the C file; bin_data["roads"] + # is the same bin read in the same order, so it's usable as-is. + self._road_data = bin_data["roads"] + Path(self.debug_goal_lane_dir).mkdir(parents=True, exist_ok=True) + self._goal_lane_debug_file = open(Path(self.debug_goal_lane_dir) / f"{self.video_tag}.csv", "w") + self._goal_lane_debug_file.write( + "step,ego_x,ego_y,goal_x,goal_y,gdir_x,gdir_y,goal_lane_idx,lane_dir_dot_route,lane_dist_to_goal_m\n" + ) + + self.lights = list(self.world.get_actors().filter("traffic.traffic_light")) + light_map, bin_num_traffic = cb.map_lights_to_bin(self.lights, self.transform, self.town_bin) + # the shadow env runs on CARLA's trigger volumes: the bin's exported stop lines sit up to 9 m off and miss a few + self._stop_sign_lines, stop_sign_headings = cb.stop_signs_from_carla(self.world, self.cmap, self.transform) + self.num_traffic = self.env.set_stop_signs(self._stop_sign_lines, stop_sign_headings) + print( + f"[puffer_agent] stop signs: {len(stop_sign_headings)} CARLA trigger volumes replace the bin's " + f"(traffic elements {bin_num_traffic} -> {self.num_traffic})" + ) + # One id -> bin-indices table, reused by both passes in + # _read_light_states, so they can't disagree on which bin element a + # given light maps to (a prior independent geometric lookup for the + # ego-governance pass picked the wrong element ~5% of the time). + self.light_bin_indices_by_id = {lt.id: light_map[li] for li, lt in enumerate(self.lights)} + self.last_light_states = np.zeros(self.num_traffic, np.int32) + + wheelbase, max_steer = read_vehicle_geometry(self.vehicle) + self.wheelbase_m = wheelbase + body = self.vehicle.bounding_box + self.body_front_m = body.location.x + body.extent.x + self.body_rear_m = body.location.x - body.extent.x + self.controller = TrackingController(wheelbase_m=wheelbase, max_steer_rad=max_steer, horizon_s=self.dt) + + if self.obs_html_dir: + from pufferlib.ocean.cosim.obs_replay import ObsReplayCapture + + Path(self.obs_html_dir).mkdir(parents=True, exist_ok=True) + self._obs_html = ObsReplayCapture( + self.env, self.policy, Path(self.obs_html_dir) / self.video_tag, max_steps=self.obs_html_max_steps + ) + + if self.debug_carla_view_dir: + from pufferlib.ocean.cosim.carla_cosim import Mp4Writer + + Path(self.debug_carla_view_dir).mkdir(parents=True, exist_ok=True) + out = str(Path(self.debug_carla_view_dir) / f"{self.video_tag}.mp4") + self.carla_view_writer = Mp4Writer(out, fps=round(1.0 / self.tick_dt)) + + if self.telemetry_dir: + Path(self.telemetry_dir).mkdir(parents=True, exist_ok=True) + self.telemetry_file = open(Path(self.telemetry_dir) / f"{self.video_tag}.csv", "w") + self.telemetry_file.write( + "step,current_speed,target_speed,ego_action,goal_cursor,goal_dist_m," + "near_light_dist_m,near_light_state," + "pd_infr_collision,pd_infr_offroad,pd_infr_red,pd_infr_stop," + "carla_infr_collision,carla_infr_offroad,carla_infr_red,carla_infr_stop\n" + ) + + if self.record_infractions_dir: + from collections import deque + + Path(self.record_infractions_dir).mkdir(parents=True, exist_ok=True) + # Rolling window of the last INFRACTION_CLIP_SECONDS of chase-cam + self.infraction_buffer = deque(maxlen=int(INFRACTION_CLIP_SECONDS / self.tick_dt)) + self.infraction_counter = 0 + self.last_infraction_location = self.vehicle.get_location() + + if self.telemetry_dir or self.record_infractions_dir: + self._init_carla_infraction_detectors() + + print( + f"[puffer_agent] town={town} tick_dt={self.tick_dt} dt={self.dt} route_goals={len(self.route_goals)} " + f"max_speed_mps={self.env.max_speed_mps:.1f} (C_vel={self.env.max_speed_mps / self.env.base_max_speed_mps:.2f}) " + f"zero_partner_stopped_time={int(self.zero_partner_stopped_time)} " + f"pedestrian_min_size_m={self.pedestrian_min_size_m:g}" + ) + self.initialized = True + + # --- shadow-env sync (read-only w.r.t. CARLA) -------------------------- + + def _nearby_actors(self): + ego_loc = self.vehicle.get_location() + candidates = [] + for a in self.world.get_actors(): + if a.id == self.vehicle.id or not ("vehicle" in a.type_id or "walker.pedestrian" in a.type_id): + continue + loc = a.get_location() + # scenario_runner parks pending scenario actors 50-500 m underground at the ego's own waypoint + if abs(loc.z - ego_loc.z) > PARTNER_MAX_ABS_DZ_M: + continue + candidates.append((loc.distance(ego_loc), a)) + candidates.sort(key=lambda item: item[0]) + return [a for _, a in candidates[: self.num_agents - 1]] + + def _read_states(self, actors): + idx, x, y, z, h, vx, vy, yaw_rate, accel_long, stopped_s = [], [], [], [], [], [], [], [], [], [] + stopped_by_id = {} + for j, a in enumerate(actors): + idx.append(1 + j) # agent 0 = ego; others fill 1..M + bx, by, bz, bh, bvx, bvy, byr, bal = self.transform.actor_state_to_bin(a) + x.append(bx) + y.append(by) + z.append(bz) + h.append(bh) + vx.append(bvx) + vy.append(bvy) + yaw_rate.append(byr) + accel_long.append(bal) + # stopped time follows the CARLA actor, not the shadow slot (slots reshuffle by distance every step) + is_stopped = math.hypot(bvx, bvy) <= self._partner_stopped_speed_threshold + stopped_by_id[a.id] = self._partner_stopped_s.get(a.id, 0.0) + self.dt if is_stopped else 0.0 + stopped_s.append(stopped_by_id[a.id]) + self._partner_stopped_s = stopped_by_id + if self.zero_partner_stopped_time: + stopped_s = [0.0] * len(actors) + return ( + np.array(idx, np.int32), + np.array(x, np.float32), + np.array(y, np.float32), + np.array(z, np.float32), + np.array(h, np.float32), + np.array(vx, np.float32), + np.array(vy, np.float32), + np.array(yaw_rate, np.float32), + np.array(accel_long, np.float32), + np.array(stopped_s, np.float32), + ) + + def _read_sizes(self, actors): + idx, length, width = [], [], [] + for j, a in enumerate(actors): + idx.append(1 + j) + ext = a.bounding_box.extent + walker_floor_m = self.pedestrian_min_size_m if "walker" in a.type_id else 0.0 + length.append(max(2.0 * ext.x, 0.1, walker_floor_m)) + width.append(max(2.0 * ext.y, 0.1, walker_floor_m)) + return (np.array(idx, np.int32), np.array(length, np.float32), np.array(width, np.float32)) + + def _read_light_states(self): + """Ground truth for every mapped bin traffic element, via the + precomputed light_bin_indices_by_id table (see _init_on_first_step).""" + # unmapped light elements read OFF (a training state), never UNKNOWN + states = np.full(self.num_traffic, cb.TRAFFIC_LIGHT_STATE_OFF, np.int32) + for lt in self.lights: + state = cb.carla_light_to_puffer(lt.get_state()) + for j in self.light_bin_indices_by_id[lt.id]: + if 0 <= j < self.num_traffic: + states[j] = state + ego_light = self.vehicle.get_traffic_light() + if ego_light is not None and ego_light.id in self.light_bin_indices_by_id: + state = cb.carla_light_to_puffer(ego_light.get_state()) + for j in self.light_bin_indices_by_id[ego_light.id]: + if 0 <= j < self.num_traffic: + states[j] = state + return states + + def _sync_ego_from_carla(self, zero_velocity=False): + """Overwrite agent 0 (ego) from CARLA's ground-truth pose/size.""" + ego_state = self.transform.actor_state_to_bin(self.vehicle) + if zero_velocity: + x, y, z, heading, *_ = ego_state + ego_state = (x, y, z, heading, 0.0, 0.0, 0.0, 0.0) + self.env.set_agent_states(np.array([0], np.int32), *[np.array([v], np.float32) for v in ego_state]) + ego_ext = self.vehicle.bounding_box.extent + self.env.set_agent_sizes( + np.array([0], np.int32), np.array([2.0 * ego_ext.x], np.float32), np.array([2.0 * ego_ext.y], np.float32) + ) + + def _sync_carla(self): + """Overwrite the shadow env's background agents/lights (+ ego, only in + dynamics_source='carla' mode) from CARLA ground truth and return the + recomputed observation array (num_agents, obs_dim). Row 0 = ego.""" + if self.dynamics_source == "carla": + self._sync_ego_from_carla() + ego = self.env.get_global_agent_state() + ebx, eby = float(ego["x"][0]), float(ego["y"][0]) + + actors = self._nearby_actors() + if actors: + states = self._read_states(actors) + sizes = self._read_sizes(actors) + self.env.set_agent_states(*states) + self.env.set_agent_sizes(*sizes) + is_walker = np.array(["walker" in a.type_id for a in actors], np.float32) + self._last_partners = (states[1], states[2], states[4], states[5], states[6], sizes[1], sizes[2], is_walker) + else: + self._last_partners = None + n_used = 1 + len(actors) + if n_used < self.num_agents: + sp = np.arange(n_used, self.num_agents, dtype=np.int32) + zf = np.full(len(sp), FAR_AWAY, np.float32) + zz = np.zeros_like(zf) + self.env.set_agent_states(sp, zf, zf, zf, zz, zz, zz, zz, zz) + + self.last_light_states = self._read_light_states() + self.env.set_traffic_light_states(self.last_light_states) + + self.goal_window.sync(ebx, eby, float(ego["heading"][0])) + if self._goal_lane_debug_file is not None: + self._write_goal_lane_debug_row(ebx, eby, self.route_goals[self.goal_window.current_index]) + return np.asarray(self.env.recompute_observations()) + + def _write_goal_lane_debug_row(self, ebx, eby, current_goal): + """One CSV row: the CURRENT goal (window slot 0, current_goal_idx after + the reset in c_set_agent_goals) vs the lane find_goal_lane snapped it + to -- that lane's direction (nearest segment to the goal, mirroring + find_goal_lane's own point-to-segment search) dotted against the route + direction used at snap time, and the snap distance. dot < 0 means the + snapped lane runs opposite the route (oncoming lane); a large snap + distance means no nearby lane passed find_goal_lane's gates at all.""" + gx, gy, _, gdir_x, gdir_y = current_goal + state = self.env.get_state() + scenario = state[0] if isinstance(state, list) else state + ego_agent = (scenario.get("agents") or [{}])[0] + lane_idx = int(ego_agent.get("goal_lane_idx", -1)) + dot, dist = float("nan"), float("nan") + if lane_idx >= 0 and self._road_data is not None and lane_idx < len(self._road_data): + road = self._road_data[lane_idx] + xs, ys = np.asarray(road["x"], dtype=np.float64), np.asarray(road["y"], dtype=np.float64) + if len(xs) >= 2: + seg_dx, seg_dy = xs[1:] - xs[:-1], ys[1:] - ys[:-1] + seg_len = np.hypot(seg_dx, seg_dy) + seg_len_safe = np.where(seg_len > 1e-6, seg_len, 1.0) + t = np.clip(((gx - xs[:-1]) * seg_dx + (gy - ys[:-1]) * seg_dy) / (seg_len_safe**2), 0.0, 1.0) + px, py = xs[:-1] + t * seg_dx, ys[:-1] + t * seg_dy + d = np.hypot(gx - px, gy - py) + k = int(np.argmin(d)) + dist = float(d[k]) + route_norm = math.hypot(gdir_x, gdir_y) + if seg_len[k] > 1e-6 and route_norm > 1e-6: + dot = float((gdir_x * seg_dx[k] + gdir_y * seg_dy[k]) / (seg_len[k] * route_norm)) + self._goal_lane_debug_file.write( + f"{self.step},{ebx:.2f},{eby:.2f},{gx:.2f},{gy:.2f},{gdir_x:.3f},{gdir_y:.3f}," + f"{lane_idx},{dot:.3f},{dist:.2f}\n" + ) + self._goal_lane_debug_file.flush() + + def _carla_integrate(self, actions): + """Step the shadow env ONCE (one policy step == one shadow tick). + + dynamics_source='carla': return the ego's target kinematic state for + the tracking controller + + dynamics_source='pufferdrive': PufferDrive's own jerk/classic dynamics + (the ones the policy trained on) ARE the ego's motion -- no target to + chase. The CARLA ego is teleported to the post-step pose (see + _teleport_carla_ego), where the next tick's observation reads it. + """ + if self.step >= MAX_POLICY_STEPS: + raise RuntimeError(f"route exceeded MAX_POLICY_STEPS={MAX_POLICY_STEPS} policy steps") + before = self.env.get_global_agent_state() + speed_before = self._ego_speed() + self.env.step(actions) + after = self.env.get_global_agent_state() + target_yaw_deg = self.transform.bin_heading_to_yaw(float(after["heading"][0])) + speed_after = self._ego_speed() + accel_after = float(np.asarray(self.env.observations)[0][4]) * self._accel_long_norm() + + if self.dynamics_source == "pufferdrive": + x0, y0 = self.transform.bin_to_loc(float(before["x"][0]), float(before["y"][0])) + x1, y1 = self.transform.bin_to_loc(float(after["x"][0]), float(after["y"][0])) + yaw0_deg = self.transform.bin_heading_to_yaw(float(before["heading"][0])) + self._motion = (x0, y0, yaw0_deg, speed_before, x1, y1, target_yaw_deg, speed_after, float(after["z"][0])) + self._teleport_carla_ego(1.0) + return speed_after, target_yaw_deg # logged, not chased (see run_step) + + target_speed = speed_after + max(accel_after, 0.0) * self.speed_intent_extension_s + return target_speed, target_yaw_deg + + def _teleport_carla_ego(self, fraction): + """Place the CARLA ego at `fraction` (0 = pre-step, 1 = post-step) of the current policy step's motion.""" + x0, y0, yaw0_deg, speed0, x1, y1, yaw1_deg, speed1, sim_z = self._motion + yaw_delta_deg = cb.wrap_deg_180(yaw1_deg - yaw0_deg) + x = x0 + fraction * (x1 - x0) + y = y0 + fraction * (y1 - y0) + yaw_deg = yaw0_deg + fraction * yaw_delta_deg + speed = speed0 + fraction * (speed1 - speed0) + # CARLA's live road-mesh height, not the shadow env's lane-averaged sim_z: on graded + # multi-level roads the average can land the body mid-structure (measured: 20-100+ + # road collisions per Town03/04 route). sim_z only off the drivable network. + # get_waypoint is nearest-in-3D: without sim_z the z=0 default snaps an overpass ego to the road beneath + carla_z = self.transform.z_to_carla(sim_z) + wp = self.cmap.get_waypoint(carla.Location(x=x, y=y, z=carla_z)) + z = wp.transform.location.z if wp is not None else carla_z + road_up = wp.transform.rotation.get_up_vector() if wp is not None else carla.Vector3D(x=0.0, y=0.0, z=1.0) + pitch_deg, roll_deg, forward = road_aligned_attitude(road_up, yaw_deg) + # Waypoint z is quantised in ~0.5 m steps on steep grades (measured 0.32 m low at a Town03 + # descent); the mesh raycast is exact and resting the body on its bumper chord keeps the overhangs out of sags. + surface = self._road_surface(x, y, z, yaw_deg) + if surface is not None: + z, pitch_deg = surface + pitch_rad = math.radians(pitch_deg) + yaw_rad = math.radians(yaw_deg) + forward = ( + math.cos(yaw_rad) * math.cos(pitch_rad), + math.sin(yaw_rad) * math.cos(pitch_rad), + math.sin(pitch_rad), + ) + # Zero momentum before the teleport: CARLA's collision resolver reacts violently to a + # physics body carrying velocity into a new pose (carla issue #8076). + zero = carla.Vector3D(x=0.0, y=0.0, z=0.0) + self.vehicle.set_target_velocity(zero) + self.vehicle.set_target_angular_velocity(zero) + self.vehicle.set_transform( + carla.Transform(carla.Location(x=x, y=y, z=z), carla.Rotation(pitch=pitch_deg, yaw=yaw_deg, roll=roll_deg)) + ) + self.vehicle.set_target_velocity( + carla.Vector3D(x=speed * forward[0], y=speed * forward[1], z=speed * forward[2]) + ) + self.vehicle.set_target_angular_velocity(carla.Vector3D(x=0.0, y=0.0, z=yaw_delta_deg / self.dt)) + + def _road_surface(self, x, y, reference_z, yaw_deg): + """(z, pitch_deg) resting the body on the road mesh: bumper-to-bumper chord, lifted until no sample is below the mesh; None off the mesh.""" + yaw_rad = math.radians(yaw_deg) + cos_yaw, sin_yaw = math.cos(yaw_rad), math.sin(yaw_rad) + half_wheelbase = 0.5 * self.wheelbase_m + sample_offsets_m = (self.body_rear_m, -half_wheelbase, 0.0, half_wheelbase, self.body_front_m) + sample_z = [road_mesh_z(self.world, x + s * cos_yaw, y + s * sin_yaw, reference_z) for s in sample_offsets_m] + if any(z is None for z in sample_z): + return None + slope = (sample_z[-1] - sample_z[0]) / (self.body_front_m - self.body_rear_m) + z = max(z_i - slope * s_i for z_i, s_i in zip(sample_z, sample_offsets_m)) + return z, math.degrees(math.atan(slope)) + + def _ego_speed(self): + return float(np.asarray(self.env.observations)[0][0]) * self._max_speed() + + def _max_speed(self): + return self.env.obs_norm_speed_mps + + def _accel_long_norm(self): + from pufferlib.ocean.drive import binding + + return binding.ACCEL_LONG_NORM + + def _ego_infractions(self): + """{'collision': f, 'offroad': f, 'red_light': f, 'stop_sign': f} from the shadow + pufferdrive env's own model of the ego (compute_metrics, refreshed by + the last integrate()) -- what the policy would have caused according + to PufferDrive's own dynamics, not necessarily what the real CARLA + vehicle did (see _carla_infractions).""" + state = self.env.get_state() + scenario = state[0] if isinstance(state, list) else state + agents = scenario.get("agents") or [] + if not agents: + return {name: 0.0 for name in EGO_INFRACTION_METRICS} + metrics = agents[0].get("metrics_array") or [] + return { + name: float(metrics[idx]) if idx < len(metrics) else 0.0 for name, idx in EGO_INFRACTION_METRICS.items() + } + + def _init_carla_infraction_detectors(self): + """Real CARLA ground truth for collision/offroad/red-light/stop-sign, reusing + CaRL's own standalone criteria (CARL_WORK_DIR/team_code/reward). + RunRedLight uses _get_traffic_light_waypoints and do + RunRedLight's rear-bumper-crossing check in _poll_carla_red_light.""" + if "CARL_WORK_DIR" not in os.environ: + raise RuntimeError( + "CARL_WORK_DIR is not set; required to import CaRL's reward.criteria " + "collision/offroad detectors for COSIM_TELEMETRY/COSIM_RECORD_INFRACTIONS." + ) + team_code_dir = str(Path(os.environ["CARL_WORK_DIR"]) / "team_code") + if team_code_dir not in sys.path: + sys.path.insert(0, team_code_dir) + from reward.criteria.collision import Collision + from reward.criteria.outside_route_lanes import OutsideRouteLanesTest + from reward.criteria.run_stop_sign2 import RunStopSign2 + from birds_eye_view.traffic_light import _get_traffic_light_waypoints + + self._carla_collision = Collision(self.vehicle, self.world) + self._carla_collision_pending = False + self._carla_offroad_test = OutsideRouteLanesTest(self.vehicle, self.cmap) + self._carla_stop_sign = RunStopSign2(self.world, self.cmap) # the training-side definition, on CARLA's actors + self._ran_stop_sign_pending = False + + self._red_light_geometry = {} + for lt in self.lights: + tv_loc, stopline_wps, _stopline_vertices, long_line, _junction_paths = _get_traffic_light_waypoints( + lt, self.cmap + ) + self._red_light_geometry[lt.id] = (tv_loc, list(zip(stopline_wps, long_line))) + self._last_red_light_id = None + self._ran_red_light_pending = False + + def _poll_carla_collision(self): + elapsed = self.world.get_snapshot().timestamp.elapsed_seconds + if self._carla_collision.tick(self.vehicle, elapsed) is not None: + self._carla_collision_pending = True + + def _poll_carla_red_light(self): + """Real CARLA ground truth for 'ran a red light'""" + import shapely.geometry + + ev_tra = self.vehicle.get_transform() + ev_dir = ev_tra.get_forward_vector() + ev_extent = self.vehicle.bounding_box.extent.x + tail_close = ev_tra.transform(carla.Location(x=-0.8 * ev_extent)) + tail_far = ev_tra.transform(carla.Location(x=-ev_extent - 1.0)) + tail_line = shapely.geometry.LineString([(tail_close.x, tail_close.y), (tail_far.x, tail_far.y)]) + speed = self.vehicle.get_velocity().length() + if speed <= 0.001: + return + for lt in self.lights: + if lt.get_state() != carla.TrafficLightState.Red or lt.id == self._last_red_light_id: + continue + tv_loc, crossings = self._red_light_geometry[lt.id] + if tv_loc.distance(ev_tra.location) > RED_LIGHT_CHECK_DISTANCE_M: + continue + for wp, (stop_left, stop_right) in crossings: + wp_dir = wp.transform.get_forward_vector() + if ev_dir.x * wp_dir.x + ev_dir.y * wp_dir.y + ev_dir.z * wp_dir.z <= 0: + continue # this light's stop line doesn't face our lane + stop_line = shapely.geometry.LineString([(stop_left.x, stop_left.y), (stop_right.x, stop_right.y)]) + if not tail_line.intersection(stop_line).is_empty: + self._last_red_light_id = lt.id + self._ran_red_light_pending = True + return + + def _poll_carla_stop_sign(self): + """Real CARLA ground truth for 'ran a stop sign' (RunStopSign2: the centre crossed the trigger + volume's line without a standstill inside the volume first).""" + info = self._carla_stop_sign.tick(self.vehicle) + if info is not None and info["event"] == "run": + self._ran_stop_sign_pending = True + + def _carla_infractions(self): + """{'collision': f, 'offroad': f, 'red_light': f, 'stop_sign': f} from real CARLA + ground truth (_init_carla_infraction_detectors) -- contrast with + _ego_infractions, which is the shadow env's model of the ego.""" + collision, self._carla_collision_pending = self._carla_collision_pending, False + red_light, self._ran_red_light_pending = self._ran_red_light_pending, False + stop_sign, self._ran_stop_sign_pending = self._ran_stop_sign_pending, False + self._carla_offroad_test.update() + offroad = self._carla_offroad_test.outside_lane_active or self._carla_offroad_test.wrong_lane_active + return { + "collision": float(collision), + "offroad": float(offroad), + "red_light": float(red_light), + "stop_sign": float(stop_sign), + } + + # --- policy + capture --------------------------------------------------- + + def _adapt_obs_for_policy(self, obs): + """Strip the trailing zero-padded boundary columns when the checkpoint + predates them (see _load_policy_and_env). No-op otherwise.""" + if self._boundary_feat_ckpt is None: + return obs + env = self.env + n_slots = env.obs_slots_boundary_kept + feat_env, feat_ckpt = env.boundary_features, self._boundary_feat_ckpt + b0 = ( + env.ego_features + + env.num_reward_coefs + + env.goal_dim + + env.obs_slots_partners_n * env.partner_features + + env.obs_slots_lane_kept * env.lane_features + ) + b1 = b0 + n_slots * feat_env + obs = np.asarray(obs) + boundary = obs[:, b0:b1].reshape(obs.shape[0], n_slots, feat_env)[:, :, :feat_ckpt] + return np.concatenate([obs[:, :b0], boundary.reshape(obs.shape[0], -1), obs[:, b1:]], axis=1) + + def _policy_actions(self, obs): + """-> (actions (num_agents, act_dim) int32, aux dict). aux carries + per-agent value/entropy/action-probs/pool for the obs_html capture""" + import torch + import pufferlib.pytorch + + import pufferlib.spaces + + obs = self._adapt_obs_for_policy(obs) + # A discrete policy head on a continuous env needs the bin->continuous + # mapping (pufferl.py feeds cont_action to the env, never the bin indices). + env_continuous = isinstance(self.env.single_action_space, pufferlib.spaces.Box) + action_selection = ( + pufferlib.pytorch.ACTION_SELECT_MEAN + if env_continuous and not self.policy.is_continuous + else pufferlib.pytorch.ACTION_SELECT_MODE + ) + with torch.no_grad(): + logits, value = self.policy.forward_eval(torch.as_tensor(obs).to(self.device)) + action, _, entropy, cont_action = pufferlib.pytorch.sample_logits( + logits, action_selection=action_selection, env_continuous=env_continuous, policy=self.policy + ) + env_action = cont_action if env_continuous and cont_action is not None else action + actions = env_action.cpu().numpy().reshape(1, -1) + if not env_continuous: + actions = actions.astype(np.int32) + aux = {} + if self._obs_html is not None: + aux = self._obs_html.policy_outputs(torch.as_tensor(obs).to(self.device), logits, value, entropy) + aux["action_index"] = action.cpu().numpy().reshape(-1) if not self.policy.is_continuous else None + return actions, aux + + def _record_world_state(self, obs): + """Bin-frame snapshot at sync time (pre-step ego, partners as streamed, light states).""" + ego = self.env.get_global_agent_state() + accel_long = float(obs[0][4]) * self._accel_long_norm() + step_idx = len(self._world_log["ego"]) + self._world_log["ego"].append( + ( + self.step, + float(ego["x"][0]), + float(ego["y"][0]), + float(ego["heading"][0]), + self._ego_speed(), + accel_long, + float(self.goal_window.current_index), + ) + ) + if self._last_partners is not None: + x, y, h, vx, vy, length, width, is_walker = self._last_partners + speed = np.hypot(vx, vy) + rows = np.column_stack([np.full(len(x), step_idx, np.float32), x, y, h, length, width, speed, is_walker]) + self._world_log["partners"].append(rows.astype(np.float32)) + self._world_log["lights"].append(self.last_light_states.astype(np.int8)) + + def _write_world_log(self): + Path(self.world_log_dir).mkdir(parents=True, exist_ok=True) + dense = np.array( + [self.transform.loc_to_bin(t.location.x, t.location.y) for t, _ in self.dense_global_plan_world_coord], + np.float32, + ) + partners = self._world_log["partners"] + np.savez_compressed( + Path(self.world_log_dir) / f"{self.video_tag}.npz", + ego=np.array(self._world_log["ego"], np.float32), + partners=np.concatenate(partners) if partners else np.zeros((0, 8), np.float32), + lights=np.stack(self._world_log["lights"]) if self._world_log["lights"] else np.zeros((0, 0), np.int8), + route_goals=self.route_goals, + stop_signs=self._stop_sign_lines, + dense_route=dense, + meta=json.dumps( + { + "town": Path(self.town_bin).stem.split("__")[-1], + "town_bin": self.town_bin, + "dt": self.dt, + "tick_dt": self.tick_dt, + "offset": [self.transform.tx, self.transform.ty], + "z_offset": self.transform.tz, + } + ), + ) + + def _write_telemetry_row(self, ego_action, pd_flags, carla_flags): + """One CSV row per policy step: what the loop commanded vs achieved, + plus the nearest mapped light's state and both infraction sources + (pd_* = shadow pufferdrive env, carla_* = real CARLA ground truth).""" + ego = self.env.get_global_agent_state() + ex, ey = float(ego["x"][0]), float(ego["y"][0]) + cur = min(self.goal_window.current_index, len(self.route_goals) - 1) + goal_dist = float(np.hypot(self.route_goals[cur, 0] - ex, self.route_goals[cur, 1] - ey)) + near_dist, near_state = -1.0, -1 + if len(self.stop_line_centers): + d2 = (self.stop_line_centers[:, 0] - ex) ** 2 + (self.stop_line_centers[:, 1] - ey) ** 2 + j = int(d2.argmin()) + near_dist, near_state = float(np.sqrt(d2[j])), int(self.last_light_states[j]) + current_speed = ( + float(np.asarray(self.env.observations)[0, 0]) * self._max_speed() + if self.dynamics_source == "pufferdrive" + else self.vehicle.get_velocity().length() + ) + self.telemetry_file.write( + f"{self.step},{current_speed:.3f},{self.target[0]:.3f}," + f"{ego_action},{self.goal_window.current_index},{goal_dist:.1f},{near_dist:.1f},{near_state}," + f"{pd_flags['collision']:.0f},{pd_flags['offroad']:.0f},{pd_flags['red_light']:.0f}," + f"{pd_flags['stop_sign']:.0f}," + f"{carla_flags['collision']:.0f},{carla_flags['offroad']:.0f},{carla_flags['red_light']:.0f}," + f"{carla_flags['stop_sign']:.0f}\n" + ) + + def _maybe_save_infraction_clip(self, pd_flags, carla_flags): + """Dump the rolling chase-cam buffer as one mp4 when either infraction + source flags an ego infraction (collision/offroad/red-light/stop-sign), at most + once per INFRACTION_MIN_SEPARATION_M of ego travel (CaRL eval_agent- + style).""" + fired = [f"pd_{name}" for name, value in pd_flags.items() if value > 0.0] + fired += [f"carla_{name}" for name, value in carla_flags.items() if value > 0.0] + if not fired or not self.infraction_buffer: + return + location = self.vehicle.get_location() + if location.distance(self.last_infraction_location) <= INFRACTION_MIN_SEPARATION_M: + return + from pufferlib.ocean.cosim.carla_cosim import write_mp4 + + out = str( + Path(self.record_infractions_dir) / f"{self.video_tag}_{'_'.join(fired)}_{self.infraction_counter:02d}.mp4" + ) + write_mp4(out, list(self.infraction_buffer), fps=round(1.0 / self.tick_dt)) + print(f"[puffer_agent] infraction {fired} -> {out}") + self.infraction_counter += 1 + self.last_infraction_location = location + + def run_step(self, input_data, timestamp, sensors=None): + self.step += 1 + if not self.initialized: + self._init_on_first_step() + return carla.VehicleControl(steer=0.0, throttle=0.0, brake=1.0) + + if CARLA_VIEW_SENSOR_ID in input_data: + _, bgra = input_data[CARLA_VIEW_SENSOR_ID] # (H, W, 4) uint8, leaderboard's CallBack format + rgb = bgra[:, :, [2, 1, 0]] # BGRA -> RGB + if self.carla_view_writer is not None: + self.carla_view_writer.append_data(rgb) # streamed to disk + if self.record_infractions_dir: + self.infraction_buffer.append(rgb.copy()) + + if self._carla_collision is not None: + self._poll_carla_collision() + self._poll_carla_red_light() # filters single-tick get_traffic_light() noise + self._poll_carla_stop_sign() + + obs = self._sync_carla() # shadow env <- CARLA ground truth + actions, aux = self._policy_actions(obs) + if self.obs_dump_dir: + self._obs_dump.append((self.step, obs[0].astype(np.float32), actions[0].astype(np.float32))) + if self.world_log_dir: + self._record_world_state(obs) + if self._obs_html is not None: + self._obs_html.capture(obs, actions, aux, aux.get("action_index")) + self.target = self._carla_integrate(actions) # policy intent, one dt ahead + if self.telemetry_file is not None or self.record_infractions_dir: + # _init_carla_infraction_detectors ran for either dir being set + pd_flags, carla_flags = self._ego_infractions(), self._carla_infractions() + if self.telemetry_file is not None: + self._write_telemetry_row(float(actions[0, 0]), pd_flags, carla_flags) + if self.record_infractions_dir: + self._maybe_save_infraction_clip(pd_flags, carla_flags) + + if self.dynamics_source == "pufferdrive": + return carla.VehicleControl() + + # Controller runs every tick against the latest CARLA state, chasing the + # target held from the last policy step. + current_speed = self.vehicle.get_velocity().length() + current_yaw = self.vehicle.get_transform().rotation.yaw + target_speed, target_yaw = self.target + return self.controller.step(current_speed, current_yaw, target_speed, target_yaw, self.tick_dt) + + def destroy(self, results=None): + if not self.initialized: + return + print( + f"[puffer_agent] route done: goals {self.goal_window.current_index + 1}/{len(self.route_goals)}, " + f"tracking {self.controller.stats()}" + ) + if self.carla_view_writer is not None: + self.carla_view_writer.close() + print(f"[puffer_agent] wrote CARLA chase-cam video for route {self.video_tag}") + if self.telemetry_file is not None: + self.telemetry_file.close() + if self._carla_collision is not None: + self._carla_collision.clean() # stop+destroy the spawned collision sensor actor + if self._goal_lane_debug_file is not None: + self._goal_lane_debug_file.close() + if self._obs_html is not None: + written = self._obs_html.write(render_html=self.obs_html_render) + print(f"[puffer_agent] wrote obs replay ({len(self._obs_html)} frames) -> {written}") + if self.world_log_dir and self._world_log["ego"]: + self._write_world_log() + if self.obs_dump_dir and self._obs_dump: + Path(self.obs_dump_dir).mkdir(parents=True, exist_ok=True) + np.savez_compressed( + Path(self.obs_dump_dir) / f"{self.video_tag}.npz", + step=np.array([s for s, _, _ in self._obs_dump], np.int32), + obs=np.stack([o for _, o, _ in self._obs_dump]), + action=np.stack([a for _, _, a in self._obs_dump]), + ) + self.env.close() + self.initialized = False diff --git a/pufferlib/ocean/cosim/carla/routes/longest6_no_scenarios.xml b/pufferlib/ocean/cosim/carla/routes/longest6_no_scenarios.xml new file mode 100644 index 0000000000..d2c1755e17 --- /dev/null +++ b/pufferlib/ocean/cosim/carla/routes/longest6_no_scenarios.xml @@ -0,0 +1,1721 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/pufferlib/ocean/cosim/carla/run_evaluator.py b/pufferlib/ocean/cosim/carla/run_evaluator.py new file mode 100644 index 0000000000..b9ef6db4a4 --- /dev/null +++ b/pufferlib/ocean/cosim/carla/run_evaluator.py @@ -0,0 +1,68 @@ +"""Run CaRL's leaderboard_evaluator.py for one model directory, with all outputs under its eval folder. + +usage: run_evaluator.py --model-dir [evaluator args...] + + holds final_model.pt + config.yaml (blank -> DEFAULT_MODEL_DIR). The launcher adds +--agent-config /final_model.pt and --checkpoint /eval/carla_leaderboard/result.json, +creates that directory (the evaluator opens the result file before any agent code runs), and +points COSIM_TELEMETRY / COSIM_WORLD_LOG / COSIM_OBS_HTML / COSIM_DEBUG_CARLA_VIEW into it unless +they are already set. Remaining arguments go to the evaluator, located through CARL_WORK_DIR. +""" + +import os +import runpy +import sys +from pathlib import Path + + +DEFAULT_MODEL_DIR = "experiments/k_scaled_0036_1000" +EVAL_SUBDIR = "eval/carla_leaderboard" +OUTPUT_ENV_DIRS = { + "COSIM_TELEMETRY": "telemetry", + "COSIM_WORLD_LOG": "world_log", + "COSIM_OBS_HTML": "obs_html", + "COSIM_DEBUG_CARLA_VIEW": "carla_view", +} + + +def main(): + argv = sys.argv[1:] + model_dir = "" + if "--model-dir" in argv: + k = argv.index("--model-dir") + has_value = k + 1 < len(argv) and not argv[k + 1].startswith("--") + model_dir = argv[k + 1] if has_value else "" + del argv[k : k + 2 if has_value else k + 1] + if not model_dir: + model_dir = DEFAULT_MODEL_DIR + print(f"[run_evaluator] --model-dir blank, using {DEFAULT_MODEL_DIR}") + model_dir = Path(model_dir) + if not model_dir.is_absolute(): + model_dir = Path.cwd() / model_dir + checkpoint = model_dir / "final_model.pt" + if not checkpoint.is_file(): + raise SystemExit(f"run_evaluator.py: no checkpoint at {checkpoint}") + eval_dir = model_dir / EVAL_SUBDIR + eval_dir.mkdir(parents=True, exist_ok=True) + if "--agent-config" not in argv: + argv += ["--agent-config", str(checkpoint)] + if "--checkpoint" not in argv: + argv += ["--checkpoint", str(eval_dir / "result.json")] + for env_name, subdir in OUTPUT_ENV_DIRS.items(): + if env_name not in os.environ: + os.environ[env_name] = str(eval_dir / subdir) + carl_work_dir = os.environ.get("CARL_WORK_DIR") + if not carl_work_dir: + raise SystemExit("run_evaluator.py: CARL_WORK_DIR is not set") + evaluator = ( + Path(carl_work_dir) / "original_leaderboard" / "leaderboard" / "leaderboard" / "leaderboard_evaluator.py" + ) + if not evaluator.is_file(): + raise SystemExit(f"run_evaluator.py: evaluator not found at {evaluator}") + print(f"[run_evaluator] model {model_dir} -> outputs {eval_dir}") + sys.argv = [str(evaluator)] + argv + runpy.run_path(str(evaluator), run_name="__main__") + + +if __name__ == "__main__": + main() diff --git a/pufferlib/ocean/cosim/carla/run_leaderboard.sh b/pufferlib/ocean/cosim/carla/run_leaderboard.sh new file mode 100755 index 0000000000..f6a56d30c3 --- /dev/null +++ b/pufferlib/ocean/cosim/carla/run_leaderboard.sh @@ -0,0 +1,100 @@ +#!/bin/bash +# Run pufferlib/ocean/cosim/carla/leaderboard_agent.py under CaRL's unmodified +# original_leaderboard evaluator. See README.md "How to run". +# +# Usage (from a GPU allocation's job id — squeue -u $USER to find it): +# CKPT=/path/to/model_puffer_drive_xxxxxx.pt \ +# srun --jobid= --overlap bash run_leaderboard.sh +# +# Required env: +# CKPT PufferDrive checkpoint .pt (experiment dir or models/*.pt file) +# Optional env: +# ROUTES routes xml. Default: one longest6 split route (Town01). +# ROUTES_SUBSET route id within ROUTES. Default: 0. Set to empty +# (ROUTES_SUBSET=) to run every route in ROUTES sequentially +# (e.g. point ROUTES at the combined longest6.xml for all 36). +# OUT result json path. Default: runs/cosim_leaderboard_/result.json +# CARLA_PORT default 2000 (use a distinct port per concurrent instance). +# COSIM_DEBUG_CARLA_VIEW dir for one CARLA chase-cam mp4 per route (an +# actual camera sensor on the ego, native 20 Hz tick rate, +# streamed to disk frame-by-frame so it's safe for long +# routes). Default: "$(dirname "$OUT")/carla_view". +# Set COSIM_DEBUG_CARLA_VIEW= (empty) to disable. +# COSIM_DEVICE / COSIM_DT / COSIM_NUM_AGENTS +# forwarded to leaderboard_agent.py as-is if already exported. +if [ -z "$INSIDE_PUFFER_CONTAINER" ]; then + source /share/apps/images/slurm-ib-bind-apptainer.sh + export APPTAINERENV_PREPEND_PATH=/share/apps/apptainer/bin:$APPTAINERENV_PREPEND_PATH + exec singularity exec --nv \ + --bind /opt/slurm:/opt/slurm \ + --bind /scratch/yw4142:/scratch/yw4142 \ + --bind /run/munge:/run/munge \ + --bind /etc/passwd:/etc/passwd \ + --bind /etc/group:/etc/group \ + --bind /usr/share/vulkan/icd.d:/usr/share/vulkan/icd.d \ + --bind /etc/vulkan:/etc/vulkan \ + --bind /usr/lib64/libGLX_nvidia.so.0:/usr/lib64/libGLX_nvidia.so.0 \ + --bind /usr/sbin/fuser:/usr/sbin/fuser \ + /share/apps/images/cuda13.0.1-cudnn9.13.0-ubuntu-24.04.3.sif \ + env INSIDE_PUFFER_CONTAINER=1 bash "$0" +fi +set -u + +: "${CKPT:?set CKPT=/path/to/model_xxx.pt}" +PD=${PD:-/scratch/yw4142/PufferDrive_cosim} +CARL_PY=/scratch/yw4142/conda_envs/carl/bin/python +CARLA_ROOT=/scratch/yw4142/CaRL/CARLA/carla +CARL_WORK_DIR=/scratch/yw4142/CaRL/CARLA +CARLA_PORT=${CARLA_PORT:-2000} +ROUTES=${ROUTES:-$CARL_WORK_DIR/custom_leaderboard/leaderboard/data/longest6.xml} +ROUTES_SUBSET=${ROUTES_SUBSET-0-35} # ROUTES_SUBSET= (empty) runs every route in ROUTES +TIMESTAMP=$(date +"%Y%m%d_%H%M%S") +OUT=${OUT:-/scratch/yw4142/runs/cosim_leaderboard_${TIMESTAMP}/result.json} +mkdir -p "$(dirname "$OUT")" +# CARLA chase-cam mp4 per route, next to OUT by default. +export COSIM_DEBUG_CARLA_VIEW=${COSIM_DEBUG_CARLA_VIEW-"$(dirname "$OUT")/carla_view"} + +export CARLA_ROOT CARL_WORK_DIR +export PYTHONPATH="$PD:$CARL_WORK_DIR/original_leaderboard/leaderboard:$CARL_WORK_DIR/original_leaderboard/scenario_runner:$CARLA_ROOT/PythonAPI/carla:${PYTHONPATH:-}" +# route_scenario.py globs $SCENARIO_RUNNER_ROOT/srunner/scenarios/*.py to +# discover scenario classes; leaderboard_agent.py refuses to start without it. +export SCENARIO_RUNNER_ROOT="$CARL_WORK_DIR/original_leaderboard/scenario_runner" + +"$CARLA_ROOT/CarlaUE4.sh" -RenderOffScreen -nosound -carla-rpc-port="$CARLA_PORT" & +SERVER_PID=$! +up=1 +for i in $(seq 1 60); do + if ! kill -0 $SERVER_PID 2>/dev/null; then + echo "CARLA server process died"; break + fi + "$CARL_PY" -c " +import carla, sys +try: + c = carla.Client('localhost', $CARLA_PORT); c.set_timeout(10.0) + c.get_world().get_map() +except Exception: + sys.exit(1)" && up=0 && break + sleep 5 +done +if [ $up -ne 0 ]; then + echo "FAIL: CARLA server not ready on port $CARLA_PORT" + kill $SERVER_PID 2>/dev/null + exit 1 +fi +echo "[run_leaderboard] CARLA server ready (pid $SERVER_PID, port $CARLA_PORT)" + +# Distinct traffic-manager port per instance (default 8000 collides when two +# CARLA servers share a node, killing one evaluator with an rpc bind error). +TM_PORT=${TM_PORT:-$((CARLA_PORT + 6000))} +ARGS=(--routes "$ROUTES" + --agent "$PD/pufferlib/ocean/cosim/carla/leaderboard_agent.py" + --agent-config "$CKPT" --checkpoint "$OUT" --track MAP --port "$CARLA_PORT" + --traffic-manager-port "$TM_PORT") +[ -n "$ROUTES_SUBSET" ] && ARGS+=(--routes-subset "$ROUTES_SUBSET") + +echo "[run_leaderboard] routes=$ROUTES subset=${ROUTES_SUBSET:-} ckpt=$CKPT out=$OUT" +"$CARL_PY" -u "$CARL_WORK_DIR/original_leaderboard/leaderboard/leaderboard/leaderboard_evaluator.py" "${ARGS[@]}" +RC=$? +kill $SERVER_PID 2>/dev/null +echo "[run_leaderboard] evaluator exit $RC; result -> $OUT" +exit $RC diff --git a/pufferlib/ocean/cosim/carla_bridge.py b/pufferlib/ocean/cosim/carla_bridge.py new file mode 100644 index 0000000000..8931f3dae2 --- /dev/null +++ b/pufferlib/ocean/cosim/carla_bridge.py @@ -0,0 +1,474 @@ +"""CARLA <-> PufferDrive coordinate transform + state readback for co-simulation. + +PufferDrive's `carla*/opendrive__Town*.bin` maps are OpenDRIVE exports of the +CARLA towns, stored in a y-flipped, per-town-translated frame. Empirically +(road-network alignment + heading match to 1e-3 rad): + + bin_x = carla_x + tx + bin_y = -carla_y + ty + bin_heading = -radians(carla_yaw) # CARLA yaw is degrees, left-handed + bin_vx = carla_vx + bin_vy = -carla_vy + +`(tx, ty)` is the town's georeference offset -- a hardcoded constant per town +(TOWN_OFFSETS below), not computed at runtime. History: this used to be +recovered on the fly by ICP-aligning the loaded CARLA map's road points to the +bin's lane points, on the theory that computing it fresh beats trusting a +possibly-stale constant. In practice ICP's local gradient descent (start = +centroid delta, refine by nearest-heading-bucket matching) silently settled a +few meters from the true optimum for Town04/Town06 -- close enough that +aggregate alignment stats still looked fine, but consistently off by about a +lane width, so CARLA-synced background agents rendered off-lane in PufferDrive. +The fix that actually worked: a brute-force (tx, ty) grid search minimizing +median nearest-neighbor distance from transformed CARLA waypoints to bin lane +points (coarse then refined). +For Town6, TWO independently geometry-refined Town06 candidates -- (-150.1, +113.35) from the py123d arrow match, and (-150.93, 109.40) -- both scored +dramatically WORSE on a full longest6 Town06 leaderboard sweep than the +original (-150.93, 111.40). + +The PufferDrive env additionally subtracts its own `world_mean` inside +`set_agent_states`/`set_agent_goals`, so the values produced here are in the +*bin* frame (== sim frame + world_mean), matching `get_global_agent_state`. +""" + +import math +from pathlib import Path + +import numpy as np + +import data_utils.mirror_map_bin as _mbin + +# Verified per-town offsets -- see the module docstring for how these were +# derived and why they're hardcoded instead of computed on the fly. +TOWN_OFFSETS = { + "Town01": (-204.34, 148.75), + "Town02": (-93.70, 213.06), + "Town03": (-43.56, -4.60), + "Town04": (-13.73, -10.49), + "Town05": (49.04, 0.95), + "Town06": (-150.93, 111.40), + "Town10HD": (8.13, 32.98), +} + +_BIN_DIR = Path(__file__).resolve().parents[2] / "resources" / "drive" / "binaries" / "carla" + + +def bin_path_for_town(town: str) -> str: + """Path to the OpenDRIVE bin for a CARLA town name (e.g. 'Town01').""" + return str(_BIN_DIR / f"opendrive__{town}.bin") + + +def wrap_deg_180(d): + """Wrap a degree value (e.g. a yaw difference) to (-180, 180].""" + return (d + 180.0) % 360.0 - 180.0 + + +def _bin_lane_points(bin_path: str) -> np.ndarray: + """All lane-polyline (x, y) points from a bin (type 0..9 == lanes).""" + data = _mbin.read_bin(Path(bin_path)) + pts = [] + for road in data["roads"]: + if 0 <= road["type"] <= 9: + pts.extend(zip(road["x"], road["y"])) + return np.asarray(pts, dtype=np.float64) + + +def town_offset(bin_path: str): + """CARLA<->bin (tx, ty): the bin's stored centroid when present (bin = + original - centroid, and CARLA = original y-flipped, so the offset is just + -centroid_xy).""" + centroid = _mbin.read_bin(Path(bin_path)).get("centroid") + if centroid is not None: + return (-float(centroid[0]), -float(centroid[1])) + town = Path(bin_path).stem.split("__")[-1] + return TOWN_OFFSETS[town] + + +OFFSET_CALIBRATION_MAX_RESIDUAL_M = ( + 1.0 # median lane-normal residual left after calibration: larger = wrong bin for this map +) +OFFSET_CALIBRATION_SPACING_M = 10.0 +OFFSET_CALIBRATION_MATCH_MAX_M = 2.0 # waypoint-to-bin-lane residuals beyond this are mismatches, not offset +OFFSET_CALIBRATION_MAX_Z_OFFSET_M = 5.0 # exported bins sit 0-1.7 m below CARLA's ground; more means a frame mix-up + + +def calibrate_town_offset(carla_map, transform, town_bin): + """(tx, ty) refined so the bin's lane polylines coincide with CARLA's lane centres. + + The exported bins sit a constant fraction of a metre off the CARLA frame (measured 2026-09-23 with + the stored offsets: Town01 0.27 m, Town02 0.6 m), and the ego plus every streamed actor inherited it, + driving 0.44 m right of CARLA's lane centre on Town02 and clipping curbs the shadow env could not see. + Least squares of the translation over the lane-normal residuals of CARLA's driving waypoints against + the nearest bin lane segment, two passes (re-matched after the first shift), mismatches beyond + OFFSET_CALIBRATION_MATCH_MAX_M dropped. The bins also sit a constant per-town height below CARLA's + ground (Town03 0.77 m, Town04 1.24 m, Town05 1.64 m, Town06 0), so the same matches give the z offset as + the median of bin lane z minus waypoint z. Returns (offset, z_offset_m, residual_before_m, residual_after_m).""" + data = _mbin.read_bin(Path(town_bin)) + seg_start, seg_end, seg_z_start, seg_z_end = [], [], [], [] + for road in data["roads"]: + if not (0 <= road["type"] <= 9) or len(road["x"]) < 2: + continue + pts = np.column_stack([road["x"], road["y"]]) + heights = np.asarray(road["z"], dtype=np.float64) + seg_start.append(pts[:-1]) + seg_end.append(pts[1:]) + seg_z_start.append(heights[:-1]) + seg_z_end.append(heights[1:]) + seg_start = np.vstack(seg_start) + seg_end = np.vstack(seg_end) + seg_z_start = np.concatenate(seg_z_start) + seg_z_end = np.concatenate(seg_z_end) + seg_dir = seg_end - seg_start + seg_len_sq = np.maximum((seg_dir**2).sum(1), 1e-9) + points, normals, waypoint_z = [], [], [] + for wp in carla_map.generate_waypoints(OFFSET_CALIBRATION_SPACING_M): + if wp.is_junction or str(wp.lane_type) != "Driving": + continue + loc, right = wp.transform.location, wp.transform.get_right_vector() + points.append(transform.loc_to_bin(loc.x, loc.y)) + normals.append((right.x, -right.y)) # y flips into the bin frame + waypoint_z.append(loc.z) + points = np.array(points, dtype=np.float64).reshape(-1, 2) + normals = np.array(normals, dtype=np.float64).reshape(-1, 2) + normals /= np.maximum(np.hypot(normals[:, 0], normals[:, 1]), 1e-9)[:, None] + waypoint_z = np.array(waypoint_z, dtype=np.float64) + + def normal_residuals(shift): + residuals = np.empty(len(points)) + foot_z = np.empty(len(points)) + for start in range(0, len(points), 256): + chunk = points[start : start + 256] - shift + t = ( + (chunk[:, None, 0] - seg_start[None, :, 0]) * seg_dir[None, :, 0] + + (chunk[:, None, 1] - seg_start[None, :, 1]) * seg_dir[None, :, 1] + ) / seg_len_sq[None, :] + t = np.clip(t, 0.0, 1.0) + foot_x = seg_start[None, :, 0] + t * seg_dir[None, :, 0] + foot_y = seg_start[None, :, 1] + t * seg_dir[None, :, 1] + dist = np.hypot(chunk[:, None, 0] - foot_x, chunk[:, None, 1] - foot_y) + nearest = np.argmin(dist, axis=1) + rows = np.arange(len(chunk)) + delta = np.column_stack([foot_x[rows, nearest], foot_y[rows, nearest]]) - chunk + residuals[start : start + 256] = (delta * normals[start : start + 256]).sum(1) + t_nearest = t[rows, nearest] + foot_z[start : start + 256] = seg_z_start[nearest] + t_nearest * (seg_z_end[nearest] - seg_z_start[nearest]) + return residuals, foot_z + + shift = np.zeros(2) # bin lanes = CARLA lanes + shift -> loc_to_bin must add shift + residual_before = float(np.median(np.abs(normal_residuals(shift)[0]))) + for _ in range(2): + residuals, _ = normal_residuals(-shift) + keep = np.abs(residuals) <= OFFSET_CALIBRATION_MATCH_MAX_M + n = normals[keep] + # residual_i = n_i . v -> (N^T N) v = N^T r + shift += np.linalg.lstsq(n, residuals[keep], rcond=None)[0] + residuals, foot_z = normal_residuals(-shift) + residual_after = float(np.median(np.abs(residuals))) + if residual_after > OFFSET_CALIBRATION_MAX_RESIDUAL_M: + raise RuntimeError( + f"{Path(town_bin).name}: bin lanes do not match this CARLA map (median lane residual " + f"{residual_after:.2f} m after offset calibration)" + ) + keep = np.abs(residuals) <= OFFSET_CALIBRATION_MATCH_MAX_M + z_offset = float(np.median(foot_z[keep] - waypoint_z[keep])) # bin z = CARLA z + z_offset + if not np.isfinite(z_offset) or abs(z_offset) > OFFSET_CALIBRATION_MAX_Z_OFFSET_M: + raise RuntimeError( + f"{Path(town_bin).name}: bin lane height is {z_offset:.2f} m off this CARLA map (limit " + f"{OFFSET_CALIBRATION_MAX_Z_OFFSET_M} m)" + ) + return (transform.tx + float(shift[0]), transform.ty + float(shift[1])), z_offset, residual_before, residual_after + + +class CarlaTransform: + """Bidirectional CARLA <-> PufferDrive-bin-frame transform for one town. + + z_offset: bin z = CARLA z + z_offset (calibrate_town_offset; the bins sit 0.8-1.6 m below CARLA's + ground on Town03/04/05, so every injected height must go through z_to_bin).""" + + def __init__(self, town: str, offset=None, z_offset=0.0): + self.town = town + self.tx, self.ty = offset if offset is not None else TOWN_OFFSETS[town] + self.tz = float(z_offset) + + # --- CARLA -> bin frame --- + def loc_to_bin(self, cx, cy): + return cx + self.tx, -cy + self.ty + + def z_to_bin(self, cz): + return cz + self.tz + + def yaw_to_bin(self, yaw_deg): + return -math.radians(yaw_deg) + + def vel_to_bin(self, vx, vy): + return vx, -vy + + def actor_state_to_bin(self, actor): + """Return (x, y, z, heading, vx, vy, yaw_rate, accel_long) in the bin frame for a CARLA actor. + yaw_rate/accel_long come from CARLA's own physics (get_angular_velocity/get_acceleration), not + finite-differenced from a previous PufferDrive state: env.step() may have already overwritten + this actor's previous state with a throwaway dummy-action rollout by the time PufferDrive would + otherwise read "previous" state itself.""" + tf = actor.get_transform() + v = actor.get_velocity() + av = actor.get_angular_velocity() # deg/s, world frame (CARLA's rotation convention) + acc = actor.get_acceleration() # m/s^2, world frame + bx, by = self.loc_to_bin(tf.location.x, tf.location.y) + heading = self.yaw_to_bin(tf.rotation.yaw) + yaw_rate = -math.radians(av.z) # mirrored y flips rotation sense, like yaw_to_bin's negation + accel_x, accel_y = acc.x, -acc.y # mirror y, like vel_to_bin + accel_long = accel_x * math.cos(heading) + accel_y * math.sin(heading) + box = actor.bounding_box # pivot differs per asset (vehicles: ground, walkers: capsule centre); bin z = ground + ground_z = tf.location.z + box.location.z - box.extent.z + return (bx, by, self.z_to_bin(ground_z), heading, v.x, -v.y, yaw_rate, accel_long) + + # --- bin frame -> CARLA (to teleport the ego back into CARLA) --- + def bin_to_loc(self, bx, by): + return bx - self.tx, -(by - self.ty) + + def z_to_carla(self, bz): + return bz - self.tz + + def bin_heading_to_yaw(self, heading_rad): + return -math.degrees(heading_rad) + + +# CARLA traffic-light state -> PufferDrive enum (datatypes.h:61-67) +# UNKNOWN=0 RED=1 YELLOW=2 GREEN=3 OFF=4 +TRAFFIC_LIGHT_STATE_OFF = 4 + + +def carla_light_to_puffer(state) -> int: + import carla + + return { + carla.TrafficLightState.Red: 1, + carla.TrafficLightState.Yellow: 2, + carla.TrafficLightState.Green: 3, + carla.TrafficLightState.Off: 4, + carla.TrafficLightState.Unknown: 0, + }.get(state, 0) + + +LIGHT_LANE_MATCH_MAX_DIST_M = 12.0 # measured Town01 controlled-lane stub distance (see below) +LIGHT_LANE_FORWARD_MATCH_MAX_DIST_M = ( + 3.0 # forward fallback lands ON the connector; a lane width away is the neighbour's +) +LIGHT_LANE_ALIGN_COS_MIN = ( + 0.5 # forward fallback: the lane must run along the waypoint's heading (no crossing connectors) +) +JUNCTION_CLUSTER_M = 60.0 # stop lines this close are treated as one junction (outlier repair, see below) +# An assigned light farther from its junction cluster-mates' lights than this +# multiple of the cluster-mates' own spread is a wrong-junction match. +OUTLIER_SPREAD_MULTIPLE = 2.0 +OUTLIER_MIN_SPREAD_M = 5.0 # floor for tiny/degenerate clusters (e.g. two lights a few meters apart) + + +def _cluster_by_proximity(points, keys, radius_m): + """Single-linkage clustering of `keys` by `points[key]` proximity <= radius_m. + Returns {cluster_root_key: [member_keys]}. points: {key: (x, y)}.""" + parent = {k: k for k in keys} + + def find(k): + while parent[k] != k: + parent[k] = parent[parent[k]] + k = parent[k] + return k + + for i, k1 in enumerate(keys): + for k2 in keys[i + 1 :]: + if np.hypot(*(np.subtract(points[k1], points[k2]))) <= radius_m: + root1, root2 = find(k1), find(k2) + if root1 != root2: + parent[root1] = root2 + clusters = {} + for k in keys: + clusters.setdefault(find(k), []).append(k) + return clusters + + +def _drop_junction_outliers(mapping, lights, transform, stop_centers): + """(light_idx, element_idx) pairs to remove from `mapping`: elements whose + assigned light sits far outside where its junction cluster-mates' lights + actually are -- see map_lights_to_bin's docstring.""" + element_light = {} + for li, elements in enumerate(mapping): + for j in elements: + element_light.setdefault(j, []).append(li) + element_light = {j: v[0] for j, v in element_light.items() if len(v) == 1} # single-owner only + if len(element_light) < 3: + return set() + + stop_center_of = {j: tuple(stop_centers[j]) for j in element_light} + clusters = _cluster_by_proximity(stop_center_of, sorted(element_light), JUNCTION_CLUSTER_M) + + to_drop = set() + for elements in clusters.values(): + if len(elements) < 3: + continue + light_pos = {} + for j in elements: + loc = lights[element_light[j]].get_location() + light_pos[j] = np.array(transform.loc_to_bin(loc.x, loc.y)) + for j in elements: + others = np.array([light_pos[j2] for j2 in elements if j2 != j]) + centroid = others.mean(axis=0) + dist_to_centroid = float(np.hypot(*(light_pos[j] - centroid))) + spread = 0.0 + for a in range(len(others)): + for b in range(a + 1, len(others)): + spread = max(spread, float(np.hypot(*(others[a] - others[b])))) + if dist_to_centroid > OUTLIER_SPREAD_MULTIPLE * max(spread, OUTLIER_MIN_SPREAD_M): + to_drop.add((element_light[j], j)) + return to_drop + + +def map_lights_to_bin(lights, transform, town_bin): + """mapping[i] = list of bin traffic-element indices controlled by lights[i]. + + Semantic matching: each of the light's STOP WAYPOINTS is snapped to its bin + LANE (nearest drivable-lane segment -- stop lines lie ON lane segments, so + this is a near-zero-distance match), then lane -> element via the bin's + controlled_lanes. Geometry-only alternatives mis-bind systematically: the + light's own transform is the pole across the junction, and stop-line-to- + stop-line matching is off by ~9 m in these bins while adjacent approaches + are only 15-25 m apart. + + CARLA's stop waypoints sit at the junction ENTRY, up to ~10 m past the + bin's controlled-lane stub, so each waypoint is walked backward along its + own lane until a controlled lane resolves (0/36 raw matches without this). + Where the bin element only lists the junction connectors (Town01: 3 of 36, + Town02: 4 of 24 approaches, connectors starting 12-15 m past the waypoint), + the backward walk finds nothing; a fallback then probes forward into the + junction for a lane within LIGHT_LANE_FORWARD_MATCH_MAX_DIST_M that runs + along the waypoint's heading (tight radius + alignment: on multi-lane + approaches the neighbour lane's connector is only a lane width away). + + A second pass then repairs occasional wrong-junction matches: the walk-back + above resolves per light independently, and for a small number of lights + per town it lands on a bin element geometrically nowhere near that light + (measured: Town04 light 0 -> an element 44 m away, Town06 light 5 -> 34 m + away, while every OTHER approach in each of those same junctions matched + within its own junction's ~15-20 m span). Elements never collide (two + lights never claim the same one), so nothing else catches this -- the + element just silently carries a confidently wrong state forever. Fix: + cluster single-owner elements by stop-line proximity (one cluster ~= one + junction), and within each cluster of >=3, drop any element whose + assigned light sits far outside where the cluster's OTHER lights actually + are. A dropped element goes unmapped (state stays UNKNOWN) rather than + guessing a replacement -- no other candidate light contests these, so + there's nothing to reassign to with confidence. + + Returns (mapping, num_traffic_elements); the state array passed to + set_traffic_light_states is sized to the bin's element count.""" + import data_utils.mirror_map_bin as mbin + + data = mbin.read_bin(Path(town_bin)) + seg_start, seg_end, seg_lane = [], [], [] + for road in data["roads"]: + if not (0 <= road["type"] <= 9): + continue + xs, ys = np.asarray(road["x"]), np.asarray(road["y"]) + for k in range(len(xs) - 1): + seg_start.append((xs[k], ys[k])) + seg_end.append((xs[k + 1], ys[k + 1])) + seg_lane.append(road["id"]) + seg_start = np.array(seg_start).reshape(-1, 2) + seg_end = np.array(seg_end).reshape(-1, 2) + seg_lane = np.array(seg_lane, dtype=int) + element_of_lane = {} + for element_idx, t in enumerate(data["traffic"]): + for lane_id in t.get("controlled_lanes", ()): + element_of_lane.setdefault(int(lane_id), []).append(element_idx) + + seg_dir = seg_end - seg_start + seg_dir = seg_dir / np.maximum(np.hypot(seg_dir[:, 0], seg_dir[:, 1]), 1e-9)[:, None] + + def controlling_elements_near(px, py, max_dist_m, heading=None): + d = seg_end - seg_start + length_sq = np.maximum((d**2).sum(1), 1e-9) + t = np.clip(((px - seg_start[:, 0]) * d[:, 0] + (py - seg_start[:, 1]) * d[:, 1]) / length_sq, 0.0, 1.0) + dist = np.hypot(px - (seg_start[:, 0] + t * d[:, 0]), py - (seg_start[:, 1] + t * d[:, 1])) + candidate = dist <= max_dist_m + if heading is not None: + candidate &= ( + seg_dir[:, 0] * math.cos(heading) + seg_dir[:, 1] * math.sin(heading) >= LIGHT_LANE_ALIGN_COS_MIN + ) + lane_best = {} + for j in np.nonzero(candidate)[0]: + lane_id = int(seg_lane[j]) + if dist[j] < lane_best.get(lane_id, np.inf): + lane_best[lane_id] = float(dist[j]) + for lane_id in sorted(lane_best, key=lane_best.get): + controlling = element_of_lane.get(lane_id, []) + if controlling: + return controlling + return [] + + probe_steps_m = (0.0, 3.0, 6.0, 10.0, 15.0) + mapping = [] + for lt in lights: + element_indices = [] + for wp in lt.get_stop_waypoints(): + if not len(seg_lane): + continue + controlling = [] + for step_m in probe_steps_m: + probes = [wp] if step_m == 0.0 else wp.previous(step_m) + for probe in probes[:1]: + bx, by = transform.loc_to_bin(probe.transform.location.x, probe.transform.location.y) + controlling = controlling_elements_near(bx, by, LIGHT_LANE_MATCH_MAX_DIST_M) + if controlling: + break + for step_m in probe_steps_m[1:] if not controlling else (): # fallback: forward into the junction + for probe in (wp.next(step_m) or [])[:1]: + bx, by = transform.loc_to_bin(probe.transform.location.x, probe.transform.location.y) + heading = transform.yaw_to_bin(probe.transform.rotation.yaw) + controlling = controlling_elements_near(bx, by, LIGHT_LANE_FORWARD_MATCH_MAX_DIST_M, heading) + if controlling: + break + element_indices.extend(controlling) + mapping.append(sorted(set(element_indices))) + + stop_centers = np.array( + [ + [0.5 * (t["stop_line"][0] + t["stop_line"][3]), 0.5 * (t["stop_line"][1] + t["stop_line"][4])] + for t in data["traffic"] + ] + ).reshape(-1, 2) + for light_idx, element_idx in _drop_junction_outliers(mapping, lights, transform, stop_centers): + mapping[light_idx].remove(element_idx) + + return mapping, len(data["traffic"]) + + +def stop_signs_from_carla(world, carla_map, transform): + """(lines (K, 6), headings (K,)) in the bin frame, one per CARLA `traffic.stop` actor: the trigger + volume's centre projected onto its driving lane, spanning the trigger's width across that lane, with + the lane's travel direction as heading and the lane waypoint's height (a few trigger volumes sit up + to 1.5 m above or below their road). Feed to Drive.set_stop_signs so the shadow env runs on the + volumes the leaderboard scores against instead of the bin's exported stop lines (which sit up to + 9 m away on a few Town03/05 approaches and miss four signs).""" + import carla + + lines, headings = [], [] + for actor in world.get_actors().filter("traffic.stop"): + actor_transform = actor.get_transform() + trigger = actor.trigger_volume + center = actor_transform.transform(trigger.location) + waypoint = carla_map.get_waypoint(center, project_to_road=True, lane_type=carla.LaneType.Driving) + if waypoint is None: + continue + forward = waypoint.transform.get_forward_vector() + lane_yaw = math.atan2(forward.y, forward.x) + box_yaw = math.radians(actor_transform.rotation.yaw) + box_x_along_lane = abs(math.cos(box_yaw - lane_yaw)) >= math.cos(math.pi / 4.0) + half_width = trigger.extent.y if box_x_along_lane else trigger.extent.x + across_x, across_y = -math.sin(lane_yaw), math.cos(lane_yaw) + left = transform.loc_to_bin(center.x - half_width * across_x, center.y - half_width * across_y) + right = transform.loc_to_bin(center.x + half_width * across_x, center.y + half_width * across_y) + line_z = transform.z_to_bin(waypoint.transform.location.z) + lines.append([left[0], left[1], line_z, right[0], right[1], line_z]) + headings.append(transform.yaw_to_bin(math.degrees(lane_yaw))) + return np.array(lines, np.float32).reshape(-1, 6), np.array(headings, np.float32) diff --git a/pufferlib/ocean/cosim/carla_cosim.py b/pufferlib/ocean/cosim/carla_cosim.py new file mode 100644 index 0000000000..1909f0b9a2 --- /dev/null +++ b/pufferlib/ocean/cosim/carla_cosim.py @@ -0,0 +1,570 @@ +"""CARLA <-> PufferDrive co-simulation driver. + +PufferDrive simulates only the EGO (its own kinematics) and teleports it into +CARLA; CARLA's Traffic Manager drives all background vehicles and the traffic +lights. Each tick we read CARLA's background poses + light states and overwrite +the matching PufferDrive agents/lights, so the ego's observation reflects the +CARLA world. See pufferlib/ocean/cosim/carla_bridge.py for the transform. + +Loop: + obs = ego observation (PufferDrive, synced to CARLA) + act = policy(obs) # or a constant dummy action + env.step(act) # PufferDrive integrates the ego + teleport CARLA ego to the ego's new pose + world.tick() x N # CARLA advances background + lights + overwrite PufferDrive background + lights from CARLA + obs = env.recompute_observations() + +Usage (dummy action, no checkpoint): + python -m pufferlib.ocean.cosim.carla_cosim --route-id 0 --steps 60 +With a policy: + python -m pufferlib.ocean.cosim.carla_cosim --route-id 0 \ + --checkpoint experiments/puffer_drive_zg6rezam/models/model_puffer_drive_004769.pt +""" + +import argparse +import math +import xml.etree.ElementTree as ET +from pathlib import Path + +import numpy as np + +# `carla` is imported lazily inside the functions that need it (build_carla, +# _route_goal_xy, attach_chase_camera, main), NOT at module scope: Mp4Writer +# and the other pure-python helpers here are reused by +# pufferlib/ocean/cosim/nuplan/planner.py, which runs in the pufferdrive venv +# (cp312) where the carla package (CARLA 0.9.15, cp310/cp37 wheels only) isn't +# installed at all — same principle as carla_bridge.py's lazy carla import. + +from pufferlib.ocean.drive.drive import Drive +from pufferlib.ocean.cosim import carla_bridge as cb +from pufferlib.ocean.cosim.arch import checkpoint_config_path, shadow_env_kwargs +from pufferlib.ocean.cosim.goals import RouteGoalWindow +# carla_scenarios imports carla at module scope (it's pure CARLA-actor +# manipulation, unlike this module's carla_bridge deps) and is only used +# inside main() below, so it's imported there too. + +DEFAULT_ROUTES = "/scratch/yw4142/CaRL/PlanT/data/longest6.xml" +FAR_AWAY = 1.0e6 # park surplus PufferDrive agents out of observation range +SCENARIO_LENGTH_MARGIN_STEPS = 2 # shadow env must never hit its own truncation while the co-sim runs +DEFAULT_VEHICLE_LENGTH_M = 4.5 # fallback size for a parked/dead actor slot (out of obs range anyway) +DEFAULT_VEHICLE_WIDTH_M = 2.0 +EGO_BLUEPRINT = "vehicle.lincoln.mkz_2017" # the longest6/CARLA-leaderboard hero vehicle + + +def clean_policy_state_dict(state_dict): + """Strip torch.compile / DDP prefixes. Inlined from pufferlib.pufferl to + avoid importing the training stack (wandb, neptune, the _C kernel, ...) in + the co-sim environment — same as cosim/carla/leaderboard_agent.py.""" + + def clean(key): + while key.startswith(("module.", "_orig_mod.")): + key = key.split(".", 1)[1] + return key + + return {clean(k): v for k, v in state_dict.items()} + + +def load_checkpoint_config(checkpoint): + """The checkpoint's sibling config.yaml — the single source of truth for the + obs/action layout the policy expects (and the policy arch). None if no ckpt.""" + if not checkpoint: + return None + import yaml + + return yaml.safe_load(open(checkpoint_config_path(checkpoint))) + + +DEFAULT_DT = 0.1 # co-sim runs lockstep with CARLA at 0.1s (sub_ticks=1) + + +def parse_route(xml_path, route_id): + root = ET.parse(xml_path).getroot() + routes = [r for r in root.findall("route") if r.get("id") == str(route_id)] + if not routes: + raise ValueError(f"route id {route_id} not found in {xml_path}") + r = routes[0] + town = r.get("town") + wps = np.array( + [[float(p.get("x")), float(p.get("y")), float(p.get("z"))] for p in r.find("waypoints").findall("position")], + dtype=np.float64, + ) + return town, wps + + +def build_carla(client, town, route_wps, num_background, dt_sub): + import carla + + client.load_world(town) + world = client.get_world() + settings = world.get_settings() + settings.synchronous_mode = True + settings.fixed_delta_seconds = dt_sub + world.apply_settings(settings) + tm = client.get_trafficmanager() + tm.set_synchronous_mode(True) + bplib = world.get_blueprint_library() + car_bps = [b for b in bplib.filter("vehicle.*") if int(b.get_attribute("number_of_wheels")) == 4] + cmap = world.get_map() + spawn_points = cmap.get_spawn_points() + + # Ego: spawn at the CARLA spawn point nearest the route start; physics OFF + # (we teleport it from PufferDrive each tick). + start = carla.Location(x=route_wps[0, 0], y=route_wps[0, 1], z=route_wps[0, 2]) + ego_sp = min(spawn_points, key=lambda sp: sp.location.distance(start)) + ego = world.spawn_actor(bplib.find(EGO_BLUEPRINT), ego_sp) + ego.set_simulate_physics(False) + + # Background: spawn at the spawn points NEAREST the ego (TM autopilot) so the + # traffic clusters around the ego — visible in the camera and within the ego's + # observation range, rather than scattered across the whole town. + bg = [] + for sp in sorted(spawn_points, key=lambda s: s.location.distance(ego_sp.location)): + if len(bg) >= num_background: + break + if sp.location.distance(ego_sp.location) < 8.0: + continue + actor = world.try_spawn_actor(car_bps[len(bg) % len(car_bps)], sp) + if actor is not None: + actor.set_autopilot(True, tm.get_port()) + bg.append(actor) + + world.tick() + lights = list(world.get_actors().filter("traffic.traffic_light")) + return world, tm, ego, bg, lights + + +def read_background(bg_actors, transform): + idx, x, y, z, h, vx, vy, yaw_rate, accel_long = [], [], [], [], [], [], [], [], [] + for j, a in enumerate(bg_actors): + idx.append(1 + j) # agent 0 = ego; background fills 1..M + if a is not None and a.is_alive: + bx, by, bz, bh, bvx, bvy, byr, bal = transform.actor_state_to_bin(a) + else: # TM may remove a vehicle; park its PufferDrive slot out of range + bx = by = bz = FAR_AWAY + bh = bvx = bvy = byr = bal = 0.0 + x.append(bx) + y.append(by) + z.append(bz) + h.append(bh) + vx.append(bvx) + vy.append(bvy) + yaw_rate.append(byr) + accel_long.append(bal) + return ( + np.array(idx, np.int32), + np.array(x, np.float32), + np.array(y, np.float32), + np.array(z, np.float32), + np.array(h, np.float32), + np.array(vx, np.float32), + np.array(vy, np.float32), + np.array(yaw_rate, np.float32), + np.array(accel_long, np.float32), + ) + + +def read_actor_sizes(actors): + """Bounding-box (length, width) in meters for each CARLA actor, so the ego + observes a truck as a truck. CARLA extent is half-size along the actor's + local axes (x=forward, y=lateral).""" + idx, length, width = [], [], [] + for j, a in enumerate(actors): + idx.append(1 + j) + if a is not None and a.is_alive: + ext = a.bounding_box.extent + length.append(2.0 * ext.x) + width.append(2.0 * ext.y) + else: + length.append(DEFAULT_VEHICLE_LENGTH_M) + width.append(DEFAULT_VEHICLE_WIDTH_M) + return (np.array(idx, np.int32), np.array(length, np.float32), np.array(width, np.float32)) + + +def densify_route(route_wps, step=2.0): + """Interpolate the sparse longest6v2 route waypoints to ~step-m spacing.""" + pts = [route_wps[0, :2].astype(float)] + for i in range(len(route_wps) - 1): + a, b = route_wps[i, :2].astype(float), route_wps[i + 1, :2].astype(float) + seg = float(np.hypot(*(b - a))) + n = max(1, int(seg / step)) + for k in range(1, n + 1): + pts.append(a + (b - a) * (k / n)) + return np.asarray(pts) + + +def _route_goal_xy(cmap, cx, cy, route_yaw_deg): + """CARLA-frame (x, y, z) for a route goal: snap to the nearest DRIVING lane + whose travel direction matches the route (within 90 deg) so a goal never lands + in the oncoming lane. `cmap.get_waypoint` returns the nearest lane regardless of + direction, so at the center of a two-way road it can pick the oncoming one; we + then search lateral neighbors for a matching-direction lane. If none is reachable + (e.g. across a solid center line), keep the raw route point — it is at least in + the correct travel direction even if not perfectly lane-centered.""" + import carla + + def matches(w): + return abs(((route_yaw_deg - w.transform.rotation.yaw) + 180.0) % 360.0 - 180.0) <= 90.0 + + wp = cmap.get_waypoint(carla.Location(x=float(cx), y=float(cy))) + if wp is None: + return (cx, cy, 0.0) + if matches(wp): + loc = wp.transform.location + return (loc.x, loc.y, loc.z) + for go_left in (True, False): + w = wp + for _ in range(4): + w = w.get_left_lane() if go_left else w.get_right_lane() + if w is None or w.road_id != wp.road_id: + break + if str(w.lane_type) == "Driving" and matches(w): + loc = w.transform.location + return (loc.x, loc.y, loc.z) + return (cx, cy, 0.0) + + +def build_route_goals(dense_route, transform, cmap, spacing=20.0): + """Fixed sequence of lane-centered goals along the WHOLE route, one every + `spacing` m, each snapped to a DIRECTION-MATCHED driving lane, in the bin frame. + Returns an (N, 5) array (x, y, z, dir_x, dir_y) where dir_* is the local route + travel direction at the goal (bin frame), consumed by set_agent_goals' lane + snapping. The ego marches through these via a cursor that only advances on + arrival (GOAL_RADIUS_M) — goals do not float with the ego.""" + + def goal_at(i): + d = dense_route[i] - dense_route[i - 1] + route_yaw = np.degrees(np.arctan2(d[1], d[0])) # route travel direction (CARLA frame) + gx, gy, gz = _route_goal_xy(cmap, dense_route[i][0], dense_route[i][1], route_yaw) + return (*transform.loc_to_bin(gx, gy), transform.z_to_bin(gz), d[0], -d[1]) # y flips into the bin frame + + goals, next_at, cum = [], spacing, 0.0 + for i in range(1, len(dense_route)): + cum += float(np.hypot(*(dense_route[i] - dense_route[i - 1]))) + if cum >= next_at: + goals.append(goal_at(i)) + next_at += spacing + goals.append(goal_at(len(dense_route) - 1)) # always finish on the route's end + return np.array(goals, np.float32) + + +def attach_chase_camera(world, ego, w=960, h=540): + """RGB chase camera attached to the ego (behind + above, looking forward). + Returns (camera_actor, image_queue). Requires CARLA running WITH rendering + (-RenderOffScreen), not -nullrhi.""" + import queue + + import carla + + bp = world.get_blueprint_library().find("sensor.camera.rgb") + bp.set_attribute("image_size_x", str(w)) + bp.set_attribute("image_size_y", str(h)) + bp.set_attribute("fov", "90") + cam = world.spawn_actor( + bp, carla.Transform(carla.Location(x=-6.5, z=3.2), carla.Rotation(pitch=-12.0)), attach_to=ego + ) + q = queue.Queue() + cam.listen(q.put) + return cam, q + + +def carla_image_to_rgb(img): + arr = np.frombuffer(img.raw_data, dtype=np.uint8).reshape(img.height, img.width, 4) + return arr[:, :, [2, 1, 0]].copy() # BGRA -> RGB + + +class Mp4Writer: + """Streams RGB uint8 frames to an mp4 via OpenCV.""" + + def __init__(self, out_path, fps): + self.out_path = str(out_path) + self.fps = float(fps) + self.writer = None + self.frame_count = 0 + + def append_data(self, frame): + import cv2 + + frame = np.ascontiguousarray(frame) + if self.writer is None: + height, width = frame.shape[:2] + self.writer = cv2.VideoWriter(self.out_path, cv2.VideoWriter_fourcc(*"mp4v"), self.fps, (width, height)) + if not self.writer.isOpened(): + raise RuntimeError(f"Mp4Writer({self.out_path}): OpenCV could not open the video writer") + self.writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) + self.frame_count += 1 + + def close(self): + if self.writer is not None: + self.writer.release() + self.writer = None + + +def write_mp4(out_path, frames, fps=10): + if not frames: + raise ValueError(f"write_mp4({out_path}): no frames") + writer = Mp4Writer(out_path, fps) + for frame in frames: + writer.append_data(frame) + writer.close() + + +def main(): + import carla + + from pufferlib.ocean.cosim import carla_scenarios + + ap = argparse.ArgumentParser() + ap.add_argument("--routes", default=DEFAULT_ROUTES) + ap.add_argument("--route-id", type=int, default=0) + ap.add_argument("--checkpoint", default=None) + ap.add_argument("--num-background", type=int, default=30) + ap.add_argument( + "--num-agents", + type=int, + default=None, + help="shadow agent pool (default: the checkpoint's max_agents_per_env, else 64)", + ) + ap.add_argument("--steps", type=int, default=60) + ap.add_argument("--carla-host", default="localhost") + ap.add_argument("--carla-port", type=int, default=2000) + ap.add_argument("--device", default="cpu") + ap.add_argument("--sub-ticks", type=int, default=None) # default: round(dt / 0.1) + ap.add_argument( + "--dt", type=float, default=None, help="ego dynamics dt (default: the checkpoint's training dt, else 0.1)" + ) + ap.add_argument( + "--carla-view", + default=None, + help="output mp4 path for a CARLA chase-camera view (needs CARLA -RenderOffScreen)", + ) + ap.add_argument( + "--town-bin", + default=None, + help="override the town .bin (e.g. a shoulder-retyped variant); default: repo carla bin", + ) + args = ap.parse_args() + + town, route_wps = parse_route(args.routes, args.route_id) + town_bin = args.town_bin or cb.bin_path_for_town(town) + print(f"[cosim] route {args.route_id} town={town} waypoints={len(route_wps)} bin={town_bin}") + + cfg = load_checkpoint_config(args.checkpoint) # for the env arch + policy arch (below) + # Policy dt defaults to the checkpoint's training dt (matching dynamics). + dt = args.dt if args.dt is not None else ((cfg or {}).get("env", {}).get("dt", DEFAULT_DT)) + sub_ticks = args.sub_ticks or max(1, round(dt / 0.1)) # CARLA at 0.1s, PufferDrive at dt + + # Shadow agent pool: the checkpoint's per-env training cap by default, so + # binding.shared builds exactly one gigaflow C env (the co-sim setters only + # address env 0). + num_agents = args.num_agents or int(((cfg or {}).get("env") or {}).get("max_agents_per_env", 64)) + + # PufferDrive env: one policy agent (the ego) plus static partner slots + # streamed from CARLA. Env kwargs come from the checkpoint's config.yaml with + # the clean-eval profile applied on top (see cosim/arch.py). + env = Drive( + **shadow_env_kwargs( + cfg, + overrides=dict( + map_dir=town_bin, + num_maps=1, + num_agents=1, + min_agents_per_env=1, + cosim_partner_slots=num_agents - 1, + goal_source="external", + scenario_length=args.steps + SCENARIO_LENGTH_MARGIN_STEPS, + resample_frequency=0, + dt=dt, + # External sim owns the episode: a training-config + # termination_mode=1 would c_reset() the pool (ego included) once + # the parked FAR_AWAY slots latch under "stop" infraction behaviors. + termination_mode=0, + # Enforcement off (flags still fire): in one endless episode a + # "stop" latch is permanent and would freeze the ego for good. + collision_behavior="ignore", + offroad_behavior="ignore", + traffic_light_behavior="ignore", + ), + ) + ) + obs, _ = env.reset() + obs = np.asarray(obs) + n_slots = 1 + env.cosim_partner_slots + print(f"[cosim] env reset: obs {obs.shape}, action {env.single_action_space}, dt={dt}, sub_ticks={sub_ticks}") + + # Policy (optional) — falls back to a constant forward jerk action. + policy = None + if cfg is not None: + import torch + import pufferlib.ocean.torch as drive_torch + + # pre-3.0 checkpoints keep action_type only in the env section + cfg["policy"].setdefault("action_type", cfg["env"]["action_type"]) + policy = getattr(drive_torch, cfg.get("policy_name", "Drive"))(env, **cfg["policy"]).to(args.device) + sd = torch.load(args.checkpoint, map_location=args.device, weights_only=False) + policy.load_state_dict(clean_policy_state_dict(sd)) + policy.eval() + print(f"[cosim] loaded policy from {args.checkpoint}") + # jerk action: idx = j_long_idx*3 + j_lat_idx; (3,1) = +long jerk, straight (accelerate) + dummy_action = np.full((1, 1), 3 * 3 + 1, dtype=np.int32) + + # CARLA + client = carla.Client(args.carla_host, args.carla_port) + client.set_timeout(60.0) + world, tm, ego, bg, lights = build_carla(client, town, route_wps, args.num_background, 0.1) + cmap = world.get_map() + transform = cb.CarlaTransform(town, offset=cb.town_offset(town_bin)) + offset, z_offset, _, _ = cb.calibrate_town_offset(cmap, transform, town_bin) + transform = cb.CarlaTransform(town, offset=offset, z_offset=z_offset) + _bin_lanes = cb._bin_lane_points(town_bin) # bin lane points (== global frame) for diagnostics + dense_route = densify_route(route_wps) # fine-sampled route for lane-centered goal placement + route_goals = build_route_goals(dense_route, transform, cmap) # fixed 20-m lane-centered goal sequence + goal_window = RouteGoalWindow(env, route_goals) + light_map, num_traffic = cb.map_lights_to_bin(lights, transform, town_bin) + print( + f"[cosim] carla: ego + {len(bg)} background + {len(lights)} lights; " + f"offset={transform.tx:.1f},{transform.ty:.1f} z={transform.tz:+.2f}" + ) + + car_bps = [ + b for b in world.get_blueprint_library().filter("vehicle.*") if int(b.get_attribute("number_of_wheels")) == 4 + ] + walker_bps = list(world.get_blueprint_library().filter("walker.pedestrian.*")) + scenario_mgr = carla_scenarios.ScenarioManager( + carla_scenarios.parse_scenarios(args.routes, args.route_id), world, tm, car_bps, walker_bps + ) + print(f"[cosim] {len(scenario_mgr.scenarios)} scenarios loaded (ControlLoss skipped)") + + # --- init sync: ego pose + size + goals; background; park surplus agents --- + ego_state = transform.actor_state_to_bin(ego) + env.set_agent_states(np.array([0], np.int32), *[np.array([v], np.float32) for v in ego_state]) + ego_ext = ego.bounding_box.extent # match the ego to its CARLA blueprint box (static) + env.set_agent_sizes( + np.array([0], np.int32), np.array([2.0 * ego_ext.x], np.float32), np.array([2.0 * ego_ext.y], np.float32) + ) + goal_window.sync(ego_state[0], ego_state[1], ego_state[3]) + bg_idx, *_ = read_background(bg, transform) + surplus = np.arange(1 + len(bg), n_slots, dtype=np.int32) + if len(surplus): + z6 = np.full(len(surplus), FAR_AWAY, np.float32) + zero = np.zeros_like(z6) + env.set_agent_states(surplus, z6, z6, z6, zero, zero, zero, zero, zero) + obs = np.asarray(env.recompute_observations()) + + cam, cam_q = attach_chase_camera(world, ego) if args.carla_view else (None, None) + carla_frames = [] + + # --- co-sim loop --- + for step in range(args.steps): + if policy is not None: + import torch + import pufferlib.pytorch + + import pufferlib.spaces + + # A discrete policy head on a continuous env needs the bin->continuous + # mapping (pufferl.py feeds cont_action to the env, never the bin indices). + env_continuous = isinstance(env.single_action_space, pufferlib.spaces.Box) + action_selection = ( + pufferlib.pytorch.ACTION_SELECT_MEAN + if env_continuous and not policy.is_continuous + else pufferlib.pytorch.ACTION_SELECT_MODE + ) + with torch.no_grad(): + logits, _ = policy.forward_eval(torch.as_tensor(obs).to(args.device)) + action, _, _, cont_action = pufferlib.pytorch.sample_logits( + logits, + action_selection=action_selection, + env_continuous=env_continuous, + policy=policy, + ) + env_action = cont_action if env_continuous and cont_action is not None else action + act = env_action.cpu().numpy().reshape(1, -1) + if not env_continuous: + act = act.astype(np.int32) + else: + act = dummy_action + + env.step(act) # PufferDrive integrates the ego (and others; overwritten below) + ego_bin = env.get_global_agent_state() + # teleport CARLA ego to the ego's new pose + ex, ey = transform.bin_to_loc(float(ego_bin["x"][0]), float(ego_bin["y"][0])) + eyaw = transform.bin_heading_to_yaw(float(ego_bin["heading"][0])) + ego.set_transform(carla.Transform(carla.Location(x=ex, y=ey, z=0.3), carla.Rotation(yaw=eyaw))) + scenario_mgr.tick(ego.get_location()) # trigger/spawn hazards near the ego + cam_img = None + for _ in range(sub_ticks): + world.tick() + if cam_q is not None: + cam_img = cam_q.get(timeout=20.0) + if cam_img is not None: + carla_frames.append(carla_image_to_rgb(cam_img)) + # overwrite background + scenario actors + lights from CARLA + actors = [a for a in bg if a.is_alive] + scenario_mgr.alive_actors() + env.set_agent_states(*read_background(actors, transform)) + env.set_agent_sizes(*read_actor_sizes(actors)) # true CARLA bounding-box sizes + n_used = 1 + len(actors) # ego + actors; park the rest (count varies) + if n_used < n_slots: + sp = np.arange(n_used, n_slots, dtype=np.int32) + zf = np.full(len(sp), FAR_AWAY, np.float32) + zz = np.zeros_like(zf) + env.set_agent_states(sp, zf, zf, zf, zz, zz, zz, zz, zz) + states = np.zeros(num_traffic, np.int32) + for li, lt in enumerate(lights): + state = cb.carla_light_to_puffer(lt.get_state()) + for j in light_map[li]: + if 0 <= j < num_traffic: + states[j] = state + env.set_traffic_light_states(states) + ebx, eby = float(ego_bin["x"][0]), float(ego_bin["y"][0]) + goal_window.sync(ebx, eby, float(ego_bin["heading"][0])) + gx, gy = goal_window.window[:, 0], goal_window.window[:, 1] + obs = np.asarray(env.recompute_observations()) + + if step % 10 == 0: + el = ego.get_location() + nl = min(lights, key=lambda lt: lt.get_location().distance(el)) if lights else None + ls = f"{nl.get_state()}@{nl.get_location().distance(el):.0f}m" if nl else "n/a" + goal_cursor = goal_window.current_index + gd = float(np.hypot(route_goals[goal_cursor, 0] - ebx, route_goals[goal_cursor, 1] - eby)) + near = sum(1 for a in bg if a.is_alive and a.get_location().distance(el) < 40.0) + wp = cmap.get_waypoint(ego.get_location()) # nearest drivable lane center + lat = ego.get_location().distance(wp.transform.location) if wp else -1.0 + onroad = cmap.get_waypoint(ego.get_location(), project_to_road=False) is not None + bin_off = ( + float(np.min(np.hypot(_bin_lanes[:, 0] - ebx, _bin_lanes[:, 1] - eby))) if len(_bin_lanes) else -1.0 + ) + print( + f"[cosim] step {step}: ego carla=({ex:.1f},{ey:.1f}) carla_off={lat:.1f}m onroad={onroad} bin_off={bin_off:.1f}m goal0={gd:.0f}m[{goal_cursor}/{len(route_goals)}] light={ls} scn={scenario_mgr.active_count()}" + ) + + # cleanup + if carla_frames: + write_mp4(args.carla_view, carla_frames, fps=10) + print(f"[cosim] wrote {args.carla_view} ({len(carla_frames)} frames)") + # Teardown while STILL synchronous: stop sensors, detach the TM from its + # vehicles, destroy actors, tick to apply — and only then release the world + # to async. Going async first races the TM thread against the destruction + # batch and aborts the process with "trying to operate on a destroyed + # actor" (std::terminate in the TM worker thread). + if cam is not None and cam.is_alive: + cam.stop() + tm_vehicles = [ + a for a in [*bg, *scenario_mgr.alive_actors()] if a is not None and a.is_alive and "vehicle" in a.type_id + ] + client.apply_batch_sync([carla.command.SetAutopilot(a.id, False, tm.get_port()) for a in tm_vehicles], True) + world.tick() # let the TM observe the detach before anything is destroyed + scenario_mgr.cleanup() + client.apply_batch_sync( + [carla.command.DestroyActor(a) for a in [cam, ego, *bg] if a is not None and a.is_alive], True + ) + world.tick() + s = world.get_settings() + s.synchronous_mode = False + world.apply_settings(s) + tm.set_synchronous_mode(False) + print("[cosim] done") + + +if __name__ == "__main__": + main() diff --git a/pufferlib/ocean/cosim/carla_scenarios.py b/pufferlib/ocean/cosim/carla_scenarios.py new file mode 100644 index 0000000000..b2e0ac3112 --- /dev/null +++ b/pufferlib/ocean/cosim/carla_scenarios.py @@ -0,0 +1,207 @@ +"""longest6v2 scenario hazards reimplemented for the teleport co-sim loop. + +scenario_runner drives these via py_trees + the full leaderboard (which owns the +ego). We keep our PufferDrive-teleport ego and instead, when the ego nears a +scenario's XML trigger point, spawn the hazard actor(s) and run a simple scripted +behavior; the actor then syncs into the ego's observation like any background +vehicle (carla_cosim feeds `ScenarioManager.alive_actors()` into set_agent_states). + +Implemented: DynamicObjectCrossing (pedestrian) + a parameterized JunctionVehicle +for SignalizedJunctionLeftTurn / SignalizedJunctionRightTurn / +OppositeVehicleRunningRedLight / VehicleTurningRoute. ControlLoss is intentionally +skipped (it perturbs the ego's own control, which a teleported ego can't model). +""" + +import math +import xml.etree.ElementTree as ET + +import carla + +TRIGGER_RADIUS_M = 35.0 + + +def parse_scenarios(xml_path, route_id): + root = ET.parse(xml_path).getroot() + routes = [x for x in root.findall("route") if x.get("id") == str(route_id)] + if not routes: + return [] + out = [] + sc = routes[0].find("scenarios") + for s in sc if sc is not None else []: + tp = s.find("trigger_point") + if tp is None: + continue + out.append( + dict( + type=s.get("type"), + name=s.get("name"), + x=float(tp.get("x")), + y=float(tp.get("y")), + z=float(tp.get("z")), + yaw=float(tp.get("yaw")), + ) + ) + return out + + +def _fwd(yaw_deg): + r = math.radians(yaw_deg) + return math.cos(r), math.sin(r) + + +class Scenario: + def __init__(self, spec, world, tm, car_bps, walker_bps): + self.spec, self.world, self.tm = spec, world, tm + self.car_bps, self.walker_bps = car_bps, walker_bps + self.triggered = self.done = False + self.actors = [] + self._t = 0 + + def trigger_loc(self): + return carla.Location(x=self.spec["x"], y=self.spec["y"], z=self.spec["z"]) + + def maybe_trigger(self, ego_loc): + if self.triggered or self.done: + return + if ego_loc.distance(self.trigger_loc()) < TRIGGER_RADIUS_M: + self.triggered = True + try: + self._spawn() + print(f"[scenario] triggered {self.spec['type']} ({self.spec['name']})") + except Exception as e: # never let a scenario break the co-sim loop + print(f"[scenario] {self.spec['type']} spawn failed: {e}") + self.done = True + + def step(self): + if self.triggered and not self.done: + self._t += 1 + try: + self._behave() + except Exception: + self.done = True + + def cleanup(self): + for a in self.actors: + try: + if a is not None and a.is_alive: + a.destroy() + except Exception: + pass + self.actors = [] + + def alive(self): + return [a for a in self.actors if a is not None and a.is_alive] + + def _spawn(self): + raise NotImplementedError + + def _behave(self): + pass + + +class DynamicObjectCrossing(Scenario): + """A pedestrian crosses the road in front of the ego at the trigger.""" + + def _spawn(self): + fx, fy = _fwd(self.spec["yaw"]) # ego forward at the trigger + lx, ly = -fy, fx # left of forward + loc = self.trigger_loc() + ahead, side = 6.0, 4.5 # a bit ahead, offset to one side + sx, sy = loc.x + fx * ahead + lx * side, loc.y + fy * ahead + ly * side + w = self.world.try_spawn_actor(self.walker_bps[0], carla.Transform(carla.Location(x=sx, y=sy, z=loc.z + 1.0))) + if w is None: + self.done = True + return + self.actors = [w] + self._dir = carla.Vector3D(-lx, -ly, 0.0) # walk across to the far side + w.apply_control(carla.WalkerControl(direction=self._dir, speed=2.0)) + + def _behave(self): + if self._t > 60: # ~6 s crossing, then stop + finish + if self.alive(): + self.actors[0].apply_control(carla.WalkerControl(speed=0.0)) + self.done = True + + +class JunctionVehicle(Scenario): + """A vehicle drives through the junction near the trigger (cross / oncoming + traffic). The red-light variant ignores traffic lights.""" + + ignore_lights = False + + def _spawn(self): + cmap = self.world.get_map() + wp = cmap.get_waypoint(self.trigger_loc()) + jwp = wp + for _ in range(25): # walk forward to the next junction + nxt = jwp.next(2.0) + if not nxt: + break + jwp = nxt[0] + if jwp.is_junction: + break + if not jwp.is_junction: + self.done = True + return + fx, fy = _fwd(self.spec["yaw"]) + spawn_wp = None + for entry, _exit in jwp.get_junction().get_waypoints(carla.LaneType.Driving): + d = entry.transform.get_forward_vector() + if d.x * fx + d.y * fy < 0.3: # cross or oncoming (not the ego's direction) + back = entry.previous(12.0) + spawn_wp = back[0] if back else entry + break + if spawn_wp is None: + self.done = True + return + tf = spawn_wp.transform + tf.location.z += 0.3 + v = self.world.try_spawn_actor(self.car_bps[0], tf) + if v is None: + self.done = True + return + v.set_autopilot(True, self.tm.get_port()) + self.tm.vehicle_percentage_speed_difference(v, -25) + if self.ignore_lights: + self.tm.ignore_lights_percentage(v, 100.0) + self.actors = [v] + + def _behave(self): + if self._t > 120: # cleared the junction; leave it as ambient TM traffic + self.done = True + + +def _make(spec, world, tm, car_bps, walker_bps): + t = spec["type"] + if t == "DynamicObjectCrossing": + return DynamicObjectCrossing(spec, world, tm, car_bps, walker_bps) + if t == "OppositeVehicleRunningRedLight": + s = JunctionVehicle(spec, world, tm, car_bps, walker_bps) + s.ignore_lights = True + return s + if t in ("SignalizedJunctionLeftTurn", "SignalizedJunctionRightTurn", "VehicleTurningRoute"): + return JunctionVehicle(spec, world, tm, car_bps, walker_bps) + return None # ControlLoss and anything else: skipped + + +class ScenarioManager: + def __init__(self, specs, world, tm, car_bps, walker_bps): + self.scenarios = [s for s in (_make(sp, world, tm, car_bps, walker_bps) for sp in specs) if s] + + def tick(self, ego_loc): + for s in self.scenarios: + s.maybe_trigger(ego_loc) + s.step() + + def alive_actors(self): + out = [] + for s in self.scenarios: + out.extend(s.alive()) + return out + + def active_count(self): + return sum(1 for s in self.scenarios if s.triggered and not s.done) + + def cleanup(self): + for s in self.scenarios: + s.cleanup() diff --git a/pufferlib/ocean/cosim/goals.py b/pufferlib/ocean/cosim/goals.py new file mode 100644 index 0000000000..d486481ca8 --- /dev/null +++ b/pufferlib/ocean/cosim/goals.py @@ -0,0 +1,143 @@ +"""Route goal windows for a co-sim ego (Drive goal_source="external"). + +The shadow env consumes the goals of its window itself, exactly like training +with goal_regen_mode=finite (consumed slots are zeroed in the obs, the window +is replaced only once every goal in it was reached). The co-sim only decides +WHICH `num_goals` route goals form the next window. A sliding window instead +refills to `num_goals` goals ahead after every consumed goal, so the ego sees a +window-final (speed-gated) goal only at the true route end. +""" + +import math + +import numpy as np + +ROUTE_GOAL_COLUMNS = 5 # x, y, z, dir_x, dir_y in the bin frame +ROUTE_MAX_LATERAL_M = 8.0 # farther from the goal polyline the ego is off-route: never skip goals then + + +class RouteGoalWindow: + def __init__(self, env, route_goals, agent_idx=0, sliding=False): + """route_goals: (N, 5) float32 (x, y, z, dir_x, dir_y), bin frame, in + travel order. dir_* is the local route direction at the goal, used by + set_agent_goals' route-aligned lane snapping. sliding: advance the + window past every consumed goal instead of replacing it once exhausted.""" + self.env = env + self.goals = np.ascontiguousarray(route_goals, dtype=np.float32).reshape(-1, ROUTE_GOAL_COLUMNS) + if len(self.goals) == 0: + raise ValueError("RouteGoalWindow needs at least one route goal") + self.agent_idx = int(agent_idx) + self.sliding = bool(sliding) + self.num_goals = int(env.num_goals) + self.goal_radius = float(env.goal_radius) + segment_lengths = np.hypot(*np.diff(self.goals[:, :2], axis=0).T) + self.arc_length = np.concatenate([[0.0], np.cumsum(segment_lengths)]) # route progress of each goal + self.window_start = 0 + self.current_index = 0 # route index of the goal the ego is heading for + self.pushed = False + + @property + def window(self): + return self.goals[self.window_start : self.window_start + self.num_goals] + + @property + def consumed_count(self): + """Goals of the current window the shadow env has already consumed (zeroed in the obs).""" + if not self.pushed: + return 0 + current_goal_idx, goal_count = self.env.get_agent_goal_progress(self.agent_idx) + return min(current_goal_idx, goal_count) + + @property + def exhausted(self): + return self.pushed and self.window_start + self.num_goals >= len(self.goals) and self._consumed_all() + + def _consumed_all(self): + current_goal_idx, goal_count = self.env.get_agent_goal_progress(self.agent_idx) + return current_goal_idx >= goal_count + + def route_progress(self, ego_x, ego_y, first_goal=0, last_goal=None): + """(arc length of the ego projected onto the goal polyline between two goal indices, lateral + distance to it). Restricting the goal range keeps a route that loops back near the ego from + projecting onto its far end.""" + pts = self.goals[:, :2].astype(np.float64) + last_goal = len(pts) - 1 if last_goal is None else min(last_goal, len(pts) - 1) + if last_goal <= first_goal: + return float(self.arc_length[first_goal]), float( + np.hypot(pts[first_goal, 0] - ego_x, pts[first_goal, 1] - ego_y) + ) + a, b = pts[first_goal:last_goal], pts[first_goal + 1 : last_goal + 1] + d = b - a + length_sq = np.maximum((d * d).sum(axis=1), 1e-9) + t = np.clip(((ego_x - a[:, 0]) * d[:, 0] + (ego_y - a[:, 1]) * d[:, 1]) / length_sq, 0.0, 1.0) + proj = a + t[:, None] * d + dist = np.hypot(proj[:, 0] - ego_x, proj[:, 1] - ego_y) + k = int(np.argmin(dist)) + return float(self.arc_length[first_goal + k] + t[k] * math.sqrt(length_sq[k])), float(dist[k]) + + def sync(self, ego_x, ego_y, ego_heading): + """Push the next window when the current one is exhausted (sliding: when any goal was + consumed), or when the ego has driven past its current goal along the route (e.g. in the + adjacent lane) without consuming it. An ego far off the route keeps its window: skipping + goals there cascades through the whole route.""" + if not self.pushed: + self._push(0) + return + current_goal_idx, goal_count = self.env.get_agent_goal_progress(self.agent_idx) + if current_goal_idx >= goal_count: + next_start = self.window_start + goal_count + else: + self.current_index = self.window_start + current_goal_idx + progress, lateral = self.route_progress( + ego_x, ego_y, max(self.current_index - 1, 0), self.current_index + self.num_goals + ) + passed = ( + lateral <= ROUTE_MAX_LATERAL_M and progress > self.arc_length[self.current_index] + self.goal_radius + ) + next_start = int(np.searchsorted(self.arc_length, progress, side="right")) if passed else self.window_start + if self.sliding and not passed: + next_start = self.current_index + if next_start == self.window_start or next_start >= len(self.goals): + return # window still live, or route exhausted: the last window stays saturated + self._push(next_start) + + def _push(self, start): + sel = self.goals[start : start + self.num_goals] + self.env.set_agent_goals( + self.agent_idx, sel[:, 0].copy(), sel[:, 1].copy(), sel[:, 2].copy(), sel[:, 3].copy(), sel[:, 4].copy() + ) + self.window_start = start + self.current_index = start + self.pushed = True + + +def route_goals_from_xy(goals_xy, goals_z=None, origin_xy=None): + """(N, 2) goal points -> (N, 5) route goals; dir = previous goal -> goal. origin_xy (the ego + position) gives the first goal its direction too; without it the first dir is zero and the + env snaps that goal to the nearest lane by distance alone (wrong branch at junctions).""" + xy = np.asarray(goals_xy, dtype=np.float64).reshape(-1, 2) + z = np.zeros(len(xy)) if goals_z is None else np.asarray(goals_z, dtype=np.float64).reshape(-1) + first_prev = xy[:1] if origin_xy is None else np.asarray(origin_xy, dtype=np.float64).reshape(1, 2) + prev = np.vstack([first_prev, xy[:-1]]) + return np.column_stack([xy, z, xy - prev]).astype(np.float32) + + +def route_goals_from_target_points(target_points_xyz, dense_route_xyz, to_bin, start_xy_bin, skip_radius): + """Leaderboard target points (junction entries/exits, lane changes, every 200 m) -> (N, 5) route + goals in the bin frame. Each target point is a point of the dense route; its direction is the dense + route's local direction there. Leading points within skip_radius of start_xy_bin are dropped (the + route starts under the ego, and a goal there would be consumed on the first step).""" + targets = np.asarray(target_points_xyz, dtype=np.float64).reshape(-1, 3) + dense = np.asarray(dense_route_xyz, dtype=np.float64).reshape(-1, 3) + if len(targets) == 0 or len(dense) < 2: + raise ValueError("route_goals_from_target_points needs target points and a dense route of >= 2 points") + dense_bin = np.array([to_bin(x, y) for x, y, _ in dense], dtype=np.float64) + goals = np.zeros((len(targets), ROUTE_GOAL_COLUMNS), dtype=np.float64) + for goal_idx, (tx, ty, tz) in enumerate(targets): + k = max(int(np.argmin(np.hypot(dense[:, 0] - tx, dense[:, 1] - ty))), 1) + bx, by = to_bin(tx, ty) + goals[goal_idx] = (bx, by, tz, *(dense_bin[k] - dense_bin[k - 1])) + first = 0 + while first < len(goals) - 1 and math.hypot(*(goals[first, :2] - start_xy_bin)) < skip_radius: + first += 1 + return goals[first:].astype(np.float32) diff --git a/pufferlib/ocean/cosim/nuplan/README.md b/pufferlib/ocean/cosim/nuplan/README.md new file mode 100644 index 0000000000..a035df5e5e --- /dev/null +++ b/pufferlib/ocean/cosim/nuplan/README.md @@ -0,0 +1,127 @@ +# PufferDrive policy under nuPlan's closed-loop evaluation + +## Goal + +Evaluate a PufferDrive-trained policy with nuPlan's **unmodified** `run_simulation.py` closed-loop pipeline — the same simulator, metrics, and scoring CaRL's own nuPlan planners are evaluated with — so scores are directly comparable. The nuPlan analog of `pufferlib/ocean/cosim/carla/`. + +## Design + +Zero changes to nuplan-devkit or CaRL's `carl_nuplan`. Hydra loads any planner via `planner=pufferdrive_planner` (a config pointing at this package's `PufferDrivePlanner`), so the whole integration is this package: + +- `planner.py` — `PufferDrivePlanner(AbstractPlanner)`. The shadow `Drive` + env holds ONE policy agent (the ego) plus `num_agents - 1` static partner + slots (`cosim_partner_slots`) that are never spawned or stepped by + PufferDrive. Per planning iteration (dt = the scenario's + `database_interval`): stream nuPlan's `PlannerInput` (`DetectionsTracks`, + traffic lights) into the partner slots (`nuplan_bridge` transforms), + recompute observations, run the policy on the ego row, `env.step` + (PufferDrive integrates the ego one dt), and return the integrated pose as + the trajectory. The ego is synced from nuPlan telemetry only at the first + iteration; afterwards the shadow env owns it and the planner raises if + nuPlan's ego pose ever diverges from the integrated one (the configs pin + `perfect_tracking_controller`). +- Route goals (`goal_source: external`): PDM-style lane-graph route -> + goals every `goal_spacing` m. Planner `goal_source: roadblock` (env + `GOAL_SOURCE=roadblock`) uses the centroids of the CaRL-corrected route + roadblocks instead (off-polygon centroids moved onto a lane baseline, thinned + to `goal_spacing`), i.e. the route information CaRL rendered, without a lane + choice. `goal_source: roadblock_lane` keeps the roadblock goals but puts each + on the lane that continues the ego's own lane through the route (a lane + change only where the lane graph forces one), so multi-lane roads stop + pulling the ego toward the road centre. All come from the challenge's route + roadblock ids only; the planner + never reads the logged ego. Eval hack `pedestrian_min_size_m` (env + `PEDESTRIAN_MIN_SIZE_M`): pedestrian and bicycle partner boxes are grown to at least + this size in the policy's observation (training spawns nothing below 0.8 x 0.8 m, + nuPlan pedestrians are 0.4-0.8 m); nuPlan still scores the true boxes. Off by + default, script 9 sets 0.8 m. The shadow env consumes the goals of its + `num_goals` window itself exactly like training (`goal_regen_mode: finite`, + consumed slots zeroed in the obs); the planner pushes the next window only + when the current one is exhausted or its current goal is clearly behind the + ego. `sliding_goal_window: true` (env `SLIDING_GOAL_WINDOW=true`) instead + refills the window after every consumed goal, so the policy sees a + window-final speed-gated goal (which it parks at) only at the true route + end. Lights nuPlan does not report are GREEN (training never produces + UNKNOWN). +- Start-up jerk caps (`startup_accel_jerk_cap_mps3` / + `startup_brake_jerk_cap_mps3`, env `STARTUP_ACCEL_JERK_CAP` / + `STARTUP_BRAKE_JERK_CAP`, 0 = off) clip the ego's continuous jerk action for + the first `startup_jerk_cap_seconds` (1.5) of each scenario. nuPlan's + `ego_lon_jerk` is a Savitzky-Golay derivative that extrapolates at the + trajectory edges: the policy's full +4 m/s³ ramp reads 2.2 m/s³ mid-scenario + but 5.0 m/s³ when it starts at t=0 (bound 4.13); a 2 m/s³ cap reads 2.9. + In the 2026-09-03 val14 run 199 of the 232 lon-jerk comfort failures were in + the first 1.5 s. +- `pufferlib/ocean/cosim/nuplan_bridge.py` +- `config/` — the Hydra configs that plug the planner in without touching + nuplan-devkit or `carl_nuplan`: `planner/pufferdrive_planner.yaml` and two + challenge presets, `closed_loop_{nonreactive,reactive}_agents_pufferdrive` + (differing only in the `observation` override: log-replay vs IDM agents). + Found via `hydra.searchpath=[pkg://pufferlib.ocean.cosim.nuplan.config, ...]`. + Use city bins + +## Visualization + +- `COSIM_OBS_HTML=all|failures|0` (default `failures`): one interactive pufferlib.viz + replay per scenario -> `$GROUP/obs_html/.html`, showing the exact observation + vector the policy received each step (partner/lane/boundary/light slots, goals), its + action/value/entropy/probabilities and the encoder max-pool winners. This is what the + agent saw; road geometry is cropped to 250 m around the driven path. `failures` renders + (and keeps) only scenarios scoring below `COSIM_OBS_HTML_MAX_SCORE` (default 0.9); + `all` renders every scenario, `infractions` only collision / drivable-area / direction / + no-progress failures. `obs_html/index.html` is the same navigator as the self-play eval + replays (pages named `s___.html`, worst first). Pages can be + rendered later from the saved `.replay.zlib` files with `scripts/eval/render_obs_html.py`. +- nuPlan ground truth: `carl_visualization_callback` in `CALLBACKS`, or nuBoard on the + simulation logs (`scripts/eval/nuboard_failures.py` builds a failures-only nuBoard folder). + +## How to run + +``` +JOBID=13838103 +export CKPT=/scratch/yw4142/runs/2026-07-07_multi_agent_nightly_best_seed2_run_name2026-07-07_seed2_499f818/puffer_drive_nlemvgc0/models/model_puffer_drive_000954.pt +export LIMIT_TOTAL_SCENARIOS=10 + +srun --jobid=$JOBID --overlap \ + bash /scratch/yw4142/PufferDrive_nightly/pufferlib/ocean/cosim/nuplan/run_nuplan_planner.sh +``` + +## Lane graph / GPS goal features + +The deployed city bins (`/scratch/yw4142/datasets/ad/nuplan/maps/nuplan__*.bin`, +converted 2026-07-09) DO carry lane graphs — built by 123Drive's bin_factory +(`src/bin_factory/transforms/graph.py`: directed scipy Dijkstra over lane +entry/exit connectivity; the n×n distance matrix dominates bin size, e.g. +Vegas 4583 lanes → 84 MB). With `set_agent_goals`' nearest-lane snapping, the +GPS lane-distance goal columns (`obs_goal_lane_distance`) are therefore live +here, same as the CARLA side. + +To (re)generate city bins WITH lane graphs, use the 123Drive converter (from +`/scratch/ev2237/123Drive`): + +```bash +uv run convert --preset nuplan --map_only \ + --py123d_path /scratch/ev2237/data/nuplan/py123d_output \ + --output /scratch/yw4142/datasets/ad/nuplan/maps +``` + +Do NOT use `nuplan_bridge.write_drive_bin` for city bins — it writes an empty +lane graph (`n: 0`), which silently zeroes the GPS goal columns. + +## Features not obtainable from nuPlan + +- Background (DetectionsTracks) agents carry no acceleration/angular velocity — + perception tracks, not telemetry. Zeros are exact for partner observations + (never read for non-ego agents); ego telemetry comes from + `EgoState.dynamic_car_state`. +- Partner `seconds_stopped` is approximated at the planner's 10 Hz cadence from + the synced speeds (same caveat as the CARLA side). + +## Remaining TODOs + +- [ ] The full Val14 split (both challenges) has never been run to + completion, so total wall time are unmeasured. +- [ ] Double check static variables +- [ ] `nuplan_bridge.write_drive_bin` should build a lane graph (or refuse + `--map_only`-style use) so a regenerated bin can't silently lose the GPS + goal features (deployed city bins already have graphs; see above) diff --git a/pufferlib/ocean/cosim/nuplan/__init__.py b/pufferlib/ocean/cosim/nuplan/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/pufferlib/ocean/cosim/nuplan/config/__init__.py b/pufferlib/ocean/cosim/nuplan/config/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/pufferlib/ocean/cosim/nuplan/config/planner/pufferdrive_planner.yaml b/pufferlib/ocean/cosim/nuplan/config/planner/pufferdrive_planner.yaml new file mode 100644 index 0000000000..6bef40984c --- /dev/null +++ b/pufferlib/ocean/cosim/nuplan/config/planner/pufferdrive_planner.yaml @@ -0,0 +1,32 @@ +pufferdrive_planner: + _target_: pufferlib.ocean.cosim.nuplan.planner.PufferDrivePlanner + _convert_: 'all' + + checkpoint_path: ??? + bin_cache_dir: ${oc.env:NUPLAN_EXP_ROOT}/pufferdrive_bin_cache + # Whole-city map-only bins -- the only map source, no fallback. + city_bin_dir: ??? + device: "cpu" + map_radius: 400.0 # TODO: load from config + goal_spacing: 20.0 # TODO: load from config + goal_source: route # route (lane-graph route goal windows) | roadblock (route roadblock centroid windows) | roadblock_lane (roadblock goals on the lane continuing the ego lane) + num_agents: 64 # TODO: load from config + horizon_seconds: 8.0 # nuplan default + deterministic: true + # Ablations (defaults = off). sliding_goal_window: refill the goal window after every consumed goal so + # the policy never sees a window-final (speed-gated) goal before the route end. Start-up jerk caps + # [m/s^3, 0 = off] bound the ego's longitudinal jerk for the first startup_jerk_cap_seconds: nuPlan's + # ego_lon_jerk extrapolates at the trajectory edges, ~2x the raw jerk of a ramp that starts at t=0. + sliding_goal_window: false + startup_jerk_cap_seconds: 1.5 + startup_accel_jerk_cap_mps3: 0.0 + startup_brake_jerk_cap_mps3: 0.0 + # eval hack: grow pedestrian/bicycle partner boxes to at least this size [m] in the policy's obs (training min 0.8); 0 = off + pedestrian_min_size_m: 0.0 + env_overrides: + obs_slots_partners_n: 40 # GIGAFLOW eval value (Table A4); slots are max-pooled, so any checkpoint transfers + stop_signs_enabled: false # nuPlan city bins export TURN_STOP zones as stop signs; keep them out of the obs + # Write the interactive pufferlib.viz observation replay (exact policy input/outputs) per scenario + obs_html_dir: null + obs_html_max_steps: 800 + obs_html_render: true # false: save .replay.zlib only, render later (scripts/eval/render_obs_html.py) diff --git a/pufferlib/ocean/cosim/nuplan/config/simulation/closed_loop_nonreactive_agents_pufferdrive.yaml b/pufferlib/ocean/cosim/nuplan/config/simulation/closed_loop_nonreactive_agents_pufferdrive.yaml new file mode 100644 index 0000000000..e58c61fe38 --- /dev/null +++ b/pufferlib/ocean/cosim/nuplan/config/simulation/closed_loop_nonreactive_agents_pufferdrive.yaml @@ -0,0 +1,10 @@ +# @package _global_ +job_name: closed_loop_nonreactive_agents_pufferdrive + +defaults: + - override /observation: box_observation + - override /ego_controller: perfect_tracking_controller # suitable for jerk-based ego dynamics + - override /planner: pufferdrive_planner + - override /simulation_metric: simulation_closed_loop_nonreactive_agents + - override /metric_aggregator: + - closed_loop_nonreactive_agents_weighted_average diff --git a/pufferlib/ocean/cosim/nuplan/config/simulation/closed_loop_reactive_agents_pufferdrive.yaml b/pufferlib/ocean/cosim/nuplan/config/simulation/closed_loop_reactive_agents_pufferdrive.yaml new file mode 100644 index 0000000000..1f1dafc246 --- /dev/null +++ b/pufferlib/ocean/cosim/nuplan/config/simulation/closed_loop_reactive_agents_pufferdrive.yaml @@ -0,0 +1,10 @@ +# @package _global_ +job_name: closed_loop_reactive_agents_pufferdrive + +defaults: + - override /observation: idm_agents_observation + - override /ego_controller: perfect_tracking_controller # suitable for jerk-based ego dynamics + - override /planner: pufferdrive_planner + - override /simulation_metric: simulation_closed_loop_reactive_agents + - override /metric_aggregator: + - closed_loop_reactive_agents_weighted_average diff --git a/pufferlib/ocean/cosim/nuplan/planner.py b/pufferlib/ocean/cosim/nuplan/planner.py new file mode 100644 index 0000000000..512f590d45 --- /dev/null +++ b/pufferlib/ocean/cosim/nuplan/planner.py @@ -0,0 +1,651 @@ +import json +import math +from pathlib import Path +from typing import Dict, Optional, Type + +import numpy as np + +from nuplan.common.actor_state.ego_state import EgoState +from nuplan.common.actor_state.state_representation import StateSE2, StateVector2D, TimePoint +from nuplan.planning.scenario_builder.abstract_scenario import AbstractScenario +from nuplan.planning.simulation.observation.observation_type import DetectionsTracks, Observation +from nuplan.planning.simulation.planner.abstract_planner import ( + AbstractPlanner, + PlannerInitialization, + PlannerInput, +) +from nuplan.planning.simulation.trajectory.abstract_trajectory import AbstractTrajectory +from nuplan.planning.simulation.trajectory.interpolated_trajectory import InterpolatedTrajectory + +from pufferlib.ocean.cosim import nuplan_bridge as nb +from pufferlib.ocean.cosim.arch import checkpoint_config_path +from pufferlib.ocean.cosim.goals import RouteGoalWindow, route_goals_from_xy +from pufferlib.ocean.drive.drive import Drive + +from pufferlib.ocean.cosim.nuplan_bridge import DEFAULT_ARCH, FAR_AWAY +from pufferlib.ocean.drive import binding + +EGO_OBS_ACCEL_LONG_IDX = 4 # write_ego_obs column order: speed, width, length, steering, accel_long, ... +EGO_TRACKING_TOL_M = 0.05 # perfect_tracking_controller hands back our integrated pose up to log-timestamp jitter +EGO_TRACKING_TOL_RAD = 0.005 +SCENARIO_LENGTH_MARGIN_STEPS = 2 # shadow env must never hit its own truncation while nuPlan still plans +NUPLAN_COMFORT_MAX_LON_ACCEL_MPS2 = 2.40 # ego_lon_acceleration_statistics.yaml max_lon_accel +TRAJECTORY_TAIL_SPACING_S = 0.5 # constant-velocity tail past the integrated step; the controller only reads t+dt +COMFORT_ACCEL_MARGIN = 0.96 # nuPlan Savitzky-Golay-smooths the reported accel and overshoots a hard cap by ~0.5% +ROADBLOCK_GOAL_MARGIN_M = 5.0 # first roadblock goal sits at least this far beyond the goal radius ahead of the ego + + +def startup_jerk_action_bounds(accel_cap_mps3: float, brake_cap_mps3: float): + """[lo, hi] of the continuous longitudinal jerk action for the caps (0 = uncapped): the env maps + action < 0 onto [JERK_LONG[0], 0] and action > 0 onto [0, JERK_LONG[-1]] m/s³.""" + lo = -1.0 if brake_cap_mps3 <= 0 else max(-1.0, -brake_cap_mps3 / abs(float(binding.JERK_LONG[0]))) + hi = 1.0 if accel_cap_mps3 <= 0 else min(1.0, accel_cap_mps3 / float(binding.JERK_LONG[-1])) + return lo, hi + + +def clean_policy_state_dict(state_dict): + """Strip torch.compile / DDP prefixes. Inlined from pufferlib.pufferl to + avoid importing the training stack (wandb, neptune, the _C kernel, ...) + inside the evaluation environment.""" + + def clean(key): + while key.startswith(("module.", "_orig_mod.")): + key = key.split(".", 1)[1] + return key + + return {clean(k): v for k, v in state_dict.items()} + + +# per-process caches: one planner per scenario, same checkpoint/bins each time +_state_dict_cache: Dict[str, dict] = {} +_bin_geometry_cache: Dict[str, dict] = {} + + +def _angle_diff(a: float, b: float) -> float: + return (a - b + math.pi) % (2.0 * math.pi) - math.pi + + +def _steering_from(yaw_rate: float, speed: float, wheel_base: float) -> float: + if abs(speed) < 0.1: + return 0.0 + return float(np.clip(math.atan(yaw_rate * wheel_base / speed), -0.83, 0.83)) + + +class PufferDrivePlanner(AbstractPlanner): + """nuPlan planner that delegates observation + ego dynamics to PufferDrive.""" + + requires_scenario: bool = True + + def __init__( + self, + checkpoint_path: str, + bin_cache_dir: str, + city_bin_dir: Optional[str] = None, + scenario: Optional[AbstractScenario] = None, + device: str = "cpu", + map_radius: float = 400.0, + goal_spacing: float = 20.0, + goal_source: str = "route", + num_agents: int = 64, + horizon_seconds: float = 8.0, + deterministic: bool = True, + sliding_goal_window: bool = False, + startup_jerk_cap_seconds: float = 1.5, + startup_accel_jerk_cap_mps3: float = 0.0, + startup_brake_jerk_cap_mps3: float = 0.0, + pedestrian_min_size_m: float = 0.0, + env_overrides: Optional[Dict] = None, + obs_html_dir: Optional[str] = None, + obs_html_max_steps: int = 800, + obs_html_render: bool = True, + ): + """ + :param checkpoint_path: PufferDrive policy checkpoint (.pt); config.yaml + two levels up defines the policy architecture. + :param bin_cache_dir: fallback cache dir for a city bin's `.origin.json` + sidecar when the bin's own directory isn't writable. + :param city_bin_dir: directory of whole-city map-only bins named + `nuplan__.bin` (the only map source; see module + docstring). A missing bin is a hard error, no fallback. + :param scenario: injected by the devkit (requires_scenario). + :param map_radius: map extraction / light-matching radius [m]. + :param goal_spacing: goal spacing along the route / logged path fed to the policy [m]. + :param goal_source: "route" (goal windows along the lane-graph route through the route + roadblocks), "roadblock" (goal windows of route roadblock centroids, no lane choice) or + "roadblock_lane" (roadblock goals on the lane continuing the ego's own lane, a lane change + only where the lane graph forces one). + :param num_agents: PufferDrive agent-pool size (ego + streamed background). + :param horizon_seconds: returned trajectory horizon (constant-velocity + extrapolation past the integrated first step) [s]. + :param sliding_goal_window: refill the goal window to `num_goals` goals ahead after every + consumed goal (default: replace it only once exhausted, as in training), so the policy + sees a window-final speed-gated goal only at the true route end. + :param startup_jerk_cap_seconds: length of the scenario-start window the jerk caps apply to [s]. + :param startup_accel_jerk_cap_mps3: cap on the ego's positive (accelerating) longitudinal jerk + during the start window [m/s³]; 0 = uncapped. nuPlan's Savitzky-Golay jerk extrapolates at + the trajectory edges, so a ramp that starts at t=0 reads ~2x its raw jerk. + :param startup_brake_jerk_cap_mps3: same cap for braking jerk; 0 = uncapped. + :param pedestrian_min_size_m: eval hack: pedestrian and bicycle partner boxes are grown to at + least this length and width [m] before the policy sees them (training spawns nothing below + 0.8 x 0.8 m, nuPlan pedestrians are 0.4-0.8 m); nuPlan keeps scoring the true boxes. 0 = off. + :param env_overrides: Drive env kwargs overriding DEFAULT_ARCH. + :param obs_html_dir: write the interactive pufferlib.viz observation replay per + scenario (the exact obs the policy received, its outputs and encoder pool winners). + :param obs_html_render: render the html right away; False saves only the compact + .replay.zlib so pages can be rendered later for selected scenarios + (scripts/eval/render_obs_html.py, e.g. only those scoring below a threshold). + """ + self._checkpoint_path = checkpoint_path + self._bin_cache_dir = Path(bin_cache_dir) + self._city_bin_dir = Path(city_bin_dir) if city_bin_dir else None + self._scenario = scenario + self._device = device + self._map_radius = float(map_radius) + self._goal_spacing = float(goal_spacing) + if goal_source not in ("route", "roadblock", "roadblock_lane"): + raise ValueError(f"goal_source must be 'route', 'roadblock' or 'roadblock_lane', got {goal_source!r}") + self._goal_source = goal_source + self._num_agents = int(num_agents) + self._horizon_seconds = float(horizon_seconds) + self._deterministic = deterministic + self._sliding_goal_window = bool(sliding_goal_window) + self._startup_jerk_cap_seconds = float(startup_jerk_cap_seconds) + self._startup_accel_jerk_cap_mps3 = float(startup_accel_jerk_cap_mps3) + self._startup_brake_jerk_cap_mps3 = float(startup_brake_jerk_cap_mps3) + if ( + min(self._startup_jerk_cap_seconds, self._startup_accel_jerk_cap_mps3, self._startup_brake_jerk_cap_mps3) + < 0 + ): + raise ValueError("startup_jerk_cap_seconds and the startup jerk caps must be >= 0") + self._pedestrian_min_size_m = float(pedestrian_min_size_m) + if self._pedestrian_min_size_m < 0: + raise ValueError("pedestrian_min_size_m must be >= 0") + self._startup_jerk_cap_steps = 0 # resolved in _build from the scenario dt + self._startup_action_bounds = None # (lo, hi) on the continuous jerk action while the cap is live + self._env_overrides = env_overrides or {} + self._obs_html_dir = Path(obs_html_dir) if obs_html_dir else None + self._obs_html_max_steps = int(obs_html_max_steps) + self._obs_html_render = bool(obs_html_render) + self._obs_replay = None # ObsReplayCapture, built in _build if obs_html_dir is set + self._arch: Optional[Dict] = None # resolved in _build from the checkpoint config + + # lazy state (built on the first compute call, which knows the ego pose) + self._initialization: Optional[PlannerInitialization] = None + self._env: Optional[Drive] = None + self._policy = None + self._transform: Optional[nb.NuPlanTransform] = None + self._connector_map: Dict[str, int] = {} + self._route_connector_ids = () # lane ids along the Dijkstra route: they decide shared stop-line elements + self._num_traffic = 0 + self._goal_window: Optional[RouteGoalWindow] = None + self._last_integrated = None # (bin_x, bin_y, heading) the shadow ego was left at by the last step + self._last_light_states = None # traffic-light state array from the most recent _sync + self._static_on_lane: Dict[str, bool] = {} # static object track_token -> stands inside a lane polygon + + # --- AbstractPlanner interface ------------------------------------------- + def initialize(self, initialization: PlannerInitialization) -> None: + self._initialization = initialization + + def name(self) -> str: + return self.__class__.__name__ + + def observation_type(self) -> Type[Observation]: + return DetectionsTracks # type: ignore + + def generate_planner_report(self, clear_stats: bool = True): + # runners stay referenced until the whole process exits; free per-scenario state + report = super().generate_planner_report(clear_stats) + if self._env is not None: + self._env.close() + self._env = None + self._policy = None + self._obs_replay = None + return report + + # --- whole-city map-only bins ------------------------------------------- + def _find_city_bin(self) -> Path: + """City bin for this scenario's map (the map id the devkit read from + the log db's `map_version`). Assumed to exist -- city_bin_dir covers + every map, no fallback.""" + map_name = str(self._initialization.map_api.map_name).replace(".gpkg", "") + return self._city_bin_dir / f"nuplan__{map_name}.bin" + + def _gpkg_layer(self, layer_name: str): + """Whole-city vector layer (GeoDataFrame, UTM) from the scenario's map.""" + map_api = self._initialization.map_api + try: + return map_api._load_vector_map_layer(layer_name) + except AttributeError: + return map_api._maps_db.load_vector_layer(map_api.map_name, layer_name) + + def _city_bin_origin(self, bin_path: Path, stop_line_centers: np.ndarray) -> nb.NuPlanTransform: + """Recover a city bin's frame origin (it stores no centroid): global + translation vote on stop-line landmarks, then translation-only ICP of + the bin's lane centerlines onto the GPKG baseline paths. Cached in a + sidecar json — one registration per city, ever.""" + sidecar = bin_path.with_suffix(".origin.json") + cache = sidecar if sidecar.exists() else self._bin_cache_dir / sidecar.name + if cache.exists(): + origin = json.loads(cache.read_text())["origin"] + return nb.NuPlanTransform(origin[0], origin[1]) + + if not len(stop_line_centers): + raise RuntimeError(f"{bin_path}: no traffic elements to register the origin with") + import shapely + + stops = self._gpkg_layer("stop_polygons").geometry.centroid + init = nb.coarse_translation_vote(stop_line_centers, np.stack([stops.x.to_numpy(), stops.y.to_numpy()], axis=1)) + lane_utm = shapely.get_coordinates(self._gpkg_layer("baseline_paths").geometry.values) + t, resid = nb.fit_translation(nb.read_bin_lane_points(bin_path), lane_utm, init=init) + if resid > 0.5: + raise RuntimeError( + f"{bin_path}: lane-geometry registration residual {resid:.2f} m — " + "bin does not match this city's GPKG map, refusing to guess the origin." + ) + payload = json.dumps({"origin": [float(t[0]), float(t[1])], "residual_m": resid}) + for target in (sidecar, self._bin_cache_dir / sidecar.name): + try: + target.parent.mkdir(parents=True, exist_ok=True) + target.write_text(payload) + break + except OSError: + continue + print( + f"[pufferdrive_planner] registered {bin_path.name}: " + f"origin=({t[0]:.2f}, {t[1]:.2f}) resid={resid * 100:.1f} cm" + ) + return nb.NuPlanTransform(t[0], t[1]) + + def _resolve_map_bin(self): + """-> (bin_path, NuPlanTransform, bin geometry dict from read_bin_geometry).""" + city_bin = self._find_city_bin() + geo = _bin_geometry_cache.get(str(city_bin)) + if geo is None: + geo = nb.read_bin_geometry(city_bin) + _bin_geometry_cache[str(city_bin)] = geo + tf = ( + nb.NuPlanTransform(*geo["origin"]) + if geo["origin"] is not None + else self._city_bin_origin(city_bin, geo["stop_line_centers"]) + ) + return city_bin, tf, geo + + def _match_traffic_lights(self, geo: dict, ex: float, ey: float) -> Dict[str, int]: + """Signalized lane connectors within map_radius -> bin traffic elements, + matched by entry pose to co-directional stop lines (no sidecar file needed).""" + from nuplan.common.actor_state.state_representation import Point2D + from nuplan.common.maps.maps_datatypes import SemanticMapLayer + + objs = self._initialization.map_api.get_proximal_map_objects( + Point2D(ex, ey), self._map_radius, [SemanticMapLayer.LANE_CONNECTOR] + ) + entries = {} + for e in objs[SemanticMapLayer.LANE_CONNECTOR]: + if e.has_traffic_lights(): + p0 = e.baseline_path.discrete_path[0] + entries[str(e.id)] = (float(p0.x), float(p0.y), float(p0.heading)) + return nb.match_connectors_to_stop_lines( + entries, self._transform, geo["stop_lines"], geo["stop_line_headings"], geo["traffic_types"] + ) + + def _resolve_arch(self, cfg: Dict, bin_path: Path) -> Dict: + """Shadow-env Drive kwargs: the checkpoint's training env section + decides every Drive-accepted key, the clean-eval profile is applied on + top (see cosim/arch.py), DEFAULT_ARCH fills the no-checkpoint gaps, + `env_overrides` wins -- except for the structural co-sim keys (map/ + pool wiring), which are not overridable.""" + from pufferlib.ocean.cosim.arch import shadow_env_kwargs + + if self._scenario is None: + raise RuntimeError("PufferDrivePlanner needs the scenario injected (requires_scenario)") + return shadow_env_kwargs( + cfg, + defaults=DEFAULT_ARCH, + overrides={ + **self._env_overrides, + "map_dir": str(bin_path), + "num_maps": 1, + # One policy agent (the ego); every other slot is a static partner streamed from nuPlan. + "num_agents": 1, + "min_agents_per_env": 1, + "cosim_partner_slots": self._num_agents - 1, + "goal_source": "external", + # Training semantics: a window's final goal clears only below goal_speed (3 m/s) + "goal_reach_requires_speed": True, + # C_acc conditioning: cap the jerk model's positive accel at nuPlan's comfort bound instead of 2.5 + "conditioning_accel_scale": COMFORT_ACCEL_MARGIN + * NUPLAN_COMFORT_MAX_LON_ACCEL_MPS2 + / binding.ACCEL_LONG_MAX, + # lockstep with nuPlan's planning iterations, not the training dt (mimolette: 0.3) + "dt": float(self._scenario.database_interval), + "scenario_length": int(self._scenario.get_number_of_iterations()) + SCENARIO_LENGTH_MARGIN_STEPS, + "resample_frequency": 0, + # External sim owns the episode: a training-config + # termination_mode=1 would c_reset() the pool (ego included) + # once parked FAR_AWAY slots latch under "stop" behaviors. + "termination_mode": 0, + # native-eval scenario batching builds zero envs on a map-only bin + "eval_mode": False, + # Enforcement off (flags still fire): a "stop" latch would freeze the ego for good. + "collision_behavior": "ignore", + "offroad_behavior": "ignore", + "traffic_light_behavior": "ignore", + }, + ) + + @property + def _dummy(self) -> bool: + """checkpoint_path in ('', 'dummy'): no policy — constant forward-jerk + actions, the no-checkpoint wiring test (same as the CARLA agent).""" + return str(self._checkpoint_path or "").lower() in ("", "dummy", "none") + + # --- lazy construction ---------------------------------------------------- + def _build(self, ego_state: EgoState) -> None: + import torch + import yaml + + import pufferlib.ocean.torch as drive_torch + + init = self._initialization + ex, ey = float(ego_state.center.x), float(ego_state.center.y) + + cfg = {} if self._dummy else yaml.safe_load(open(checkpoint_config_path(self._checkpoint_path))) + + bin_path, self._transform, geo = self._resolve_map_bin() + self._num_traffic = geo["num_traffic"] + self._connector_map = self._match_traffic_lights(geo, ex, ey) + + self._arch = self._resolve_arch(cfg, bin_path) + self._env = Drive(**self._arch) + self._env.reset() + torch.set_num_threads(1) # one scenario per worker process; BLAS threads only fight the other workers + if max(self._startup_accel_jerk_cap_mps3, self._startup_brake_jerk_cap_mps3) > 0: + import pufferlib.spaces + + if not isinstance(self._env.single_action_space, pufferlib.spaces.Box): + raise RuntimeError("startup jerk caps need a continuous (jerk) env action space") + self._startup_jerk_cap_steps = int(round(self._startup_jerk_cap_seconds / float(self._arch["dt"]))) + self._startup_action_bounds = startup_jerk_action_bounds( + self._startup_accel_jerk_cap_mps3, self._startup_brake_jerk_cap_mps3 + ) + + # Round-trip probe: slot 0 must be the ego we set. It is silently wrong when + # gigaflow spawning failed (e.g. a city bin without lane connectivity). + probe_x, probe_y = self._transform.loc_to_bin(ex, ey) + zero = np.zeros(1, np.float32) + self._env.set_agent_states( + np.array([0], np.int32), + np.array([probe_x], np.float32), + np.array([probe_y], np.float32), + zero, + np.array([float(ego_state.center.heading)], np.float32), + zero, + zero, + zero, + zero, + ) + readback = self._env.get_global_agent_state() + if abs(float(readback["x"][0]) - probe_x) > 0.5 or abs(float(readback["y"][0]) - probe_y) > 0.5: + raise RuntimeError( + f"{bin_path.name}: shadow env agent slot 0 is not the ego " + f"(set ({probe_x:.1f}, {probe_y:.1f}), read ({readback['x'][0]:.1f}, {readback['y'][0]:.1f})). " + "Gigaflow spawning likely failed on this bin -- check for '[GIGAFLOW WARNING]'/'[ERROR]' in the " + "worker stdout; a map-only city bin without lane connectivity (exit_lanes) is the known cause." + ) + + if self._dummy: + self._policy = None + else: + # pre-3.0 checkpoints keep action_type only in the env section + cfg["policy"].setdefault("action_type", cfg["env"]["action_type"]) + policy = getattr(drive_torch, cfg.get("policy_name", "Drive"))(self._env, **cfg["policy"]) + sd = _state_dict_cache.get(str(self._checkpoint_path)) + if sd is None: + sd = clean_policy_state_dict( + torch.load(self._checkpoint_path, map_location=self._device, weights_only=False) + ) + _state_dict_cache[str(self._checkpoint_path)] = sd + policy.load_state_dict(sd) + self._policy = policy.to(self._device).eval() + + # The lane-graph route through the CaRL-corrected route roadblocks always decides the shared + # stop-line elements and seeds the route goals. + from carl_nuplan.planning.simulation.planner.pdm_planner.utils.route_utils import ( + route_roadblock_correction_v2, + ) + + route_ids = route_roadblock_correction_v2(ego_state, init.map_api, list(init.route_roadblock_ids)) + centerline, self._route_connector_ids = nb.route_centerline( + init.map_api, route_ids, ex, ey, float(ego_state.center.heading) + ) + if self._goal_source in ("roadblock", "roadblock_lane"): + min_ahead_m = float(self._env.goal_radius) + ROADBLOCK_GOAL_MARGIN_M + goal_route_ids = nb.extend_route_past_loop_cut(route_ids, init.route_roadblock_ids) + goal_args = ( + init.map_api, + goal_route_ids, + ex, + ey, + float(ego_state.center.heading), + self._goal_spacing, + min_ahead_m, + ) + if self._goal_source == "roadblock_lane": + goals = nb.roadblock_lane_goals(*goal_args) + else: + goals = nb.roadblock_centroid_goals(*goal_args) + else: + goals = nb.goals_along(centerline, self._goal_spacing) + if len(goals) == 0: # degenerate route: fall back to the mission goal + goals = np.array([[init.mission_goal.x, init.mission_goal.y]]) + goals -= (self._transform.ox, self._transform.oy) + route_goals = route_goals_from_xy(goals, origin_xy=self._transform.loc_to_bin(ex, ey)) + self._goal_window = RouteGoalWindow(self._env, route_goals, sliding=self._sliding_goal_window) + + # ego bounding box from nuPlan vehicle parameters (static) + fp = ego_state.car_footprint + self._env.set_agent_sizes( + np.array([0], np.int32), np.array([fp.length], np.float32), np.array([fp.width], np.float32) + ) + + if self._obs_html_dir and self._policy is not None: + from pufferlib.ocean.cosim.obs_replay import ObsReplayCapture + + token = self._scenario.token if self._scenario else "scenario" + self._obs_replay = ObsReplayCapture( + self._env, + self._policy, + self._obs_html_dir / token, + max_steps=self._obs_html_max_steps, + ghost_trajectory=nb.logged_ego_boxes(self._scenario, self._transform), + ) + + print( + f"[pufferdrive_planner] bin={bin_path.name} lights={len(self._connector_map)}/" + f"{self._num_traffic} goals={len(goals)} goal_source={self._goal_source}" + ) + + # --- world sync ----------------------------------------------------------- + def _sync(self, ego_state: EgoState, detections: DetectionsTracks, traffic_light_data) -> None: + tf = self._transform + env = self._env + + # ego (slot 0): synced from nuPlan telemetry once, at the scenario's initial state. Afterwards the + # shadow env owns the ego: nuPlan's perfect_tracking_controller hands back exactly the pose we + # integrated, and re-deriving accel_long/steering from telemetry every tick re-injects + # finite-difference accel after every stop and drives the ego into reverse. + h = float(ego_state.center.heading) + dcs = ego_state.dynamic_car_state + v_local = dcs.center_velocity_2d + vx_g = v_local.x * math.cos(h) - v_local.y * math.sin(h) + vy_g = v_local.x * math.sin(h) + v_local.y * math.cos(h) + ex, ey = tf.loc_to_bin(ego_state.center.x, ego_state.center.y) + if self._last_integrated is not None: + self._check_ego_tracking(ex, ey, h) + else: + env.set_agent_states( + np.array([0], np.int32), + np.array([ex], np.float32), + np.array([ey], np.float32), + np.array([0.0], np.float32), + np.array([h], np.float32), + np.array([vx_g], np.float32), + np.array([vy_g], np.float32), + np.array([float(dcs.angular_velocity)], np.float32), + np.array([float(dcs.center_acceleration_2d.x)], np.float32), + ) + + # background (slots 1..): streamed from nuPlan, never simulated here. nuPlan's TrackedObject + # carries no acceleration/angular-velocity (perception detections, not ego telemetry), but + # write_partner_obs never reads those fields for non-ego agents, so zero is exact, not a stopgap. + objs = nb.partner_tracked_objects( + detections.tracked_objects, self._initialization.map_api, self._static_on_lane + ) + if len(objs) > self._num_agents - 1: + # keep the nearest: devkit ordering is by type/token, so blind + # truncation could drop a close vehicle while keeping far ones + ox, oy = ego_state.center.x, ego_state.center.y + objs.sort(key=lambda o: (o.center.x - ox) ** 2 + (o.center.y - oy) ** 2) + objs = objs[: self._num_agents - 1] + if objs: + idx, x, y, z, hh, vx, vy, tp, ln, wd = nb.tracked_objects_to_arrays(objs, tf) + if self._pedestrian_min_size_m > 0: + ln, wd = nb.floor_vru_partner_sizes(tp, ln, wd, self._pedestrian_min_size_m) + env.set_agent_states(idx, x, y, z, hh, vx, vy, np.zeros_like(vx), np.zeros_like(vx)) + env.set_agent_sizes(idx, ln, wd) + surplus = np.arange(1 + len(objs), self._num_agents, dtype=np.int32) + if len(surplus): + far = np.full(len(surplus), FAR_AWAY, np.float32) + zero = np.zeros_like(far) + env.set_agent_states(surplus, far, far, far, zero, zero, zero, zero, zero) + + # traffic lights: state array sized to the bin's element count + if self._num_traffic: + self._last_light_states = nb.traffic_light_states( + traffic_light_data, self._connector_map, self._num_traffic, self._route_connector_ids + ) + env.set_traffic_light_states(self._last_light_states) + + self._goal_window.sync(ex, ey, h) + + def _check_ego_tracking(self, bin_x: float, bin_y: float, heading: float) -> None: + lx, ly, lh = self._last_integrated + if ( + abs(bin_x - lx) > EGO_TRACKING_TOL_M + or abs(bin_y - ly) > EGO_TRACKING_TOL_M + or abs(_angle_diff(heading, lh)) > EGO_TRACKING_TOL_RAD + ): + raise RuntimeError( + f"nuPlan ego pose ({bin_x:.3f}, {bin_y:.3f}, {heading:.4f}) diverged from the integrated " + f"shadow pose ({lx:.3f}, {ly:.3f}, {lh:.4f}); the planner assumes perfect_tracking_controller" + ) + + # --- planning ------------------------------------------------------------- + def compute_planner_trajectory(self, current_input: PlannerInput) -> AbstractTrajectory: + import torch + + import pufferlib.pytorch + + ego_state, detections = current_input.history.current_state + if self._env is None: + self._build(ego_state) + + self._sync(ego_state, detections, current_input.traffic_light_data) + if self._env.tick >= self._arch["scenario_length"] - 1: + raise RuntimeError( + f"shadow env tick {self._env.tick} reached scenario_length {self._arch['scenario_length']}; " + "nuPlan planned more iterations than the scenario declared" + ) + obs = np.asarray(self._env.recompute_observations()) + + if self._policy is None: # dummy wiring test: constant forward jerk + act = np.full((self._env.num_agents, 1), 3 * 3 + 1, dtype=np.int32) + else: + import pufferlib.spaces + + # A discrete policy head on a continuous env needs the bin->continuous + # mapping (pufferl.py feeds cont_action to the env, never the bin indices). + env_continuous = isinstance(self._env.single_action_space, pufferlib.spaces.Box) + if self._deterministic: + action_selection = ( + pufferlib.pytorch.ACTION_SELECT_MEAN + if env_continuous and not self._policy.is_continuous + else pufferlib.pytorch.ACTION_SELECT_MODE + ) + else: + action_selection = pufferlib.pytorch.ACTION_SELECT_SAMPLE + obs_tensor = torch.as_tensor(obs).to(self._device) + with torch.no_grad(): + logits, value = self._policy.forward_eval(obs_tensor) + action, _, entropy, cont_action = pufferlib.pytorch.sample_logits( + logits, + action_selection=action_selection, + env_continuous=env_continuous, + policy=self._policy, + ) + env_action = cont_action if env_continuous and cont_action is not None else action + act = env_action.cpu().numpy().reshape(self._env.num_agents, -1) + if not env_continuous: + act = act.astype(np.int32) + if self._startup_action_bounds is not None and self._env.tick < self._startup_jerk_cap_steps: + np.clip(act[:, 0], *self._startup_action_bounds, out=act[:, 0]) + if self._obs_replay is not None: + # sample_logits' discrete `action` is the class taken (mode/sample) or the argmax under mean selection + action_index = action.cpu().numpy().reshape(-1) if not self._policy.is_continuous else None + self._obs_replay.capture( + obs, act, self._obs_replay.policy_outputs(obs_tensor, logits, value, entropy), action_index + ) + + self._env.step(act) # integrates the ego one dt; background is re-synced next call + + if ( + self._obs_replay is not None + and current_input.iteration.index >= self._scenario.get_number_of_iterations() - 2 + ): + out = self._obs_replay.write(render_html=self._obs_html_render) + print(f"[pufferdrive_planner] wrote obs replay ({len(self._obs_replay)} steps) -> {out}") + self._obs_replay = None + + # integrated ego -> nuPlan EgoState at t + dt + dt = float(self._arch["dt"]) + state = self._env.get_global_agent_state() + nx_, ny_ = self._transform.bin_to_loc(float(state["x"][0]), float(state["y"][0])) + nh = float(state["heading"][0]) + self._last_integrated = (float(state["x"][0]), float(state["y"][0]), nh) + + # speed = the shadow ego's POST-STEP velocity state (ego obs column 0), + # not displacement/dt, which halves the intent during acceleration and + # makes the closed loop crawl (see WorldSync.integrate). + ego_obs = np.asarray(self._env.observations)[0] + speed = float(ego_obs[0]) * self._env.obs_norm_speed_mps + accel_long = float(ego_obs[EGO_OBS_ACCEL_LONG_IDX]) * binding.ACCEL_LONG_NORM + yaw_rate = _angle_diff(nh, float(ego_state.center.heading)) / dt + + params = ego_state.car_footprint.vehicle_parameters + t0 = ego_state.time_point + + def _state_at(i: int) -> EgoState: + """i-th 10 Hz pose: i=0 current, i=1 the PufferDrive-integrated step, + then constant velocity + yaw-rate extrapolation for the horizon.""" + if i == 0: + return ego_state + t = (i - 1) * dt + heading = nh + yaw_rate * t + x = nx_ + speed * math.cos(heading) * t + y = ny_ + speed * math.sin(heading) * t + return EgoState.build_from_center( + center=StateSE2(x, y, heading), + center_velocity_2d=StateVector2D(speed, 0.0), + center_acceleration_2d=StateVector2D(accel_long if i == 1 else 0.0, 0.0), + tire_steering_angle=_steering_from(yaw_rate, speed, params.wheel_base), + time_point=TimePoint(t0.time_us + int(i * dt * 1e6)), + vehicle_parameters=params, + angular_vel=yaw_rate, + ) + + n_poses = int(round(self._horizon_seconds / dt)) + tail_stride = max(1, int(round(TRAJECTORY_TAIL_SPACING_S / dt))) + pose_indices = [0, 1] + list(range(1 + tail_stride, n_poses + 1, tail_stride)) + if pose_indices[-1] != n_poses: + pose_indices.append(n_poses) + return InterpolatedTrajectory([_state_at(i) for i in pose_indices]) diff --git a/pufferlib/ocean/cosim/nuplan/run_nuplan_planner.sh b/pufferlib/ocean/cosim/nuplan/run_nuplan_planner.sh new file mode 100755 index 0000000000..bc21e10f92 --- /dev/null +++ b/pufferlib/ocean/cosim/nuplan/run_nuplan_planner.sh @@ -0,0 +1,141 @@ +#!/bin/bash +# Run pufferlib/ocean/cosim/nuplan/planner.py under nuPlan's unmodified +# closed-loop run_simulation.py: BOTH agent-reactivity challenges (nonreactive +# log-replay + reactive IDM), with both debug videos enabled. See README.md +# "How to run" and planner.py's module docstring. +# +# Usage (from a compute-node allocation's job id -- squeue -u $USER to find it): +# CKPT=/path/to/model_puffer_drive_xxxxxx.pt \ +# srun --jobid= --overlap bash run_nuplan_planner.sh +# +# Required env: +# CKPT PufferDrive checkpoint .pt, or "dummy" for the no-policy wiring test. +# Optional env: +# SPLIT carl_nuplan scenario_filter. Default: val14_split (the full +# closed-loop Val14 benchmark -- ~900 scenarios, "all the +# scenarios"). filter_pgh is a much smaller Pittsburgh-only +# filter, useful to validate a new checkpoint quickly first. +# CHALLENGES space-separated challenge list. Default: both +# "closed_loop_nonreactive_agents_pufferdrive +# closed_loop_reactive_agents_pufferdrive" (log-replay + IDM). +# THREADS_PER_NODE caps worker.threads_per_node. ray_distributed sizes its +# pool from the NODE's cpu count (128 on cs), not the slurm +# allocation -- 128 nuPlan workers OOMed a 160G job in 34 min +# (2026-08-09); size this to job_mem / ~12G per worker. +# WORKER nuPlan worker config. Default: unset -> falls through to the +# devkit's own default (ray_distributed), matching +# scripts/eval/sim_pufferdrive.sh's established convention for a +# full-benchmark run. NOT verified in this container this +# session -- only worker=sequential has been exercised end-to-end +# (one scenario). If ray_distributed misbehaves here, retry with +# WORKER=sequential (much slower, but proven) while investigating. +# LIMIT_TOTAL_SCENARIOS caps scenario_filter.limit_total_scenarios (unset = +# no cap, SPLIT's own scenario count applies). Handy for a quick +# sanity check before committing to the full SPLIT. +# GROUP output group dir. Default: runs/nuplan_leaderboard_. +# GOAL_SOURCE=route|roadblock planner goal source (default route, see pufferdrive_planner.yaml). +# SLIDING_GOAL_WINDOW=true|false refill the goal window after every consumed goal (default false). +# STARTUP_ACCEL_JERK_CAP / STARTUP_BRAKE_JERK_CAP [m/s^3] ego jerk caps for the first +# STARTUP_JERK_CAP_SECONDS (default 1.5) of every scenario; unset/0 = off. +# PEDESTRIAN_MIN_SIZE_M [m] grow pedestrian/bicycle partner boxes to at least this size in the +# policy's observation (training never spawns below 0.8 m); unset/0 = off. +# OBS_SLOTS_PARTNERS_N shadow-env partner obs slots (unset = planner yaml, 40). +# EVAL_PERCEIVED_SIZE_MARGIN_M [m per side] ego box inflation in the policy's obs (unset = arch.py, 0.2). +# COSIM_OBS_HTML=all|failures|infractions|0 interactive observation replay (exact policy +# input/outputs per step, pufferlib.viz HTML) -> $GROUP/obs_html. all: every +# scenario. failures (default): only scenarios scoring below +# COSIM_OBS_HTML_MAX_SCORE (default 0.9). infractions: only scenarios with an +# at-fault collision, drivable-area or driving-direction violation, or no +# progress. Non-selected replays are deleted after the run. 0: off. +# (nuPlan's own ground-truth video is always requested via +# carl_visualization_callback in the callback list below -- one .avi per +# scenario under $GROUP/simulation///visualization/) +if [ -z "$INSIDE_PUFFER_CONTAINER" ] && [ -f /share/apps/images/slurm-ib-bind-apptainer.sh ]; then + source /share/apps/images/slurm-ib-bind-apptainer.sh + export APPTAINERENV_PREPEND_PATH=/share/apps/apptainer/bin:$APPTAINERENV_PREPEND_PATH + exec singularity exec \ + --bind /scratch/yw4142:/scratch/yw4142 \ + /share/apps/images/cuda13.0.1-cudnn9.13.0-ubuntu-24.04.3.sif \ + env INSIDE_PUFFER_CONTAINER=1 bash "$0" +fi +set -u + +: "${CKPT:?set CKPT=/path/to/model_xxx.pt (or CKPT=dummy for the wiring test)}" +PY=${PY:-/scratch/yw4142/venvs/pufferdrive/bin/python} +PD=${PD:-/scratch/yw4142/PufferDrive_cosim} +export NUPLAN_DATA_ROOT=${NUPLAN_DATA_ROOT:-/scratch/yw4142/datasets/ad/nuplan} +export NUPLAN_MAPS_ROOT=${NUPLAN_MAPS_ROOT:-/scratch/yw4142/datasets/ad/nuplan/maps-gpkg} +export NUPLAN_EXP_ROOT=${NUPLAN_EXP_ROOT:-/scratch/yw4142/runs/nuplan_exp} +export NUPLAN_DEVKIT_ROOT=${NUPLAN_DEVKIT_ROOT:-/scratch/yw4142/nuplan-devkit} +mkdir -p "$NUPLAN_EXP_ROOT" + +SPLIT=${SPLIT:-val14_split} +CHALLENGES=${CHALLENGES:-"closed_loop_nonreactive_agents_pufferdrive closed_loop_reactive_agents_pufferdrive"} +TIMESTAMP=$(date +"%Y%m%d_%H%M%S") +GROUP=${GROUP:-$NUPLAN_EXP_ROOT/nuplan_leaderboard_${TIMESTAMP}} +OBS_HTML=${COSIM_OBS_HTML:-failures} +[ "$OBS_HTML" = "1" ] && OBS_HTML=all + +EXTRA_ARGS=() +[ "$OBS_HTML" != "0" ] && EXTRA_ARGS+=("planner.pufferdrive_planner.obs_html_dir=$GROUP/obs_html" "planner.pufferdrive_planner.obs_html_render=false") +CITY_BIN_DIR=${CITY_BIN_DIR:-/scratch/yw4142/datasets/ad/nuplan/maps} +EXTRA_ARGS+=("planner.pufferdrive_planner.city_bin_dir=$CITY_BIN_DIR") +[ -n "${GOAL_SOURCE:-}" ] && EXTRA_ARGS+=("planner.pufferdrive_planner.goal_source=$GOAL_SOURCE") +[ -n "${SLIDING_GOAL_WINDOW:-}" ] && EXTRA_ARGS+=("planner.pufferdrive_planner.sliding_goal_window=$SLIDING_GOAL_WINDOW") +[ -n "${STARTUP_JERK_CAP_SECONDS:-}" ] && EXTRA_ARGS+=("planner.pufferdrive_planner.startup_jerk_cap_seconds=$STARTUP_JERK_CAP_SECONDS") +[ -n "${STARTUP_ACCEL_JERK_CAP:-}" ] && EXTRA_ARGS+=("planner.pufferdrive_planner.startup_accel_jerk_cap_mps3=$STARTUP_ACCEL_JERK_CAP") +[ -n "${STARTUP_BRAKE_JERK_CAP:-}" ] && EXTRA_ARGS+=("planner.pufferdrive_planner.startup_brake_jerk_cap_mps3=$STARTUP_BRAKE_JERK_CAP") +[ -n "${PEDESTRIAN_MIN_SIZE_M:-}" ] && EXTRA_ARGS+=("planner.pufferdrive_planner.pedestrian_min_size_m=$PEDESTRIAN_MIN_SIZE_M") +[ -n "${OBS_SLOTS_PARTNERS_N:-}" ] && EXTRA_ARGS+=("++planner.pufferdrive_planner.env_overrides.obs_slots_partners_n=$OBS_SLOTS_PARTNERS_N") +[ -n "${EVAL_PERCEIVED_SIZE_MARGIN_M:-}" ] && EXTRA_ARGS+=("++planner.pufferdrive_planner.env_overrides.eval_perceived_size_margin_m=$EVAL_PERCEIVED_SIZE_MARGIN_M") +[ -n "${WORKER:-}" ] && EXTRA_ARGS+=("worker=$WORKER") +[ -n "${THREADS_PER_NODE:-}" ] && EXTRA_ARGS+=("worker.threads_per_node=$THREADS_PER_NODE") +[ -n "${LIMIT_TOTAL_SCENARIOS:-}" ] && EXTRA_ARGS+=("scenario_filter.limit_total_scenarios=$LIMIT_TOTAL_SCENARIOS") +[ -n "${SCENARIO_TOKENS:-}" ] && EXTRA_ARGS+=("scenario_filter.scenario_tokens=$SCENARIO_TOKENS") +[ -n "${EXTRA_HYDRA:-}" ] && EXTRA_ARGS+=($EXTRA_HYDRA) + +cd "$PD" +declare -A STATUS +# main_callback below omits metric_summary_callback (PDF histograms): it +# crashes on a read-only numpy array from pyarrow-backed parquet columns in +# this nuplan-devkit checkout (nuboard_histogram_utils.py), which aborts the +# whole main_callback chain and skips csv_main_callback -- the actual +# results. csv_main_callback reads aggregator parquet files directly and +# doesn't need metric_summary_callback's output. +for CHALLENGE in $CHALLENGES; do + echo "[run_nuplan_planner] challenge=$CHALLENGE split=$SPLIT group=$GROUP" + "$PY" "$NUPLAN_DEVKIT_ROOT/nuplan/planning/script/run_simulation.py" \ + +simulation=$CHALLENGE \ + scenario_builder=nuplan \ + scenario_builder.data_root="${NUPLAN_VAL_DB_DIR:-$NUPLAN_DATA_ROOT/nuplan-v1.1/splits/val}" \ + scenario_filter=$SPLIT \ + planner=pufferdrive_planner \ + planner.pufferdrive_planner.checkpoint_path="$CKPT" \ + callback="${CALLBACKS:-[simulation_log_callback, carl_visualization_callback]}" \ + main_callback="[time_callback, metric_file_callback, metric_aggregator_callback, csv_main_callback]" \ + hydra.searchpath="[pkg://pufferlib.ocean.cosim.nuplan.config, pkg://carl_nuplan.planning.script.config.common, pkg://carl_nuplan.planning.script.config.simulation, pkg://nuplan.planning.script.config.common, pkg://nuplan.planning.script.experiments]" \ + group="$GROUP" \ + "${EXTRA_ARGS[@]}" + STATUS[$CHALLENGE]=$? + echo "[run_nuplan_planner] $CHALLENGE exit ${STATUS[$CHALLENGE]}" +done + +if [ "$OBS_HTML" = "all" ]; then + "$PY" "$PD/scripts/eval/render_obs_html.py" "$GROUP" --max-score 1.01 +elif [ "$OBS_HTML" = "failures" ]; then + "$PY" "$PD/scripts/eval/render_obs_html.py" "$GROUP" --max-score "${COSIM_OBS_HTML_MAX_SCORE:-0.9}" --prune +elif [ "$OBS_HTML" = "infractions" ]; then + "$PY" "$PD/scripts/eval/render_obs_html.py" "$GROUP" --max-score 0 --prune \ + --metric no_ego_at_fault_collisions --metric drivable_area_compliance \ + --metric driving_direction_compliance --metric ego_is_making_progress +fi + +echo "=========== SUMMARY ===========" +rc=0 +for c in "${!STATUS[@]}"; do + if [ "${STATUS[$c]}" -eq 0 ]; then s=PASS; else s="FAIL(${STATUS[$c]})"; rc=1; fi + echo "$s $c" +done +echo "group -> $GROUP" +[ -f "$GROUP/obs_html/index.html" ] && echo "obs replays -> $GROUP/obs_html/index.html" +exit $rc diff --git a/pufferlib/ocean/cosim/nuplan_bridge.py b/pufferlib/ocean/cosim/nuplan_bridge.py new file mode 100644 index 0000000000..599ca2fdc7 --- /dev/null +++ b/pufferlib/ocean/cosim/nuplan_bridge.py @@ -0,0 +1,678 @@ +"""nuPlan <-> PufferDrive map conversion + state readback for co-simulation. + +Unlike CARLA (see carla_bridge.py), nuPlan is already a right-handed, z-up, +meters, radians frame — no reflection is needed. The only transform is a +translation: nuPlan map coordinates are large (UTM-ish, ~1e5-1e6 m) and the +PufferDrive bins store float32, so each bin is translated by an `origin` +recovered per city (registered against the GPKG map, see `planner.py`'s +`_city_bin_origin`) or read from a stored `centroid`: + + bin_x = nuplan_x - origin_x bin_y = nuplan_y - origin_y + bin_heading = nuplan_heading bin_v = nuplan_v (global frame) + +Bins are pre-converted per-city map-only bins (`city_bin_dir`, see +`planner.py`), holding only the road graph (lanes / boundaries / crosswalks / +traffic-light stop lines) — no logged trajectories. Agent states are streamed +from the nuPlan simulation every tick, exactly like the CARLA co-sim streams +from CARLA. Traffic lights are matched geometrically at planner-build time +(`match_connectors_to_stop_lines`) so live traffic-light status +(`PlannerInput.traffic_light_data`) can be written with set_traffic_light_states. + +Nothing in this module imports `nuplan` at module scope, so the pure-python bin +writer stays testable from an env without the nuplan-devkit installed. +""" + +from pathlib import Path + +import numpy as np + +import data_utils.mirror_map_bin as _mbin + +# PufferDrive road types (datatypes.h): lanes 0-9, road lines 10-19, edges 20-29 +LANE_SURFACE_STREET = 2 +ROAD_LINE_BROKEN_WHITE = 11 +ROAD_EDGE_BOUNDARY = 21 +MISC_CROSSWALK = 31 +TRAFFIC_TYPE_LIGHT = 1 # matches the CARLA bins' traffic-element type + +# nuPlan TrackedObjectType -> PufferDrive agent type (1=vehicle 2=ped 3=cyclist) +_NUPLAN_AGENT_TYPE = {"VEHICLE": 1, "PEDESTRIAN": 2, "BICYCLE": 3} + +# nuPlan TrafficLightStatusType -> PufferDrive light enum (datatypes.h: RED=1 YELLOW=2 GREEN=3). +# Training never produces UNKNOWN (0), so an unknown or unreported light is treated as GREEN. +LIGHT_STATE_GREEN = 3 +_NUPLAN_LIGHT_STATE = {"RED": 1, "YELLOW": 2, "GREEN": LIGHT_STATE_GREEN, "UNKNOWN": LIGHT_STATE_GREEN} + +FAR_AWAY = 1.0e6 # park surplus PufferDrive agents out of observation range +ROUTE_SEARCH_DEPTH_BLOCKS = 30 # PDM's Dijkstra window over route roadblocks + +# Env/obs layout the carla_combined gigaflow policy expects at eval time (a +# fallback for shadow_env_kwargs' checkpoint-config adoption, see +# cosim/arch.py -- only used for keys the checkpoint config doesn't set, i.e. +# chiefly the no-checkpoint dummy run). Override per-checkpoint via the +# planner's `env_overrides` when the training config differs. +DEFAULT_ARCH = dict( + goal_source="external", # route goal windows are pushed by the planner + num_goals=3, + obs_slots_lane_n=80, + obs_slots_boundary_n=40, + obs_slots_partners_n=16, + obs_slots_traffic_controls_n=4, + obs_range_partner_m=200.0, + obs_range_road_front_m=200.0, + obs_range_road_behind_m=40.0, + obs_range_road_side_m=50.0, + obs_range_traffic_control_m=100.0, + obs_norm_xy_offset_m=200.0, + obs_norm_goal_offset_m=200.0, + obs_norm_road_seg_length_m=10.0, + obs_norm_road_seg_width_m=5.0, + obs_norm_veh_length_m=15.0, + obs_norm_veh_width_m=10.0, + reward_conditioning=True, + goal_speed=20.0, # reward conditioning: arrive at goals at up to 20 m/s + goal_radius=6.0, + dynamics_model="jerk", + dt=0.1, # lockstep with nuPlan's 10 Hz planner interval +) + + +class NuPlanTransform: + """Translation-only nuPlan <-> bin-frame transform for one scenario.""" + + def __init__(self, origin_x: float, origin_y: float): + self.ox = float(origin_x) + self.oy = float(origin_y) + + def loc_to_bin(self, x, y): + return x - self.ox, y - self.oy + + def bin_to_loc(self, bx, by): + return bx + self.ox, by + self.oy + + +def _polyline_heading(xy: np.ndarray) -> np.ndarray: + """Per-point forward-tangent headings; last point repeats.""" + d = np.diff(xy, axis=0) + h = np.arctan2(d[:, 1], d[:, 0]) + return np.append(h, h[-1] if len(h) else 0.0).astype(np.float32) + + +def _road_entry(road_id: int, road_type: int, xy: np.ndarray, entry=None, exit_=None, speed_limit=-1.0) -> dict: + """One road element in mirror_map_bin's dict schema.""" + xy = np.asarray(xy, dtype=np.float32) + S = len(xy) + e = { + "id": road_id, + "type": road_type, + "S": S, + "x": tuple(xy[:, 0].tolist()), + "y": tuple(xy[:, 1].tolist()), + "z": tuple([0.0] * S), + "headings": tuple(_polyline_heading(xy).tolist()), + } + if 0 <= road_type <= 9: + seg = np.hypot(*np.diff(xy, axis=0).T) if S > 1 else np.array([0.0]) + cum = np.concatenate([[0.0], np.cumsum(seg)]).astype(np.float32)[:S] + e["entry_lanes"] = tuple(entry or []) + e["exit_lanes"] = tuple(exit_ or []) + e["speed_limit"] = float(speed_limit if speed_limit is not None else -1.0) + e["length"] = float(cum[-1]) + e["cum_lengths"] = tuple(cum.tolist()) + return e + + +def write_drive_bin(roads, traffic, out_path: Path, scenario_id: str, centroid=(0.0, 0.0, 0.0)): + """Write a map-only PufferDrive bin (0 agents, 0 objects, empty lane graph), + like the CARLA town bins. `roads`/`traffic` follow mirror_map_bin's schema.""" + data = { + "agents": [], + "roads": roads, + "traffic": traffic, + "objects": [], + "lane_graph": {"n": 0, "lane_ids": (), "distances": ()}, + "scenario_id": scenario_id.encode("utf-8")[:128].ljust(128, b"\0"), + "dataset_name": b"nuplan".ljust(32, b"\0"), + "log_length": 0, + "log_dt": 0.0, + "objects_of_interest": (), + "tracks_to_predict": (), + "centroid": (float(centroid[0]), float(centroid[1]), float(centroid[2])), + } + out_path.parent.mkdir(parents=True, exist_ok=True) + _mbin.write_bin(data, out_path) + + +def expert_route_xy(scenario) -> np.ndarray: + """(N, 2) logged ego-center trajectory for the whole scenario window, in + nuPlan map coordinates -- the ground-truth path the expert actually drove. + Preferred goal source over `route_centerline`: a lane-graph walk only knows + which roadblocks are on-route, not which lane within a multi-lane block or + which fork at a split, so it can place goals on a parallel or turning lane; + the logged trajectory has neither ambiguity. Starts at the scenario's + initial ego state (iteration 0), so no start-position trim is needed + (unlike route_centerline, whose first lane can start well behind the ego). + Duck-typed on `EgoState`-like objects (`.center.x/.y`), not a real nuPlan + import, so this stays testable without the devkit installed.""" + return np.array( + [[s.center.x, s.center.y] for s in scenario.get_expert_ego_trajectory()], + dtype=np.float64, + ).reshape(-1, 2) + + +def route_centerline(map_api, route_roadblock_ids, start_x: float, start_y: float, start_heading: float): + """Lane-graph route through the route roadblocks -> ((N, 2) centerline in + nuPlan map coordinates, [lane ids along it]), PDM-style: the starting lane + is the on-route lane under the ego with the smallest heading error (never + a crossing lane of the same junction), then Dijkstra over the route's lane + graph to the last roadblock. The first lane is trimmed to the point + nearest the ego.""" + from nuplan.common.actor_state.state_representation import Point2D + from nuplan.common.maps.maps_datatypes import SemanticMapLayer + from carl_nuplan.planning.simulation.planner.pdm_planner.utils.graph_search.dijkstra import Dijkstra + + blocks = [] + for rid in dict.fromkeys(route_roadblock_ids): + block = map_api.get_map_object(str(rid), SemanticMapLayer.ROADBLOCK) or map_api.get_map_object( + str(rid), SemanticMapLayer.ROADBLOCK_CONNECTOR + ) + if block is not None and block.interior_edges: + blocks.append(block) + if not blocks: + return np.zeros((0, 2)), [] + route_lanes = {lane.id: lane for block in blocks for lane in block.interior_edges} + + def _lane_pts(lane): + return np.array([[p.x, p.y] for p in lane.baseline_path.discrete_path]) + + def _heading_error(lane): + path = lane.baseline_path.discrete_path + pts = _lane_pts(lane) + nearest = int(np.argmin(np.hypot(*(pts - (start_x, start_y)).T))) + return abs((path[nearest].heading - start_heading + np.pi) % (2.0 * np.pi) - np.pi) + + def _distance(lane): + return float(np.min(np.hypot(*(_lane_pts(lane) - (start_x, start_y)).T))) + + ego_point = Point2D(start_x, start_y) + containing = sorted((lane for lane in route_lanes.values() if lane.contains_point(ego_point)), key=_heading_error) + block_ids = [block.id for block in blocks] + start_block_idx = block_ids.index(containing[0].get_roadblock_id()) if containing else 0 + # The ego's own lane may not connect to the next route block (wrong lane for the turn, PDM's + # route correction keeps the block): fall back to the sibling lanes of its block, nearest first. + siblings = sorted( + (lane for lane in blocks[start_block_idx].interior_edges if lane not in containing), key=_distance + ) + candidates = containing + siblings or sorted(route_lanes.values(), key=_distance)[:1] + target_block = blocks[min(len(blocks) - 1, start_block_idx + ROUTE_SEARCH_DEPTH_BLOCKS - 1)] + path = [] + for start_lane in candidates: + candidate_path, found = Dijkstra(start_lane, list(route_lanes.keys())).search(target_block) + if found: + path = candidate_path + break + if len(candidate_path) > len(path): + path = candidate_path + + lane_points = [_lane_pts(lane) for lane in path] + start_idx = int(np.argmin(np.hypot(*(lane_points[0] - (start_x, start_y)).T))) + lane_points[0] = lane_points[0][start_idx:] + return np.concatenate(lane_points, axis=0), [str(lane.id) for lane in path] + + +def indices_along(polyline: np.ndarray, spacing: float) -> np.ndarray: + """(N, 2) polyline -> vertex indices every `spacing` meters of arc length (+ the endpoint).""" + if len(polyline) < 2: + return np.arange(len(polyline)) + seg = np.hypot(*np.diff(polyline, axis=0).T) + cum = np.concatenate([[0.0], np.cumsum(seg)]) + indices = [int(np.searchsorted(cum, s)) for s in np.arange(spacing, cum[-1], spacing)] + indices.append(len(polyline) - 1) + return np.asarray(indices, dtype=np.int64) + + +def goals_along(centerline: np.ndarray, spacing: float) -> np.ndarray: + """(N, 2) polyline -> fixed goal sequence every `spacing` meters (+ endpoint).""" + return np.asarray(centerline, dtype=np.float64)[indices_along(centerline, spacing)] + + +def _route_blocks(map_api, route_roadblock_ids): + """Route roadblock ids -> [roadblock or roadblock connector] with interior lanes, consecutive repeats merged.""" + from nuplan.common.maps.maps_datatypes import SemanticMapLayer + + blocks = [] + for rid in route_roadblock_ids: + block = map_api.get_map_object(str(rid), SemanticMapLayer.ROADBLOCK) or map_api.get_map_object( + str(rid), SemanticMapLayer.ROADBLOCK_CONNECTOR + ) + if block is None or not block.interior_edges or (blocks and blocks[-1].id == block.id): + continue + blocks.append(block) + return blocks + + +def _baseline_xy(lane) -> np.ndarray: + return np.array([[p.x, p.y] for p in lane.baseline_path.discrete_path], dtype=np.float64) + + +def _ego_lane(lanes, x, y, heading): + """The lane nearest to the pose among the co-directional ones (baseline heading within 90 deg at the + nearest point), else the nearest lane of all.""" + heading_dir = np.array([np.cos(heading), np.sin(heading)]) + best_key, best_lane = None, None + for lane in lanes: + pts = _baseline_xy(lane) + k = int(np.argmin(np.hypot(*(pts - (x, y)).T))) + seg = pts[min(k + 1, len(pts) - 1)] - pts[max(k - 1, 0)] + key = (seg @ heading_dir <= 0.0, float(np.hypot(*(pts[k] - (x, y))))) + if best_key is None or key < best_key: + best_key, best_lane = key, lane + return best_lane + + +def _continuing_lane(block, prev_lane, next_block, prev_xy): + """The block's lane the previous lane flows into and that flows on into the next route block; when no + lane does both, one that flows on beats one that only flows in (change lanes early, not late); ties go + to the lane nearest prev_xy.""" + successor_ids = {edge.id for edge in prev_lane.outgoing_edges} + + def key(lane): + flows_in = lane.id in successor_ids + flows_on = next_block is None or any(edge.get_roadblock_id() == next_block.id for edge in lane.outgoing_edges) + return ( + not (flows_in and flows_on), + not flows_on, + not flows_in, + float(np.min(np.hypot(*(_baseline_xy(lane) - prev_xy).T))), + ) + + return min(block.interior_edges, key=key) + + +def roadblock_lane_goals( + map_api, route_roadblock_ids, start_x, start_y, start_heading, min_spacing: float, min_ahead_m: float +): + """Route roadblock goals on lane baselines chosen for lane continuity -> (N, 2) in nuPlan map coordinates. + Blocks, start block, min_ahead_m and min_spacing as in roadblock_centroid_goals; each block's goal is the + point nearest its centroid on the block's continuing lane (see _continuing_lane, seeded with the ego's + lane), so goals cue a lane change only where the lane graph forces one instead of pulling toward the + road centre on multi-lane roads.""" + from shapely.geometry import Point + + blocks = _route_blocks(map_api, route_roadblock_ids) + if not blocks: + return np.zeros((0, 2)) + start_idx = int(np.argmin([block.polygon.distance(Point(start_x, start_y)) for block in blocks])) + heading_dir = np.array([np.cos(start_heading), np.sin(start_heading)]) + lane = _ego_lane(blocks[start_idx].interior_edges, start_x, start_y, start_heading) + prev_xy = np.array([start_x, start_y], dtype=np.float64) + goals = [] + for block_idx in range(start_idx, len(blocks)): + block = blocks[block_idx] + if block_idx > start_idx: + next_block = blocks[block_idx + 1] if block_idx + 1 < len(blocks) else None + lane = _continuing_lane(block, lane, next_block, prev_xy) + centroid = block.polygon.centroid + pts = _baseline_xy(lane) + goal = pts[int(np.argmin(np.hypot(pts[:, 0] - centroid.x, pts[:, 1] - centroid.y)))] + if block_idx == start_idx and (goal - (start_x, start_y)) @ heading_dir < min_ahead_m: + continue + if goals and np.hypot(*(goal - goals[-1])) < min_spacing: + continue + goals.append(goal) + prev_xy = goal + return np.array(goals, dtype=np.float64).reshape(-1, 2) + + +def roadblock_centroid_goals( + map_api, route_roadblock_ids, start_x, start_y, start_heading, min_spacing: float, min_ahead_m: float +): + """Route roadblock centroids as goals -> (N, 2) in nuPlan map coordinates, from the ego's roadblock on + (the block containing the ego, else the nearest one). The ego block's own centroid is dropped when it + is behind the ego or less than min_ahead_m ahead; later centroids follow the route regardless of the + ego heading (turns). A centroid outside its (curved) polygon moves to the nearest interior-lane + baseline point; consecutive goals closer than min_spacing are thinned.""" + from shapely.geometry import Point + + blocks = _route_blocks(map_api, route_roadblock_ids) + if not blocks: + return np.zeros((0, 2)) + ego_point = Point(start_x, start_y) + start_idx = int(np.argmin([block.polygon.distance(ego_point) for block in blocks])) + heading_dir = np.array([np.cos(start_heading), np.sin(start_heading)]) + goals = [] + for block_idx in range(start_idx, len(blocks)): + block = blocks[block_idx] + centroid = block.polygon.centroid + goal = np.array([centroid.x, centroid.y], dtype=np.float64) + if not block.polygon.contains(centroid): + lane_pts = np.concatenate( + [[[p.x, p.y] for p in lane.baseline_path.discrete_path] for lane in block.interior_edges] + ) + goal = lane_pts[int(np.argmin(np.hypot(*(lane_pts - goal).T)))].astype(np.float64) + if block_idx == start_idx and (goal - (start_x, start_y)) @ heading_dir < min_ahead_m: + continue + if goals and np.hypot(*(goal - goals[-1])) < min_spacing: + continue + goals.append(goal) + return np.array(goals, dtype=np.float64).reshape(-1, 2) + + +def extend_route_past_loop_cut(corrected_ids, raw_ids): + """CaRL's route correction cuts the route where a later roadblock overlaps an earlier one (a loop), + which its Dijkstra needs; goal windows follow the route in travel order, so the logged remainder + after the cut is appended again -> [str ids].""" + corrected = [str(rid) for rid in corrected_ids] + raw = [str(rid) for rid in raw_ids] + if not corrected or corrected[-1] not in raw: + return corrected + return corrected + raw[raw.index(corrected[-1]) + 1 :] + + +def logged_ego_boxes(scenario, transform: NuPlanTransform) -> np.ndarray: + """Human-driven ego per scenario iteration -> (N, 5) float32 [x, y, heading, length, width], bin frame.""" + iteration_count = scenario.get_number_of_iterations() + boxes = np.zeros((iteration_count, 5), np.float32) + for i in range(iteration_count): + state = scenario.get_ego_state_at_iteration(i) + bx, by = transform.loc_to_bin(float(state.center.x), float(state.center.y)) + footprint = state.car_footprint + boxes[i] = (bx, by, float(state.center.heading), float(footprint.length), float(footprint.width)) + return boxes + + +def read_bin_geometry(bin_path: Path) -> dict: + """Read the co-sim-relevant geometry out of an existing PufferDrive bin + (city_bin_dir map-only bins or pre-converted training-format bins): + + origin (ox, oy) from the stored centroid, or None if unset + (nuPlan map coords are ~1e5-1e6 m, so a (0, 0)/missing + centroid means "not stored", never a real origin) + stop_line_centers (K, 2) bin-frame stop-line midpoints of all traffic + elements, for origin registration + stop_lines (K, 4) bin-frame stop-line endpoints [x1, y1, x2, y2] + stop_line_headings (K,) travel direction each stop line faces [rad] + traffic_types (K,) element type (TRAFFIC_TYPE_LIGHT = traffic light) + num_traffic K, the size set_traffic_light_states expects + ego_t0 (x, y, heading) of agent 0 at its first valid log step, + or None if the bin has no agents — fallback origin + recovery: origin = nuplan_ego_t0_xy - bin_ego_t0_xy + ego_traj (N, 2) agent-0 valid trajectory in the bin frame, or + None — fallback origin recovery for whole-log bins: + fit_translation against the log's UTM ego trajectory + """ + data = _mbin.read_bin(Path(bin_path)) + + centroid = data.get("centroid") + origin = None + if centroid is not None and (abs(centroid[0]) > 1.0 or abs(centroid[1]) > 1.0): + origin = (float(centroid[0]), float(centroid[1])) + + stop_lines = np.array( + [[t["stop_line"][0], t["stop_line"][1], t["stop_line"][3], t["stop_line"][4]] for t in data["traffic"]], + dtype=np.float64, + ).reshape(-1, 4) + centers = 0.5 * (stop_lines[:, 0:2] + stop_lines[:, 2:4]) + stop_line_headings = np.array([t["heading"] for t in data["traffic"]], dtype=np.float64) + traffic_types = np.array([t["type"] for t in data["traffic"]], dtype=np.int32) + + ego_t0, ego_traj = None, None + if data["agents"]: + cols = data["agents"][0]["cols"] + valid = np.asarray(cols["valid"]) > 0 + if valid.any(): + xs = np.asarray(cols["x"], np.float64)[valid] + ys = np.asarray(cols["y"], np.float64)[valid] + hs = np.asarray(cols["h"], np.float64)[valid] + ego_t0 = (float(xs[0]), float(ys[0]), float(hs[0])) + ego_traj = np.stack([xs, ys], axis=1) + + return { + "origin": origin, + "stop_line_centers": centers, + "stop_lines": stop_lines, + "stop_line_headings": stop_line_headings, + "traffic_types": traffic_types, + "num_traffic": len(centers), + "ego_t0": ego_t0, + "ego_traj": ego_traj, + } + + +def fit_translation(src: np.ndarray, ref: np.ndarray, init=None, iterations: int = 50, max_points: int = 2000): + """Translation-only alignment of two samplings of the SAME physical curve + (e.g. a bin's centered ego trajectory vs the log's UTM ego trajectory): + find t minimizing nearest-neighbor distance from src + t onto ref. + + Because the transform is a pure translation and both point sets trace the + identical path, the centroid-difference initialization lands within meters + and the ICP iterations converge to sub-centimeter. Returns + (t (2,) float64, median residual in meters) — callers should reject large + residuals rather than trust a bad fit.""" + src = np.asarray(src, np.float64).reshape(-1, 2) + ref = np.asarray(ref, np.float64).reshape(-1, 2) + if len(src) < 2 or len(ref) < 2: + raise ValueError("fit_translation needs at least 2 points in src and ref") + if len(src) > max_points: + src = src[np.linspace(0, len(src) - 1, max_points).astype(int)] + + try: + from scipy.spatial import cKDTree + + nearest = cKDTree(ref).query + except ImportError: # brute force on a subsample + if len(ref) > 5000: + ref = ref[np.linspace(0, len(ref) - 1, 5000).astype(int)] + + def nearest(pts): + d2 = ((pts[:, None, :] - ref[None, :, :]) ** 2).sum(-1) + j = d2.argmin(1) + return np.sqrt(d2[np.arange(len(pts)), j]), j + + t = np.asarray(init, np.float64) if init is not None else ref.mean(0) - src.mean(0) + for _ in range(iterations): + dist, j = nearest(src + t) + step = (ref[j] - (src + t)).mean(0) + t = t + step + if float(np.hypot(*step)) < 1e-6: + break + dist, _ = nearest(src + t) + return t, float(np.median(dist)) + + +def read_bin_lane_points(bin_path: Path, max_points: int = 200_000) -> np.ndarray: + """(N, 2) float64 lane-centerline vertices of a bin (road types 0-9), the + bin-side point cloud for whole-city origin registration. Uniformly + subsampled to max_points.""" + data = _mbin.read_bin(Path(bin_path)) + pts = [ + np.stack([np.asarray(r["x"], np.float64), np.asarray(r["y"], np.float64)], axis=1) + for r in data["roads"] + if 0 <= r["type"] <= 9 + ] + if not pts: + raise ValueError(f"{bin_path}: no lane elements to register against") + out = np.concatenate(pts) + if len(out) > max_points: + out = out[np.linspace(0, len(out) - 1, max_points).astype(int)] + return out + + +def coarse_translation_vote(src: np.ndarray, ref: np.ndarray, grid: float = 5.0) -> np.ndarray: + """Global translation-only registration of two sparse constellations of the + SAME landmarks (e.g. a city bin's traffic stop-line centers vs the GPKG + map's stop polygons): every (ref - src) pair difference votes on a `grid`-m + cell; the true translation collects one vote per real landmark match while + mismatched pairs scatter. Returns the median of the diffs near the winning + cell — a few-meter-accurate init for fit_translation, found with no prior.""" + src = np.asarray(src, np.float64).reshape(-1, 2) + ref = np.asarray(ref, np.float64).reshape(-1, 2) + if len(src) < 3 or len(ref) < 3: + raise ValueError("coarse_translation_vote needs at least 3 landmarks per side") + if len(src) > 3000: + src = src[np.linspace(0, len(src) - 1, 3000).astype(int)] + if len(ref) > 3000: + ref = ref[np.linspace(0, len(ref) - 1, 3000).astype(int)] + + d = (ref[None, :, :] - src[:, None, :]).reshape(-1, 2) + keys = np.round(d / grid).astype(np.int64) + hashed = keys[:, 0] * 1_000_003 + keys[:, 1] + vals, counts = np.unique(hashed, return_counts=True) + t0 = d[hashed == vals[counts.argmax()]].mean(axis=0) + near = d[np.hypot(d[:, 0] - t0[0], d[:, 1] - t0[1]) < 1.5 * grid] + return np.median(near, axis=0) + + +LIGHT_MATCH_MAX_DIST_M = 15.0 # nuPlan connector entries sit up to 14 m before/after the bin stop line +LIGHT_MATCH_MAX_HEADING_DIFF_RAD = np.radians( + 75.0 +) # keeps skewed stop lines, rejects cross-street (90) and oncoming (180) + + +def point_to_segment_distance(px: float, py: float, segments: np.ndarray) -> np.ndarray: + """Distance from (px, py) to each segment of a (K, 4) [x1, y1, x2, y2] array.""" + ax, ay, bx, by = segments[:, 0], segments[:, 1], segments[:, 2], segments[:, 3] + dx, dy = bx - ax, by - ay + length_sq = np.maximum(dx * dx + dy * dy, 1e-9) + t = np.clip(((px - ax) * dx + (py - ay) * dy) / length_sq, 0.0, 1.0) + return np.hypot(ax + t * dx - px, ay + t * dy - py) + + +def match_connectors_to_stop_lines( + connector_entries: dict, + transform: NuPlanTransform, + stop_lines: np.ndarray, + stop_line_headings: np.ndarray, + traffic_types: np.ndarray, + max_dist_m: float = LIGHT_MATCH_MAX_DIST_M, + max_heading_diff_rad: float = LIGHT_MATCH_MAX_HEADING_DIFF_RAD, +) -> dict: + """Geometric traffic-light mapping, the runtime replacement for the + `.tl.json` sidecar (works for any bin source). `connector_entries` maps + lane_connector_id(str) -> (x, y, heading) entry pose in nuPlan map + coordinates. Each connector takes the nearest traffic light whose stop + line faces its travel direction, measured to the line segment (a multi-lane + stop line's midpoint can sit >10 m from the outer lanes; matching every + lane of a line matters, or the route rule cannot override neighbours). Returns + {lane_connector_id: bin traffic-element idx}, skipping connectors with no + such stop line within max_dist_m.""" + mapping = {} + if not len(stop_lines): + return mapping + is_light = traffic_types == TRAFFIC_TYPE_LIGHT + for cid, (x, y, heading) in connector_entries.items(): + bx, by = transform.loc_to_bin(x, y) + heading_diff = np.abs((stop_line_headings - heading + np.pi) % (2.0 * np.pi) - np.pi) + candidate = is_light & (heading_diff <= max_heading_diff_rad) + if not candidate.any(): + continue + dist = np.where(candidate, point_to_segment_distance(bx, by, stop_lines), np.inf) + j = int(dist.argmin()) + if dist[j] <= max_dist_m: + mapping[str(cid)] = j + return mapping + + +MOVING_PARTNER_TYPES = ("VEHICLE", "PEDESTRIAN", "BICYCLE") # the only types the training bins turn into agents +STATIC_PARTNER_TYPES = ("TRAFFIC_CONE", "BARRIER", "CZONE_SIGN", "GENERIC_OBJECT") + + +def partner_tracked_objects(tracked_objects, map_api, static_on_lane: dict): + """Objects the shadow env should see: moving agents always; static clutter (cones, barriers, signs, + generic objects) only when it stands inside a lane or lane-connector polygon, where the ego can hit it. + The wider drivable area also covers intersection corners and crosswalks, where barrier rows line the + curb: those filled the nearest-N partner slots as 0.6 x 3 m stopped vehicles the policy never saw in + training. static_on_lane: track_token -> bool, filled here (statics never move).""" + from nuplan.common.actor_state.state_representation import Point2D + from nuplan.common.maps.maps_datatypes import SemanticMapLayer + + kept = [] + for obj in tracked_objects: + type_name = obj.tracked_object_type.name + if type_name in MOVING_PARTNER_TYPES: + kept.append(obj) + continue + if type_name not in STATIC_PARTNER_TYPES: + continue + on_lane = static_on_lane.get(obj.track_token) + if on_lane is None: + point = Point2D(obj.center.x, obj.center.y) + # is_in_layer(LANE_CONNECTOR) tests the centerline layer; the polygon lookup is get_all_map_objects + on_lane = bool( + map_api.is_in_layer(point, SemanticMapLayer.LANE) + or map_api.get_all_map_objects(point, SemanticMapLayer.LANE_CONNECTOR) + ) + static_on_lane[obj.track_token] = on_lane + if on_lane: + kept.append(obj) + return kept + + +VRU_PARTNER_TYPE_IDS = (_NUPLAN_AGENT_TYPE["PEDESTRIAN"], _NUPLAN_AGENT_TYPE["BICYCLE"]) + + +def floor_vru_partner_sizes(types, lengths, widths, min_size_m: float): + """Pedestrian/bicycle boxes below min_size_m on either axis grow to min_size_m (training never + spawns agents under 0.8 x 0.8 m; nuPlan pedestrians are mostly 0.4-0.8 m). Other types untouched.""" + vru = np.isin(types, VRU_PARTNER_TYPE_IDS) + lengths = np.where(vru, np.maximum(lengths, min_size_m), lengths).astype(np.float32) + widths = np.where(vru, np.maximum(widths, min_size_m), widths).astype(np.float32) + return lengths, widths + + +def tracked_objects_to_arrays(tracked_objects, transform: NuPlanTransform, first_slot: int = 1): + """nuPlan DetectionsTracks agents -> (idx, x, y, z, h, vx, vy, types, lengths, widths) + arrays in the bin frame, filling PufferDrive slots first_slot..N.""" + idx, x, y, z, h, vx, vy, tp, ln, wd = [], [], [], [], [], [], [], [], [], [] + for j, obj in enumerate(tracked_objects): + bx, by = transform.loc_to_bin(obj.center.x, obj.center.y) + v = getattr(obj, "velocity", None) + idx.append(first_slot + j) + x.append(bx) + y.append(by) + z.append(0.0) + h.append(float(obj.center.heading)) + vx.append(float(v.x) if v is not None else 0.0) + vy.append(float(v.y) if v is not None else 0.0) + tp.append(_NUPLAN_AGENT_TYPE.get(obj.tracked_object_type.name, 1)) + ln.append(float(obj.box.length)) + wd.append(float(obj.box.width)) + return ( + np.array(idx, np.int32), + np.array(x, np.float32), + np.array(y, np.float32), + np.array(z, np.float32), + np.array(h, np.float32), + np.array(vx, np.float32), + np.array(vy, np.float32), + np.array(tp, np.int32), + np.array(ln, np.float32), + np.array(wd, np.float32), + ) + + +_LIGHT_RESTRICTIVENESS = {1: 0, 2: 1, LIGHT_STATE_GREEN: 2} # RED < YELLOW < GREEN + + +def traffic_light_states( + traffic_light_data, connector_map: dict, num_traffic: int, route_connector_ids=() +) -> np.ndarray: + """PlannerInput.traffic_light_data -> per-element state array for + set_traffic_light_states. Several lane connectors (straight/left/right + from one lane) share one stop-line element: an element on the ego's route + is decided by its route connector alone, any other element by the most + restrictive reported status. Unreported means GREEN.""" + states = np.full(num_traffic, LIGHT_STATE_GREEN, dtype=np.int32) + on_route = set(str(cid) for cid in route_connector_ids) + route_elements = {connector_map[cid] for cid in on_route if cid in connector_map} + for tl in traffic_light_data: + cid = str(tl.lane_connector_id) + j = connector_map.get(cid) + if j is None: + continue + state = _NUPLAN_LIGHT_STATE[tl.status.name] + if cid in on_route: + states[j] = state + elif j not in route_elements and _LIGHT_RESTRICTIVENESS[state] < _LIGHT_RESTRICTIVENESS[int(states[j])]: + states[j] = state + return states diff --git a/pufferlib/ocean/cosim/obs_replay.py b/pufferlib/ocean/cosim/obs_replay.py new file mode 100644 index 0000000000..444b442a33 --- /dev/null +++ b/pufferlib/ocean/cosim/obs_replay.py @@ -0,0 +1,250 @@ +"""Interactive observation replay (pufferlib.viz) for a co-sim shadow env: the exact obs vector the +policy received each step, its outputs (action, value, entropy, action probabilities) and the +encoder max-pool winners, rendered as one self-contained HTML per scenario/route.""" + +import json +from pathlib import Path + +import numpy as np + +from pufferlib.ocean.drive import binding + +OBS_CLIP = 100.0 # parked FAR_AWAY slots carry ~5e3 goal offsets that would blow up the viewer's int16 quantization +ROAD_CROP_MARGIN_M = 250.0 # whole-city bins: keep only road elements near the driven trajectory +POOL_NAMES = ("pool_partner", "pool_lane", "pool_boundary", "pool_traffic") +OBS_TRAILING_COUNT_FEATURES = 4 # lane/boundary/partner/traffic-control counts appended after the slots + + +class ObsReplayCapture: + def __init__(self, env, policy, out_stem, max_steps=800, ghost_trajectory=None): + """ghost_trajectory: (N, 5) logged/human ego boxes [x, y, heading, length, width] in the bin frame, + row f = frame f (the viewer's red ghost); None when the external sim has no logged ego.""" + self.env = env + self.policy = policy + self.out_stem = Path(out_stem) + self.max_steps = int(max_steps) + self.ghost_trajectory = ( + None if ghost_trajectory is None else np.asarray(ghost_trajectory, np.float32).reshape(-1, 5) + ) + state = env.get_state() + self.scenario = state[0] if isinstance(state, list) else state + self.agent_cap = int(self.scenario["num_total_agents"]) + self.traffic_cap = max(int(self.scenario["num_traffic_elements"]), 1) + self.frames = { + key: [] + for key in ( + "agent_f32", + "agent_i32", + "metrics_f32", + "puffer_f32", + "traffic_i16", + "goals_f32", + "rewards_f32", + "coefs_f32", + "obs", + "raw_action", + "action_index", + "value", + "entropy", + "policy_probs", + ) + } + + def __len__(self): + return len(self.frames["obs"]) + + def policy_outputs(self, obs_tensor, logits, value, entropy): + """-> aux dict for capture(): value/entropy/action probabilities (+ pool winners when the policy exposes them).""" + import torch + + probs = torch.softmax(logits if isinstance(logits, torch.Tensor) else logits[0], dim=-1) + aux = { + "value": value.detach().cpu().numpy().reshape(-1).astype(np.float32), + "entropy": entropy.detach().cpu().numpy().reshape(-1).astype(np.float32), + "policy_probs": probs.detach().cpu().numpy().astype(np.float32), + } + pool_method = getattr(self.policy, "pool_slot_counts", None) + if pool_method is not None: + with torch.no_grad(): + pool = pool_method(obs_tensor) + aux["pool"] = {name: counts.cpu().numpy().astype(np.int16) for name, counts in pool.items()} + return aux + + def capture(self, obs, actions, aux, action_index=None): + """action_index: discrete class per agent behind `actions` (argmax when the executed action is the + distribution mean, the sampled/mode class otherwise); None or -1 when there is no class.""" + if len(self) >= self.max_steps: + return + cap, tcap = self.agent_cap, self.traffic_cap + agent_f32 = np.zeros((1, cap, binding.AGENT_F32_FIELDS), np.float32) + agent_i32 = np.zeros((1, cap, binding.AGENT_I32_FIELDS), np.int32) + metrics_f32 = np.zeros((1, cap, binding.METRICS_F32_FIELDS), np.float32) + puffer_f32 = np.zeros((1, cap, binding.SCORE_F32_FIELDS), np.float32) + traffic_i16 = np.zeros((1, tcap, binding.TRAFFIC_I16_FIELDS), np.int16) + goals_f32 = np.zeros((1, cap, int(self.env.num_goals) * binding.GOAL_XY_FIELDS), np.float32) + rewards_f32 = np.zeros((1, cap, binding.REWARD_F32_FIELDS), np.float32) + coefs_f32 = np.zeros((1, cap, binding.NUM_REWARD_COEFS), np.float32) + self.env.get_obs_html_frame( + agent_f32, agent_i32, metrics_f32, puffer_f32, traffic_i16, goals_f32, rewards_f32, coefs_f32 + ) + frames = self.frames + frames["agent_f32"].append(agent_f32[0]) + frames["agent_i32"].append(agent_i32[0]) + frames["metrics_f32"].append(metrics_f32[0]) + frames["puffer_f32"].append(puffer_f32[0]) + frames["traffic_i16"].append(traffic_i16[0]) + frames["goals_f32"].append(goals_f32[0]) + frames["rewards_f32"].append(rewards_f32[0]) + frames["coefs_f32"].append(coefs_f32[0]) + obs = np.asarray(obs, dtype=np.float32) + frames["obs"].append(np.clip(obs, -OBS_CLIP, OBS_CLIP)) + frames["raw_action"].append(np.asarray(actions, dtype=np.float32).reshape(obs.shape[0], -1)) + index = ( + np.full(obs.shape[0], -1, np.int32) + if action_index is None + else np.asarray(action_index, np.int32).reshape(obs.shape[0]) + ) + frames["action_index"].append(index) + frames["value"].append(aux.get("value", np.zeros(obs.shape[0], np.float32))) + frames["entropy"].append(aux.get("entropy", np.zeros(obs.shape[0], np.float32))) + if "policy_probs" in aux: + frames["policy_probs"].append(aux["policy_probs"]) + for pool_name, counts in aux.get("pool", {}).items(): + frames.setdefault(pool_name, []).append(counts) + + def _env_cfg(self): + env = self.env + return { + "init_step": 0, + "goal_regen_mode": "finite", + "action_type": "discrete", + "dynamics_model": env.dynamics_model, + "num_goals": int(env.num_goals), + "goal_radius": float(env.goal_radius), + "reward_conditioning": bool(env.num_reward_coefs), + "obs_slots_partners_n": int(env.obs_slots_partners_n), + "obs_slots_lane_n": int(env.obs_slots_lane_n), + "obs_slots_boundary_n": int(env.obs_slots_boundary_n), + "obs_lane_stride": int(env.obs_lane_stride), + "obs_boundary_stride": int(env.obs_boundary_stride), + "obs_slots_traffic_controls_n": int(env.obs_slots_traffic_controls_n), + "obs_dropout_lane": float(env.obs_dropout_lane), + "obs_dropout_boundary": float(env.obs_dropout_boundary), + "obs_norm_goal_offset_m": float(env.obs_norm_goal_offset_m), + "obs_norm_xy_offset_m": float(env.obs_norm_xy_offset_m), + "obs_norm_veh_width_m": float(env.obs_norm_veh_width_m), + "obs_norm_veh_length_m": float(env.obs_norm_veh_length_m), + "obs_norm_road_seg_length_m": float(env.obs_norm_road_seg_length_m), + "obs_norm_road_seg_width_m": float(env.obs_norm_road_seg_width_m), + "obs_norm_speed_mps": float(env.obs_norm_speed_mps), + "obs_partner_relative_velocity": bool(env.obs_partner_relative_velocity), + "obs_lane_speed_limit": bool(env.obs_lane_speed_limit), + "obs_goal_lane_distance": bool(env.obs_goal_lane_distance), + "ego_features": int(env.ego_features), + "num_reward_coefs": int(env.num_reward_coefs), + "goal_dim": int(env.goal_dim), + "partner_features": int(env.partner_features), + "lane_features": int(env.lane_features), + "boundary_features": int(env.boundary_features), + } + + def _check_layout(self, obs_dim): + """The viewer decodes the obs from binding constants + env_cfg; refuse to write a replay it would misread.""" + env = self.env + viewer_dim = ( + env.ego_features + + env.num_reward_coefs + + env.goal_dim + + env.obs_slots_partners_n * env.partner_features + + env.obs_slots_lane_kept * env.lane_features + + env.obs_slots_boundary_kept * env.boundary_features + + env.obs_slots_traffic_controls_n * binding.TRAFFIC_CONTROL_FEATURES + ) + if obs_dim < viewer_dim or obs_dim - viewer_dim > OBS_TRAILING_COUNT_FEATURES: + raise RuntimeError( + f"obs replay layout mismatch: obs has {obs_dim} columns, viewer layout expects {viewer_dim}" + ) + + def _cropped_scenario(self, agent_f32): + """Road elements within ROAD_CROP_MARGIN_M of the ego's driven positions (agent_f32 x/y are the road frame).""" + ego_xy = agent_f32[:, 0, :2] + lo = ego_xy.min(axis=0) - ROAD_CROP_MARGIN_M + hi = ego_xy.max(axis=0) + ROAD_CROP_MARGIN_M + roads = [] + for elem in self.scenario.get("road_elements", []) or []: + xs, ys = np.asarray(elem.get("x") or [], np.float32), np.asarray(elem.get("y") or [], np.float32) + if len(xs) and xs.max() >= lo[0] and xs.min() <= hi[0] and ys.max() >= lo[1] and ys.min() <= hi[1]: + roads.append(elem) + scenario = dict(self.scenario) + scenario["road_elements"] = roads + scenario["map_corners"] = [float(lo[0]), float(lo[1]), float(hi[0]), float(hi[1])] + return scenario + + def write(self, render_html=True, save_npz=False): + """Save .replay.zlib (+ .html when render_html, + .npz raw arrays when save_npz). + -> path of the html (or the zlib when not rendered), None when nothing was captured. + Deferred rendering: `render_replay_html(zlib_path)` turns a saved replay into the page later, + e.g. only for scenarios that scored badly.""" + from pufferlib import viz + + frames = self.frames + if not frames["obs"]: + return None + env_cfg = self._env_cfg() + self._check_layout(frames["obs"][0].shape[-1]) + agent_f32 = np.stack(frames["agent_f32"]) + replay = { + "schema": "obs_html_compact_v1", + "env": env_cfg, + "agent_f32": agent_f32, + "agent_i32": np.stack(frames["agent_i32"]), + "metrics_f32": np.stack(frames["metrics_f32"]), + "puffer_f32": np.stack(frames["puffer_f32"]), + "traffic_i16": np.stack(frames["traffic_i16"]), + "goals_f32": np.stack(frames["goals_f32"]), + "rewards_f32": np.stack(frames["rewards_f32"]), + "coefs_f32": np.stack(frames["coefs_f32"]), + "obs": np.stack(frames["obs"]), + "raw_action": np.stack(frames["raw_action"]), + "clipped_action": np.stack(frames["raw_action"]), + "action_index": np.stack(frames["action_index"]), + "value": np.stack(frames["value"]), + "entropy": np.stack(frames["entropy"]), + "policy_probs": np.stack(frames["policy_probs"]) if frames["policy_probs"] else None, + "policy_mean": None, + "policy_std": None, + "policy_log_prob": None, + } + for pool_name in POOL_NAMES: + if frames.get(pool_name): + replay[pool_name] = np.stack(frames[pool_name]) + if self.ghost_trajectory is not None: + frame_count = agent_f32.shape[0] + ghost = np.zeros((frame_count, replay["raw_action"].shape[1], 5), np.float32) + ghost_count = min(frame_count, len(self.ghost_trajectory)) + ghost[:ghost_count, 0] = self.ghost_trajectory[:ghost_count] + replay["ghost_f32"] = ghost + self.out_stem.parent.mkdir(parents=True, exist_ok=True) + zlib_path = str(self.out_stem) + ".replay.zlib" + html_path = str(self.out_stem) + ".html" + viz.save_interactive_replay_zlib(self._cropped_scenario(agent_f32), replay, zlib_path) + if save_npz: + np.savez( + str(self.out_stem) + ".npz", + obs=replay["obs"], + agent_f32=agent_f32, + env_cfg_json=np.array(json.dumps(env_cfg)), + ) + if not render_html: + return zlib_path + render_replay_html(zlib_path) + return html_path + + +def render_replay_html(zlib_path): + """.replay.zlib -> .html""" + from pufferlib import viz + + html_path = str(zlib_path)[: -len(".replay.zlib")] + ".html" + viz.render_interactive_replay_zlib(str(zlib_path), html_path) + return html_path diff --git a/pufferlib/ocean/drive/binding.c b/pufferlib/ocean/drive/binding.c index f599c0de90..0c73e69a79 100644 --- a/pufferlib/ocean/drive/binding.c +++ b/pufferlib/ocean/drive/binding.c @@ -43,13 +43,18 @@ static PyObject *map_cache_release_py(PyObject *self __attribute__((unused)), Py // Seeds are 63-bit non-negative so they survive int64 round-trips (numpy, pandas, CSV). static int unpack_seed(PyObject *kwargs, uint64_t *seed_out) { + // Unpacks kwargs dict can silently set a Python exception. Further processing it would overwrite the error masking + // it. We check here whether this has occured and if so return which reports the original error. + if (PyErr_Occurred()) { + return -1; + } PyObject *seed_obj = PyDict_GetItemString(kwargs, "seed"); if (seed_obj == NULL || !PyLong_Check(seed_obj)) { PyErr_SetString(PyExc_TypeError, "Missing or non-integer keyword argument 'seed'"); return -1; } long long seed = PyLong_AsLongLong(seed_obj); - if (PyErr_Occurred() || seed < 0) { + if (seed < 0) { PyErr_SetString(PyExc_ValueError, "seed must be a non-negative 63-bit integer"); return -1; } @@ -878,6 +883,21 @@ static PyObject *my_get(PyObject *dict, Env *env) { } Py_DECREF(tmp); + // Snap agents goal to -1 once all goal are consumed. CARLA debug CSV writer treats -1 as "no snapped lane" + tmp = PyLong_FromLong((a->current_goal_idx < a->goal_count) ? a->list_goal_lane[a->current_goal_idx] : -1); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "goal_lane_idx", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + tmp = PyLong_FromLong(a->active_agent); if (!tmp) { Py_DECREF(agent); @@ -1309,7 +1329,7 @@ static PyObject *my_get(PyObject *dict, Env *env) { } Py_DECREF(tmp); - PyObject *pf = PyFloat_FromDouble((double) r->speed_limit); + PyObject *pf = PyFloat_FromDouble((double) env->lane_speed_limit_mps[i]); if (!pf) { Py_DECREF(road); Py_DECREF(road_list); @@ -1990,10 +2010,16 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { env->reward_stop_line = (float) unpack(kwargs, "reward_stop_line"); env->reward_timestep = (float) unpack(kwargs, "reward_timestep"); env->reward_overspeed = (float) unpack(kwargs, "reward_overspeed"); + env->overspeed_tolerance_mps = (float) unpack(kwargs, "overspeed_tolerance_mps"); + if (!(env->overspeed_tolerance_mps >= 0.0f) || !isfinite(env->overspeed_tolerance_mps)) { + PyErr_SetString(PyExc_ValueError, "overspeed_tolerance_mps must be finite and >= 0"); + return -1; + } env->reward_ade = (float) unpack(kwargs, "reward_ade"); env->collision_behavior = (int) unpack(kwargs, "collision_behavior"); env->offroad_behavior = (int) unpack(kwargs, "offroad_behavior"); env->disable_red_light_infractions = (int) unpack(kwargs, "disable_red_light_infractions"); + env->disable_stop_sign_infractions = (int) unpack(kwargs, "disable_stop_sign_infractions"); env->traffic_light_junction_phases = (int) unpack(kwargs, "traffic_light_junction_phases"); env->traffic_light_behavior = (int) unpack(kwargs, "traffic_light_behavior"); env->stop_sign_behavior = (int) unpack(kwargs, "stop_sign_behavior"); @@ -2029,6 +2055,8 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { env->obs_slots_lane_n = (int) unpack(kwargs, "obs_slots_lane_n"); env->obs_slots_partners_n = (int) unpack(kwargs, "obs_slots_partners_n"); env->obs_partner_relative_velocity = (int) unpack(kwargs, "obs_partner_relative_velocity"); + env->obs_lane_heading_signed = (int) unpack(kwargs, "obs_lane_heading_signed"); + env->obs_lane_speed_limit = (int) unpack(kwargs, "obs_lane_speed_limit"); env->obs_slots_traffic_controls_n = (int) unpack(kwargs, "obs_slots_traffic_controls_n"); env->traffic_lights_enabled = (bool) unpack(kwargs, "traffic_lights_enabled"); env->stop_signs_enabled = (bool) unpack(kwargs, "stop_signs_enabled"); @@ -2047,8 +2075,31 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { env->spawn_heading_max_deg = (float) unpack(kwargs, "spawn_heading_max_deg"); env->pose_noise_xy_m = (float) unpack(kwargs, "pose_noise_xy_m"); env->pose_noise_yaw_rad = (float) unpack(kwargs, "pose_noise_yaw_deg") * (float) M_PI / 180.0f; + env->speed_limit_random_prob = (float) unpack(kwargs, "speed_limit_random_prob"); + env->speed_limit_random_delta_mps = (float) unpack(kwargs, "speed_limit_random_delta_mps"); + env->speed_limit_random_min_mps = (float) unpack(kwargs, "speed_limit_random_min_mps"); + env->speed_limit_random_max_mps = (float) unpack(kwargs, "speed_limit_random_max_mps"); + if (!(env->speed_limit_random_prob >= 0.0f && env->speed_limit_random_prob <= 1.0f)) { + PyErr_SetString(PyExc_ValueError, "speed_limit_random_prob must be in [0, 1]"); + return -1; + } + if (!(env->speed_limit_random_delta_mps >= 0.0f) || !isfinite(env->speed_limit_random_delta_mps)) { + PyErr_SetString(PyExc_ValueError, "speed_limit_random_delta_mps must be finite and >= 0"); + return -1; + } + if (!(env->speed_limit_random_min_mps > 0.0f && env->speed_limit_random_min_mps <= env->speed_limit_random_max_mps) + || !isfinite(env->speed_limit_random_max_mps)) { + PyErr_SetString(PyExc_ValueError, "speed limit clip must satisfy 0 < min <= max (finite)"); + return -1; + } env->goal_speed = (float) unpack(kwargs, "goal_speed"); env->goal_speed_randomization = (int) unpack(kwargs, "goal_speed_randomization"); + env->conditioning_accel_scale = (float) unpack(kwargs, "conditioning_accel_scale"); + env->conditioning_speed_scale = (float) unpack(kwargs, "conditioning_speed_scale"); + if (!(env->conditioning_speed_scale >= 1.0f) || !isfinite(env->conditioning_speed_scale)) { + PyErr_SetString(PyExc_ValueError, "conditioning_speed_scale must be finite and >= 1"); + return -1; + } env->goal_reach_requires_speed = (int) unpack(kwargs, "goal_reach_requires_speed"); env->scenario_length = (int) unpack(kwargs, "scenario_length"); env->termination_mode = (int) unpack(kwargs, "termination_mode"); @@ -2058,9 +2109,20 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { env->map_name = map_file; env->num_controllable_agents = (int) unpack(kwargs, "max_agents"); env->num_max_agents = (int) unpack(kwargs, "max_agents_per_env"); + env->cosim_partner_slots = (int) unpack(kwargs, "cosim_partner_slots"); + env->cosim_eval_semantics = (int) unpack(kwargs, "cosim_eval_semantics"); int init_step = (int) unpack(kwargs, "init_step"); env->init_step = init_step; env->timestep = init_step; + env->init_step_min_horizon = (int) unpack(kwargs, "init_step_min_horizon"); + env->stagger_first_episode = (int) unpack(kwargs, "stagger_first_episode"); + if (env->stagger_first_episode + && (env->init_step_min_horizon < 1 || env->init_step + env->init_step_min_horizon > env->scenario_length)) { + PyErr_SetString( + PyExc_ValueError, + "stagger_first_episode needs 1 <= init_step_min_horizon <= scenario_length - init_step"); + return -1; + } env->init_mode = (int) unpack(kwargs, "init_mode"); env->control_mode = (int) unpack(kwargs, "control_mode"); env->sdc_controller = (int) unpack(kwargs, "sdc_controller"); @@ -2164,6 +2226,14 @@ static int my_log(PyObject *dict, Env *env, Log *log, float n) { float avg_distance_per_infraction = total_distance_travelled / fmaxf(1.0f, total_infractions); assign_to_dict(dict, "n", log->n); + assign_to_dict(dict, "spawn_failed", log->spawn_failed); + assign_to_dict(dict, "spawn_reject_collision", log->spawn_reject_collision); + assign_to_dict(dict, "spawn_reject_offroad", log->spawn_reject_offroad); + assign_to_dict(dict, "spawn_reject_stop_line", log->spawn_reject_stop_line); + assign_to_dict(dict, "spawn_reject_empty_cell", log->spawn_reject_empty_cell); + assign_to_dict(dict, "spawn_failed_goal", log->spawn_failed_goal); + assign_to_dict(dict, "stopped_at_reset", log->stopped_at_reset); + assign_to_dict(dict, "early_reset_short", log->early_reset_short); assign_to_dict(dict, "offroad_rate", log->offroad_rate); assign_to_dict(dict, "episode_length", log->episode_length); assign_to_dict(dict, "collision_rate", log->collision_rate); diff --git a/pufferlib/ocean/drive/constants.h b/pufferlib/ocean/drive/constants.h index 5b90c80638..9977ff483d 100644 --- a/pufferlib/ocean/drive/constants.h +++ b/pufferlib/ocean/drive/constants.h @@ -106,9 +106,11 @@ #define GOAL_REGEN_ROLLING 1 // slide window: drop reached goal, append one at the frontier // Where goals are sampled from -#define GOAL_SOURCE_ROUTE 0 // seed from the agent's own forward route -#define GOAL_SOURCE_MAP 1 // seed from a uniformly sampled map lane -#define GOAL_SOURCE_GT 2 // seed directly from the logged ground-truth trajectory +#define GOAL_SOURCE_ROUTE 0 // seed from the agent's own forward route +#define GOAL_SOURCE_MAP 1 // seed from a uniformly sampled map lane +#define GOAL_SOURCE_GT 2 // seed directly from the logged ground-truth trajectory +#define GOAL_SOURCE_EXTERNAL 3 // co-sim: goal windows pushed by the external sim (c_set_agent_goals) +#define GOAL_SOURCE_GT_MAP 4 // logged-trajectory goals projected onto the nearest co-directional lane center // Dynamics model #define DYNAMICS_MODEL_CLASSIC 0 @@ -164,7 +166,18 @@ static const float STEERING_VALUES[NUM_STEERING_ACTIONS] #define COLLISION_SKIP_DISP_M 0.1f #define COLLISION_PAIR_MARGIN_M 0.5f // Extra slack on the radius+displacement quick-check before OBB SAT #define SPAWN_CLEARANCE_M 1.5f // Min box-to-box gap between agents at spawn +#define SPAWN_REJECT_COLLISION 0 +#define SPAWN_REJECT_OFFROAD 1 +#define SPAWN_REJECT_STOP_LINE 2 +#define SPAWN_REJECT_EMPTY_CELL 3 +#define SPAWN_REJECT_REASON_COUNT 4 +#define EARLY_RESET_SHORT_TIMESTEPS 2 // early reset this soon after c_reset means the reset itself failed +#define SHORT_RESET_MAX_PRINTS 3 +#define COSIM_PARTNER_DEFAULT_LENGTH_M 4.5f // co-sim partner slot box until the external sim sets sizes +#define COSIM_PARTNER_DEFAULT_WIDTH_M 2.0f +#define COSIM_PARTNER_DEFAULT_HEIGHT_M 1.5f #define SPAWN_OFFROAD_SCALE_FACTOR 1.1f +#define EVAL_SPAWN_EDGE_CLEARANCE_M 0.5f // Rear swing on a first full-lock turn must not clip the road edge // Replay self-play: logged vehicles failing these are created static instead of policy-controlled #define REPLAY_SPAWN_EDGE_CLEARANCE_M 0.5f // Car box must stay this far from the road edge #define REPLAY_SPAWN_LONGITUDINAL_CLEARANCE_M SPAWN_CLEARANCE_M // Gap needed to other cars in front of and behind @@ -175,14 +188,21 @@ static const float STEERING_VALUES[NUM_STEERING_ACTIONS] #define STOP_LINE_DIST_SQ (10.0f * 10.0f) #define STOP_LINE_EXTENSION_FACTOR 1.5f #define STOP_LINE_HEADING_THRESHOLD (M_PI / 4.0f) -#define STOP_SIGN_HEADING_TOLERANCE_RADIANS 1e-5f -#define STOP_SIGN_REQUIRED_STOP_DURATION_SECONDS 0.5f // Red light violation detection #define RED_LIGHT_TRIGGER_DIST_SQ (30.0f * 30.0f) #define RED_LIGHT_LATERAL_EXTENSION_M 15.0f // Beyond each stop line endpoint, so the line cannot be driven around #define RED_LIGHT_ENTER_HEADING_THRESHOLD (M_PI / 2.0f) +// Stop sign violation detection (CaRL RunStopSign2 semantics, trigger box centred on the stop line) +#define STOP_SIGN_PROXIMITY_DIST_SQ (20.0f * 20.0f) +#define STOP_SIGN_TRIGGER_HALF_DEPTH_M 1.7f // CARLA traffic.stop trigger volume half extent along the lane +#define STOP_SIGN_AFFECTED_BOX_SCALE 1.2f +#define STOP_SIGN_STOP_SPEED_MPS 0.1f +#define STOP_SIGN_REVERSING_SPEED_MPS 0.17f +#define STOP_SIGN_APPROACH_HEADING_THRESHOLD (M_PI / 4.0f) +#define STOP_SIGN_LATERAL_EXTENSION_M 17.5f // Five lane widths beyond each endpoint, cannot be driven around + #define BEHIND_COS_THRESHOLD -0.8660254f // cos(150 degrees) // ===================================================================================== @@ -193,8 +213,11 @@ static const float STEERING_VALUES[NUM_STEERING_ACTIONS] #define MAX_GRID_CELL_COUNT 100000000 // Depends on resolution of data Formula: 3 * (2 + GRID_CELL_SIZE*sqrt(2)/resolution) // => For each entity type in gridmap, diagonal poly-lines -> sqrt(2), include diagonal ends -> 2 -#define MAX_ENTITIES_PER_CELL 30 +#define MAX_ENTITIES_PER_CELL 64 #define ROAD_QUERY_ENTITY_COUNT (MAX_ENTITIES_PER_CELL * 25) // 5x5 cell neighborhood +#define OBS_ENTITY_NONE 0 // grid entity not sampled for road observations +#define OBS_ENTITY_LANE 1 +#define OBS_ENTITY_EDGE 2 // 5x5 cell neighborhood swept by a road query, centered on the agent's cell static const int ROAD_OFFSETS[25][2] @@ -216,6 +239,9 @@ static const int ROAD_OFFSETS[25][2] #define ROUTE_EXIT_MAX_CANDIDATES 5 #define GOAL_HEADING_MAX_ATTEMPTS 8 #define GT_GOAL_RADIUS_M 6.0f +// Max snap distance from an externally-set (co-sim) goal waypoint to its lane; route goals are +// already lane-centered upstream, so anything farther is off the drivable network -> no lane. +#define GOAL_LANE_SNAP_MAX_DIST_M 6.0f #define LANE_GRAPH_DISTANCE_NORM_M 500.0f // normalization for the GPS lane-distance feature // ===================================================================================== @@ -226,7 +252,11 @@ static const int ROAD_OFFSETS[25][2] #define TL_DEFAULT_YELLOW_DURATION 3.0f #define TL_DEFAULT_GREEN_DURATION 10.0f #define TL_EPISODE_DISABLE_PROB 0.20f +// Junction lanes inherit the min limit over their entry lanes; passes bound chained connectors +#define SPEED_LIMIT_JUNCTION_INHERIT_PASSES 4 +#define UNKNOWN_LANE_SPEED_LIMIT_MPS 15.0f // lanes without a map limit (nuPlan Boston/Singapore); CaRL's IDM fallback #define TL_INDIVIDUAL_REMOVE_PROB 0.20f +#define TL_GROUP_REMOVE_PROB 0.20f #define TL_ALWAYS_GREEN_PROB 0.05f // ===================================================================================== @@ -235,6 +265,7 @@ static const int ROAD_OFFSETS[25][2] #define EGO_FEATURES 11 #define LANE_FEATURES 9 +#define LANE_SPEED_LIMIT_FEATURES 1 // appended per lane row when obs_lane_speed_limit is set #define BOUNDARY_FEATURES 6 #define PARTNER_FEATURES 9 #define PARTNER_RELATIVE_VELOCITY_FEATURES 2 // appended per partner row when obs_partner_relative_velocity is set diff --git a/pufferlib/ocean/drive/datatypes.h b/pufferlib/ocean/drive/datatypes.h index 0067e222e8..0daefdfd39 100644 --- a/pufferlib/ocean/drive/datatypes.h +++ b/pufferlib/ocean/drive/datatypes.h @@ -96,9 +96,9 @@ struct Agent { int displacement_sample_count; float distance_since_spawn; float seconds_stopped; - float lane_curvature; // 1/m at the closest segment of current lane, positive = left turn + float lane_curvature; // 1/m at the closest segment of current lane, positive = left turn + float lane_heading_error_rad; // agent heading minus lane heading, positive = agent points left of lane int comfort_violation_last_window_idx; - int stop_sign_stopped_timestep_count; // Goal positions float list_goal_x[MAX_GOALS]; @@ -114,8 +114,13 @@ struct Agent { float gt_goal_y; // Last valid ground-truth goal position y float gt_goal_z; // Last valid ground-truth goal position z - int stopped; // 0/1 -> freeze if set - int removed; // 0/1 -> remove from sim if set + int stopped; // 0/1 -> freeze if set + int removed; // 0/1 -> remove from sim if set + int first_collision_partner_idx; // agent index of the first collision partner this episode, -1 if none + int stop_sign_target_idx; // traffic element being approached, -1 if none + int stop_sign_stop_completed; // 0/1 -> stood still inside the target's trigger box + int stop_sign_last_failed_idx; // element run without stopping, not re-targeted until 20 m away + int stop_sign_standstill_idx; // element whose trigger box the agent last stood still in, -1 if none // Jerk dynamics float accel_long; @@ -134,6 +139,16 @@ struct Agent { unsigned char is_phantom_braker; // episode-level flag: agent may phantom-brake }; +typedef struct { + float mid_x_m; + float mid_y_m; + float mid_z_m; + float half_length_m; + float direction_x; + float direction_y; + float width_m; +} RoadObservationSegment; + struct RoadMapElement { int type; @@ -141,7 +156,8 @@ struct RoadMapElement { float *x; float *y; float *z; - float *headings; // Pre-computed heading for each segment + float *headings; // Pre-computed heading for each segment + RoadObservationSegment *observation_segments; // Immutable; owned with the shared road geometry. // Lane specific info int num_entries; @@ -151,7 +167,8 @@ struct RoadMapElement { float speed_limit; float length; float *cum_lengths; - float *widths; // per-point lane width, meters + float *widths; // per-point lane width, meters + int speed_zone_idx; // lanes sharing one posted limit; -1 = none (junction lane or map without zones) }; struct TrafficControlElement { @@ -193,6 +210,7 @@ void free_road_element(struct RoadMapElement *element) { free(element->y); free(element->z); free(element->headings); + free(element->observation_segments); free(element->entry_lanes); free(element->exit_lanes); free(element->cum_lengths); diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h index d1120e3bf7..88b83bc039 100644 --- a/pufferlib/ocean/drive/drive.h +++ b/pufferlib/ocean/drive/drive.h @@ -87,12 +87,20 @@ struct Log { float reward_reverse; float reward_overspeed; float reward_ade; + float spawn_failed; + float spawn_reject_collision; + float spawn_reject_offroad; + float spawn_reject_stop_line; + float spawn_reject_empty_cell; + float spawn_failed_goal; + float stopped_at_reset; + float early_reset_short; }; struct GridMapEntity { - int entity_idx; // Index into the road_elements array - int geometry_idx; // Index into element's geometry array - int valid_for_obs; // Whether this entity should be included in observations + int entity_idx; // Index into the road_elements array + int geometry_idx; // Index into element's geometry array + int obs_kind; // OBS_ENTITY_NONE / LANE / EDGE: how road observations sample this entity }; struct GridMap { @@ -105,11 +113,13 @@ struct GridMap { int vision_range; int *cell_entities_count; int *neighbor_cache_count; + int *neighbor_cache_lane_count; // per cell: leading lane entries of its neighbor cache; edge entries follow int *grid_index_drivable; int num_drivable_grid_cell; int total_entities; + GridMapEntity *entity_pool; // one block; cells[i] point into it GridMapEntity **cells; - GridMapEntity **neighbor_cache_entities; + uint16_t **neighbor_cache_pool_idx; // per cell: entity_pool indices of its spiral neighborhood }; // Static, read-only map geometry shared across envs loading the same map file @@ -149,6 +159,7 @@ struct Drive { // Agents Agent *agents; int num_controllable_agents; + int cosim_partner_slots; // gigaflow co-sim: static slots owned by the external sim (never spawned/stepped) int active_agent_count; int *active_agent_indices; int num_total_agents; @@ -163,6 +174,7 @@ struct Drive { char *map_name; RoadMapElement *road_elements; int num_road_elements; + int num_speed_zones; TrafficControlElement *traffic_elements; int num_traffic_elements; struct LaneGraph lane_graph; @@ -187,7 +199,12 @@ struct Drive { int *tracks_to_predict; // Simulation int timestep; + int autoreset_pending; int init_step; + int init_step_min_horizon; + int stagger_first_episode; + int first_reset_pending; + int episode_end_timestep; float dt; float base_max_speed_mps; float max_speed_mps; @@ -196,6 +213,13 @@ struct Drive { float spawn_heading_max_deg; float pose_noise_xy_m; float pose_noise_yaw_rad; + float speed_limit_random_prob; + float speed_limit_random_delta_mps; + float speed_limit_random_min_mps; + float speed_limit_random_max_mps; + float *lane_speed_limit_mps; // per-episode effective limit per road element (zone-randomized) + float *speed_zone_offset_mps; // num_speed_zones + unsigned char *lane_limit_resolved; // scratch for junction-lane inheritance int dynamics_model; int reset_accel_on_stop; int init_mode; @@ -204,6 +228,7 @@ struct Drive { int offroad_behavior; int traffic_light_behavior; int disable_red_light_infractions; + int disable_stop_sign_infractions; int traffic_light_junction_phases; int stop_sign_behavior; int sdc_controller; @@ -231,6 +256,7 @@ struct Drive { float reward_stop_line; float reward_timestep; float reward_overspeed; + float overspeed_tolerance_mps; float reward_ade; int reward_conditioning; int reward_randomization; @@ -240,9 +266,11 @@ struct Drive { float goal_speed; float min_goal_spacing; float max_goal_spacing; - float goal_heading_max_deg; // 0 disables the successive-waypoint heading constraint - int goal_speed_randomization; // 0 pins the goal-speed coef to goal_speed (paper: v_goal fixed) - int goal_reach_requires_speed; // 1: final goal is consumed only below goal speed (paper semantics) + float goal_heading_max_deg; // 0 disables the successive-waypoint heading constraint + int goal_speed_randomization; // 0 pins the goal-speed coef to goal_speed (paper: v_goal fixed) + float conditioning_accel_scale; // eval C_acc: scales the positive accel cap (ACCEL_LONG_LIMIT[1]); 1.0 = paper eval + float conditioning_speed_scale; // training C_vel ~ X(a): speed cap = base_max_speed_mps * [1/a, a]; 1.5 = paper + int goal_reach_requires_speed; // 1: final goal is consumed only below goal speed (paper semantics) int num_goals; int goal_regen_mode; int goal_source; @@ -252,6 +280,8 @@ struct Drive { int obs_slots_lane_n; int obs_slots_partners_n; int obs_partner_relative_velocity; + int obs_lane_heading_signed; + int obs_lane_speed_limit; int obs_slots_traffic_controls_n; int traffic_lights_enabled; int stop_signs_enabled; @@ -270,6 +300,7 @@ struct Drive { float obs_norm_road_seg_width_m; float obs_norm_z_m; float eval_perceived_size_margin_m; + int cosim_eval_semantics; // co-sim: apply the eval-only obs/action tweaks without native eval batching float eval_standstill_jerk_deadband_mps3; // 0 disables; eval-only standstill deadband on j_long float obs_range_traffic_control_m; float obs_range_partner_m; @@ -289,6 +320,9 @@ struct Drive { // Logging Log log; Log *logs; + int spawn_reject_counts[SPAWN_REJECT_REASON_COUNT]; + int spawn_goal_failed; + int short_reset_print_count; int logs_capacity; // Seed int eval_episode_done; @@ -476,6 +510,11 @@ static void reset_agent_state(Agent *agent) { agent->displacement_sample_count = 0; agent->stopped = 0; agent->removed = 0; + agent->first_collision_partner_idx = -1; + agent->stop_sign_target_idx = -1; + agent->stop_sign_stop_completed = 0; + agent->stop_sign_last_failed_idx = -1; + agent->stop_sign_standstill_idx = -1; agent->current_lane_idx = -1; agent->previous_lane_idx = -1; agent->current_route_idx = 0; @@ -487,8 +526,8 @@ static void reset_agent_state(Agent *agent) { agent->distance_since_spawn = 0.0f; agent->seconds_stopped = 0.0f; agent->lane_curvature = 0.0f; + agent->lane_heading_error_rad = 0.0f; agent->comfort_violation_last_window_idx = -1; - agent->stop_sign_stopped_timestep_count = 0; agent->phantom_braking_counter = 0; agent->partner_blindness_counter = 0; agent->is_blind_partner = 0; @@ -1077,6 +1116,117 @@ static bool compute_new_route(Drive *env, Agent *agent, int current_lane_idx) { return true; } +// Nearest drivable lane for a goal waypoint (sim frame) plus the waypoint's projection onto it, so +// the GPS lane-distance observation columns (write_road_obs: list_goal_lane -> lane-graph +// distance) stay live for external and logged routes, matching goals produced by the map/route +// generators. Alignment gate: at a junction, the nearest lane to a goal can be the +// CROSSING road's (9% of Town01 route goals measured), which points the GPS features +// down the wrong road exactly where a turn decision happens. +#define GOAL_LANE_ALIGN_SIN_LIMIT 0.7071f // sin(45 deg): reject > 45 deg divergence mod 180 +#define GOAL_LANE_ALIGN_COS_MIN 0.0f // cos(90 deg): reject lanes not generally co-directional +static int find_goal_lane( + Drive *env, + float goal_x, + float goal_y, + float route_dir_x, + float route_dir_y, + float *out_snap_x, + float *out_snap_y) { + *out_snap_x = goal_x; + *out_snap_y = goal_y; + if (env->grid_map == NULL || get_grid_index(env, goal_x, goal_y) == -1) { + return -1; + } + float route_norm = sqrtf(route_dir_x * route_dir_x + route_dir_y * route_dir_y); + int use_alignment_gate = route_norm > 1e-6f; + if (use_alignment_gate) { + route_dir_x /= route_norm; + route_dir_y /= route_norm; + } + GridMapEntity entity_list[ROAD_QUERY_ENTITY_COUNT]; + int list_size = get_neighbors_entities(env, goal_x, goal_y, entity_list, ROAD_QUERY_ENTITY_COUNT, ROAD_OFFSETS, 25); + int best_lane_idx = -1; + float best_dist_sq = GOAL_LANE_SNAP_MAX_DIST_M * GOAL_LANE_SNAP_MAX_DIST_M; + for (int i = 0; i < list_size; i++) { + if (entity_list[i].entity_idx == -1) { + continue; + } + RoadMapElement *element = &env->road_elements[entity_list[i].entity_idx]; + if (!is_drivable_road_lane(element->type)) { + continue; + } + int geometry_idx = entity_list[i].geometry_idx; + if (geometry_idx + 1 >= element->segment_size) { + continue; + } + float seg_start_x = element->x[geometry_idx]; + float seg_start_y = element->y[geometry_idx]; + float seg_dx = element->x[geometry_idx + 1] - seg_start_x; + float seg_dy = element->y[geometry_idx + 1] - seg_start_y; + float seg_length_sq = seg_dx * seg_dx + seg_dy * seg_dy; + if (use_alignment_gate && seg_length_sq > 1e-6f) { + float seg_length = sqrtf(seg_length_sq); + float seg_dir_x = seg_dx / seg_length; + float seg_dir_y = seg_dy / seg_length; + float cross = route_dir_x * seg_dir_y - route_dir_y * seg_dir_x; + if (fabsf(cross) > GOAL_LANE_ALIGN_SIN_LIMIT) { + continue; // crossing road's lane, not the route's + } + float dot = route_dir_x * seg_dir_x + route_dir_y * seg_dir_y; + if (dot < GOAL_LANE_ALIGN_COS_MIN) { + continue; // oncoming lane (mirror-image direction of the route), not the route's + } + } + float to_goal_x = goal_x - seg_start_x; + float to_goal_y = goal_y - seg_start_y; + float t = (seg_length_sq > 1e-6f) ? (to_goal_x * seg_dx + to_goal_y * seg_dy) / seg_length_sq : 0.0f; + t = clip(t, 0.0f, 1.0f); + float dx = to_goal_x - t * seg_dx; + float dy = to_goal_y - t * seg_dy; + float dist_sq = dx * dx + dy * dy; + if (dist_sq < best_dist_sq) { + best_dist_sq = dist_sq; + best_lane_idx = entity_list[i].entity_idx; + *out_snap_x = seg_start_x + t * seg_dx; + *out_snap_y = seg_start_y + t * seg_dy; + } + } + return best_lane_idx; +} + +static void generate_new_goals_from_log(Drive *env, Agent *agent) { + // GT keeps the raw logged points (no lane -> no GPS lane-distance). GT_MAP projects each point onto + // the nearest lane co-directional with the logged heading so the expert's exact pose never reaches the policy. + int start = env->init_step > 0 ? env->init_step : 0; + int remaining = agent->trajectory_size - 1 - start; + if (remaining < 1) { + remaining = 1; + } + int num_wp = env->num_goals; + for (int g = 0; g < num_wp; g++) { + int t = start + (g + 1) * remaining / num_wp; + if (t >= agent->trajectory_size) { + t = agent->trajectory_size - 1; + } + float goal_x = agent->log_trajectory_x[t]; + float goal_y = agent->log_trajectory_y[t]; + int goal_lane_idx = -1; + if (env->goal_source == GOAL_SOURCE_GT_MAP) { + float log_heading = agent->log_heading[t]; + goal_lane_idx = find_goal_lane(env, goal_x, goal_y, cosf(log_heading), sinf(log_heading), &goal_x, &goal_y); + } + agent->list_goal_x[g] = goal_x; + agent->list_goal_y[g] = goal_y; + agent->list_goal_z[g] = agent->log_trajectory_z[t]; + agent->list_goal_lane[g] = goal_lane_idx; + } + agent->goal_count = num_wp; + agent->current_goal_idx = 0; + agent->current_goal_x = agent->list_goal_x[0]; + agent->current_goal_y = agent->list_goal_y[0]; + agent->current_goal_z = agent->list_goal_z[0]; +} + static bool generate_new_goals_from_route(Drive *env, Agent *agent) { // Places num_goals goals along the agent's route by native lane arc-length. // Replay follows the loaded route to its end; gigaflow route source random-walks a fresh route @@ -1491,6 +1641,14 @@ static bool check_segment_crosses_moving_box(float ax, float ay, float bx, float return true; } + // Centre moved less than the inscribed radius: a segment over its path already hit the cur test above. + float center_step_x = agent->sim_x - agent->prev_x; + float center_step_y = agent->sim_y - agent->prev_y; + float inscribed_radius = fminf(half_length, half_width); + if (center_step_x * center_step_x + center_step_y * center_step_y < inscribed_radius * inscribed_radius) { + return false; + } + // All edges can miss while the swept region still covers the box center: consistent cross-product // sign means the origin is inside the quad. The quad chords the prev/cur segments, but the true // endpoint paths are arcs, so at large per-step yaw the chords under-cover and a swept center reads @@ -1576,6 +1734,25 @@ static bool check_agent_on_stop_line(Drive *env, Agent *agent, bool include_yell return false; } +static bool check_corner_boxes_overlap(float corners_a[4][2], float corners_b[4][2], float axes[4][2]) { + for (int i = 0; i < 4; i++) { + float min_a = INFINITY, max_a = -INFINITY; + float min_b = INFINITY, max_b = -INFINITY; + for (int j = 0; j < 4; j++) { + float proj_a = corners_a[j][0] * axes[i][0] + corners_a[j][1] * axes[i][1]; + min_a = fminf(min_a, proj_a); + max_a = fmaxf(max_a, proj_a); + float proj_b = corners_b[j][0] * axes[i][0] + corners_b[j][1] * axes[i][1]; + min_b = fminf(min_b, proj_b); + max_b = fmaxf(max_b, proj_b); + } + if (max_a < min_b || min_a > max_b) { + return false; + } + } + return true; +} + static bool check_red_light_violation(Drive *env, int agent_idx) { Agent *agent = &env->agents[agent_idx]; // Vehicle center crosses on red; the laterally extended line cannot be driven @@ -1635,111 +1812,185 @@ static bool check_red_light_violation(Drive *env, int agent_idx) { return false; } -static bool agent_front_crossed_stop_line(Agent *agent, TrafficControlElement *traffic_control) { - float line_dx = traffic_control->stop_line[3] - traffic_control->stop_line[0]; - float line_dy = traffic_control->stop_line[4] - traffic_control->stop_line[1]; - float line_length = sqrtf(line_dx * line_dx + line_dy * line_dy); - if (line_length <= 0.0f) { - return false; - } - - float line_unit_x = line_dx / line_length; - float line_unit_y = line_dy / line_length; - float normal_x = -line_unit_y; - float normal_y = line_unit_x; - if (normal_x * cosf(traffic_control->heading) + normal_y * sinf(traffic_control->heading) < 0.0f) { - normal_x = -normal_x; - normal_y = -normal_y; - } - - float midpoint_x = 0.5f * (traffic_control->stop_line[0] + traffic_control->stop_line[3]); - float midpoint_y = 0.5f * (traffic_control->stop_line[1] + traffic_control->stop_line[4]); - float front_offset = 0.5f * agent->sim_length; - float previous_front_x = agent->prev_x + front_offset * agent->prev_cos_heading; - float previous_front_y = agent->prev_y + front_offset * agent->prev_sin_heading; - float current_front_x = agent->sim_x + front_offset * agent->cos_heading; - float current_front_y = agent->sim_y + front_offset * agent->sin_heading; - float previous_side = (previous_front_x - midpoint_x) * normal_x + (previous_front_y - midpoint_y) * normal_y; - float current_side = (current_front_x - midpoint_x) * normal_x + (current_front_y - midpoint_y) * normal_y; - if (!(previous_side < 0.0f && current_side >= 0.0f)) { +typedef struct { + float mid_x; + float mid_y; + float along_x; // unit travel direction, normal to the stop line + float along_y; + float half_len; +} StopLineFrame; + +static bool compute_stop_line_frame(TrafficControlElement *tc, StopLineFrame *frame) { + float line_dx = tc->stop_line[3] - tc->stop_line[0]; + float line_dy = tc->stop_line[4] - tc->stop_line[1]; + float line_len = sqrtf(line_dx * line_dx + line_dy * line_dy); + if (line_len <= 0.0f) { return false; } - - float crossing_fraction = previous_side / (previous_side - current_side); - float crossing_x = previous_front_x + crossing_fraction * (current_front_x - previous_front_x); - float crossing_y = previous_front_y + crossing_fraction * (current_front_y - previous_front_y); - float lateral_offset = (crossing_x - midpoint_x) * line_unit_x + (crossing_y - midpoint_y) * line_unit_y; - return fabsf(lateral_offset) <= 0.5f * STOP_LINE_EXTENSION_FACTOR * line_length; + float along_x = -line_dy / line_len; + float along_y = line_dx / line_len; + if (along_x * cosf(tc->heading) + along_y * sinf(tc->heading) < 0.0f) { + along_x = -along_x; + along_y = -along_y; + } + frame->mid_x = (tc->stop_line[0] + tc->stop_line[3]) * 0.5f; + frame->mid_y = (tc->stop_line[1] + tc->stop_line[4]) * 0.5f; + frame->along_x = along_x; + frame->along_y = along_y; + frame->half_len = 0.5f * line_len; + return true; } -static bool check_stop_sign_violation(Drive *env, Agent *agent) { - bool near_stop_sign = false; - bool stopped_before_stop_line = false; - bool crossed_stop_line = false; +// RunStopSign2 acquisition: the path ahead reaches the scaled trigger box within 20 m, never while reversing +static int find_stop_sign_target(Drive *env, Agent *agent) { + if (agent->sim_speed_signed < -STOP_SIGN_REVERSING_SPEED_MPS) { + return -1; + } + int target_idx = -1; + float target_dist_sq = STOP_SIGN_PROXIMITY_DIST_SQ; for (int i = 0; i < env->num_traffic_elements; i++) { - TrafficControlElement *traffic_control = &env->traffic_elements[i]; - if (traffic_control->type != TRAFFIC_CONTROL_TYPE_STOP_SIGN) { + TrafficControlElement *tc = &env->traffic_elements[i]; + if (tc->type != TRAFFIC_CONTROL_TYPE_STOP_SIGN || i == agent->stop_sign_last_failed_idx) { + continue; + } + float mid_z = (tc->stop_line[2] + tc->stop_line[5]) * 0.5f; + if (fabsf(agent->sim_z - mid_z) > Z_BUFFER) { continue; } - if (fabsf(compute_heading_diff(agent->sim_heading, traffic_control->heading)) - > STOP_LINE_HEADING_THRESHOLD + STOP_SIGN_HEADING_TOLERANCE_RADIANS) { + StopLineFrame frame; + if (!compute_stop_line_frame(tc, &frame)) { continue; } - - float midpoint_x = 0.5f * (traffic_control->stop_line[0] + traffic_control->stop_line[3]); - float midpoint_y = 0.5f * (traffic_control->stop_line[1] + traffic_control->stop_line[4]); - float midpoint_z = 0.5f * (traffic_control->stop_line[2] + traffic_control->stop_line[5]); - if (fabsf(agent->sim_z - midpoint_z) > Z_BUFFER) { + float dx = agent->sim_x - frame.mid_x; + float dy = agent->sim_y - frame.mid_y; + float dist_sq = dx * dx + dy * dy; + if (dist_sq >= target_dist_sq) { continue; } - - crossed_stop_line = crossed_stop_line || agent_front_crossed_stop_line(agent, traffic_control); - float dx = agent->sim_x - midpoint_x; - float dy = agent->sim_y - midpoint_y; - float distance_sq = dx * dx + dy * dy; - if (distance_sq > STOP_LINE_DIST_SQ) { + if (fabsf(compute_heading_diff(agent->sim_heading, tc->heading)) > STOP_SIGN_APPROACH_HEADING_THRESHOLD) { continue; } - near_stop_sign = true; + float along = dx * frame.along_x + dy * frame.along_y; + float across = dx * frame.along_y - dy * frame.along_x; + if (along > STOP_SIGN_AFFECTED_BOX_SCALE * STOP_SIGN_TRIGGER_HALF_DEPTH_M + || fabsf(across) > STOP_SIGN_AFFECTED_BOX_SCALE * frame.half_len) { + continue; + } + target_idx = i; + target_dist_sq = dist_sq; + } + return target_idx; +} - float line_dx = traffic_control->stop_line[3] - traffic_control->stop_line[0]; - float line_dy = traffic_control->stop_line[4] - traffic_control->stop_line[1]; - float normal_x = -line_dy; - float normal_y = line_dx; - if (normal_x * cosf(traffic_control->heading) + normal_y * sinf(traffic_control->heading) < 0.0f) { - normal_x = -normal_x; - normal_y = -normal_y; +static bool check_agent_in_stop_sign_box(Agent *agent, StopLineFrame *frame) { + float agent_corners[4][2]; + compute_bounding_box_corners( + agent->sim_x, + agent->sim_y, + agent->cos_heading, + agent->sin_heading, + agent->sim_length / 2.0f, + agent->sim_width / 2.0f, + agent_corners); + float box_corners[4][2]; + compute_bounding_box_corners( + frame->mid_x, + frame->mid_y, + frame->along_x, + frame->along_y, + STOP_SIGN_TRIGGER_HALF_DEPTH_M, + frame->half_len, + box_corners); + float axes[4][2] + = {{agent->cos_heading, agent->sin_heading}, + {-agent->sin_heading, agent->cos_heading}, + {frame->along_x, frame->along_y}, + {-frame->along_y, frame->along_x}}; + return check_corner_boxes_overlap(agent_corners, box_corners, axes); +} + +static bool stop_sign_beyond_proximity(Drive *env, Agent *agent, int element_idx) { + TrafficControlElement *tc = &env->traffic_elements[element_idx]; + float dx = agent->sim_x - (tc->stop_line[0] + tc->stop_line[3]) * 0.5f; + float dy = agent->sim_y - (tc->stop_line[1] + tc->stop_line[4]) * 0.5f; + return dx * dx + dy * dy > STOP_SIGN_PROXIMITY_DIST_SQ; +} + +// A standstill inside a sign's trigger box is remembered so it still counts once that sign becomes the target +static int find_stop_sign_standstill(Drive *env, Agent *agent) { + if (agent->sim_speed >= STOP_SIGN_STOP_SPEED_MPS) { + return -1; + } + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *tc = &env->traffic_elements[i]; + if (tc->type != TRAFFIC_CONTROL_TYPE_STOP_SIGN || stop_sign_beyond_proximity(env, agent, i)) { + continue; + } + float mid_z = (tc->stop_line[2] + tc->stop_line[5]) * 0.5f; + if (fabsf(agent->sim_z - mid_z) > Z_BUFFER) { + continue; + } + StopLineFrame frame; + if (compute_stop_line_frame(tc, &frame) && check_agent_in_stop_sign_box(agent, &frame)) { + return i; } - float front_x = agent->sim_x + 0.5f * agent->sim_length * agent->cos_heading; - float front_y = agent->sim_y + 0.5f * agent->sim_length * agent->sin_heading; - bool before_stop_line = (front_x - midpoint_x) * normal_x + (front_y - midpoint_y) * normal_y < 0.0f; - stopped_before_stop_line - = stopped_before_stop_line || (before_stop_line && agent->sim_speed <= AGENT_STOPPED_SPEED_THRESHOLD); } + return -1; +} - int required_stop_steps = (int) (STOP_SIGN_REQUIRED_STOP_DURATION_SECONDS / env->dt); - if (required_stop_steps < 1) { - required_stop_steps = 1; +// True on the step the centre crosses the target's stop line without a completed stop. +static bool update_stop_sign_state(Drive *env, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; + StopLineFrame frame = {0}; + if (agent->stop_sign_last_failed_idx >= 0 + && stop_sign_beyond_proximity(env, agent, agent->stop_sign_last_failed_idx)) { + agent->stop_sign_last_failed_idx = -1; + } + if (agent->stop_sign_standstill_idx >= 0 + && stop_sign_beyond_proximity(env, agent, agent->stop_sign_standstill_idx)) { + agent->stop_sign_standstill_idx = -1; + } + if (agent->stop_sign_target_idx >= 0 && stop_sign_beyond_proximity(env, agent, agent->stop_sign_target_idx)) { + agent->stop_sign_target_idx = -1; + agent->stop_sign_stop_completed = 0; + } + if (agent->stop_sign_target_idx < 0) { + int standstill_idx = find_stop_sign_standstill(env, agent); + if (standstill_idx >= 0) { + agent->stop_sign_standstill_idx = standstill_idx; + } + agent->stop_sign_target_idx = find_stop_sign_target(env, agent); + agent->stop_sign_stop_completed + = agent->stop_sign_target_idx >= 0 && agent->stop_sign_target_idx == agent->stop_sign_standstill_idx; + if (agent->stop_sign_target_idx < 0) { + return false; + } } - bool stop_satisfied = agent->stop_sign_stopped_timestep_count >= required_stop_steps; - if (crossed_stop_line) { - agent->stop_sign_stopped_timestep_count = 0; - return !stop_satisfied; + compute_stop_line_frame(&env->traffic_elements[agent->stop_sign_target_idx], &frame); + if (!agent->stop_sign_stop_completed && agent->sim_speed < STOP_SIGN_STOP_SPEED_MPS + && check_agent_in_stop_sign_box(agent, &frame)) { + agent->stop_sign_stop_completed = 1; } - if (!near_stop_sign) { - agent->stop_sign_stopped_timestep_count = 0; + if (agent->stop_sign_stop_completed) { return false; } - if (stop_satisfied) { + float s_prev = (agent->prev_x - frame.mid_x) * frame.along_x + (agent->prev_y - frame.mid_y) * frame.along_y; + float s_cur = (agent->sim_x - frame.mid_x) * frame.along_x + (agent->sim_y - frame.mid_y) * frame.along_y; + if (!(s_prev < 0.0f && s_cur >= 0.0f)) { return false; } - if (stopped_before_stop_line) { - agent->stop_sign_stopped_timestep_count++; - } else { - agent->stop_sign_stopped_timestep_count = 0; + float crossing_frac = s_prev / (s_prev - s_cur); + float cross_x = agent->prev_x + crossing_frac * (agent->sim_x - agent->prev_x); + float cross_y = agent->prev_y + crossing_frac * (agent->sim_y - agent->prev_y); + float lateral = (cross_x - frame.mid_x) * frame.along_y - (cross_y - frame.mid_y) * frame.along_x; + if (fabsf(lateral) > frame.half_len + STOP_SIGN_LATERAL_EXTENSION_M) { + return false; } - return false; + agent->stop_sign_last_failed_idx = agent->stop_sign_target_idx; + agent->stop_sign_target_idx = -1; + return true; } + static bool check_obb_collision(Agent *car1, Agent *car2) { // OBB collision via SAT (Separating Axis Theorem). // Projects both boxes onto 4 axes (2 per car) and checks for overlap on all axes. @@ -1776,23 +2027,7 @@ static bool check_obb_collision(Agent *car1, Agent *car2) { {-car1->sin_heading, car1->cos_heading}, {car2->cos_heading, car2->sin_heading}, {-car2->sin_heading, car2->cos_heading}}; - - for (int i = 0; i < 4; i++) { - float min1 = INFINITY, max1 = -INFINITY; - float min2 = INFINITY, max2 = -INFINITY; - for (int j = 0; j < 4; j++) { - float proj1 = car1_corners[j][0] * axes[i][0] + car1_corners[j][1] * axes[i][1]; - min1 = fminf(min1, proj1); - max1 = fmaxf(max1, proj1); - float proj2 = car2_corners[j][0] * axes[i][0] + car2_corners[j][1] * axes[i][1]; - min2 = fminf(min2, proj2); - max2 = fmaxf(max2, proj2); - } - if (max1 < min2 || min1 > max2) { - return false; - } - } - return true; + return check_corner_boxes_overlap(car1_corners, car2_corners, axes); } static bool check_moving_obb_collision(Agent *a, Agent *b, float a_disp, float b_disp) { @@ -2161,6 +2396,26 @@ static float calculate_puffer_score(Log *agent_log, float duration_steps, float return agent_log->puffer_score; } +// True when the first collisions of logged policy agent i and its partner are with each other. +static bool is_shared_policy_collision(Drive *env, int log_idx) { + int agent_idx = env->active_agent_indices[log_idx]; + int partner_idx = env->agents[agent_idx].first_collision_partner_idx; + if (partner_idx == -1 || env->agents[partner_idx].first_collision_partner_idx != agent_idx) { + return false; + } + for (int j = 0; j < env->active_agent_count; j++) { + if (env->active_agent_indices[j] != partner_idx) { + continue; + } + Agent *partner = &env->agents[partner_idx]; + if (partner->is_blind_partner || partner->is_phantom_braker) { + return false; + } + return env->logs[j].collision_rate > 0.0f; + } + return false; +} + static void add_log(Drive *env) { int safe_timestep = (env->timestep > 0) ? env->timestep : 1; Log episode_log = {0}; @@ -2182,7 +2437,11 @@ static void add_log(Drive *env) { episode_log.red_light_violation_rate += red_light_violations; int stop_sign_violations = env->logs[i].stop_sign_violation_rate; episode_log.stop_sign_violation_rate += stop_sign_violations; - int total_infractions = (offroad || collided || red_light_violations || stop_sign_violations) ? 1 : 0; + float collision_share = collided ? 1.0f : 0.0f; + if (collided && is_shared_policy_collision(env, i)) { + collision_share = 0.5f; + } + float infraction_count = offroad + red_light_violations + stop_sign_violations + collision_share; float avg_speed_per_agent = env->logs[i].avg_speed_per_agent; episode_log.avg_speed_per_agent += avg_speed_per_agent / safe_timestep; int num_goals_reached = env->logs[i].num_goals_reached; @@ -2195,13 +2454,19 @@ static void add_log(Drive *env) { episode_log.dnf_rate += 1.0f; } episode_log.total_distance_travelled += agent->distance_since_spawn; - if (total_infractions > 0) { - episode_log.total_infractions += 1.0f; - } + episode_log.total_infractions += infraction_count; float displacement_error = env->logs[i].avg_displacement_error; episode_log.avg_displacement_error += displacement_error; episode_log.episode_length += env->logs[i].episode_length; episode_log.episode_return += env->logs[i].episode_return; + episode_log.spawn_failed += env->logs[i].spawn_failed; + episode_log.spawn_reject_collision += env->logs[i].spawn_reject_collision; + episode_log.spawn_reject_offroad += env->logs[i].spawn_reject_offroad; + episode_log.spawn_reject_stop_line += env->logs[i].spawn_reject_stop_line; + episode_log.spawn_reject_empty_cell += env->logs[i].spawn_reject_empty_cell; + episode_log.spawn_failed_goal += env->logs[i].spawn_failed_goal; + episode_log.stopped_at_reset += env->logs[i].stopped_at_reset; + episode_log.early_reset_short += env->logs[i].early_reset_short; // Per-component reward sums (mirrors compute_rewards' env->rewards[i]+= sites). episode_log.reward_collision += env->logs[i].reward_collision; episode_log.reward_offroad += env->logs[i].reward_offroad; @@ -2315,7 +2580,7 @@ static void generate_reward_coefs(Drive *env, Agent *agent) { agent->reward_coefs[REWARD_COEF_THROTTLE] = sample_mixed_uniform(&env->rng_state, 1.25f); agent->reward_coefs[REWARD_COEF_STEER] = sample_mixed_uniform(&env->rng_state, 1.25f); agent->reward_coefs[REWARD_COEF_ACC] = sample_mixed_uniform(&env->rng_state, 1.5f); - agent->reward_coefs[REWARD_COEF_SPEED] = sample_mixed_uniform(&env->rng_state, 1.5f); + agent->reward_coefs[REWARD_COEF_SPEED] = sample_mixed_uniform(&env->rng_state, env->conditioning_speed_scale); } else { agent->reward_coefs[REWARD_COEF_GOAL_RADIUS] = env->goal_radius; agent->reward_coefs[REWARD_COEF_GOAL_SPEED] = env->goal_speed; @@ -2333,7 +2598,7 @@ static void generate_reward_coefs(Drive *env, Agent *agent) { agent->reward_coefs[REWARD_COEF_REVERSE] = env->reward_reverse; agent->reward_coefs[REWARD_COEF_THROTTLE] = 1.0f; agent->reward_coefs[REWARD_COEF_STEER] = 1.0f; - agent->reward_coefs[REWARD_COEF_ACC] = 1.0f; + agent->reward_coefs[REWARD_COEF_ACC] = env->conditioning_accel_scale; agent->reward_coefs[REWARD_COEF_SPEED] = env->max_speed_mps / env->base_max_speed_mps; } } @@ -2346,10 +2611,10 @@ typedef struct { int offset; } TrafficLightCycle; -// Lights of one junction share the cycle sampled for the first light of that junction. -static int traffic_light_cycle_leader(Drive *env, int light_idx) { +// Junction-wide draws (shared cycle, group removal) are made once, for the junction's first light. +static int traffic_light_junction_leader(Drive *env, int light_idx) { TrafficControlElement *tc = &env->traffic_elements[light_idx]; - if (tc->junction_id < 0 || !env->traffic_light_junction_phases) { + if (tc->junction_id < 0) { return light_idx; } for (int i = 0; i < light_idx; i++) { @@ -2425,6 +2690,85 @@ static void fill_traffic_light_states( } } +static void sample_traffic_light_group_removal(Drive *env, int *group_removed, int enabled) { + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *tc = &env->traffic_elements[i]; + group_removed[i] = 0; + if (!enabled || tc->type != TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT || tc->junction_id < 0) { + continue; + } + int leader_idx = traffic_light_junction_leader(env, i); + if (leader_idx == i) { + group_removed[i] = sample_uniform(&env->rng_state, 0.0f, 1.0f) < TL_GROUP_REMOVE_PROB; + continue; + } + group_removed[i] = group_removed[leader_idx]; + } +} + +static float min_resolved_entry_limit(const Drive *env, const RoadMapElement *road) { + float min_limit = INFINITY; + for (int k = 0; k < road->num_entries; k++) { + int entry_idx = road->entry_lanes[k]; + if (env->lane_limit_resolved[entry_idx]) { + min_limit = fminf(min_limit, env->lane_speed_limit_mps[entry_idx]); + } + } + return min_limit; +} + +static int inherit_junction_speed_limits_pass(Drive *env) { + int changed = 0; + for (int i = 0; i < env->num_road_elements; i++) { + const RoadMapElement *road = &env->road_elements[i]; + if (env->lane_limit_resolved[i] || !is_road_lane(road->type) || road->speed_limit <= 0.0f) { + continue; + } + float min_limit = min_resolved_entry_limit(env, road); + if (!isfinite(min_limit)) { + continue; + } + env->lane_speed_limit_mps[i] = min_limit; + env->lane_limit_resolved[i] = 1; + changed = 1; + } + return changed; +} + +// Per-episode: one additive offset per speed zone; lanes without a zone inherit the min over their entries. +static void sample_zone_speed_limits(Drive *env) { + for (int i = 0; i < env->num_road_elements; i++) { + const RoadMapElement *road = &env->road_elements[i]; + int limit_unknown = is_road_lane(road->type) && road->speed_limit <= 0.0f; + env->lane_speed_limit_mps[i] = limit_unknown ? UNKNOWN_LANE_SPEED_LIMIT_MPS : road->speed_limit; + } + if (env->speed_limit_random_prob <= 0.0f || env->num_speed_zones <= 0) { + return; + } + if (sample_uniform(&env->rng_state, 0.0f, 1.0f) >= env->speed_limit_random_prob) { + return; + } + float delta = env->speed_limit_random_delta_mps; + for (int zone_idx = 0; zone_idx < env->num_speed_zones; zone_idx++) { + env->speed_zone_offset_mps[zone_idx] = sample_uniform(&env->rng_state, -delta, delta); + } + for (int i = 0; i < env->num_road_elements; i++) { + const RoadMapElement *road = &env->road_elements[i]; + int has_zone_limit = road->speed_zone_idx >= 0 && road->speed_limit > 0.0f; + env->lane_limit_resolved[i] = (unsigned char) has_zone_limit; + if (!has_zone_limit) { + continue; + } + float limit = road->speed_limit + env->speed_zone_offset_mps[road->speed_zone_idx]; + env->lane_speed_limit_mps[i] = clip(limit, env->speed_limit_random_min_mps, env->speed_limit_random_max_mps); + } + for (int pass = 0; pass < SPEED_LIMIT_JUNCTION_INHERIT_PASSES; pass++) { + if (!inherit_junction_speed_limits_pass(env)) { + break; + } + } +} + static void generate_traffic_light_states(Drive *env) { int steps = env->scenario_length; int training_mode = !env->eval_mode || env->eval_training_render; @@ -2432,7 +2776,10 @@ static void generate_traffic_light_states(Drive *env) { // 20% chance: disable ALL lights for this episode int disable_all = training_mode && (sample_uniform(&env->rng_state, 0.0f, 1.0f) < TL_EPISODE_DISABLE_PROB); - TrafficLightCycle cycles[env->num_traffic_elements > 0 ? env->num_traffic_elements : 1]; + int element_capacity = env->num_traffic_elements > 0 ? env->num_traffic_elements : 1; + TrafficLightCycle cycles[element_capacity]; + int group_removed[element_capacity]; + sample_traffic_light_group_removal(env, group_removed, training_mode && !disable_all); for (int i = 0; i < env->num_traffic_elements; i++) { TrafficControlElement *tc = &env->traffic_elements[i]; if (tc->type != TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT || tc->states == NULL || tc->state_size <= 0) { @@ -2453,14 +2800,14 @@ static void generate_traffic_light_states(Drive *env) { int junction_id = env->traffic_light_junction_phases ? tc->junction_id : -1; int own_phase_idx = junction_id < 0 ? 0 : tc->phase_idx; - int leader_idx = traffic_light_cycle_leader(env, i); + int leader_idx = junction_id < 0 ? i : traffic_light_junction_leader(env, i); if (leader_idx == i) { cycles[i] = sample_traffic_light_cycle(env, junction_id, training_mode); } if (training_mode) { - // Individual removal - if (sample_uniform(&env->rng_state, 0.0f, 1.0f) < TL_INDIVIDUAL_REMOVE_PROB) { + // Group or individual removal + if (group_removed[i] || sample_uniform(&env->rng_state, 0.0f, 1.0f) < TL_INDIVIDUAL_REMOVE_PROB) { for (int t = 0; t < fill_steps; t++) { tc->states[t] = TRAFFIC_CONTROL_STATE_OFF; } @@ -2479,14 +2826,14 @@ static void generate_traffic_light_states(Drive *env) { } } -static bool check_spawn_collision(Drive *env, int num_existing_agents, Agent *tmp_agent) { +static bool check_spawn_collision(Drive *env, Agent *tmp_agent) { // Inflate the candidate box so agents keep SPAWN_CLEARANCE_M of gap to every neighbor Agent inflated = *tmp_agent; inflated.sim_length += 2.0f * SPAWN_CLEARANCE_M; inflated.sim_width += 2.0f * SPAWN_CLEARANCE_M; update_agent_radius(&inflated); - for (int i = 0; i < num_existing_agents; i++) { + for (int i = 0; i < env->num_total_agents; i++) { Agent *other = &env->agents[i]; if (other->sim_x == INVALID_POSITION || other->sim_valid != 1) { @@ -2549,6 +2896,10 @@ static bool check_spawn_offroad(Drive *env, Agent *tmp_agent, float edge_clearan static bool spawn_agent(Drive *env, int agent_idx, int num_agents) { Agent *agent = &env->agents[agent_idx]; + for (int reason = 0; reason < SPAWN_REJECT_REASON_COUNT; reason++) { + env->spawn_reject_counts[reason] = 0; + } + env->spawn_goal_failed = 0; // Free existing route on reset if (agent->route != NULL) { @@ -2563,15 +2914,17 @@ static bool spawn_agent(Drive *env, int agent_idx, int num_agents) { agent->active_agent = 1; agent->mark_as_expert = 0; - float spawn_length, spawn_width; + float spawn_length, spawn_width, spawn_edge_clearance_m; if (env->eval_mode && !env->eval_training_render) { // Eval: uniform random car-sized boxes spawn_length = sample_uniform(&env->rng_state, 2.0f, 5.5f); spawn_width = sample_uniform(&env->rng_state, 1.5f, 2.5f); + spawn_edge_clearance_m = EVAL_SPAWN_EDGE_CLEARANCE_M; } else { // Training: random size spawn_length = sample_uniform(&env->rng_state, 0.8f, 7.0f); spawn_width = sample_uniform(&env->rng_state, 0.8f, 3.0f); + spawn_edge_clearance_m = 0.0f; } if (spawn_width > spawn_length) { spawn_width = spawn_length; @@ -2606,6 +2959,7 @@ static bool spawn_agent(Drive *env, int agent_idx, int num_agents) { } if (candidate_count == 0) { + env->spawn_reject_counts[SPAWN_REJECT_EMPTY_CELL]++; continue; } @@ -2645,15 +2999,18 @@ static bool spawn_agent(Drive *env, int agent_idx, int num_agents) { update_agent_radius(&tmp_agent); tmp_agent.current_lane_idx = start_lane_idx; - if (check_spawn_collision(env, num_agents, &tmp_agent)) { + if (check_spawn_collision(env, &tmp_agent)) { + env->spawn_reject_counts[SPAWN_REJECT_COLLISION]++; continue; } - if (check_spawn_offroad(env, &tmp_agent, 0.0f)) { + if (check_spawn_offroad(env, &tmp_agent, spawn_edge_clearance_m)) { + env->spawn_reject_counts[SPAWN_REJECT_OFFROAD]++; continue; } if (check_agent_on_stop_line(env, &tmp_agent, true)) { + env->spawn_reject_counts[SPAWN_REJECT_STOP_LINE]++; continue; } @@ -2686,9 +3043,15 @@ static bool spawn_agent(Drive *env, int agent_idx, int num_agents) { agent->yaw_rate = 0.0f; update_agent_speed(agent); + if (env->goal_source == GOAL_SOURCE_EXTERNAL) { + agent->goal_count = 0; + agent->current_goal_idx = 0; + return true; + } if (env->goal_source == GOAL_SOURCE_MAP) { if (!generate_new_goals_from_map(env, agent)) { printf("[GIGAFLOW WARNING] -> Failed to generate map goals for agent %d\n", agent_idx); + env->spawn_goal_failed = 1; return false; } return true; @@ -2696,11 +3059,13 @@ static bool spawn_agent(Drive *env, int agent_idx, int num_agents) { if (!compute_new_route(env, agent, start_lane_idx)) { printf("[GIGAFLOW WARNING] -> Failed to compute a new route for agent %d\n", agent_idx); - return false; // Failed to compute new goal + env->spawn_goal_failed = 1; + return false; } // Compute initial goal if (!generate_new_goals_from_route(env, agent)) { + env->spawn_goal_failed = 1; return false; } @@ -2910,7 +3275,8 @@ static bool should_control_agent(Drive *env, int agent_idx) { } // In REPLAY mode without route data, control agents spawning far enough from their goal - if (env->goal_source == GOAL_SOURCE_GT && agent->route_length == 0) { + bool log_goal_source = env->goal_source == GOAL_SOURCE_GT || env->goal_source == GOAL_SOURCE_GT_MAP; + if (log_goal_source && agent->route_length == 0) { float dx = agent->gt_goal_x - agent->log_trajectory_x[env->init_step]; float dy = agent->gt_goal_y - agent->log_trajectory_y[env->init_step]; float dz = agent->gt_goal_z - agent->log_trajectory_z[env->init_step]; @@ -2953,9 +3319,11 @@ void set_active_agents(Drive *env) { // In GIGAFLOW mode, spawn agents dynamically on the map if (env->simulation_mode == SIMULATION_MODE_GIGAFLOW) { int num_agents_to_create = env->num_controllable_agents; + int partner_slots = env->cosim_partner_slots; // Initialize agents for GIGAFLOW mode - env->agents = (Agent *) calloc(num_agents_to_create, sizeof(Agent)); + env->agents = (Agent *) calloc(num_agents_to_create + partner_slots, sizeof(Agent)); + env->num_total_agents = num_agents_to_create + partner_slots; int *active_agent_indices = (int *) malloc(num_agents_to_create * sizeof(int)); int successfully_created = 0; @@ -2970,10 +3338,24 @@ void set_active_agents(Drive *env) { } } - env->num_total_agents = num_agents_to_create; env->active_agent_indices = (int *) malloc(successfully_created * sizeof(int)); - env->static_agent_indices = NULL; + env->static_agent_indices = partner_slots > 0 ? (int *) malloc(partner_slots * sizeof(int)) : NULL; env->expert_static_agent_indices = NULL; + for (int k = 0; k < partner_slots; k++) { + int partner_idx = num_agents_to_create + k; + Agent *partner = &env->agents[partner_idx]; + partner->id = partner_idx; + partner->type = VEHICLE; + partner->controller = CONTROLLER_STATIC; + partner->sim_length = COSIM_PARTNER_DEFAULT_LENGTH_M; + partner->sim_width = COSIM_PARTNER_DEFAULT_WIDTH_M; + partner->sim_height = COSIM_PARTNER_DEFAULT_HEIGHT_M; + partner->wheelbase = 0.6f * partner->sim_length; + update_agent_radius(partner); + invalidate_agent(partner); + env->static_agent_indices[k] = partner_idx; + } + env->static_agent_count = partner_slots; for (int i = 0; i < successfully_created; i++) { env->active_agent_indices[i] = active_agent_indices[i]; @@ -2982,8 +3364,16 @@ void set_active_agents(Drive *env) { } free(active_agent_indices); + if (successfully_created == 0) { + fprintf( + stderr, + "[ERROR] -> gigaflow spawn created 0 of %d agents on map %s (no drivable lanes or missing lane " + "connectivity?)\n", + num_agents_to_create, + env->map_name); + } env->active_agent_count = successfully_created; - env->num_agents = successfully_created; + env->num_agents = successfully_created + partner_slots; return; } @@ -3001,7 +3391,8 @@ void set_active_agents(Drive *env) { bool filter_spawns = !is_log_replay && env->control_mode != CONTROL_MODE_WOSAC; // Iterate through entities to find agents to create and/or control - for (int i = 0; i < env->num_total_agents && env->num_agents < max_agents; i++) { + int created_agent_count = 0; + for (int i = 0; i < env->num_total_agents && created_agent_count < max_agents; i++) { Agent *agent = &env->agents[i]; // Skip if not valid at initialization @@ -3028,20 +3419,20 @@ void set_active_agents(Drive *env) { if (filter_spawns) { load_pose_from_log(agent, env->init_step); } - if (filter_spawns && log_pose_overlaps_created_agent(env, agent, created_agent_indices, env->num_agents)) { + if (filter_spawns && log_pose_overlaps_created_agent(env, agent, created_agent_indices, created_agent_count)) { continue; } - created_agent_indices[env->num_agents] = i; - env->num_agents++; + created_agent_indices[created_agent_count] = i; + created_agent_count++; } // Control decisions need every created agent's pose, so they run after creation - for (int created_idx = 0; created_idx < env->num_agents; created_idx++) { + for (int created_idx = 0; created_idx < created_agent_count; created_idx++) { int i = created_agent_indices[created_idx]; Agent *agent = &env->agents[i]; bool is_controlled = should_control_agent(env, i); if (is_controlled && filter_spawns && agent->type == VEHICLE - && !replay_spawn_fit_for_control(env, agent, created_agent_indices, env->num_agents)) { + && !replay_spawn_fit_for_control(env, agent, created_agent_indices, created_agent_count)) { is_controlled = false; } @@ -3065,6 +3456,8 @@ void set_active_agents(Drive *env) { } } + env->num_agents = env->active_agent_count + env->static_agent_count; + // Set up initial active agents env->active_agent_indices = (int *) malloc(env->active_agent_count * sizeof(int)); env->static_agent_indices = (int *) malloc(env->static_agent_count * sizeof(int)); @@ -3197,6 +3590,7 @@ void remove_bad_trajectories(Drive *env) { void init(Drive *env) { env->timestep = 0; + env->first_reset_pending = 1; struct SharedMapData *shared = env->use_map_cache ? map_cache_lookup(env) : NULL; if (shared != NULL) { // Cache hit: load only the per-env data (agents, traffic-control elements), @@ -3239,7 +3633,7 @@ void init(Drive *env) { fprintf(stderr, "[ERROR] -> Replay map %s has invalid log_dt %f\n", env->map_name, (double) env->log_dt); return; } - if (env->use_neighbor_cache && env->grid_map->neighbor_cache_entities == NULL) { + if (env->use_neighbor_cache && env->grid_map->neighbor_cache_pool_idx == NULL) { cache_neighbor_offsets(env); } if (!env->use_neighbor_cache) { @@ -3248,7 +3642,18 @@ void init(Drive *env) { env->road_dropout_enabled = (env->obs_slots_lane_kept < env->obs_slots_lane_n) || (env->obs_slots_boundary_kept < env->obs_slots_boundary_n); env->logs_capacity = 0; + if (env->speed_limit_random_prob > 0.0f && env->num_speed_zones <= 0) { + raise_error_with_message( + ERROR_INVALID_ARGUMENT, + "speed_limit_random_prob > 0 but map %s carries no speed zones", + env->map_name); + } + env->lane_speed_limit_mps = (float *) malloc(env->num_road_elements * sizeof(float)); + env->lane_limit_resolved = (unsigned char *) calloc(env->num_road_elements, sizeof(unsigned char)); + env->speed_zone_offset_mps + = (float *) malloc((env->num_speed_zones > 0 ? env->num_speed_zones : 1) * sizeof(float)); begin_episode_rng(env); + sample_zone_speed_limits(env); if (env->simulation_mode == SIMULATION_MODE_GIGAFLOW) { int steps = env->scenario_length; if (steps > 0) { @@ -3281,36 +3686,10 @@ void init(Drive *env) { set_start_position(env); env->logs = (Log *) calloc(env->active_agent_count, sizeof(Log)); - if (env->goal_source == GOAL_SOURCE_GT) { + if (env->goal_source == GOAL_SOURCE_GT || env->goal_source == GOAL_SOURCE_GT_MAP) { for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - Agent *agent = &env->agents[agent_idx]; - // For replay mode, always place goals along the logged - // trajectory. Route-based goal generation can produce goals that - // diverge from the actual path the SDC should follow. - { - int start = env->init_step > 0 ? env->init_step : 0; - int remaining = agent->trajectory_size - 1 - start; - if (remaining < 1) { - remaining = 1; - } - int num_wp = env->num_goals; - for (int g = 0; g < num_wp; g++) { - int t = start + (g + 1) * remaining / num_wp; - if (t >= agent->trajectory_size) { - t = agent->trajectory_size - 1; - } - agent->list_goal_x[g] = agent->log_trajectory_x[t]; - agent->list_goal_y[g] = agent->log_trajectory_y[t]; - agent->list_goal_z[g] = agent->log_trajectory_z[t]; - agent->list_goal_lane[g] = -1; // logged goals have no lane idx (no GPS lane-distance) - } - agent->goal_count = num_wp; - agent->current_goal_idx = 0; - agent->current_goal_x = agent->list_goal_x[0]; - agent->current_goal_y = agent->list_goal_y[0]; - agent->current_goal_z = agent->list_goal_z[0]; - } + Agent *agent = &env->agents[env->active_agent_indices[i]]; + generate_new_goals_from_log(env, agent); } } else if (env->goal_source == GOAL_SOURCE_MAP) { for (int i = 0; i < env->active_agent_count; i++) { @@ -3328,6 +3707,9 @@ void init(Drive *env) { void c_close(Drive *env) { free(env->active_agent_indices); free(env->logs); + free(env->lane_speed_limit_mps); + free(env->speed_zone_offset_mps); + free(env->lane_limit_resolved); free(env->obs_neighbor_scratch); free(env->static_agent_indices); free(env->expert_static_agent_indices); @@ -3338,9 +3720,13 @@ static inline int partner_feature_count(const Drive *env) { return PARTNER_FEATURES + (env->obs_partner_relative_velocity ? PARTNER_RELATIVE_VELOCITY_FEATURES : 0); } +static inline int lane_feature_count(const Drive *env) { + return LANE_FEATURES + (env->obs_lane_speed_limit ? LANE_SPEED_LIMIT_FEATURES : 0); +} + static int compute_observation_size(Drive *env) { return EGO_FEATURES + partner_feature_count(env) * env->obs_slots_partners_n - + LANE_FEATURES * env->obs_slots_lane_kept + BOUNDARY_FEATURES * env->obs_slots_boundary_kept + + lane_feature_count(env) * env->obs_slots_lane_kept + BOUNDARY_FEATURES * env->obs_slots_boundary_kept + TRAFFIC_CONTROL_FEATURES * env->obs_slots_traffic_controls_n + OBS_VALID_COUNT_FEATURES + env->reward_conditioning * NUM_REWARD_COEFS + env->num_goals * GOAL_FEATURES; } @@ -3382,8 +3768,10 @@ int get_track_id_or_placeholder(Drive *env, int agent_idx) { return -1; } +// include_static: also write the static partner slots after the active agents (co-sim BEV/debug). void c_get_global_agent_state( Drive *env, + int include_static, float *x_out, float *y_out, float *z_out, @@ -3391,8 +3779,10 @@ void c_get_global_agent_state( int *id_out, float *length_out, float *width_out) { - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; + int count = include_static ? env->num_agents : env->active_agent_count; + for (int i = 0; i < count; i++) { + int agent_idx = (i < env->active_agent_count) ? env->active_agent_indices[i] + : env->static_agent_indices[i - env->active_agent_count]; Agent *agent = &env->agents[agent_idx]; // For WOSAC, we need the original world coordinates, so we add the world means back @@ -3406,161 +3796,55 @@ void c_get_global_agent_state( } } -void c_get_global_ground_truth_trajectories( +// Pure geometry: nearest-drivable-lane search + road-edge offroad check for one agent's current +// pose. No side effects (no agent_log, no metrics_array, no infraction handling) -- shared by +// compute_metrics (which owns everything side-effecting: infraction application, goal advancement, +// eval-stat accumulation) and refresh_lane_association (cosim-only snapshot refresh) below. +static void find_lane_and_offroad( Drive *env, - float *x_out, - float *y_out, - float *z_out, - float *heading_out, - int *valid_out, - int *id_out, - int *scenario_id_out) { - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - Agent *agent = &env->agents[agent_idx]; - id_out[i] = get_track_id_or_placeholder(env, agent_idx); - scenario_id_out[i] = 0; // TODO: FIXME + Agent *agent, + bool *is_offroad_out, + int *lane_idx_out, + int *lane_seg_idx_out, + float *signed_lane_distance_out, + float *lane_heading_out) { + bool is_offroad = false; - for (int t = env->init_step; t < agent->trajectory_size; t++) { - int out_idx = i * (agent->trajectory_size - env->init_step) + (t - env->init_step); - // Add world means back to get original world coordinates - x_out[out_idx] = agent->log_trajectory_x[t] + env->world_mean_x; - y_out[out_idx] = agent->log_trajectory_y[t] + env->world_mean_y; - z_out[out_idx] = agent->log_trajectory_z[t]; - heading_out[out_idx] = agent->log_heading[t]; - valid_out[out_idx] = agent->log_valid[t]; - } - } -} + // Track best candidate by combined distance/heading score + float best_score = 1e9f; + int lane_idx = -1; + int lane_seg_idx = 0; + float signed_lane_distance = 0.0f, lane_heading = 0.0f; -void c_get_road_edge_counts(Drive *env, int *num_polylines_out, int *total_points_out) { - int count = 0, points = 0; - for (int i = 0; i < env->num_road_elements; i++) { - if (is_road_edge(env->road_elements[i].type)) { - count++; - points += env->road_elements[i].segment_size; - } + GridMapEntity entity_list[ROAD_QUERY_ENTITY_COUNT]; + int list_size = get_neighbors_entities( + env, + agent->sim_x, + agent->sim_y, + entity_list, + ROAD_QUERY_ENTITY_COUNT, + ROAD_OFFSETS, + 25); + + if (list_size <= 0) { + is_offroad = true; } - *num_polylines_out = count; - *total_points_out = points; -} -void c_get_road_edge_polylines(Drive *env, float *x_out, float *y_out, int *lengths_out, int *scenario_ids_out) { - int poly_idx = 0, pt_idx = 0; - for (int i = 0; i < env->num_road_elements; i++) { - RoadMapElement *e = &env->road_elements[i]; - if (is_road_edge(e->type)) { - lengths_out[poly_idx] = e->segment_size; - scenario_ids_out[poly_idx] = 0; // TODO: FIXME - for (int j = 0; j < e->segment_size; j++) { - x_out[pt_idx] = e->x[j] + env->world_mean_x; - y_out[pt_idx] = e->y[j] + env->world_mean_y; - pt_idx++; - } - poly_idx++; + // Vehicle-width based distance threshold (3x width) + float max_distance_threshold = 3.0f * agent->sim_width; + + int checked_lanes[MAX_CHECKED_LANES]; + int num_checked_lanes = 0; + + // Loop through road entities and compute associated metrics (offroad, lane alignment) + for (int i = 0; i < list_size; i++) { + if (entity_list[i].entity_idx == -1) { + continue; } - } -} -// ======================================== -// Noise & Robustness Functions -// ======================================== - -static void subsample_road_observation_rows( - Rng *rng_state, - float *buffer, - int collected_count, - int keep_count, - int feature_count) { - if (keep_count <= 0 || collected_count <= keep_count) { - return; - } - float tmp[feature_count]; - for (int sample_idx = 0; sample_idx < keep_count; sample_idx++) { - int remaining = collected_count - sample_idx; - int swap_idx = (remaining > 1) ? sample_idx + rng_below(rng_state, remaining) : sample_idx; - if (swap_idx == sample_idx) { - continue; - } - float *a = &buffer[sample_idx * feature_count]; - float *b = &buffer[swap_idx * feature_count]; - memcpy(tmp, a, sizeof(tmp)); - memcpy(a, b, sizeof(tmp)); - memcpy(b, tmp, sizeof(tmp)); - } -} - -// ======================================== -// Core Simulation Functions -// ======================================== - -static void compute_metrics(Drive *env, int agent_idx, int log_idx) { - Agent *agent = &env->agents[agent_idx]; - Log *agent_log = &env->logs[log_idx]; - - reset_agent_metrics(env, agent_idx); - - if (agent->sim_x == INVALID_POSITION) { - return; // invalid agent position - } - if (get_grid_index(env, agent->sim_x, agent->sim_y) == -1) { - // Current agent is offgrid, treat as offroad - agent->metrics_array[OFFROAD_IDX] = 1.0f; - apply_infraction_behavior(agent, env->offroad_behavior); - return; - } - - // Compute log-replay metrics - if (env->simulation_mode == SIMULATION_MODE_REPLAY) { - // Compute displacement error - float displacement_error = compute_displacement_error(agent, env->timestep); - if (displacement_error > 0.0f) { // Only count valid displacements - agent->cumulative_displacement += displacement_error; - agent->displacement_sample_count++; - - // Compute running average - agent->metrics_array[AVG_DISPLACEMENT_ERROR_IDX] - = agent->cumulative_displacement / agent->displacement_sample_count; - } - } - - bool is_offroad = false; - - // Track best candidate by combined distance/heading score - float best_score = 1e9f; - int lane_idx = -1; - int lane_seg_idx = 0; - float signed_lane_distance = 0.0f, lane_heading = 0.0f; - - GridMapEntity entity_list[ROAD_QUERY_ENTITY_COUNT]; - int list_size = get_neighbors_entities( - env, - agent->sim_x, - agent->sim_y, - entity_list, - ROAD_QUERY_ENTITY_COUNT, - ROAD_OFFSETS, - 25); - - if (list_size <= 0) { - is_offroad = true; - } - - // Vehicle-width based distance threshold (3x width) - float max_distance_threshold = 3.0f * agent->sim_width; - - int checked_lanes[MAX_CHECKED_LANES]; - int num_checked_lanes = 0; - - // Loop through road entities and compute associated metrics (offroad, lane alignment) - for (int i = 0; i < list_size; i++) { - if (entity_list[i].entity_idx == -1) { - continue; - } - - int entity_idx = entity_list[i].entity_idx; - int geometry_idx = entity_list[i].geometry_idx; - RoadMapElement *element = &env->road_elements[entity_idx]; + int entity_idx = entity_list[i].entity_idx; + int geometry_idx = entity_list[i].geometry_idx; + RoadMapElement *element = &env->road_elements[entity_idx]; // Check for offroad crossing with road edges if (is_road_edge(element->type)) { @@ -3688,6 +3972,382 @@ static void compute_metrics(Drive *env, int agent_idx, int log_idx) { } } + *is_offroad_out = is_offroad; + *lane_idx_out = lane_idx; + *lane_seg_idx_out = lane_seg_idx; + *signed_lane_distance_out = signed_lane_distance; + *lane_heading_out = lane_heading; +} + +// Cosim-only: refresh current_lane_idx / metrics_array[LANE_DIST_IDX/LANE_ANGLE_IDX] for one agent +// using the same geometry compute_metrics relies on. Deliberately does none of compute_metrics's +// other side effects (infraction application, goal advancement, agent_log/eval-stat accumulation) -- +// those stay exclusively c_step's job, so this is safe to call as often as an externally-set agent's +// pose changes without double-counting anything or corrupting goal/termination state. +static void refresh_lane_association(Drive *env, Agent *agent) { + if (env->grid_map == NULL || agent->sim_x == INVALID_POSITION + || get_grid_index(env, agent->sim_x, agent->sim_y) == -1) { + agent->previous_lane_idx = -1; + agent->current_lane_idx = -1; + agent->metrics_array[LANE_DIST_IDX] = LANE_DISTANCE_NORMALIZATION; + agent->metrics_array[LANE_ANGLE_IDX] = 0.0f; + agent->lane_heading_error_rad = 0.0f; + agent->lane_curvature = 0.0f; + return; + } + + bool is_offroad; + int lane_idx, lane_seg_idx; + float signed_lane_distance, lane_heading; + find_lane_and_offroad(env, agent, &is_offroad, &lane_idx, &lane_seg_idx, &signed_lane_distance, &lane_heading); + + if (is_offroad || lane_idx == -1) { + agent->previous_lane_idx = -1; + agent->current_lane_idx = -1; + agent->metrics_array[LANE_DIST_IDX] = LANE_DISTANCE_NORMALIZATION; + agent->metrics_array[LANE_ANGLE_IDX] = 0.0f; + agent->lane_heading_error_rad = 0.0f; + agent->lane_curvature = 0.0f; + return; + } + + agent->previous_lane_idx = agent->current_lane_idx; + agent->current_lane_idx = lane_idx; + agent->metrics_array[LANE_DIST_IDX] = signed_lane_distance; + float theta_f = compute_heading_diff(agent->sim_heading, lane_heading); + agent->metrics_array[LANE_ANGLE_IDX] = cosf(theta_f); + agent->lane_heading_error_rad = theta_f; + agent->lane_curvature = compute_lane_curvature(&env->road_elements[lane_idx], lane_seg_idx); +} + +// ── Co-simulation external-state setters ───────────────────────────────────── +// Co-sim setters return 0 on success, -1 on invalid external input (the binding raises). +int c_set_agent_states( + Drive *env, + int count, + const int *idx, + const float *x, + const float *y, + const float *z, + const float *heading, + const float *vx, + const float *vy, + const float *yaw_rate, + const float *accel_long, + const float *seconds_stopped) { + for (int k = 0; k < count; k++) { + int agent_idx = idx[k]; + if (agent_idx < 0 || agent_idx >= env->num_total_agents) { + return -1; + } + if (!isfinite(x[k]) || !isfinite(y[k]) || !isfinite(heading[k]) || !isfinite(vx[k]) || !isfinite(vy[k])) { + return -1; + } + if (seconds_stopped && !(seconds_stopped[k] >= 0.0f)) { + return -1; + } + Agent *agent = &env->agents[agent_idx]; + agent->sim_x = x[k] - env->world_mean_x; + agent->sim_y = y[k] - env->world_mean_y; + agent->sim_z = z[k]; + agent->sim_heading = heading[k]; + agent->cos_heading = cosf(agent->sim_heading); + agent->sin_heading = sinf(agent->sim_heading); + // Teleport: reset prev pose now, else prev-based swept checks (offroad crossing, goal-reach) sweep the gap and + // fire spuriously. + copy_pose_to_prev(agent); + agent->sim_vx = vx[k]; + agent->sim_vy = vy[k]; + update_agent_speed(agent); + agent->sim_valid = 1; + // External sim owns this agent: a removed/stopped latch (e.g. failed gigaflow + // respawn) must not survive the overwrite, else move_dynamics re-invalidates it. + agent->removed = 0; + agent->stopped = 0; + + // yaw_rate/accel_long come from the external sim's own physics (e.g. CARLA's + // get_angular_velocity()/get_acceleration(), nuPlan's EgoState.dynamic_car_state) rather than + // being finite-differenced from the agent's previous state here: c_step's move_dynamics runs + // for every active agent (including ones cosim is about to overwrite) before this setter is + // called, so "the agent's previous state" at this point is a throwaway dummy-action rollout, + // not the true previous external state. + agent->yaw_rate = yaw_rate[k]; + float speed_for_steering = fmaxf(fabsf(agent->sim_speed_signed), 1.0f); + float steering = atanf(agent->yaw_rate * agent->wheelbase / speed_for_steering); + agent->steering_angle = clip(steering, -STEERING_LIMIT, STEERING_LIMIT); + agent->accel_long = accel_long[k]; + agent->accel_lat = agent->sim_speed_signed * agent->yaw_rate; + refresh_lane_association(env, agent); // current_lane_idx / lane-dist / lane-angle for the new pose + + // NULL keeps c_step's own accumulation (co-sim default); an array injects stopped-time as state. + if (seconds_stopped) { + agent->seconds_stopped = seconds_stopped[k]; + } + } + return 0; +} + +// Overwrite agent bounding-box dimensions +int c_set_agent_sizes(Drive *env, int count, const int *idx, const float *length, const float *width) { + for (int k = 0; k < count; k++) { + int agent_idx = idx[k]; + if (agent_idx < 0 || agent_idx >= env->num_total_agents) { + return -1; + } + if (!(length[k] > 0.0f) || !(width[k] > 0.0f)) { + return -1; + } + Agent *agent = &env->agents[agent_idx]; + agent->sim_length = length[k]; + agent->sim_width = width[k]; + update_agent_radius(agent); // collision broad-phase uses this; stale radius under/over-detects hits + agent->wheelbase = 0.6f * agent->sim_length; // matches spawn/log-replay sizing (see move_expert) + } + return 0; +} + +int c_set_traffic_light_states(Drive *env, const int *states) { + int ts = env->timestep; + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *traffic = &env->traffic_elements[i]; + if (traffic->type != TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT) { + continue; + } + if (traffic->states == NULL) { + continue; // element without a state schedule cannot hold a state + } + if (ts < 0 || ts >= traffic->state_size) { + return -1; + } + if (states[i] < TRAFFIC_CONTROL_STATE_UNKNOWN || states[i] > TRAFFIC_CONTROL_STATE_OFF) { + return -1; + } + traffic->states[ts] = states[i]; + } + return 0; +} + +// Co-sim: retire the map's stop signs in place (light indices stay valid) and append the external sim's own, +// lines = count x [x1, y1, z1, x2, y2, z2] in world coordinates, headings = travel direction across each line. +int c_set_stop_signs(Drive *env, int count, const float *lines, const float *headings) { + if (count < 0 || (count > 0 && (lines == NULL || headings == NULL))) { + return -1; + } + for (int v = 0; v < count * 6; v++) { + if (!isfinite(lines[v])) { + return -1; + } + } + for (int k = 0; k < count; k++) { + float line_dx = lines[k * 6 + 3] - lines[k * 6]; + float line_dy = lines[k * 6 + 4] - lines[k * 6 + 1]; + if (!isfinite(headings[k]) || line_dx * line_dx + line_dy * line_dy <= 0.0f) { + return -1; + } + } + int old_count = env->num_traffic_elements; + int new_count = old_count + count; + if (new_count > 0) { + TrafficControlElement *elements + = (TrafficControlElement *) realloc(env->traffic_elements, new_count * sizeof(TrafficControlElement)); + if (elements == NULL) { + return -1; + } + env->traffic_elements = elements; + } + for (int i = 0; i < old_count; i++) { + if (env->traffic_elements[i].type == TRAFFIC_CONTROL_TYPE_STOP_SIGN) { + env->traffic_elements[i].type = TRAFFIC_CONTROL_TYPE_NONE; + } + } + for (int k = 0; k < count; k++) { + TrafficControlElement *tc = &env->traffic_elements[old_count + k]; + memset(tc, 0, sizeof(TrafficControlElement)); + tc->type = TRAFFIC_CONTROL_TYPE_STOP_SIGN; + tc->stop_line[0] = lines[k * 6] - env->world_mean_x; + tc->stop_line[1] = lines[k * 6 + 1] - env->world_mean_y; + tc->stop_line[2] = lines[k * 6 + 2]; + tc->stop_line[3] = lines[k * 6 + 3] - env->world_mean_x; + tc->stop_line[4] = lines[k * 6 + 4] - env->world_mean_y; + tc->stop_line[5] = lines[k * 6 + 5]; + tc->heading = headings[k]; + tc->junction_id = -1; + tc->phase_idx = -1; + } + env->num_traffic_elements = new_count; + for (int i = 0; i < env->num_total_agents; i++) { + env->agents[i].stop_sign_target_idx = -1; + env->agents[i].stop_sign_stop_completed = 0; + env->agents[i].stop_sign_last_failed_idx = -1; + env->agents[i].stop_sign_standstill_idx = -1; + } + return 0; +} + +void c_get_agent_goal_progress(Drive *env, int agent_idx, int *current_goal_idx_out, int *goal_count_out) { + Agent *agent = &env->agents[agent_idx]; + *current_goal_idx_out = agent->current_goal_idx; + *goal_count_out = agent->goal_count; +} + +int c_set_agent_goals( + Drive *env, + int agent_idx, + int num_wp, + const float *gx, + const float *gy, + const float *gz, + const float *gdir_x, + const float *gdir_y) { + if (agent_idx < 0 || agent_idx >= env->num_total_agents || num_wp < 1 || num_wp > MAX_GOALS) { + return -1; + } + Agent *agent = &env->agents[agent_idx]; + for (int w = 0; w < num_wp; w++) { + if (!isfinite(gx[w]) || !isfinite(gy[w])) { + return -1; + } + agent->list_goal_x[w] = gx[w] - env->world_mean_x; + agent->list_goal_y[w] = gy[w] - env->world_mean_y; + agent->list_goal_z[w] = gz[w]; + // Snap each waypoint to its nearest route-aligned drivable lane so the GPS + // lane-distance features work for external routes. + float snap_x, snap_y; // external goals keep their pushed position, only the lane idx is used + agent->list_goal_lane[w] + = find_goal_lane(env, agent->list_goal_x[w], agent->list_goal_y[w], gdir_x[w], gdir_y[w], &snap_x, &snap_y); + } + agent->goal_count = num_wp; + agent->current_goal_idx = 0; + agent->current_goal_x = agent->list_goal_x[0]; + agent->current_goal_y = agent->list_goal_y[0]; + agent->current_goal_z = agent->list_goal_z[0]; + return 0; +} +// ─────────────────────────────────────── + +void c_get_global_ground_truth_trajectories( + Drive *env, + float *x_out, + float *y_out, + float *z_out, + float *heading_out, + int *valid_out, + int *id_out, + int *scenario_id_out) { + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + Agent *agent = &env->agents[agent_idx]; + id_out[i] = get_track_id_or_placeholder(env, agent_idx); + scenario_id_out[i] = 0; // TODO: FIXME + + for (int t = env->init_step; t < agent->trajectory_size; t++) { + int out_idx = i * (agent->trajectory_size - env->init_step) + (t - env->init_step); + // Add world means back to get original world coordinates + x_out[out_idx] = agent->log_trajectory_x[t] + env->world_mean_x; + y_out[out_idx] = agent->log_trajectory_y[t] + env->world_mean_y; + z_out[out_idx] = agent->log_trajectory_z[t]; + heading_out[out_idx] = agent->log_heading[t]; + valid_out[out_idx] = agent->log_valid[t]; + } + } +} + +void c_get_road_edge_counts(Drive *env, int *num_polylines_out, int *total_points_out) { + int count = 0, points = 0; + for (int i = 0; i < env->num_road_elements; i++) { + if (is_road_edge(env->road_elements[i].type)) { + count++; + points += env->road_elements[i].segment_size; + } + } + *num_polylines_out = count; + *total_points_out = points; +} + +void c_get_road_edge_polylines(Drive *env, float *x_out, float *y_out, int *lengths_out, int *scenario_ids_out) { + int poly_idx = 0, pt_idx = 0; + for (int i = 0; i < env->num_road_elements; i++) { + RoadMapElement *e = &env->road_elements[i]; + if (is_road_edge(e->type)) { + lengths_out[poly_idx] = e->segment_size; + scenario_ids_out[poly_idx] = 0; // TODO: FIXME + for (int j = 0; j < e->segment_size; j++) { + x_out[pt_idx] = e->x[j] + env->world_mean_x; + y_out[pt_idx] = e->y[j] + env->world_mean_y; + pt_idx++; + } + poly_idx++; + } + } +} + +// ======================================== +// Noise & Robustness Functions +// ======================================== + +static void subsample_road_observation_rows( + Rng *rng_state, + float *buffer, + int collected_count, + int keep_count, + int feature_count) { + if (keep_count <= 0 || collected_count <= keep_count) { + return; + } + float tmp[feature_count]; + for (int sample_idx = 0; sample_idx < keep_count; sample_idx++) { + int remaining = collected_count - sample_idx; + int swap_idx = (remaining > 1) ? sample_idx + rng_below(rng_state, remaining) : sample_idx; + if (swap_idx == sample_idx) { + continue; + } + float *a = &buffer[sample_idx * feature_count]; + float *b = &buffer[swap_idx * feature_count]; + memcpy(tmp, a, sizeof(tmp)); + memcpy(a, b, sizeof(tmp)); + memcpy(b, tmp, sizeof(tmp)); + } +} + +// ======================================== +// Core Simulation Functions +// ======================================== + +static void compute_metrics(Drive *env, int agent_idx, int log_idx) { + Agent *agent = &env->agents[agent_idx]; + Log *agent_log = &env->logs[log_idx]; + + reset_agent_metrics(env, agent_idx); + + if (agent->sim_x == INVALID_POSITION) { + return; // invalid agent position + } + if (get_grid_index(env, agent->sim_x, agent->sim_y) == -1) { + // Current agent is offgrid, treat as offroad + agent->metrics_array[OFFROAD_IDX] = 1.0f; + apply_infraction_behavior(agent, env->offroad_behavior); + return; + } + + // Compute log-replay metrics + if (env->simulation_mode == SIMULATION_MODE_REPLAY) { + // Compute displacement error + float displacement_error = compute_displacement_error(agent, env->timestep); + if (displacement_error > 0.0f) { // Only count valid displacements + agent->cumulative_displacement += displacement_error; + agent->displacement_sample_count++; + + // Compute running average + agent->metrics_array[AVG_DISPLACEMENT_ERROR_IDX] + = agent->cumulative_displacement / agent->displacement_sample_count; + } + } + + bool is_offroad; + int lane_idx, lane_seg_idx; + float signed_lane_distance, lane_heading; + find_lane_and_offroad(env, agent, &is_offroad, &lane_idx, &lane_seg_idx, &signed_lane_distance, &lane_heading); + // Update lane alignment metric (running average) if (lane_idx != -1) { agent->previous_lane_idx = agent->current_lane_idx; @@ -3704,6 +4364,7 @@ static void compute_metrics(Drive *env, int agent_idx, int log_idx) { // theta_f = angle relative to lane heading float theta_f = compute_heading_diff(agent->sim_heading, lane_heading); agent->metrics_array[LANE_ANGLE_IDX] = cosf(theta_f); // Store cos(θ_f) + agent->lane_heading_error_rad = theta_f; agent->lane_curvature = compute_lane_curvature(&env->road_elements[lane_idx], lane_seg_idx); } else { // Agent not on any lane @@ -3711,6 +4372,7 @@ static void compute_metrics(Drive *env, int agent_idx, int log_idx) { agent->current_lane_idx = -1; agent->metrics_array[LANE_DIST_IDX] = LANE_DISTANCE_NORMALIZATION; // Max distance (far from lane) agent->metrics_array[LANE_ANGLE_IDX] = 0.0f; // Perpendicular (no alignment) + agent->lane_heading_error_rad = 0.0f; agent->lane_curvature = 0.0f; } @@ -3719,13 +4381,13 @@ static void compute_metrics(Drive *env, int agent_idx, int log_idx) { agent_log->avg_speed_per_agent += agent->sim_speed; // Speed limit metric (CUSTOM) - float target_speed = 15.0f; // Default target speed + float target_speed = UNKNOWN_LANE_SPEED_LIMIT_MPS; int current_lane_idx = agent->current_lane_idx; - if (current_lane_idx != -1 && env->road_elements[current_lane_idx].speed_limit > 0) { - target_speed = env->road_elements[current_lane_idx].speed_limit; + if (current_lane_idx != -1 && env->lane_speed_limit_mps[current_lane_idx] > 0) { + target_speed = env->lane_speed_limit_mps[current_lane_idx]; } - // Binary overspeed metric, 1.0 if overspeeding by more than 2 m/s - agent->metrics_array[SPEED_LIMIT_IDX] = (agent->sim_speed > target_speed + 2.0f) ? 1.0f : 0.0f; + agent->metrics_array[SPEED_LIMIT_IDX] + = (agent->sim_speed > target_speed + env->overspeed_tolerance_mps) ? 1.0f : 0.0f; if (env->compute_eval_metrics) { agent_log->speed_violation_sum += fmaxf(agent->sim_speed - target_speed, 0.0f) * env->dt; } @@ -3753,7 +4415,7 @@ static void compute_metrics(Drive *env, int agent_idx, int log_idx) { // Handle terminal events - NOTE: move it elsewhere? // IMPORTANT: early returns after offroad and collision enforce mutual exclusivity of terminal flags. - // Order matters: offroad > collision > red_light. + // Order matters: offroad > collision > red_light > stop_sign. // Priority 1: Handle offroad if (is_offroad) { @@ -3766,6 +4428,9 @@ static void compute_metrics(Drive *env, int agent_idx, int log_idx) { int car_collided_with_index = collision_check(env, agent_idx); if (car_collided_with_index != -1) { agent->metrics_array[COLLISION_IDX] = 1.0f; + if (agent->first_collision_partner_idx == -1) { + agent->first_collision_partner_idx = car_collided_with_index; + } if (env->compute_eval_metrics && is_at_fault_collision(env, agent_idx, car_collided_with_index)) { agent_log->at_fault_collision_rate = 1.0f; agent->metrics_array[AT_FAULT_COLLISION_IDX] = 1.0f; @@ -3782,8 +4447,9 @@ static void compute_metrics(Drive *env, int agent_idx, int log_idx) { return; } - // Priority 4: Handle stop sign violation - if (env->stop_signs_enabled && check_stop_sign_violation(env, agent)) { + // Priority 4: Handle stop sign violation; the state update runs even when disabled so the sign obs stay identical + if (env->stop_signs_enabled && env->obs_slots_traffic_controls_n && update_stop_sign_state(env, agent_idx) + && !env->disable_stop_sign_infractions) { agent->metrics_array[STOP_SIGN_IDX] = 1.0f; apply_infraction_behavior(agent, env->stop_sign_behavior); return; @@ -3896,7 +4562,7 @@ static void compute_rewards(Drive *env, int i) { float lane_center_reward = -agent->reward_coefs[REWARD_COEF_LANE_CENTER] * env->dt * ((cos_theta > 0.5f) * adjusted_dist - exp_decay); env->rewards[i] += lane_center_reward; - agent_log->lane_center_rate += fabsf(lane_center_distance) < 0.5f ? 1.0f : 0.0f; + agent_log->lane_center_rate += adjusted_dist < 0.5f ? 1.0f : 0.0f; agent_log->reward_lane_center += lane_center_reward; // Comfort reward @@ -4000,8 +4666,8 @@ static void compute_rewards(Drive *env, int i) { } static int write_ego_obs(Drive *env, Agent *ego, float *obs, int obs_idx) { - float perceived_margin - = (env->eval_mode && !env->eval_training_render) ? 2.0f * env->eval_perceived_size_margin_m : 0.0f; + bool perceived_margin_active = (env->eval_mode && !env->eval_training_render) || env->cosim_eval_semantics; + float perceived_margin = perceived_margin_active ? 2.0f * env->eval_perceived_size_margin_m : 0.0f; obs[obs_idx++] = ego->sim_speed_signed / env->obs_norm_speed_mps; obs[obs_idx++] = (ego->sim_width + perceived_margin) / env->obs_norm_veh_width_m; obs[obs_idx++] = (ego->sim_length + perceived_margin) / env->obs_norm_veh_length_m; @@ -4009,9 +4675,10 @@ static int write_ego_obs(Drive *env, Agent *ego, float *obs, int obs_idx) { obs[obs_idx++] = ego->accel_long / fabsf(ACCEL_LONG_LIMIT[0]); obs[obs_idx++] = ego->accel_lat / ACCEL_LAT_LIMIT[1]; obs[obs_idx++] = fmaxf(-1.0f, fminf(1.0f, ego->metrics_array[LANE_DIST_IDX] / LANE_DISTANCE_NORMALIZATION)); - obs[obs_idx++] = ego->metrics_array[LANE_ANGLE_IDX]; + obs[obs_idx++] = env->obs_lane_heading_signed ? ego->lane_heading_error_rad / (float) M_PI + : ego->metrics_array[LANE_ANGLE_IDX]; float current_lane_speed_limit - = (ego->current_lane_idx != -1) ? env->road_elements[ego->current_lane_idx].speed_limit : -1.0f; + = (ego->current_lane_idx != -1) ? env->lane_speed_limit_mps[ego->current_lane_idx] : -1.0f; obs[obs_idx++] = current_lane_speed_limit / env->obs_norm_speed_mps; obs[obs_idx++] = fminf(1.0f, ego->seconds_stopped / MAX_STOPPED_SECONDS); obs[obs_idx++] = fmaxf(-1.0f, fminf(1.0f, ego->lane_curvature / LANE_CURVATURE_NORM)); @@ -4024,6 +4691,10 @@ static int write_reward_target_obs(Drive *env, Agent *ego, float *obs, int obs_i for (int coef_idx = 0; coef_idx < NUM_REWARD_COEFS; coef_idx++) { float lo = reward_bounds[coef_idx].min_val; float hi = reward_bounds[coef_idx].max_val; + if (coef_idx == REWARD_COEF_SPEED) { + lo = 1.0f / env->conditioning_speed_scale; + hi = env->conditioning_speed_scale; + } float coef = ego->reward_coefs[coef_idx]; float normalized_coef; if (reward_bounds[coef_idx].log_scale) { @@ -4161,28 +4832,97 @@ static int write_partner_obs(Drive *env, Agent *ego, int agent_idx, float *obs, return obs_idx + (env->obs_slots_partners_n - partners_written) * partner_feature_count(env); } -static int write_road_obs(Drive *env, Agent *ego, float *obs, int obs_idx, int *lane_count, int *boundary_count) { - int grid_idx = get_grid_index(env, ego->sim_x, ego->sim_y); - int neighbor_count = 0; - const GridMapEntity *neighbor_entities = NULL; - if (!(grid_idx < 0 || grid_idx >= (env->grid_map->grid_cols * env->grid_map->grid_rows))) { - if (env->use_neighbor_cache) { - neighbor_count = env->grid_map->neighbor_cache_count[grid_idx]; - neighbor_entities = env->grid_map->neighbor_cache_entities[grid_idx]; - } else { - // Same spiral order as the cache build, so obs are bit-identical to the cached path. - neighbor_count = get_neighbors_entities( - env, - ego->sim_x, - ego->sim_y, - env->obs_neighbor_scratch, - env->grid_map->total_entities, - (const int (*)[2]) env->neighbor_offsets, - env->grid_map->vision_range * env->grid_map->vision_range); - neighbor_entities = env->obs_neighbor_scratch; +// Lane-graph distance from a lane to the goal lane: INFINITY = unreachable, negative = lane not in the graph. +static float lane_graph_distance_to_goal_m(Drive *env, int lane_idx, int goal_graph_idx) { + if (lane_idx < 0 || lane_idx >= env->num_road_elements) { + return -1.0f; + } + int lane_graph_idx = env->lane_graph.lane_to_graph_idx[lane_idx]; + if (lane_graph_idx < 0) { + return -1.0f; + } + float distance_m = env->lane_graph.distances[lane_graph_idx * env->lane_graph.n_lanes + goal_graph_idx]; + return (isfinite(distance_m) && distance_m >= 0.0f) ? distance_m : INFINITY; +} + +// GPS columns [abs, rel]; an unreachable lane reads as far, and as worse than an ego lane that can reach the goal. +static void write_lane_goal_distance_obs(float lane_dist_m, float ego_dist_to_goal_m, float *dest) { + dest[0] = 0.0f; + dest[1] = 0.0f; + if (lane_dist_m < 0.0f) { + return; + } + bool lane_reachable = isfinite(lane_dist_m); + dest[0] = lane_reachable ? clip(lane_dist_m / LANE_GRAPH_DISTANCE_NORM_M, 0.0f, 1.0f) : 1.0f; + if (ego_dist_to_goal_m < 0.0f) { + return; + } + bool ego_reachable = isfinite(ego_dist_to_goal_m); + if (lane_reachable && ego_reachable) { + dest[1] = clip((lane_dist_m - ego_dist_to_goal_m) / LANE_GRAPH_DISTANCE_NORM_M, -1.0f, 1.0f); + } else if (lane_reachable) { + dest[1] = -1.0f; + } else if (ego_reachable) { + dest[1] = 1.0f; + } +} + +// Writes up to slot_cap rows of the cursor's road obs kind around the ego; returns the rows written. +static int write_road_obs_rows( + Drive *env, + Agent *ego, + NeighborCursor *neighbor_cursor, + float *dest, + int slot_cap, + int goal_graph_idx, + float ego_dist_to_goal_m) { + int is_lane = neighbor_cursor->obs_kind == OBS_ENTITY_LANE; + int segment_features = is_lane ? lane_feature_count(env) : BOUNDARY_FEATURES; + int rows_written = 0; + + for (const GridMapEntity *entity = neighbor_cursor_next(env, neighbor_cursor); + entity != NULL && rows_written < slot_cap; + entity = neighbor_cursor_next(env, neighbor_cursor)) { + int entity_idx = entity->entity_idx; + int geometry_idx = entity->geometry_idx; + RoadMapElement *road_element = &env->road_elements[entity_idx]; + + const RoadObservationSegment *segment = &road_element->observation_segments[geometry_idx]; + float rel_x, rel_y; + float rel_z = segment->mid_z_m - ego->sim_z; + project_point_to_ego_frame(ego, segment->mid_x_m, segment->mid_y_m, &rel_x, &rel_y); + if (rel_x < -env->obs_range_road_behind_m || rel_x > env->obs_range_road_front_m) { + continue; + } + if (fabsf(rel_y) > env->obs_range_road_side_m) { + continue; + } + + float rel_seg_dir_x, rel_seg_dir_y; + project_vector_to_ego_frame(ego, segment->direction_x, segment->direction_y, &rel_seg_dir_x, &rel_seg_dir_y); + + float *row = &dest[rows_written++ * segment_features]; + row[0] = rel_x / env->obs_norm_xy_offset_m; + row[1] = rel_y / env->obs_norm_xy_offset_m; + row[2] = rel_z / env->obs_norm_z_m; + row[3] = segment->half_length_m / env->obs_norm_road_seg_length_m; + row[4] = rel_seg_dir_x; + row[5] = rel_seg_dir_y; + // Goal-distance features: absolute and relative to ego's lane->goal distance. + if (is_lane) { + row[6] = segment->width_m / env->obs_norm_road_seg_width_m; + float lane_dist_m + = goal_graph_idx >= 0 ? lane_graph_distance_to_goal_m(env, entity_idx, goal_graph_idx) : -1.0f; + write_lane_goal_distance_obs(lane_dist_m, ego_dist_to_goal_m, &row[7]); + } + if (is_lane && env->obs_lane_speed_limit) { + row[LANE_FEATURES] = env->lane_speed_limit_mps[entity_idx] / env->obs_norm_speed_mps; } } + return rows_written; +} +static int write_road_obs(Drive *env, Agent *ego, float *obs, int obs_idx, int *lane_count, int *boundary_count) { // GPS lane-distance features int goal_graph_idx = -1; if (env->obs_goal_lane_distance && env->lane_graph.lane_to_graph_idx != NULL @@ -4195,105 +4935,37 @@ static int write_road_obs(Drive *env, Agent *ego, float *obs, int obs_idx, int * // Ego's own lane->goal distance: reference for the relative column (delta vs ego lane). float ego_dist_to_goal_m = -1.0f; // <0 = no valid reference -> relative column stays 0 if (goal_graph_idx >= 0) { // implies obs_goal_lane_distance and a non-NULL lane_to_graph_idx - int ego_lane = ego->current_lane_idx; - if (ego_lane >= 0 && ego_lane < env->num_road_elements) { - int ego_graph_idx = env->lane_graph.lane_to_graph_idx[ego_lane]; - if (ego_graph_idx >= 0) { - float d = env->lane_graph.distances[ego_graph_idx * env->lane_graph.n_lanes + goal_graph_idx]; - // Map binaries store unreachable pairs as a negative sentinel or NaN; both clamp to max. - ego_dist_to_goal_m = (!isfinite(d) || d < 0.0f) ? LANE_GRAPH_DISTANCE_NORM_M : d; - } - } + ego_dist_to_goal_m = lane_graph_distance_to_goal_m(env, ego->current_lane_idx, goal_graph_idx); } + int lane_row_features = lane_feature_count(env); int lane_obs_idx = obs_idx; - int boundary_obs_idx = lane_obs_idx + env->obs_slots_lane_kept * LANE_FEATURES; + int boundary_obs_idx = lane_obs_idx + env->obs_slots_lane_kept * lane_row_features; obs_idx = boundary_obs_idx + env->obs_slots_boundary_kept * BOUNDARY_FEATURES; - float lanes_buffer[env->obs_slots_lane_n * LANE_FEATURES]; + float lanes_buffer[env->obs_slots_lane_n * lane_row_features]; float boundaries_buffer[env->obs_slots_boundary_n * BOUNDARY_FEATURES]; float *lane_obs_dest = env->road_dropout_enabled ? lanes_buffer : &obs[lane_obs_idx]; float *boundary_obs_dest = env->road_dropout_enabled ? boundaries_buffer : &obs[boundary_obs_idx]; - int lanes_found = 0; - int boundaries_found = 0; - - for (int k = 0; k < neighbor_count; k++) { - if (lanes_found >= env->obs_slots_lane_n && boundaries_found >= env->obs_slots_boundary_n) { - break; - } - if (!neighbor_entities[k].valid_for_obs) { - continue; - } - int entity_idx = neighbor_entities[k].entity_idx; - int geometry_idx = neighbor_entities[k].geometry_idx; - RoadMapElement *road_element = &env->road_elements[entity_idx]; - int is_lane = is_road_lane(road_element->type); - int is_edge = is_road_edge(road_element->type); - if (!is_lane && !is_edge) { - continue; - } - - float start_x = road_element->x[geometry_idx]; - float start_y = road_element->y[geometry_idx]; - float start_z = road_element->z[geometry_idx]; - float end_x = road_element->x[geometry_idx + 1]; - float end_y = road_element->y[geometry_idx + 1]; - float end_z = road_element->z[geometry_idx + 1]; - float mid_x = (start_x + end_x) / 2.0f; - float mid_y = (start_y + end_y) / 2.0f; - float mid_z = (start_z + end_z) / 2.0f; - float rel_x, rel_y; - float rel_z = mid_z - ego->sim_z; - project_point_to_ego_frame(ego, mid_x, mid_y, &rel_x, &rel_y); - if (rel_x < -env->obs_range_road_behind_m || rel_x > env->obs_range_road_front_m) { - continue; - } - if (fabsf(rel_y) > env->obs_range_road_side_m) { - continue; - } - - float seg_dx = end_x - mid_x; - float seg_dy = end_y - mid_y; - float seg_half_len = sqrtf(seg_dx * seg_dx + seg_dy * seg_dy); - float seg_dir_x = (seg_half_len > 0) ? seg_dx / seg_half_len : seg_dx; - float seg_dir_y = (seg_half_len > 0) ? seg_dy / seg_half_len : seg_dy; - float rel_seg_dir_x, rel_seg_dir_y; - project_vector_to_ego_frame(ego, seg_dir_x, seg_dir_y, &rel_seg_dir_x, &rel_seg_dir_y); - - float *segment_dest = is_lane ? lane_obs_dest : boundary_obs_dest; - int *segment_count = is_lane ? &lanes_found : &boundaries_found; - int segment_cap = is_lane ? env->obs_slots_lane_n : env->obs_slots_boundary_n; - int segment_features = is_lane ? LANE_FEATURES : BOUNDARY_FEATURES; - if (*segment_count >= segment_cap) { - continue; - } - int feature_base = (*segment_count)++ * segment_features; - segment_dest[feature_base] = rel_x / env->obs_norm_xy_offset_m; - segment_dest[feature_base + 1] = rel_y / env->obs_norm_xy_offset_m; - segment_dest[feature_base + 2] = rel_z / env->obs_norm_z_m; - segment_dest[feature_base + 3] = seg_half_len / env->obs_norm_road_seg_length_m; - segment_dest[feature_base + 4] = rel_seg_dir_x; - segment_dest[feature_base + 5] = rel_seg_dir_y; - // Goal-distance features: absolute and relative to ego's lane->goal distance. - if (is_lane) { - float seg_width = 0.5f * (road_element->widths[geometry_idx] + road_element->widths[geometry_idx + 1]); - segment_dest[feature_base + 6] = seg_width / env->obs_norm_road_seg_width_m; - float goal_dist_abs = 0.0f, goal_dist_rel = 0.0f; // 0 when flag off / unresolved - if (env->obs_goal_lane_distance && goal_graph_idx >= 0 && entity_idx < env->num_road_elements) { - int lane_graph_idx = env->lane_graph.lane_to_graph_idx[entity_idx]; - if (lane_graph_idx >= 0) { - float d = env->lane_graph.distances[lane_graph_idx * env->lane_graph.n_lanes + goal_graph_idx]; - float d_m = (!isfinite(d) || d < 0.0f) ? LANE_GRAPH_DISTANCE_NORM_M : d; // unreachable/NaN -> max - goal_dist_abs = clip(d_m / LANE_GRAPH_DISTANCE_NORM_M, 0.0f, 1.0f); - if (ego_dist_to_goal_m >= 0.0f) { - goal_dist_rel = clip((d_m - ego_dist_to_goal_m) / LANE_GRAPH_DISTANCE_NORM_M, -1.0f, 1.0f); - } - } - } - segment_dest[feature_base + 7] = goal_dist_abs; - segment_dest[feature_base + 8] = goal_dist_rel; - } - } + int scratch_count = fill_neighbor_scratch(env, ego->sim_x, ego->sim_y); + NeighborCursor lane_cursor = neighbor_cursor_begin(env, ego->sim_x, ego->sim_y, OBS_ENTITY_LANE, scratch_count); + NeighborCursor edge_cursor = neighbor_cursor_begin(env, ego->sim_x, ego->sim_y, OBS_ENTITY_EDGE, scratch_count); + int lanes_found = write_road_obs_rows( + env, + ego, + &lane_cursor, + lane_obs_dest, + env->obs_slots_lane_n, + goal_graph_idx, + ego_dist_to_goal_m); + int boundaries_found = write_road_obs_rows( + env, + ego, + &edge_cursor, + boundary_obs_dest, + env->obs_slots_boundary_n, + goal_graph_idx, + ego_dist_to_goal_m); if (env->road_dropout_enabled) { int lanes_to_copy = (lanes_found < env->obs_slots_lane_kept) ? lanes_found : env->obs_slots_lane_kept; @@ -4301,18 +4973,18 @@ static int write_road_obs(Drive *env, Agent *ego, float *obs, int obs_idx, int * = (boundaries_found < env->obs_slots_boundary_kept) ? boundaries_found : env->obs_slots_boundary_kept; *lane_count = lanes_to_copy; *boundary_count = boundaries_to_copy; - subsample_road_observation_rows(&env->rng_state, lanes_buffer, lanes_found, lanes_to_copy, LANE_FEATURES); + subsample_road_observation_rows(&env->rng_state, lanes_buffer, lanes_found, lanes_to_copy, lane_row_features); subsample_road_observation_rows( &env->rng_state, boundaries_buffer, boundaries_found, boundaries_to_copy, BOUNDARY_FEATURES); - memcpy(&obs[lane_obs_idx], lanes_buffer, lanes_to_copy * LANE_FEATURES * sizeof(float)); + memcpy(&obs[lane_obs_idx], lanes_buffer, lanes_to_copy * lane_row_features * sizeof(float)); memset( - &obs[lane_obs_idx + lanes_to_copy * LANE_FEATURES], + &obs[lane_obs_idx + lanes_to_copy * lane_row_features], 0, - (env->obs_slots_lane_kept - lanes_to_copy) * LANE_FEATURES * sizeof(float)); + (env->obs_slots_lane_kept - lanes_to_copy) * lane_row_features * sizeof(float)); memcpy(&obs[boundary_obs_idx], boundaries_buffer, boundaries_to_copy * BOUNDARY_FEATURES * sizeof(float)); memset( &obs[boundary_obs_idx + boundaries_to_copy * BOUNDARY_FEATURES], @@ -4324,9 +4996,9 @@ static int write_road_obs(Drive *env, Agent *ego, float *obs, int obs_idx, int * *lane_count = lanes_found; *boundary_count = boundaries_found; memset( - &obs[lane_obs_idx + lanes_found * LANE_FEATURES], + &obs[lane_obs_idx + lanes_found * lane_row_features], 0, - (env->obs_slots_lane_kept - lanes_found) * LANE_FEATURES * sizeof(float)); + (env->obs_slots_lane_kept - lanes_found) * lane_row_features * sizeof(float)); memset( &obs[boundary_obs_idx + boundaries_found * BOUNDARY_FEATURES], 0, @@ -4393,6 +5065,9 @@ static int write_traffic_control_obs(Drive *env, Agent *ego, float *obs, int obs light_state = (env->timestep >= 0 && env->timestep < tc->state_size && tc->states != NULL) ? tc->states[env->timestep] : TRAFFIC_CONTROL_STATE_OFF; + } else if (tc->type == TRAFFIC_CONTROL_TYPE_STOP_SIGN && visible_controls[j].idx == ego->stop_sign_target_idx) { + // the ego's own stop sign reads red until it stood still in the trigger box, then green + light_state = ego->stop_sign_stop_completed ? TRAFFIC_CONTROL_STATE_GREEN : TRAFFIC_CONTROL_STATE_RED; } obs[obs_idx++] = rel_x1 / env->obs_norm_xy_offset_m; @@ -4575,7 +5250,7 @@ static void move_dynamics(Drive *env, int action_idx, int agent_idx) { } } - if (env->eval_mode && agent->sim_speed < STANDSTILL_SPEED_EPSILON_MPS + if ((env->eval_mode || env->cosim_eval_semantics) && agent->sim_speed < STANDSTILL_SPEED_EPSILON_MPS && fabsf(j_long) < env->eval_standstill_jerk_deadband_mps3) { j_long = 0.0f; } @@ -4705,7 +5380,24 @@ static void apply_pose_noise(Drive *env, Agent *agent) { #include "idm.h" +static void update_rollout_masks(Drive *env) { + for (int i = 0; i < env->active_agent_count; i++) { + Agent *a = &env->agents[env->active_agent_indices[i]]; + env->masks[i] = !(a->stopped || a->removed || a->is_blind_partner || a->is_phantom_braker); + } +} + void c_reset(Drive *env) { + env->autoreset_pending = 0; + env->episode_end_timestep = env->scenario_length; + if (env->first_reset_pending) { + env->first_reset_pending = 0; + // first episode after creation ends early so sub-envs do not run their episodes in lockstep + if (env->stagger_first_episode && !env->eval_mode) { + int offset_range = env->scenario_length - env->init_step - env->init_step_min_horizon; + env->episode_end_timestep -= rng_below(&env->rng_state, offset_range + 1); + } + } if (env->timestep == 0) { for (int i = 0; i < env->num_total_agents; i++) { copy_pose_to_prev(&env->agents[i]); @@ -4716,6 +5408,7 @@ void c_reset(Drive *env) { sample_erratic_flags(env, &env->agents[agent_idx]); compute_metrics(env, agent_idx, x); } + update_rollout_masks(env); compute_observations(env); return; } @@ -4723,26 +5416,38 @@ void c_reset(Drive *env) { env->timestep = env->init_step; begin_episode_rng(env); + sample_zone_speed_limits(env); if (env->simulation_mode == SIMULATION_MODE_GIGAFLOW) { generate_traffic_light_states(env); + // stale poses must neither block nor escape the spawn overlap check + for (int x = 0; x < env->active_agent_count; x++) { + invalidate_agent(&env->agents[env->active_agent_indices[x]]); + } int num_reset = 0; for (int x = 0; x < env->active_agent_count; x++) { int agent_idx = env->active_agent_indices[x]; + env->logs[x] = (Log) {0}; // Respawn agent at new random position if (spawn_agent(env, agent_idx, num_reset)) { + env->agents[agent_idx].removed = 0; // a removal from an earlier episode must not outlive a respawn num_reset++; } else { // Failed spawn: ensure agent is properly invalidated invalidate_agent(&env->agents[agent_idx]); env->agents[agent_idx].removed = 1; + env->logs[x].spawn_failed = 1.0f; } + env->logs[x].spawn_reject_collision = (float) env->spawn_reject_counts[SPAWN_REJECT_COLLISION]; + env->logs[x].spawn_reject_offroad = (float) env->spawn_reject_counts[SPAWN_REJECT_OFFROAD]; + env->logs[x].spawn_reject_stop_line = (float) env->spawn_reject_counts[SPAWN_REJECT_STOP_LINE]; + env->logs[x].spawn_reject_empty_cell = (float) env->spawn_reject_counts[SPAWN_REJECT_EMPTY_CELL]; + env->logs[x].spawn_failed_goal = (float) env->spawn_goal_failed; } // GIGAFLOW: spawn_agent already set positions, routes, paths, goals. // Only need to generate reward coefs and compute initial metrics. for (int x = 0; x < env->active_agent_count; x++) { - env->logs[x] = (Log) {0}; int agent_idx = env->active_agent_indices[x]; Agent *agent = &env->agents[agent_idx]; if (agent->removed) { @@ -4753,7 +5458,9 @@ void c_reset(Drive *env) { sample_erratic_flags(env, agent); generate_reward_coefs(env, agent); compute_metrics(env, agent_idx, x); + env->logs[x].stopped_at_reset = agent->stopped ? 1.0f : 0.0f; } + update_rollout_masks(env); compute_observations(env); return; } @@ -4773,36 +5480,64 @@ void c_reset(Drive *env) { init_dynamics_state_from_log(env, agent); } - if (env->goal_source == GOAL_SOURCE_GT) { - int start = env->init_step > 0 ? env->init_step : 0; - int remaining = agent->trajectory_size - 1 - start; - if (remaining < 1) { - remaining = 1; - } - int num_wp = env->num_goals; - for (int g = 0; g < num_wp; g++) { - int t = start + (g + 1) * remaining / num_wp; - if (t >= agent->trajectory_size) { - t = agent->trajectory_size - 1; - } - agent->list_goal_x[g] = agent->log_trajectory_x[t]; - agent->list_goal_y[g] = agent->log_trajectory_y[t]; - agent->list_goal_z[g] = agent->log_trajectory_z[t]; - agent->list_goal_lane[g] = -1; // logged goals have no lane idx (no GPS lane-distance) - } - agent->goal_count = num_wp; - agent->current_goal_idx = 0; - agent->current_goal_x = agent->list_goal_x[0]; - agent->current_goal_y = agent->list_goal_y[0]; - agent->current_goal_z = agent->list_goal_z[0]; + if (env->goal_source == GOAL_SOURCE_GT || env->goal_source == GOAL_SOURCE_GT_MAP) { + generate_new_goals_from_log(env, agent); } else { generate_new_goals_from_route(env, agent); } compute_metrics(env, agent_idx, x); } + update_rollout_masks(env); compute_observations(env); } +static void record_short_early_reset(Drive *env) { + int removed_count = 0; + int stopped_count = 0; + float spawn_failed_sum = 0.0f; + float stopped_at_reset_sum = 0.0f; + float reject_collision_sum = 0.0f; + float reject_offroad_sum = 0.0f; + float reject_stop_line_sum = 0.0f; + float reject_empty_cell_sum = 0.0f; + float goal_failed_sum = 0.0f; + for (int i = 0; i < env->active_agent_count; i++) { + Agent *agent = &env->agents[env->active_agent_indices[i]]; + env->logs[i].early_reset_short = 1.0f; + removed_count += agent->removed ? 1 : 0; + stopped_count += agent->stopped ? 1 : 0; + spawn_failed_sum += env->logs[i].spawn_failed; + stopped_at_reset_sum += env->logs[i].stopped_at_reset; + reject_collision_sum += env->logs[i].spawn_reject_collision; + reject_offroad_sum += env->logs[i].spawn_reject_offroad; + reject_stop_line_sum += env->logs[i].spawn_reject_stop_line; + reject_empty_cell_sum += env->logs[i].spawn_reject_empty_cell; + goal_failed_sum += env->logs[i].spawn_failed_goal; + } + if (env->short_reset_print_count >= SHORT_RESET_MAX_PRINTS) { + return; + } + env->short_reset_print_count++; + fprintf( + stderr, + "[DRIVE DIAG] short early reset: map=%s timestep=%d active=%d removed=%d stopped=%d spawn_failed=%.0f " + "stopped_at_reset=%.0f rejects collision=%.0f offroad=%.0f stop_line=%.0f empty_cell=%.0f goal_failed=%.0f " + "episode_seed=%llu\n", + env->map_name, + env->timestep, + env->active_agent_count, + removed_count, + stopped_count, + (double) spawn_failed_sum, + (double) stopped_at_reset_sum, + (double) reject_collision_sum, + (double) reject_offroad_sum, + (double) reject_stop_line_sum, + (double) reject_empty_cell_sum, + (double) goal_failed_sum, + (unsigned long long) env->episode_seed); +} + void c_step(Drive *env) { // In eval, a scenario is evaluated once: hold the env after its episode // ended, so a short episode does not replay and re-emit within the window. @@ -4815,16 +5550,9 @@ void c_step(Drive *env) { memset(env->rewards, 0, env->active_agent_count * sizeof(float)); memset(env->terminals, 0, env->active_agent_count * sizeof(unsigned char)); memset(env->truncations, 0, env->active_agent_count * sizeof(unsigned char)); - - // Update masks for stopped/removed agents - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - Agent *a = &env->agents[agent_idx]; - if (a->stopped || a->removed || a->is_blind_partner || a->is_phantom_braker) { - env->masks[i] = 0; - } else { - env->masks[i] = 1; - } + if (env->autoreset_pending) { + c_reset(env); + return; } env->timestep++; @@ -4848,7 +5576,7 @@ void c_step(Drive *env) { Agent *agent = &env->agents[agent_idx]; if (agent->controller == CONTROLLER_POLICY) { move_dynamics(env, i, agent_idx); - if ((env->pose_noise_xy_m > 0.0f || env->pose_noise_yaw_rad > 0.0f) + if (!agent->stopped && !agent->removed && (env->pose_noise_xy_m > 0.0f || env->pose_noise_yaw_rad > 0.0f) && (!env->eval_mode || env->eval_training_render)) { apply_pose_noise(env, agent); } @@ -4883,12 +5611,12 @@ void c_step(Drive *env) { compute_rewards(env, i); } - // Mark terminals for stopped or removed agents + // Masks describe the next action's eligibility; terminal rewards belong to the previous action. + update_rollout_masks(env); for (int i = 0; i < env->active_agent_count; i++) { int agent_idx = env->active_agent_indices[i]; if (env->agents[agent_idx].stopped || env->agents[agent_idx].removed) { env->terminals[i] = 1; - env->masks[i] = 0; } } @@ -4918,7 +5646,10 @@ void c_step(Drive *env) { } } - if (env->timestep == env->scenario_length || early_reset) { + if (env->timestep == env->episode_end_timestep || early_reset) { + if (early_reset && env->timestep <= EARLY_RESET_SHORT_TIMESTEPS) { + record_short_early_reset(env); + } for (int i = 0; i < env->active_agent_count; i++) { env->truncations[i] = 1; } @@ -4927,24 +5658,24 @@ void c_step(Drive *env) { env->eval_episode_done = 1; return; } - c_reset(env); + // Expose the terminal state for the value bootstrap; the next c_step resets without applying its action. + compute_observations(env); + memset(env->masks, 0, env->active_agent_count * sizeof(unsigned char)); + env->autoreset_pending = 1; return; } - // -> 4. Compute observations - compute_observations(env); - - // -> 5. Update goals for agents that reached their goal + // -> 4. Update goals for agents that reached their goal for (int i = 0; i < env->active_agent_count; i++) { int agent_idx = env->active_agent_indices[i]; Agent *agent = &env->agents[agent_idx]; if (agent->metrics_array[REACHED_GOAL_IDX] == 0.0f) { continue; } - if (env->goal_source == GOAL_SOURCE_GT) { - // Replay mode: leave current_goal_idx saturated so the - // reached-goal condition won't fire again. Re-generating - // route-based goals on WOMD maps fails (removed=1). + if (env->goal_source == GOAL_SOURCE_GT || env->goal_source == GOAL_SOURCE_GT_MAP + || env->goal_source == GOAL_SOURCE_EXTERNAL) { + // Replay/co-sim: leave current_goal_idx saturated so the reached-goal condition won't fire + // again; the next window comes from the log layout (GT) or c_set_agent_goals (external). continue; } // Rolling slides the window forward by one goal; finite advances the alias to the next goal in the set. @@ -4970,6 +5701,11 @@ void c_step(Drive *env) { if (!regen_ok) { invalidate_agent(agent); agent->removed = 1; + env->masks[i] = 0; + env->terminals[i] = 1; } } + + // -> 5. Observations must describe the goals used by the next action. + compute_observations(env); } diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py index 1fb5a3924c..a0783b0670 100644 --- a/pufferlib/ocean/drive/drive.py +++ b/pufferlib/ocean/drive/drive.py @@ -6,6 +6,7 @@ import pufferlib from pufferlib.ocean.drive import binding +LOG_MIN_AGENT_EPISODES = 1 # emit env logs as soon as any episode completed; the trainer weights them by n TRAFFIC_CONTROL_CATEGORICAL_FEATURE_COUNT = 2 # type and state @@ -33,6 +34,7 @@ def __init__( reward_stop_line=1.0, reward_timestep=0.000025, reward_overspeed=0.05, + overspeed_tolerance_mps=0.0, reward_ade=0.0, min_goal_spacing=20.0, max_goal_spacing=60.0, @@ -43,6 +45,7 @@ def __init__( offroad_behavior="ignore", traffic_light_behavior="ignore", disable_red_light_infractions=False, + disable_stop_sign_infractions=False, traffic_light_junction_phases=False, stop_sign_behavior="ignore", use_map_cache=False, @@ -58,8 +61,14 @@ def __init__( spawn_heading_max_deg=0.0, pose_noise_xy_m=0.0, pose_noise_yaw_deg=0.0, + speed_limit_random_prob=0.0, + speed_limit_random_delta_mps=9.72, + speed_limit_random_min_mps=1.39, + speed_limit_random_max_mps=36.11, goal_speed=3.0, goal_speed_randomization=True, + conditioning_accel_scale=1.0, + conditioning_speed_scale=1.5, goal_reach_requires_speed=False, scenario_length=None, resample_frequency=91, @@ -68,6 +77,8 @@ def __init__( num_agents=512, min_agents_per_env=32, max_agents_per_env=64, + cosim_partner_slots=0, + cosim_eval_semantics=False, action_type="discrete", dynamics_model="classic", reset_accel_on_stop=False, @@ -80,6 +91,7 @@ def __init__( init_step=0, init_step_spread=False, init_step_min_horizon=20, + stagger_first_episode=False, eval_mode=0, num_eval_scenarios=16, max_scenarios_per_batch=None, @@ -108,6 +120,8 @@ def __init__( obs_boundary_stride=1, obs_slots_partners_n=16, obs_partner_relative_velocity=False, + obs_lane_heading_signed=False, + obs_lane_speed_limit=False, obs_slots_traffic_controls_n=4, traffic_lights_enabled=True, stop_signs_enabled=False, @@ -152,8 +166,33 @@ def __init__( self.pose_noise_yaw_deg = float(pose_noise_yaw_deg) if self.pose_noise_xy_m < 0.0 or self.pose_noise_yaw_deg < 0.0: raise ValueError(f"pose noise must be >= 0, got xy {pose_noise_xy_m}, yaw {pose_noise_yaw_deg}") + self.speed_limit_random_prob = float(speed_limit_random_prob) + self.speed_limit_random_delta_mps = float(speed_limit_random_delta_mps) + self.speed_limit_random_min_mps = float(speed_limit_random_min_mps) + self.speed_limit_random_max_mps = float(speed_limit_random_max_mps) + if not 0.0 <= self.speed_limit_random_prob <= 1.0: + raise ValueError(f"speed_limit_random_prob must be in [0, 1], got {speed_limit_random_prob}") + if not np.isfinite(self.speed_limit_random_delta_mps) or self.speed_limit_random_delta_mps < 0.0: + raise ValueError( + f"speed_limit_random_delta_mps must be finite and >= 0, got {speed_limit_random_delta_mps}" + ) + if not (0.0 < self.speed_limit_random_min_mps <= self.speed_limit_random_max_mps < float("inf")): + raise ValueError( + "speed_limit_random_min_mps/max_mps must satisfy 0 < min <= max, " + f"got {speed_limit_random_min_mps} / {speed_limit_random_max_mps}" + ) self.goal_speed = float(goal_speed) self.goal_speed_randomization = int(bool(goal_speed_randomization)) + # Eval-time C_acc conditioning coefficient (training samples it in REWARD_BOUNDS[REWARD_COEF_ACC]). + self.conditioning_speed_scale = float(conditioning_speed_scale) + if not np.isfinite(self.conditioning_speed_scale) or self.conditioning_speed_scale < 1.0: + raise ValueError(f"conditioning_speed_scale must be finite and >= 1, got {conditioning_speed_scale}") + self.conditioning_accel_scale = float(conditioning_accel_scale) + if not (binding.CONDITIONING_ACC_MIN <= self.conditioning_accel_scale <= binding.CONDITIONING_ACC_MAX): + raise ValueError( + f"conditioning_accel_scale must be within the trained C_acc range " + f"[{binding.CONDITIONING_ACC_MIN}, {binding.CONDITIONING_ACC_MAX}]. Got: {conditioning_accel_scale}" + ) self.goal_reach_requires_speed = int(bool(goal_reach_requires_speed)) self.reward_conditioning = reward_conditioning self.reward_randomization = reward_randomization @@ -176,6 +215,9 @@ def __init__( self.reward_stop_line = reward_stop_line self.reward_timestep = reward_timestep self.reward_overspeed = reward_overspeed + self.overspeed_tolerance_mps = float(overspeed_tolerance_mps) + if not (self.overspeed_tolerance_mps >= 0.0) or not np.isfinite(self.overspeed_tolerance_mps): + raise ValueError(f"overspeed_tolerance_mps must be finite and >= 0, got {overspeed_tolerance_mps}") self.reward_ade = reward_ade self.goal_radius = goal_radius self.min_goal_spacing = min_goal_spacing @@ -190,6 +232,8 @@ def __init__( "route": binding.GOAL_SOURCE_ROUTE, "map": binding.GOAL_SOURCE_MAP, "gt": binding.GOAL_SOURCE_GT, + "external": binding.GOAL_SOURCE_EXTERNAL, + "gt_map": binding.GOAL_SOURCE_GT_MAP, }[goal_source] self.obs_goal_lane_distance = int(bool(obs_goal_lane_distance)) infraction_behavior_values = { @@ -201,6 +245,7 @@ def __init__( self.offroad_behavior = infraction_behavior_values[offroad_behavior] self.traffic_light_behavior = infraction_behavior_values[traffic_light_behavior] self.disable_red_light_infractions = bool(disable_red_light_infractions) + self.disable_stop_sign_infractions = bool(disable_stop_sign_infractions) self.traffic_light_junction_phases = bool(traffic_light_junction_phases) if replay_expert_agents not in (0, 1): raise ValueError(f"replay_expert_agents must be 0 or 1. Got: {replay_expert_agents}") @@ -240,6 +285,17 @@ def __init__( self.rng = np.random.default_rng(seed) self.min_agents_per_env = min_agents_per_env self.max_agents_per_env = max_agents_per_env + # Co-sim: static agent slots per env owned by the external simulator (never spawned or stepped). + self.cosim_partner_slots = int(cosim_partner_slots) + if self.cosim_partner_slots < 0: + raise ValueError(f"cosim_partner_slots must be >= 0. Got: {cosim_partner_slots}") + if self.cosim_partner_slots > 0 and simulation_mode != "gigaflow": + raise ValueError("cosim_partner_slots is only supported in gigaflow simulation_mode") + # Co-sim envs stay out of eval_mode (its scenario batching needs a full agent pool) but still want the + # eval-only observation/action semantics: perceived-size margin and standstill jerk deadband. + self.cosim_eval_semantics = int(bool(cosim_eval_semantics)) + if goal_source == "external" and simulation_mode != "gigaflow": + raise ValueError("goal_source 'external' is only supported in gigaflow simulation_mode (co-sim)") self.ego_features = binding.EGO_FEATURES @@ -250,6 +306,8 @@ def __init__( self.obs_boundary_stride = obs_boundary_stride self.obs_slots_partners_n = obs_slots_partners_n self.obs_partner_relative_velocity = int(bool(obs_partner_relative_velocity)) + self.obs_lane_heading_signed = int(bool(obs_lane_heading_signed)) + self.obs_lane_speed_limit = int(bool(obs_lane_speed_limit)) self.traffic_lights_enabled = traffic_lights_enabled self.stop_signs_enabled = stop_signs_enabled self.yield_signs_enabled = yield_signs_enabled @@ -295,7 +353,9 @@ def __init__( self.partner_features = binding.PARTNER_FEATURES + ( binding.PARTNER_RELATIVE_VELOCITY_FEATURES if self.obs_partner_relative_velocity else 0 ) - self.lane_features = binding.LANE_FEATURES + self.lane_features = binding.LANE_FEATURES + ( + binding.LANE_SPEED_LIMIT_FEATURES if self.obs_lane_speed_limit else 0 + ) self.boundary_features = binding.BOUNDARY_FEATURES self.traffic_control_features = binding.TRAFFIC_CONTROL_FEATURES self.obs_valid_count_features = binding.OBS_VALID_COUNT_FEATURES @@ -336,6 +396,7 @@ def __init__( self.init_step_spread = bool(init_step_spread) # limit at which we set the starting point from the end of the total episode length self.init_step_min_horizon = int(init_step_min_horizon) + self.stagger_first_episode = int(bool(stagger_first_episode)) self.init_mode_str = init_mode self.control_mode_str = control_mode self.sdc_controller_str = sdc_controller @@ -490,11 +551,13 @@ def _env_init_kwargs(self, map_file, max_agents): "reward_stop_line": self.reward_stop_line, "reward_timestep": self.reward_timestep, "reward_overspeed": self.reward_overspeed, + "overspeed_tolerance_mps": self.overspeed_tolerance_mps, "reward_ade": self.reward_ade, "collision_behavior": self.collision_behavior, "offroad_behavior": self.offroad_behavior, "traffic_light_behavior": self.traffic_light_behavior, "disable_red_light_infractions": self.disable_red_light_infractions, + "disable_stop_sign_infractions": self.disable_stop_sign_infractions, "traffic_light_junction_phases": self.traffic_light_junction_phases, "stop_sign_behavior": self.stop_sign_behavior, "use_map_cache": self.use_map_cache, @@ -513,6 +576,8 @@ def _env_init_kwargs(self, map_file, max_agents): "obs_boundary_stride": self.obs_boundary_stride, "obs_slots_partners_n": self.obs_slots_partners_n, "obs_partner_relative_velocity": self.obs_partner_relative_velocity, + "obs_lane_heading_signed": self.obs_lane_heading_signed, + "obs_lane_speed_limit": self.obs_lane_speed_limit, "obs_slots_traffic_controls_n": self.obs_slots_traffic_controls_n, "traffic_lights_enabled": self.traffic_lights_enabled, "stop_signs_enabled": self.stop_signs_enabled, @@ -525,8 +590,14 @@ def _env_init_kwargs(self, map_file, max_agents): "spawn_heading_max_deg": self.spawn_heading_max_deg, "pose_noise_xy_m": self.pose_noise_xy_m, "pose_noise_yaw_deg": self.pose_noise_yaw_deg, + "speed_limit_random_prob": self.speed_limit_random_prob, + "speed_limit_random_delta_mps": self.speed_limit_random_delta_mps, + "speed_limit_random_min_mps": self.speed_limit_random_min_mps, + "speed_limit_random_max_mps": self.speed_limit_random_max_mps, "goal_speed": self.goal_speed, "goal_speed_randomization": self.goal_speed_randomization, + "conditioning_accel_scale": self.conditioning_accel_scale, + "conditioning_speed_scale": self.conditioning_speed_scale, "goal_reach_requires_speed": self.goal_reach_requires_speed, "scenario_length": int(self.scenario_length) if self.scenario_length is not None else None, "termination_mode": int(self.termination_mode), @@ -535,7 +606,11 @@ def _env_init_kwargs(self, map_file, max_agents): "map_file": map_file, "max_agents": max_agents, "max_agents_per_env": self.max_agents_per_env, + "cosim_partner_slots": self.cosim_partner_slots, + "cosim_eval_semantics": self.cosim_eval_semantics, "init_step": self._sample_init_step(), + "init_step_min_horizon": self.init_step_min_horizon, + "stagger_first_episode": self.stagger_first_episode, "init_mode": self.init_mode, "control_mode": self.control_mode, "sdc_controller": self.sdc_controller, @@ -608,6 +683,7 @@ def reset(self, seed=None): else: binding.vec_reset(self.c_envs) self.tick = 0 + self._resample_pending = False self.truncations[:] = 0 if self.capture_replay: self._initialize_replay_captures() @@ -619,20 +695,27 @@ def step(self, actions): self.terminals[:] = 0 self.truncations[:] = 0 return (self.observations, self.rewards, self.terminals, self.truncations, []) - if self.capture_replay: - self._capture_replay_step() - self.actions[:] = actions - binding.vec_step(self.c_envs) - self.tick += 1 + if not self._resample_pending: + if self.capture_replay: + self._capture_replay_step() + self.actions[:] = actions + binding.vec_step(self.c_envs) + self.tick += 1 info = [] # vec_log is the training aggregate; it resets env->log, which eval reads # per episode, so it must not run in eval mode. - if not self.eval_mode and self.tick % self.report_interval == 0: - log = binding.vec_log(self.c_envs, self.num_agents) + if not self.eval_mode and not self._resample_pending and self.tick % self.report_interval == 0: + log = binding.vec_log(self.c_envs, LOG_MIN_AGENT_EPISODES) if log: info.append(log) - # print(log) if self.tick > 0 and self.resample_frequency > 0 and self.tick % self.resample_frequency == 0: + if not self.eval_mode and not self._resample_pending: + # Training resample is a truncation: expose the pre-resample state for the value bootstrap first. + self._resample_pending = True + self.truncations[:] = 1 + self.masks[:] = 0 + return (self.observations, self.rewards, self.terminals, self.truncations, info) + self._resample_pending = False self.tick = 0 will_resample = 1 if will_resample: @@ -706,18 +789,24 @@ def step(self, actions): binding.vec_reset(self.c_envs) if self.capture_replay: self._initialize_replay_captures() - # Map resampling is an external reset boundary (dataset/map switch). Treat as truncation. - self.truncations[:] = 1 + # Eval reads the map switch as a truncation on the post-reset step; training already emitted it. + self.truncations[:] = self.eval_mode + if not self.eval_mode: + self.rewards[:] = 0 + self.terminals[:] = 0 return (self.observations, self.rewards, self.terminals, self.truncations, info) - def get_global_agent_state(self): + def get_global_agent_state(self, include_static=False): """Get current global state of all active agents. + include_static: also return the co-sim partner slots after the active agents + (num_active_agents + cosim_partner_slots * num_envs rows). + Returns: dict with keys 'x', 'y', 'z', 'heading', 'id', 'length', 'width' containing numpy arrays of shape (num_active_agents,) """ - num_agents = self.num_agents + num_agents = self.num_agents + (self.cosim_partner_slots * self.num_envs if include_static else 0) states = { "x": np.zeros(num_agents, dtype=np.float32), @@ -738,10 +827,102 @@ def get_global_agent_state(self): states["id"], states["length"], states["width"], + int(bool(include_static)), ) return states + def set_agent_states(self, idx, x, y, z, heading, vx, vy, yaw_rate, accel_long, seconds_stopped=None): + """Co-sim: overwrite the sim state of agents at global indices `idx` + (e.g. CARLA background) with world-frame pose/velocity. The C side + subtracts world_mean, recaches heading trig and recomputes speed. + `yaw_rate` (rad/s) and `accel_long` (m/s^2) must come from the external + sim's own physics (e.g. CARLA's get_angular_velocity()/get_acceleration(), + nuPlan's EgoState.dynamic_car_state) -- not finite-differenced here, since + this agent's previous state may already have been overwritten this tick by + env.step()'s own dynamics before this call runs. + `seconds_stopped` (seconds each agent's speed has been below + binding.AGENT_STOPPED_SPEED_THRESHOLD) is optional: pass it when the + external sim owns the agent's kinematics every tick, so stopped-time is + injected as state instead of derived from the dummy in-sim rollout; + leave it None to keep c_step's own per-tick accumulation.""" + binding.vec_set_agent_states( + self.c_envs, + np.ascontiguousarray(idx, dtype=np.int32), + np.ascontiguousarray(x, dtype=np.float32), + np.ascontiguousarray(y, dtype=np.float32), + np.ascontiguousarray(z, dtype=np.float32), + np.ascontiguousarray(heading, dtype=np.float32), + np.ascontiguousarray(vx, dtype=np.float32), + np.ascontiguousarray(vy, dtype=np.float32), + np.ascontiguousarray(yaw_rate, dtype=np.float32), + np.ascontiguousarray(accel_long, dtype=np.float32), + None if seconds_stopped is None else np.ascontiguousarray(seconds_stopped, dtype=np.float32), + ) + + # ── Co-simulation external-state setters ───────────────────────────────────── + def set_agent_sizes(self, idx, length, width): + """overwrite the bounding-box length/width (meters) of agents at + global indices `idx`, so the ego observes/collides against a CARLA + actor's true size instead of the gigaflow-spawned default.""" + binding.vec_set_agent_sizes( + self.c_envs, + np.ascontiguousarray(idx, dtype=np.int32), + np.ascontiguousarray(length, dtype=np.float32), + np.ascontiguousarray(width, dtype=np.float32), + ) + + def recompute_observations(self): + """recompute observations from current state without stepping + dynamics or advancing the timestep (call after set_agent_states).""" + binding.vec_recompute_observations(self.c_envs) + return self.observations + + def set_traffic_light_states(self, states): + """override each traffic-light element's state at the current + timestep (states length == num_traffic_elements).""" + binding.vec_set_traffic_light_states(self.c_envs, np.ascontiguousarray(states, dtype=np.int32)) + + def set_stop_signs(self, lines, headings): + """replace every stop-sign element with the external sim's own (co-sim): + lines (K, 6) world-frame endpoints [x1, y1, z1, x2, y2, z2], headings (K,) + travel direction across each line. The map's stop signs are retired in + place, so light element indices stay valid; returns the new element + count (the length set_traffic_light_states expects from now on).""" + lines = np.ascontiguousarray(np.asarray(lines, dtype=np.float32).reshape(-1, 6)) + headings = np.ascontiguousarray(np.asarray(headings, dtype=np.float32).reshape(-1)) + return int(binding.vec_set_stop_signs(self.c_envs, lines, headings)) + + def set_agent_goals(self, agent_idx, gx, gy, gz, gdir_x=None, gdir_y=None): + """set an agent's goal waypoints (e.g. the ego's route) in world + coords (C subtracts world_mean). Each waypoint is snapped to its + nearest route-aligned drivable lane (find_goal_lane) so the GPS + lane-distance observation columns stay live when the map carries a + lane graph; waypoints off the drivable network keep lane -1 + (columns zero-filled). gdir_x/gdir_y: per-waypoint local route travel + direction (need not be normalized) used to reject crossing-road lanes + at junctions; omit (or pass zeros) to snap by distance alone.""" + gx = np.ascontiguousarray(gx, dtype=np.float32) + if gdir_x is None or gdir_y is None: + gdir_x = np.zeros_like(gx) + gdir_y = np.zeros_like(gx) + binding.vec_set_agent_goals( + self.c_envs, + int(agent_idx), + gx, + np.ascontiguousarray(gy, dtype=np.float32), + np.ascontiguousarray(gz, dtype=np.float32), + np.ascontiguousarray(gdir_x, dtype=np.float32), + np.ascontiguousarray(gdir_y, dtype=np.float32), + ) + + def get_agent_goal_progress(self, agent_idx): + """Co-sim: (current_goal_idx, goal_count) of one agent. current_goal_idx == goal_count + means every goal of the current window has been consumed.""" + return binding.vec_get_agent_goal_progress(self.c_envs, int(agent_idx)) + + # ─────────────────────────────────────── + def get_ground_truth_trajectories(self): """Get ground truth trajectories for all active agents. diff --git a/pufferlib/ocean/drive/idm.h b/pufferlib/ocean/drive/idm.h index a884938dbb..f0936f72e2 100644 --- a/pufferlib/ocean/drive/idm.h +++ b/pufferlib/ocean/drive/idm.h @@ -518,7 +518,7 @@ static inline float idm_lane_speed_limit(Drive *env, int lane_idx) { if (lane_idx < 0 || lane_idx >= env->num_road_elements) { return 0.0f; } - return env->road_elements[lane_idx].speed_limit; + return env->lane_speed_limit_mps[lane_idx]; } static float idm_desired_speed(Drive *env, Agent *agent) { diff --git a/pufferlib/ocean/drive/map_data.h b/pufferlib/ocean/drive/map_data.h index c7f9a7b0fe..8254dc4fbb 100644 --- a/pufferlib/ocean/drive/map_data.h +++ b/pufferlib/ocean/drive/map_data.h @@ -33,7 +33,7 @@ static void add_entity_to_grid( int grid_index, int entity_idx, int geometry_idx, - int valid_for_obs, + int obs_kind, int *cell_entities_insert_index) { if (grid_index == -1) { return; @@ -52,11 +52,51 @@ static void add_entity_to_grid( env->grid_map->cells[grid_index][count].entity_idx = entity_idx; env->grid_map->cells[grid_index][count].geometry_idx = geometry_idx; - env->grid_map->cells[grid_index][count].valid_for_obs = valid_for_obs; + env->grid_map->cells[grid_index][count].obs_kind = obs_kind; cell_entities_insert_index[grid_index] = count + 1; } +// Build after loading lane widths and before sharing the immutable road elements. +static void cache_road_observation_geometry(Drive *env) { + for (int road_idx = 0; road_idx < env->num_road_elements; road_idx++) { + RoadMapElement *road = &env->road_elements[road_idx]; + if (!is_road_grid_candidate(road->type)) { + continue; + } + if (road->segment_size < 0) { + raise_error_with_message(ERROR_INVALID_ARGUMENT, "Negative point count for road %d", road_idx); + } + if (road->segment_size < 2) { + continue; + } + size_t segment_count = (size_t) road->segment_size - 1; + if (segment_count > SIZE_MAX / sizeof(RoadObservationSegment)) { + raise_error_with_message(ERROR_INVALID_ARGUMENT, "Road %d geometry cache is too large", road_idx); + } + road->observation_segments = (RoadObservationSegment *) calloc(segment_count, sizeof(RoadObservationSegment)); + if (road->observation_segments == NULL) { + raise_error_with_message(ERROR_MEMORY_ALLOCATION, "Failed to cache geometry for road %d", road_idx); + } + int has_lane_width = is_road_lane(road->type); + for (int segment_idx = 0; segment_idx < road->segment_size - 1; segment_idx++) { + RoadObservationSegment *segment = &road->observation_segments[segment_idx]; + // Preserve the observation formulas and float rounding, including zero-length segments. + segment->mid_x_m = (road->x[segment_idx] + road->x[segment_idx + 1]) / 2.0f; + segment->mid_y_m = (road->y[segment_idx] + road->y[segment_idx + 1]) / 2.0f; + segment->mid_z_m = (road->z[segment_idx] + road->z[segment_idx + 1]) / 2.0f; + float half_dx_m = road->x[segment_idx + 1] - segment->mid_x_m; + float half_dy_m = road->y[segment_idx + 1] - segment->mid_y_m; + segment->half_length_m = sqrtf(half_dx_m * half_dx_m + half_dy_m * half_dy_m); + segment->direction_x = segment->half_length_m > 0.0f ? half_dx_m / segment->half_length_m : half_dx_m; + segment->direction_y = segment->half_length_m > 0.0f ? half_dy_m / segment->half_length_m : half_dy_m; + segment->width_m + = has_lane_width ? 0.5f * (road->widths[segment_idx] + road->widths[segment_idx + 1]) : 0.0f; + } + } +} + static int init_grid_map(Drive *env) { + cache_road_observation_geometry(env); env->grid_map = (GridMap *) calloc(1, sizeof(GridMap)); float top_left_x = 0.0f, top_left_y = 0.0f, bottom_right_x = 0.0f, bottom_right_y = 0.0f; @@ -137,12 +177,29 @@ static int init_grid_map(Drive *env) { env->grid_map->cell_entities_count[grid_index]++; } } + for (int grid_index = 0; grid_index < grid_cell_count; grid_index++) { + int count = env->grid_map->cell_entities_count[grid_index]; + if (count > MAX_ENTITIES_PER_CELL) { + fprintf( + stderr, + "[ERROR] -> Grid cell %d holds %d road entities, exceeding MAX_ENTITIES_PER_CELL %d\n", + grid_index, + count, + MAX_ENTITIES_PER_CELL); + return 1; + } + } // Allocate grid cells based on counts int *cell_entities_insert_index = (int *) calloc(grid_cell_count, sizeof(int)); + for (int grid_index = 0; grid_index < grid_cell_count; grid_index++) { + env->grid_map->total_entities += env->grid_map->cell_entities_count[grid_index]; + } + env->grid_map->entity_pool = (GridMapEntity *) calloc(env->grid_map->total_entities + 1, sizeof(GridMapEntity)); + int pool_offset = 0; for (int grid_index = 0; grid_index < grid_cell_count; grid_index++) { int count = env->grid_map->cell_entities_count[grid_index]; - env->grid_map->total_entities += count; - env->grid_map->cells[grid_index] = (GridMapEntity *) calloc(count, sizeof(GridMapEntity)); + env->grid_map->cells[grid_index] = count > 0 ? &env->grid_map->entity_pool[pool_offset] : NULL; + pool_offset += count; } // Track which grid cells have drivable lanes bool *drivable_grid_seen = (bool *) calloc(grid_cell_count, sizeof(bool)); @@ -151,8 +208,9 @@ static int init_grid_map(Drive *env) { continue; } RoadMapElement *element = &env->road_elements[i]; + int is_lane = is_road_lane(element->type); int obs_stride = 1; - if (is_road_lane(element->type)) { + if (is_lane) { obs_stride = env->obs_lane_stride; } else if (is_road_edge(element->type)) { obs_stride = env->obs_boundary_stride; @@ -166,8 +224,10 @@ static int init_grid_map(Drive *env) { float heading_dev = fabsf(normalize_heading(element->headings[j] - element->headings[last_kept_idx])); valid_for_obs = j - last_kept_idx >= obs_stride || heading_dev > OBS_STRIDE_HEADING_THRESHOLD; } + int obs_kind = OBS_ENTITY_NONE; if (valid_for_obs) { last_kept_idx = j; + obs_kind = is_lane ? OBS_ENTITY_LANE : OBS_ENTITY_EDGE; } float x_center = (element->x[j] + element->x[j + 1]) / 2; float y_center = (element->y[j] + element->y[j + 1]) / 2; @@ -175,7 +235,7 @@ static int init_grid_map(Drive *env) { if (grid_index == -1) { continue; } - add_entity_to_grid(env, grid_index, i, j, valid_for_obs, cell_entities_insert_index); + add_entity_to_grid(env, grid_index, i, j, obs_kind, cell_entities_insert_index); if (is_drivable_road_lane(element->type) && !drivable_grid_seen[grid_index]) { drivable_grid_seen[grid_index] = true; env->grid_map->num_drivable_grid_cell++; @@ -229,60 +289,66 @@ static void init_neighbor_offsets(Drive *env) { } } -static void cache_neighbor_offsets(Drive *env) { - int count = 0; - int cell_count = env->grid_map->grid_cols * env->grid_map->grid_rows; - env->grid_map->neighbor_cache_entities = (GridMapEntity **) calloc(cell_count, sizeof(GridMapEntity *)); - env->grid_map->neighbor_cache_count = (int *) calloc(cell_count + 1, sizeof(int)); - for (int i = 0; i < cell_count; i++) { - int cell_x = i % env->grid_map->grid_cols; // Convert to 2D coordinates - int cell_y = i / env->grid_map->grid_cols; - int current_cell_neighbor_count = 0; - for (int j = 0; j < env->grid_map->vision_range * env->grid_map->vision_range; j++) { - int x = cell_x + env->neighbor_offsets[j * 2]; - int y = cell_y + env->neighbor_offsets[j * 2 + 1]; - int grid_index = env->grid_map->grid_cols * y + x; - if (x < 0 || x >= env->grid_map->grid_cols || y < 0 || y >= env->grid_map->grid_rows) { +// Spiral-order entity_pool indices of a cell's vision window, observed lanes first then edges; NULL dest only counts. +// lane_count is written by the counting call and read by the filling call to place the edge block. +static int collect_neighbor_pool_indices(Drive *env, int cell_idx, uint16_t *dest, int *lane_count) { + GridMap *grid = env->grid_map; + int cell_x = cell_idx % grid->grid_cols; + int cell_y = cell_idx / grid->grid_cols; + int window_size = grid->vision_range * grid->vision_range; + int lanes_seen = 0; + int edges_seen = 0; + for (int j = 0; j < window_size; j++) { + int x = cell_x + env->neighbor_offsets[j * 2]; + int y = cell_y + env->neighbor_offsets[j * 2 + 1]; + if (x < 0 || x >= grid->grid_cols || y < 0 || y >= grid->grid_rows) { + continue; + } + int grid_index = grid->grid_cols * y + x; + int cell_entity_count = grid->cell_entities_count[grid_index]; + int first_pool_idx = (int) (grid->cells[grid_index] - grid->entity_pool); + for (int e = 0; e < cell_entity_count; e++) { + int obs_kind = grid->cells[grid_index][e].obs_kind; + if (obs_kind == OBS_ENTITY_NONE) { continue; } - int grid_count = env->grid_map->cell_entities_count[grid_index]; - current_cell_neighbor_count += grid_count; - } - env->grid_map->neighbor_cache_count[i] = current_cell_neighbor_count; - count += current_cell_neighbor_count; - if (current_cell_neighbor_count == 0) { - env->grid_map->neighbor_cache_entities[i] = NULL; - continue; + int is_lane = obs_kind == OBS_ENTITY_LANE; + if (dest != NULL) { + dest[is_lane ? lanes_seen : *lane_count + edges_seen] = (uint16_t) (first_pool_idx + e); + } + lanes_seen += is_lane; + edges_seen += !is_lane; } - env->grid_map->neighbor_cache_entities[i] - = (GridMapEntity *) calloc(current_cell_neighbor_count, sizeof(GridMapEntity)); } + *lane_count = lanes_seen; + return lanes_seen + edges_seen; +} - env->grid_map->neighbor_cache_count[cell_count] = count; +static void cache_neighbor_offsets(Drive *env) { + GridMap *grid = env->grid_map; + int cell_count = grid->grid_cols * grid->grid_rows; + if (grid->total_entities > UINT16_MAX) { + raise_error_with_message( + ERROR_INVALID_ARGUMENT, + "%d grid entities exceed the uint16 neighbor cache index", + grid->total_entities); + } + grid->neighbor_cache_pool_idx = (uint16_t **) calloc(cell_count, sizeof(uint16_t *)); + grid->neighbor_cache_count = (int *) calloc(cell_count + 1, sizeof(int)); + grid->neighbor_cache_lane_count = (int *) calloc(cell_count, sizeof(int)); + int total_count = 0; for (int i = 0; i < cell_count; i++) { - int cell_x = i % env->grid_map->grid_cols; - int cell_y = i / env->grid_map->grid_cols; - int base_index = 0; - for (int j = 0; j < env->grid_map->vision_range * env->grid_map->vision_range; j++) { - int x = cell_x + env->neighbor_offsets[j * 2]; - int y = cell_y + env->neighbor_offsets[j * 2 + 1]; - int grid_index = env->grid_map->grid_cols * y + x; - if (x < 0 || x >= env->grid_map->grid_cols || y < 0 || y >= env->grid_map->grid_rows) { - continue; - } - int grid_count = env->grid_map->cell_entities_count[grid_index]; - // Skip if no entities or source is NULL - if (grid_count == 0 || env->grid_map->cells[grid_index] == NULL) { - continue; - } - // Copy grid_count pairs (entity_idx, geometry_idx) at once - memcpy( - &env->grid_map->neighbor_cache_entities[i][base_index], - env->grid_map->cells[grid_index], - grid_count * sizeof(GridMapEntity)); - base_index += grid_count; + int neighbor_count = collect_neighbor_pool_indices(env, i, NULL, &grid->neighbor_cache_lane_count[i]); + grid->neighbor_cache_count[i] = neighbor_count; + total_count += neighbor_count; + if (neighbor_count == 0) { + grid->neighbor_cache_pool_idx[i] = NULL; + continue; } + grid->neighbor_cache_pool_idx[i] = (uint16_t *) malloc(neighbor_count * sizeof(uint16_t)); + collect_neighbor_pool_indices(env, i, grid->neighbor_cache_pool_idx[i], &grid->neighbor_cache_lane_count[i]); } + grid->neighbor_cache_count[cell_count] = total_count; } static int get_neighbors_entities( @@ -320,6 +386,64 @@ static int get_neighbors_entities( return entity_list_count; } +// Walks one obs kind of the spiral neighborhood of a query point: the kind's block of cached pool indices, or the +// scratch list filtered by kind. +typedef struct NeighborCursor { + const GridMapEntity *entities; + const uint16_t *pool_indices; + int count; + int pos; + int obs_kind; +} NeighborCursor; + +// Uncached path only: spiral neighborhood of a query point into the scratch list, shared by both obs kinds. +static int fill_neighbor_scratch(Drive *env, float x, float y) { + if (env->use_neighbor_cache) { + return 0; + } + return get_neighbors_entities( + env, + x, + y, + env->obs_neighbor_scratch, + env->grid_map->total_entities, + (const int (*)[2]) env->neighbor_offsets, + env->grid_map->vision_range * env->grid_map->vision_range); +} + +static NeighborCursor neighbor_cursor_begin(Drive *env, float x, float y, int obs_kind, int scratch_count) { + NeighborCursor cursor = {0}; + cursor.obs_kind = obs_kind; + if (!env->use_neighbor_cache) { + cursor.entities = env->obs_neighbor_scratch; + cursor.count = scratch_count; + return cursor; + } + int grid_idx = get_grid_index(env, x, y); + if (grid_idx < 0 || grid_idx >= env->grid_map->grid_cols * env->grid_map->grid_rows) { + return cursor; + } + int lane_count = env->grid_map->neighbor_cache_lane_count[grid_idx]; + int total_count = env->grid_map->neighbor_cache_count[grid_idx]; + const uint16_t *pool_indices = env->grid_map->neighbor_cache_pool_idx[grid_idx]; + cursor.pool_indices = obs_kind == OBS_ENTITY_LANE ? pool_indices : pool_indices + lane_count; + cursor.count = obs_kind == OBS_ENTITY_LANE ? lane_count : total_count - lane_count; + return cursor; +} + +static inline const GridMapEntity *neighbor_cursor_next(Drive *env, NeighborCursor *cursor) { + for (int k = cursor->pos; k < cursor->count; k++) { + cursor->pos = k + 1; + if (cursor->pool_indices != NULL) { + return &env->grid_map->entity_pool[cursor->pool_indices[k]]; + } + if (cursor->entities[k].obs_kind == cursor->obs_kind) { + return &cursor->entities[k]; + } + } + return NULL; +} + // ======================================== // Map Loading Functions // ======================================== @@ -328,6 +452,8 @@ static int get_neighbors_entities( #define TRAFFIC_PHASE_SECTION_TAG_LEN 8 #define LANE_WIDTH_SECTION_TAG "LANEWID1" #define LANE_WIDTH_SECTION_TAG_LEN 8 +#define SPEED_ZONE_SECTION_TAG "SPDZONE1" +#define SPEED_ZONE_SECTION_TAG_LEN 8 // Optional tail section; files written before it existed end right after the metadata. static int load_traffic_phase_section(FILE *file, Drive *drive) { @@ -405,9 +531,48 @@ static int load_lane_width_section(FILE *file, Drive *drive) { } } } + return 0; +} + +// Optional tail section; files without it get -1 (no zone) for every lane. +static int load_speed_zone_section(FILE *file, Drive *drive) { + char tag[SPEED_ZONE_SECTION_TAG_LEN]; + size_t tag_bytes_read = fread(tag, sizeof(char), SPEED_ZONE_SECTION_TAG_LEN, file); + int has_section = tag_bytes_read == SPEED_ZONE_SECTION_TAG_LEN + && memcmp(tag, SPEED_ZONE_SECTION_TAG, SPEED_ZONE_SECTION_TAG_LEN) == 0; + if (tag_bytes_read != 0 && !has_section) { + printf( + "[ERROR] -> Unexpected bytes after %s section (expected EOF or %s).\n", + LANE_WIDTH_SECTION_TAG, + SPEED_ZONE_SECTION_TAG); + return -1; + } + int lane_count = 0; + for (int i = 0; i < drive->num_road_elements; i++) { + lane_count += is_road_lane(drive->road_elements[i].type); + } + drive->num_speed_zones = 0; + for (int i = 0; i < drive->num_road_elements; i++) { + RoadMapElement *road = &drive->road_elements[i]; + road->speed_zone_idx = -1; + if (!is_road_lane(road->type) || !has_section) { + continue; + } + if (fread(&road->speed_zone_idx, sizeof(int), 1, file) != 1) { + printf("[ERROR] -> Truncated %s section at lane %d.\n", SPEED_ZONE_SECTION_TAG, i); + return -1; + } + if (road->speed_zone_idx < -1 || road->speed_zone_idx >= lane_count) { + printf("[ERROR] -> Lane %d has invalid speed zone %d.\n", i, road->speed_zone_idx); + return -1; + } + if (road->speed_zone_idx + 1 > drive->num_speed_zones) { + drive->num_speed_zones = road->speed_zone_idx + 1; + } + } char trailing_byte; if (fread(&trailing_byte, sizeof(char), 1, file) == 1) { - printf("[ERROR] -> Trailing bytes after %s section.\n", LANE_WIDTH_SECTION_TAG); + printf("[ERROR] -> Trailing bytes after %s section.\n", SPEED_ZONE_SECTION_TAG); return -1; } return 0; @@ -831,6 +996,10 @@ int load_map_binary(const char *filename, Drive *drive) { fclose(file); return -1; } + if (load_speed_zone_section(file, drive) != 0) { + fclose(file); + return -1; + } fclose(file); return 0; @@ -881,19 +1050,18 @@ static void free_grid_map(GridMap *grid_map) { return; } int grid_cell_count = grid_map->grid_cols * grid_map->grid_rows; - for (int grid_index = 0; grid_index < grid_cell_count; grid_index++) { - free(grid_map->cells[grid_index]); - } + free(grid_map->entity_pool); free(grid_map->cells); free(grid_map->cell_entities_count); free(grid_map->grid_index_drivable); - if (grid_map->neighbor_cache_entities != NULL) { + if (grid_map->neighbor_cache_pool_idx != NULL) { for (int grid_index = 0; grid_index < grid_cell_count; grid_index++) { - free(grid_map->neighbor_cache_entities[grid_index]); + free(grid_map->neighbor_cache_pool_idx[grid_index]); } } - free(grid_map->neighbor_cache_entities); + free(grid_map->neighbor_cache_pool_idx); free(grid_map->neighbor_cache_count); + free(grid_map->neighbor_cache_lane_count); free(grid_map); } @@ -993,7 +1161,7 @@ static int preload_map_cache(Drive *config, const char **map_files, int num_map_ if (shared != NULL) { env.grid_map = shared->grid_map; env.neighbor_offsets = shared->neighbor_offsets; - if (env.use_neighbor_cache && env.grid_map->neighbor_cache_entities == NULL) { + if (env.use_neighbor_cache && env.grid_map->neighbor_cache_pool_idx == NULL) { cache_neighbor_offsets(&env); } shared->ref_count++; diff --git a/pufferlib/ocean/env_binding.h b/pufferlib/ocean/env_binding.h index cac096430f..cedb4c1215 100644 --- a/pufferlib/ocean/env_binding.h +++ b/pufferlib/ocean/env_binding.h @@ -912,15 +912,16 @@ static PyObject *get_global_agent_state(PyObject *self, PyObject *args) { float *length_data = (float *) PyArray_DATA((PyArrayObject *) length_arr); float *width_data = (float *) PyArray_DATA((PyArrayObject *) width_arr); - c_get_global_agent_state(drive, x_data, y_data, z_data, heading_data, id_data, length_data, width_data); + c_get_global_agent_state(drive, 0, x_data, y_data, z_data, heading_data, id_data, length_data, width_data); Py_RETURN_NONE; } static PyObject *vec_get_global_agent_state(PyObject *self, PyObject *args) { - if (PyTuple_Size(args) != 8) { - PyErr_SetString(PyExc_TypeError, "vec_get_global_agent_state requires 8 arguments"); + if (PyTuple_Size(args) != 9) { + PyErr_SetString(PyExc_TypeError, "vec_get_global_agent_state requires 9 arguments"); return NULL; } + int include_static = (int) PyLong_AsLong(PyTuple_GetItem(args, 8)); VecEnv *vec = unpack_vecenv(args); if (!vec) { @@ -967,6 +968,7 @@ static PyObject *vec_get_global_agent_state(PyObject *self, PyObject *args) { // Write to the arrays at the current offset c_get_global_agent_state( drive, + include_static, &x_base[offset], &y_base[offset], &z_base[offset], @@ -976,9 +978,253 @@ static PyObject *vec_get_global_agent_state(PyObject *self, PyObject *args) { &width_base[offset]); // Move offset forward by the number of agents in this environment - offset += drive->active_agent_count; + offset += include_static ? drive->num_agents : drive->active_agent_count; + } + + Py_RETURN_NONE; +} + +// ── Co-simulation external-state setters (mirror vec_get_global_agent_state) ── +// Co-sim runs a single env (num_envs == 1); these operate on vec->envs[0]. +static PyObject *vec_set_agent_states(PyObject *self, PyObject *args) { + if (PyTuple_Size(args) != 11) { + PyErr_SetString(PyExc_TypeError, "vec_set_agent_states requires 11 arguments"); + return NULL; + } + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + return NULL; + } + PyObject *idx_arr = PyTuple_GetItem(args, 1); + PyObject *x_arr = PyTuple_GetItem(args, 2); + PyObject *y_arr = PyTuple_GetItem(args, 3); + PyObject *z_arr = PyTuple_GetItem(args, 4); + PyObject *heading_arr = PyTuple_GetItem(args, 5); + PyObject *vx_arr = PyTuple_GetItem(args, 6); + PyObject *vy_arr = PyTuple_GetItem(args, 7); + PyObject *yaw_rate_arr = PyTuple_GetItem(args, 8); + PyObject *accel_long_arr = PyTuple_GetItem(args, 9); + PyObject *seconds_stopped_arr = PyTuple_GetItem(args, 10); // None keeps c_step's accumulation + if (!PyArray_Check(idx_arr) || !PyArray_Check(x_arr) || !PyArray_Check(y_arr) || !PyArray_Check(z_arr) + || !PyArray_Check(heading_arr) || !PyArray_Check(vx_arr) || !PyArray_Check(vy_arr) + || !PyArray_Check(yaw_rate_arr) || !PyArray_Check(accel_long_arr)) { + PyErr_SetString(PyExc_TypeError, "All arrays must be NumPy arrays"); + return NULL; + } + if (seconds_stopped_arr != Py_None && !PyArray_Check(seconds_stopped_arr)) { + PyErr_SetString(PyExc_TypeError, "seconds_stopped must be a NumPy array or None"); + return NULL; + } + int *idx = (int *) PyArray_DATA((PyArrayObject *) idx_arr); + float *x = (float *) PyArray_DATA((PyArrayObject *) x_arr); + float *y = (float *) PyArray_DATA((PyArrayObject *) y_arr); + float *z = (float *) PyArray_DATA((PyArrayObject *) z_arr); + float *heading = (float *) PyArray_DATA((PyArrayObject *) heading_arr); + float *vx = (float *) PyArray_DATA((PyArrayObject *) vx_arr); + float *vy = (float *) PyArray_DATA((PyArrayObject *) vy_arr); + float *yaw_rate = (float *) PyArray_DATA((PyArrayObject *) yaw_rate_arr); + float *accel_long = (float *) PyArray_DATA((PyArrayObject *) accel_long_arr); + float *seconds_stopped + = seconds_stopped_arr == Py_None ? NULL : (float *) PyArray_DATA((PyArrayObject *) seconds_stopped_arr); + int count = (int) PyArray_SIZE((PyArrayObject *) idx_arr); + PyObject *value_arrs[9] + = {x_arr, y_arr, z_arr, heading_arr, vx_arr, vy_arr, yaw_rate_arr, accel_long_arr, seconds_stopped_arr}; + for (int k = 0; k < 9; k++) { + if (value_arrs[k] == Py_None) { + continue; + } + if ((int) PyArray_SIZE((PyArrayObject *) value_arrs[k]) != count) { + PyErr_SetString(PyExc_ValueError, "vec_set_agent_states: every array must have the same length as idx"); + return NULL; + } + } + if (c_set_agent_states( + (Drive *) vec->envs[0], + count, + idx, + x, + y, + z, + heading, + vx, + vy, + yaw_rate, + accel_long, + seconds_stopped) + != 0) { + PyErr_SetString( + PyExc_ValueError, + "vec_set_agent_states: agent index out of range, non-finite state or negative seconds_stopped"); + return NULL; + } + Py_RETURN_NONE; +} + +static PyObject *vec_set_agent_sizes(PyObject *self, PyObject *args) { + if (PyTuple_Size(args) != 4) { + PyErr_SetString(PyExc_TypeError, "vec_set_agent_sizes requires 4 arguments"); + return NULL; + } + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + return NULL; + } + PyObject *idx_arr = PyTuple_GetItem(args, 1); + PyObject *length_arr = PyTuple_GetItem(args, 2); + PyObject *width_arr = PyTuple_GetItem(args, 3); + if (!PyArray_Check(idx_arr) || !PyArray_Check(length_arr) || !PyArray_Check(width_arr)) { + PyErr_SetString(PyExc_TypeError, "All arrays must be NumPy arrays"); + return NULL; + } + int *idx = (int *) PyArray_DATA((PyArrayObject *) idx_arr); + float *length = (float *) PyArray_DATA((PyArrayObject *) length_arr); + float *width = (float *) PyArray_DATA((PyArrayObject *) width_arr); + int count = (int) PyArray_SIZE((PyArrayObject *) idx_arr); + if ((int) PyArray_SIZE((PyArrayObject *) length_arr) != count + || (int) PyArray_SIZE((PyArrayObject *) width_arr) != count) { + PyErr_SetString(PyExc_ValueError, "vec_set_agent_sizes: length/width must have the same length as idx"); + return NULL; + } + if (c_set_agent_sizes((Drive *) vec->envs[0], count, idx, length, width) != 0) { + PyErr_SetString(PyExc_ValueError, "vec_set_agent_sizes: agent index out of range or non-positive size"); + return NULL; + } + Py_RETURN_NONE; +} + +static PyObject *vec_recompute_observations(PyObject *self, PyObject *args) { + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + return NULL; + } + for (int i = 0; i < vec->num_envs; i++) { + compute_observations((Drive *) vec->envs[i]); + } + Py_RETURN_NONE; +} + +static PyObject *vec_set_traffic_light_states(PyObject *self, PyObject *args) { + if (PyTuple_Size(args) != 2) { + PyErr_SetString(PyExc_TypeError, "vec_set_traffic_light_states requires 2 arguments"); + return NULL; + } + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + return NULL; } + PyObject *states_arr = PyTuple_GetItem(args, 1); + if (!PyArray_Check(states_arr)) { + PyErr_SetString(PyExc_TypeError, "states must be a NumPy array"); + return NULL; + } + int *states = (int *) PyArray_DATA((PyArrayObject *) states_arr); + Drive *drive = (Drive *) vec->envs[0]; + if ((int) PyArray_SIZE((PyArrayObject *) states_arr) != drive->num_traffic_elements) { + PyErr_Format( + PyExc_ValueError, + "vec_set_traffic_light_states: expected %d states (one per traffic element), got %d", + drive->num_traffic_elements, + (int) PyArray_SIZE((PyArrayObject *) states_arr)); + return NULL; + } + if (c_set_traffic_light_states(drive, states) != 0) { + PyErr_SetString( + PyExc_ValueError, + "vec_set_traffic_light_states: timestep outside the light state buffer or invalid state"); + return NULL; + } + Py_RETURN_NONE; +} +static PyObject *vec_set_stop_signs(PyObject *self, PyObject *args) { + if (PyTuple_Size(args) != 3) { + PyErr_SetString(PyExc_TypeError, "vec_set_stop_signs requires 3 arguments (vec_env, lines, headings)"); + return NULL; + } + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + return NULL; + } + PyObject *lines_arr = PyTuple_GetItem(args, 1); + PyObject *headings_arr = PyTuple_GetItem(args, 2); + if (!PyArray_Check(lines_arr) || !PyArray_Check(headings_arr)) { + PyErr_SetString(PyExc_TypeError, "vec_set_stop_signs: lines and headings must be NumPy arrays"); + return NULL; + } + int count = (int) PyArray_SIZE((PyArrayObject *) headings_arr); + if ((int) PyArray_SIZE((PyArrayObject *) lines_arr) != count * 6) { + PyErr_SetString(PyExc_ValueError, "vec_set_stop_signs: lines must hold 6 floats per heading"); + return NULL; + } + Drive *drive = (Drive *) vec->envs[0]; + const float *lines = (const float *) PyArray_DATA((PyArrayObject *) lines_arr); + const float *headings = (const float *) PyArray_DATA((PyArrayObject *) headings_arr); + if (c_set_stop_signs(drive, count, lines, headings) != 0) { + PyErr_SetString(PyExc_ValueError, "vec_set_stop_signs: non-finite value or zero-length stop line"); + return NULL; + } + return PyLong_FromLong(drive->num_traffic_elements); +} + +static PyObject *vec_get_agent_goal_progress(PyObject *self, PyObject *args) { + if (PyTuple_Size(args) != 2) { + PyErr_SetString(PyExc_TypeError, "vec_get_agent_goal_progress requires 2 arguments"); + return NULL; + } + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + return NULL; + } + Drive *drive = (Drive *) vec->envs[0]; + int agent_idx = (int) PyLong_AsLong(PyTuple_GetItem(args, 1)); + if (agent_idx < 0 || agent_idx >= drive->num_total_agents) { + PyErr_SetString(PyExc_ValueError, "vec_get_agent_goal_progress: agent index out of range"); + return NULL; + } + int current_goal_idx, goal_count; + c_get_agent_goal_progress(drive, agent_idx, ¤t_goal_idx, &goal_count); + return Py_BuildValue("(ii)", current_goal_idx, goal_count); +} + +static PyObject *vec_set_agent_goals(PyObject *self, PyObject *args) { + if (PyTuple_Size(args) != 7) { + PyErr_SetString(PyExc_TypeError, "vec_set_agent_goals requires 7 arguments"); + return NULL; + } + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + return NULL; + } + int agent_idx = (int) PyLong_AsLong(PyTuple_GetItem(args, 1)); + PyObject *gx_arr = PyTuple_GetItem(args, 2); + PyObject *gy_arr = PyTuple_GetItem(args, 3); + PyObject *gz_arr = PyTuple_GetItem(args, 4); + PyObject *gdx_arr = PyTuple_GetItem(args, 5); + PyObject *gdy_arr = PyTuple_GetItem(args, 6); + if (!PyArray_Check(gx_arr) || !PyArray_Check(gy_arr) || !PyArray_Check(gz_arr) || !PyArray_Check(gdx_arr) + || !PyArray_Check(gdy_arr)) { + PyErr_SetString(PyExc_TypeError, "goal arrays must be NumPy arrays"); + return NULL; + } + float *gx = (float *) PyArray_DATA((PyArrayObject *) gx_arr); + float *gy = (float *) PyArray_DATA((PyArrayObject *) gy_arr); + float *gz = (float *) PyArray_DATA((PyArrayObject *) gz_arr); + float *gdx = (float *) PyArray_DATA((PyArrayObject *) gdx_arr); + float *gdy = (float *) PyArray_DATA((PyArrayObject *) gdy_arr); + int num_wp = (int) PyArray_SIZE((PyArrayObject *) gx_arr); + if ((int) PyArray_SIZE((PyArrayObject *) gy_arr) != num_wp || (int) PyArray_SIZE((PyArrayObject *) gz_arr) != num_wp + || (int) PyArray_SIZE((PyArrayObject *) gdx_arr) != num_wp + || (int) PyArray_SIZE((PyArrayObject *) gdy_arr) != num_wp) { + PyErr_SetString(PyExc_ValueError, "vec_set_agent_goals: goal arrays must all have the same length"); + return NULL; + } + if (c_set_agent_goals((Drive *) vec->envs[0], agent_idx, num_wp, gx, gy, gz, gdx, gdy) != 0) { + PyErr_Format( + PyExc_ValueError, + "vec_set_agent_goals: agent index out of range, waypoint count outside 1..%d, or non-finite goal", + MAX_GOALS); + return NULL; + } Py_RETURN_NONE; } @@ -1247,6 +1493,31 @@ static PyMethodDef methods[] {"shared", (PyCFunction) my_shared, METH_VARARGS | METH_KEYWORDS, "Shared state"}, {"get_global_agent_state", get_global_agent_state, METH_VARARGS, "Get global agent state"}, {"vec_get_global_agent_state", vec_get_global_agent_state, METH_VARARGS, "Get agent state from vectorized env"}, + {"vec_set_agent_states", + vec_set_agent_states, + METH_VARARGS, + "Overwrite agent states from an external source (co-sim)"}, + {"vec_set_agent_sizes", + vec_set_agent_sizes, + METH_VARARGS, + "Overwrite agent bounding-box sizes from an external source (co-sim)"}, + {"vec_recompute_observations", + vec_recompute_observations, + METH_VARARGS, + "Recompute observations without stepping (co-sim)"}, + {"vec_set_traffic_light_states", + vec_set_traffic_light_states, + METH_VARARGS, + "Override traffic light states (co-sim)"}, + {"vec_set_stop_signs", + vec_set_stop_signs, + METH_VARARGS, + "Replace the stop signs with the external sim's own (co-sim)"}, + {"vec_set_agent_goals", vec_set_agent_goals, METH_VARARGS, "Set an agent's goal waypoints (co-sim)"}, + {"vec_get_agent_goal_progress", + vec_get_agent_goal_progress, + METH_VARARGS, + "(current_goal_idx, goal_count) of one agent (co-sim)"}, {"get_ground_truth_trajectories", get_ground_truth_trajectories, METH_VARARGS, "Get ground truth trajectories"}, {"vec_get_global_ground_truth_trajectories", vec_get_global_ground_truth_trajectories, @@ -1299,6 +1570,7 @@ PyMODINIT_FUNC PyInit_binding(void) { // Make constants accessible from Python PyModule_AddIntConstant(m, "MAX_ENTITIES_PER_CELL", MAX_ENTITIES_PER_CELL); PyModule_AddIntConstant(m, "LANE_FEATURES", LANE_FEATURES); + PyModule_AddIntConstant(m, "LANE_SPEED_LIMIT_FEATURES", LANE_SPEED_LIMIT_FEATURES); PyModule_AddObject(m, "LANE_CURVATURE_NORM", PyFloat_FromDouble(LANE_CURVATURE_NORM)); PyModule_AddIntConstant(m, "BOUNDARY_FEATURES", BOUNDARY_FEATURES); PyModule_AddIntConstant(m, "PARTNER_FEATURES", PARTNER_FEATURES); @@ -1327,13 +1599,14 @@ PyMODINIT_FUNC PyInit_binding(void) { PyModule_AddIntConstant(m, "SCORE_F32_FIELDS", SCORE_F32_FIELDS); PyModule_AddIntConstant(m, "REWARD_F32_FIELDS", REWARD_F32_FIELDS); PyModule_AddIntConstant(m, "TRAFFIC_I16_FIELDS", TRAFFIC_I16_FIELDS); - PyModule_AddIntConstant(m, "REWARD_F32_FIELDS", REWARD_F32_FIELDS); PyModule_AddIntConstant(m, "NUM_REWARD_COEFS", NUM_REWARD_COEFS); PyModule_AddIntConstant(m, "GOAL_REGEN_FINITE", GOAL_REGEN_FINITE); PyModule_AddIntConstant(m, "GOAL_REGEN_ROLLING", GOAL_REGEN_ROLLING); PyModule_AddIntConstant(m, "GOAL_SOURCE_ROUTE", GOAL_SOURCE_ROUTE); PyModule_AddIntConstant(m, "GOAL_SOURCE_MAP", GOAL_SOURCE_MAP); PyModule_AddIntConstant(m, "GOAL_SOURCE_GT", GOAL_SOURCE_GT); + PyModule_AddIntConstant(m, "GOAL_SOURCE_EXTERNAL", GOAL_SOURCE_EXTERNAL); + PyModule_AddIntConstant(m, "GOAL_SOURCE_GT_MAP", GOAL_SOURCE_GT_MAP); PyModule_AddIntConstant(m, "CONTROLLER_STATIC", CONTROLLER_STATIC); PyModule_AddIntConstant(m, "CONTROLLER_POLICY", CONTROLLER_POLICY); PyModule_AddIntConstant(m, "CONTROLLER_REPLAY", CONTROLLER_REPLAY); @@ -1343,6 +1616,14 @@ PyMODINIT_FUNC PyInit_binding(void) { PyModule_AddIntConstant(m, "INFRACTION_BEHAVIOR_REMOVE", INFRACTION_BEHAVIOR_REMOVE); PyModule_AddIntConstant(m, "SIMULATION_MODE_GIGAFLOW", SIMULATION_MODE_GIGAFLOW); PyModule_AddIntConstant(m, "SIMULATION_MODE_REPLAY", SIMULATION_MODE_REPLAY); + // Ego obs normalization (obs[4] * ACCEL_LONG_NORM = accel_long m/s^2); co-sim reads + // the shadow ego's post-step accel intent back out of the observation row. The speed + // norm (obs[0]) is per-env config: env.obs_norm_speed_mps. + PyModule_AddObject(m, "ACCEL_LONG_NORM", PyFloat_FromDouble(fabsf(ACCEL_LONG_LIMIT[0]))); + PyModule_AddObject(m, "ACCEL_LONG_MAX", PyFloat_FromDouble(ACCEL_LONG_LIMIT[1])); + PyModule_AddObject(m, "AGENT_STOPPED_SPEED_THRESHOLD", PyFloat_FromDouble(AGENT_STOPPED_SPEED_THRESHOLD)); + PyModule_AddObject(m, "CONDITIONING_ACC_MIN", PyFloat_FromDouble(REWARD_BOUNDS[REWARD_COEF_ACC].min_val)); + PyModule_AddObject(m, "CONDITIONING_ACC_MAX", PyFloat_FromDouble(REWARD_BOUNDS[REWARD_COEF_ACC].max_val)); PyModule_AddIntConstant(m, "ACTION_TYPE_DISCRETE", ACTION_TYPE_DISCRETE); PyModule_AddIntConstant(m, "ACTION_TYPE_CONTINUOUS", ACTION_TYPE_CONTINUOUS); PyModule_AddIntConstant(m, "DYNAMICS_MODEL_CLASSIC", DYNAMICS_MODEL_CLASSIC); diff --git a/pufferlib/ocean/evaluation_utils/cosim_evaluator.py b/pufferlib/ocean/evaluation_utils/cosim_evaluator.py new file mode 100644 index 0000000000..50233ffad9 --- /dev/null +++ b/pufferlib/ocean/evaluation_utils/cosim_evaluator.py @@ -0,0 +1,414 @@ +"""Evaluator for the CARLA closed-loop co-simulation benchmark.""" + +import contextlib +import json +import os +import time + +import numpy as np +import pandas as pd + +import pufferlib +from pufferlib.ocean.evaluation_utils.evaluation_utils import ( + _load_yaml_mapping, + _positive_int, + _require_mapping, +) + +COSIM_SIMULATION_MODES = ("carla_cosim",) + +_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(pufferlib.__file__))) +CARLA_LEADERBOARD_SCRIPT = os.path.join(_REPO_ROOT, "pufferlib/ocean/cosim/carla/run_leaderboard.sh") +CARLA_PORT_STRIDE = 20 # distinct CARLA_PORT per route job, guards against two landing on one node + + +# ─── benchmark config parsing ──────────────────────────────────────────────── +def parse_cosim_benchmark(name, benchmark): + simulation_mode = benchmark.get("simulation_mode") + if simulation_mode == "carla_cosim": + return _parse_carla_benchmark(name, benchmark) + raise pufferlib.APIUsageError(f"Benchmark {name} has unsupported cosim simulation_mode: {simulation_mode}") + + +def _parse_carla_benchmark(name, benchmark): + route_ids = benchmark.get("route_ids") + if not isinstance(route_ids, list) or not route_ids or any(not isinstance(r, int) for r in route_ids): + raise pufferlib.APIUsageError(f"Benchmark {name} route_ids must be a non-empty list of integers") + + return { + "name": name, + "simulation_mode": "carla_cosim", + "route_ids": route_ids, + "routes": benchmark.get("routes"), # None -> run_leaderboard.sh's own default routes xml + "base_carla_port": _positive_int(benchmark.get("base_carla_port", 2000), f"Benchmark {name} base_carla_port"), + "device": benchmark.get("device"), + "dynamics_source": benchmark.get("dynamics_source"), + "compute_config": benchmark.get("compute_config"), + } + + +# ─── submitit launcher (mirrors scripts/submit_cluster.py's executor setup) ── +def _load_compute_config(compute_config): + if compute_config is None: + return {} + if isinstance(compute_config, str): + return _load_yaml_mapping(compute_config, "cosim compute config") + return _require_mapping(compute_config, "cosim compute config") + + +@contextlib.contextmanager +def _clean_env_prefixes(*prefixes): + saved = {key: os.environ.pop(key) for key in list(os.environ) if key.startswith(prefixes)} + try: + yield + finally: + os.environ.update(saved) + + +def _clean_slurm_env(): + """Submitting a new sbatch job from inside an already-running SLURM job + (as the training-loop debug hook does) leaks the parent job's SLURM_* env + vars -- SLURM_CPU_BIND in particular -- into `sbatch`'s inherited + environment, and the child job then fails at its own srun step with "CPU + binding outside of job step allocation" once it lands on a different + node/allocation. Strip them for just the submission call.""" + return _clean_env_prefixes("SLURM_") + + +def _clean_slurm_and_wandb_env(): + """Same leak as _clean_slurm_env, but also strips WANDB_* -- the parent + training process's live WandbLogger sets WANDB_SERVICE (and friends) + pointing at a local wandb-core socket under its own SLURM job's + node-local scratch dir. Submitting the orchestrator job (which later + calls wandb.init itself, possibly on a different node, long after the + parent's socket is gone) with that inherited would make it try to reuse + a dead service and silently fall back to logging a disconnected run.""" + return _clean_env_prefixes("SLURM_", "WANDB_") + + +def _build_executor(compute_config, folder, job_name): + import submitit + + executor = submitit.AutoExecutor(folder=folder, slurm_max_num_timeout=20) + if compute_config.get("gpu_type") is not None: + gres = f"gpu:{compute_config['gpu_type']}:{compute_config.get('gpus', 1)}" + elif compute_config.get("gpus") is not None: + gres = f"gpu:{compute_config['gpus']}" + else: + gres = None + executor.update_parameters( + slurm_account=compute_config.get("account"), + slurm_partition=compute_config.get("partition"), + cpus_per_task=compute_config.get("cpus", 8), + nodes=compute_config.get("nodes", 1), + tasks_per_node=1, + slurm_gres=gres, + slurm_exclude=compute_config.get("exclude") or None, + slurm_mem=compute_config.get("mem", "64gb") or "64gb", + slurm_time=compute_config.get("time", 120), + slurm_job_name=job_name, + ) + return executor + + +def _run_script(cmd, env, subdir): + import subprocess + + os.makedirs(subdir, exist_ok=True) + with open(os.path.join(subdir, "launcher.log"), "w") as log_file: + result = subprocess.run(cmd, env=env, stdout=log_file, stderr=subprocess.STDOUT, check=False) + return result.returncode + + +# ─── CARLA: one SLURM job per route, via run_leaderboard.sh ────────────────── +def _run_carla_route_job(subdir, checkpoint_path, script_path, routes, route_id, carla_port, device, dynamics_source): + """Runs inside the SLURM job: run_leaderboard.sh starts its own CARLA + server, runs CaRL's original_leaderboard evaluator against `route_id` + with leaderboard_agent.py as the driving agent, and writes result.json.""" + out_path = os.path.join(subdir, "result.json") + env = os.environ.copy() + env["CKPT"] = checkpoint_path + env["ROUTES_SUBSET"] = str(route_id) + env["OUT"] = out_path + env["CARLA_PORT"] = str(carla_port) + if routes: + env["ROUTES"] = routes + if device: + env["COSIM_DEVICE"] = device + if dynamics_source: + env["COSIM_DYNAMICS_SOURCE"] = dynamics_source + + returncode = _run_script(["bash", script_path], env, subdir) + if returncode != 0: + raise RuntimeError(f"run_leaderboard.sh failed (exit {returncode}) for route {route_id}; see {subdir}") + return _read_leaderboard_result(out_path, route_id) + + +# Crash statuses CaRL's own evaluate_routes_slurm.py resubmits on (leaderboard +# process/simulator died mid-route) -- as opposed to a legitimate bad-driving +# outcome ("Failed - Agent deviated from the route", "Failed - Collision", ... +# any status not in this set), which is real data, not a failure to retry. +CARLA_CRASH_STATUSES = frozenset( + { + "Failed - Agent couldn't be set up", + "Failed", + "Failed - Simulation crashed", + "Failed - Agent crashed", + } +) +CARLA_MAX_ATTEMPTS = 3 # matches CaRL's PlanT evaluate_routes_slurm.py default; CARLA's own script uses 5 + + +def _read_leaderboard_result(out_path, route_id): + """CARLA leaderboard's own result.json schema (leaderboard_evaluator.py's + StatisticsManager): _checkpoint.records[i].scores.{score_composed, + score_route, score_penalty} (driving score / route completion % / + infraction-penalty multiplier), .status, .infractions.""" + if not os.path.isfile(out_path): + raise RuntimeError(f"run_leaderboard.sh did not write a result at {out_path}") + with open(out_path) as result_file: + data = json.load(result_file) + records = data.get("_checkpoint", {}).get("records", []) + if not records: + raise RuntimeError(f"run_leaderboard.sh result at {out_path} has no route records") + crashed = [r for r in records if r.get("status") in CARLA_CRASH_STATUSES] + if crashed: + raise RuntimeError(f"CARLA route {route_id} crashed ({crashed[0].get('status')}); see {out_path}") + rows = [] + for record in records: + scores = record.get("scores", {}) + infractions = record.get("infractions", {}) + rows.append( + { + "route_id": record.get("route_id", route_id), + "status": record.get("status"), + "driving_score": scores.get("score_composed"), + "route_completion": scores.get("score_route"), + "infraction_penalty": scores.get("score_penalty"), + "num_infractions": sum(len(v) for v in infractions.values()) if isinstance(infractions, dict) else None, + } + ) + return rows + + +def _submit_carla_route_entry(benchmark, checkpoint_path, output_dir, executor, job_idx, route_id, attempt): + subdir = os.path.join(output_dir, f"route_{route_id:03d}") + if attempt > 1: + subdir += f"_attempt{attempt}" + # Unique port per (route, attempt) pair -- a crashed attempt's CARLA server + # may still be tearing down when we resubmit, so never reuse its port. + carla_port = benchmark["base_carla_port"] + (job_idx * CARLA_MAX_ATTEMPTS + (attempt - 1)) * CARLA_PORT_STRIDE + with _clean_slurm_env(): + job = executor.submit( + _run_carla_route_job, + subdir, + checkpoint_path, + CARLA_LEADERBOARD_SCRIPT, + benchmark["routes"], + route_id, + carla_port, + benchmark["device"], + benchmark["dynamics_source"], + ) + return { + "job": job, + "route_id": route_id, + "job_idx": job_idx, + "attempt": attempt, + "benchmark": benchmark, + "checkpoint_path": checkpoint_path, + "output_dir": output_dir, + "executor": executor, + } + + +def _submit_carla_jobs(benchmark, checkpoint_path, output_dir, executor): + entries = [ + _submit_carla_route_entry(benchmark, checkpoint_path, output_dir, executor, job_idx, route_id, attempt=1) + for job_idx, route_id in enumerate(benchmark["route_ids"]) + ] + print( + f"[cosim_eval] submitted {len(entries)} CARLA route jobs (run_leaderboard.sh) for benchmark {benchmark['name']}" + ) + return entries + + +def _resolve_carla_entries(entries): + """Check each route's current attempt; on a genuine crash (see + CARLA_CRASH_STATUSES / a process-level failure in _run_carla_route_job), + resubmit a fresh job for that route up to CARLA_MAX_ATTEMPTS times -- + mirrors CaRL's own evaluate_routes_slurm.py resubmit-on-crash loop. + Returns (rows_from_routes_that_finished_this_call, still_running_entries).""" + rows = [] + still_running = [] + for entry in entries: + if not entry["job"].done(): + still_running.append(entry) + continue + try: + rows.extend(entry["job"].result()) + except Exception as exc: + if entry["attempt"] >= CARLA_MAX_ATTEMPTS: + print( + f"[cosim_eval] CARLA route {entry['route_id']} crashed on all {entry['attempt']} " + f"attempt(s), giving up: {exc}" + ) + continue + print( + f"[cosim_eval] CARLA route {entry['route_id']} attempt {entry['attempt']} crashed ({exc}); " + f"resubmitting as attempt {entry['attempt'] + 1}" + ) + still_running.append( + _submit_carla_route_entry( + entry["benchmark"], + entry["checkpoint_path"], + entry["output_dir"], + entry["executor"], + entry["job_idx"], + entry["route_id"], + entry["attempt"] + 1, + ) + ) + return rows, still_running + + +def _run_carla_benchmark(benchmark, checkpoint_path, output_dir, executor): + entries = _submit_carla_jobs(benchmark, checkpoint_path, output_dir, executor) + rows = [] + while entries: + finished_rows, entries = _resolve_carla_entries(entries) + rows.extend(finished_rows) + if entries: + time.sleep(10) + return rows + + +# ─── result aggregation + wandb ────────────────────────────────────────────── +def _summarize_rows(rows): + df = pd.DataFrame(rows) + numeric = df.select_dtypes(include=[np.number]) + metrics_mean = {column: float(numeric[column].dropna().mean()) for column in numeric.columns} + summary = {"num_scenarios": len(df), "num_episodes": len(df), "metrics_mean": metrics_mean} + return df, summary + + +def _write_cosim_report(rows, output_dir): + if not rows: + print("No co-sim evaluation episodes were recorded; skipping report.") + return None + df, summary = _summarize_rows(rows) + os.makedirs(output_dir, exist_ok=True) + df.to_csv(os.path.join(output_dir, "episode_metrics.csv"), index=False) + with open(os.path.join(output_dir, "evaluation_summary.json"), "w") as summary_file: + json.dump(summary, summary_file, indent=2) + print(f"Wrote {len(df)} co-sim episode rows and summary to {output_dir}") + return summary + + +def _resolve_wandb_run_id(checkpoint_path): + """A checkpoint lives at /_/models/*.pt; recover + the run id from that directory name so co-sim results resume onto the same + training run instead of a disconnected one. None if it doesn't parse.""" + run_dir_name = os.path.basename(os.path.dirname(os.path.dirname(os.path.abspath(checkpoint_path)))) + if "_" not in run_dir_name: + return None + return run_dir_name.rsplit("_", 1)[-1] + + +def _log_cosim_to_wandb(args, checkpoint_path, benchmark_name, summary): + if not args.get("wandb") or summary is None: + return + import wandb + + metrics = {"num_scenarios": summary["num_scenarios"], "num_episodes": summary["num_episodes"]} + metrics.update(summary["metrics_mean"]) + metrics = {f"cosim_{benchmark_name}/{key}": value for key, value in metrics.items()} + + run_id = _resolve_wandb_run_id(checkpoint_path) + try: + if run_id is None: + raise RuntimeError("no run id parsed from checkpoint path") + wandb.init(id=run_id, project=args["wandb_project"], resume="must", settings=wandb.Settings(console="off")) + except Exception as exc: + print(f"[cosim_eval] could not resume training run's wandb run ({exc}); logging a new run instead") + wandb.init( + project=args["wandb_project"], + group=args["wandb_group"], + job_type="cosim_eval", + name=f"cosim_eval_{benchmark_name}", + settings=wandb.Settings(console="off"), + ) + wandb.log(metrics) + wandb.finish() + print(f"[cosim_eval] logged {len(metrics)} metrics to wandb run {wandb.run.id if wandb.run else '?'}") + + +def _prepare_cosim_submission(benchmark, base_args, output_dir): + checkpoint_path = base_args.get("load_model_path") + if not isinstance(checkpoint_path, str) or not os.path.isfile(checkpoint_path): + raise pufferlib.APIUsageError( + f"Cosim benchmark {benchmark['name']} requires a checkpoint on disk (eval.load_model_path); " + "co-sim runs launch as separate SLURM jobs that load the policy from a file, not the live " + "in-process training policy, so use_training_config is not supported for cosim benchmarks" + ) + compute_config = _load_compute_config(benchmark.get("compute_config")) + executor = _build_executor(compute_config, os.path.join(output_dir, "submitit"), job_name=benchmark["name"]) + return checkpoint_path, executor + + +def _run_cosim_benchmark_job(benchmark, base_args, output_dir): + """Runs inside its own detached SLURM job (an orchestrator -- it submits + and waits on the real CARLA work, it doesn't do that work itself): + blocks until every route job finishes (including CARLA's + retry-on-crash), then logs to wandb. Because this orchestrator is its own + SLURM job with its own time budget, wandb gets the result whenever the + real work finishes -- even long after the training process that + requested it has exited.""" + return run_cosim_benchmark(benchmark, base_args, output_dir, log_to_wandb=True) + + +def submit_cosim_benchmark_async(benchmark, base_args, output_dir, orchestrator_time_minutes=360): + """Fire-and-forget for the training-loop debug hook: submit ONE small + orchestrator job (see _run_cosim_benchmark_job) and return immediately. + Unlike submit_cosim_benchmark()+collect_cosim_results(), the caller does + not need to still be running to see the result reach wandb -- the + orchestrator does that itself, independently. The orchestrator needs no + GPU (it only submits/waits on the real jobs), just a generous time + budget to cover CARLA's queue wait + runtime.""" + checkpoint_path = base_args.get("load_model_path") + if not isinstance(checkpoint_path, str) or not os.path.isfile(checkpoint_path): + raise pufferlib.APIUsageError( + f"Cosim benchmark {benchmark['name']} requires a checkpoint on disk (eval.load_model_path)" + ) + orchestrator_config = dict(_load_compute_config(benchmark.get("compute_config"))) + orchestrator_config.update(cpus=2, mem="8gb", gpus=None, gpu_type=None, time=orchestrator_time_minutes) + executor = _build_executor( + orchestrator_config, os.path.join(output_dir, "submitit_orchestrator"), job_name=f"{benchmark['name']}_log" + ) + with _clean_slurm_and_wandb_env(): + job = executor.submit(_run_cosim_benchmark_job, benchmark, base_args, output_dir) + print( + f"[cosim_eval] submitted orchestrator for benchmark {benchmark['name']}: will submit the real CARLA " + f"jobs and log to wandb once they finish, independent of this process; see {output_dir}" + ) + return job + + +def run_cosim_benchmark(benchmark, base_args, output_dir, log_to_wandb=True): + """Submit, wait for, and aggregate a carla_cosim benchmark. + Returns {"episodes": [...], "summary": {...}} -- same shape pufferl.py's + eval() already expects from the native gigaflow/replay benchmarks. This + blocks until every job finishes -- use submit_cosim_benchmark instead for + a fire-and-forget launch (e.g. from inside the training loop). + + log_to_wandb=False when the caller already owns an open wandb run and + will log the summary itself -- this function's own wandb.init/finish + would otherwise tear down that already-active run.""" + checkpoint_path, executor = _prepare_cosim_submission(benchmark, base_args, output_dir) + + rows = _run_carla_benchmark(benchmark, checkpoint_path, output_dir, executor) + + summary = _write_cosim_report(rows, output_dir) + if log_to_wandb: + _log_cosim_to_wandb(base_args, checkpoint_path, benchmark["name"], summary) + return {"episodes": rows, "summary": summary} diff --git a/pufferlib/ocean/evaluation_utils/evaluation_utils.py b/pufferlib/ocean/evaluation_utils/evaluation_utils.py index 735867db60..878ed309c8 100644 --- a/pufferlib/ocean/evaluation_utils/evaluation_utils.py +++ b/pufferlib/ocean/evaluation_utils/evaluation_utils.py @@ -24,6 +24,7 @@ "at_fault_collision_rate", "offroad_rate", "red_light_violation_rate", + "stop_sign_violation_rate", ) @@ -56,6 +57,12 @@ def _load_yaml_mapping(path, label): return _require_mapping(value, label) +def _positive_int(value, label): + if isinstance(value, bool) or not isinstance(value, int) or value <= 0: + raise pufferlib.APIUsageError(f"{label} must be a positive integer") + return value + + def _resolve_map_indices(map_dir, map_names): """Map each logged map name back to its index in the sorted .bin map set.""" if os.path.isfile(map_dir) and str(map_dir).endswith(".bin"): @@ -130,6 +137,11 @@ def load_benchmark_config(config_path, selected_names, map_dir_override=None, nu selected_benchmark_configs = [] for name in selected_names: benchmark = configured_benchmarks[name] + if benchmark.get("simulation_mode") == "carla_cosim": + from pufferlib.ocean.evaluation_utils.cosim_evaluator import parse_cosim_benchmark + + resolved_benchmarks.append(parse_cosim_benchmark(name, benchmark)) + continue if num_scenarios_override is not None: benchmark = {**benchmark, "num_scenarios": num_scenarios_override} if map_dir_override is not None: @@ -228,6 +240,7 @@ def _build_benchmark_args(base_args, benchmark, environment_config): "goal_source", "obs_slots_partners_n", "disable_red_light_infractions", + "disable_stop_sign_infractions", ): override_value = args["eval"].get(override_key) if override_value is not None: diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py index 5ec1e9592b..2704ab32aa 100644 --- a/pufferlib/pufferl.py +++ b/pufferlib/pufferl.py @@ -39,6 +39,7 @@ import pufferlib from pufferlib.ocean.evaluation_utils import evaluation_utils as drive_benchmark from pufferlib.ocean.evaluation_utils import eval_replay as drive_eval_replay +from pufferlib.ocean.evaluation_utils import cosim_evaluator as drive_cosim_eval import pufferlib.sweep import pufferlib.utils import pufferlib.vector @@ -322,6 +323,10 @@ def __init__(self, config, vecenv, policy, logger=None): self.obs_stats_feature_idx = torch.as_tensor( np.flatnonzero(vecenv.driver_env.obs_stats_feature_mask), device=config["device"] ) + # Workers already exist; registration belongs to this rank's CUDA context. + if use_cuda and hasattr(vecenv, "pin_observations"): + with torch.cuda.device(device): + vecenv.pin_observations() self.epoch = 0 self.global_step = 0 self.agent_steps = 0 @@ -331,6 +336,7 @@ def __init__(self, config, vecenv, policy, logger=None): self.utilization = Utilization() self.profile = Profile() self.stats = defaultdict(list) + self.env_metric_sums = {} self.last_stats = defaultdict(list) self.losses = {} self.best_score = -float("inf") @@ -459,19 +465,15 @@ def evaluate(self): self.actions[batch_rows, l] = action self.logprobs[batch_rows, l] = logprob.float() - # Truncation bootstrap hack for auto-reset envs. - # Ideally we add `gamma * V(s_{t+1})` on truncation steps, but Drive resets in C so - # the value at index `l` is post-reset. We use `values[..., l-1]` as a heuristic - # proxy for the pre-reset terminal value (bootstrap term is not clipped). - if l > 0 and config["use_value_bootstrapping"]: + # Truncated envs expose the pre-reset state here, so V(o) is the bootstrap value (unclipped). + if config["use_value_bootstrapping"]: trunc_mask = (t > 0) & (d == 0) - r = r + trunc_mask.to(r.dtype) * config["gamma"] * self.values[batch_rows, l - 1] + r = r + trunc_mask.to(r.dtype) * config["gamma"] * value.flatten().float() self.rewards[batch_rows, l] = r self.terminals[batch_rows, l] = done_mask.bool() self.values[batch_rows, l] = value.flatten().float() self.masks[batch_rows, l] = m - # Note: We are not yet handling masks in this version self.ep_lengths[env_id] += 1 if l + 1 >= config["bptt_horizon"]: num_full = env_id.stop - env_id.start @@ -482,13 +484,10 @@ def evaluate(self): profile("eval_misc", epoch) for i in info: + # every completed episode counts once: weight each env log by its episode count + weight = float(i.get(pufferlib.utils.EPISODE_COUNT_KEY, 1.0)) if isinstance(i, dict) else 1.0 for k, v in pufferlib.unroll_nested_dict(i): - if isinstance(v, np.ndarray): - v = v.tolist() - elif isinstance(v, (list, tuple)): - self.stats[k].extend(v) - else: - self.stats[k].append(v) + pufferlib.utils.accumulate_environment_metric(self.env_metric_sums, k, v, weight) profile("env", epoch) @@ -506,7 +505,7 @@ def evaluate(self): self.ep_indices = torch.arange(self.total_agents, dtype=torch.int32) self.ep_lengths.zero_() profile.end() - return pufferlib.utils.reduce_environment_metrics(self.stats) + return pufferlib.utils.finalize_environment_metrics(self.collect_environment_metric_sums()) @record def train(self): @@ -539,6 +538,7 @@ def train(self): logs = self.mean_and_log() self.print_dashboard() self.stats = defaultdict(list) + self.env_metric_sums = {} self.last_log_time = time.time() self.last_log_step = self.global_step profile.clear() @@ -635,12 +635,13 @@ def _ppo_loss(self, mb_obs, mb_actions, mb_logprobs, mb_values, mb_returns, mb_a ) def _clip_gradients(self, losses): + # train() converts these logging-only scalars after the optimizer updates. max_grad_norm = self.config["max_grad_norm"] if self.separate_grad_clip: - losses["actor_grad_norm"] = torch.nn.utils.clip_grad_norm_(self.actor_params, max_grad_norm).item() - losses["critic_grad_norm"] = torch.nn.utils.clip_grad_norm_(self.critic_params, max_grad_norm).item() + losses["actor_grad_norm"] = torch.nn.utils.clip_grad_norm_(self.actor_params, max_grad_norm).detach() + losses["critic_grad_norm"] = torch.nn.utils.clip_grad_norm_(self.critic_params, max_grad_norm).detach() else: - losses["grad_norm"] = torch.nn.utils.clip_grad_norm_(self.policy.parameters(), max_grad_norm).item() + losses["grad_norm"] = torch.nn.utils.clip_grad_norm_(self.policy.parameters(), max_grad_norm).detach() def _compute_advantages(self, ratio, rho_clip, c_clip): config = self.config @@ -794,17 +795,12 @@ def _train_ppo_transition(self, losses, profile, epoch): self.optimizer.zero_grad() total_minibatches = 0 - pending_minibatches = 0 - - # Disabled for now: dropping the partial final minibatch means zero optimizer - # steps (silently) whenever fewer than minibatch_size transitions survive the - # advantage filter, which permanently freezes a plateaued policy. - # full_minibatch_transitions = (keep_idx.numel() // self.minibatch_size) * self.minibatch_size - full_minibatch_transitions = keep_idx.numel() + retained_transition_count = keep_idx.numel() + optimizer_batch_size = self.minibatch_size * self.accumulate_minibatches for _ in range(config["update_epochs"]): - permutation = keep_idx[torch.randperm(keep_idx.numel(), device=keep_idx.device)] - for start in range(0, full_minibatch_transitions, self.minibatch_size): + permutation = keep_idx[torch.randperm(retained_transition_count, device=keep_idx.device)] + for start in range(0, retained_transition_count, self.minibatch_size): profile("train_copy", epoch) mb_idx = permutation[start : start + self.minibatch_size] if config["cpu_offload"]: @@ -832,20 +828,20 @@ def _train_ppo_transition(self, losses, profile, epoch): losses[key] += value profile("learn", epoch) + accumulation_start = (start // optimizer_batch_size) * optimizer_batch_size + accumulation_end = min(accumulation_start + optimizer_batch_size, retained_transition_count) + # Mean losses contribute in proportion to their samples, including a partial final group. + if self.accumulate_minibatches > 1: + loss = loss * (mb_idx.numel() / (accumulation_end - accumulation_start)) loss.backward() total_minibatches += 1 - pending_minibatches += 1 - if pending_minibatches >= self.accumulate_minibatches: - self._clip_gradients(losses) - self.optimizer.step() - self.optimizer.zero_grad() - pending_minibatches = 0 - - if pending_minibatches > 0: - self._clip_gradients(losses) - self.optimizer.step() - self.optimizer.zero_grad() + # Flush at the end of each PPO epoch so updates never span two permutations. + if start + self.minibatch_size < accumulation_end: + continue + self._clip_gradients(losses) + self.optimizer.step() + self.optimizer.zero_grad() if total_minibatches > 0: for key in ("policy_loss", "value_loss", "entropy", "ent_coef", "old_approx_kl", "approx_kl", "clipfrac"): @@ -858,9 +854,12 @@ def _train_ppo_transition(self, losses, profile, epoch): float("nan") if var_y == 0 else (1 - (y_true - y_pred).var(unbiased=False) / var_y).item() ) + def collect_environment_metric_sums(self): + return {**pufferlib.utils.environment_metric_sums(self.stats), **self.env_metric_sums} + def mean_and_log(self): config = self.config - env_metric_sums = pufferlib.utils.environment_metric_sums(self.stats) + env_metric_sums = self.collect_environment_metric_sums() losses = {k: float(v) for k, v in self.losses.items()} if torch.distributed.is_initialized(): world_size = torch.distributed.get_world_size() @@ -1478,6 +1477,11 @@ def derive_rank_seeds(vec_seed, train_seed, world_size, global_rank): return torch_seed, env_seed +def _latest_checkpoint_path(run_dir): + model_files = glob.glob(os.path.join(run_dir, "models", "model_*.pt")) + return max(model_files, key=os.path.getctime) if model_files else None + + def train(env_name, args=None, vecenv=None, policy=None, logger=None, early_stop_fn=None): args = args or load_config(env_name) @@ -1634,6 +1638,17 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None, early_stop if is_rank0: _save_experiment_config(args, path) + # Debug hook: force an initial checkpoint (epoch 0, before any training + # step) and submit the co-sim benchmarks against it (fire-and-forget -- + # see run_cosim_debug_benchmarks), so the SLURM wiring can be verified + # without stalling training on a real CARLA run. Each benchmark's + # own orchestrator job logs its result to wandb once it's ready, whenever + # that is -- independent of this process's lifetime. + if training_evaluation_scheduled and args["train"]["cosim_debug_evals"] and is_rank0: + initial_checkpoint = pufferl.save_checkpoint() + if initial_checkpoint is not None: + run_cosim_debug_benchmarks(env_name, args, initial_checkpoint, "start") + # Sweep needs data for early stopped runs, so send data when steps > 100M logging_threshold = min(0.20 * train_config["total_timesteps"], 100_000_000) all_logs = [] @@ -1722,6 +1737,16 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None, early_stop run_dir=path, ) + # Debug hook: co-sim benchmarks against the final checkpoint (train()'s + # PPO loop always save_checkpoint()s once done_training, so this exists). + # Fire-and-forget, same as the start-of-training hook -- each benchmark's + # orchestrator job logs to wandb once it finishes, whenever that is, so + # this process doesn't need to wait around for it. + if training_evaluation_scheduled and args["train"]["cosim_debug_evals"] and is_rank0: + final_checkpoint = _latest_checkpoint_path(path) + if final_checkpoint is not None: + run_cosim_debug_benchmarks(env_name, args, final_checkpoint, "end") + logs = pufferl.mean_and_log() if logs is not None: all_logs.append(logs) @@ -1789,6 +1814,21 @@ def eval( benchmark_results = {} evaluation_policy_cache = {"policy": policy} for benchmark in benchmarks: + if benchmark.get("simulation_mode") in drive_cosim_eval.COSIM_SIMULATION_MODES: + if failure_replay_csv is not None: + raise pufferlib.APIUsageError( + f"eval.failure_replay_csv is not supported for cosim benchmark {benchmark['name']}" + ) + output_directory_name = benchmark["name"] + if output_name is not None: + output_directory_name = f"{output_directory_name}_{output_name}" + cosim_output_dir = os.path.join(eval_output_dir, output_directory_name, eval_output_subdir) + os.makedirs(cosim_output_dir) + benchmark_results[benchmark["name"]] = drive_cosim_eval.run_cosim_benchmark( + benchmark, base_args, cosim_output_dir + ) + continue + run_args = drive_benchmark.build_benchmark_args( base_args, benchmark, @@ -2463,6 +2503,8 @@ def render_training_replays(env_name, args, policy, epoch, global_step, run_dir) # Fixed-length episodes: eval-mode summaries flush on the resample boundary. env_config["termination_mode"] = False env_config["compute_eval_metrics"] = True + # Without it eval mode spawns max_agents_per_env eval-sized cars per map, not the training distribution. + env_config["eval_training_render"] = True env_config["num_agents"] = env_config["max_agents_per_env"] run_args["eval"]["action_selection"] = pufferlib.pytorch.ACTION_SELECT_SAMPLE capture_observations = run_args["eval"]["capture_observations"] @@ -2535,6 +2577,44 @@ def run_training_evaluation(env_name, args, policy, logger, epoch, global_step, restore_rng_state({"rng_state": rng_state}) +# Hardcoded for now -- wired to only the start and end of training (see +# train()), not the periodic eval cadence. Debug hook: promote to an +# eval.cosim_benchmarks config once the wiring itself has been exercised +# end to end. +COSIM_DEBUG_BENCHMARKS = ("carla_cosim",) + + +def run_cosim_debug_benchmarks(env_name, args, checkpoint_path, label): + """Fire-and-forget smoke-test hook: submit the co-sim benchmarks in + COSIM_DEBUG_BENCHMARKS against `checkpoint_path` and return immediately. + Each benchmark gets its own orchestrator SLURM job (see + cosim_evaluator.submit_cosim_benchmark_async) that submits the real + CARLA work, waits for it, and logs the result to wandb itself -- + so results reach wandb whenever they're ready, independent of whether + this training process is still running. Failures are non-fatal (each + co-sim run depends on external infra -- a CARLA server -- that + training itself doesn't own).""" + eval_config = args["eval"] + try: + _, benchmarks = drive_benchmark.load_benchmark_config( + eval_config["benchmark_config"], list(COSIM_DEBUG_BENCHMARKS) + ) + except pufferlib.APIUsageError as exc: + print(f"[cosim_eval] skipping {label}-of-training cosim debug eval: {exc}") + return + + cosim_args = copy.deepcopy(args) + cosim_args["load_model_path"] = checkpoint_path + run_dir = os.path.dirname(os.path.dirname(checkpoint_path)) + for benchmark in benchmarks: + output_dir = os.path.join(run_dir, "eval", "cosim", label, benchmark["name"]) + try: + drive_cosim_eval.submit_cosim_benchmark_async(benchmark, cosim_args, output_dir) + except Exception: + print(f"\n[cosim_eval] {label}-of-training {benchmark['name']} submission failed; continuing training:") + traceback.print_exc() + + def _render_eval_failures( env_name, run_args, diff --git a/pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin b/pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin index 524a24e203..b0b3f0402f 100644 Binary files a/pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin and b/pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin differ diff --git a/pufferlib/resources/drive/binaries/carla/opendrive__Town02.bin b/pufferlib/resources/drive/binaries/carla/opendrive__Town02.bin index eb4e489ece..64aec35867 100644 Binary files a/pufferlib/resources/drive/binaries/carla/opendrive__Town02.bin and b/pufferlib/resources/drive/binaries/carla/opendrive__Town02.bin differ diff --git a/pufferlib/resources/drive/binaries/carla/opendrive__Town03.bin b/pufferlib/resources/drive/binaries/carla/opendrive__Town03.bin index 73e9fdf97e..0612a6ca66 100644 Binary files a/pufferlib/resources/drive/binaries/carla/opendrive__Town03.bin and b/pufferlib/resources/drive/binaries/carla/opendrive__Town03.bin differ diff --git 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a/pufferlib/resources/drive/binaries/carla/opendrive__Town07.bin b/pufferlib/resources/drive/binaries/carla/opendrive__Town07.bin index 316e49636b..2573c12d15 100644 Binary files a/pufferlib/resources/drive/binaries/carla/opendrive__Town07.bin and b/pufferlib/resources/drive/binaries/carla/opendrive__Town07.bin differ diff --git a/pufferlib/resources/drive/binaries/carla/opendrive__Town10HD.bin b/pufferlib/resources/drive/binaries/carla/opendrive__Town10HD.bin index 7e3a58cb03..bba155c792 100644 Binary files a/pufferlib/resources/drive/binaries/carla/opendrive__Town10HD.bin and b/pufferlib/resources/drive/binaries/carla/opendrive__Town10HD.bin differ diff --git a/pufferlib/utils.py b/pufferlib/utils.py index 788da190fd..246e2e71e1 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -3,6 +3,27 @@ import numpy as np +EPISODE_COUNT_KEY = "n" +EPISODE_SUM_KEYS = (EPISODE_COUNT_KEY, "total_distance_travelled_sum", "total_infraction_count") + + +def accumulate_environment_metric(metric_sums, key, value, weight): + """Per-key [weighted_sum, weight_sum]; episode-count and window-sum keys accumulate plain sums.""" + values = value.tolist() if isinstance(value, np.ndarray) else value + if not isinstance(values, (list, tuple)): + values = [values] + for item in values: + if not isinstance(item, numbers.Number): + continue + entry = metric_sums.setdefault(key, [0.0, 0.0]) + if key in EPISODE_SUM_KEYS: + entry[0] += float(item) + entry[1] += 1 + else: + entry[0] += float(item) * weight + entry[1] += weight + + def environment_metric_sums(metric_lists): """Per-key [value_sum, sample_count] over the collected log dicts.""" metric_sums = {} @@ -20,6 +41,9 @@ def finalize_environment_metrics(metric_sums): for key, (value_sum, value_count) in metric_sums.items(): if key in ("total_distance_travelled_sum", "total_infraction_count"): continue + if key == EPISODE_COUNT_KEY: + reduced_metrics[key] = value_sum + continue reduced_metrics[key] = value_sum / max(value_count, 1) if total_distance is not None and total_infractions is not None: diff --git a/pufferlib/vector.py b/pufferlib/vector.py index 6d07989739..2d9254fa57 100644 --- a/pufferlib/vector.py +++ b/pufferlib/vector.py @@ -448,6 +448,7 @@ def __init__( self.initialized = False self.zero_copy = zero_copy self.sync_traj = sync_traj + self._observation_registration = None self.ready_workers = [] self.waiting_workers = [] @@ -581,11 +582,39 @@ def async_reset(self, seed=0): def notify(self): self.buf["notify"][:] = True + def pin_observations(self): + """Register stable shared observations in the trainer's current CUDA context.""" + if self.processes is None: + raise pufferlib.APIUsageError("Cannot pin observations after the vector environment is closed") + # Non-zero-copy recv() returns newly allocated arrays, not this shared buffer. + if not self.zero_copy or self._observation_registration is not None: + return + + import torch + + observations = self.buf["observations"] + device = torch.cuda.current_device() + error = torch.cuda.cudart().cudaHostRegister(observations.ctypes.data, observations.nbytes, 0) + if int(error) != 0: + raise RuntimeError(f"cudaHostRegister failed for observations ({observations.nbytes} bytes): {error}") + self._observation_registration = (observations, device) + def close(self): if self.processes is None: return for p in self.processes: p.terminate() + if self._observation_registration is not None: + import torch + + observations, device = self._observation_registration + # Finish any asynchronous reads before releasing this rank's registration. + with torch.cuda.device(device): + torch.cuda.synchronize() + error = torch.cuda.cudart().cudaHostUnregister(observations.ctypes.data) + if int(error) != 0: + raise RuntimeError(f"cudaHostUnregister failed for observations: {error}") + self._observation_registration = None if self._driver_env_open: self.driver_env.close() self._driver_env_open = False diff --git a/pufferlib/viz.py b/pufferlib/viz.py index 7ac11532d8..8461f02df5 100644 --- a/pufferlib/viz.py +++ b/pufferlib/viz.py @@ -119,6 +119,7 @@ def _obs_scales( obs_norm_veh_length_m=15.0, obs_norm_road_seg_length_m=5.0, obs_norm_road_seg_width_m=5.0, + obs_norm_speed_mps=60.0, ): env_cfg = env_cfg or {} obs_norm_goal_offset_m = float(env_cfg.get("obs_norm_goal_offset_m", obs_norm_goal_offset_m)) @@ -127,6 +128,7 @@ def _obs_scales( obs_norm_veh_length_m = float(env_cfg.get("obs_norm_veh_length_m", obs_norm_veh_length_m)) obs_norm_road_seg_length_m = float(env_cfg.get("obs_norm_road_seg_length_m", obs_norm_road_seg_length_m)) obs_norm_road_seg_width_m = float(env_cfg.get("obs_norm_road_seg_width_m", obs_norm_road_seg_width_m)) + obs_norm_speed_mps = float(env_cfg.get("obs_norm_speed_mps", obs_norm_speed_mps)) inverse_xy_scale = None if obs_norm_xy_offset_m == 0 else 1.0 / obs_norm_xy_offset_m return { "obs_norm_goal_offset_m": obs_norm_goal_offset_m, @@ -136,6 +138,7 @@ def _obs_scales( "goal_to_position": 1.0 if inverse_xy_scale is None else obs_norm_goal_offset_m * inverse_xy_scale, "road_length_to_position": 1.0 if inverse_xy_scale is None else obs_norm_road_seg_length_m * inverse_xy_scale, "road_width_to_position": 1.0 if inverse_xy_scale is None else obs_norm_road_seg_width_m * inverse_xy_scale, + "speed_to_mps": obs_norm_speed_mps, } @@ -483,6 +486,7 @@ def unpack_obs( obs_dropout_boundary: float = 0.0, agent_idx: int = 0, obs_partner_relative_velocity: bool = False, + obs_lane_speed_limit: bool = False, ): """ Unpack the flattened observation into ego, map, partner, and traffic-control views. @@ -502,7 +506,7 @@ def unpack_obs( binding.PARTNER_RELATIVE_VELOCITY_FEATURES if obs_partner_relative_velocity else 0 ) # Road obs - lane_feature_size = binding.LANE_FEATURES + lane_feature_size = binding.LANE_FEATURES + (binding.LANE_SPEED_LIMIT_FEATURES if obs_lane_speed_limit else 0) boundary_feature_size = binding.BOUNDARY_FEATURES # Traffic control obs traffic_control_feature_size = binding.TRAFFIC_CONTROL_FEATURES @@ -586,6 +590,7 @@ def plot_observation( true_length_m=None, true_width_m=None, obs_partner_relative_velocity=False, + obs_lane_speed_limit=False, ) -> np.ndarray: """Plot observation in ego-centric frame. @@ -607,6 +612,7 @@ def plot_observation( obs_dropout_boundary=obs_dropout_boundary, agent_idx=agent_idx, obs_partner_relative_velocity=obs_partner_relative_velocity, + obs_lane_speed_limit=obs_lane_speed_limit, ) scales = _obs_scales( obs_norm_goal_offset_m=obs_norm_goal_offset_m, @@ -951,6 +957,8 @@ def encode_interactive_replay(scenario, replay): chunks["obs_scale"] = observation_scale_per_dim.astype(np.float32) if replay.get("policy_probs") is not None: chunks["policy_probs"] = replay["policy_probs"].astype(np.float32, copy=False) + if replay.get("action_index") is not None: # discrete class behind a continuous executed action (-1: none) + chunks["action_index"] = replay["action_index"].astype(np.int32, copy=False) if replay.get("policy_mean") is not None: chunks["policy_mean"] = replay["policy_mean"].astype(np.float32, copy=False) chunks["policy_std"] = replay["policy_std"].astype(np.float32, copy=False) @@ -969,6 +977,13 @@ def encode_interactive_replay(scenario, replay): active_count = int(replay["raw_action"].shape[1]) init_step = int(env_cfg.get("init_step", 0)) ghost = np.zeros((frame_count, max(1, active_count), 5), dtype=np.float32) + explicit_ghost = replay.get("ghost_f32") + if explicit_ghost is not None: # co-sim: the logged trajectory lives outside the bin scenario + explicit_ghost = np.asarray(explicit_ghost, dtype=np.float32) + if explicit_ghost.shape != ghost.shape: + raise ValueError(f"ghost_f32 shape {explicit_ghost.shape} != expected {ghost.shape}") + ghost = explicit_ghost + active_indices = [] for slot in range(min(active_count, len(active_indices))): agent_idx = active_indices[slot] if agent_idx < 0 or agent_idx >= len(agents): @@ -1013,7 +1028,9 @@ def encode_interactive_replay(scenario, replay): "reward_coef_count": int(binding.NUM_REWARD_COEFS), "partner_features": int(binding.PARTNER_FEATURES) + (int(binding.PARTNER_RELATIVE_VELOCITY_FEATURES) if env_cfg.get("obs_partner_relative_velocity") else 0), - "lane_features": int(binding.LANE_FEATURES), + "lane_features": int(binding.LANE_FEATURES) + + (int(binding.LANE_SPEED_LIMIT_FEATURES) if env_cfg.get("obs_lane_speed_limit") else 0), + "lane_speed_limit_idx": int(binding.LANE_FEATURES) if env_cfg.get("obs_lane_speed_limit") else -1, "boundary_features": int(binding.BOUNDARY_FEATURES), "traffic_features": int(binding.TRAFFIC_CONTROL_FEATURES), "lane_count": int(lane_count), @@ -1082,6 +1099,7 @@ def _render_interactive_replay_payload(compressed_payload, filename): .perturbation-line { width:28px; height:0; border-top:3px solid; } .perturbation-line.blindness { border-color:#6d28d9; border-top-style:dashed; } .perturbation-line.phantom-braking { border-color:#b45309; } + .perturbation-line.human-log { border-color:#ff0000; } /* Agent panel: dark instrument-cluster surface in both themes — scoped variable overrides restyle all children. */ #hud-telemetry { --border:#4a5468; --muted:#aab3c5; --field:rgba(255,255,255,.07); --accent:#7cbcff; position:absolute; top:14px; right:14px; width:372px; max-height:calc(100vh - 90px); padding:12px 14px; overflow-y:auto; display:none; background:rgba(54,62,77,.95); color:#eef1f6; } [data-theme="dark"] #hud-telemetry { background:rgba(48,56,70,.95); } @@ -1116,6 +1134,7 @@ def _render_interactive_replay_payload(compressed_payload, filename): #obs-title span { flex:1; } .obs-tool { padding:3px 8px; border:1px solid var(--border); border-radius:5px; background:transparent; color:var(--muted); font-size:9.5px; font-weight:600; letter-spacing:.05em; cursor:pointer; } .obs-tool:hover { color:var(--accent); border-color:var(--accent); } + .obs-tool.on { color:var(--accent); border-color:var(--accent); } #obs-canvas { width:100%; height:100%; background:#fff; } @@ -1131,6 +1150,7 @@ def _render_interactive_replay_payload(compressed_payload, filename):
Active perturbations
Partner blindness
Phantom braking
+ @@ -1166,12 +1186,12 @@ def _render_interactive_replay_payload(compressed_payload, filename):
-
Ego-centric observation (dashed = true footprint)
+
Ego-centric observation (dashed = true footprint)
0 / 0 - +
@@ -1185,7 +1205,7 @@ def _render_interactive_replay_payload(compressed_payload, filename): // Order must match the REWARD_COEF_* indices in constants.h. const COEF_LABELS = ["goal radius","goal speed","collision","offroad","comfort","lane align","vel align","lane center","center bias","velocity","reverse","stop line","timestep","overspeed","throttle","steer","acc","speed"]; const REWARD_LABELS = ["collision","offroad","red light","stop sign","goal","lane align","lane center","comfort","velocity","timestep","reverse","overspeed","ADE"]; - const EGO_OBS_LABELS = ["speed","width","length","steer","accel lon","accel lat","lane dist","lane angle cos","speed limit","stopped","lane curvature"]; + const EGO_OBS_LABELS = ["speed","width","length","steer","accel lon","accel lat","lane dist","lane angle (cos | err/pi)","speed limit","stopped","lane curvature"]; const EGO_COND_LABELS = ["goal radius","goal speed","collision","offroad","comfort","lane align","vel align","lane center","center bias","velocity","reverse","stop line","timestep","overspeed","C_throttle","C_steer","C_acc","C_vel"]; const ACCEL = [-4,-2.667,-1.333,0,1.333,2.667,4], STEER = [-0.667,-0.5,-0.333,-0.167,0,0.167,0.333,0.5,0.667]; const JLONG = [-15,-4,0,4], JLAT = [-4,0,4]; @@ -1204,10 +1224,12 @@ def _render_interactive_replay_payload(compressed_payload, filename): const dpr = window.devicePixelRatio || 1; let step = 0, play = false, speed = 4, lastTick = 0; let cam = {x:0,y:0,z:5,drag:false,lx:0,ly:0}; - let followedId = null, isEgoCam = false, darkMode = false, showGhost = false; - let obsZoom = 2.2, obsExpanded = false, obsMode = 2; + let followedId = null, isEgoCam = false, darkMode = false, showGhost = false, ghostPaths = []; + const OBS_ZOOM_DEFAULT = 2.2, OBS_ZOOM_MIN = .45, OBS_ZOOM_MAX = 8; + let obsZoom = OBS_ZOOM_DEFAULT, obsExpanded = false, obsMode = 2, obsShowLimits = true; let expertAgentIndices = new Set(); const OBS_MODES = ["ALL","POOL","BOTH"]; + const EGO_LANE_SPEED_LIMIT_FEATURE = 8, MPS_TO_KMH = 3.6, SAME_LIMIT_LABEL_SPACING_PX = 100; function chunk(name) { const m = H.chunks[name], start = H.dataStart + m.offset, n = m.nbytes / ({float32:4,int32:4,int16:2,uint8:1}[m.dtype]); @@ -1312,7 +1334,10 @@ def _render_interactive_replay_payload(compressed_payload, filename): document.getElementById('meta-map').textContent = String(H.map_name).split('/').pop(); document.getElementById('meta-id').textContent = H.scenario_id || "-"; document.getElementById('meta-agents').textContent = H.active_count + ' / ' + H.total_agents; - showGhost = (H.active_count === 1) && !!(H.chunks && H.chunks.ghost_f32); + buildGhostPaths(); + const hasGhost = ghostPaths.length > 0 && C.ghost_f32.some((v, i) => i % 5 === 4 && v > 0); + showGhost = (H.active_count === 1) && hasGhost; + document.getElementById('ghost-key').style.display = hasGhost ? '' : 'none'; const ov = H.eval_overrides || {}, ovKeys = Object.keys(ov); if (ovKeys.length) document.getElementById('overrides-body').innerHTML = ovKeys.map(k=>`
${k}${ov[k]}
`).join(''); else document.getElementById('overrides-header').style.display = 'none'; @@ -1420,9 +1445,17 @@ def _render_interactive_replay_payload(compressed_payload, filename): return {idx:idx, id:C.agent_i32[ib], type:agentType, cl:C.agent_i32[ib+6], slot:C.agent_i32[ib+7], partnerBlindnessActive:C.agent_i32[ib+8] === 1, phantomBrakingActive:C.agent_i32[ib+9] === 1, x:C.agent_f32[fb], y:C.agent_f32[fb+1], h:C.agent_f32[fb+3], l:C.agent_f32[fb+4], w:C.agent_f32[fb+5], s:C.agent_f32[fb+6], st:C.agent_f32[fb+7], al:C.agent_f32[fb+8], alat:C.agent_f32[fb+9], jl:C.agent_f32[fb+10], jlat:C.agent_f32[fb+11], goalRadius:goalRadius, c:agentColor}; } function getFrameAgents(frame) { const out = []; for (let i=0;i { if(!H || e.target.tagName === 'INPUT') return; if(e.code === 'Space'){ toggle(); e.preventDefault(); } if(e.code === 'ArrowRight'){ play=false; updateBtn(); step=Math.min(step+1,frameMax()); draw(true); } if(e.code === 'ArrowLeft'){ play=false; updateBtn(); step=Math.max(step-1,0); draw(true); } if(e.code === 'Escape'){ followedId=null; isEgoCam=false; updateUI(); draw(true); } if(e.code === 'KeyG'){ showGhost=!showGhost; draw(true); } }); @@ -1456,7 +1491,7 @@ def _render_interactive_replay_payload(compressed_payload, filename): c.onmousedown = e => { if(!H) return; const r=c.getBoundingClientRect(), wx=(e.clientX-r.left-c.width/2)/cam.z+cam.x, wy=(e.clientY-r.top-c.height/2)/-cam.z+cam.y; let hit=null, agents=getFrameAgents(Math.floor(step)); if(!isEgoCam) for(const a of agents) if(Math.hypot(wx-a.x, wy-a.y) < Math.max(a.l,3)){ hit=a.id; break; } if(hit !== null){ followedId=hit; cam.drag=false; } else { followedId=null; isEgoCam=false; cam.drag=true; cam.lx=e.clientX; cam.ly=e.clientY; } draw(true); }; window.onmouseup = () => cam.drag = false; c.onmousemove = e => { if(cam.drag && !isEgoCam){ cam.x -= (e.clientX-cam.lx)/cam.z; cam.y -= (e.clientY-cam.ly)/-cam.z; cam.lx=e.clientX; cam.ly=e.clientY; draw(true); } }; - obsC.addEventListener('wheel', e => { e.preventDefault(); obsZoom = Math.max(.45, Math.min(8, obsZoom * Math.exp(-e.deltaY * .001))); draw(true); }, {passive:false}); + obsC.addEventListener('wheel', e => { e.preventDefault(); obsZoom = Math.max(OBS_ZOOM_MIN, Math.min(OBS_ZOOM_MAX, obsZoom * Math.exp(-e.deltaY * .001))); draw(true); }, {passive:false}); function dragPanel(handleId, panelId) { const h=document.getElementById(handleId), p=document.getElementById(panelId); let on=false,sx=0,sy=0,sl=0,st=0; h.addEventListener('mousedown', e => { if(e.target.closest('button')) return; on=true; sx=e.clientX; sy=e.clientY; const r=p.getBoundingClientRect(); sl=r.left; st=r.top; p.style.right='auto'; p.style.bottom='auto'; p.style.left=sl+'px'; p.style.top=st+'px'; }); window.addEventListener('mousemove', e => { if(on){ p.style.left=(sl+e.clientX-sx)+'px'; p.style.top=(st+e.clientY-sy)+'px'; }}); window.addEventListener('mouseup', () => on=false); } dragPanel('obs-title','obs-container'); document.querySelectorAll('.obs-tool').forEach(btn => { @@ -1481,6 +1516,43 @@ def _render_interactive_replay_payload(compressed_payload, filename): function heatColor(t) { t = t < 0 ? 0 : (t > 1 ? 1 : t); const f = t * (HEAT_STOPS.length - 1), i = Math.floor(f), k = f - i, a = HEAT_STOPS[i], b = HEAT_STOPS[Math.min(i + 1, HEAT_STOPS.length - 1)]; return `rgb(${Math.round(a[0]+(b[0]-a[0])*k)},${Math.round(a[1]+(b[1]-a[1])*k)},${Math.round(a[2]+(b[2]-a[2])*k)})`; } function poolColor(t) { t = t < 0 ? 0 : (t > 1 ? 1 : t); const f = t * (POOL_STOPS.length - 1), i = Math.floor(f), k = f - i, a = POOL_STOPS[i], b = POOL_STOPS[Math.min(i + 1, POOL_STOPS.length - 1)]; return `rgb(${Math.round(a[0]+(b[0]-a[0])*k)},${Math.round(a[1]+(b[1]-a[1])*k)},${Math.round(a[2]+(b[2]-a[2])*k)})`; } function drawPoolLegend(maxN) { const w = 116*dpr, h = 9*dpr, x = obsC.width - w - 12*dpr, y = obsC.height - 20*dpr, grad = obsCtx.createLinearGradient(x, 0, x+w, 0); for (let i=0;i<=10;i++) grad.addColorStop(i/10, poolColor(i/10)); obsCtx.fillStyle = grad; obsCtx.fillRect(x, y, w, h); obsCtx.strokeStyle = "rgba(0,0,0,.45)"; obsCtx.lineWidth = dpr; obsCtx.strokeRect(x, y, w, h); obsCtx.fillStyle = "#111"; obsCtx.font = `bold ${9.5*dpr}px system-ui`; obsCtx.textAlign = "left"; obsCtx.fillText("pool wins 1", x, y - 4*dpr); obsCtx.textAlign = "right"; obsCtx.fillText(maxN, x+w, y - 4*dpr); } + const LIMIT_PILL_STYLES = {same: ["rgba(255,255,255,.55)", "rgba(0,0,0,.2)", "#444"], differs: ["rgba(255,237,213,.6)", "rgba(194,65,12,.8)", "#9a3412"], ego: ["rgba(255,255,255,.95)", "rgba(0,0,0,.45)", "#111"]}; + function drawLimitPill(x, y, w, h, text, style) { + const [fill, stroke, ink] = LIMIT_PILL_STYLES[style]; + obsCtx.beginPath(); + if (obsCtx.roundRect) obsCtx.roundRect(x - w/2, y - h/2, w, h, h/2); else obsCtx.rect(x - w/2, y - h/2, w, h); + obsCtx.fillStyle = fill; + obsCtx.strokeStyle = stroke; + obsCtx.lineWidth = dpr; + obsCtx.fill(); obsCtx.stroke(); + obsCtx.fillStyle = ink; + obsCtx.fillText(text, x, y); + } + function drawSpeedLimits(frame, scale) { + const cx = obsC.width/2, cy = obsC.height/2, h = 13*dpr, gap = 2*dpr, top = document.getElementById('obs-title').offsetHeight*dpr; + const egoLimit = frame.ego.limit, egoKmh = egoLimit === null || egoLimit < 0 ? null : Math.round(egoLimit * MPS_TO_KMH); + obsCtx.save(); + obsCtx.font = `600 ${9*dpr}px system-ui`; obsCtx.textAlign = "center"; obsCtx.textBaseline = "middle"; + const differs = r => egoKmh !== null && Math.round(r[7] * MPS_TO_KMH) !== egoKmh; + const lanes = frame.lanes.filter(r => r[7] !== null).sort((a, b) => (differs(b) - differs(a)) || ((a[0]*a[0] + a[1]*a[1]) - (b[0]*b[0] + b[1]*b[1]))); + const zoomIn = Math.min(1, Math.max(0, Math.log(obsZoom / OBS_ZOOM_DEFAULT) / Math.log(OBS_ZOOM_MAX / OBS_ZOOM_DEFAULT))); + const sameValueSpacing = SAME_LIMIT_LABEL_SPACING_PX * dpr * (1 - zoomIn), labelEveryLane = zoomIn >= 1; + const placed = []; + // limit changes first, then nearest; thinning fades with zoom and is off at max zoom so every visible element is labelled + for (const r of lanes) { + const kmh = Math.round(r[7] * MPS_TO_KMH), text = String(kmh), x = cx + r[0]*scale, y = cy - r[1]*scale, w = obsCtx.measureText(text).width + 8*dpr; + if (x < w/2 || x > obsC.width - w/2 || y < top + h/2 || y > obsC.height - h/2) continue; + if (!labelEveryLane && placed.some(p => Math.abs(p.x - x) < (p.w + w)/2 + gap && Math.abs(p.y - y) < h + gap)) continue; + if (placed.some(p => p.kmh === kmh && Math.hypot(p.x - x, p.y - y) < sameValueSpacing)) continue; + placed.push({x, y, w, kmh, text, style: differs(r) ? "differs" : "same"}); + } + for (let i = placed.length - 1; i >= 0; i--) drawLimitPill(placed[i].x, placed[i].y, placed[i].w, h, placed[i].text, placed[i].style); + if (egoLimit !== null) { + const text = egoKmh === null ? "ego off lane" : `ego lane ${egoKmh} km/h`, w = obsCtx.measureText(text).width + 12*dpr; + drawLimitPill(12*dpr + w/2, obsC.height - 12*dpr - h/2, w, h, text, "ego"); + } + obsCtx.restore(); + } function obsRow(frame, slot) { const D = H.obs_dim, S = C.obs_scale, raw = C.obs, row = new Float32Array(D); if (H.obs_layout === "agent_dim_frame_delta") { const T = H.frames, base = slot * D * T + frame; for (let k = 0; k < D; k++) row[k] = raw[base + k * T] * S[k]; } @@ -1526,11 +1598,12 @@ def _render_interactive_replay_payload(compressed_payload, filename): const trafficStart = p; const rot = (x,y) => [-y,x]; const zero = (off,n) => { for(let i=0;i { const out=[]; for(let i=0;i { const out=[]; for(let i=0;i= 0 ? v(o+limitIdx)*speedScale : null]); } return out; }; const partners = []; for(let i=0;i 0){ obsCtx.strokeStyle=poolColor(r[5]/poolMax); obsCtx.lineWidth=pw(r[5]/poolMax); obsCtx.beginPath(); obsCtx.moveTo(r[0]+r[3]*r[2]/2,r[1]+r[4]*r[2]/2); obsCtx.lineTo(r[0]-r[3]*r[2]/2,r[1]-r[4]*r[2]/2); obsCtx.stroke(); } } } for(const g of frame.gps){ obsCtx.fillStyle="magenta"; obsCtx.beginPath(); obsCtx.arc(g[0],g[1],5*px,0,7); obsCtx.fill(); } - for(const t of frame.traffic_controls){ if(showAll){ obsCtx.strokeStyle = bothMode ? "#000" : (t.type === 1 ? trafficColor({state:t.state}) : (t.type === 2 ? "#cc0000" : "#ffd700")); obsCtx.lineWidth=2.5*px; obsCtx.beginPath(); obsCtx.moveTo(t.x1,t.y1); obsCtx.lineTo(t.x2,t.y2); obsCtx.stroke(); } if(showPool && t.pool > 0){ obsCtx.strokeStyle=poolColor(t.pool/poolMax); obsCtx.lineWidth=pw(t.pool/poolMax)+0.8*px; obsCtx.beginPath(); obsCtx.moveTo(t.x1,t.y1); obsCtx.lineTo(t.x2,t.y2); obsCtx.stroke(); } } + for(const t of frame.traffic_controls){ if(showAll){ obsCtx.strokeStyle = bothMode ? "#000" : (t.type === 1 || t.type === 2 ? trafficColor({state:t.state}) : "#ffd700"); obsCtx.lineWidth=2.5*px; obsCtx.setLineDash(t.type === 2 ? [6*px, 4*px] : []); obsCtx.beginPath(); obsCtx.moveTo(t.x1,t.y1); obsCtx.lineTo(t.x2,t.y2); obsCtx.stroke(); obsCtx.setLineDash([]); } if(showPool && t.pool > 0){ obsCtx.strokeStyle=poolColor(t.pool/poolMax); obsCtx.lineWidth=pw(t.pool/poolMax)+0.8*px; obsCtx.beginPath(); obsCtx.moveTo(t.x1,t.y1); obsCtx.lineTo(t.x2,t.y2); obsCtx.stroke(); } } for(const p of frame.partners){ const win = showPool && p.pool > 0; if(!showAll && !win) continue; obsCtx.save(); obsCtx.translate(p.x,p.y); obsCtx.rotate(p.h); if(showAll){ obsCtx.fillStyle=bothMode?"rgba(0,0,0,.55)":"rgba(136,136,136,.8)"; obsCtx.strokeStyle=bothMode?"#000":"#333"; obsCtx.lineWidth=1.5*px; obsCtx.beginPath(); obsCtx.rect(-p.l/2,-p.w/2,p.l,p.w); obsCtx.fill(); obsCtx.stroke(); } if(win){ obsCtx.strokeStyle=poolColor(p.pool/poolMax); obsCtx.lineWidth=pw(p.pool/poolMax); obsCtx.strokeRect(-p.l/2,-p.w/2,p.l,p.w); } obsCtx.restore(); } if(frame.ego){ obsCtx.save(); obsCtx.rotate(Math.PI/2); obsCtx.fillStyle="rgba(0,102,255,.8)"; obsCtx.strokeStyle="#000"; obsCtx.lineWidth=1.5*px; obsCtx.beginPath(); obsCtx.rect(-frame.ego.l/2,-frame.ego.w/2,frame.ego.l,frame.ego.w); obsCtx.fill(); obsCtx.stroke(); if(trueSize){ obsCtx.strokeStyle="#ffd700"; obsCtx.setLineDash([3*px,2*px]); obsCtx.strokeRect(-trueSize.l/2,-trueSize.w/2,trueSize.l,trueSize.w); obsCtx.setLineDash([]); } obsCtx.restore(); } obsCtx.restore(); if(showPool && poolMax > 1) drawPoolLegend(poolMax); + if(obsShowLimits) drawSpeedLimits(frame, scale); } let panelKey = null, refs = null, lastWarnKey = ""; function ensurePanels() { @@ -1597,6 +1671,11 @@ def _render_interactive_replay_payload(compressed_payload, filename): html += '
' + cols.map(v=>`
${v.toFixed(1)}
`).join(''); for (let r=0;r${rows[r].toFixed(1)}`; for (let cI=0;cI
${jerk ? 'jerk_long ↓ / jerk_lat →' : 'accel ↓ / steer →'}
`; + if (C.action_index && actionDims === 2) { + // continuous env driven by the distribution mean: the grid highlights the argmax, these are the executed values + labels = jerk ? ["jerk_long","jerk_lat"] : ["accel","steer"]; + labels.forEach(l => html += `
executed ${l} (mean)-
`); + } } else { labels = H.action_type === "continuous" ? (H.dynamics_model === "jerk" ? ["jerk_long","jerk_lat"] : ["accel","steer"]) : Array.from({length:actionDims}, (_,i)=>`p${i}`); labels.forEach(l => html += `
${l}-
`); @@ -1624,7 +1703,7 @@ def _render_interactive_replay_payload(compressed_payload, filename): refs.polE.textContent = C.entropy[s].toFixed(3); const ab = s * refs.actionDims; if (refs.discrete) { - const n = refs.heat.length, pb = s * n, selected = Math.round(C.raw_action[ab]); + const n = refs.heat.length, pb = s * n, selected = C.action_index ? C.action_index[s] : Math.round(C.raw_action[ab]); let maxP = 1e-9; for (let i=0;i (header dict, chunk name -> ndarray view) of a saved .replay.zlib.""" + with open(replay_path, "rb") as replay_file: + raw = zlib.decompress(replay_file.read()) + header_len = struct.unpack(" -> {metric_name: value}`, the index exposes a filter for each supported infraction present in the metrics. Previous/next navigation follows the active filtered list. + `links` ([(label, href), ...]) are shown under the title, e.g. a run's report. """ files = [f for f in os.listdir(folder_path) if f != "index.html" and f.endswith(".html")] @@ -1779,6 +1894,7 @@ def build_gallery_index(folder_path=".", file_metrics=None): ("collision", "collision_rate", "Collisions"), ("atfault", "at_fault_collision_rate", "At-fault collisions"), ("redlight", "red_light_violation_rate", "Red-light violations"), + ("stopsign", "stop_sign_violation_rate", "Stop-sign violations"), ) available_failure_filters = [ failure_filter for failure_filter in FAILURE_FILTERS if failure_filter[1] in present_metrics @@ -1855,6 +1971,7 @@ def build_gallery_index(folder_path=".", file_metrics=None): --collision: #b42318; --atfault: #7e22ce; --redlight: #d92d20; + --stopsign: #0e7490; } * { box-sizing: border-box; } @@ -1903,6 +2020,16 @@ def build_gallery_index(folder_path=".", file_metrics=None): font-weight: 700; } + .brand-link { + margin-top: 2px; + color: var(--accent); + font-size: 12px; + font-weight: 600; + text-decoration: none; + } + + .brand-link:hover { text-decoration: underline; } + .top-section { min-width: 0; padding: 8px 12px; @@ -1953,6 +2080,7 @@ def build_gallery_index(folder_path=".", file_metrics=None): .collision-dot { background: var(--collision); } .atfault-dot { background: var(--atfault); } .redlight-dot { background: var(--redlight); } + .stopsign-dot { background: var(--stopsign); } select { cursor: pointer; @@ -2183,6 +2311,11 @@ def build_gallery_index(folder_path=".", file_metrics=None): color: var(--redlight); } + .scenario-badge.stopsign { + border-left: 3px solid var(--stopsign); + color: var(--stopsign); + } + #viewer { flex: 1 1 auto; width: 100%; @@ -2244,6 +2377,7 @@ def build_gallery_index(folder_path=".", file_metrics=None):
PufferDrive Replay index + __BRAND_LINKS__
__CATEGORY_FILTER_UI__
@@ -2319,6 +2453,7 @@ def build_gallery_index(folder_path=".", file_metrics=None): if (selectedOption.dataset.collision === 'true') addFailureBadge('Collision', 'collision'); if (selectedOption.dataset.atfault === 'true') addFailureBadge('At-fault collision', 'atfault'); if (selectedOption.dataset.redlight === 'true') addFailureBadge('Red-light violation', 'redlight'); + if (selectedOption.dataset.stopsign === 'true') addFailureBadge('Stop-sign violation', 'stopsign'); if (!currentFailures.childElementCount) { const badge = document.createElement('span'); badge.className = 'scenario-badge'; @@ -2394,10 +2529,12 @@ def build_gallery_index(folder_path=".", file_metrics=None): """ + links_html = "".join(f'{label}' for label, href in (links or [])) final_html = ( html_content.replace("__OPTIONS__", options_html) .replace("__FIRST__", files[0]) .replace("__CATEGORY_FILTER_UI__", category_filter_ui) + .replace("__BRAND_LINKS__", links_html) ) index_path = os.path.join(folder_path, "index.html") diff --git a/scripts/eval/add_nuplan_human_ghost.py b/scripts/eval/add_nuplan_human_ghost.py new file mode 100644 index 0000000000..7a23329920 --- /dev/null +++ b/scripts/eval/add_nuplan_human_ghost.py @@ -0,0 +1,147 @@ +"""Fill the human-driver ghost (logged ego trajectory) into the obs_html/.replay.zlib files of a +nuPlan run that was recorded before the planner wrote it, then re-render with render_obs_html.py. + +usage: python scripts/eval/add_nuplan_human_ghost.py [--data-root DIR] [--maps-root DIR] [--tokens tok ...] + +Needs the nuPlan devkit (carl_nuplan venv) and the split's log .db files under +/nuplan-v1.1/splits/val. Scenarios are rebuilt with the run's own hydra scenario mapping / +vehicle parameters; the bin-frame origin is recovered from the replay's frame-0 ego (synced from nuPlan). +""" + +import argparse +import glob +import os +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +import yaml + +REPO_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO_ROOT)) +from pufferlib import viz # noqa: E402 +from pufferlib.ocean.cosim import nuplan_bridge as nb # noqa: E402 + +EGO_HEADING_IDX = 3 # agent_f32 columns: x, y, z, heading, ... +FRAME0_HEADING_TOL_RAD = 1e-3 # replay frame 0 is the nuPlan initial ego state, synced verbatim + + +def token_log_names(group_dir): + rows = {} + for csv in glob.glob(f"{group_dir}/simulation/*/20*/aggregator_metric/*_weighted_average_metrics_*.csv"): + agg = pd.read_csv(csv) + for _, row in agg[agg["scenario_type"] != "final_score"].iterrows(): + rows[str(row["scenario"])] = str(row["log_name"]) + return rows + + +def run_config(group_dir): + configs = glob.glob(f"{group_dir}/simulation/*/20*/code/hydra/config.yaml") + if not configs: + raise SystemExit(f"no code/hydra/config.yaml under {group_dir}") + return yaml.safe_load(open(configs[0])) + + +def build_scenarios(cfg, data_root, maps_root, wanted): + """token -> NuPlanScenario for every (token, log_name) in `wanted`, extracted like the run did.""" + from nuplan.common.actor_state.vehicle_parameters import VehicleParameters + from nuplan.planning.scenario_builder.nuplan_db.nuplan_scenario_builder import NuPlanScenarioBuilder + from nuplan.planning.scenario_builder.nuplan_db.nuplan_scenario_utils import ScenarioMapping + from nuplan.planning.scenario_builder.scenario_filter import ScenarioFilter + from nuplan.planning.utils.multithreading.worker_sequential import Sequential + + builder_cfg = cfg["scenario_builder"] + mapping_cfg = builder_cfg["scenario_mapping"] + vehicle_cfg = {k: v for k, v in builder_cfg["vehicle_parameters"].items() if not k.startswith("_")} + log_names = sorted(set(wanted.values())) + db_files = [str(Path(data_root) / "nuplan-v1.1" / "splits" / "val" / f"{log_name}.db") for log_name in log_names] + missing = [db for db in db_files if not os.path.exists(db)] + if missing: + raise SystemExit(f"{len(missing)} log db(s) missing locally, e.g. {missing[0]}") + builder = NuPlanScenarioBuilder( + data_root=str(Path(data_root) / "nuplan-v1.1" / "splits" / "val"), + map_root=str(maps_root), + sensor_root=None, + db_files=db_files, + map_version=builder_cfg["map_version"], + include_cameras=False, + verbose=False, + scenario_mapping=ScenarioMapping(mapping_cfg["scenario_map"], mapping_cfg.get("subsample_ratio_override")), + vehicle_parameters=VehicleParameters(**vehicle_cfg), + ) + scenario_filter = ScenarioFilter( + scenario_types=None, + scenario_tokens=sorted(wanted.keys()), + log_names=log_names, + map_names=None, + num_scenarios_per_type=None, + limit_total_scenarios=None, + timestamp_threshold_s=None, + ego_displacement_minimum_m=None, + expand_scenarios=False, + remove_invalid_goals=False, + shuffle=False, + ) + return {scenario.token: scenario for scenario in builder.get_scenarios(scenario_filter, Sequential())} + + +def replay_ghost(replay_path, scenario): + """(frames, 1, 5) ghost in the replay's bin frame, or None with a reason when the replay can't be matched.""" + header, chunks = viz.read_replay_zlib(replay_path) + if header["active_count"] != 1: + return None, f"active_count {header['active_count']} != 1" + ego0 = chunks["agent_f32"][0, 0] + initial = scenario.get_ego_state_at_iteration(0) + heading_delta = float(initial.center.heading) - float(ego0[EGO_HEADING_IDX]) + heading_error = abs((heading_delta + np.pi) % (2.0 * np.pi) - np.pi) + if heading_error > FRAME0_HEADING_TOL_RAD: + return None, f"frame-0 heading mismatch {heading_error:.4f} rad" + origin = nb.NuPlanTransform(float(initial.center.x) - float(ego0[0]), float(initial.center.y) - float(ego0[1])) + boxes = nb.logged_ego_boxes(scenario, origin) + frames = int(header["frames"]) + ghost = np.zeros((frames, 1, 5), np.float32) + count = min(frames, len(boxes)) + ghost[:count, 0] = boxes[:count] + return ghost, None + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("group_dir") + ap.add_argument("--data-root", default=os.environ.get("NUPLAN_DATA_ROOT")) + ap.add_argument("--maps-root", default=os.environ.get("NUPLAN_MAPS_ROOT")) + ap.add_argument("--tokens", nargs="*", default=[]) + args = ap.parse_args() + if not args.data_root or not args.maps_root: + raise SystemExit("--data-root / --maps-root (or NUPLAN_DATA_ROOT / NUPLAN_MAPS_ROOT) required") + obs_dir = Path(args.group_dir) / "obs_html" + replays = {p.name[: -len(".replay.zlib")]: p for p in obs_dir.glob("*.replay.zlib")} + if args.tokens: + replays = {token: path for token, path in replays.items() if token in set(args.tokens)} + if not replays: + raise SystemExit(f"no obs_html/*.replay.zlib under {args.group_dir}") + log_names = token_log_names(args.group_dir) + wanted = {token: log_names[token] for token in replays if token in log_names} + scenarios = build_scenarios(run_config(args.group_dir), args.data_root, args.maps_root, wanted) + written, skipped = 0, [] + for token, path in sorted(replays.items()): + scenario = scenarios.get(token) + if scenario is None: + skipped.append(f"{token}: no scenario ({'no log_name' if token not in wanted else 'not built'})") + continue + ghost, reason = replay_ghost(path, scenario) + if ghost is None: + skipped.append(f"{token}: {reason}") + continue + viz.set_replay_ghost(path, ghost) + written += 1 + for line in skipped: + print(f"[add_nuplan_human_ghost] skipped {line}") + print( + f"[add_nuplan_human_ghost] {written}/{len(replays)} replays updated -> now run scripts/eval/render_obs_html.py {args.group_dir}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/eval/analyze_carla_cosim.py b/scripts/eval/analyze_carla_cosim.py new file mode 100644 index 0000000000..7e8a008c22 --- /dev/null +++ b/scripts/eval/analyze_carla_cosim.py @@ -0,0 +1,427 @@ +"""Failure analysis + visualizations for CARLA leaderboard runs of the PufferDrive co-sim agent +(pufferlib/ocean/cosim/carla/leaderboard_agent.py run with COSIM_WORLD_LOG and COSIM_TELEMETRY set). +Same report shape as analyze_nuplan_cosim.py: score table worst first, failure-category counts, +per-route diagnosis, six-frame top-down strip + speed plot + chase-cam / top-down video. + +usage: python scripts/eval/analyze_carla_cosim.py [ ...] [--max-inline N] [--no-video] + [--obs-html-dir D] + + holds result.json (leaderboard_evaluator --checkpoint), world_log/*.npz, telemetry/*.csv and +carla_view/*.mp4; the records of one run pair with the newest per-route files in timestamp order (retried +routes leave the files of crashed attempts behind). --obs-html-dir: the run's obs replay gallery +(scripts/eval/render_carla_obs_html.py), linked per route. +""" + +import argparse +import glob +import json +import math +import os +import re +import shutil +import sys +from pathlib import Path + +import matplotlib +import numpy as np +import pandas as pd + + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib.patches import Polygon + + +REPO_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO_ROOT)) +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from render_carla_obs_html import page_name + +import data_utils.mirror_map_bin as mbin +from pufferlib.ocean.cosim.carla_cosim import write_mp4 + + +LIGHT_COLOR = {1: "red", 2: "orange", 3: "limegreen", 4: "0.5", 0: "0.7"} +STOPPED_SPEED_MPS = 0.3 +CREEP_SPEED_MPS = 1.0 +CLOSE_AGENT_M = 8.0 +MAP_CROP_MARGIN_M = 80.0 +STRIP_SPAN_M = 40.0 +VIDEO_FRAME_STRIDE = 2 +VIDEO_FPS = 5 +INFRACTION_KEYS = ( + ("collisions_pedestrian", "collision"), + ("collisions_vehicle", "collision"), + ("collisions_layout", "collision"), + ("red_light", "red light"), + ("stop_infraction", "stop sign"), + ("outside_route_lanes", "offroad"), + ("route_dev", "route deviation"), + ("route_timeout", "timeout"), + ("vehicle_blocked", "blocked"), +) +TABLE_COLS = [ + "route", + "town", + "status", + "driving_score", + "route_completion", + "infraction_penalty", + "collisions", + "red_lights", + "offroad_pct", + "route_dev", + "min_speed_infractions", + "stopped_frac", + "creep_frac", + "categories", +] + + +def draw_box(ax, cx, cy, heading, length, width, color, alpha=0.7): + c, s = math.cos(heading), math.sin(heading) + corners = [(length / 2, width / 2), (length / 2, -width / 2), (-length / 2, -width / 2), (-length / 2, width / 2)] + pts = [(cx + c * dx - s * dy, cy + s * dx + c * dy) for dx, dy in corners] + ax.add_patch(Polygon(pts, closed=True, color=color, alpha=alpha)) + ax.plot([cx, cx + c * length / 2], [cy, cy + s * length / 2], color="k", lw=0.8) + + +class RouteLog: + def __init__(self, npz_path): + d = np.load(npz_path) + self.ego = d["ego"] # step, x, y, heading, speed, accel, goal_cursor + self.partners = d["partners"] # step_idx, x, y, heading, length, width, speed, is_walker + self.lights = d["lights"] # (T, num_traffic) + self.route_goals = d["route_goals"] + self.dense_route = d["dense_route"] + self.meta = json.loads(str(d["meta"])) + self.tick_dt = float(self.meta["tick_dt"]) + self.t = self.ego[:, 0] * self.tick_dt + bin_data = mbin.read_bin(Path(self.meta["town_bin"])) + lo = self.ego[:, 1:3].min(axis=0) - MAP_CROP_MARGIN_M + hi = self.ego[:, 1:3].max(axis=0) + MAP_CROP_MARGIN_M + self.lanes, self.edges = [], [] + for road in bin_data["roads"]: + xs, ys = np.asarray(road["x"]), np.asarray(road["y"]) + if not len(xs) or xs.max() < lo[0] or xs.min() > hi[0] or ys.max() < lo[1] or ys.min() > hi[1]: + continue + (self.lanes if 0 <= road["type"] <= 9 else self.edges).append((xs, ys)) + self.stop_lines = [(t["type"], np.asarray(t["stop_line"]).reshape(2, 3)[:, :2]) for t in bin_data["traffic"]] + self.partner_rows = {} + for row in self.partners: + self.partner_rows.setdefault(int(row[0]), []).append(row) + + def carla_to_bin(self, x, y): + tx, ty = self.meta["offset"] + return x + tx, -y + ty + + def step_nearest(self, bx, by): + return int(np.argmin(np.hypot(self.ego[:, 1] - bx, self.ego[:, 2] - by))) + + def own_light_state(self, i, ahead_m=40.0): + """state of the nearest light stop line ahead of the ego (within ahead_m, roughly facing it), else None""" + ex, ey, eh = self.ego[i, 1], self.ego[i, 2], self.ego[i, 3] + c, s = math.cos(eh), math.sin(eh) + best, best_d = None, ahead_m + for j, (kind, line) in enumerate(self.stop_lines): + if kind != 1 or j >= self.lights.shape[1]: + continue + mx, my = line.mean(axis=0) + dx, dy = mx - ex, my - ey + along, lateral = dx * c + dy * s, -dx * s + dy * c + if -3.0 < along < best_d and abs(lateral) < 4.0: + best, best_d = int(self.lights[i, j]), along + return best + + +def draw_scene(ax, log, i, infraction_pts, title=""): + ex, ey, eh = log.ego[i, 1], log.ego[i, 2], log.ego[i, 3] + for xs, ys in log.lanes: + ax.plot(xs, ys, color="0.8", lw=0.7) + for xs, ys in log.edges: + ax.plot(xs, ys, color="0.3", lw=0.6) + for j, (kind, line) in enumerate(log.stop_lines): + state = int(log.lights[i, j]) if kind == 1 and j < log.lights.shape[1] else 0 + ax.plot(line[:, 0], line[:, 1], color=LIGHT_COLOR.get(state, "0.7"), lw=2.2 if kind == 1 else 1.0) + ax.plot(log.dense_route[:, 0], log.dense_route[:, 1], "g--", lw=1.2) + cur = int(log.ego[i, 6]) + ax.scatter(log.route_goals[:, 0], log.route_goals[:, 1], s=18, c="g", zorder=3) + if 0 <= cur < len(log.route_goals): + ax.scatter([log.route_goals[cur, 0]], [log.route_goals[cur, 1]], s=60, c="none", edgecolors="g", zorder=3) + for row in log.partner_rows.get(i, []): + _, px, py, ph, length, width, _, is_walker = row + if abs(px - ex) > STRIP_SPAN_M * 1.2 or abs(py - ey) > STRIP_SPAN_M * 1.2: + continue + d = math.hypot(px - ex, py - ey) + color = "tab:red" if d < CLOSE_AGENT_M else ("tab:purple" if is_walker else "tab:blue") + draw_box(ax, px, py, ph, length, width, color) + for kind, bx, by in infraction_pts: + ax.plot([bx], [by], marker="x", color="k" if kind == "collision" else "r", ms=8, mew=2) + draw_box(ax, ex, ey, eh, 4.9, 2.1, "tab:orange", 0.95) + ax.set_xlim(ex - STRIP_SPAN_M, ex + STRIP_SPAN_M) + ax.set_ylim(ey - STRIP_SPAN_M, ey + STRIP_SPAN_M) + ax.set_aspect("equal") + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_title(title, fontsize=9) + + +def fig_to_rgb(fig): + fig.canvas.draw() + return np.asarray(fig.canvas.buffer_rgba())[:, :, :3].copy() + + +def parse_infractions(record, log): + """-> [(category, bin_x, bin_y, message)] for every infraction with a position""" + out = [] + for key, category in INFRACTION_KEYS: + for message in record.get("infractions", {}).get(key, []) or []: + m = re.search(r"x=([-\d.]+), y=([-\d.]+)", message) + if m: + bx, by = log.carla_to_bin(float(m.group(1)), float(m.group(2))) + out.append((category, bx, by, message)) + else: + out.append((category, float("nan"), float("nan"), message)) + return out + + +def stall_segments(log): + """[(start_idx, end_idx)] of maximal runs with speed < STOPPED_SPEED_MPS, longest first""" + stopped = np.abs(log.ego[:, 4]) < STOPPED_SPEED_MPS + segs, start = [], None + for i, s in enumerate(stopped): + if s and start is None: + start = i + if not s and start is not None: + segs.append((start, i - 1)) + start = None + if start is not None: + segs.append((start, len(stopped) - 1)) + return sorted(segs, key=lambda seg: seg[0] - seg[1]) + + +def diagnose(record, log, infractions): + scores = record["scores"] + speed = np.abs(log.ego[:, 4]) + stopped_frac = float((speed < STOPPED_SPEED_MPS).mean()) + creep_frac = float(((speed >= STOPPED_SPEED_MPS) & (speed < CREEP_SPEED_MPS)).mean()) + categories, reasons = [], [] + for seg in stall_segments(log)[:2]: + dur = log.t[seg[1]] - log.t[seg[0]] + if dur < 8.0: + continue + states = [log.own_light_state(k) for k in range(seg[0], seg[1] + 1)] + greens = sum(1 for s in states if s == 3) * log.tick_dt * (log.ego[1, 0] - log.ego[0, 0]) + light = "no light ahead" if all(s is None for s in states) else f"own light green for {greens:.0f} s of it" + reasons.append(f"standstill {dur:.0f} s from t={log.t[seg[0]]:.0f} s ({light})") + if stopped_frac > 0.4: + categories.append("stalled") + elif creep_frac > 0.15: + categories.append("creeping") + for category, bx, by, message in infractions: + categories.append(category) + if math.isnan(bx): + reasons.append(f"{category}: {message[:90]}") + continue + i = log.step_nearest(bx, by) + state = log.own_light_state(i) + reasons.append( + f"{category} at t={log.t[i]:.0f} s (v={log.ego[i, 4]:.1f} m/s, own light {LIGHT_COLOR.get(state, 'none') if state is not None else 'none'})" + ) + if scores["score_route"] < 100 and "route deviation" not in categories and "timeout" not in categories: + reasons.append(f"route completion {scores['score_route']:.0f}%: {record.get('status')}") + for message in record.get("infractions", {}).get("min_speed_infractions", []) or []: + m = re.search(r"([\d.]+)%", message) + if m and float(m.group(1)) < 80: + categories.append("slow") + reasons.append(f"min-speed: {message[:70]}") + break + counts = {} + for c in categories: + counts[c] = counts.get(c, 0) + 1 + return sorted(counts), reasons or ["no infraction"], stopped_frac, creep_frac + + +def render_route(record, log, chase_mp4, report_dir, tag, make_video): + infractions = parse_infractions(record, log) + categories, reasons, stopped_frac, creep_frac = diagnose(record, log, infractions) + n = len(log.ego) + picks = {0, n // 5, 2 * n // 5, 3 * n // 5, 4 * n // 5, n - 1} + for seg in stall_segments(log)[:1]: + picks.add((seg[0] + seg[1]) // 2) + for _, bx, by, _ in infractions[:2]: + if not math.isnan(bx): + picks.add(log.step_nearest(bx, by)) + picks = sorted(picks)[:8] + pts = [(c, bx, by) for c, bx, by, _ in infractions if not math.isnan(bx)] + fig, axes = plt.subplots(1, len(picks), figsize=(3.6 * len(picks), 3.8)) + for ax, i in zip(np.atleast_1d(axes), picks): + draw_scene(ax, log, i, pts, title=f"t={log.t[i]:.0f}s v={log.ego[i, 4]:.1f} m/s goal {int(log.ego[i, 6])}") + fig.suptitle( + f"{tag} {log.meta['town']} DS={record['scores']['score_composed']:.1f} RC={record['scores']['score_route']:.0f}%", + fontsize=10, + ) + plt.tight_layout() + fig.savefig(report_dir / "strips" / f"{tag}.png", dpi=70) + plt.close(fig) + + fig, ax = plt.subplots(figsize=(9, 2.4)) + ax.plot(log.t, log.ego[:, 4], color="tab:orange", label="ego speed") + states = np.array([log.own_light_state(i) or 0 for i in range(n)]) + for state, color in LIGHT_COLOR.items(): + mask = states == state + if state and mask.any(): + ax.scatter(log.t[mask], np.full(mask.sum(), -0.6), s=6, c=color, marker="s") + for seg in stall_segments(log): + if log.t[seg[1]] - log.t[seg[0]] >= 8.0: + ax.axvspan(log.t[seg[0]], log.t[seg[1]], color="0.9") + for category, bx, by, _ in infractions: + if not math.isnan(bx): + ax.axvline(log.t[log.step_nearest(bx, by)], color="k" if category == "collision" else "r", lw=1.0, ls="--") + ax.set_xlabel("s") + ax.set_ylabel("m/s") + ax.set_ylim(bottom=-1.0) + ax.legend(fontsize=8, loc="upper right") + plt.tight_layout() + fig.savefig(report_dir / "speed" / f"{tag}.png", dpi=70) + plt.close(fig) + + if chase_mp4 is not None: + shutil.copy(chase_mp4, report_dir / "videos" / f"{tag}_chase.mp4") + if make_video: + frames = [] + for i in range(0, n, VIDEO_FRAME_STRIDE): + fig, ax = plt.subplots(figsize=(7, 7), dpi=90) + draw_scene(ax, log, i, pts, title=f"{tag} t={log.t[i]:.0f}s v={log.ego[i, 4]:.1f} m/s") + frames.append(fig_to_rgb(fig)) + plt.close(fig) + write_mp4(report_dir / "videos" / f"{tag}_topdown.mp4", frames, fps=VIDEO_FPS) + infr = record.get("infractions", {}) + return { + "route": tag, + "town": log.meta["town"], + "status": record.get("status"), + "driving_score": float(record["scores"]["score_composed"]), + "route_completion": float(record["scores"]["score_route"]), + "infraction_penalty": float(record["scores"]["score_penalty"]), + "collisions": sum( + len(infr.get(k, []) or []) for k in ("collisions_pedestrian", "collisions_vehicle", "collisions_layout") + ), + "red_lights": len(infr.get("red_light", []) or []), + "offroad_pct": sum( + float(m.group(1)) + for m in (re.search(r"\(([\d.]+)%", s) for s in infr.get("outside_route_lanes", []) or []) + if m + ), + "route_dev": len(infr.get("route_dev", []) or []), + "min_speed_infractions": len(infr.get("min_speed_infractions", []) or []), + "stopped_frac": stopped_frac, + "creep_frac": creep_frac, + "duration_s": float(log.t[-1]), + "categories": "|".join(categories) if categories else "none", + "diagnosis": "; ".join(reasons), + "has_chase": chase_mp4 is not None, + "has_topdown": make_video, + "obs_page": page_name(record, log.meta["town"]), + } + + +def route_section(r, inline, obs_href_dir): + tag = r["route"] + html = [ + f"

{tag} · {r['town']} · DS {r['driving_score']:.1f} · RC {r['route_completion']:.0f}% · {r['status']}

", + f"

{r['categories']}: {r['diagnosis']}

", + ] + if obs_href_dir: + html.append(f"

obs replay (what the policy saw)

") + if inline: + html.append(f"

") + if r["has_chase"]: + html.append(f"") + if r["has_topdown"]: + html.append(f"") + return "\n".join(html) + + +def collect_routes(run_dir): + run_dir = Path(run_dir) + with open(run_dir / "result.json") as result_file: + result = json.load(result_file) + records = result["_checkpoint"]["records"] + logs = sorted(glob.glob(str(run_dir / "world_log" / "*.npz"))) + videos = sorted(glob.glob(str(run_dir / "carla_view" / "*.mp4"))) + if len(logs) != len(records): + print( + f"[{run_dir.name}] {len(records)} route records but {len(logs)} world logs; pairing the newest {min(len(logs), len(records))}" + ) + count = min(len(records), len(logs)) + records, logs, videos = records[-count:], logs[-count:], videos[-count:] + out = [] + for k, (record, log_path) in enumerate(zip(records, logs)): + tag = f"{run_dir.name}_{record.get('route_id', k)}" + out.append((record, log_path, videos[k] if k < len(videos) else None, tag)) + return out + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("paths", nargs="+", help="run dirs followed by the report dir") + parser.add_argument("--max-inline", type=int, default=12) + parser.add_argument("--no-video", action="store_true") + parser.add_argument("--obs-html-dir", help="the run's obs replay gallery dir, linked per route") + args = parser.parse_args() + run_dirs, report_dir = args.paths[:-1], Path(args.paths[-1]) + obs_href_dir = os.path.relpath(args.obs_html_dir, report_dir) if args.obs_html_dir else "" + for sub in ("strips", "speed", "videos"): + (report_dir / sub).mkdir(parents=True, exist_ok=True) + rows = [] + for run_dir in run_dirs: + for record, log_path, chase_mp4, tag in collect_routes(run_dir): + rows.append(render_route(record, RouteLog(log_path), chase_mp4, report_dir, tag, not args.no_video)) + print(f"{tag}: {rows[-1]['categories']} | {rows[-1]['diagnosis']}") + df = pd.DataFrame(rows).sort_values("driving_score") + df.to_csv(report_dir / "routes.csv", index=False) + category_counts = pd.Series([c for cats in df["categories"] for c in cats.split("|")]).value_counts() + table = df[TABLE_COLS].round(2).copy() + table["route"] = table["route"].map(lambda t: f"{t}") + html = [ + "PufferDrive CARLA leaderboard report", + "", + f"

PufferDrive CARLA leaderboard · {len(df)} routes · mean driving score {df['driving_score'].mean():.1f} · mean route completion {df['route_completion'].mean():.0f}%

", + ( + "

Orange = ego, blue = vehicles (red within 8 m), purple = pedestrians, green dashed = route with goal dots (current goal circled), " + "stop lines colored by light state, x = infraction (black collision, red other). Speed plot: grey bands = standstills ≥ 8 s, " + "squares along the bottom = state of the ego's own next light.

" + ), + "

Mean metrics

", + df[ + [ + "driving_score", + "route_completion", + "infraction_penalty", + "collisions", + "red_lights", + "stopped_frac", + "creep_frac", + ] + ] + .mean() + .to_frame("mean") + .T.round(3) + .to_html(index=False), + "

Failure categories (route counts)

", + category_counts.to_frame("routes").to_html(), + "

All routes, worst first

", + table.to_html(index=False, escape=False), + f"

Worst {min(args.max_inline, len(df))} routes

", + ] + for k, (_, r) in enumerate(df.iterrows()): + html.append(route_section(r, k < args.max_inline, obs_href_dir)) + html.append("") + (report_dir / "index.html").write_text("\n".join(html)) + print("failure categories:", category_counts.to_dict()) + print("report:", report_dir / "index.html") + + +if __name__ == "__main__": + main() diff --git a/scripts/eval/analyze_nuplan_cosim.py b/scripts/eval/analyze_nuplan_cosim.py new file mode 100644 index 0000000000..21f811d969 --- /dev/null +++ b/scripts/eval/analyze_nuplan_cosim.py @@ -0,0 +1,396 @@ +"""Failure analysis + visualizations for a nuPlan closed-loop run of the PufferDrive planner +(pufferlib/ocean/cosim/nuplan). Needs the nuPlan devkit env (same interpreter as the simulation). + +usage: python scripts/eval/analyze_nuplan_cosim.py [--workers N] [--max-inline N] [--no-video] + + is the run_simulation.py `group` directory (holds simulation///...). +Writes /index.html (score table sorted worst-first, failure-category counts, per-scenario +diagnosis, the worst --max-inline scenarios inline with a six-frame strip + speed plot + video), plus +strips/, speed/, videos/ and scenarios.csv. Per-scenario work runs in a multiprocessing pool. +""" + +import argparse +import glob +import math +import sys +from multiprocessing import Pool +from pathlib import Path + +import numpy as np +import pandas as pd +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib.patches import Polygon # noqa: E402 + +from nuplan.common.actor_state.state_representation import Point2D # noqa: E402 +from nuplan.common.maps.maps_datatypes import SemanticMapLayer # noqa: E402 +from nuplan.planning.simulation.simulation_log import SimulationLog # noqa: E402 + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import nuplan_log_remap # noqa: E402 logs written on another machine (cluster paths, planner classes) + +REPO_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO_ROOT)) +from pufferlib.ocean.cosim.carla_cosim import write_mp4 # noqa: E402 + +SCORE_COLS = [ + "score", + "ego_progress_along_expert_route", + "ego_is_making_progress", + "drivable_area_compliance", + "driving_direction_compliance", + "no_ego_at_fault_collisions", + "time_to_collision_within_bound", + "ego_is_comfortable", + "speed_limit_compliance", +] +COMFORT_METRICS = [ + "ego_lon_acceleration", + "ego_lon_jerk", + "ego_jerk", + "ego_lat_acceleration", + "ego_yaw_acceleration", + "ego_yaw_rate", +] +LIGHT_COLOR = {"RED": "red", "YELLOW": "orange", "GREEN": "limegreen", "UNKNOWN": "0.6"} +STOPPED_SPEED_MPS = 0.3 +CLOSE_AGENT_M = 8.0 +LANE_CHOICE_OFFSET_M = 3.0 +VIDEO_FRAME_STRIDE = 2 +VIDEO_FPS = 5 + + +def draw_box(ax, cx, cy, heading, length, width, color, alpha=0.7): + c, s = math.cos(heading), math.sin(heading) + corners = [(length / 2, width / 2), (length / 2, -width / 2), (-length / 2, -width / 2), (-length / 2, width / 2)] + pts = [(cx + c * dx - s * dy, cy + s * dx + c * dy) for dx, dy in corners] + ax.add_patch(Polygon(pts, closed=True, color=color, alpha=alpha)) + ax.plot([cx, cx + c * length / 2], [cy, cy + s * length / 2], color="k", lw=0.8) + + +def draw_scene(ax, sample, scenario, expert_xy, span=40.0, title=""): + ego = sample.ego_state + objs = scenario.map_api.get_proximal_map_objects( + Point2D(ego.center.x, ego.center.y), span * 1.3, [SemanticMapLayer.LANE, SemanticMapLayer.LANE_CONNECTOR] + ) + lights = {str(t.lane_connector_id): t.status.name for t in sample.traffic_light_status} + for layer, lanes in objs.items(): + for lane in lanes: + pts = np.array([[p.x, p.y] for p in lane.baseline_path.discrete_path]) + is_connector = layer == SemanticMapLayer.LANE_CONNECTOR + color = LIGHT_COLOR.get(lights.get(str(lane.id)), "0.8") if is_connector else "0.8" + ax.plot(pts[:, 0], pts[:, 1], color=color, lw=1.6 if str(lane.id) in lights else 0.7) + ax.plot(expert_xy[:, 0], expert_xy[:, 1], "g--", lw=1.2) + for o in sample.observation.tracked_objects.get_agents(): + d = math.hypot(o.center.x - ego.center.x, o.center.y - ego.center.y) + color = "tab:red" if d < CLOSE_AGENT_M else "tab:blue" + draw_box(ax, o.center.x, o.center.y, o.center.heading, o.box.length, o.box.width, color) + for o in sample.observation.tracked_objects.get_static_objects(): + draw_box(ax, o.center.x, o.center.y, o.center.heading, o.box.length, o.box.width, "0.4", 0.5) + fp = ego.car_footprint + draw_box(ax, ego.center.x, ego.center.y, ego.center.heading, fp.length, fp.width, "tab:orange", 0.95) + ax.set_xlim(ego.center.x - span, ego.center.x + span) + ax.set_ylim(ego.center.y - span, ego.center.y + span) + ax.set_aspect("equal") + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_title(title, fontsize=9) + + +def fig_to_rgb(fig): + fig.canvas.draw() + return np.asarray(fig.canvas.buffer_rgba())[:, :, :3].copy() + + +def trajectory_stats(hist, scenario): + ego = np.array( + [ + [ + s.ego_state.center.x, + s.ego_state.center.y, + s.ego_state.center.heading, + s.ego_state.dynamic_car_state.speed, + s.ego_state.dynamic_car_state.center_acceleration_2d.x, + ] + for s in hist.data + ] + ) + expert = np.array( + [[s.center.x, s.center.y, s.dynamic_car_state.speed] for s in scenario.get_expert_ego_trajectory()] + ) + min_dist, min_dist_iter, min_dist_speed = np.inf, 0, 0.0 + for i, s in enumerate(hist.data): + e = s.ego_state.center + for o in s.observation.tracked_objects.get_agents(): + d = math.hypot(o.center.x - e.x, o.center.y - e.y) + if d < min_dist: + min_dist, min_dist_iter, min_dist_speed = d, i, o.velocity.magnitude() + lateral = [0.0] # offset from the expert path, only while the ego is still alongside it (not past its end) + for x, y in ego[:, :2]: + d = np.hypot(expert[:, 0] - x, expert[:, 1] - y) + k = int(np.argmin(d)) + if k < len(expert) - 1: + lateral.append(float(d[k])) + stats = dict( + iterations=len(ego), + ego_dist_m=float(np.hypot(*np.diff(ego[:, :2], axis=0).T).sum()), + expert_dist_m=float(np.hypot(*np.diff(expert[:, :2], axis=0).T).sum()), + ego_max_speed=float(ego[:, 3].max()), + expert_max_speed=float(expert[:, 2].max()), + stopped_frac=float((ego[:, 3] < STOPPED_SPEED_MPS).mean()), + expert_stopped_frac=float((expert[:, 2] < STOPPED_SPEED_MPS).mean()), + max_lateral_from_expert_m=float(np.max(lateral)), + min_agent_dist_m=float(min_dist), + min_agent_dist_iter=int(min_dist_iter), + min_agent_dist_other_speed=float(min_dist_speed), + min_accel=float(ego[:, 4].min()), + max_accel=float(ego[:, 4].max()), + ) + return ego, expert, stats + + +def comfort_failures(sim_dir, token): + """nuPlan's own within-bound flags per comfort sub-metric -> ['ego_lon_jerk range -2.7..5.0', ...].""" + out = [] + for name in COMFORT_METRICS: + f = Path(sim_dir) / "metrics" / f"{name}.parquet" + if not f.exists(): + continue + df = pd.read_parquet(f) + rows = df[df["scenario_name"] == token] + if rows.empty: + continue + r = rows.iloc[0] + flag = [c for c in df.columns if c.endswith("within_bounds_stat_value")] + if flag and not bool(r[flag[0]]): + out.append( + f"{name} range {float(r[f'min_{name}_stat_value']):.2f}..{float(r[f'max_{name}_stat_value']):.2f}" + ) + return out + + +def diagnose(row, stats, comfort): + """-> (categories, reasons). Categories are the aggregate failure buckets, reasons the per-scenario text.""" + categories, reasons = [], [] + if row["ego_progress_along_expert_route"] < 0.5 and stats["expert_dist_m"] > 5: + if stats["stopped_frac"] > 0.6: + categories.append("stalled") + reasons.append( + f"stalled: stopped {stats['stopped_frac']:.0%} of the time (expert {stats['expert_stopped_frac']:.0%})" + ) + else: + categories.append("slow progress") + reasons.append( + f"slow/short progress: drove {stats['ego_dist_m']:.0f} m vs expert {stats['expert_dist_m']:.0f} m" + ) + if row["no_ego_at_fault_collisions"] < 1: + categories.append("collision") + reasons.append( + f"at-fault collision (closest agent {stats['min_agent_dist_m']:.1f} m at iter {stats['min_agent_dist_iter']})" + ) + if row["drivable_area_compliance"] < 1: + categories.append("offroad") + reasons.append(f"left drivable area (max {stats['max_lateral_from_expert_m']:.1f} m from expert path)") + if row["driving_direction_compliance"] < 1: + categories.append("wrong way") + reasons.append("drove against traffic direction") + if row["time_to_collision_within_bound"] < 1: + categories.append("TTC") + reasons.append( + f"TTC below bound: closest agent {stats['min_agent_dist_m']:.1f} m (moving {stats['min_agent_dist_other_speed']:.1f} m/s) " + f"at iter {stats['min_agent_dist_iter']}" + ) + if row["ego_is_comfortable"] < 1: + categories.append("comfort") + reasons.append("uncomfortable: " + ", ".join(comfort or ["sub-metric not identified"])) + if row["speed_limit_compliance"] < 1: + categories.append("speeding") + reasons.append(f"speeding: max {stats['ego_max_speed']:.1f} m/s (expert {stats['expert_max_speed']:.1f})") + if stats["max_lateral_from_expert_m"] > LANE_CHOICE_OFFSET_M and row["drivable_area_compliance"] >= 1: + reasons.append( + f"lane choice differs from expert (up to {stats['max_lateral_from_expert_m']:.1f} m off the expert path)" + ) + if stats["ego_dist_m"] > stats["expert_dist_m"] + 5: + reasons.append(f"drove further than the expert ({stats['ego_dist_m']:.0f} m vs {stats['expert_dist_m']:.0f} m)") + return categories or ["none"], reasons or ["no metric failed"] + + +def process_scenario(job): + sim_dir, token, row, report_dir, make_video = job + report_dir = Path(report_dir) + log = nuplan_log_remap.load_log(row["log_path"]) + hist, scenario = log.simulation_history, log.scenario + ego, expert, stats = trajectory_stats(hist, scenario) + categories, reasons = diagnose(row, stats, comfort_failures(sim_dir, token)) + n = len(hist.data) + picks = [0, n // 5, 2 * n // 5, 3 * n // 5, 4 * n // 5, n - 1] + if stats["min_agent_dist_iter"] not in picks: + picks[3] = stats["min_agent_dist_iter"] + picks = sorted(set(picks)) + fig, axes = plt.subplots(1, len(picks), figsize=(3.6 * len(picks), 3.8)) + for ax, it in zip(axes, picks): + draw_scene(ax, hist.data[it], scenario, expert[:, :2], title=f"it {it} v={ego[it, 3]:.1f} m/s") + fig.suptitle(f"{token} {row['scenario_type']} score={row['score']:.2f}", fontsize=10) + plt.tight_layout() + fig.savefig(report_dir / "strips" / f"{token}.png", dpi=70) + plt.close(fig) + + fig, ax = plt.subplots(figsize=(7, 2.2)) + dt = scenario.database_interval + ax.plot(np.arange(len(ego)) * dt, ego[:, 3], label="ego") + ax.plot(np.arange(len(expert)) * dt, expert[:, 2], "g--", label="expert") + ax.set_xlabel("s") + ax.set_ylabel("m/s") + ax.legend(fontsize=8) + plt.tight_layout() + fig.savefig(report_dir / "speed" / f"{token}.png", dpi=70) + plt.close(fig) + + if make_video: + frames = [] + for it in range(0, n, VIDEO_FRAME_STRIDE): + fig, ax = plt.subplots(figsize=(7, 7), dpi=90) + draw_scene( + ax, + hist.data[it], + scenario, + expert[:, :2], + title=f"{row['scenario_type']} it {it} v={ego[it, 3]:.1f} m/s", + ) + frames.append(fig_to_rgb(fig)) + plt.close(fig) + write_mp4(report_dir / "videos" / f"{token}.mp4", frames, fps=VIDEO_FPS) + return { + "token": token, + "type": row["scenario_type"], + "log": row["log_name"], + "map": scenario.map_api.map_name, + **{c: float(row[c]) for c in SCORE_COLS}, + **stats, + "categories": "|".join(categories), + "diagnosis": "; ".join(reasons), + } + + +def collect_jobs(group_dir, report_dir, make_video): + jobs = [] + for sim_dir in sorted(glob.glob(f"{group_dir}/simulation/*/20*")): + agg_files = glob.glob(f"{sim_dir}/aggregator_metric/*_weighted_average_metrics_*.csv") + if not agg_files: + continue + agg = pd.read_csv(agg_files[0]) + per_scenario = agg[(agg["scenario_type"] != "final_score") & (agg["scenario"] != agg["scenario_type"])] + logs = {Path(p).parent.name: p for p in glob.glob(f"{sim_dir}/simulation_log/**/*.msgpack.xz", recursive=True)} + for _, row in per_scenario.iterrows(): + token = str(row["scenario"]) + if token not in logs: + continue + row = row.to_dict() + row["log_path"] = logs[token] + jobs.append((sim_dir, token, row, str(report_dir), make_video)) + return jobs + + +def scenario_section(r, report_dir, inline): + header = f"

{r['token']} · {r['type']} · {r['map']} · score {r['score']:.2f}

" + scores = "   ".join(f"{c.replace('_', ' ')}: {r[c]:.2f}" for c in SCORE_COLS[1:]) + facts = ( + f"ego {r['ego_dist_m']:.0f} m / expert {r['expert_dist_m']:.0f} m, max speed {r['ego_max_speed']:.1f} / {r['expert_max_speed']:.1f} m/s, " + f"stopped {r['stopped_frac']:.0%} / {r['expert_stopped_frac']:.0%}, closest agent {r['min_agent_dist_m']:.1f} m" + ) + media = ( + f'

' + + ( + f'' + if (report_dir / "videos" / f"{r['token']}.mp4").exists() + else "" + ) + if inline + else f'frames · speed' + + ( + f' · video' + if (report_dir / "videos" / f"{r['token']}.mp4").exists() + else "" + ) + ) + return f"{header}

Diagnosis: {r['diagnosis']}

{scores}

{facts}

{media}" + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("group_dir") + ap.add_argument("report_dir") + ap.add_argument("--workers", type=int, default=16) + ap.add_argument("--max-inline", type=int, default=40, help="worst scenarios shown inline; the rest get links") + ap.add_argument("--no-video", action="store_true") + args = ap.parse_args() + report_dir = Path(args.report_dir) + for sub in ("strips", "speed", "videos"): + (report_dir / sub).mkdir(parents=True, exist_ok=True) + jobs = collect_jobs(args.group_dir, report_dir, not args.no_video) + if not jobs: + raise SystemExit(f"no scenarios with aggregator metrics + simulation logs under {args.group_dir}") + print(f"[analyze_nuplan_cosim] {len(jobs)} scenarios, {args.workers} workers") + with Pool(args.workers) as pool: + rows = pool.map(process_scenario, jobs, chunksize=1) + df = pd.DataFrame(rows).sort_values("score") + df.to_csv(report_dir / "scenarios.csv", index=False) + + category_counts = pd.Series([c for cats in df["categories"] for c in cats.split("|")]).value_counts() + by_type = ( + df.groupby("type")[ + [ + "score", + "ego_progress_along_expert_route", + "no_ego_at_fault_collisions", + "drivable_area_compliance", + "ego_is_comfortable", + "speed_limit_compliance", + ] + ] + .mean() + .sort_values("score") + ) + table_cols = [ + "token", + "type", + "map", + "score", + "ego_progress_along_expert_route", + "no_ego_at_fault_collisions", + "drivable_area_compliance", + "time_to_collision_within_bound", + "ego_is_comfortable", + "speed_limit_compliance", + "categories", + ] + table = df[table_cols].round(2).copy() + table["token"] = table["token"].map(lambda t: f"{t}") + html = [ + "PufferDrive nuPlan closed-loop report", + "", + f"

PufferDrive nuPlan closed-loop · {len(df)} scenarios · mean score {df['score'].mean():.3f}

", + "

Orange = ego, blue = other agents (red within 8 m), green dashed = expert path, lane connectors colored by reported light state.

", + "

Mean metrics

", + df[SCORE_COLS].mean().to_frame("mean").T.round(3).to_html(index=False), + "

Failure categories (scenario counts)

", + category_counts.to_frame("scenarios").to_html(), + "

By scenario type

", + by_type.round(3).to_html(), + "

All scenarios, worst first

", + table.to_html(index=False, escape=False), + f"

Worst {min(args.max_inline, len(df))} scenarios

", + ] + for k, (_, r) in enumerate(df.iterrows()): + html.append(scenario_section(r, report_dir, inline=k < args.max_inline)) + html.append("") + (report_dir / "index.html").write_text("\n".join(html)) + print(df[SCORE_COLS].mean().round(3).to_string()) + print("failure categories:", category_counts.to_dict()) + print("report:", report_dir / "index.html") + + +if __name__ == "__main__": + main() diff --git a/scripts/eval/nuboard_failures.py b/scripts/eval/nuboard_failures.py new file mode 100644 index 0000000000..3aab43beb8 --- /dev/null +++ b/scripts/eval/nuboard_failures.py @@ -0,0 +1,124 @@ +"""Build a nuBoard folder that contains only the failing scenarios of a nuPlan run, then launch nuBoard on it. + +usage: python scripts/eval/nuboard_failures.py [--max-score 0.5] [--metric no_ego_at_fault_collisions ...] + [--out ] [--launch] [--port 5006] + +nuBoard has no score filter of its own, so this mirrors /simulation/// into + with symlinks to the selected scenarios' simulation logs, metric/aggregator tables filtered to +those scenarios, and a .nuboard descriptor. --launch starts nuBoard on the mirror (Hydra +run_nuboard.py from $NUPLAN_DEVKIT_ROOT); logs written on another machine are loaded through +scripts/eval/nuplan_log_remap.py (set NUPLAN_DATA_ROOT / NUPLAN_MAPS_ROOT). + +Selection: scenarios with aggregated score < --max-score, OR any --metric below 1.0. +""" + +import argparse +import glob +import os +import shutil +import sys +from pathlib import Path + +import pandas as pd + + +def select_failures(sim_dir, max_score, metrics): + agg = pd.read_csv(glob.glob(f"{sim_dir}/aggregator_metric/*_weighted_average_metrics_*.csv")[0]) + per_scenario = agg[(agg["scenario_type"] != "final_score") & (agg["scenario"] != agg["scenario_type"])] + mask = per_scenario["score"] < max_score + for metric in metrics: + if metric not in per_scenario: + raise SystemExit( + f"unknown metric {metric}; available: {[c for c in per_scenario.columns if c not in ('scenario', 'scenario_type', 'log_name', 'planner_name', 'aggregator_type', 'num_scenarios')]}" + ) + mask |= per_scenario[metric] < 1.0 + return per_scenario[mask].sort_values("score") + + +def build_mirror(sim_dir, out_dir, selected): + from nuplan.planning.nuboard.base.data_class import NuBoardFile + + sim_dir, out_dir = ( + Path(sim_dir).resolve(), + Path(out_dir).resolve(), + ) # absolute: symlink targets must not be relative + tokens = set(selected["scenario"].astype(str)) + if out_dir.exists(): + shutil.rmtree(out_dir) + (out_dir / "simulation_log").mkdir(parents=True) + linked = 0 + for log_path in glob.glob(f"{sim_dir}/simulation_log/**/*.xz", recursive=True): + rel = Path(log_path).relative_to(sim_dir / "simulation_log") + if rel.parent.name not in tokens: + continue + target = out_dir / "simulation_log" / rel + target.parent.mkdir(parents=True, exist_ok=True) + os.symlink(log_path, target) + linked += 1 + (out_dir / "metrics").mkdir() + for f in glob.glob(f"{sim_dir}/metrics/*.parquet"): + df = pd.read_parquet(f) + df[df["scenario_name"].astype(str).isin(tokens)].to_parquet(out_dir / "metrics" / Path(f).name) + (out_dir / "aggregator_metric").mkdir() + for f in glob.glob(f"{sim_dir}/aggregator_metric/*"): + if not f.endswith((".parquet", ".csv")): + continue + df = pd.read_parquet(f) if f.endswith(".parquet") else pd.read_csv(f) + if "scenario" in df: # per-scenario rows -> keep failures plus the per-type / final summary rows + keep = df["scenario"].astype(str).isin(tokens) + if "scenario_type" in df: + keep |= (df["scenario"] == df["scenario_type"]) | (df["scenario_type"] == "final_score") + df = df[keep] + if f.endswith(".parquet"): + df.to_parquet(out_dir / "aggregator_metric" / Path(f).name) + else: + df.to_csv(out_dir / "aggregator_metric" / Path(f).name, index=False) + NuBoardFile( + simulation_main_path=str(out_dir), + metric_main_path=str(out_dir), + metric_folder="metrics", + aggregator_metric_folder="aggregator_metric", + simulation_folder="simulation_log", + ).save_nuboard_file(out_dir / "failures.nuboard") + return linked + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("group_dir") + ap.add_argument("--max-score", type=float, default=0.5) + ap.add_argument( + "--metric", action="append", default=[], help="also select scenarios where this metric is < 1 (repeatable)" + ) + ap.add_argument("--out", default=None, help="mirror directory (default /nuboard_failures)") + ap.add_argument("--launch", action="store_true") + ap.add_argument("--port", type=int, default=5006) + args = ap.parse_args() + sim_dirs = sorted(glob.glob(f"{args.group_dir}/simulation/*/20*")) + if len(sim_dirs) != 1: + raise SystemExit( + f"expected exactly one simulation// under {args.group_dir}, found {sim_dirs}" + ) + selected = select_failures(sim_dirs[0], args.max_score, args.metric) + out_dir = Path(args.out or f"{args.group_dir}/nuboard_failures").resolve() + linked = build_mirror(sim_dirs[0], out_dir, selected) + print(f"[nuboard_failures] {len(selected)} scenarios selected, {linked} logs linked -> {out_dir}") + print(selected[["scenario", "scenario_type", "score"]].head(15).to_string(index=False)) + devkit = os.environ.get("NUPLAN_DEVKIT_ROOT", "") + cmd = f"python {devkit}/nuplan/planning/script/run_nuboard.py simulation_path=[{out_dir}] port_number={args.port}" + if not args.launch: + print("launch with:\n " + cmd) + return + sys.path.insert(0, str(Path(__file__).resolve().parent)) + import nuplan_log_remap # noqa: F401 patches scenario/map paths for logs written elsewhere + from nuplan.planning.simulation import simulation_log as _sl + + _sl.SimulationLog.load_data = classmethod(lambda cls, file_path: nuplan_log_remap.load_log(file_path)) + sys.argv = [sys.argv[0], f"simulation_path=[{out_dir}]", f"port_number={args.port}"] + import runpy + + runpy.run_path(f"{devkit}/nuplan/planning/script/run_nuboard.py", run_name="__main__") + + +if __name__ == "__main__": + main() diff --git a/scripts/eval/nuplan_log_remap.py b/scripts/eval/nuplan_log_remap.py new file mode 100644 index 0000000000..2f6496a044 --- /dev/null +++ b/scripts/eval/nuplan_log_remap.py @@ -0,0 +1,98 @@ +"""Load nuPlan simulation logs produced on another machine (e.g. the cluster) locally. + +Simulation logs pickle the NuPlanScenario with the data/map roots baked in and the planner object +with its classes. Importing this module patches the scenario and map-db constructors to remap +CLUSTER_NUPLAN_ROOT -> NUPLAN_DATA_ROOT / NUPLAN_MAPS_ROOT (val logs are looked up in splits/trainval +when splits/val is absent) and provides `load_log`, which unpickles with placeholder classes for any +module that cannot be imported here. + +Env: CLUSTER_NUPLAN_ROOT (default /home/shared/data/nuplan), NUPLAN_DATA_ROOT, NUPLAN_MAPS_ROOT. +""" + +import io +import lzma +import os +import pickle +from pathlib import Path + +import msgpack + +from nuplan.database.maps_db import gpkg_mapsdb as _gpkg +from nuplan.planning.scenario_builder.nuplan_db import nuplan_scenario as _scenario + +CLUSTER_ROOT = os.environ.get("CLUSTER_NUPLAN_ROOT", "/home/shared/data/nuplan") +LOCAL_DATA_ROOT = os.environ.get("NUPLAN_DATA_ROOT", CLUSTER_ROOT) +LOCAL_MAPS_ROOT = os.environ.get("NUPLAN_MAPS_ROOT", CLUSTER_ROOT + "/maps") + + +def remap_path(path): + if not isinstance(path, str) or not path.startswith(CLUSTER_ROOT): + return path + if path.startswith(CLUSTER_ROOT + "/maps"): + return LOCAL_MAPS_ROOT + path[len(CLUSTER_ROOT + "/maps") :] + local = LOCAL_DATA_ROOT + path[len(CLUSTER_ROOT) :] + if "/splits/val/" in local and not Path(local).exists(): + local = local.replace("/splits/val/", "/splits/trainval/") + return local + + +_orig_scenario_init = _scenario.NuPlanScenario.__init__ +_orig_gpkg_init = _gpkg.GPKGMapsDB.__init__ + + +def _scenario_init( + self, + data_root, + log_file_load_path, + initial_lidar_token, + initial_lidar_timestamp, + scenario_type, + map_root, + *args, + **kwargs, +): + _orig_scenario_init( + self, + remap_path(data_root), + remap_path(log_file_load_path), + initial_lidar_token, + initial_lidar_timestamp, + scenario_type, + remap_path(map_root), + *args, + **kwargs, + ) + + +def _gpkg_init(self, map_version, map_root, *args, **kwargs): + _orig_gpkg_init(self, map_version, remap_path(map_root), *args, **kwargs) + + +_scenario.NuPlanScenario.__init__ = _scenario_init +_gpkg.GPKGMapsDB.__init__ = _gpkg_init + + +class _Placeholder: + def __init__(self, *args, **kwargs): + pass + + def __setstate__(self, state): + self.__dict__.update(state if isinstance(state, dict) else {"state": state}) + + +class _LenientUnpickler(pickle.Unpickler): + def find_class(self, module, name): + try: + return super().find_class(module, name) + except (ImportError, AttributeError): + return type(name, (_Placeholder,), {}) + + +def load_log(path): + """SimulationLog.load_data with the remap + placeholder classes (msgpack.xz and pkl.xz).""" + path = Path(path) + with lzma.open(str(path), "rb") as f: + raw = f.read() + if path.suffixes[-2] == ".msgpack": + raw = msgpack.unpackb(raw) + return _LenientUnpickler(io.BytesIO(raw)).load() diff --git a/scripts/eval/render_carla_obs_html.py b/scripts/eval/render_carla_obs_html.py new file mode 100644 index 0000000000..37b47f1efc --- /dev/null +++ b/scripts/eval/render_carla_obs_html.py @@ -0,0 +1,199 @@ +"""Render the interactive observation replays of a CARLA longest6 run (scripts/kesai/11_carla_longest6.sh: +/routes/route_XX/obs_html/*.replay.zlib) into /obs_html/, one page per route plus an +index.html gallery (same navigator as the nuPlan and self-play eval replays). + +usage: python scripts/eval/render_carla_obs_html.py [--routes ] [--workers N] + +Pages are named ds___route.html so the gallery lists the worst first +(flags: COL collision, RED red light, STOP stop sign, OFF outside route lanes, DEV route deviation, +BLOCK blocked, TIMEOUT route/scenario timeout); each page carries the route's leaderboard scores and +infractions as a panel. --routes (the evaluated xml) names the town of every route, else the world log does. +Retried routes leave the files of crashed attempts behind: the newest files pair with the records. +""" + +import argparse +import glob +import html +import json +import os +import re +import sys +import xml.etree.ElementTree as ET +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +import numpy as np + + +REPO_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO_ROOT)) +from pufferlib import viz + + +COLLISION_KEYS = ("collisions_pedestrian", "collisions_vehicle", "collisions_layout") +FLAG_INFRACTIONS = ( + ("COL", COLLISION_KEYS), + ("RED", ("red_light",)), + ("STOP", ("stop_infraction",)), + ("OFF", ("outside_route_lanes",)), + ("DEV", ("route_dev",)), + ("BLOCK", ("vehicle_blocked",)), + ("TIMEOUT", ("route_timeout", "scenario_timeouts")), +) +PANEL_INFRACTIONS = ( + ("collisions_pedestrian", "pedestrian collisions"), + ("collisions_vehicle", "vehicle collisions"), + ("collisions_layout", "layout collisions"), + ("red_light", "red lights"), + ("stop_infraction", "stop signs"), + ("outside_route_lanes", "outside route lanes"), + ("route_dev", "route deviations"), + ("vehicle_blocked", "blocked"), + ("route_timeout", "route timeouts"), + ("scenario_timeouts", "scenario timeouts"), + ("min_speed_infractions", "min speed"), + ("yield_emergency_vehicle_infractions", "emergency vehicles"), +) +MESSAGE_MAX_CHARS = 120 +ROUTE_ID_PATTERN = re.compile(r"RouteScenario_(\d+)(?:_rep(\d+))?") + + +def infraction_messages(record, key): + return record.get("infractions", {}).get(key, []) or [] + + +def route_index_rep(record): + """-> (route id, repetition) of a leaderboard record ('RouteScenario_12_rep0').""" + match = ROUTE_ID_PATTERN.fullmatch(str(record.get("route_id", ""))) + if match is None: + raise ValueError(f"unexpected route_id {record.get('route_id')!r}") + return int(match.group(1)), int(match.group(2) or 0) + + +def page_name(record, town): + route_idx, rep = route_index_rep(record) + parts = [f"ds{float(record['scores']['score_composed']):05.1f}"] + parts += [flag for flag, keys in FLAG_INFRACTIONS if any(infraction_messages(record, key) for key in keys)] + if town: + parts.append(town) + parts.append(f"route{route_idx:02d}" + (f"_rep{rep}" if rep else "")) + return "_".join(parts) + ".html" + + +def score_panel(record, town): + """Fixed panel injected into the viewer: leaderboard scores, every infraction category, the messages.""" + scores, meta = record["scores"], record.get("meta", {}) + route_idx, rep = route_index_rep(record) + lines = [] + for key, label in PANEL_INFRACTIONS: + messages = infraction_messages(record, key) + color = "#ff6b6b" if messages else "#7ed491" + mark = "✗" if messages else "✓" + lines.append( + f'
{mark} {label}' + f'{len(messages)}
' + ) + for message in messages: + lines.append( + f'
↳ {html.escape(message[:MESSAGE_MAX_CHARS])}
' + ) + if meta: + lines.append( + f'
route {float(meta.get("route_length", 0)):.0f} m · ' + f"game {float(meta.get('duration_game', 0)):.0f} s · wall {float(meta.get('duration_system', 0)):.0f} s
" + ) + title = f"{town + ' ' if town else ''}route {route_idx}" + (f" rep {rep}" if rep else "") + return ( + '
' + f'driving score {float(scores["score_composed"]):.1f} · ' + f"RC {float(scores['score_route']):.0f}% · IP {float(scores['score_penalty']):.3f} · " + f"{html.escape(title)} · {html.escape(str(record.get('status', '')))}" + + "".join(lines) + + "
" + ) + + +def render_page(zlib_path, html_path, panel): + viz.render_interactive_replay_zlib(str(zlib_path), str(html_path)) + page = Path(html_path).read_text() + Path(html_path).write_text(page.replace("", "" + panel, 1)) + + +def gallery_metrics(record): + """Infraction counts under the names the gallery filters on (> 0 = flagged).""" + return { + "driving_score": float(record["scores"]["score_composed"]), + "collision_rate": float(sum(len(infraction_messages(record, key)) for key in COLLISION_KEYS)), + "red_light_violation_rate": float(len(infraction_messages(record, "red_light"))), + "offroad_rate": float(len(infraction_messages(record, "outside_route_lanes"))), + } + + +def route_towns(routes_xml): + return { + int(route.attrib["id"]): route.attrib.get("town", "") for route in ET.parse(routes_xml).getroot().iter("route") + } + + +def world_log_town(npz_path): + return json.loads(str(np.load(npz_path)["meta"])).get("town", "") + + +def newest_files(route_dir, subdir, suffix, count): + """The `count` newest (timestamp-named) files of /.""" + files = sorted(glob.glob(str(route_dir / subdir / f"*{suffix}"))) + return files[max(0, len(files) - count) :] + + +def collect_jobs(run_dir, towns): + """-> [(record, replay zlib path, town)] over routes/route_*, the newest replay per route record.""" + jobs = [] + for route_dir in sorted(Path(run_dir, "routes").glob("route_*")): + result_path = route_dir / "result.json" + if not result_path.is_file(): + print(f"[{route_dir.name}] no result.json, skipped") + continue + with open(result_path) as result_file: + records = json.load(result_file)["_checkpoint"]["records"] + replays = newest_files(route_dir, "obs_html", ".replay.zlib", len(records)) + if len(replays) != len(records): + print(f"[{route_dir.name}] {len(records)} route records but {len(replays)} replays; pairing the newest") + if not replays: + continue + records = records[-len(replays) :] + world_logs = newest_files(route_dir, "world_log", ".npz", len(replays)) + for k, (record, replay) in enumerate(zip(records, replays)): + town = towns.get(route_index_rep(record)[0]) + if town is None and k < len(world_logs): + town = world_log_town(world_logs[k]) + jobs.append((record, replay, town or "")) + return jobs + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("run_dir", help="output dir of 11_carla_longest6.sh (holds routes/route_*/)") + ap.add_argument("--routes", help="evaluated route xml, names the town of every route") + ap.add_argument("--workers", type=int, default=min(16, os.cpu_count() or 1)) + args = ap.parse_args() + run_dir = Path(args.run_dir) + towns = route_towns(args.routes) if args.routes else {} + jobs = collect_jobs(run_dir, towns) + if not jobs: + raise SystemExit(f"no routes/route_*/obs_html/*.replay.zlib with a result.json under {run_dir}") + obs_dir = run_dir / "obs_html" + obs_dir.mkdir(exist_ok=True) + pages = [(obs_dir / page_name(record, town), record, replay, town) for record, replay, town in jobs] + with ThreadPoolExecutor(max_workers=max(1, args.workers)) as pool: + list(pool.map(lambda job: render_page(job[2], job[0], score_panel(job[1], job[3])), pages)) + file_metrics = {page.name: gallery_metrics(record) for page, record, _, _ in pages} + report = run_dir / "report" / "index.html" + links = [("Analysis report", os.path.relpath(report, obs_dir))] if report.is_file() else [] + viz.build_gallery_index(str(obs_dir), file_metrics=file_metrics, links=links) + print(f"[render_carla_obs_html] {len(pages)} route pages rendered -> {obs_dir / 'index.html'}") + + +if __name__ == "__main__": + main() diff --git a/scripts/eval/render_obs_html.py b/scripts/eval/render_obs_html.py new file mode 100644 index 0000000000..7cad4b7b31 --- /dev/null +++ b/scripts/eval/render_obs_html.py @@ -0,0 +1,198 @@ +"""Render the interactive observation replays (obs_html/.replay.zlib) of a nuPlan run into HTML +pages plus an index.html gallery (same navigator as the self-play eval replays), only for the +scenarios worth looking at. + +usage: python scripts/eval/render_obs_html.py [--max-score 0.9] [--metric no_ego_at_fault_collisions ...] + [--tokens tok1 tok2 ...] [--prune] [--workers N] + +Selection: aggregated score < --max-score OR any --metric < 1 OR explicitly listed --tokens. +Pages are named s___.html so the gallery lists the worst first +(flags: COL at-fault collision, OFF drivable area, DIR driving direction, STALL no progress). +--prune deletes the .replay.zlib of every scenario that was not selected. +""" + +import argparse +import glob +import os +import sys +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +import pandas as pd + +REPO_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO_ROOT)) +from pufferlib import viz # noqa: E402 + +FLAG_METRICS = ( + ("COL", "no_ego_at_fault_collisions"), + ("OFF", "drivable_area_compliance"), + ("DIR", "driving_direction_compliance"), + ("STALL", "ego_is_making_progress"), +) + + +SCORED_METRICS = ( + ("no_ego_at_fault_collisions", "no at-fault collision", "multiplier"), + ("drivable_area_compliance", "drivable area", "multiplier"), + ("driving_direction_compliance", "driving direction", "multiplier"), + ("ego_is_making_progress", "making progress", "multiplier"), + ("time_to_collision_within_bound", "time to collision", "weight 5"), + ("ego_progress_along_expert_route", "progress along expert route", "weight 5"), + ("speed_limit_compliance", "speed limit", "weight 4"), + ("ego_is_comfortable", "comfort", "weight 2"), +) +COMFORT_METRICS = ( + "ego_lon_acceleration", + "ego_lon_jerk", + "ego_jerk", + "ego_lat_acceleration", + "ego_yaw_acceleration", + "ego_yaw_rate", +) +COMFORT_BOUNDS = { + "ego_lon_acceleration": "-4.05..2.40 m/s^2", + "ego_lon_jerk": "|j| <= 4.13 m/s^3", + "ego_jerk": "|j| <= 8.37 m/s^3", + "ego_lat_acceleration": "|a| <= 4.89 m/s^2", + "ego_yaw_acceleration": "|a| <= 1.93 rad/s^2", + "ego_yaw_rate": "|w| <= 0.95 rad/s", +} + + +def scenario_rows(group_dir): + """token -> aggregator row (score + per-metric values) over every challenge of the run.""" + rows = {} + for csv in glob.glob(f"{group_dir}/simulation/*/20*/aggregator_metric/*_weighted_average_metrics_*.csv"): + agg = pd.read_csv(csv) + per_scenario = agg[(agg["scenario_type"] != "final_score") & (agg["scenario"] != agg["scenario_type"])] + for _, row in per_scenario.iterrows(): + rows[str(row["scenario"])] = row + return rows + + +def comfort_failures(group_dir): + """token -> ['ego_lon_jerk -2.7..5.0', ...]: nuPlan's own out-of-bound comfort sub-metrics.""" + out = {} + for sim_dir in glob.glob(f"{group_dir}/simulation/*/20*"): + for name in COMFORT_METRICS: + f = Path(sim_dir) / "metrics" / f"{name}.parquet" + if not f.exists(): + continue + df = pd.read_parquet(f) + flag = [c for c in df.columns if c.endswith("within_bounds_stat_value")] + if not flag: + continue + for _, r in df[~df[flag[0]].astype(bool)].iterrows(): + out.setdefault(str(r["scenario_name"]), []).append( + f"{name} {float(r[f'min_{name}_stat_value']):.2f}..{float(r[f'max_{name}_stat_value']):.2f} (bound {COMFORT_BOUNDS[name]})" + ) + return out + + +def score_panel(token, row, comfort): + """Fixed panel injected into the viewer: nuPlan score, every scored sub-metric, violated ones highlighted.""" + if row is None: + return "" + lines = [] + for metric, label, role in SCORED_METRICS: + value = float(row[metric]) + color = "#ff6b6b" if value < 1.0 else "#7ed491" + mark = "✗" if value < 1.0 else "✓" + lines.append( + f'
{mark} {label}' + f'{value:.2f} ({role})
' + ) + for detail in comfort: + lines.append(f'
↳ {detail}
') + return ( + '
' + f'nuPlan score {float(row["score"]):.2f} · {row["scenario_type"]} · {token}' + + "".join(lines) + + "
" + ) + + +def render_page(zlib_path, html_path, panel): + viz.render_interactive_replay_zlib(str(zlib_path), str(html_path)) + if panel: + html = Path(html_path).read_text() + Path(html_path).write_text(html.replace("", "" + panel, 1)) + + +def selected_tokens(rows, max_score, metrics): + tokens = set() + for token, row in rows.items(): + if row["score"] < max_score or any(row[metric] < 1.0 for metric in metrics): + tokens.add(token) + return tokens + + +def page_name(token, row): + if row is None: + return f"{token}.html" + flags = "".join(f"{flag}_" for flag, metric in FLAG_METRICS if row[metric] < 1.0) + return f"s{row['score']:.2f}_{flags}{row['scenario_type']}_{token}.html" + + +def gallery_metrics(row): + """Failure flags in the names the self-play gallery filters on (rate > 0 = flagged).""" + if row is None: + return {} + return { + "score": float(row["score"]), + "at_fault_collision_rate": 1.0 - float(row["no_ego_at_fault_collisions"]), + "offroad_rate": 1.0 - float(row["drivable_area_compliance"]), + "collision_rate": 1.0 - float(row["no_ego_at_fault_collisions"]), + } + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("group_dir") + ap.add_argument("--max-score", type=float, default=1.01) + ap.add_argument("--metric", action="append", default=[]) + ap.add_argument("--tokens", nargs="*", default=[]) + ap.add_argument("--prune", action="store_true") + ap.add_argument("--workers", type=int, default=min(16, os.cpu_count() or 1)) + args = ap.parse_args() + obs_dir = Path(args.group_dir) / "obs_html" + replays = {p.name[: -len(".replay.zlib")]: p for p in obs_dir.glob("*.replay.zlib")} + if not replays: + raise SystemExit(f"no obs_html/*.replay.zlib under {args.group_dir}") + rows = scenario_rows(args.group_dir) + comfort = comfort_failures(args.group_dir) + wanted = selected_tokens(rows, args.max_score, args.metric) | set(args.tokens) + jobs = [ + (token, replays[token], obs_dir / page_name(token, rows.get(token))) + for token in sorted(wanted) + if token in replays + ] + with ThreadPoolExecutor(max_workers=max(1, args.workers)) as pool: + list( + pool.map( + lambda job: render_page(job[1], job[2], score_panel(job[0], rows.get(job[0]), comfort.get(job[0], []))), + jobs, + ) + ) + file_metrics = {html.name: gallery_metrics(rows.get(token)) for token, _, html in jobs} + pruned = 0 + if args.prune: + for token, path in replays.items(): + if token not in wanted: + path.unlink() + pruned += 1 + if jobs: + viz.build_gallery_index(str(obs_dir), file_metrics=file_metrics) + missing = len(wanted) - len(jobs) + print( + f"[render_obs_html] {len(replays)} replays, {len(wanted)} scenarios selected, {len(jobs)} pages rendered" + f"{f', {missing} selected without a replay' if missing else ''}{f', {pruned} replays pruned' if pruned else ''}" + f" -> {obs_dir / 'index.html'}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/kesai/10_diag_reset_loop_8GPU_slurm.sh b/scripts/kesai/10_diag_reset_loop_8GPU_slurm.sh new file mode 100755 index 0000000000..38f4f575a9 --- /dev/null +++ b/scripts/kesai/10_diag_reset_loop_8GPU_slurm.sh @@ -0,0 +1,100 @@ +#!/bin/bash +#SBATCH --job-name diag_reset_loop +#SBATCH --ntasks 1 +#SBATCH --nodes 1 +#SBATCH --time 0-02:00 +#SBATCH --gres gpu:8 +#SBATCH --mem=1007G +#SBATCH --cpus-per-task 144 +#SBATCH --output /home/bjaeger/PufferDrive/experiments/logs/log_%a_%A.out +#SBATCH --error /home/bjaeger/PufferDrive/experiments/logs/log_%a_%A.err +#SBATCH --partition dev +#SBATCH --array=0-1 + +# Short 1-node run to catch sub-envs that early-reset every tick right after +# creation (see environment/early_reset_short, environment/spawn_* and the +# [DRIVE DIAG] lines in the .err). Task 0 = training spawn noise, task 1 = noise off. +echo "START TIME: $(date)" +start=$(date +%s) + +export SEED=1000 +if [ "${SLURM_ARRAY_TASK_ID}" -eq 0 ]; then + export RUN_NAME=diag_reset_noise_on_${SEED} + SPAWN_NOISE_ARGS="env.spawn_lateral_offset_max_frac=1.0 env.spawn_heading_max_deg=30.0" +else + export RUN_NAME=diag_reset_noise_off_${SEED} + SPAWN_NOISE_ARGS="env.spawn_lateral_offset_max_frac=0.0 env.spawn_heading_max_deg=0.0" +fi +echo ${RUN_NAME} +export DATA_DIR=/home/bjaeger/PufferDrive/experiments/${RUN_NAME} +echo ${DATA_DIR} + +export NUMEXPR_NUM_THREADS=1 +export MKL_NUM_THREADS=1 +export VECLIB_MAXIMUM_THREADS=1 +export OPENBLAS_NUM_THREADS=1 +export OMP_NUM_THREADS=1 + +source .venv/bin/activate + +# Only task 0 builds; concurrent in-place builds race on shared NFS build files. +BUILD_STATUS_FILE=/home/bjaeger/PufferDrive/experiments/logs/build_status_${SLURM_ARRAY_JOB_ID} +if [ "${SLURM_ARRAY_TASK_ID}" -eq 0 ]; then + bash scripts/kesai/build_ext_if_changed.sh /home/bjaeger/PufferDrive + BUILD_STATUS=$? + echo ${BUILD_STATUS} > ${BUILD_STATUS_FILE} +else + BUILD_WAIT_SECONDS=0 + BUILD_TIMEOUT_SECONDS=1800 + while [ ! -f ${BUILD_STATUS_FILE} ]; do + if [ ${BUILD_WAIT_SECONDS} -ge ${BUILD_TIMEOUT_SECONDS} ]; then + echo "Timed out after ${BUILD_TIMEOUT_SECONDS}s waiting for task 0 build; aborting." + exit 1 + fi + sleep 10 + BUILD_WAIT_SECONDS=$((BUILD_WAIT_SECONDS + 10)) + done + BUILD_STATUS=$(cat ${BUILD_STATUS_FILE}) +fi +if [ "${BUILD_STATUS}" -ne 0 ]; then + echo "C extension build failed with status ${BUILD_STATUS}; aborting." + exit 1 +fi + +# 2B steps = ~15k ticks at 8 GPUs; the stuck state formed within ~1.5k ticks in the 64-GPU runs. +torchrun --standalone --nnodes=1 --nproc-per-node=8 --max_restarts=0 --start-method spawn \ + -m pufferlib.pufferl train puffer_drive \ + wandb=True \ + wandb_project=nightly-multi-long \ + wandb_group=diag_reset_loop \ + train.data_dir=${DATA_DIR} \ + env.map_dir=/home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla_128_affine_zones \ + env.num_maps=128 \ + train.name=${RUN_NAME} \ + run_name=${RUN_NAME} \ + train.total_timesteps=2000000000 \ + vec.num_envs=16 \ + train.compile=True \ + train.max_minibatch_size=131072 \ + train.minibatch_size=131072 \ + train.precision=bfloat16 \ + policy.fp32_heads=true \ + train.tf32=false \ + env.goal_speed_randomization=false \ + env.goal_reach_requires_speed=true \ + env.obs_partner_relative_velocity=true \ + env.pose_noise_xy_m=0.025 \ + env.pose_noise_yaw_deg=0.25 \ + env.speed_limit_random_prob=1.0 \ + env.conditioning_speed_scale=2.0 \ + policy.mask_padded_features=true \ + train.evaluation_benchmarks=null \ + train.seed=${SEED} \ + ${SPAWN_NOISE_ARGS} \ + tb=True + +echo "Training exited with status $?" +echo "Summarize with: python scripts/kesai/summarize_reset_diag.py /home/bjaeger/PufferDrive/experiments/logs/log_${SLURM_ARRAY_TASK_ID}_${SLURM_ARRAY_JOB_ID}.err" +end=$(date +%s) +echo "END TIME: $(date)" +echo "Runtime: $((end-start))" diff --git a/scripts/kesai/11_carla_longest6.sh b/scripts/kesai/11_carla_longest6.sh new file mode 100644 index 0000000000..3f589db93e --- /dev/null +++ b/scripts/kesai/11_carla_longest6.sh @@ -0,0 +1,216 @@ +#!/bin/bash +#SBATCH --job-name eval_longest6 +#SBATCH --ntasks 1 +#SBATCH --nodes 1 +#SBATCH --time 1-00:00 +#SBATCH --gres gpu:8 +#SBATCH --mem=1007G +#SBATCH --cpus-per-task 144 +#SBATCH --output /home/bjaeger/PufferDrive/experiments/logs/log_%j.out +#SBATCH --error /home/bjaeger/PufferDrive/experiments/logs/log_%j.err +#SBATCH --partition dev +# CARLA longest6 evaluation of a PufferDrive checkpoint through CaRL's unmodified original_leaderboard +# (pufferlib/ocean/cosim/carla/leaderboard_agent.py), the CARLA counterpart of 9_nuPlan.sh. +# +# One CARLA server + one evaluator per GPU (1-8: whatever the allocation holds, e.g. `sbatch --gres gpu:2`); +# the 36 routes are dealt round-robin over the GPUs, every route gets its own server (restarted per route, +# CaRL's own recipe), crashed routes are retried, and the per-route result jsons are aggregated at the end +# with CaRL's tools/result_parser.py (results.csv) plus the nuPlan-style HTML report ($OUT/report/index.html) +# and the obs replay gallery of all routes ($OUT/obs_html/index.html, as in 9_nuPlan.sh). +# +# Overridable env: RUN_DIR, ROUTES, SCENARIOS (0 = pufferlib's scenario-free longest6), REPETITIONS, +# ROUTE_SUBSET ("0 5 17": only these route ids), NUM_GPUS, PORT_BASE (CARLA rpc port of gpu 0; gpu w uses +# PORT_BASE+50w, TM PORT_BASE+6000+50w), LOGGING (1, default: telemetry, world log and the HTML report per +# route; 0: scores only), CARLA_VIEW (0, default: CARLA runs without rendering (-nullrhi; collision sensors +# and telemetry work, an RGB camera segfaults the server); 1: chase-cam mp4 per route on a rendering server, +# needs LOGGING=1), OBS_HTML (1 = also the interactive obs replay per route, rendered into the one gallery +# folder $OUT/obs_html at the end, large; needs LOGGING=1), REPORT (0 = skip the HTML report), +# MAX_ATTEMPTS, CARLA_ROOT, CARL_WORK_DIR, PY, PD, COSIM_MAX_SPEED_MPS (ego speed cap, default 30), +# COSIM_ZERO_PARTNER_STOPPED_TIME (default 1: partners' stopped-time obs held at 0; 0 = real stopped times), +# COSIM_PEDESTRIAN_MIN_SIZE_M (default 0: true CARLA walker boxes; 0.8 = the training spawn floor). +set -u + +export PD=${PD:-/home/bjaeger/PufferDrive} +export PY=${PY:-/home/bjaeger/miniconda3/envs/carl/bin/python} # cp310: CARLA 0.9.15 ships no newer wheel +export CARLA_ROOT=${CARLA_ROOT:-/home/bjaeger/CARLA_0.9.15} +export CARL_WORK_DIR=${CARL_WORK_DIR:-/home/bjaeger/CaRL/CARLA} +RUN_DIR=${RUN_DIR:-/home/bjaeger/PufferDrive/experiments/k_scaled_0041_1000} +# the agent finds config.yaml next to final_model.pt (or one level above a models/*.pt) +export CKPT=$RUN_DIR/final_model.pt +SCENARIOS=${SCENARIOS:-1} +if [ "$SCENARIOS" = "1" ]; then + ROUTES=${ROUTES:-$CARL_WORK_DIR/custom_leaderboard/leaderboard/data/longest6.xml} +else + ROUTES=${ROUTES:-$PD/pufferlib/ocean/cosim/carla/routes/longest6_no_scenarios.xml} +fi +REPETITIONS=${REPETITIONS:-1} +ROUTE_SUBSET=${ROUTE_SUBSET:-} +PORT_BASE=${PORT_BASE:-2000} +MAX_ATTEMPTS=${MAX_ATTEMPTS:-3} +LOGGING=${LOGGING:-1} +CARLA_VIEW=${CARLA_VIEW:-0} +[ "$CARLA_VIEW" = "1" ] && [ "$LOGGING" != "1" ] && { echo "CARLA_VIEW=1 needs LOGGING=1"; exit 1; } +OBS_HTML=${OBS_HTML:-1} +REPORT=${REPORT:-$LOGGING} +EVALUATOR=$CARL_WORK_DIR/original_leaderboard/leaderboard/leaderboard/leaderboard_evaluator.py +for path in "$CKPT" "$PY" "$CARLA_ROOT/CarlaUE4.sh" "$EVALUATOR" "$ROUTES" "$CARL_WORK_DIR/tools/result_parser.py"; do + [ -e "$path" ] || { echo "missing $path"; exit 1; } +done +for path in "$PY" "$CARLA_ROOT/CarlaUE4.sh"; do + [ -x "$path" ] || { echo "not executable: $path (chmod +x, or the filesystem is mounted noexec)"; exit 1; } +done +echo "Evaluating checkpoint: $CKPT" + +# GPUs of this allocation; one CARLA server + one evaluator per GPU +NUM_GPUS=${NUM_GPUS:-$(nvidia-smi -L 2>/dev/null | grep -c "^GPU")} +if [ "$NUM_GPUS" -lt 1 ] || [ "$NUM_GPUS" -gt 8 ]; then echo "NUM_GPUS=$NUM_GPUS must be 1..8"; exit 1; fi + +# PD first, and drop inherited entries from other PufferDrive checkouts (see 9_nuPlan.sh) +INHERITED_PYTHONPATH=$(echo "${PYTHONPATH:-}" | tr ':' '\n' | grep -v "cosim_Puffer\|/PufferDrive" | paste -sd: -) +export PYTHONPATH="$PD:$CARL_WORK_DIR/original_leaderboard/leaderboard:$CARL_WORK_DIR/original_leaderboard/scenario_runner:$CARLA_ROOT/PythonAPI/carla${INHERITED_PYTHONPATH:+:$INHERITED_PYTHONPATH}" +export SCENARIO_RUNNER_ROOT=$CARL_WORK_DIR/original_leaderboard/scenario_runner +cd "$PD" || exit 1 +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 NUMEXPR_NUM_THREADS=4 +export COSIM_DEVICE=${COSIM_DEVICE:-cpu} +export COSIM_DYNAMICS_SOURCE=pufferdrive +export COSIM_MAX_SPEED_MPS=${COSIM_MAX_SPEED_MPS:-30} +export COSIM_ZERO_PARTNER_STOPPED_TIME=${COSIM_ZERO_PARTNER_STOPPED_TIME:-1} +export COSIM_PEDESTRIAN_MIN_SIZE_M=${COSIM_PEDESTRIAN_MIN_SIZE_M:-0} +export COSIM_OBS_HTML_MAX_STEPS=${COSIM_OBS_HTML_MAX_STEPS:-20000} +export COSIM_OBS_HTML_RENDER=0 # routes save the compact replay only; render_carla_obs_html.py renders all pages into $OUT/obs_html + +TAG=longest6$([ "$SCENARIOS" = "1" ] || echo "_noscen")$([ "$REPETITIONS" = "1" ] || echo "_rep$REPETITIONS") +# results live in the model's own eval folder, next to the PufferDrive benchmark evals +OUT=$RUN_DIR/eval/carla_${TAG}_$(date +%Y%m%d_%H%M%S)_${SLURM_JOB_ID:-local} +mkdir -p "$OUT/routes" "$OUT/aggregate" +{ + echo "checkpoint $CKPT"; echo "routes $ROUTES"; echo "repetitions $REPETITIONS"; echo "gpus $NUM_GPUS" + echo "git $(git -C "$PD" rev-parse --short HEAD 2>/dev/null) $(git -C "$PD" diff --quiet 2>/dev/null || echo dirty)" +} > "$OUT/run_info.txt" +echo "Results -> $OUT" + +mkdir -p "$PD/experiments/logs" # the build helper keeps its source hash there +PATH="$(dirname "$PY"):$PATH" bash "$PD/scripts/kesai/build_ext_if_changed.sh" "$PD" || exit 1 +IMPORTED_AGENT=$("$PY" -c "import pufferlib.ocean.cosim.carla.leaderboard_agent as a; print(a.__file__)") || { echo "ERROR: agent import failed (see traceback above)"; exit 1; } +case "$IMPORTED_AGENT" in + "$PD"/*) echo "agent: $IMPORTED_AGENT" ;; + *) echo "ERROR: agent imported from '$IMPORTED_AGENT', expected $PD"; exit 1 ;; +esac + +# route ids of the xml, dealt round-robin: gpu w runs ids w, w+N, w+2N, ... +mapfile -t ROUTE_IDS < <("$PY" -c " +import sys, xml.etree.ElementTree as ET +print('\n'.join(r.attrib['id'] for r in ET.parse(sys.argv[1]).getroot().iter('route')))" "$ROUTES") +[ -n "$ROUTE_SUBSET" ] && read -r -a ROUTE_IDS <<< "$ROUTE_SUBSET" +echo "${#ROUTE_IDS[@]} routes over $NUM_GPUS GPUs" + +route_done() { # 0 when the route json holds a record that is not one of the crash states CaRL resubmits + "$PY" - "$1" <<'EOF' +import json, sys +try: + records = json.load(open(sys.argv[1]))["_checkpoint"]["records"] +except Exception: + sys.exit(1) +crashed = ("Failed - Agent couldn't be set up", "Failed", "Failed - Simulation crashed", "Failed - Agent crashed") +sys.exit(0 if records and all(r["status"] not in crashed for r in records) else 1) +EOF +} + +run_route() { # $1 gpu, $2 route id, $3 attempt: one CARLA server + one evaluator run, into $OUT/routes/route_ + local gpu=$1 route_id=$2 attempt=$3 + local route_dir=$OUT/routes/route_$(printf "%02d" "$route_id") + local port=$((PORT_BASE + gpu * 50)) tm_port=$((PORT_BASE + 6000 + gpu * 50)) + local server_log=$route_dir/carla_server_attempt$attempt.log evaluator_log=$route_dir/evaluator_attempt$attempt.log + mkdir -p "$route_dir" + rm -f "$route_dir/result.json" + # only the chase camera needs the renderer; collision sensors and telemetry work under -nullrhi + local render_args=(-RenderOffScreen -nullrhi) + [ "$CARLA_VIEW" = "1" ] && render_args=(-RenderOffScreen -graphicsadapter="$gpu") + "$CARLA_ROOT/CarlaUE4.sh" "${render_args[@]}" -nosound \ + -carla-rpc-port="$port" -carla-streaming-port=$((port + 1)) > "$server_log" 2>&1 & + local server_pid=$! up=1 + echo "$server_pid" >> "$OUT/server_pids" + for _ in $(seq 1 60); do + kill -0 "$server_pid" 2>/dev/null || { echo "[gpu$gpu route$route_id] CARLA server died"; break; } + "$PY" -c " +import carla, sys +try: + c = carla.Client('localhost', $port); c.set_timeout(10.0); c.get_world().get_map() +except Exception: + sys.exit(1)" 2>/dev/null && up=0 && break + sleep 5 + done + if [ $up -ne 0 ]; then + pkill -9 -P "$server_pid" 2>/dev/null; kill -9 "$server_pid" 2>/dev/null + echo "[gpu$gpu route$route_id] last lines of $server_log:"; tail -n 20 "$server_log" + return 1 + fi + local log_env=() + if [ "$LOGGING" = "1" ]; then + log_env=(COSIM_TELEMETRY="$route_dir/telemetry" COSIM_WORLD_LOG="$route_dir/world_log") + [ "$CARLA_VIEW" = "1" ] && log_env+=(COSIM_DEBUG_CARLA_VIEW="$route_dir/carla_view") + [ "$OBS_HTML" = "1" ] && log_env+=(COSIM_OBS_HTML="$route_dir/obs_html") + fi + env CUDA_VISIBLE_DEVICES="$gpu" "${log_env[@]}" \ + "$PY" -u "$EVALUATOR" --routes "$ROUTES" --routes-subset "$route_id" --repetitions "$REPETITIONS" \ + --agent "$PD/pufferlib/ocean/cosim/carla/leaderboard_agent.py" --agent-config "$CKPT" \ + --checkpoint "$route_dir/result.json" --track MAP --port "$port" --traffic-manager-port "$tm_port" \ + > "$evaluator_log" 2>&1 + # children first: killing the wrapper shell first reparents the UE4 binary and leaves it running + pkill -9 -P "$server_pid" 2>/dev/null; kill -9 "$server_pid" 2>/dev/null; wait "$server_pid" 2>/dev/null + route_done "$route_dir/result.json" && return 0 + echo "[gpu$gpu route$route_id] no valid route record; last lines of $evaluator_log:"; tail -n 20 "$evaluator_log" + return 1 +} + +run_worker() { # $1 gpu: its share of the routes, sequentially, each retried up to MAX_ATTEMPTS + local gpu=$1 + for ((k = gpu; k < ${#ROUTE_IDS[@]}; k += NUM_GPUS)); do + local route_id=${ROUTE_IDS[k]} attempt + for ((attempt = 1; attempt <= MAX_ATTEMPTS; attempt++)); do + echo "[gpu$gpu] route $route_id attempt $attempt $(date +%H:%M:%S)" + run_route "$gpu" "$route_id" "$attempt" && break + echo "[gpu$gpu] route $route_id attempt $attempt failed" + done + done +} + +# only the servers this job started (children first: the UE4 binary outlives its wrapper shell otherwise) +trap 'for p in $(cat "$OUT/server_pids" 2>/dev/null); do pkill -9 -P "$p" 2>/dev/null; kill -9 "$p" 2>/dev/null; done' EXIT +WORKER_PIDS=() +for ((gpu = 0; gpu < NUM_GPUS; gpu++)); do + run_worker "$gpu" > "$OUT/worker_gpu$gpu.log" 2>&1 & + WORKER_PIDS+=($!) +done +wait "${WORKER_PIDS[@]}" +cat "$OUT"/worker_gpu*.log + +# aggregate: CaRL's parser over the per-route jsons -> $OUT/aggregate/results.csv, plus a per-route table +for f in "$OUT"/routes/route_*/result.json; do cp "$f" "$OUT/aggregate/$(basename "$(dirname "$f")").json"; done +"$PY" "$CARL_WORK_DIR/tools/result_parser.py" --xml "$ROUTES" --results "$OUT/aggregate" +"$PY" - "$OUT/aggregate" "${#ROUTE_IDS[@]}" <<'EOF' +import glob, json, sys +rows = [] +for f in sorted(glob.glob(sys.argv[1] + "/route_*.json")): + for r in json.load(open(f))["_checkpoint"]["records"]: + rows.append((r["route_id"], r["status"], r["scores"]["score_composed"], r["scores"]["score_route"], r["scores"]["score_penalty"])) +print(f"\n{len(rows)} route records of {sys.argv[2]} routes") +for route_id, status, ds, rc, ip in rows: + print(f" {route_id:22s} {status:34s} DS {ds:6.2f} RC {rc:6.2f} IP {ip:5.3f}") +if rows: + n = len(rows) + print(f"mean DS {sum(r[2] for r in rows)/n:.2f} RC {sum(r[3] for r in rows)/n:.2f} IP {sum(r[4] for r in rows)/n:.3f}") +EOF + +REPORT_ARGS=() +[ "$OBS_HTML" = "1" ] && REPORT_ARGS+=(--obs-html-dir "$OUT/obs_html") +if [ "$REPORT" = "1" ] && [ "$LOGGING" = "1" ]; then + "$PY" "$PD/scripts/eval/analyze_carla_cosim.py" "$OUT"/routes/route_* "$OUT/report" "${REPORT_ARGS[@]}" || echo "HTML report failed (results above are unaffected)" +fi +# after the report: the gallery links to it when it exists +if [ "$OBS_HTML" = "1" ] && [ "$LOGGING" = "1" ]; then + "$PY" "$PD/scripts/eval/render_carla_obs_html.py" "$OUT" --routes "$ROUTES" || echo "obs replay gallery failed (results above are unaffected)" +fi +[ -f "$OUT/report/index.html" ] && echo "report -> $OUT/report/index.html" +[ -f "$OUT/obs_html/index.html" ] && echo "obs replays -> $OUT/obs_html/index.html" +echo "Done -> $OUT" diff --git a/scripts/kesai/12_alpasim.sh b/scripts/kesai/12_alpasim.sh new file mode 100644 index 0000000000..dcc2882388 --- /dev/null +++ b/scripts/kesai/12_alpasim.sh @@ -0,0 +1,189 @@ +#!/bin/bash +#SBATCH --job-name eval_alpasim +#SBATCH --ntasks 1 +#SBATCH --nodes 1 +#SBATCH --time 1-00:00 +#SBATCH --gres gpu:8 +#SBATCH --mem=1007G +#SBATCH --cpus-per-task 144 +#SBATCH --output /home/bjaeger/PufferDrive/experiments/logs/log_%j.out +#SBATCH --error /home/bjaeger/PufferDrive/experiments/logs/log_%j.err +#SBATCH --partition dev +# AlpaSim closed-loop evaluation of a PufferDrive checkpoint on NuRec scenes, the AlpaSim counterpart of 9_nuPlan.sh: +# alpasim-training's teacher driver runs the policy in a shadow env of this checkout (PUFFERDRIVE_ROOT=$PD) on the +# scenes converted to PufferDrive bins; AlpaSim renders, simulates the ego and replays the logged traffic. +# +# One-time setup: alpasim-training at ALPAGYM_ROOT (`uv sync --all-packages`), the AlpaSim fork at ALPASIM_ROOT, the +# NuRec scenes and converted bins under NUREC_ROOT (alpasim-training README steps 3-4; all-usdzs/ must hold real files), +# and the AlpaSim base image archived to ALPASIM_IMAGE_TAR (REPO=$ALPASIM_ROOT OUT_DIR= sbatch +# --output= --error= $ALPAGYM_ROOT/scripts/build_alpasim_image.sbatch). +# +# Overridable env: RUN_DIR, CKPT (default RUN_DIR/final_model.pt, else the newest RUN_DIR/models/model_*.pt), PD, +# ALPAGYM_ROOT, ALPASIM_ROOT, NUREC_ROOT, BINS, USDZ_DIR, ALPASIM_IMAGE_TAR, TOPOLOGY (8gpu_64rollouts = whole node; +# 1gpu for a quick check), PARALLEL (scenes in flight: 32 on 8gpu, at most 4 on 1gpu), N_SCENES (0 = every scene that +# passed the conversion checks), SCENE_LIST (file of scene ids), GOAL_MODE (gt | route), CAM_W/CAM_H (16:9 render size; +# the teacher never sees the cameras), RENDER_VIDEO (1 = one mp4 per scene), SIM_TIMEOUT_S (per batch of PARALLEL scenes). +set -u + +export PD=${PD:-/home/bjaeger/PufferDrive} +ALPAGYM_ROOT=${ALPAGYM_ROOT:-/home/bjaeger/alpasim-training} +export ALPASIM_ROOT=${ALPASIM_ROOT:-/home/bjaeger/alpasim} +NUREC_ROOT=${NUREC_ROOT:-/home/shared/data/nurec} +BINS=${BINS:-$NUREC_ROOT/bins} +USDZ_DIR=${USDZ_DIR:-$NUREC_ROOT/all-usdzs} +RUN_DIR=${RUN_DIR:-/home/bjaeger/PufferDrive/experiments/k_scaled_0043_1000} +TOPOLOGY=${TOPOLOGY:-8gpu_64rollouts} +case "$TOPOLOGY" in + 8gpu*) PARALLEL=${PARALLEL:-32} ;; + *) PARALLEL=${PARALLEL:-4} ;; +esac +N_SCENES=${N_SCENES:-0} +SCENE_LIST=${SCENE_LIST:-} +GOAL_MODE=${GOAL_MODE:-gt} +CAM_W=${CAM_W:-640} +CAM_H=${CAM_H:-360} +RENDER_VIDEO=${RENDER_VIDEO:-1} +SIM_TIMEOUT_S=${SIM_TIMEOUT_S:-3600} +ALPAGYM_PY=$ALPAGYM_ROOT/.venv/bin/python +# uv: the host syncs the AlpaSim checkout's venv before it starts the Wizard +export PATH="$HOME/.local/bin:$PATH" + +if [ -z "${CKPT:-}" ]; then + CKPT=$RUN_DIR/final_model.pt + [ -f "$CKPT" ] || CKPT=$(ls "$RUN_DIR"/models/model_*.pt 2>/dev/null | sort | tail -n 1) +fi +[ -n "$CKPT" ] || { echo "no checkpoint in $RUN_DIR (final_model.pt or models/model_*.pt)"; exit 1; } +for path in "$CKPT" "$ALPAGYM_PY" "$ALPASIM_ROOT/pyproject.toml" "$BINS/scene_manifest.jsonl" "$USDZ_DIR"; do + [ -e "$path" ] || { echo "missing $path"; exit 1; } +done +[ -f "$(dirname "$CKPT")/config.yaml" ] || [ -f "$(dirname "$(dirname "$CKPT")")/config.yaml" ] \ + || { echo "no config.yaml next to or one level above $CKPT"; exit 1; } +[ -z "$(find "$USDZ_DIR" -maxdepth 1 -type l -print -quit)" ] \ + || { echo "$USDZ_DIR holds symlinks, which dangle inside the renderer container; point USDZ_DIR at the real files"; exit 1; } +for tool in docker nvidia-smi uv; do + command -v "$tool" > /dev/null || { echo "missing tool: $tool"; exit 1; } +done +case "$TOPOLOGY" in + 8gpu*) + NUM_GPUS=$(nvidia-smi -L | grep -c "^GPU") + [ "$NUM_GPUS" -eq 8 ] || { echo "$TOPOLOGY pins its services to GPUs 0-7 and needs all 8, got $NUM_GPUS"; exit 1; } + ;; +esac +ALPASIM_VERSION=$(grep -m1 '^version' "$ALPASIM_ROOT/pyproject.toml" | sed -E 's/.*"(.*)".*/\1/') +BASE_TAG=alpasim-base:$ALPASIM_VERSION +ALPASIM_IMAGE_TAR=${ALPASIM_IMAGE_TAR:-/home/bjaeger/docker_images/alpasim-base-$ALPASIM_VERSION.tar.zst} +NRE_TAG=$(grep -m1 -oE 'nvcr.io/nvidia/nre/[^[:space:]"]+' "$ALPASIM_ROOT/src/wizard/configs/base_config.yaml") +[ -n "$NRE_TAG" ] || { echo "no NRE renderer image in $ALPASIM_ROOT/src/wizard/configs/base_config.yaml"; exit 1; } +echo "Evaluating checkpoint: $CKPT" + +# drop inherited entries from other PufferDrive checkouts: the teacher would import their pufferlib instead of $PD's +export PYTHONPATH=$(echo "${PYTHONPATH:-}" | tr ':' '\n' | grep -v "cosim_Puffer\|/PufferDrive" | paste -sd: -) +export PUFFERDRIVE_ROOT=$PD +mkdir -p "$PD/experiments/logs" # the build helper keeps its source hash there +# cp312 binding for the alpasim-training venv; NO_TRAIN skips the training-only CUDA extension +NO_TRAIN=1 PATH="$ALPAGYM_ROOT/.venv/bin:$PATH" bash "$PD/scripts/kesai/build_ext_if_changed.sh" "$PD" || exit 1 +IMPORTED_PUFFERLIB=$("$ALPAGYM_PY" -c "import alpagym_distill.teacher, pufferlib; from pufferlib.ocean.drive import binding; print(pufferlib.__file__)") \ + || { echo "ERROR: teacher import failed (see traceback above)"; exit 1; } +case "$IMPORTED_PUFFERLIB" in + "$PD"/*) echo "pufferlib: $IMPORTED_PUFFERLIB" ;; + *) echo "ERROR: pufferlib imported from '$IMPORTED_PUFFERLIB', expected $PD"; exit 1 ;; +esac + +mapfile -t SCENES < <("$ALPAGYM_PY" - "$BINS/scene_manifest.jsonl" "$N_SCENES" "$SCENE_LIST" <<'EOF' +import json, sys +manifest, n_scenes, scene_list = sys.argv[1], int(sys.argv[2]), sys.argv[3] +rows = [json.loads(line) for line in open(manifest).read().splitlines()[1:]] +scenes = [r["alpasim_scene_id"] for r in rows if r["source_parity_pass"] and r["gt_replay_pass"]] +print(f"{len(scenes)} of {len(rows)} converted scenes passed the conversion checks", file=sys.stderr) +if scene_list: + usable = set(scenes) + listed = [s.strip() for s in open(scene_list) if s.strip() and not s.startswith("#")] + scenes = [s for s in (s if s.startswith("clipgt-") else "clipgt-" + s for s in listed) if s in usable] + print(f"{len(scenes)} of {len(listed)} scenes in {scene_list} usable", file=sys.stderr) +sys.stdout.write("".join(s + "\n" for s in (scenes if n_scenes == 0 else scenes[:n_scenes]))) +EOF +) +[ ${#SCENES[@]} -gt 0 ] || { echo "no scenes to evaluate"; exit 1; } + +# results live in the model's own eval folder, next to the PufferDrive benchmark evals +OUT=$RUN_DIR/eval/alpasim_${GOAL_MODE}_$(basename "$CKPT" .pt)_$(date +%Y%m%d_%H%M%S)_${SLURM_JOB_ID:-local} +mkdir -p "$OUT" +{ + echo "checkpoint $CKPT"; echo "scenes ${#SCENES[@]}"; echo "topology $TOPOLOGY parallel $PARALLEL goal_mode $GOAL_MODE" + for repo in "$PD" "$ALPAGYM_ROOT" "$ALPASIM_ROOT"; do + echo "git $repo $(git -C "$repo" rev-parse --short HEAD 2>/dev/null) $(git -C "$repo" diff --quiet 2>/dev/null || echo dirty)" + done +} > "$OUT/run_info.txt" +echo "Results -> $OUT" + +docker info > /dev/null 2>&1 || { echo "docker is not usable on $(hostname)"; exit 1; } +# own compose project and port window per job, so evals sharing a node never touch each other's services +export COMPOSE_PROJECT_NAME=alpasim_${SLURM_JOB_ID:-$$} +BASEPORT=$((6000 + (${SLURM_JOB_ID:-$$} % 90) * 20)) +LOADED_BASE=0 +PULLED_NRE=0 +cleanup() { + local compose_file=$OUT/alpagym/alpasim/wizard_0/docker-compose.yaml + [ -f "$compose_file" ] && docker compose -f "$compose_file" down --volumes --remove-orphans > /dev/null 2>&1 + for container in $(docker ps -aq --filter "label=com.docker.compose.project=$COMPOSE_PROJECT_NAME"); do + docker rm -f "$container" > /dev/null 2>&1 + done + # /var/lib/docker is tmpfs on the compute nodes: an image left behind keeps holding ~40 GB of node RAM + [ "$LOADED_BASE" = 1 ] && docker rmi "$BASE_TAG" > /dev/null 2>&1 + [ "$PULLED_NRE" = 1 ] && docker rmi "$NRE_TAG" > /dev/null 2>&1 +} +trap cleanup EXIT +if ! docker image inspect "$BASE_TAG" > /dev/null 2>&1; then + [ -f "$ALPASIM_IMAGE_TAR" ] || { echo "$BASE_TAG is not on $(hostname) and $ALPASIM_IMAGE_TAR is missing"; exit 1; } + echo "Loading $BASE_TAG from $ALPASIM_IMAGE_TAR" + LOADED_BASE=1 + zstd -dc "$ALPASIM_IMAGE_TAR" | docker load || exit 1 +fi +if ! docker image inspect "$NRE_TAG" > /dev/null 2>&1; then + echo "Pulling $NRE_TAG" + PULLED_NRE=1 + docker pull "$NRE_TAG" || exit 1 +fi + +# the teacher is an external driver, but the 8gpu topologies re-assert driver.skip=false and abort at startup +OVERRIDES="wizard.baseport=$BASEPORT runtime.endpoints.driver.skip=true" +[ "$RENDER_VIDEO" = "1" ] || OVERRIDES="$OVERRIDES eval.video.render_video=false" +echo "${#SCENES[@]} scenes, $TOPOLOGY with $PARALLEL in flight; full log: $OUT/evaluate.log" +cd "$ALPAGYM_ROOT" || exit 1 +"$ALPAGYM_PY" -m alpagym_host.evaluate \ + --model_kind teacher \ + --teacher_checkpoint "$CKPT" \ + --teacher_bins_dir "$BINS" \ + --teacher_goal_mode "$GOAL_MODE" \ + --local_scene_dir "$USDZ_DIR" \ + --scene_ids "${SCENES[@]}" \ + --topology "$TOPOLOGY" \ + --num_parallel_scenes "$PARALLEL" \ + --camera_width "$CAM_W" --camera_height "$CAM_H" \ + --simulation_timeout_s "$SIM_TIMEOUT_S" \ + --run_dir "$OUT/alpagym" \ + --extra_overrides "$OVERRIDES" \ + > "$OUT/evaluate.log" 2>&1 +EVAL_STATUS=$? + +if [ -f "$OUT/alpagym/evaluation.yaml" ]; then + "$ALPAGYM_PY" - "$OUT/alpagym" <<'EOF' +import sys +from pathlib import Path +import yaml +run = Path(sys.argv[1]) +summary = yaml.safe_load((run / "evaluation.yaml").read_text()) +print(f"\n{len(summary['scenes'])} scenes evaluated, complete: {summary['complete']}") +aggregate = run / "metrics" / "aggregate.yaml" +if aggregate.exists(): + for name, stats in yaml.safe_load(aggregate.read_text())["metrics"].items(): + print(f" {name:34s} mean {stats['mean']:9.4f} median {stats['median']:9.4f} n {stats['num_finite']}") +EOF +fi +if [ $EVAL_STATUS -ne 0 ]; then + echo "AlpaSim evaluation failed (exit $EVAL_STATUS); last host lines of $OUT/evaluate.log:" + grep -vE '^[a-z0-9_-]+-[0-9]+-[0-9]+ +\|' "$OUT/evaluate.log" | tail -n 30 +fi +echo "per-scene metrics -> $OUT/alpagym/metrics/per_scene.csv; videos -> $OUT/alpagym/alpasim/wizard_0/rollouts" +echo "Done -> $OUT" +exit $EVAL_STATUS diff --git a/scripts/kesai/13_nuPlan_nonreactive.sh b/scripts/kesai/13_nuPlan_nonreactive.sh new file mode 100644 index 0000000000..18d75f0c9b --- /dev/null +++ b/scripts/kesai/13_nuPlan_nonreactive.sh @@ -0,0 +1,16 @@ +#!/bin/bash +#SBATCH --job-name eval_nuplan_nr +#SBATCH --ntasks 1 +#SBATCH --nodes 1 +#SBATCH --time 1-00:00 +#SBATCH --gres gpu:8 +#SBATCH --mem=1007G +#SBATCH --cpus-per-task 144 +#SBATCH --output /home/bjaeger/PufferDrive/experiments/logs/log_%j.out +#SBATCH --error /home/bjaeger/PufferDrive/experiments/logs/log_%j.err +#SBATCH --partition dev +# nuPlan Val14 closed-loop evaluation against non-reactive log-replay agents (CLS-NR): 9_nuPlan.sh with the non-reactive +# challenge, so the reactive and non-reactive scores always come from the same planner settings. Overridable env: as in +# 9_nuPlan.sh; results go to $RUN_DIR/eval/nuplan_val14_nonreactive_*. +PD=${PD:-/home/bjaeger/PufferDrive} +CHALLENGES=closed_loop_nonreactive_agents_pufferdrive exec bash "$PD/scripts/kesai/9_nuPlan.sh" diff --git a/scripts/kesai/1_eval_model_slurm.sh b/scripts/kesai/1_eval_model_slurm.sh index 273342ae7a..0a6590e5a0 100644 --- a/scripts/kesai/1_eval_model_slurm.sh +++ b/scripts/kesai/1_eval_model_slurm.sh @@ -29,7 +29,7 @@ bash scripts/kesai/build_ext_if_changed.sh /home/bjaeger/PufferDrive || exit 1 # wandb=True python -m pufferlib.pufferl eval puffer_drive nuplan_single \ - env.map_dir=/home/shared/data/nuPlan/PufferDrive \ + eval.map_dir=/home/shared/data/nuplan/PufferDrive \ vec.num_envs=64 \ eval.max_sdc_replay_workers=64 \ env.goal_source=gt \ diff --git a/scripts/kesai/2_train_8GPU_nightly_SLURM.sh b/scripts/kesai/2_train_8GPU_nightly_SLURM.sh index 1e7075c342..b8811e6c8f 100644 --- a/scripts/kesai/2_train_8GPU_nightly_SLURM.sh +++ b/scripts/kesai/2_train_8GPU_nightly_SLURM.sh @@ -97,7 +97,7 @@ python -m pufferlib.pufferl eval puffer_drive carla \ wandb=True python -m pufferlib.pufferl eval puffer_drive nuplan_single \ - env.map_dir=/home/shared/data/nuPlan/PufferDrive \ + eval.map_dir=/home/shared/data/nuplan/PufferDrive \ eval.output_name=${RUN_NAME} \ load_model_path=${MODEL_PATH} \ wandb=True diff --git a/scripts/kesai/3_train_64GPU_multinode_slurm.sh b/scripts/kesai/3_train_64GPU_multinode_slurm.sh index df3def3797..39334d9503 100644 --- a/scripts/kesai/3_train_64GPU_multinode_slurm.sh +++ b/scripts/kesai/3_train_64GPU_multinode_slurm.sh @@ -1,11 +1,11 @@ #!/bin/bash #SBATCH --job-name train_puffer -#SBATCH --nodes 8 # Number of nodes requested +#SBATCH --nodes 4 # Number of nodes requested #SBATCH --ntasks-per-node 1 # Run 1 srun task per node (which fires up torchrun) #SBATCH --gres gpu:8 # GPUs per node #SBATCH --cpus-per-task 144 #SBATCH --mem=1007G -#SBATCH --time 3-00:00 +#SBATCH --time 6-00:00 #SBATCH --output /home/bjaeger/PufferDrive/experiments/logs/log_%a_%A.out #SBATCH --error /home/bjaeger/PufferDrive/experiments/logs/log_%a_%A.err #SBATCH --partition dev @@ -22,7 +22,7 @@ start=$(date +%s) export SEED=1000 -export RUN_NAME=k_scaled_0036_${SEED} +export RUN_NAME=k_scaled_0044_${SEED} echo ${RUN_NAME} export DATA_DIR=/home/bjaeger/PufferDrive/experiments/${RUN_NAME} @@ -58,7 +58,7 @@ srun torchrun \ wandb_project=nightly-multi-long \ wandb_group=emerge_ \ train.data_dir=${DATA_DIR} \ - env.map_dir=/home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla_128_affine \ + env.map_dir=/home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla_128_affine_zones_stop \ env.num_maps=128 \ train.name=${RUN_NAME} \ run_name=${RUN_NAME} \ @@ -73,8 +73,15 @@ srun torchrun \ env.goal_speed_randomization=false \ env.goal_reach_requires_speed=true \ env.obs_partner_relative_velocity=true \ + env.obs_lane_heading_signed=true \ + env.obs_lane_speed_limit=true \ + env.stop_signs_enabled=true \ env.pose_noise_xy_m=0.025 \ env.pose_noise_yaw_deg=0.25 \ + env.speed_limit_random_prob=1.0 \ + env.stagger_first_episode=true \ + env.resample_frequency=0 \ + env.conditioning_speed_scale=2.0 \ policy.mask_padded_features=true \ train.evaluation_benchmarks=carla_fast \ train.final_model_name=${FINAL_MODEL_NAME} \ @@ -91,7 +98,7 @@ fi echo "Training done, evaluating ${MODEL_PATH}" .venv/bin/python scripts/parallel_eval.py carla \ --total-scenarios 40000 \ - --num-nodes 8 \ + --num-nodes 4 \ env.map_dir=/home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla \ vec.num_envs=64 \ eval.reward_comfort=0.0 \ @@ -100,6 +107,7 @@ echo "Training done, evaluating ${MODEL_PATH}" eval.min_goal_spacing=20 \ eval.max_goal_spacing=200 \ env.disable_red_light_infractions=1 \ + env.disable_stop_sign_infractions=1 \ env.traffic_light_junction_phases=0 \ env.eval_standstill_jerk_deadband_mps3=1.5 \ eval.render_filter=all_infractions \ @@ -108,20 +116,19 @@ echo "Training done, evaluating ${MODEL_PATH}" load_model_path=${MODEL_PATH} \ wandb=True -python -m pufferlib.pufferl eval puffer_drive nuplan_multi \ - env.map_dir=/home/shared/data/nuPlan/PufferDrive \ - vec.num_envs=64 \ - eval.num_agents=300 \ - eval.reward_comfort=0.0 \ - eval.reward_lane_center=0.0075 \ - env.eval_perceived_size_margin_m=0.0 \ - eval.disable_red_light_infractions=1 \ - eval.render_filter=all_infractions \ - eval.capture_observations=true \ - eval.output_name=${RUN_NAME} \ - load_model_path=${MODEL_PATH} \ - wandb=True - +# nuPlan (reactive and non-reactive), longest6 and AlpaSim evals need one 8-GPU node each: submit them as their own jobs so this 4-node allocation ends now. +echo "CARLA eval done, submitting nuPlan reactive eval for ${MODEL_PATH}" +RUN_DIR=${DATA_DIR} sbatch scripts/kesai/9_nuPlan.sh \ + || echo "nuPlan eval submission failed; run by hand: RUN_DIR=${DATA_DIR} sbatch scripts/kesai/9_nuPlan.sh" +echo "Submitting nuPlan non-reactive eval for ${MODEL_PATH}" +RUN_DIR=${DATA_DIR} sbatch scripts/kesai/13_nuPlan_nonreactive.sh \ + || echo "nuPlan non-reactive eval submission failed; run by hand: RUN_DIR=${DATA_DIR} sbatch scripts/kesai/13_nuPlan_nonreactive.sh" +echo "Submitting longest6 eval for ${MODEL_PATH}" +RUN_DIR=${DATA_DIR} sbatch scripts/kesai/11_carla_longest6.sh \ + || echo "longest6 eval submission failed; run by hand: RUN_DIR=${DATA_DIR} sbatch scripts/kesai/11_carla_longest6.sh" +echo "Submitting AlpaSim eval for ${MODEL_PATH}" +RUN_DIR=${DATA_DIR} sbatch scripts/kesai/12_alpasim.sh \ + || echo "AlpaSim eval submission failed; run by hand: RUN_DIR=${DATA_DIR} sbatch scripts/kesai/12_alpasim.sh" end=$(date +%s) runtime=$((end-start)) diff --git a/scripts/kesai/4_train_1GPU_slurm.sh b/scripts/kesai/4_train_1GPU_slurm.sh index 563fd114bd..84e7226abd 100644 --- a/scripts/kesai/4_train_1GPU_slurm.sh +++ b/scripts/kesai/4_train_1GPU_slurm.sh @@ -71,7 +71,7 @@ python -m pufferlib.pufferl eval puffer_drive carla \ wandb=True python -m pufferlib.pufferl eval puffer_drive nuplan_single \ - env.map_dir=/home/shared/data/nuPlan/PufferDrive \ + eval.map_dir=/home/shared/data/nuplan/PufferDrive \ eval.output_name=${RUN_NAME} \ load_model_path=${MODEL_PATH} \ wandb=True diff --git a/scripts/kesai/5_train_8GPU_experimental_slurm.sh b/scripts/kesai/5_train_8GPU_experimental_slurm.sh index 91848fcf10..3494083e4e 100644 --- a/scripts/kesai/5_train_8GPU_experimental_slurm.sh +++ b/scripts/kesai/5_train_8GPU_experimental_slurm.sh @@ -112,7 +112,7 @@ python -m pufferlib.pufferl eval puffer_drive carla \ python -m pufferlib.pufferl eval puffer_drive nuplan_single \ eval.render_filter=all_infractions \ - env.map_dir=/home/shared/data/nuPlan/PufferDrive \ + eval.map_dir=/home/shared/data/nuplan/PufferDrive \ eval.capture_observations=true \ eval.output_name=${RUN_NAME} \ load_model_path=${MODEL_PATH} \ diff --git a/scripts/kesai/7_eval_8node_slurm.sh b/scripts/kesai/7_eval_8node_slurm.sh index cc844f2055..58a71cf93d 100644 --- a/scripts/kesai/7_eval_8node_slurm.sh +++ b/scripts/kesai/7_eval_8node_slurm.sh @@ -13,7 +13,7 @@ echo "START TIME: $(date)" start=$(date +%s) -export RUN_NAME=k_scaled_0036_1000 +export RUN_NAME=k_scaled_0038_1000 echo ${RUN_NAME} export MODEL_PATH=/home/bjaeger/PufferDrive/experiments/${RUN_NAME}/final_model.pt @@ -36,24 +36,26 @@ fi # parallel_eval places one shard per allocated node via srun, so each shard's 64 # env workers get a full node's cores instead of sharing the batch host. -#.venv/bin/python scripts/parallel_eval.py carla \ -# --total-scenarios 40000 \ -# --num-nodes 8 \ -# env.map_dir=/home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla \ -# vec.num_envs=64 \ -# eval.render_filter=all_infractions \ -# eval.capture_observations=true \ -# eval.reward_comfort=0.0 \ -# eval.reward_lane_center=0.0075 \ -# env.eval_perceived_size_margin_m=0.2 \ -# eval.min_goal_spacing=20 \ -# eval.max_goal_spacing=200 \ -# env.disable_red_light_infractions=1 \ -# env.traffic_light_junction_phases=0 \ -# env.eval_standstill_jerk_deadband_mps3=1.5 \ -# eval.output_name=${RUN_NAME}_fast \ -# load_model_path=${MODEL_PATH} \ -# wandb=True +.venv/bin/python scripts/parallel_eval.py carla \ + --total-scenarios 5000 \ + --num-nodes 8 \ + env.map_dir=/home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla \ + vec.num_envs=64 \ + eval.render_filter=all_infractions \ + eval.capture_observations=true \ + eval.reward_comfort=0.0 \ + eval.reward_lane_center=0.0075 \ + env.eval_perceived_size_margin_m=0.2 \ + eval.min_goal_spacing=20 \ + eval.max_goal_spacing=200 \ + env.disable_red_light_infractions=1 \ + env.disable_stop_sign_infractions=1 \ + env.traffic_light_junction_phases=0 \ + env.eval_standstill_jerk_deadband_mps3=1.5 \ + eval.output_name=${RUN_NAME}_random_tl2 \ + load_model_path=${MODEL_PATH} \ + wandb=True + # eval.obs_slots_partners_n=40 \ # eval.goal_regen_mode=rolling \ # env.max_speed_mps=13.33 \ @@ -68,10 +70,11 @@ python -m pufferlib.pufferl eval puffer_drive nuplan_multi \ eval.reward_lane_center=0.0075 \ env.eval_perceived_size_margin_m=0.0 \ eval.disable_red_light_infractions=1 \ + eval.disable_stop_sign_infractions=1 \ env.eval_standstill_jerk_deadband_mps3=1.5 \ eval.render_filter=all_infractions \ eval.capture_observations=true \ - eval.output_name=${RUN_NAME}_fast5 \ + eval.output_name=${RUN_NAME}_random_tl2 \ load_model_path=${MODEL_PATH} \ wandb=True diff --git a/scripts/kesai/8_train_64GPU_multinode_slurm.sh b/scripts/kesai/8_train_64GPU_multinode_slurm.sh index 360031c8ff..f7ea094770 100644 --- a/scripts/kesai/8_train_64GPU_multinode_slurm.sh +++ b/scripts/kesai/8_train_64GPU_multinode_slurm.sh @@ -6,8 +6,8 @@ #SBATCH --cpus-per-task 144 #SBATCH --mem=1007G #SBATCH --time 3-00:00 -#SBATCH --output /home/bjaeger/cosim_Puffer/experiments/logs/log_%a_%A.out -#SBATCH --error /home/bjaeger/cosim_Puffer/experiments/logs/log_%a_%A.err +#SBATCH --output /home/bjaeger/PufferDrive/experiments/logs/log_%a_%A.out +#SBATCH --error /home/bjaeger/PufferDrive/experiments/logs/log_%a_%A.err #SBATCH --partition dev # Set up PyTorch Distributed Rendezvous parameters from Slurm variables @@ -25,7 +25,7 @@ export SEED=1000 export RUN_NAME=k_scaled_0034_${SEED} echo ${RUN_NAME} -export DATA_DIR=/home/bjaeger/cosim_Puffer/experiments/${RUN_NAME} +export DATA_DIR=/home/bjaeger/PufferDrive/experiments/${RUN_NAME} echo ${DATA_DIR} # Name the training run writes its final model under, so the eval steps below can @@ -43,7 +43,7 @@ export OPENBLAS_NUM_THREADS=1 export OMP_NUM_THREADS=1 source .venv/bin/activate -bash scripts/kesai/build_ext_if_changed.sh /home/bjaeger/cosim_Puffer || exit 1 +bash scripts/kesai/build_ext_if_changed.sh /home/bjaeger/PufferDrive || exit 1 # Execute torchrun across all nodes using srun srun torchrun \ --nnodes=${SLURM_JOB_NUM_NODES} \ @@ -58,7 +58,7 @@ srun torchrun \ wandb_project=nightly-multi-long \ wandb_group=emerge_ \ train.data_dir=${DATA_DIR} \ - env.map_dir=/home/bjaeger/cosim_Puffer/pufferlib/resources/drive/binaries/carla_128_affine \ + env.map_dir=/home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla_128_affine \ env.num_maps=128 \ env.goal_speed_randomization=false \ env.goal_reach_requires_speed=true \ @@ -89,7 +89,7 @@ echo "Training done, evaluating ${MODEL_PATH}" .venv/bin/python scripts/parallel_eval.py carla \ --total-scenarios 40000 \ --num-nodes 8 \ - env.map_dir=/home/bjaeger/cosim_Puffer/pufferlib/resources/drive/binaries/carla \ + env.map_dir=/home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla \ vec.num_envs=64 \ eval.reward_comfort=0.0 \ eval.reward_lane_center=0.0075 \ @@ -98,6 +98,7 @@ echo "Training done, evaluating ${MODEL_PATH}" eval.max_goal_spacing=30 \ env.max_speed_mps=13.33 \ env.disable_red_light_infractions=1 \ + env.disable_stop_sign_infractions=1 \ env.traffic_light_junction_phases=0 \ env.eval_standstill_jerk_deadband_mps3=1.5 \ eval.render_filter=all_infractions \ @@ -107,7 +108,7 @@ echo "Training done, evaluating ${MODEL_PATH}" wandb=True python -m pufferlib.pufferl eval puffer_drive nuplan_multi \ - env.map_dir=/home/shared/data/nuPlan/PufferDrive \ + eval.map_dir=/home/shared/data/nuplan/PufferDrive \ vec.num_envs=64 \ eval.num_agents=300 \ eval.reward_comfort=0.0 \ @@ -115,6 +116,7 @@ python -m pufferlib.pufferl eval puffer_drive nuplan_multi \ env.eval_perceived_size_margin_m=0.15 \ env.max_speed_mps=13.33 \ eval.disable_red_light_infractions=1 \ + eval.disable_stop_sign_infractions=1 \ eval.render_filter=all_infractions \ eval.capture_observations=true \ eval.output_name=${RUN_NAME} \ diff --git a/scripts/kesai/9_nuPlan.sh b/scripts/kesai/9_nuPlan.sh new file mode 100644 index 0000000000..53cc904afd --- /dev/null +++ b/scripts/kesai/9_nuPlan.sh @@ -0,0 +1,78 @@ +#!/bin/bash +#SBATCH --job-name eval_nuplan +#SBATCH --ntasks 1 +#SBATCH --nodes 1 +#SBATCH --time 1-00:00 +#SBATCH --gres gpu:8 +#SBATCH --mem=1007G +#SBATCH --cpus-per-task 144 +#SBATCH --output /home/bjaeger/PufferDrive/experiments/logs/log_%j.out +#SBATCH --error /home/bjaeger/PufferDrive/experiments/logs/log_%j.err +#SBATCH --partition dev +set -u + +export PD=/home/bjaeger/PufferDrive +export PY=$(conda info --base)/envs/carl_nuplan/bin/python +# Overridable env: RUN_DIR (3_train_64GPU_multinode_slurm.sh submits this job with RUN_DIR=), CHALLENGES +RUN_DIR=${RUN_DIR:-/home/bjaeger/PufferDrive/experiments/k_scaled_0041_1000} +# the planner finds config.yaml next to final_model.pt (or one level above a models/*.pt) +export CKPT=$RUN_DIR/final_model.pt +[ -f "$CKPT" ] || { echo "missing $CKPT"; exit 1; } +# reactive = IDM agents (CLS-R), nonreactive = log-replay agents (CLS-NR) +export CHALLENGES=${CHALLENGES:-closed_loop_reactive_agents_pufferdrive} +case "$CHALLENGES" in + closed_loop_reactive_agents_pufferdrive) CHALLENGE_TAG="" ;; + closed_loop_nonreactive_agents_pufferdrive) CHALLENGE_TAG="_nonreactive" ;; + *) echo "CHALLENGES must be closed_loop_reactive_agents_pufferdrive or closed_loop_nonreactive_agents_pufferdrive"; exit 1 ;; +esac +echo "Evaluating checkpoint: $CKPT" +export CITY_BIN_DIR=/home/shared/data/nuplan/PufferDrive +# shell env still exports the pre-rename nuPlan casing; pin the renamed paths here +export NUPLAN_DATA_ROOT=/home/shared/data/nuplan +export NUPLAN_MAPS_ROOT=/home/shared/data/nuplan/maps +# PD first, and drop inherited entries from other PufferDrive checkouts (the login shell still exports +# /home/bjaeger/cosim_Puffer): Python would otherwise import that checkout's pufferlib package. +INHERITED_PYTHONPATH=$(echo "${PYTHONPATH:-}" | tr ':' '\n' | grep -v "cosim_Puffer\|/PufferDrive" | paste -sd: -) +export PYTHONPATH=$PD:$CARL_DEVKIT_ROOT:$NUPLAN_DEVKIT_ROOT${INHERITED_PYTHONPATH:+:$INHERITED_PYTHONPATH} +cd "$PD" || exit 1 +export OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=1 +export COSIM_OBS_HTML=infractions # obs replays (what the policy saw) only for collision/offroad/wrong-way/no-progress scenarios; all|failures|infractions|0 +# carl_visualization_callback: nuPlan ground-truth video per scenario -> $GROUP/simulation///visualization +export CALLBACKS="[simulation_log_callback, carl_visualization_callback]" +export WORKER=ray_distributed +# Per-worker memory is small now (1 policy agent + partner slots, light buffers sized to the scenario), so this is CPU-bound. +export THREADS_PER_NODE=128 +# route: lane-graph route goals; roadblock: route roadblock centroids; roadblock_lane: roadblock goals on the lane continuing the ego lane +export GOAL_SOURCE=roadblock +# Ablations (see run_nuplan_planner.sh): refill the goal window after every consumed goal; cap the ego's +# accelerating / braking jerk [m/s^3] for the first 1.5 s of each scenario (0 = off). +export SLIDING_GOAL_WINDOW=true +export STARTUP_ACCEL_JERK_CAP=2 +export STARTUP_BRAKE_JERK_CAP=2 +# pedestrian/bicycle partner boxes grown to the training minimum agent size in the policy's obs (0 = off) +export PEDESTRIAN_MIN_SIZE_M=0.8 +# Shadow-env obs overrides: partner slots (training 20, GIGAFLOW eval 40; 40 real partners max-pooled is OOD in +# sidewalk crowds) and the ego perceived-size margin [m per side] (0.2 = native eval; nuPlan's 2.3 x 5.2 m ego +# reads as 2.7 x 5.6 m). Unset = planner yaml / arch.py defaults (40 / 0.2). +export OBS_SLOTS_PARTNERS_N=40 +export EVAL_PERCEIVED_SIZE_MARGIN_M=0.2 +ABLATION_TAG="" +[ "$SLIDING_GOAL_WINDOW" = "true" ] && ABLATION_TAG="${ABLATION_TAG}_slide" +[ "$STARTUP_ACCEL_JERK_CAP" != "0" ] && ABLATION_TAG="${ABLATION_TAG}_jacc${STARTUP_ACCEL_JERK_CAP}" +[ "$STARTUP_BRAKE_JERK_CAP" != "0" ] && ABLATION_TAG="${ABLATION_TAG}_jbrk${STARTUP_BRAKE_JERK_CAP}" +[ "$PEDESTRIAN_MIN_SIZE_M" != "0" ] && ABLATION_TAG="${ABLATION_TAG}_ped${PEDESTRIAN_MIN_SIZE_M}m" +[ -n "${OBS_SLOTS_PARTNERS_N:-}" ] && ABLATION_TAG="${ABLATION_TAG}_slots${OBS_SLOTS_PARTNERS_N}" +[ -n "${EVAL_PERCEIVED_SIZE_MARGIN_M:-}" ] && ABLATION_TAG="${ABLATION_TAG}_margin${EVAL_PERCEIVED_SIZE_MARGIN_M}" +# results live in the model's own eval folder, next to the PufferDrive benchmark evals +export GROUP=$RUN_DIR/eval/nuplan_val14${CHALLENGE_TAG}_${GOAL_SOURCE}${ABLATION_TAG}_$(date +%Y%m%d_%H%M%S)_${SLURM_JOB_ID:-local} + +PATH="$(dirname "$PY"):$PATH" bash "$PD/scripts/kesai/build_ext_if_changed.sh" "$PD" || exit 1 + +# The planner must come from this checkout (needs the freshly built C extension above). +IMPORTED_PLANNER=$("$PY" -c "import pufferlib.ocean.cosim.nuplan.planner as p; print(p.__file__)") || { echo "ERROR: planner import failed (see traceback above)"; exit 1; } +case "$IMPORTED_PLANNER" in + "$PD"/*) echo "planner: $IMPORTED_PLANNER" ;; + *) echo "ERROR: planner imported from '$IMPORTED_PLANNER', expected $PD"; exit 1 ;; +esac + +bash "$PD/pufferlib/ocean/cosim/nuplan/run_nuplan_planner.sh" diff --git a/scripts/kesai/build_ext_if_changed.sh b/scripts/kesai/build_ext_if_changed.sh index 222c322092..e06aeff300 100755 --- a/scripts/kesai/build_ext_if_changed.sh +++ b/scripts/kesai/build_ext_if_changed.sh @@ -2,7 +2,13 @@ # Rebuild env before training if the source has changed. # Minimize rebuilds as they crash running jobs that are using the env. REPO_DIR=$1 -BUILD_HASH_FILE=${REPO_DIR}/experiments/logs/build_source_hash +# .so files are tagged per python version; track builds per interpreter +PYTHON_TAG=$(python -c 'import sys; print("cp%d%d" % sys.version_info[:2])') +BUILD_HASH_FILE=${REPO_DIR}/experiments/logs/build_source_hash_${PYTHON_TAG} +# jobs of one interpreter that start together (e.g. both nuPlan evals) would otherwise build into the same files at once +mkdir -p "$(dirname "${BUILD_HASH_FILE}")" +exec 9> "${BUILD_HASH_FILE}.lock" +flock 9 SOURCE_HASH=$({ find ${REPO_DIR}/pufferlib \( -name '*.c' -o -name '*.h' \) -type f; echo ${REPO_DIR}/setup.py; } | sort | xargs sha256sum | sha256sum | cut -d' ' -f1) diff --git a/scripts/kesai/summarize_reset_diag.py b/scripts/kesai/summarize_reset_diag.py new file mode 100644 index 0000000000..c695db2021 --- /dev/null +++ b/scripts/kesai/summarize_reset_diag.py @@ -0,0 +1,67 @@ +"""Summarize [DRIVE DIAG] short-early-reset lines from training stderr logs. + +Usage: python scripts/kesai/summarize_reset_diag.py LOG.err [LOG2.err ...] +""" + +import re +import sys +from collections import defaultdict + +LINE_RE = re.compile(r"\[DRIVE DIAG\] short early reset: (.*)") +FIELD_RE = re.compile(r"(\w+)=(\S+)") +SUM_FIELDS = [ + "active", + "removed", + "stopped", + "spawn_failed", + "stopped_at_reset", + "collision", + "offroad", + "stop_line", + "empty_cell", + "goal_failed", +] + + +def main(paths): + per_map = defaultdict(lambda: {"lines": 0, **{k: 0.0 for k in SUM_FIELDS}}) + seeds = defaultdict(list) + for path in paths: + with open(path, errors="replace") as handle: + for line in handle: + match = LINE_RE.search(line) + if not match: + continue + fields = dict(FIELD_RE.findall(match.group(1))) + entry = per_map[fields["map"].rsplit("/", 1)[-1]] + entry["lines"] += 1 + for key in SUM_FIELDS: + entry[key] += float(fields[key]) + seeds[fields["map"].rsplit("/", 1)[-1]].append(fields["episode_seed"]) + if not per_map: + print("no [DRIVE DIAG] lines found") + return + print( + f"{'map':40s} {'lines':>5s} {'active':>7s} {'removed':>8s} {'stopped':>8s} {'spawnfail':>9s} " + f"{'stop@rst':>8s} {'coll':>6s} {'offrd':>6s} {'stopl':>6s} {'empty':>6s} {'goal':>5s}" + ) + for map_name, entry in sorted(per_map.items(), key=lambda kv: -kv[1]["lines"]): + n = entry["lines"] + print( + f"{map_name:40s} {n:5d} " + + " ".join(f"{entry[k] / n:{w}.1f}" for k, w in zip(SUM_FIELDS, (7, 8, 8, 9, 8, 6, 6, 6, 6, 5))) + ) + print( + "\nper-line means are per sub-env at the moment of the short reset; rejects are summed over all agents " + "of that reset (max 30 per agent)." + ) + print("example episode seeds per map (rerun locally with use_exact_episode_seed):") + for map_name, values in sorted(seeds.items()): + print(f" {map_name}: {values[:3]}") + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print(__doc__) + sys.exit(1) + main(sys.argv[1:]) diff --git a/scripts/visualize_speed_zones.py b/scripts/visualize_speed_zones.py new file mode 100644 index 0000000000..b78728323a --- /dev/null +++ b/scripts/visualize_speed_zones.py @@ -0,0 +1,534 @@ +#!/usr/bin/env python +"""Render the SPDZONE1 speed zones of PufferDrive map .bin files into one self-contained HTML inspector. + +python scripts/visualize_speed_zones.py --bin-dir ~/ordnung/data/CARLA/puffer_bins_zones +python scripts/visualize_speed_zones.py --bin-dir --output zones.html --fragment # no / wrapper +python scripts/visualize_speed_zones.py --bin-dir --draw-seed 3 # overlay one C-side random limit draw +""" + +import argparse +import json +import math +import pathlib +import sys + +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent / "data_utils")) +from mirror_map_bin import read_bin # noqa: E402 + +LANE_TYPE_MAX = 9 +ROAD_EDGE_TYPES = (20, 21, 22) +COORD_DECIMALS = 1 +MPS_TO_KMH = 3.6 + + +def _polyline(road): + return [[round(x, COORD_DECIMALS), round(y, COORD_DECIMALS)] for x, y in zip(road["x"], road["y"])] + + +def _bbox_diagonal_m(points): + xs = [p[0] for p in points] + ys = [p[1] for p in points] + return math.hypot(max(xs) - min(xs), max(ys) - min(ys)) + + +def sample_limit_draw(bin_file: pathlib.Path, seed: int, delta_mps: float, min_mps: float, max_mps: float) -> dict: + """Effective per-lane limits (m/s) after one episode draw of the C sampler, keyed by road element id.""" + from pufferlib.ocean.drive.drive import Drive + + env = Drive( + map_dir=str(bin_file), + num_maps=1, + num_agents=32, + allow_map_subset=True, + scenario_length=300, + resample_frequency=1_000_000, + termination_mode=0, + report_interval=1, + speed_limit_random_prob=1.0, + speed_limit_random_delta_mps=delta_mps, + speed_limit_random_min_mps=min_mps, + speed_limit_random_max_mps=max_mps, + ) + env.reset(seed=seed) + roads = env.get_state()[0]["road_elements"] + env.close() + return {road["id"]: road["speed_limit"] for road in roads} + + +def collect_map(bin_file: pathlib.Path, draw: dict | None = None) -> dict: + data = read_bin(bin_file) + lanes, edges = [], [] + for road in data["roads"]: + if 0 <= road["type"] <= LANE_TYPE_MAX: + lanes.append( + { + "id": road["id"], + "zone": road["speed_zone_idx"], + "kmh": round(road["speed_limit"] * MPS_TO_KMH, 1), + "kmh_draw": None if draw is None else round(draw[road["id"]] * MPS_TO_KMH, 1), + "length": round(road["length"], 1), + "pts": _polyline(road), + } + ) + elif road["type"] in ROAD_EDGE_TYPES: + edges.append(_polyline(road)) + + zones = {} + for lane in lanes: + if lane["zone"] < 0: + continue + zone = zones.setdefault( + lane["zone"], {"idx": lane["zone"], "lanes": 0, "length": 0.0, "kmh": set(), "kmh_draw": set(), "pts": []} + ) + zone["lanes"] += 1 + if lane["kmh_draw"] is not None: + zone["kmh_draw"].add(lane["kmh_draw"]) + zone["length"] += lane["length"] + zone["kmh"].add(lane["kmh"]) + zone["pts"].extend(lane["pts"]) + zone_rows = [] + for zone in sorted(zones.values(), key=lambda z: z["idx"]): + xs = [p[0] for p in zone["pts"]] + ys = [p[1] for p in zone["pts"]] + zone_rows.append( + { + "idx": zone["idx"], + "lanes": zone["lanes"], + "length": round(zone["length"], 0), + "extent": round(_bbox_diagonal_m(zone["pts"]), 0), + "kmh": sorted(zone["kmh"]), + "kmh_draw": sorted(zone["kmh_draw"]), + "bbox": [min(xs), min(ys), max(xs), max(ys)], + } + ) + return { + "name": bin_file.stem.split("__")[-1], + "file": bin_file.name, + "has_zone_section": data["has_zone_section"], + "lanes": lanes, + "edges": edges, + "zones": zone_rows, + } + + +STYLE = """ +:root { + --ground: #F2F3F1; --panel: #FAFAF8; --line: #D9DCD8; --text: #1C2126; --muted: #6E7882; + --accent: #C9552B; --accent-soft: rgba(201, 85, 43, 0.12); --edge: #C3C7C2; --junction: #9AA1A8; + --row-hover: #ECEEEA; --tooltip: #1C2126; --tooltip-text: #F2F3F1; --lane-l: 44%; + color-scheme: light; +} +@media (prefers-color-scheme: dark) { + :root:not([data-theme="light"]) { + --ground: #141719; --panel: #1B1F22; --line: #2C3237; --text: #E4E8EA; --muted: #8B95A0; + --accent: #E2764B; --accent-soft: rgba(226, 118, 75, 0.18); --edge: #33393E; --junction: #5C656E; + --row-hover: #23282C; --tooltip: #E4E8EA; --tooltip-text: #141719; --lane-l: 62%; + color-scheme: dark; + } +} +:root[data-theme="dark"] { + --ground: #141719; --panel: #1B1F22; --line: #2C3237; --text: #E4E8EA; --muted: #8B95A0; + --accent: #E2764B; --accent-soft: rgba(226, 118, 75, 0.18); --edge: #33393E; --junction: #5C656E; + --row-hover: #23282C; --tooltip: #E4E8EA; --tooltip-text: #141719; --lane-l: 62%; + color-scheme: dark; +} +* { box-sizing: border-box; } +html, body { height: 100%; } +body { + margin: 0; background: var(--ground); color: var(--text); + font-family: "IBM Plex Sans", "Segoe UI", system-ui, sans-serif; font-size: 13px; line-height: 1.4; +} +.app { display: grid; grid-template-rows: auto 1fr; grid-template-columns: 1fr 340px; height: 100vh; min-height: 560px; } +header { + grid-column: 1 / -1; display: flex; align-items: center; gap: 20px; padding: 10px 16px; + border-bottom: 1px solid var(--line); background: var(--panel); flex-wrap: wrap; +} +header h1 { font-size: 15px; font-weight: 600; margin: 0; letter-spacing: 0.01em; white-space: nowrap; } +.tabs { display: flex; gap: 4px; flex-wrap: wrap; } +.tabs button, .seg button { + font: inherit; color: var(--text); background: transparent; border: 1px solid var(--line); border-radius: 4px; + padding: 4px 10px; cursor: pointer; +} +.tabs button[aria-pressed="true"], .seg button[aria-pressed="true"] { background: var(--accent-soft); border-color: var(--accent); } +.tabs button:focus-visible, .seg button:focus-visible, .zone-table tr:focus-visible { outline: 2px solid var(--accent); outline-offset: 1px; } +.seg { display: flex; } +.seg button:first-child { border-radius: 4px 0 0 4px; } +.seg button:last-child { border-radius: 0 4px 4px 0; margin-left: -1px; } +.controls { display: flex; align-items: center; gap: 14px; margin-left: auto; color: var(--muted); } +.controls label { display: flex; align-items: center; gap: 5px; cursor: pointer; } +.stage { position: relative; overflow: hidden; } +canvas { display: block; width: 100%; height: 100%; cursor: crosshair; } +.hint { position: absolute; left: 12px; bottom: 10px; color: var(--muted); font-size: 12px; pointer-events: none; } +.legend { position: absolute; right: 12px; top: 10px; display: flex; flex-direction: column; gap: 3px; font-family: "IBM Plex Mono", ui-monospace, monospace; font-size: 11px; pointer-events: none; } +.legend span { display: inline-flex; align-items: center; gap: 6px; } +.legend i { width: 18px; height: 3px; border-radius: 2px; display: inline-block; } +.tooltip { + position: absolute; pointer-events: none; background: var(--tooltip); color: var(--tooltip-text); + padding: 6px 9px; border-radius: 4px; font-family: "IBM Plex Mono", ui-monospace, monospace; font-size: 11.5px; + white-space: nowrap; transform: translate(12px, 12px); line-height: 1.5; +} +.tooltip[hidden] { display: none; } +aside { border-left: 1px solid var(--line); background: var(--panel); display: flex; flex-direction: column; min-height: 0; } +.stats { display: grid; grid-template-columns: 1fr 1fr; gap: 8px 12px; padding: 12px 14px; border-bottom: 1px solid var(--line); } +.stat .k { color: var(--muted); font-size: 11px; text-transform: uppercase; letter-spacing: 0.06em; } +.stat .v { font-family: "IBM Plex Mono", ui-monospace, monospace; font-size: 16px; font-variant-numeric: tabular-nums; } +.hist { padding: 10px 14px 6px; border-bottom: 1px solid var(--line); } +.hist .k { color: var(--muted); font-size: 11px; text-transform: uppercase; letter-spacing: 0.06em; margin-bottom: 4px; } +.hist svg { width: 100%; height: 70px; display: block; overflow: visible; } +.hist text { fill: var(--muted); font-family: "IBM Plex Mono", ui-monospace, monospace; font-size: 9px; } +.hist rect { fill: var(--muted); opacity: 0.55; } +.table-wrap { overflow: auto; flex: 1; min-height: 0; } +.zone-table { width: 100%; border-collapse: collapse; font-variant-numeric: tabular-nums; } +.zone-table th { position: sticky; top: 0; background: var(--panel); text-align: right; font-weight: 500; color: var(--muted); font-size: 11px; text-transform: uppercase; letter-spacing: 0.06em; padding: 8px 10px 6px; border-bottom: 1px solid var(--line); } +.zone-table th:first-child, .zone-table td:first-child { text-align: left; } +.zone-table td { text-align: right; padding: 5px 10px; font-family: "IBM Plex Mono", ui-monospace, monospace; font-size: 12px; border-bottom: 1px solid var(--line); } +.zone-table tr { cursor: pointer; } +.zone-table tr:hover td { background: var(--row-hover); } +.zone-table tr[aria-selected="true"] td { background: var(--accent-soft); } +.zone-table i { width: 10px; height: 10px; border-radius: 2px; display: inline-block; vertical-align: -1px; margin-right: 6px; } +.note { padding: 8px 14px; color: var(--muted); font-size: 11.5px; border-top: 1px solid var(--line); } +@media (max-width: 860px) { .app { grid-template-columns: 1fr; grid-template-rows: auto 1fr 320px; } aside { border-left: 0; border-top: 1px solid var(--line); } } +@media (prefers-reduced-motion: no-preference) { .tabs button, .seg button { transition: background 120ms; } } +""" + +SCRIPT = r""" +const MAPS = window.__SPEED_ZONE_MAPS__; +const KMH_MIN = 10, KMH_MAX = 130; +const state = { map: 0, mode: "zone", junction: true, edges: true, hover: null, selected: null, view: null }; +const canvas = document.getElementById("map"); +const ctx = canvas.getContext("2d"); +const tooltip = document.getElementById("tooltip"); +const stage = document.querySelector(".stage"); + +function laneL() { return getComputedStyle(document.documentElement).getPropertyValue("--lane-l").trim() || "44%"; } +function zoneColor(idx) { return `hsl(${(idx * 137.508) % 360} 58% ${laneL()})`; } +function limitColor(kmh) { + const t = Math.min(1, Math.max(0, (kmh - KMH_MIN) / (KMH_MAX - KMH_MIN))); + return `hsl(${Math.round(215 - 215 * t)} 70% ${laneL()})`; +} +function laneColor(lane) { + if (state.mode === "zone") return zoneColor(lane.zone); + if (state.mode === "draw") return limitColor(lane.kmh_draw ?? lane.kmh); + return limitColor(lane.kmh); +} +function hasDraw() { return current().lanes.some(l => l.kmh_draw !== null); } +function current() { return MAPS[state.map]; } +function zoneOf(idx) { return current().zones.find(z => z.idx === idx); } + +function fitView() { + const m = current(); + const pts = m.lanes.flatMap(l => l.pts).concat(m.edges.flat()); + let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity; + for (const [x, y] of pts) { if (x < minX) minX = x; if (x > maxX) maxX = x; if (y < minY) minY = y; if (y > maxY) maxY = y; } + fitBox([minX, minY, maxX, maxY], 30); +} +function fitBox(box, pad) { + const w = canvas.clientWidth, h = canvas.clientHeight; + const scale = Math.min((w - 2 * pad) / Math.max(1, box[2] - box[0]), (h - 2 * pad) / Math.max(1, box[3] - box[1])); + state.view = { scale, tx: w / 2 - scale * (box[0] + box[2]) / 2, ty: h / 2 + scale * (box[1] + box[3]) / 2 }; +} +function toScreen(p) { const v = state.view; return [v.tx + p[0] * v.scale, v.ty - p[1] * v.scale]; } + +function resize() { + const dpr = window.devicePixelRatio || 1; + canvas.width = Math.round(canvas.clientWidth * dpr); + canvas.height = Math.round(canvas.clientHeight * dpr); + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + if (!state.view) fitView(); + draw(); +} + +function strokePolyline(pts) { + ctx.beginPath(); + pts.forEach((p, i) => { const s = toScreen(p); if (i === 0) ctx.moveTo(s[0], s[1]); else ctx.lineTo(s[0], s[1]); }); + ctx.stroke(); +} + +function draw() { + const m = current(); + const css = getComputedStyle(document.documentElement); + ctx.clearRect(0, 0, canvas.clientWidth, canvas.clientHeight); + ctx.lineCap = "round"; ctx.lineJoin = "round"; + if (state.edges) { + ctx.strokeStyle = css.getPropertyValue("--edge"); ctx.lineWidth = 1; ctx.setLineDash([]); + m.edges.forEach(strokePolyline); + } + const focusZone = state.selected ?? (state.hover ? state.hover.zone : null); + const dimmed = focusZone !== null && focusZone >= 0; + const base = Math.max(1.4, Math.min(4, 0.9 * state.view.scale)); + if (state.junction) { + ctx.setLineDash([4, 4]); ctx.lineWidth = base * 0.7; + ctx.globalAlpha = dimmed ? 0.35 : 0.9; + for (const l of m.lanes) { + if (l.zone >= 0) continue; + ctx.strokeStyle = state.mode === "draw" && l.kmh_draw !== null ? limitColor(l.kmh_draw) : css.getPropertyValue("--junction"); + strokePolyline(l.pts); + } + } + ctx.setLineDash([]); + for (const lane of m.lanes) { + if (lane.zone < 0) continue; + const inFocus = dimmed && lane.zone === focusZone; + ctx.globalAlpha = dimmed && !inFocus ? 0.22 : 1; + ctx.lineWidth = inFocus ? base * 1.6 : base; + ctx.strokeStyle = laneColor(lane); + strokePolyline(lane.pts); + } + if (state.hover) { + ctx.globalAlpha = 1; ctx.lineWidth = base * 2.2; ctx.strokeStyle = css.getPropertyValue("--accent"); + strokePolyline(state.hover.pts); + } + ctx.globalAlpha = 1; +} + +function distToSegment(p, a, b) { + const dx = b[0] - a[0], dy = b[1] - a[1]; + const len2 = dx * dx + dy * dy; + const t = len2 === 0 ? 0 : Math.max(0, Math.min(1, ((p[0] - a[0]) * dx + (p[1] - a[1]) * dy) / len2)); + return Math.hypot(p[0] - (a[0] + t * dx), p[1] - (a[1] + t * dy)); +} +function pickLane(sx, sy) { + let best = null, bestD = 8; + for (const lane of current().lanes) { + if (lane.zone < 0 && !state.junction) continue; + const scr = lane.pts.map(toScreen); + for (let i = 1; i < scr.length; i++) { + const d = distToSegment([sx, sy], scr[i - 1], scr[i]); + if (d < bestD) { bestD = d; best = lane; } + } + } + return best; +} + +function showTooltip(lane, x, y) { + if (!lane) { tooltip.hidden = true; return; } + const z = lane.zone >= 0 ? zoneOf(lane.zone) : null; + const drawText = lane.kmh_draw === null ? "" : ` → draw ${lane.kmh_draw} km/h`; + const lines = [`lane ${lane.id} · limit ${lane.kmh} km/h${drawText} · ${lane.length} m`]; + if (z) lines.push(`zone ${z.idx}: ${z.lanes} lanes, extent ${z.extent} m, ${z.length} m of lane`); + else lines.push("junction lane · no zone (inherits from entry lanes)"); + tooltip.textContent = ""; lines.forEach(t => { const d = document.createElement("div"); d.textContent = t; tooltip.appendChild(d); }); + tooltip.style.left = `${x}px`; tooltip.style.top = `${y}px`; tooltip.hidden = false; +} + +let drag = null, moved = false; +canvas.addEventListener("pointerdown", e => { drag = { x: e.clientX, y: e.clientY, tx: state.view.tx, ty: state.view.ty }; moved = false; canvas.setPointerCapture(e.pointerId); }); +canvas.addEventListener("pointermove", e => { + const rect = canvas.getBoundingClientRect(); + const sx = e.clientX - rect.left, sy = e.clientY - rect.top; + if (drag) { + const dx = e.clientX - drag.x, dy = e.clientY - drag.y; + if (Math.abs(dx) + Math.abs(dy) > 2) moved = true; + state.view.tx = drag.tx + dx; state.view.ty = drag.ty + dy; + tooltip.hidden = true; requestAnimationFrame(draw); return; + } + const lane = pickLane(sx, sy); + if (lane !== state.hover) { state.hover = lane; requestAnimationFrame(draw); } + showTooltip(lane, sx, sy); +}); +canvas.addEventListener("pointerup", e => { + canvas.releasePointerCapture(e.pointerId); + if (!moved) { + const lane = state.hover; + selectZone(lane && lane.zone >= 0 ? lane.zone : null, false); + } + drag = null; +}); +canvas.addEventListener("pointerleave", () => { state.hover = null; tooltip.hidden = true; draw(); }); +canvas.addEventListener("wheel", e => { + e.preventDefault(); + const rect = canvas.getBoundingClientRect(); + const sx = e.clientX - rect.left, sy = e.clientY - rect.top; + const factor = Math.exp(-e.deltaY * 0.0015); + const v = state.view; + v.tx = sx - (sx - v.tx) * factor; v.ty = sy - (sy - v.ty) * factor; v.scale *= factor; + requestAnimationFrame(draw); +}, { passive: false }); +window.addEventListener("keydown", e => { if (e.key === "Escape") selectZone(null, false); if (e.key === "f") { fitView(); draw(); } }); + +function selectZone(idx, zoom) { + state.selected = idx; + document.querySelectorAll(".zone-table tr[data-zone]").forEach(tr => tr.setAttribute("aria-selected", String(Number(tr.dataset.zone) === idx))); + if (idx !== null && zoom) { fitBox(zoneOf(idx).bbox, 60); } + if (idx !== null) { const row = document.querySelector(`.zone-table tr[data-zone="${idx}"]`); if (row) row.scrollIntoView({ block: "nearest" }); } + draw(); +} + +function median(a) { const s = [...a].sort((x, y) => x - y); return s.length ? (s.length % 2 ? s[(s.length - 1) / 2] : (s[s.length / 2 - 1] + s[s.length / 2]) / 2) : 0; } + +function renderPanel() { + const m = current(); + const junction = m.lanes.filter(l => l.zone < 0).length; + const extents = m.zones.map(z => z.extent); + const stats = [ + ["zones", m.zones.length], ["lanes", m.lanes.length], + ["junction lanes", junction], ["median extent", `${median(extents)} m`], + ["min extent", `${extents.length ? Math.min(...extents) : 0} m`], ["max extent", `${extents.length ? Math.max(...extents) : 0} m`], + ]; + document.getElementById("stats").innerHTML = stats.map(([k, v]) => `
${k}
${v}
`).join(""); + + const binW = 50, maxExt = Math.max(100, ...extents), nb = Math.ceil(maxExt / binW); + const counts = new Array(nb).fill(0); + extents.forEach(x => { counts[Math.min(nb - 1, Math.floor(x / binW))]++; }); + const W = 300, H = 56, peak = Math.max(1, ...counts), bw = W / nb; + let svg = ``; + counts.forEach((c, i) => { const h = (c / peak) * H; svg += ``; }); + for (let i = 0; i <= nb; i += Math.max(1, Math.round(nb / 5))) svg += `${i * binW}`; + svg += ""; + document.getElementById("hist").innerHTML = `
zone extent (bbox diagonal, m) — count per ${binW} m bin
${svg}`; + + const rows = [...m.zones].sort((a, b) => a.extent - b.extent).map(z => + `${z.idx}${z.kmh.join("/")}${z.kmh_draw.length ? z.kmh_draw.join("/") : "–"}${z.lanes}${z.extent}${z.length}` + ).join(""); + document.getElementById("rows").innerHTML = rows; + document.querySelectorAll(".zone-table tr[data-zone]").forEach(tr => { + tr.addEventListener("click", () => selectZone(Number(tr.dataset.zone), true)); + tr.addEventListener("keydown", e => { if (e.key === "Enter") selectZone(Number(tr.dataset.zone), true); }); + tr.addEventListener("mouseenter", () => { state.hover = null; state.selected = state.selected ?? null; tr.classList.add("hot"); }); + }); + renderLegend(); +} + +function renderLegend() { + const el = document.getElementById("legend"); + if (state.mode === "limit") { + const kmhs = [...new Set(current().lanes.filter(l => l.zone >= 0).map(l => l.kmh))].sort((a, b) => a - b); + el.innerHTML = kmhs.map(k => `${k} km/h`).join("") + `junction lane`; + } else if (state.mode === "draw") { + const steps = [10, 30, 50, 70, 90, 110, 130]; + el.innerHTML = steps.map(k => `${k} km/h`).join("") + `junction lane (drawn, min over entries)`; + } else { + el.innerHTML = `one hue per zonejunction lane (no zone)`; + } +} + +function selectMap(i) { + state.map = i; state.selected = null; state.hover = null; state.view = null; + document.querySelectorAll(".tabs button").forEach((b, j) => b.setAttribute("aria-pressed", String(i === j))); + fitView(); renderPanel(); draw(); +} + +const tabs = document.querySelector(".tabs"); +MAPS.forEach((m, i) => { + const b = document.createElement("button"); b.textContent = m.name; b.type = "button"; + b.setAttribute("aria-pressed", String(i === 0)); b.addEventListener("click", () => selectMap(i)); tabs.appendChild(b); +}); +document.getElementById("draw-btn").hidden = !hasDraw(); +if (hasDraw()) { + state.mode = "draw"; + document.querySelectorAll(".seg button").forEach(x => x.setAttribute("aria-pressed", String(x.dataset.mode === "draw"))); +} +document.querySelectorAll(".seg button").forEach(b => b.addEventListener("click", () => { + state.mode = b.dataset.mode; + document.querySelectorAll(".seg button").forEach(x => x.setAttribute("aria-pressed", String(x === b))); + renderLegend(); draw(); +})); +document.getElementById("junction").addEventListener("change", e => { state.junction = e.target.checked; draw(); }); +document.getElementById("edges").addEventListener("change", e => { state.edges = e.target.checked; draw(); }); +document.getElementById("fit").addEventListener("click", () => { fitView(); draw(); }); +window.addEventListener("resize", resize); +window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", () => { renderPanel(); draw(); }); +new MutationObserver(() => { renderPanel(); draw(); }).observe(document.documentElement, { attributes: true, attributeFilter: ["data-theme"] }); +renderPanel(); +resize(); +""" + + +def build_html(maps: list[dict], fragment: bool, draw_note: str) -> str: + payload = json.dumps(maps, separators=(",", ":")) + total_zones = sum(len(m["zones"]) for m in maps) + head = ( + "CARLA Speed Zones\n" + '\n' + '\n' + f"\n" + ) + body = f""" +
+
+

CARLA Speed Zones

+
+
+ + + +
+
+ + + +
+
+
+ +
+ +
drag to pan · wheel to zoom · click a lane to isolate its zone · Esc clears · {len(maps)} maps, {total_zones} zones
+
+ +
+ + +""" + if fragment: + return head + body + return f'\n\n\n\n\n{head}\n{body}\n\n' + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--bin-dir", required=True, help="Directory of *.bin map files with a SPDZONE1 section") + parser.add_argument("--output", default=None, help="Output .html (default: /speed_zones.html)") + parser.add_argument( + "--fragment", action="store_true", help="Omit the // wrapper (artifact publishing)" + ) + parser.add_argument( + "--draw-seed", type=int, default=None, help="Overlay one episode draw of the C speed-limit sampler" + ) + parser.add_argument("--draw-delta-mps", type=float, default=9.72) + parser.add_argument("--draw-min-mps", type=float, default=1.39) + parser.add_argument("--draw-max-mps", type=float, default=36.11) + args = parser.parse_args() + + bin_dir = pathlib.Path(args.bin_dir).expanduser() + bin_files = sorted(bin_dir.glob("*.bin")) + if not bin_files: + sys.exit(f"no .bin files in {bin_dir}") + draws = ( + { + f: sample_limit_draw(f, args.draw_seed, args.draw_delta_mps, args.draw_min_mps, args.draw_max_mps) + for f in bin_files + } + if args.draw_seed is not None + else {} + ) + maps = [collect_map(f, draws.get(f)) for f in bin_files] + draw_note = ( + f"Random draw: episode seed {args.draw_seed}, one offset per zone uniform in ±{args.draw_delta_mps * MPS_TO_KMH:.0f} km/h, " + f"clipped to {args.draw_min_mps * MPS_TO_KMH:.0f}–{args.draw_max_mps * MPS_TO_KMH:.0f} km/h; junction lanes take the min over their entries." + if args.draw_seed is not None + else "No random draw overlaid (pass --draw-seed)." + ) + missing = [m["name"] for m in maps if not m["has_zone_section"]] + if missing: + print(f"warning: no SPDZONE1 section in {missing}", file=sys.stderr) + output = pathlib.Path(args.output) if args.output else bin_dir / "speed_zones.html" + output.write_text(build_html(maps, args.fragment, draw_note)) + print( + f"wrote {output} ({output.stat().st_size / 1e6:.1f} MB, {len(maps)} maps, {sum(len(m['zones']) for m in maps)} zones)" + ) + + +if __name__ == "__main__": + main() diff --git a/tests/drive/Makefile b/tests/drive/Makefile index 349f5f1272..cf7b5479bc 100644 --- a/tests/drive/Makefile +++ b/tests/drive/Makefile @@ -13,11 +13,13 @@ UNIT_SRCS := \ test_drive_goals.c \ test_drive_metrics.c \ test_drive_red_light.c \ - test_drive_traffic_lights.c \ test_drive_stop_sign.c \ + test_drive_traffic_lights.c \ test_drive_dynamics.c \ test_drive_map_cache.c \ - test_drive_idm.c + test_drive_idm.c \ + test_drive_speed_limits.c \ + test_drive_cosim_setters.c SMOKE_SRCS := test_drive_env_smoke.c PERF_SRCS := test_perf.c diff --git a/tests/drive/include/drive_fixture.h b/tests/drive/include/drive_fixture.h index a5a8622969..71fed52d33 100644 --- a/tests/drive/include/drive_fixture.h +++ b/tests/drive/include/drive_fixture.h @@ -71,6 +71,7 @@ static inline Drive drive_test_env_config( env.traffic_light_behavior = INFRACTION_BEHAVIOR_IGNORE; env.stop_sign_behavior = INFRACTION_BEHAVIOR_IGNORE; env.use_map_cache = use_map_cache; + env.conditioning_speed_scale = 1.5f; env.goal_radius = 2.0f; env.goal_speed = 3.0f; env.goal_speed_randomization = 1; diff --git a/tests/drive/test_drive_cosim_setters.c b/tests/drive/test_drive_cosim_setters.c new file mode 100644 index 0000000000..16986abce0 --- /dev/null +++ b/tests/drive/test_drive_cosim_setters.c @@ -0,0 +1,344 @@ +#include "include/drive_fixture.h" +#include "include/test.h" + +// Defensive coverage for the co-sim external-state setters (drive.h "Co-simulation external-state +// setters" section). These are called from Python (pufferlib/ocean/cosim/*) every tick to overwrite +// agent state from CARLA/nuplan + +// --------------------------------------------------------------------------- +// c_set_agent_sizes +// --------------------------------------------------------------------------- + +static int test_set_agent_sizes_updates_dimensions_radius_and_wheelbase(void) { + // Overwriting bounding-box size must also refresh the derived radius (collision broad-phase) + // and wheelbase (steering derivation in c_set_agent_states) -- both are easy to forget since + // neither is read back in the same function. + Drive env = {0}; + Agent agent = drive_test_agent(0.0f, 0.0f, 0.0f); // spawns at length=4, width=2 + env.agents = &agent; + env.num_total_agents = 1; + + int idx[1] = {0}; + float length[1] = {8.0f}; + float width[1] = {3.0f}; + c_set_agent_sizes(&env, 1, idx, length, width); + + EXPECT_NEAR(agent.sim_length, 8.0f, 1e-6f); + EXPECT_NEAR(agent.sim_width, 3.0f, 1e-6f); + EXPECT_NEAR(agent.radius, 0.5f * sqrtf(8.0f * 8.0f + 3.0f * 3.0f), 1e-6f); + EXPECT_NEAR(agent.wheelbase, 0.6f * 8.0f, 1e-6f); + return 0; +} + +static int test_set_agent_sizes_rejects_out_of_range_index_and_bad_size(void) { + // External input: an index outside [0, num_total_agents) or a non-positive size fails the whole + // call (-1) instead of being clamped or skipped; the agent must not be touched. + Drive env = {0}; + Agent agent = drive_test_agent(0.0f, 0.0f, 0.0f); + env.agents = &agent; + env.num_total_agents = 1; + + int bad_idx[1] = {5}; + float length[1] = {8.0f}; + float width[1] = {3.0f}; + EXPECT_EQ_INT(c_set_agent_sizes(&env, 1, bad_idx, length, width), -1); + EXPECT_NEAR(agent.sim_length, 4.0f, 1e-6f); + + int idx[1] = {0}; + float zero_width[1] = {0.0f}; + EXPECT_EQ_INT(c_set_agent_sizes(&env, 1, idx, length, zero_width), -1); + EXPECT_NEAR(agent.sim_length, 4.0f, 1e-6f); + EXPECT_EQ_INT(c_set_agent_sizes(&env, 1, idx, length, width), 0); + EXPECT_NEAR(agent.sim_length, 8.0f, 1e-6f); + return 0; +} + +// --------------------------------------------------------------------------- +// c_set_traffic_light_states +// --------------------------------------------------------------------------- + +static TrafficControlElement make_traffic_element(int type, int state_size, int *states_storage) { + TrafficControlElement element = {0}; + element.type = type; + element.state_size = state_size; + element.states = states_storage; + return element; +} + +static int test_set_traffic_light_states_writes_current_timestep_for_lights_only(void) { + // Only TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT elements are written, only at env->timestep, and every + // other timestep slot must be left alone. + int light_states[5] = {99, 99, 99, 99, 99}; + int stop_sign_states[5] = {99, 99, 99, 99, 99}; + TrafficControlElement elements[2]; + elements[0] = make_traffic_element(TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT, 5, light_states); + elements[1] = make_traffic_element(TRAFFIC_CONTROL_TYPE_STOP_SIGN, 5, stop_sign_states); + + Drive env = {0}; + env.traffic_elements = elements; + env.num_traffic_elements = 2; + env.timestep = 2; + + int new_states[2] = {3, 7}; // one entry per traffic element, indexed like env->traffic_elements + EXPECT_EQ_INT(c_set_traffic_light_states(&env, new_states), 0); + + EXPECT_EQ_INT(light_states[2], 3); // written at the current timestep + EXPECT_EQ_INT(light_states[0], 99); + EXPECT_EQ_INT(light_states[1], 99); + EXPECT_EQ_INT(light_states[3], 99); + EXPECT_EQ_INT(light_states[4], 99); + for (int t = 0; t < 5; t++) { + EXPECT_EQ_INT(stop_sign_states[t], 99); // non-light element untouched + } + return 0; +} + +static int test_set_traffic_light_states_rejects_out_of_range_timestep_and_state(void) { + // A timestep outside [0, state_size) or a state outside the enum fails the call (-1) instead of + // being skipped; a NULL states buffer (an element with no schedule) is skipped, never dereferenced. + int light_states[3] = {99, 99, 99}; + TrafficControlElement elements[2]; + elements[0] = make_traffic_element(TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT, 3, NULL); + elements[1] = make_traffic_element(TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT, 3, light_states); + + Drive env = {0}; + env.traffic_elements = elements; + env.num_traffic_elements = 2; + + int new_states[2] = {1, 1}; + env.timestep = 10; // out of range for state_size=3 + EXPECT_EQ_INT(c_set_traffic_light_states(&env, new_states), -1); + EXPECT_EQ_INT(light_states[0], 99); + EXPECT_EQ_INT(light_states[1], 99); + EXPECT_EQ_INT(light_states[2], 99); + + env.timestep = 1; + int bad_states[2] = {1, TRAFFIC_CONTROL_STATE_OFF + 1}; + EXPECT_EQ_INT(c_set_traffic_light_states(&env, bad_states), -1); + EXPECT_EQ_INT(light_states[1], 99); + EXPECT_EQ_INT(c_set_traffic_light_states(&env, new_states), 0); + EXPECT_EQ_INT(light_states[1], 1); + return 0; +} + +// --------------------------------------------------------------------------- +// c_set_agent_states +// --------------------------------------------------------------------------- + +static int test_set_agent_states_teleport_resets_prev_pose(void) { + // An externally-set pose is a teleport: prev_* must follow it. If prev stays at + // the old pose, every prev-based swept check (find_lane_and_offroad's road-edge + // crossing, compute_metrics' goal-reach segment) sweeps the whole gap and fires + // spuriously -- observed as an on-lane teleported ego always classified offroad + // with dead lane observations. + Drive env = {0}; + Agent agent = drive_test_agent(500.0f, 500.0f, 1.0f); + agent.prev_x = 500.0f; + agent.prev_y = 500.0f; + env.agents = &agent; + env.num_total_agents = 1; + + int idx[1] = {0}; + float x[1] = {10.0f}, y[1] = {20.0f}, z[1] = {0.0f}, h[1] = {0.5f}; + float vx[1] = {1.0f}, vy[1] = {0.0f}, yr[1] = {0.0f}, al[1] = {0.0f}; + EXPECT_EQ_INT(c_set_agent_states(&env, 1, idx, x, y, z, h, vx, vy, yr, al, NULL), 0); + + EXPECT_NEAR(agent.prev_x, agent.sim_x, 1e-6f); + EXPECT_NEAR(agent.prev_y, agent.sim_y, 1e-6f); + EXPECT_NEAR(agent.prev_cos_heading, agent.cos_heading, 1e-6f); + EXPECT_NEAR(agent.prev_sin_heading, agent.sin_heading, 1e-6f); + return 0; +} + +static int test_set_agent_states_seconds_stopped_injects_or_preserves(void) { + // seconds_stopped is optional: a NULL array leaves c_step's own accumulation untouched + // (the co-sim default CARLA/nuPlan rely on), a non-NULL array injects it as state. + Drive env = {0}; + Agent agent = drive_test_agent(0.0f, 0.0f, 0.0f); + agent.seconds_stopped = 12.5f; + env.agents = &agent; + env.num_total_agents = 1; + + int idx[1] = {0}; + float x[1] = {1.0f}, y[1] = {2.0f}, z[1] = {0.0f}, h[1] = {0.0f}; + float vx[1] = {0.0f}, vy[1] = {0.0f}, yr[1] = {0.0f}, al[1] = {0.0f}; + + EXPECT_EQ_INT(c_set_agent_states(&env, 1, idx, x, y, z, h, vx, vy, yr, al, NULL), 0); + EXPECT_NEAR(agent.seconds_stopped, 12.5f, 1e-6f); + + float seconds_stopped[1] = {3.0f}; + EXPECT_EQ_INT(c_set_agent_states(&env, 1, idx, x, y, z, h, vx, vy, yr, al, seconds_stopped), 0); + EXPECT_NEAR(agent.seconds_stopped, 3.0f, 1e-6f); + + float negative_seconds_stopped[1] = {-1.0f}; + EXPECT_EQ_INT(c_set_agent_states(&env, 1, idx, x, y, z, h, vx, vy, yr, al, negative_seconds_stopped), -1); + EXPECT_NEAR(agent.seconds_stopped, 3.0f, 1e-6f); + return 0; +} + +// --------------------------------------------------------------------------- +// c_set_agent_goals +// --------------------------------------------------------------------------- + +static int test_set_agent_goals_sets_positions_lane_and_count(void) { + // Externally-set goals must land in the bin frame (world_mean subtracted), report an accurate + // goal_count, and reset the current_goal_* window to slot 0 -- all fields that used to be left + // stale from whatever a previous internal goal generation wrote. With no grid map (as here), + // goal->lane snapping (find_goal_lane) must degrade to list_goal_lane = -1, the "no GPS + // lane-distance" convention shared with GOAL_SOURCE_GT -- never read a NULL grid. + Drive env = {0}; + env.world_mean_x = 100.0f; + env.world_mean_y = 200.0f; + Agent agent = {0}; + agent.list_goal_lane[0] = 42; // stale value from a prior internal goal generation + agent.goal_count = 99; // stale value: must be overwritten, not left stale + env.agents = &agent; + env.num_total_agents = 1; + + float gx[3] = {110.0f, 130.0f, 150.0f}; + float gy[3] = {205.0f, 215.0f, 225.0f}; + float gz[3] = {1.0f, 2.0f, 3.0f}; + float gdx[3] = {1.0f, 1.0f, 1.0f}; + float gdy[3] = {0.0f, 0.0f, 0.0f}; + EXPECT_EQ_INT(c_set_agent_goals(&env, 0, 3, gx, gy, gz, gdx, gdy), 0); + + EXPECT_EQ_INT(agent.goal_count, 3); + EXPECT_EQ_INT(agent.current_goal_idx, 0); + for (int w = 0; w < 3; w++) { + EXPECT_NEAR(agent.list_goal_x[w], gx[w] - env.world_mean_x, 1e-6f); + EXPECT_NEAR(agent.list_goal_y[w], gy[w] - env.world_mean_y, 1e-6f); + EXPECT_NEAR(agent.list_goal_z[w], gz[w], 1e-6f); + EXPECT_EQ_INT(agent.list_goal_lane[w], -1); + } + EXPECT_NEAR(agent.current_goal_x, agent.list_goal_x[0], 1e-6f); + EXPECT_NEAR(agent.current_goal_y, agent.list_goal_y[0], 1e-6f); + EXPECT_NEAR(agent.current_goal_z, agent.list_goal_z[0], 1e-6f); + return 0; +} + +static int test_set_agent_goals_rejects_more_than_max_goals(void) { + // More waypoints than MAX_GOALS (or none) is invalid external input: the call fails (-1) and the + // agent's goal window is left untouched, never silently clamped. + Drive env = {0}; + Agent agent = {0}; + agent.goal_count = 2; + env.agents = &agent; + env.num_total_agents = 1; + + int requested = MAX_GOALS + 5; + float gx[MAX_GOALS + 5], gy[MAX_GOALS + 5], gz[MAX_GOALS + 5]; + float gdir[MAX_GOALS + 5]; + for (int w = 0; w < requested; w++) { + gx[w] = (float) w; + gy[w] = (float) w; + gz[w] = 0.0f; + gdir[w] = 0.0f; + } + EXPECT_EQ_INT(c_set_agent_goals(&env, 0, requested, gx, gy, gz, gdir, gdir), -1); + EXPECT_EQ_INT(agent.goal_count, 2); + EXPECT_EQ_INT(c_set_agent_goals(&env, 0, 0, gx, gy, gz, gdir, gdir), -1); + EXPECT_EQ_INT(agent.goal_count, 2); + EXPECT_EQ_INT(c_set_agent_goals(&env, 0, MAX_GOALS, gx, gy, gz, gdir, gdir), 0); + EXPECT_EQ_INT(agent.goal_count, MAX_GOALS); + return 0; +} + +static int test_set_agent_goals_rejects_out_of_range_agent_idx(void) { + Drive env = {0}; + Agent agent = {0}; + agent.goal_count = 5; // must survive untouched + env.agents = &agent; + env.num_total_agents = 1; + + float gx[1] = {1.0f}, gy[1] = {1.0f}, gz[1] = {1.0f}, gdir[1] = {0.0f}; + EXPECT_EQ_INT(c_set_agent_goals(&env, 5, 1, gx, gy, gz, gdir, gdir), -1); // agent_idx out of range + + EXPECT_EQ_INT(agent.goal_count, 5); + return 0; +} + +// --------------------------------------------------------------------------- +// c_set_stop_signs +// --------------------------------------------------------------------------- + +static int test_set_stop_signs_retires_map_signs_and_appends_external_ones(void) { + // The map's stop signs become TRAFFIC_CONTROL_TYPE_NONE in place (light indices must survive for + // c_set_traffic_light_states), the external lines are appended shifted by world_mean like every other + // setter input, and every agent's stop-sign latch resets because element indices changed. + int light_states[3] = {1, 1, 1}; + TrafficControlElement *elements = (TrafficControlElement *) calloc(2, sizeof(TrafficControlElement)); + elements[0] = make_traffic_element(TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT, 3, light_states); + elements[1] = make_traffic_element(TRAFFIC_CONTROL_TYPE_STOP_SIGN, 0, NULL); + Agent agent = drive_test_agent(0.0f, 0.0f, 0.0f); + agent.stop_sign_target_idx = 1; + agent.stop_sign_stop_completed = 1; + agent.stop_sign_last_failed_idx = 1; + Drive env = {0}; + env.traffic_elements = elements; + env.num_traffic_elements = 2; + env.agents = &agent; + env.num_total_agents = 1; + env.world_mean_x = 100.0f; + env.world_mean_y = -50.0f; + + float lines[12] = {110.0f, -52.0f, 0.5f, 110.0f, -48.0f, 0.5f, 130.0f, -60.0f, 0.0f, 134.0f, -60.0f, 0.0f}; + float headings[2] = {0.0f, 1.5f}; + EXPECT_EQ_INT(c_set_stop_signs(&env, 2, lines, headings), 0); + EXPECT_EQ_INT(env.num_traffic_elements, 4); + EXPECT_EQ_INT(env.traffic_elements[0].type, TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT); + EXPECT_EQ_INT(env.traffic_elements[0].states[1], 1); + EXPECT_EQ_INT(env.traffic_elements[1].type, TRAFFIC_CONTROL_TYPE_NONE); + EXPECT_EQ_INT(env.traffic_elements[2].type, TRAFFIC_CONTROL_TYPE_STOP_SIGN); + EXPECT_NEAR(env.traffic_elements[2].stop_line[0], 10.0f, 1e-6f); + EXPECT_NEAR(env.traffic_elements[2].stop_line[2], 0.5f, 1e-6f); + EXPECT_NEAR(env.traffic_elements[2].stop_line[4], 2.0f, 1e-6f); + EXPECT_NEAR(env.traffic_elements[3].heading, 1.5f, 1e-6f); + EXPECT_EQ_INT(env.traffic_elements[3].state_size, 0); + EXPECT_EQ_INT(env.traffic_elements[3].num_controlled_lanes, 0); + EXPECT_EQ_INT(env.traffic_elements[3].junction_id, -1); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); + EXPECT_EQ_INT(agent.stop_sign_stop_completed, 0); + EXPECT_EQ_INT(agent.stop_sign_last_failed_idx, -1); + free(env.traffic_elements); + return 0; +} + +static int test_set_stop_signs_rejects_bad_input_untouched(void) { + // A negative count, a non-finite coordinate or a zero-length line fails the whole call (-1) and + // leaves the element list as it was; zero external signs still retires the map's. + TrafficControlElement *elements = (TrafficControlElement *) calloc(1, sizeof(TrafficControlElement)); + elements[0] = make_traffic_element(TRAFFIC_CONTROL_TYPE_STOP_SIGN, 0, NULL); + Drive env = {0}; + env.traffic_elements = elements; + env.num_traffic_elements = 1; + + float degenerate[6] = {1.0f, 1.0f, 0.0f, 1.0f, 1.0f, 0.0f}; + float nan_line[6] = {0.0f, NAN, 0.0f, 0.0f, 4.0f, 0.0f}; + float heading[1] = {0.0f}; + EXPECT_EQ_INT(c_set_stop_signs(&env, -1, degenerate, heading), -1); + EXPECT_EQ_INT(c_set_stop_signs(&env, 1, degenerate, heading), -1); + EXPECT_EQ_INT(c_set_stop_signs(&env, 1, nan_line, heading), -1); + EXPECT_EQ_INT(env.num_traffic_elements, 1); + EXPECT_EQ_INT(env.traffic_elements[0].type, TRAFFIC_CONTROL_TYPE_STOP_SIGN); + EXPECT_EQ_INT(c_set_stop_signs(&env, 0, NULL, NULL), 0); + EXPECT_EQ_INT(env.num_traffic_elements, 1); + EXPECT_EQ_INT(env.traffic_elements[0].type, TRAFFIC_CONTROL_TYPE_NONE); + free(env.traffic_elements); + return 0; +} + +int main(void) { + int failures = 0; + RUN_TEST(test_set_agent_sizes_updates_dimensions_radius_and_wheelbase); + RUN_TEST(test_set_agent_sizes_rejects_out_of_range_index_and_bad_size); + RUN_TEST(test_set_traffic_light_states_writes_current_timestep_for_lights_only); + RUN_TEST(test_set_traffic_light_states_rejects_out_of_range_timestep_and_state); + RUN_TEST(test_set_stop_signs_retires_map_signs_and_appends_external_ones); + RUN_TEST(test_set_stop_signs_rejects_bad_input_untouched); + RUN_TEST(test_set_agent_states_teleport_resets_prev_pose); + RUN_TEST(test_set_agent_states_seconds_stopped_injects_or_preserves); + RUN_TEST(test_set_agent_goals_sets_positions_lane_and_count); + RUN_TEST(test_set_agent_goals_rejects_more_than_max_goals); + RUN_TEST(test_set_agent_goals_rejects_out_of_range_agent_idx); + return test_summary(failures); +} diff --git a/tests/drive/test_drive_env_smoke.c b/tests/drive/test_drive_env_smoke.c index e0afb4521b..e90b5405c4 100644 --- a/tests/drive/test_drive_env_smoke.c +++ b/tests/drive/test_drive_env_smoke.c @@ -46,6 +46,72 @@ static int test_nuplan_replay_load_step_log(void) { return run_case("nuplan-replay", drive_nuplan_map(), SIMULATION_MODE_REPLAY, 1); } +static int test_nuplan_replay_create_only_controlled(void) { + srand(7); + Drive env = drive_test_env_config(drive_nuplan_map(), SIMULATION_MODE_REPLAY, 4, 0); + env.init_mode = INIT_MODE_CREATE_ONLY_CONTROLLED; + env.control_mode = CONTROL_MODE_VEHICLES; + allocate(&env); + c_reset(&env); + EXPECT_EQ_INT(env.active_agent_count, 4); + EXPECT_EQ_INT(env.static_agent_count, 0); + EXPECT_EQ_INT(env.num_agents, env.active_agent_count + env.static_agent_count); + int valid_agent_count = 0; + for (int i = 0; i < env.num_total_agents; i++) { + valid_agent_count += env.agents[i].sim_valid == 1; + } + EXPECT_EQ_INT(valid_agent_count, env.num_agents); + + int obs_size = compute_observation_size(&env); + for (int t = 0; t < 5; t++) { + drive_set_neutral_actions(&env); + c_step(&env); + EXPECT_TRUE(drive_all_finite(env.observations, env.active_agent_count * obs_size)); + } + free_allocated(&env); + return 0; +} + +static int test_short_early_reset_flags_and_logs(void) { + const float max_spawn_attempts = 30.0f; + srand(7); + Drive env = drive_test_make_env(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 32, 0); + EXPECT_TRUE(env.active_agent_count >= 4); + env.termination_mode = 1; + env.inactive_agent_threshold = 0.4f; + + int removed_target = env.active_agent_count / 2 + 1; + for (int i = 0; i < removed_target; i++) { + Agent *agent = &env.agents[env.active_agent_indices[i]]; + invalidate_agent(agent); + agent->removed = 1; + } + drive_set_neutral_actions(&env); + c_step(&env); + + EXPECT_TRUE(env.log.n > 0.0f); + EXPECT_NEAR(env.log.early_reset_short, env.log.n, 1e-5f); + EXPECT_EQ_INT(env.short_reset_print_count, 1); + EXPECT_EQ_INT(env.autoreset_pending, 1); + + drive_set_neutral_actions(&env); + c_step(&env); + EXPECT_EQ_INT(env.autoreset_pending, 0); + EXPECT_EQ_INT(env.short_reset_print_count, 1); + for (int x = 0; x < env.active_agent_count; x++) { + Log *agent_log = &env.logs[x]; + float rejects = agent_log->spawn_reject_collision + agent_log->spawn_reject_offroad + + agent_log->spawn_reject_stop_line + agent_log->spawn_reject_empty_cell; + EXPECT_TRUE(rejects <= max_spawn_attempts); + EXPECT_TRUE(agent_log->spawn_failed == 0.0f || agent_log->spawn_failed == 1.0f); + Agent *agent = &env.agents[env.active_agent_indices[x]]; + EXPECT_EQ_INT(agent->removed, (int) agent_log->spawn_failed); + EXPECT_EQ_INT(agent->stopped, (int) agent_log->stopped_at_reset); + } + free_allocated(&env); + return 0; +} + static int test_truncation_and_episode_log(void) { srand(11); Drive env = drive_test_env_config(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 8, 0); @@ -59,10 +125,123 @@ static int test_truncation_and_episode_log(void) { } EXPECT_TRUE(env.log.n > 0.0f); + EXPECT_EQ_INT(env.timestep, 3); + EXPECT_EQ_INT(env.autoreset_pending, 1); + for (int i = 0; i < env.active_agent_count; i++) { + EXPECT_EQ_INT(env.truncations[i], 1); + EXPECT_EQ_INT(env.masks[i], 0); + } + + drive_set_neutral_actions(&env); + c_step(&env); + EXPECT_EQ_INT(env.autoreset_pending, 0); + EXPECT_EQ_INT(env.timestep, env.init_step); + for (int i = 0; i < env.active_agent_count; i++) { + Agent *agent = &env.agents[env.active_agent_indices[i]]; + int expected_mask = !(agent->stopped || agent->removed || agent->is_blind_partner || agent->is_phantom_braker); + EXPECT_EQ_INT(env.truncations[i], 0); + EXPECT_EQ_INT(env.terminals[i], 0); + EXPECT_TRUE(env.rewards[i] == 0.0f); + EXPECT_EQ_INT(env.masks[i], expected_mask); + } + + free_allocated(&env); + return 0; +} + +static int test_stagger_first_episode(void) { + const int scenario_length = 40; + const int min_horizon = 5; + const int num_seeds = 6; + int first_episode_ends[6]; + srand(5); + for (int seed_idx = 0; seed_idx < num_seeds; seed_idx++) { + Drive env = drive_test_env_config(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 8, 0); + env.scenario_length = scenario_length; + env.init_step_min_horizon = min_horizon; + env.stagger_first_episode = 1; + env.init_seed = (uint64_t) seed_idx; + allocate(&env); + c_reset(&env); + EXPECT_EQ_INT(env.first_reset_pending, 0); + EXPECT_TRUE(env.episode_end_timestep >= min_horizon); + EXPECT_TRUE(env.episode_end_timestep <= scenario_length); + first_episode_ends[seed_idx] = env.episode_end_timestep; + free_allocated(&env); + } + int distinct_end_count = 0; + for (int seed_idx = 1; seed_idx < num_seeds; seed_idx++) { + distinct_end_count += first_episode_ends[seed_idx] != first_episode_ends[0]; + } + EXPECT_TRUE(distinct_end_count > 0); + + Drive env = drive_test_env_config(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 8, 0); + env.scenario_length = scenario_length; + env.init_step_min_horizon = min_horizon; + env.stagger_first_episode = 1; + env.init_seed = 3; + allocate(&env); + c_reset(&env); + EXPECT_EQ_INT(env.episode_end_timestep, first_episode_ends[3]); + int first_episode_end = env.episode_end_timestep; + int step_count = 0; + while (!env.autoreset_pending && step_count <= scenario_length) { + drive_set_neutral_actions(&env); + c_step(&env); + step_count++; + } + EXPECT_EQ_INT(env.autoreset_pending, 1); + EXPECT_EQ_INT(step_count, first_episode_end); + EXPECT_EQ_INT(env.timestep, first_episode_end); for (int i = 0; i < env.active_agent_count; i++) { EXPECT_EQ_INT(env.truncations[i], 1); } + drive_set_neutral_actions(&env); + c_step(&env); + EXPECT_EQ_INT(env.autoreset_pending, 0); + EXPECT_EQ_INT(env.timestep, env.init_step); + EXPECT_EQ_INT(env.episode_end_timestep, scenario_length); + for (int t = 0; t < scenario_length; t++) { + drive_set_neutral_actions(&env); + c_step(&env); + } + EXPECT_EQ_INT(env.autoreset_pending, 1); + EXPECT_EQ_INT(env.timestep, scenario_length); + free_allocated(&env); + + Drive eval_env = drive_test_env_config(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 8, 0); + eval_env.scenario_length = scenario_length; + eval_env.init_step_min_horizon = min_horizon; + eval_env.stagger_first_episode = 1; + eval_env.eval_mode = 1; + allocate(&eval_env); + c_reset(&eval_env); + EXPECT_EQ_INT(eval_env.first_reset_pending, 0); + EXPECT_EQ_INT(eval_env.episode_end_timestep, scenario_length); + free_allocated(&eval_env); + return 0; +} + +static int test_eval_spawn_keeps_edge_clearance(void) { + const char *town04_map = DRIVE_TEST_REPO_ROOT "/pufferlib/resources/drive/binaries/carla/opendrive__Town04.bin"; + const int reset_count = 8; + const int agent_count = 50; + Drive env = drive_test_env_config(town04_map, SIMULATION_MODE_GIGAFLOW, agent_count, 0); + env.eval_mode = 1; + allocate(&env); + c_reset(&env); + EXPECT_EQ_INT(env.active_agent_count, agent_count); + + for (int reset_idx = 0; reset_idx < reset_count; reset_idx++) { + for (int i = 0; i < env.active_agent_count; i++) { + Agent *agent = &env.agents[env.active_agent_indices[i]]; + EXPECT_EQ_INT(agent->removed, 0); + EXPECT_FALSE(check_spawn_offroad(&env, agent, EVAL_SPAWN_EDGE_CLEARANCE_M)); + } + env.timestep = env.scenario_length; + c_reset(&env); + } free_allocated(&env); return 0; } @@ -72,6 +251,10 @@ int main(void) { RUN_TEST(test_carla_gigaflow_load_step_log); RUN_TEST(test_nuplan_gigaflow_load_step_log); RUN_TEST(test_nuplan_replay_load_step_log); + RUN_TEST(test_nuplan_replay_create_only_controlled); RUN_TEST(test_truncation_and_episode_log); + RUN_TEST(test_short_early_reset_flags_and_logs); + RUN_TEST(test_stagger_first_episode); + RUN_TEST(test_eval_spawn_keeps_edge_clearance); return test_summary(failures); } diff --git a/tests/drive/test_drive_geometry.c b/tests/drive/test_drive_geometry.c index 852299f99d..bb2ca4dbcd 100644 --- a/tests/drive/test_drive_geometry.c +++ b/tests/drive/test_drive_geometry.c @@ -171,6 +171,17 @@ static int test_swept_line_box_cases(void) { return 0; } +static int test_swept_line_box_stationary_collinear_segment(void) { + // Far segment whose extension line passes through the box centre within float rounding (Town01 eval case). + Agent agent = drive_test_agent(0.0f, 0.0f, 0.0f); + EXPECT_FALSE(check_segment_crosses_moving_box(5.7308259f, 9.52599049f, 5.27106953f, 8.76176643f, &agent)); + + agent.prev_x = -0.001f; + EXPECT_FALSE(check_segment_crosses_moving_box(5.7308259f, 9.52599049f, 5.27106953f, 8.76176643f, &agent)); + + return 0; +} + static int test_stop_line_red_light_gating(void) { Drive env = {0}; TrafficControlElement tc = {0}; @@ -268,6 +279,7 @@ int main(void) { RUN_TEST(test_collision_check_filters); RUN_TEST(test_segment_aabb_cases); RUN_TEST(test_swept_line_box_cases); + RUN_TEST(test_swept_line_box_stationary_collinear_segment); RUN_TEST(test_stop_line_red_light_gating); RUN_TEST(test_stop_line_stationary_on_red); return test_summary(failures); diff --git a/tests/drive/test_drive_goals.c b/tests/drive/test_drive_goals.c index 1d8acb5ff3..ca228c5a96 100644 --- a/tests/drive/test_drive_goals.c +++ b/tests/drive/test_drive_goals.c @@ -318,6 +318,104 @@ static int test_gt_goals_along_trajectory_are_laneless(void) { return 0; } +static int test_gt_map_goals_snap_to_lane_centers(void) { + // GT_MAP places goals at the GT timesteps but projected onto the nearest co-directional lane: a snapped + // goal carries a lane idx and moved at most the snap radius; goals farther from any lane keep the raw + // logged point without a lane. + srand(17); + Drive raw_env = drive_test_env_config(drive_nuplan_map(), SIMULATION_MODE_REPLAY, 1, 0); + raw_env.goal_source = GOAL_SOURCE_GT; + allocate(&raw_env); + c_reset(&raw_env); + srand(17); + Drive env = drive_test_env_config(drive_nuplan_map(), SIMULATION_MODE_REPLAY, 1, 0); + env.goal_source = GOAL_SOURCE_GT_MAP; + allocate(&env); + c_reset(&env); + + EXPECT_TRUE(env.active_agent_count > 0); + EXPECT_EQ_INT(env.active_agent_count, raw_env.active_agent_count); + int snapped_count = 0; + for (int i = 0; i < env.active_agent_count; i++) { + EXPECT_EQ_INT(env.active_agent_indices[i], raw_env.active_agent_indices[i]); + Agent *agent = &env.agents[env.active_agent_indices[i]]; + Agent *raw_agent = &raw_env.agents[raw_env.active_agent_indices[i]]; + EXPECT_EQ_INT(agent->goal_count, env.num_goals); + for (int slot = 0; slot < agent->goal_count; slot++) { + float dx = agent->list_goal_x[slot] - raw_agent->list_goal_x[slot]; + float dy = agent->list_goal_y[slot] - raw_agent->list_goal_y[slot]; + float moved_m = sqrtf(dx * dx + dy * dy); + EXPECT_FINITE(agent->list_goal_x[slot]); + EXPECT_FINITE(agent->list_goal_y[slot]); + EXPECT_NEAR(agent->list_goal_z[slot], raw_agent->list_goal_z[slot], 0.0f); + EXPECT_TRUE(moved_m <= GOAL_LANE_SNAP_MAX_DIST_M + 1e-3f); + if (agent->list_goal_lane[slot] == -1) { + EXPECT_NEAR(moved_m, 0.0f, 0.0f); + continue; + } + EXPECT_TRUE(is_drivable_road_lane(env.road_elements[agent->list_goal_lane[slot]].type)); + snapped_count++; + } + } + printf(" gt_map goals snapped to a lane: %d/%d\n", snapped_count, env.active_agent_count * env.num_goals); + EXPECT_TRUE(snapped_count > 0); + free_allocated(&env); + free_allocated(&raw_env); + return 0; +} + +static int test_step_observations_follow_goal_updates(void) { + const int goal_sources[] = {GOAL_SOURCE_MAP, GOAL_SOURCE_ROUTE, GOAL_SOURCE_ROUTE}; + const int regen_modes[] = {GOAL_REGEN_FINITE, GOAL_REGEN_FINITE, GOAL_REGEN_ROLLING}; + const int goal_case_count = sizeof(goal_sources) / sizeof(goal_sources[0]); + for (int case_idx = 0; case_idx < goal_case_count; case_idx++) { + Drive env = drive_test_env_config(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 1, 0); + env.goal_source = goal_sources[case_idx]; + env.goal_regen_mode = regen_modes[case_idx]; + env.obs_goal_lane_distance = 1; + env.goal_reach_requires_speed = 1; + allocate(&env); + c_reset(&env); + + Agent *agent = &env.agents[env.active_agent_indices[0]]; + int reached_goal_idx = env.goal_regen_mode == GOAL_REGEN_ROLLING ? 0 : agent->goal_count - 1; + agent->current_goal_idx = reached_goal_idx; + agent->current_goal_x = agent->list_goal_x[reached_goal_idx] = agent->sim_x; + agent->current_goal_y = agent->list_goal_y[reached_goal_idx] = agent->sim_y; + agent->current_goal_z = agent->list_goal_z[reached_goal_idx] = agent->sim_z; + agent->list_goal_lane[reached_goal_idx] = agent->current_lane_idx; + drive_set_neutral_actions(&env); + + c_step(&env); + + EXPECT_FALSE(agent->removed); + EXPECT_FALSE(env.terminals[0]); + EXPECT_FALSE(env.truncations[0]); + EXPECT_EQ_INT(agent->current_goal_idx, 0); + EXPECT_NEAR(env.logs[0].reward_goal, env.reward_goal, 1e-6f); + float goal_dx = agent->current_goal_x - agent->sim_x; + float goal_dy = agent->current_goal_y - agent->sim_y; + float goal_norm = fmaxf(env.obs_norm_goal_offset_m, sqrtf(goal_dx * goal_dx + goal_dy * goal_dy)); + EXPECT_NEAR( + env.observations[EGO_FEATURES], + (goal_dx * agent->cos_heading + goal_dy * agent->sin_heading) / goal_norm, + 1e-6f); + EXPECT_NEAR( + env.observations[EGO_FEATURES + 1], + (-goal_dx * agent->sin_heading + goal_dy * agent->cos_heading) / goal_norm, + 1e-6f); + + // Recomputing from the returned state must also preserve the lane-to-goal routing features. + int observation_size = compute_observation_size(&env); + float step_observations[observation_size]; + memcpy(step_observations, env.observations, sizeof(step_observations)); + compute_observations(&env); + EXPECT_TRUE(memcmp(step_observations, env.observations, sizeof(step_observations)) == 0); + free_allocated(&env); + } + return 0; +} + int main(void) { int failures = 0; RUN_TEST(test_commit_goals_front_align_fills_every_slot); @@ -329,6 +427,8 @@ int main(void) { RUN_TEST(test_route_goals_front_aligned_with_lanes); RUN_TEST(test_roll_goals_slides_window_and_appends); RUN_TEST(test_roll_goals_bails_on_replay_pins); + RUN_TEST(test_step_observations_follow_goal_updates); RUN_TEST(test_gt_goals_along_trajectory_are_laneless); + RUN_TEST(test_gt_map_goals_snap_to_lane_centers); return test_summary(failures); } diff --git a/tests/drive/test_drive_idm.c b/tests/drive/test_drive_idm.c index d7f5a01147..f630769551 100644 --- a/tests/drive/test_drive_idm.c +++ b/tests/drive/test_drive_idm.c @@ -59,8 +59,10 @@ static int test_desired_speed_fallback_and_lane_limit(void) { make_lane(&lanes[0], x0, y0, z0, h0, 0.0f); make_lane(&lanes[1], x1, y1, z1, h1, 12.0f); + float lane_limits[2] = {0.0f, 12.0f}; env.road_elements = lanes; env.num_road_elements = 2; + env.lane_speed_limit_mps = lane_limits; env.base_max_speed_mps = 20.0f; agent.current_lane_idx = 0; agent.route = route; @@ -68,7 +70,7 @@ static int test_desired_speed_fallback_and_lane_limit(void) { agent.current_route_idx = 0; EXPECT_NEAR(idm_desired_speed(&env, &agent), 12.0f, 1e-5f); - lanes[1].speed_limit = 0.0f; + lane_limits[1] = 0.0f; EXPECT_NEAR(idm_desired_speed(&env, &agent), IDM_DEFAULT_DESIRED_SPEED_MPS, 1e-5f); return 0; } @@ -160,6 +162,7 @@ static int test_leader_selection_and_move_idm(void) { int statics[1] = {1}; int ego_route[1] = {0}; int other_route[1] = {0}; + float lane_limits[1] = {10.0f}; make_lane(&lane, x, y, z, h, 10.0f); agents[0] = drive_test_agent(0.0f, 0.0f, 0.0f); @@ -177,6 +180,7 @@ static int test_leader_selection_and_move_idm(void) { env.road_elements = &lane; env.num_road_elements = 1; + env.lane_speed_limit_mps = lane_limits; env.agents = agents; env.active_agent_indices = active; env.static_agent_indices = statics; diff --git a/tests/drive/test_drive_map_cache.c b/tests/drive/test_drive_map_cache.c index 78b4bb99b3..a1dd9ff6d8 100644 --- a/tests/drive/test_drive_map_cache.c +++ b/tests/drive/test_drive_map_cache.c @@ -13,13 +13,13 @@ static Drive create_test_env_with_cache_modes(int use_map_cache, int use_neighbo } static size_t compute_neighbor_cache_bytes(GridMap *grid_map) { - if (grid_map->neighbor_cache_entities == NULL) { + if (grid_map->neighbor_cache_pool_idx == NULL) { return 0; } int grid_cell_count = grid_map->grid_cols * grid_map->grid_rows; - size_t bytes = grid_cell_count * sizeof(GridMapEntity *) + (grid_cell_count + 1) * sizeof(int); + size_t bytes = grid_cell_count * sizeof(uint16_t *) + (grid_cell_count + 1) * sizeof(int); for (int grid_index = 0; grid_index < grid_cell_count; grid_index++) { - bytes += grid_map->neighbor_cache_count[grid_index] * sizeof(GridMapEntity); + bytes += grid_map->neighbor_cache_count[grid_index] * sizeof(uint16_t); } return bytes; } @@ -107,13 +107,13 @@ static int test_cache_mode_matrix_has_expected_map_and_neighbor_allocations(void EXPECT_TRUE(first_neighbor_cache_bytes > 0); EXPECT_TRUE(first_neighbor_cache_bytes == second_neighbor_cache_bytes); size_t unique_neighbor_cache_bytes = first_neighbor_cache_bytes; - if (first.grid_map->neighbor_cache_entities != second.grid_map->neighbor_cache_entities) { + if (first.grid_map->neighbor_cache_pool_idx != second.grid_map->neighbor_cache_pool_idx) { unique_neighbor_cache_bytes += second_neighbor_cache_bytes; } EXPECT_TRUE(unique_neighbor_cache_bytes == first_neighbor_cache_bytes * (use_map_cache ? 1 : 2)); } else { - EXPECT_TRUE(first.grid_map->neighbor_cache_entities == NULL); - EXPECT_TRUE(second.grid_map->neighbor_cache_entities == NULL); + EXPECT_TRUE(first.grid_map->neighbor_cache_pool_idx == NULL); + EXPECT_TRUE(second.grid_map->neighbor_cache_pool_idx == NULL); EXPECT_TRUE(first.obs_neighbor_scratch != NULL); EXPECT_TRUE(second.obs_neighbor_scratch != NULL); EXPECT_TRUE(first.obs_neighbor_scratch != second.obs_neighbor_scratch); @@ -149,7 +149,7 @@ static int test_mixed_neighbor_modes_share_map_and_populate_neighbor_cache(void) EXPECT_TRUE(first.shared_map == second.shared_map); EXPECT_TRUE(first.grid_map == second.grid_map); EXPECT_EQ_INT(first.shared_map->ref_count, 2); - EXPECT_TRUE(first.grid_map->neighbor_cache_entities != NULL); + EXPECT_TRUE(first.grid_map->neighbor_cache_pool_idx != NULL); EXPECT_TRUE(first.grid_map->neighbor_cache_count != NULL); EXPECT_TRUE(compute_neighbor_cache_bytes(first.grid_map) > 0); Drive *env_without_neighbor_cache = first_env_uses_neighbor_cache ? &second : &first; @@ -233,6 +233,12 @@ static int test_forked_child_reuses_preloaded_map_cache_entry(void) { EXPECT_TRUE(preloaded != NULL); EXPECT_EQ_INT(preloaded->ref_count, 1); EXPECT_EQ_INT(preloaded->owner_pid, parent_pid); + for (int road_idx = 0; road_idx < preloaded->num_road_elements; road_idx++) { + RoadMapElement *road = &preloaded->road_elements[road_idx]; + if (is_road_grid_candidate(road->type) && road->segment_size >= 2) { + EXPECT_TRUE(road->observation_segments != NULL); + } + } int fds[2]; EXPECT_EQ_INT(pipe(fds), 0); @@ -283,8 +289,66 @@ static int test_forked_child_reuses_preloaded_map_cache_entry(void) { return 0; } +static int test_road_observation_cache_follows_map_ownership(void) { + for (int use_map_cache = 0; use_map_cache <= 1; use_map_cache++) { + drive_map_cache_clear(); + Drive first = create_test_env_with_cache_modes(use_map_cache, 1, 8); + Drive second = create_test_env_with_cache_modes(use_map_cache, 1, 8); + int cached_road_count = 0; + for (int road_idx = 0; road_idx < first.num_road_elements; road_idx++) { + RoadMapElement *road = &first.road_elements[road_idx]; + if (!is_road_grid_candidate(road->type) || road->segment_size < 2) { + continue; + } + EXPECT_TRUE(road->observation_segments != NULL); + EXPECT_EQ_INT( + road->observation_segments == second.road_elements[road_idx].observation_segments, + use_map_cache); + cached_road_count++; + } + EXPECT_TRUE(cached_road_count > 0); + free_allocated(&first); + drive_set_neutral_actions(&second); + c_step(&second); + free_allocated(&second); + EXPECT_EQ_INT(drive_map_cache_live_count(), 0); + } + drive_map_cache_clear(); + return 0; +} + +static int test_road_observation_cache_handles_widths_and_zero_length_segments(void) { + float x[] = {0.0f, 6.0f, 6.0f}; + float y[] = {0.0f, 8.0f, 8.0f}; + float z[] = {1.0f, 3.0f, 3.0f}; + float widths[] = {2.0f, 4.0f, 6.0f}; + RoadMapElement roads[] = { + {.type = LANE_SURFACE_STREET, .segment_size = 3, .x = x, .y = y, .z = z, .widths = widths}, + {.type = ROAD_EDGE_BOUNDARY, .segment_size = 3, .x = x, .y = y, .z = z}, + {.type = LANE_SURFACE_STREET, .segment_size = 1, .x = x, .y = y, .z = z, .widths = widths}, + }; + Drive env = {.road_elements = roads, .num_road_elements = 3}; + cache_road_observation_geometry(&env); + const RoadObservationSegment expected_lane[] = { + {3.0f, 4.0f, 2.0f, 5.0f, 0.6f, 0.8f, 3.0f}, + {6.0f, 8.0f, 3.0f, 0.0f, 0.0f, 0.0f, 5.0f}, + }; + RoadObservationSegment expected_boundary[2]; + memcpy(expected_boundary, expected_lane, sizeof(expected_boundary)); + expected_boundary[0].width_m = 0.0f; + expected_boundary[1].width_m = 0.0f; + EXPECT_EQ_INT(memcmp(roads[0].observation_segments, expected_lane, sizeof(expected_lane)), 0); + EXPECT_EQ_INT(memcmp(roads[1].observation_segments, expected_boundary, sizeof(expected_boundary)), 0); + EXPECT_TRUE(roads[2].observation_segments == NULL); + free(roads[0].observation_segments); + free(roads[1].observation_segments); + return 0; +} + int main(void) { int failures = 0; + RUN_TEST(test_road_observation_cache_follows_map_ownership); + RUN_TEST(test_road_observation_cache_handles_widths_and_zero_length_segments); RUN_TEST(test_all_cache_modes_produce_identical_step_outputs); RUN_TEST(test_cache_mode_matrix_has_expected_map_and_neighbor_allocations); RUN_TEST(test_mixed_neighbor_modes_share_map_and_populate_neighbor_cache); diff --git a/tests/drive/test_drive_metrics.c b/tests/drive/test_drive_metrics.c index 8e684e2df6..8b7c8ccc6c 100644 --- a/tests/drive/test_drive_metrics.c +++ b/tests/drive/test_drive_metrics.c @@ -1,6 +1,25 @@ #include "include/drive_fixture.h" #include "include/test.h" +// Town03 stop sign 43: eastbound stop line at x 101.6 m, y in [-68.3, -64.9] m, controlling lane 117. +static const char *TOWN03_MAP = DRIVE_TEST_REPO_ROOT "/pufferlib/resources/drive/binaries/carla/opendrive__Town03.bin"; +static const float TOWN03_STOP_SIGN_LANE_Y = -66.6f; +static const float TOWN03_STOP_SIGN_LANE_Z = -0.77f; +static const int TOWN03_STOP_SIGN_IDX = 43; + +static void drive_test_drive_east(Agent *agent, float prev_x, float x, float speed) { + agent->sim_x = prev_x; + agent->sim_y = TOWN03_STOP_SIGN_LANE_Y; + agent->sim_z = TOWN03_STOP_SIGN_LANE_Z; + agent->sim_heading = 0.0f; + agent->cos_heading = 1.0f; + agent->sin_heading = 0.0f; + copy_pose_to_prev(agent); + agent->sim_x = x; + agent->sim_speed = speed; + agent->sim_speed_signed = speed; +} + static int test_metric_offroad_outside_grid(void) { srand(5); Drive env = drive_test_make_env(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 2, 0); @@ -20,6 +39,32 @@ static int test_metric_offroad_outside_grid(void) { return 0; } +static int test_metric_overspeed_flag_respects_tolerance(void) { + srand(5); + Drive env = drive_test_make_env(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 2, 0); + int agent_idx = env.active_agent_indices[0]; + Agent *agent = &env.agents[agent_idx]; + compute_metrics(&env, agent_idx, 0); + float limit = agent->current_lane_idx != -1 ? env.lane_speed_limit_mps[agent->current_lane_idx] + : UNKNOWN_LANE_SPEED_LIMIT_MPS; + agent->sim_speed = limit + 1.0f; + + env.overspeed_tolerance_mps = 0.0f; + compute_metrics(&env, agent_idx, 0); + EXPECT_NEAR(agent->metrics_array[SPEED_LIMIT_IDX], 1.0f, 1e-6f); + + env.overspeed_tolerance_mps = 2.0f; + compute_metrics(&env, agent_idx, 0); + EXPECT_NEAR(agent->metrics_array[SPEED_LIMIT_IDX], 0.0f, 1e-6f); + + agent->sim_speed = limit - 0.5f; + env.overspeed_tolerance_mps = 0.0f; + compute_metrics(&env, agent_idx, 0); + EXPECT_NEAR(agent->metrics_array[SPEED_LIMIT_IDX], 0.0f, 1e-6f); + free_allocated(&env); + return 0; +} + static int test_metric_invalid_position_resets(void) { Drive env = {0}; Agent agent = drive_test_agent(0.0f, 0.0f, 0.0f); @@ -106,11 +151,104 @@ static int test_metric_final_goal_requires_speed(void) { return 0; } +static void overlap_agent_with(Drive *env, int agent_idx, int target_idx) { + Agent *agent = &env->agents[agent_idx]; + Agent *target = &env->agents[target_idx]; + agent->sim_x = target->sim_x; + agent->sim_y = target->sim_y; + agent->sim_z = target->sim_z; + agent->sim_heading = target->sim_heading; + agent->cos_heading = target->cos_heading; + agent->sin_heading = target->sin_heading; + copy_pose_to_prev(agent); +} + +static int test_metric_stop_sign_infraction_can_be_disabled(void) { + for (int disabled = 0; disabled < 2; disabled++) { + srand(5); + Drive env = drive_test_make_env(TOWN03_MAP, SIMULATION_MODE_GIGAFLOW, 1, 0); + env.stop_signs_enabled = 1; + env.stop_sign_behavior = INFRACTION_BEHAVIOR_STOP; + env.disable_stop_sign_infractions = disabled; + int agent_idx = env.active_agent_indices[0]; + Agent *agent = &env.agents[agent_idx]; + + drive_test_drive_east(agent, 96.0f, 96.8f, 8.0f); + compute_metrics(&env, agent_idx, 0); + EXPECT_EQ_INT(agent->stop_sign_target_idx, TOWN03_STOP_SIGN_IDX); + + drive_test_drive_east(agent, 101.2f, 102.0f, 8.0f); + compute_metrics(&env, agent_idx, 0); + EXPECT_NEAR(agent->metrics_array[OFFROAD_IDX], 0.0f, 1e-6f); + EXPECT_NEAR(agent->metrics_array[COLLISION_IDX], 0.0f, 1e-6f); + EXPECT_NEAR(agent->metrics_array[STOP_SIGN_IDX], disabled ? 0.0f : 1.0f, 1e-6f); + EXPECT_EQ_INT(agent->stopped, disabled ? 0 : 1); + // the state machine still ran: the run sign is parked in last_failed either way + EXPECT_EQ_INT(agent->stop_sign_last_failed_idx, TOWN03_STOP_SIGN_IDX); + EXPECT_EQ_INT(agent->stop_sign_target_idx, -1); + free_allocated(&env); + } + return 0; +} + +static int test_metric_mutual_collision_counts_one_infraction(void) { + srand(13); + Drive env = drive_test_make_env(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 2, 0); + EXPECT_EQ_INT(env.active_agent_count, 2); + int first_idx = env.active_agent_indices[0]; + int second_idx = env.active_agent_indices[1]; + overlap_agent_with(&env, second_idx, first_idx); + + for (int slot = 0; slot < env.active_agent_count; slot++) { + compute_metrics(&env, env.active_agent_indices[slot], slot); + compute_rewards(&env, slot); + } + EXPECT_EQ_INT(env.agents[first_idx].first_collision_partner_idx, second_idx); + EXPECT_EQ_INT(env.agents[second_idx].first_collision_partner_idx, first_idx); + + add_log(&env); + EXPECT_NEAR(env.log.collision_rate, 2.0f, 1e-6f); + EXPECT_NEAR(env.log.total_infractions, 1.0f, 1e-6f); + + free_allocated(&env); + return 0; +} + +static int test_metric_non_mutual_collision_counts_per_agent(void) { + srand(13); + Drive env = drive_test_make_env(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 3, 0); + EXPECT_EQ_INT(env.active_agent_count, 3); + int first_idx = env.active_agent_indices[0]; + int second_idx = env.active_agent_indices[1]; + int third_idx = env.active_agent_indices[2]; + overlap_agent_with(&env, second_idx, first_idx); + // Second agent already collided with the third earlier this episode. + env.agents[second_idx].first_collision_partner_idx = third_idx; + + for (int slot = 0; slot < env.active_agent_count; slot++) { + compute_metrics(&env, env.active_agent_indices[slot], slot); + compute_rewards(&env, slot); + } + EXPECT_EQ_INT(env.agents[first_idx].first_collision_partner_idx, second_idx); + EXPECT_EQ_INT(env.agents[second_idx].first_collision_partner_idx, third_idx); + + add_log(&env); + EXPECT_NEAR(env.log.collision_rate, 2.0f, 1e-6f); + EXPECT_NEAR(env.log.total_infractions, 2.0f, 1e-6f); + + free_allocated(&env); + return 0; +} + int main(void) { int failures = 0; RUN_TEST(test_metric_offroad_outside_grid); + RUN_TEST(test_metric_overspeed_flag_respects_tolerance); RUN_TEST(test_metric_invalid_position_resets); RUN_TEST(test_metric_on_road_lane_alignment); RUN_TEST(test_metric_final_goal_requires_speed); + RUN_TEST(test_metric_stop_sign_infraction_can_be_disabled); + RUN_TEST(test_metric_mutual_collision_counts_one_infraction); + RUN_TEST(test_metric_non_mutual_collision_counts_per_agent); return test_summary(failures); } diff --git a/tests/drive/test_drive_observations_rewards.c b/tests/drive/test_drive_observations_rewards.c index e0c8e6cbb8..2ca5f6e099 100644 --- a/tests/drive/test_drive_observations_rewards.c +++ b/tests/drive/test_drive_observations_rewards.c @@ -21,6 +21,77 @@ static int test_observation_size_formula(void) { env.obs_partner_relative_velocity = 1; expected += 2 * PARTNER_RELATIVE_VELOCITY_FEATURES; EXPECT_EQ_INT(compute_observation_size(&env), expected); + + env.obs_lane_speed_limit = 1; + expected += 5 * LANE_SPEED_LIMIT_FEATURES; + EXPECT_EQ_INT(compute_observation_size(&env), expected); + return 0; +} + +static int check_lane_speed_limit_column(int lane_slots_kept, int boundary_slots_kept) { + const float forced_limit_mps = 12.5f; + srand(21); + Drive plain = drive_test_env_config(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 4, 0); + plain.obs_slots_lane_kept = lane_slots_kept; + plain.obs_slots_boundary_kept = boundary_slots_kept; + allocate(&plain); + c_reset(&plain); + srand(21); + Drive with_limit = drive_test_env_config(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 4, 0); + with_limit.obs_slots_lane_kept = lane_slots_kept; + with_limit.obs_slots_boundary_kept = boundary_slots_kept; + with_limit.obs_lane_speed_limit = 1; + allocate(&with_limit); + c_reset(&with_limit); + + EXPECT_EQ_INT(with_limit.active_agent_count, plain.active_agent_count); + int plain_size = compute_observation_size(&plain); + int limit_size = compute_observation_size(&with_limit); + EXPECT_EQ_INT(limit_size - plain_size, plain.obs_slots_lane_kept * LANE_SPEED_LIMIT_FEATURES); + int lane_base = EGO_FEATURES + plain.num_goals * GOAL_FEATURES + plain.obs_slots_partners_n * PARTNER_FEATURES; + int limit_row_features = LANE_FEATURES + LANE_SPEED_LIMIT_FEATURES; + int plain_tail = lane_base + plain.obs_slots_lane_kept * LANE_FEATURES; + int limit_tail = lane_base + plain.obs_slots_lane_kept * limit_row_features; + + for (int agent_idx = 0; agent_idx < plain.active_agent_count; agent_idx++) { + const float *plain_obs = &plain.observations[agent_idx * plain_size]; + const float *limit_obs = &with_limit.observations[agent_idx * limit_size]; + int lane_count = (int) limit_obs[limit_size - OBS_VALID_COUNT_FEATURES]; + EXPECT_TRUE(lane_count > 0); + EXPECT_EQ_INT(memcmp(plain_obs, limit_obs, lane_base * sizeof(float)), 0); + EXPECT_EQ_INT( + memcmp(&plain_obs[plain_tail], &limit_obs[limit_tail], (plain_size - plain_tail) * sizeof(float)), + 0); + for (int row = 0; row < plain.obs_slots_lane_kept; row++) { + const float *plain_row = &plain_obs[lane_base + row * LANE_FEATURES]; + const float *limit_row = &limit_obs[lane_base + row * limit_row_features]; + EXPECT_EQ_INT(memcmp(plain_row, limit_row, LANE_FEATURES * sizeof(float)), 0); + EXPECT_TRUE(row < lane_count ? limit_row[LANE_FEATURES] > 0.0f : limit_row[LANE_FEATURES] == 0.0f); + } + } + + for (int road_idx = 0; road_idx < with_limit.num_road_elements; road_idx++) { + with_limit.lane_speed_limit_mps[road_idx] = forced_limit_mps; + } + compute_observations(&with_limit); + for (int agent_idx = 0; agent_idx < with_limit.active_agent_count; agent_idx++) { + const float *limit_obs = &with_limit.observations[agent_idx * limit_size]; + int lane_count = (int) limit_obs[limit_size - OBS_VALID_COUNT_FEATURES]; + for (int row = 0; row < with_limit.obs_slots_lane_kept; row++) { + float expected = row < lane_count ? forced_limit_mps / with_limit.obs_norm_speed_mps : 0.0f; + EXPECT_NEAR(limit_obs[lane_base + row * limit_row_features + LANE_FEATURES], expected, 1e-6f); + } + } + + free_allocated(&plain); + free_allocated(&with_limit); + return 0; +} + +static int test_lane_speed_limit_column(void) { + EXPECT_EQ_INT(check_lane_speed_limit_column(32, 32), 0); + // fewer kept than observed slots enables road dropout (buffered + subsampled rows) + EXPECT_EQ_INT(check_lane_speed_limit_column(12, 16), 0); return 0; } @@ -243,9 +314,63 @@ static int test_reward_lane_align_wrong_way(void) { return 0; } +static int test_lane_goal_distance_obs_unreachable_reads_far(void) { + const float norm_m = LANE_GRAPH_DISTANCE_NORM_M; + float dest[2]; + + write_lane_goal_distance_obs(0.25f * norm_m, 0.5f * norm_m, dest); + EXPECT_NEAR(dest[0], 0.25f, 1e-6f); + EXPECT_NEAR(dest[1], -0.25f, 1e-6f); + + // Unreachable lane next to an ego lane 3 norms from the goal: must read worse than ego, not closer. + write_lane_goal_distance_obs(INFINITY, 3.0f * norm_m, dest); + EXPECT_NEAR(dest[0], 1.0f, 1e-6f); + EXPECT_NEAR(dest[1], 1.0f, 1e-6f); + + write_lane_goal_distance_obs(3.0f * norm_m, INFINITY, dest); + EXPECT_NEAR(dest[0], 1.0f, 1e-6f); + EXPECT_NEAR(dest[1], -1.0f, 1e-6f); + + write_lane_goal_distance_obs(INFINITY, INFINITY, dest); + EXPECT_NEAR(dest[0], 1.0f, 1e-6f); + EXPECT_NEAR(dest[1], 0.0f, 1e-6f); + + write_lane_goal_distance_obs(0.25f * norm_m, -1.0f, dest); + EXPECT_NEAR(dest[0], 0.25f, 1e-6f); + EXPECT_NEAR(dest[1], 0.0f, 1e-6f); + + write_lane_goal_distance_obs(-1.0f, 0.5f * norm_m, dest); + EXPECT_NEAR(dest[0], 0.0f, 1e-6f); + EXPECT_NEAR(dest[1], 0.0f, 1e-6f); + return 0; +} + +static int test_lane_graph_distance_to_goal_sentinels(void) { + Drive env = drive_test_make_env(drive_carla_map(), SIMULATION_MODE_GIGAFLOW, 1, 0); + EXPECT_TRUE(env.lane_graph.n_lanes >= 2); + int lane_idx = env.lane_graph.lane_ids[0]; + int goal_graph_idx = 1; + float *stored_distance_m = &env.lane_graph.distances[0 * env.lane_graph.n_lanes + goal_graph_idx]; + + *stored_distance_m = 123.0f; + EXPECT_NEAR(lane_graph_distance_to_goal_m(&env, lane_idx, goal_graph_idx), 123.0f, 1e-6f); + *stored_distance_m = INFINITY; + EXPECT_TRUE(isinf(lane_graph_distance_to_goal_m(&env, lane_idx, goal_graph_idx))); + *stored_distance_m = -1.0f; + EXPECT_TRUE(isinf(lane_graph_distance_to_goal_m(&env, lane_idx, goal_graph_idx))); + EXPECT_TRUE(lane_graph_distance_to_goal_m(&env, -1, goal_graph_idx) < 0.0f); + EXPECT_TRUE(lane_graph_distance_to_goal_m(&env, env.num_road_elements, goal_graph_idx) < 0.0f); + + free_allocated(&env); + return 0; +} + int main(void) { int failures = 0; + RUN_TEST(test_lane_goal_distance_obs_unreachable_reads_far); + RUN_TEST(test_lane_graph_distance_to_goal_sentinels); RUN_TEST(test_observation_size_formula); + RUN_TEST(test_lane_speed_limit_column); RUN_TEST(test_observation_zero_fill_and_valid_counts); RUN_TEST(test_reward_terminal_components); RUN_TEST(test_reward_goal_speed_gating); diff --git a/tests/drive/test_drive_speed_limits.c b/tests/drive/test_drive_speed_limits.c new file mode 100644 index 0000000000..3af6321675 --- /dev/null +++ b/tests/drive/test_drive_speed_limits.c @@ -0,0 +1,140 @@ +#include "include/drive_fixture.h" +#include "include/test.h" + +// Synthetic map: two zone-0 lanes, one zone-1 lane, a chained pair of junction lanes, a road edge, +// a zone-2 lane near the clip floor and a dangling junction lane without entries. +#define ELEMENT_COUNT 8 +#define ZONE_COUNT 3 + +static int entries_of_3[2] = {0, 2}; +static int entries_of_4[1] = {3}; + +static void make_element( + RoadMapElement *road, + int type, + float speed_limit, + int zone_idx, + int *entries, + int entry_count) { + *road = (RoadMapElement) {0}; + road->type = type; + road->speed_limit = speed_limit; + road->speed_zone_idx = zone_idx; + road->entry_lanes = entries; + road->num_entries = entry_count; +} + +typedef struct { + RoadMapElement roads[ELEMENT_COUNT]; + float limits[ELEMENT_COUNT]; + float offsets[ZONE_COUNT]; + unsigned char resolved[ELEMENT_COUNT]; +} SpeedLimitFixture; + +static Drive make_env(SpeedLimitFixture *fixture, float prob, float delta_mps, uint64_t seed) { + make_element(&fixture->roads[0], LANE_SURFACE_STREET, 10.0f, 0, NULL, 0); + make_element(&fixture->roads[1], LANE_SURFACE_STREET, 10.0f, 0, NULL, 0); + make_element(&fixture->roads[2], LANE_SURFACE_STREET, 25.0f, 1, NULL, 0); + make_element(&fixture->roads[3], LANE_SURFACE_STREET, 10.0f, -1, entries_of_3, 2); + make_element(&fixture->roads[4], LANE_SURFACE_STREET, 10.0f, -1, entries_of_4, 1); + make_element(&fixture->roads[5], ROAD_EDGE_BOUNDARY, 0.0f, -1, NULL, 0); + make_element(&fixture->roads[6], LANE_SURFACE_STREET, 2.5f, 2, NULL, 0); + make_element(&fixture->roads[7], LANE_SURFACE_STREET, 10.0f, -1, NULL, 0); + Drive env = {0}; + env.road_elements = fixture->roads; + env.num_road_elements = ELEMENT_COUNT; + env.num_speed_zones = ZONE_COUNT; + env.lane_speed_limit_mps = fixture->limits; + env.speed_zone_offset_mps = fixture->offsets; + env.lane_limit_resolved = fixture->resolved; + env.speed_limit_random_prob = prob; + env.speed_limit_random_delta_mps = delta_mps; + env.speed_limit_random_min_mps = 2.0f; + env.speed_limit_random_max_mps = 30.0f; + rng_seed(&env.rng_state, seed); + return env; +} + +static int test_disabled_keeps_original_limits_and_rng(void) { + SpeedLimitFixture fixture; + Drive env = make_env(&fixture, 0.0f, 5.0f, 7); + Rng rng_before = env.rng_state; + sample_zone_speed_limits(&env); + for (int i = 0; i < ELEMENT_COUNT; i++) { + EXPECT_NEAR(env.lane_speed_limit_mps[i], fixture.roads[i].speed_limit, 1e-6f); + } + EXPECT_TRUE(memcmp(&rng_before, &env.rng_state, sizeof(Rng)) == 0); + return 0; +} + +static int test_unknown_lane_limit_resolves_to_fallback(void) { + SpeedLimitFixture fixture; + Drive env = make_env(&fixture, 0.0f, 5.0f, 7); + fixture.roads[7].speed_limit = -1.0f; + sample_zone_speed_limits(&env); + EXPECT_NEAR(env.lane_speed_limit_mps[7], UNKNOWN_LANE_SPEED_LIMIT_MPS, 1e-6f); + EXPECT_NEAR(env.lane_speed_limit_mps[5], 0.0f, 1e-6f); + + env = make_env(&fixture, 1.0f, 5.0f, 7); + fixture.roads[7].speed_limit = -1.0f; + sample_zone_speed_limits(&env); + EXPECT_NEAR(env.lane_speed_limit_mps[7], UNKNOWN_LANE_SPEED_LIMIT_MPS, 1e-6f); + return 0; +} + +static int test_zone_offsets_apply_and_junctions_inherit(void) { + SpeedLimitFixture fixture; + Drive env = make_env(&fixture, 1.0f, 5.0f, 7); + sample_zone_speed_limits(&env); + float *limits = env.lane_speed_limit_mps; + EXPECT_NEAR(limits[0], limits[1], 1e-6f); + EXPECT_TRUE(fabsf(limits[0] - 10.0f) <= 5.0f + 1e-5f); + EXPECT_TRUE(fabsf(limits[2] - 25.0f) <= 5.0f + 1e-5f); + EXPECT_NEAR(limits[3], fminf(limits[0], limits[2]), 1e-6f); + EXPECT_NEAR(limits[4], limits[3], 1e-6f); + EXPECT_NEAR(limits[5], 0.0f, 1e-6f); + EXPECT_TRUE(limits[6] >= 2.0f); + EXPECT_NEAR(limits[7], 10.0f, 1e-6f); + return 0; +} + +static int test_clip_bounds_hold_for_large_delta(void) { + SpeedLimitFixture fixture; + Drive env = make_env(&fixture, 1.0f, 100.0f, 3); + sample_zone_speed_limits(&env); + for (int i = 0; i < ELEMENT_COUNT; i++) { + if (fixture.roads[i].speed_zone_idx < 0) { + continue; + } + EXPECT_TRUE(env.lane_speed_limit_mps[i] >= 2.0f); + EXPECT_TRUE(env.lane_speed_limit_mps[i] <= 30.0f); + } + return 0; +} + +static int test_same_seed_reproduces_and_other_seed_differs(void) { + SpeedLimitFixture fixture_a, fixture_b, fixture_c; + Drive env_a = make_env(&fixture_a, 1.0f, 5.0f, 11); + Drive env_b = make_env(&fixture_b, 1.0f, 5.0f, 11); + Drive env_c = make_env(&fixture_c, 1.0f, 5.0f, 12); + sample_zone_speed_limits(&env_a); + sample_zone_speed_limits(&env_b); + sample_zone_speed_limits(&env_c); + int differs = 0; + for (int i = 0; i < ELEMENT_COUNT; i++) { + EXPECT_NEAR(env_a.lane_speed_limit_mps[i], env_b.lane_speed_limit_mps[i], 1e-6f); + differs |= fabsf(env_a.lane_speed_limit_mps[i] - env_c.lane_speed_limit_mps[i]) > 1e-4f; + } + EXPECT_TRUE(differs); + return 0; +} + +int main(void) { + int failures = 0; + RUN_TEST(test_disabled_keeps_original_limits_and_rng); + RUN_TEST(test_unknown_lane_limit_resolves_to_fallback); + RUN_TEST(test_zone_offsets_apply_and_junctions_inherit); + RUN_TEST(test_clip_bounds_hold_for_large_delta); + RUN_TEST(test_same_seed_reproduces_and_other_seed_differs); + return test_summary(failures); +} diff --git a/tests/drive/test_drive_stop_sign.c b/tests/drive/test_drive_stop_sign.c index 420d8b1bf7..472ccff4a2 100644 --- a/tests/drive/test_drive_stop_sign.c +++ b/tests/drive/test_drive_stop_sign.c @@ -1,187 +1,339 @@ #include "include/drive_fixture.h" #include "include/test.h" -// Stop line spans y in [-2, 2] at x = 0 and controls traffic moving in +x. -static int stop_sign_lane = 100; +// Stop line spans y in [-2, 2] at x = 0, traffic flows +x; the trigger box is centred on it, +// 1.7 m deep on each side. Agents are 4 m long, so a stop with the nose at the line overlaps it. +static int stop_lanes[1] = {100}; static TrafficControlElement drive_test_stop_sign(void) { - TrafficControlElement stop_sign = {0}; - stop_sign.type = TRAFFIC_CONTROL_TYPE_STOP_SIGN; - stop_sign.stop_line[1] = -2.0f; - stop_sign.stop_line[4] = 2.0f; - stop_sign.heading = 0.0f; - stop_sign.num_controlled_lanes = 1; - stop_sign.controlled_lanes = &stop_sign_lane; - return stop_sign; + TrafficControlElement tc = {0}; + tc.type = TRAFFIC_CONTROL_TYPE_STOP_SIGN; + tc.stop_line[1] = -2.0f; + tc.stop_line[4] = 2.0f; + tc.heading = 0.0f; + tc.num_controlled_lanes = 1; + tc.controlled_lanes = stop_lanes; + return tc; } -#define STOP_SIGN_REQUIRED_STOP_TIMESTEPS 5 - -static Drive drive_test_stop_sign_env(TrafficControlElement *stop_sign) { +static Drive drive_test_stop_env(TrafficControlElement *tc, Agent *agent) { Drive env = {0}; - env.dt = 0.1f; env.num_traffic_elements = 1; - env.traffic_elements = stop_sign; + env.traffic_elements = tc; + env.agents = agent; return env; } -static Agent drive_test_stop_sign_agent(float x, float speed) { - Agent agent = drive_test_agent(x, 0.0f, 0.0f); - agent.current_lane_idx = stop_sign_lane; - agent.previous_lane_idx = stop_sign_lane; - agent.sim_speed = speed; - agent.sim_speed_signed = speed; - agent.sim_vx = speed; +static Agent drive_test_stop_agent(float x, float y, float heading) { + Agent agent = drive_test_agent(x, y, heading); + reset_agent_state(&agent); + agent.current_lane_idx = 100; + agent.previous_lane_idx = 100; return agent; } -static void cross_stop_line(Agent *agent) { - agent->prev_x = -3.0f; - agent->prev_y = 0.0f; - agent->prev_cos_heading = 1.0f; - agent->prev_sin_heading = 0.0f; - agent->sim_x = 1.0f; - agent->sim_speed = 2.0f; - agent->sim_speed_signed = 2.0f; - agent->sim_vx = 2.0f; +// One sim step: the agent moved prev -> cur with the given speed (positive = forward). +static void drive_test_step_to(Agent *agent, float x, float y, float speed) { + copy_pose_to_prev(agent); + agent->sim_x = x; + agent->sim_y = y; + agent->sim_speed = fabsf(speed); + agent->sim_speed_signed = speed; +} + +static void drive_test_turn_to(Agent *agent, float heading) { + agent->sim_heading = heading; + agent->cos_heading = cosf(heading); + agent->sin_heading = sinf(heading); +} + +static int test_approach_acquires_target(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-15.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -14.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, 0); + EXPECT_EQ_INT(agent.stop_sign_stop_completed, 0); + return 0; +} + +static int test_far_sign_not_targeted(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-26.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -25.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); + return 0; +} + +static int test_stop_in_box_then_cross_no_flag(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + // nose 0.5 m before the line, standing still + drive_test_step_to(&agent, -2.5f, 0.0f, 0.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_stop_completed, 1); + drive_test_step_to(&agent, -0.5f, 0.0f, 3.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, 0.5f, 0.0f, 3.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, 0); + EXPECT_EQ_INT(agent.stop_sign_last_failed_idx, -1); + return 0; +} + +static int test_cross_without_stop_flags_once(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, -0.5f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, 0.5f, 0.0f, 10.0f); + EXPECT_TRUE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); + EXPECT_EQ_INT(agent.stop_sign_last_failed_idx, 0); + // still inside the scaled box: the run sign is not re-targeted, nothing fires twice + drive_test_step_to(&agent, 1.5f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); + return 0; } -static int test_crossing_without_stop_flags(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-3.0f, 2.0f); - cross_stop_line(&agent); - EXPECT_TRUE(check_stop_sign_violation(&env, &agent)); +static int test_stop_before_box_does_not_count(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + // nose 4 m before the line: outside the 1.7 m deep trigger box + drive_test_step_to(&agent, -6.0f, 0.0f, 0.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_stop_completed, 0); + drive_test_step_to(&agent, -0.5f, 0.0f, 5.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, 0.5f, 0.0f, 5.0f); + EXPECT_TRUE(update_stop_sign_state(&env, 0)); return 0; } -static int test_required_stop_allows_crossing(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-3.0f, 0.0f); - for (int i = 0; i < STOP_SIGN_REQUIRED_STOP_TIMESTEPS; i++) { - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - } - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, STOP_SIGN_REQUIRED_STOP_TIMESTEPS); +static int test_slow_roll_in_box_does_not_count(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, -2.5f, 0.0f, 0.3f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_stop_completed, 0); + drive_test_step_to(&agent, -0.5f, 0.0f, 0.3f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, 0.5f, 0.0f, 0.3f); + EXPECT_TRUE(update_stop_sign_state(&env, 0)); + return 0; +} - cross_stop_line(&agent); - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 0); +static int test_swerve_around_line_flags(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + // acquired on the lane, then crossing 6 m beside the painted line + drive_test_step_to(&agent, -0.5f, 6.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, 0.5f, 6.0f, 10.0f); + EXPECT_TRUE(update_stop_sign_state(&env, 0)); return 0; } -static int test_partial_stop_flags_crossing(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-3.0f, 0.0f); - for (int i = 0; i < STOP_SIGN_REQUIRED_STOP_TIMESTEPS - 1; i++) { - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - } +static int test_parallel_lane_never_targeted(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-12.0f, 6.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 6.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); + drive_test_step_to(&agent, -0.5f, 6.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, 0.5f, 6.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + return 0; +} - cross_stop_line(&agent); - EXPECT_TRUE(check_stop_sign_violation(&env, &agent)); +static int test_opposing_direction_not_targeted(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(6.0f, 0.0f, (float) M_PI); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, 5.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); return 0; } -static int test_moving_resets_partial_stop(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-3.0f, 0.0f); - for (int i = 0; i < STOP_SIGN_REQUIRED_STOP_TIMESTEPS - 1; i++) { - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - } - agent.sim_speed = 1.0f; - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 0); +static int test_cross_street_not_targeted(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(1.0f, -6.0f, (float) M_PI / 2.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, 1.0f, -5.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); return 0; } -static int test_lane_match_does_not_gate_crossing(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-3.0f, 2.0f); - agent.current_lane_idx = -1; - cross_stop_line(&agent); - EXPECT_TRUE(check_stop_sign_violation(&env, &agent)); +static int test_reversing_not_targeted(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-4.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -5.0f, 0.0f, -1.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); return 0; } -static int test_beyond_ten_meters_does_not_accumulate_stop(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-10.0f, 0.0f); - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 1); +static int test_different_z_level_not_targeted(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + agent.sim_z = 6.0f; + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); + return 0; +} + +static int test_state_clears_beyond_proximity(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, -0.5f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, 0.5f, 0.0f, 10.0f); + EXPECT_TRUE(update_stop_sign_state(&env, 0)); + drive_test_step_to(&agent, 25.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_last_failed_idx, -1); + // a completed stop is forgotten the same way, so a later approach must stop again + Agent again = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + env.agents = &again; + drive_test_step_to(&again, -2.5f, 0.0f, 0.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(again.stop_sign_stop_completed, 1); + drive_test_step_to(&again, 25.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(again.stop_sign_target_idx, -1); + EXPECT_EQ_INT(again.stop_sign_stop_completed, 0); + return 0; +} - agent.sim_x = -10.1f; - copy_pose_to_prev(&agent); - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 0); +static int test_spawn_inside_box_counts_as_stopped(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-1.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_stop_completed, 1); + drive_test_step_to(&agent, 1.0f, 0.0f, 2.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); return 0; } -static int test_stop_after_line_does_not_qualify(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(1.0f, 0.0f); - for (int i = 0; i < STOP_SIGN_REQUIRED_STOP_TIMESTEPS; i++) { - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - } - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 0); +// Mid U-turn: standing still inside the box while pointed 115 deg off the lane, then aligning and crossing. +static int test_standstill_before_targeting_counts(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-0.7f, 0.0f, 2.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -0.7f, 0.0f, 0.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); + EXPECT_EQ_INT(agent.stop_sign_standstill_idx, 0); + drive_test_turn_to(&agent, 0.0f); + drive_test_step_to(&agent, -0.3f, 0.0f, 1.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, 0); + EXPECT_EQ_INT(agent.stop_sign_stop_completed, 1); + drive_test_step_to(&agent, 0.5f, 0.0f, 1.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_last_failed_idx, -1); return 0; } -static int test_leaving_proximity_resets_stop(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-3.0f, 0.0f); - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 1); +static int test_standstill_outside_box_before_targeting_does_not_count(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-6.0f, 0.0f, 2.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -6.0f, 0.0f, 0.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_standstill_idx, -1); + drive_test_turn_to(&agent, 0.0f); + drive_test_step_to(&agent, -0.5f, 0.0f, 5.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_stop_completed, 0); + drive_test_step_to(&agent, 0.5f, 0.0f, 5.0f); + EXPECT_TRUE(update_stop_sign_state(&env, 0)); + return 0; +} - agent.sim_x = -20.0f; - copy_pose_to_prev(&agent); - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 0); +static int test_standstill_clears_beyond_proximity(void) { + TrafficControlElement tc = drive_test_stop_sign(); + Agent agent = drive_test_stop_agent(-0.7f, 0.0f, 2.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -0.7f, 0.0f, 0.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_standstill_idx, 0); + drive_test_step_to(&agent, -25.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_standstill_idx, -1); return 0; } -static int test_wrong_heading_is_ignored(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-3.0f, 0.0f); - agent.sim_heading = M_PI; - agent.cos_heading = -1.0f; - agent.sin_heading = 0.0f; - copy_pose_to_prev(&agent); - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 0); +static int test_traffic_light_ignored(void) { + TrafficControlElement tc = drive_test_stop_sign(); + tc.type = TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT; + Agent agent = drive_test_stop_agent(-12.0f, 0.0f, 0.0f); + Drive env = drive_test_stop_env(&tc, &agent); + drive_test_step_to(&agent, -10.0f, 0.0f, 10.0f); + EXPECT_FALSE(update_stop_sign_state(&env, 0)); + EXPECT_EQ_INT(agent.stop_sign_target_idx, -1); return 0; } -static int test_forty_five_degree_heading_is_in_scope(void) { - TrafficControlElement stop_sign = drive_test_stop_sign(); - Drive env = drive_test_stop_sign_env(&stop_sign); - Agent agent = drive_test_stop_sign_agent(-3.0f, 0.0f); - agent.sim_heading = M_PI / 5.0f; - agent.cos_heading = cosf(M_PI / 5.0f); - agent.sin_heading = sinf(M_PI / 5.0f); - copy_pose_to_prev(&agent); - EXPECT_FALSE(check_stop_sign_violation(&env, &agent)); - EXPECT_EQ_INT(agent.stop_sign_stopped_timestep_count, 1); +static int test_obb_collision_unchanged_by_shared_sat(void) { + Agent ego = drive_test_agent(0.0f, 0.0f, 0.0f); + Agent other = drive_test_agent(3.0f, 1.5f, 0.5f); + EXPECT_TRUE(check_obb_collision(&ego, &other)); + other = drive_test_agent(6.0f, 0.0f, 0.0f); + EXPECT_FALSE(check_obb_collision(&ego, &other)); return 0; } int main(void) { int failures = 0; - RUN_TEST(test_crossing_without_stop_flags); - RUN_TEST(test_required_stop_allows_crossing); - RUN_TEST(test_partial_stop_flags_crossing); - RUN_TEST(test_moving_resets_partial_stop); - RUN_TEST(test_lane_match_does_not_gate_crossing); - RUN_TEST(test_beyond_ten_meters_does_not_accumulate_stop); - RUN_TEST(test_stop_after_line_does_not_qualify); - RUN_TEST(test_leaving_proximity_resets_stop); - RUN_TEST(test_wrong_heading_is_ignored); - RUN_TEST(test_forty_five_degree_heading_is_in_scope); + RUN_TEST(test_approach_acquires_target); + RUN_TEST(test_far_sign_not_targeted); + RUN_TEST(test_stop_in_box_then_cross_no_flag); + RUN_TEST(test_cross_without_stop_flags_once); + RUN_TEST(test_stop_before_box_does_not_count); + RUN_TEST(test_slow_roll_in_box_does_not_count); + RUN_TEST(test_swerve_around_line_flags); + RUN_TEST(test_parallel_lane_never_targeted); + RUN_TEST(test_opposing_direction_not_targeted); + RUN_TEST(test_cross_street_not_targeted); + RUN_TEST(test_reversing_not_targeted); + RUN_TEST(test_different_z_level_not_targeted); + RUN_TEST(test_state_clears_beyond_proximity); + RUN_TEST(test_spawn_inside_box_counts_as_stopped); + RUN_TEST(test_standstill_before_targeting_counts); + RUN_TEST(test_standstill_outside_box_before_targeting_does_not_count); + RUN_TEST(test_standstill_clears_beyond_proximity); + RUN_TEST(test_traffic_light_ignored); + RUN_TEST(test_obb_collision_unchanged_by_shared_sat); return test_summary(failures); } diff --git a/tests/drive/test_drive_traffic_lights.c b/tests/drive/test_drive_traffic_lights.c index 5c73feb534..525fbf3662 100644 --- a/tests/drive/test_drive_traffic_lights.c +++ b/tests/drive/test_drive_traffic_lights.c @@ -118,6 +118,48 @@ static int test_training_lights_stay_exclusive_unless_removed(void) { return 0; } +// Junction fully off while the standalone light is not: individual removal alone needs three 20% draws to line up. +static int count_junction_only_off_episodes(int junction_phases, int seed_count) { + TrafficControlElement lights[LIGHT_COUNT]; + int junction_only_off_count = 0; + for (int seed = 0; seed < seed_count; seed++) { + Drive env = drive_test_lights_env(lights, 0); + env.traffic_light_junction_phases = junction_phases; + rng_seed(&env.rng_state, (uint64_t) seed); + generate_traffic_light_states(&env); + int off_lights = 0; + for (int i = 0; i < LIGHT_COUNT; i++) { + off_lights += count_state(lights[i].states, TRAFFIC_CONTROL_STATE_OFF) == STATE_COUNT; + } + int standalone_off = count_state(lights[3].states, TRAFFIC_CONTROL_STATE_OFF) == STATE_COUNT; + junction_only_off_count += off_lights == 3 && !standalone_off; + } + return junction_only_off_count; +} + +// Expected rate per episode: 0.8 (no episode disable) * ~0.206 (group removed) * 0.8 (standalone kept) ~= 13%. +static int test_training_group_removal_turns_off_whole_junction(void) { + for (int junction_phases = 0; junction_phases <= 1; junction_phases++) { + int junction_only_off_count = count_junction_only_off_episodes(junction_phases, 500); + EXPECT_TRUE(junction_only_off_count >= 40); + EXPECT_TRUE(junction_only_off_count <= 100); + } + return 0; +} + +static int test_eval_never_removes_lights(void) { + TrafficControlElement lights[LIGHT_COUNT]; + for (int seed = 0; seed < 50; seed++) { + Drive env = drive_test_lights_env(lights, 1); + rng_seed(&env.rng_state, (uint64_t) seed); + generate_traffic_light_states(&env); + for (int i = 0; i < LIGHT_COUNT; i++) { + EXPECT_EQ_INT(count_state(lights[i].states, TRAFFIC_CONTROL_STATE_OFF), 0); + } + } + return 0; +} + int main(void) { int failures = 0; RUN_TEST(test_junction_lights_never_green_together); @@ -125,5 +167,7 @@ int main(void) { RUN_TEST(test_standalone_light_keeps_single_phase_cycle); RUN_TEST(test_junction_phases_disabled_makes_every_light_standalone); RUN_TEST(test_training_lights_stay_exclusive_unless_removed); + RUN_TEST(test_training_group_removal_turns_off_whole_junction); + RUN_TEST(test_eval_never_removes_lights); return test_summary(failures); } diff --git a/tests/eval/test_eval.py b/tests/eval/test_eval.py index 5255378088..bb19d2a3ff 100644 --- a/tests/eval/test_eval.py +++ b/tests/eval/test_eval.py @@ -538,6 +538,7 @@ def _training_args(tmp_path, benchmark_config_path, evaluation_enabled): "use_map_cache": True, "scenario_length": TRAIN_HORIZON, "resample_frequency": TRAIN_HORIZON, + "stagger_first_episode": False, # the default 20-step stagger horizon exceeds TRAIN_HORIZON } ) _set_small_observation_config(args) diff --git a/tests/smoke_tests/data/drive_rollout_golden.json b/tests/smoke_tests/data/drive_rollout_golden.json index 1b50bd6f3a..4a56a6eee1 100644 --- a/tests/smoke_tests/data/drive_rollout_golden.json +++ b/tests/smoke_tests/data/drive_rollout_golden.json @@ -1,35 +1,43 @@ { "env": { - "avg_distance_per_infraction": 12.592639437743596, - "avg_speed_per_agent": 1.3020565914256232, - "collision_rate": 0.03570190045450415, - "comfort_violation_count": 0.5988778604992798, - "dnf_rate": 0.5330425633915833, - "episode_length": 14.612885866846357, - "episode_return": -1.1959698955927576, - "lane_center_rate": 0.2800422217696905, - "n": 22.75, - "num_goals_reached": 0.009094182069280319, - "offroad_rate": 0.4243934947465147, + "avg_distance_per_infraction": 13.006657809019089, + "avg_speed_per_agent": 1.3086299777030945, + "collision_rate": 0.022455357760190964, + "comfort_violation_count": 0.6155481282621622, + "dnf_rate": 0.5376322198659181, + "early_reset_short": 0.0, + "episode_length": 14.7, + "episode_return": -1.1682074233889579, + "lane_center_rate": 0.2795072810724378, + "n": 15.0125, + "num_goals_reached": 0.008072916883975267, + "offroad_rate": 0.43584993463009597, "red_light_violation_rate": 0.0, "reward_components/ade": 0.0, - "reward_components/collision": -0.06426072296952563, - "reward_components/comfort": -0.45735678928239004, - "reward_components/goal": 0.0017113095548536097, - "reward_components/lane_align": -0.02719358926905053, - "reward_components/lane_center": -0.011776993641563292, - "reward_components/offroad": -0.626507214137486, + "reward_components/collision": -0.03335988249164075, + "reward_components/comfort": -0.4453425757586956, + "reward_components/goal": 0.004010416753590107, + "reward_components/lane_align": -0.027996644657105208, + "reward_components/lane_center": -0.011789687484269961, + "reward_components/offroad": -0.64305120408535, "reward_components/overspeed": 0.0, "reward_components/red_light": 0.0, - "reward_components/reverse": -0.010730728041380644, + "reward_components/reverse": -0.01078360435203649, "reward_components/stop_sign": 0.0, - "reward_components/timestep": -8.848491318401232e-05, - "reward_components/velocity": 0.00023330708635122783, + "reward_components/timestep": -8.974586080512381e-05, + "reward_components/velocity": 0.0001955053230744852, "score": 0.0, + "spawn_failed": 0.0, + "spawn_failed_goal": 0.0, + "spawn_reject_collision": 0.0664726434275508, + "spawn_reject_empty_cell": 0.0, + "spawn_reject_offroad": 0.43846612256020306, + "spawn_reject_stop_line": 0.01114011025056243, "stop_sign_violation_rate": 0.0, - "total_distance_travelled_sum": 132.61785657065255, - "total_infraction_count": 10.446428571428571, - "velocity_progress_sum": 0.021001098764827475 + "stopped_at_reset": 0.0, + "total_distance_travelled_sum": 86.93375225067139, + "total_infraction_count": 6.7375, + "velocity_progress_sum": 0.017799060756806283 }, "meta": { "bptt_horizon": 64, diff --git a/tests/smoke_tests/data/drive_smoke_golden.json b/tests/smoke_tests/data/drive_smoke_golden.json index d224a8693c..c78d781b00 100644 --- a/tests/smoke_tests/data/drive_smoke_golden.json +++ b/tests/smoke_tests/data/drive_smoke_golden.json @@ -1,57 +1,65 @@ { "env": { - "avg_distance_per_infraction": 11.587074600347952, - "avg_speed_per_agent": 1.2391465504964192, - "collision_rate": 0.022116174300511677, - "comfort_violation_count": 0.5565358847379684, - "distance_per_agent": 5.303314913236178, - "dnf_rate": 0.5400386999050776, - "episode_length": 14.158231337865194, - "episode_return": -1.0931359827518463, - "infractions_per_agent": 0.4576923076923077, - "lane_center_rate": 0.24947774410247803, - "n": 21.666666666666668, - "num_goals_reached": 0.005208333333333333, + "avg_distance_per_infraction": 12.77927256565468, + "avg_speed_per_agent": 1.298731041161013, + "collision_rate": 0.012987013438563327, + "comfort_violation_count": 0.5917845969592338, + "distance_per_agent": 5.642795678341027, + "dnf_rate": 0.545454554743581, + "early_reset_short": 0.0, + "episode_length": 14.380952380952381, + "episode_return": -1.17685332004126, + "infractions_per_agent": 0.44155844155844154, + "lane_center_rate": 0.2932935758328541, + "n": 231.0, + "num_goals_reached": 0.012987013438563327, "obs/max": 1.0, - "obs/mean": 0.1083909766166471, - "obs/min": -1.0, - "offroad_rate": 0.4326367899775505, + "obs/mean": 0.10966602416010574, + "obs/min": -1.0000000018626451, + "offroad_rate": 0.43290043560973496, "red_light_violation_rate": 0.0, "reward_components/ade": 0.0, - "reward_components/collision": -0.039469959835211434, - "reward_components/comfort": -0.40980128447214764, - "reward_components/goal": 0.0, - "reward_components/lane_align": -0.02823772296930353, - "reward_components/lane_center": -0.011987591395154595, - "reward_components/offroad": -0.5934753715991974, + "reward_components/collision": -0.022441596373335107, + "reward_components/comfort": -0.4126336298463665, + "reward_components/goal": 0.004329004554779498, + "reward_components/lane_align": -0.027322572619690524, + "reward_components/lane_center": -0.012265666613750385, + "reward_components/offroad": -0.6959504439975276, "reward_components/overspeed": 0.0, "reward_components/red_light": 0.0, - "reward_components/reverse": -0.01029344896475474, + "reward_components/reverse": -0.010638060892863578, "reward_components/stop_sign": 0.0, - "reward_components/timestep": -8.496711901292049e-05, - "reward_components/velocity": 0.00021435123350480959, + "reward_components/timestep": -8.928571238451601e-05, + "reward_components/velocity": 0.00015886408038481715, "score": 0.0, + "spawn_failed": 0.0, + "spawn_failed_goal": 0.0, + "spawn_reject_collision": 0.056277057051142575, + "spawn_reject_empty_cell": 0.0, + "spawn_reject_offroad": 0.3636363656360866, + "spawn_reject_stop_line": 0.004329004329004329, "stop_sign_violation_rate": 0.0, - "total_distance_travelled": 1378.8618774414062, - "total_infractions": 119.0, - "velocity_progress_sum": 0.019974155952998746 + "stopped_at_reset": 0.0, + "total_distance_travelled": 1303.4858016967773, + "total_infractions": 102.0, + "velocity_progress_sum": 0.014079634136484042 }, "losses": { - "actor_grad_norm": 0.7377755045890808, - "approx_kl": 0.0009849987691268325, - "clipfrac": 0.0, - "critic_grad_norm": 1.6560184955596924, - "ema_max": 3.866555392742157, + "actor_grad_norm": 2.6260669231414795, + "approx_kl": 0.015240681357681751, + "clipfrac": 0.21147167682647705, + "critic_grad_norm": 0.75099778175354, + "ema_max": 3.856906831264496, "ent_coef": 0.01, - "entropy": 2.4803009033203125, - "explained_variance": 0.0335850715637207, - "filter_threshold": 0.03866555392742157, - "filtered_fraction": 0.15043695380774036, - "kept_fraction": 0.8495630461922596, - "masked_fraction": 0.2177734375, - "old_approx_kl": 0.0006674724281765521, - "policy_loss": -0.00014271998952608556, - "value_loss": 0.24733062088489532 + "entropy": 2.447709321975708, + "explained_variance": 0.04323387145996094, + "filter_threshold": 0.03856906831264496, + "filtered_fraction": 0.11793611793611791, + "kept_fraction": 0.8820638820638821, + "masked_fraction": 0.205078125, + "old_approx_kl": 0.01860375516116619, + "policy_loss": -0.0062951878644526005, + "value_loss": 0.25460731983184814 }, "meta": { "bptt_horizon": 64, diff --git a/tests/smoke_tests/test_drive_rollout.py b/tests/smoke_tests/test_drive_rollout.py index 69f581858b..0162ce4ac4 100644 --- a/tests/smoke_tests/test_drive_rollout.py +++ b/tests/smoke_tests/test_drive_rollout.py @@ -79,6 +79,7 @@ def _build_config(): "use_map_cache": True, "map_dir": "pufferlib/resources/drive/binaries/carla", "scenario_length": BPTT_HORIZON, + "stagger_first_episode": False, # golden expects full-length first episodes "seed": SEED, }, ) diff --git a/tests/smoke_tests/test_drive_train.py b/tests/smoke_tests/test_drive_train.py index b06a50be53..b677a5f1a9 100644 --- a/tests/smoke_tests/test_drive_train.py +++ b/tests/smoke_tests/test_drive_train.py @@ -57,7 +57,7 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from pufferlib.pufferl import PuffeRL, load_config, load_env, load_policy -from pufferlib.utils import reduce_environment_metrics +from pufferlib.utils import finalize_environment_metrics SEED = 42 EPOCHS = 2 # small: CI emulates the CPU under QEMU where torch is ~30x slower @@ -144,6 +144,7 @@ def _build_config(): "use_map_cache": True, # share map geometry across envs in a worker "map_dir": "pufferlib/resources/drive/binaries/carla", "scenario_length": BPTT_HORIZON, # short -> episodes complete each epoch + "stagger_first_episode": False, # golden expects full-length first episodes "seed": SEED, }, ) @@ -194,14 +195,14 @@ def _finalize_train_config(args, total_agents): def _capture_metrics(pufferl): - from collections import defaultdict - - env_acc = defaultdict(list) + env_acc = {} for _ in range(EPOCHS): pufferl.evaluate() # Snapshot env stats BEFORE train()'s mean_and_log() resets them. - for k, v in pufferl.stats.items(): - env_acc[k].extend(v if isinstance(v, list) else [v]) + for k, (value_sum, weight_sum) in pufferl.collect_environment_metric_sums().items(): + entry = env_acc.setdefault(k, [0.0, 0.0]) + entry[0] += value_sum + entry[1] += weight_sum # Force logging this epoch so self.losses is set deterministically # (otherwise it only updates on a wall-clock interval -> flaky). pufferl.last_log_time = 0.0 @@ -209,7 +210,7 @@ def _capture_metrics(pufferl): losses = {k: float(v) for k, v in pufferl.losses.items() if _is_number(v)} - env_means = reduce_environment_metrics(env_acc) + env_means = finalize_environment_metrics(env_acc) return losses, env_means diff --git a/tests/unit_tests/test_action_selection.py b/tests/unit_tests/test_action_selection.py index 9f2b530888..1fa51b0de0 100644 --- a/tests/unit_tests/test_action_selection.py +++ b/tests/unit_tests/test_action_selection.py @@ -195,6 +195,7 @@ def test_drive_mean_action_is_exact_in_physical_units(monkeypatch): num_agents=64, map_dir="pufferlib/resources/drive/binaries/carla", num_maps=1, + allow_map_subset=True, scenario_length=200, dynamics_model="jerk", action_type="continuous", diff --git a/tests/unit_tests/test_carla_offset_calibration.py b/tests/unit_tests/test_carla_offset_calibration.py new file mode 100644 index 0000000000..3102986b52 --- /dev/null +++ b/tests/unit_tests/test_carla_offset_calibration.py @@ -0,0 +1,80 @@ +"""calibrate_town_offset recovers a known bin-vs-CARLA translation and height from fake driving waypoints.""" + +import os +import sys +from pathlib import Path + +import numpy as np + + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + +import data_utils.mirror_map_bin as mbin +from pufferlib.ocean.cosim import carla_bridge as cb + + +TOWN_BIN = Path(__file__).resolve().parents[2] / "pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin" +TRUE_SHIFT = (-0.42, 0.31) # bin lanes = CARLA lanes + shift, in the bin frame +TRUE_Z_OFFSET = 1.24 # bin z = CARLA z + offset (Town04's measured value) + + +class _Vec: + def __init__(self, x, y, z=0.0): + self.x, self.y, self.z = x, y, z + + +class _Transform: + def __init__(self, x, y, z, heading_x, heading_y): + self.location = _Vec(x, y, z) + self._right = _Vec(heading_y, -heading_x) # right of travel in a y-up frame + + def get_right_vector(self): + return self._right + + +class _Waypoint: + def __init__(self, x, y, z, heading_x, heading_y): + self.transform = _Transform(x, y, z, heading_x, heading_y) + self.is_junction = False + self.lane_type = "Driving" + + +class _Map: + """Waypoints on the bin's own lane centres, moved by -TRUE_SHIFT / -TRUE_Z_OFFSET and mapped to the CARLA frame.""" + + def __init__(self, transform): + data = mbin.read_bin(TOWN_BIN) + self.waypoints = [] + for road in data["roads"]: + if not (0 <= road["type"] <= 9) or len(road["x"]) < 3: + continue + xs, ys, zs = np.asarray(road["x"]), np.asarray(road["y"]), np.asarray(road["z"]) + for k in range(1, len(xs) - 1, 4): + hx, hy = xs[k + 1] - xs[k - 1], ys[k + 1] - ys[k - 1] + norm = float(np.hypot(hx, hy)) + if norm < 1e-6: + continue + cx, cy = transform.bin_to_loc(xs[k] - TRUE_SHIFT[0], ys[k] - TRUE_SHIFT[1]) + cz = zs[k] - TRUE_Z_OFFSET + self.waypoints.append(_Waypoint(cx, cy, cz, hx / norm, -hy / norm)) # heading y flips with the frame + + def generate_waypoints(self, spacing_m): + return self.waypoints + + +def test_calibration_recovers_translation_and_height(): + transform = cb.CarlaTransform("Town01", offset=cb.town_offset(str(TOWN_BIN))) + offset, z_offset, residual_before, residual_after = cb.calibrate_town_offset( + _Map(transform), transform, str(TOWN_BIN) + ) + assert residual_before > 0.2 + assert residual_after < 0.02 + np.testing.assert_allclose((offset[0] - transform.tx, offset[1] - transform.ty), TRUE_SHIFT, atol=0.02) + assert abs(z_offset - TRUE_Z_OFFSET) < 0.02 + + +def test_transform_z_round_trip(): + transform = cb.CarlaTransform("Town04", z_offset=TRUE_Z_OFFSET) + assert transform.z_to_bin(0.0) == TRUE_Z_OFFSET + assert transform.z_to_carla(transform.z_to_bin(-1.24)) == -1.24 + assert cb.CarlaTransform("Town04").tz == 0.0 diff --git a/tests/unit_tests/test_cosim_config_contract.py b/tests/unit_tests/test_cosim_config_contract.py new file mode 100644 index 0000000000..08179accfd --- /dev/null +++ b/tests/unit_tests/test_cosim_config_contract.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python3 +"""Config contract for the co-simulation integrations (pufferlib/ocean/cosim). + +The CARLA leaderboard agent, the standalone CARLA co-sim, and the nuPlan +planner all rebuild the checkpoint's shadow Drive env FROM ITS config.yaml +(saved next to the checkpoint from pufferlib/config/puffer_drive.yaml) via +cosim/arch.py's shadow_env_kwargs: every Drive-accepted env key is adopted, +then CLEAN_EVAL_OVERRIDES is applied, then the co-sim's structural keys win. +A key silently dropped (renamed in Drive.__init__ or removed from the yaml) +flips the shadow env to a Drive default and breaks observation-encoding +parity with the trained policy WITHOUT any error. + +This test pins the keys the co-sim reads, the adopt-then-override precedence, +and equality between cosim's CLEAN_EVAL_OVERRIDES and the benchmark's. + +Run: python -m unittest tests.unit_tests.test_cosim_config_contract +""" + +import inspect +import os +import sys +import unittest +from pathlib import Path + +import yaml + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + +from pufferlib.ocean.drive.drive import Drive + +REPO_ROOT = Path(__file__).resolve().parents[2] +CONFIG_PATH = REPO_ROOT / "pufferlib" / "config" / "puffer_drive.yaml" + +# Env keys the co-sim reads DIRECTLY from the checkpoint config (KeyError if +# missing): leaderboard_agent.setup() for the policy-step interval, the shadow +# agent pool, and the route-goal placement; plus keys whose silent fallback to +# a Drive default would break parity for every consumer. +COSIM_ENV_KEYS = ( + "dt", + "max_agents_per_env", + "min_goal_spacing", + "max_goal_spacing", + "goal_radius", + "num_goals", + "dynamics_model", + "goal_speed", + "goal_source", + "reward_conditioning", +) + +# obs_* keys are deliberately NOT pinned: shadow_env_kwargs adopts whatever +# obs_* keys the checkpoint config carries, and a key absent from the config +# falls back to the same Drive default the training run used -- consistent. + +# Top-level keys the cosim policy loaders read (leaderboard_agent.py, +# carla_cosim.py, nuplan/planner.py): getattr(drive_torch, cfg["policy_name"]) +# and policy_cls(env, **cfg["policy"]). +COSIM_TOP_LEVEL_KEYS = ("policy", "policy_name") + + +class TestCosimConfigContract(unittest.TestCase): + @classmethod + def setUpClass(cls): + with open(CONFIG_PATH) as f: + cls.cfg = yaml.safe_load(f) + + def test_env_section_has_cosim_keys(self): + env = self.cfg.get("env") + self.assertIsInstance(env, dict, f"{CONFIG_PATH} lost its env section") + for key in COSIM_ENV_KEYS: + self.assertIn( + key, + env, + f"env.{key} was removed from puffer_drive.yaml; the co-sim shadow env " + f"(pufferlib/ocean/cosim) reads it from checkpoint configs and would " + f"either crash or silently fall back to a Drive default, breaking " + f"parity with the trained policy.", + ) + + def test_top_level_has_policy_keys(self): + for key in COSIM_TOP_LEVEL_KEYS: + self.assertIn( + key, + self.cfg, + f"'{key}' was removed from puffer_drive.yaml; the co-sim loads the " + f"policy class/kwargs from it when restoring a checkpoint.", + ) + + def test_drive_still_accepts_pinned_keys(self): + # shadow_env_kwargs filters adopted keys through Drive.__init__'s + # signature: a renamed Drive kwarg silently drops the key instead of + # erroring. + accepted = set(inspect.signature(Drive.__init__).parameters) + for key in COSIM_ENV_KEYS: + self.assertIn( + key, + accepted, + f"Drive.__init__ no longer accepts '{key}'; shadow_env_kwargs would " + f"silently drop it and the shadow env would diverge from the training env.", + ) + + +class TestShadowEnvKwargs(unittest.TestCase): + def test_clean_eval_matches_benchmark_profile(self): + """cosim/arch.py duplicates the benchmark profile's noise/light keys so + the co-sim venvs never import the training stack; each mirrored key + must match pufferlib/config/evaluation/benchmark.yaml.""" + from pufferlib.ocean.cosim.arch import CLEAN_EVAL_OVERRIDES as cosim_overrides + + benchmark_yaml = REPO_ROOT / "pufferlib" / "config" / "evaluation" / "benchmark.yaml" + bench_env = yaml.safe_load(benchmark_yaml.read_text())["env"] + for key, value in cosim_overrides.items(): + self.assertEqual( + bench_env.get(key), + value, + f"cosim/arch.py CLEAN_EVAL_OVERRIDES['{key}'] drifted from " + "config/evaluation/benchmark.yaml; the shadow env must run " + "the same clean-eval profile as the repo's own evaluations.", + ) + + def test_every_benchmark_randomization_pin_is_mirrored(self): + """A training-time randomization the benchmark profile pins off must be pinned off in the + co-sim mirror too, or the shadow env silently adopts the checkpoint's training value (or + aborts: speed_limit_random_prob > 0 rejects maps without speed zones, e.g. nuPlan/CARLA + co-sim bins).""" + from pufferlib.ocean.cosim.arch import CLEAN_EVAL_OVERRIDES as cosim_overrides + + benchmark_yaml = REPO_ROOT / "pufferlib" / "config" / "evaluation" / "benchmark.yaml" + bench_env = yaml.safe_load(benchmark_yaml.read_text())["env"] + randomization_markers = ("random", "noise", "dropout", "blindness", "phantom", "spawn_") + for key in bench_env: + if not any(marker in key for marker in randomization_markers): + continue + self.assertIn( + key, + cosim_overrides, + f"benchmark.yaml pins '{key}' but cosim/arch.py CLEAN_EVAL_OVERRIDES does not mirror it; " + "the co-sim shadow env would adopt the checkpoint's training value.", + ) + + def test_clean_eval_overrides_beat_checkpoint_config(self): + """A checkpoint trained WITH observation noise must still evaluate + clean: every CLEAN_EVAL_OVERRIDES key wins over the config's value.""" + from pufferlib.ocean.cosim.arch import CLEAN_EVAL_OVERRIDES, shadow_env_kwargs + + cfg = {"env": {key: 0.9 for key in CLEAN_EVAL_OVERRIDES if key != "traffic_light_behavior"}} + cfg["env"]["traffic_light_behavior"] = "remove" + kwargs = shadow_env_kwargs(cfg) + for key, value in CLEAN_EVAL_OVERRIDES.items(): + self.assertEqual( + kwargs.get(key), + value, + f"'{key}' from the checkpoint config leaked past the clean-eval profile into the co-sim shadow env.", + ) + + def test_int_infraction_behaviors_normalize_to_strings(self): + """Saved checkpoint configs may carry resolved enum ints for the + infraction behaviors (weights/mimolette: collision_behavior: 1); + Drive.__init__ only accepts the strings.""" + from pufferlib.ocean.cosim.arch import shadow_env_kwargs + + kwargs = shadow_env_kwargs({"env": {"collision_behavior": 1, "offroad_behavior": 0}}) + self.assertEqual(kwargs["collision_behavior"], "stop") + self.assertEqual(kwargs["offroad_behavior"], "ignore") + + def test_adopts_every_drive_accepted_key_and_filters_the_rest(self): + from pufferlib.ocean.cosim.arch import shadow_env_kwargs + + cfg = {"env": {"obs_slots_partners_n": 63, "min_goal_spacing": 11.0, "not_a_drive_kwarg": 1}} + kwargs = shadow_env_kwargs(cfg) + self.assertEqual(kwargs["obs_slots_partners_n"], 63) + self.assertEqual(kwargs["min_goal_spacing"], 11.0) + self.assertNotIn("not_a_drive_kwarg", kwargs) + + def test_structural_overrides_beat_everything(self): + """The co-sim's structural keys (map/pool wiring) must win over the + training config, which points at training maps and multi-env pools.""" + from pufferlib.ocean.cosim.arch import shadow_env_kwargs + + cfg = {"env": {"num_agents": 512, "num_maps": 100, "resample_frequency": 91}} + kwargs = shadow_env_kwargs( + cfg, overrides={"num_agents": 64, "num_maps": 1, "resample_frequency": 0, "map_dir": "town.bin"} + ) + self.assertEqual(kwargs["num_agents"], 64) + self.assertEqual(kwargs["num_maps"], 1) + self.assertEqual(kwargs["resample_frequency"], 0) + self.assertEqual(kwargs["map_dir"], "town.bin") + + def test_defaults_only_fill_gaps(self): + """`defaults` (DEFAULT_ARCH / dummy-run arch) must lose to a real + checkpoint config.""" + from pufferlib.ocean.cosim.arch import shadow_env_kwargs + + kwargs = shadow_env_kwargs({"env": {"goal_source": "route"}}, defaults={"goal_source": "map", "num_goals": 3}) + self.assertEqual(kwargs["goal_source"], "route") + self.assertEqual(kwargs["num_goals"], 3) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit_tests/test_drive_backbone.py b/tests/unit_tests/test_drive_backbone.py index 48227a7e81..d63dfacb2b 100644 --- a/tests/unit_tests/test_drive_backbone.py +++ b/tests/unit_tests/test_drive_backbone.py @@ -23,9 +23,10 @@ def encoder(x): pooled = backbone._encode_and_pool(objects, valid_counts, encoder) - # Dense encode keeps shapes static; padded rows are masked out of the pool instead + # Dense encode (static shapes for torch.compile): every row reaches the encoder once. assert len(encoded_inputs) == 1 - torch.testing.assert_close(encoded_inputs[0], objects) + assert encoded_inputs[0].shape == objects.shape + # Padded rows never win the max-pool; an all-padded row pools to zeros. torch.testing.assert_close( pooled, torch.tensor( @@ -36,3 +37,11 @@ def encoder(x): ] ), ) + + +def test_encode_and_pool_without_masking_pools_every_row(): + backbone = object.__new__(DriveBackbone) + backbone.mask_padded_features = False + objects = torch.tensor([[[1.0, 10.0], [2.0, 3.0], [100.0, 100.0]]]) + pooled = backbone._encode_and_pool(objects, torch.tensor([2]), lambda x: x) + torch.testing.assert_close(pooled, torch.tensor([[100.0, 100.0]])) diff --git a/tests/unit_tests/test_drive_config.py b/tests/unit_tests/test_drive_config.py index 5553d814bc..3e9928ae12 100644 --- a/tests/unit_tests/test_drive_config.py +++ b/tests/unit_tests/test_drive_config.py @@ -14,6 +14,8 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from pufferlib.config_schema import validate_puffer_drive_config +from pufferlib.ocean.drive import binding +from pufferlib.ocean.drive.drive import Drive from pufferlib.pufferl import load_config, pufferlib VERBOSITY = 0 @@ -54,6 +56,37 @@ def test_obs_stride_validation(self): with self.assertRaisesRegex(pufferlib.APIUsageError, "obs_boundary_stride"): validate_puffer_drive_config(args, "test") + def test_speed_limit_randomization_validation(self): + with self.assertRaisesRegex(ValueError, "speed_limit_random_prob"): + Drive(speed_limit_random_prob=1.5) + with self.assertRaisesRegex(ValueError, "speed_limit_random_delta_mps"): + Drive(speed_limit_random_delta_mps=-1.0) + with self.assertRaisesRegex(ValueError, "min <= max"): + Drive(speed_limit_random_min_mps=20.0, speed_limit_random_max_mps=10.0) + + def test_overspeed_tolerance_validation(self): + with self.assertRaisesRegex(ValueError, "overspeed_tolerance_mps"): + Drive(overspeed_tolerance_mps=-0.5) + with self.assertRaisesRegex(ValueError, "overspeed_tolerance_mps"): + Drive(overspeed_tolerance_mps=float("nan")) + + def test_conditioning_speed_scale_validation(self): + with self.assertRaisesRegex(ValueError, "conditioning_speed_scale"): + Drive(conditioning_speed_scale=0.9) + + @patch("sys.argv", ["pufferl.py", "env.obs_lane_speed_limit=true"]) + def test_lane_speed_limit_cli_override(self): + args = load_config("puffer_drive") + self.assertTrue(args["env"]["obs_lane_speed_limit"]) + + def test_lane_speed_limit_obs_widens_lane_rows(self): + plain = Drive(config_only=True) + with_limit = Drive(config_only=True, obs_lane_speed_limit=True) + self.assertEqual(with_limit.lane_features, plain.lane_features + binding.LANE_SPEED_LIMIT_FEATURES) + self.assertEqual( + with_limit.num_obs - plain.num_obs, plain.obs_slots_lane_kept * binding.LANE_SPEED_LIMIT_FEATURES + ) + @patch("sys.argv", ["pufferl.py", "train.learning_rate=0.5"]) def test_cli_override(self): """Test that Hydra CLI overrides win over the config file values.""" diff --git a/tests/unit_tests/test_environment_metric_weighting.py b/tests/unit_tests/test_environment_metric_weighting.py new file mode 100644 index 0000000000..41d75531ea --- /dev/null +++ b/tests/unit_tests/test_environment_metric_weighting.py @@ -0,0 +1,40 @@ +"""Env log dicts are weighted by their episode count when reduced to one metric per key.""" + +import numpy as np + +from pufferlib.utils import accumulate_environment_metric, finalize_environment_metrics + + +def _accumulate(logs): + sums = {} + for log in logs: + weight = float(log["n"]) + for key, value in log.items(): + accumulate_environment_metric(sums, key, value, weight) + return sums + + +def test_weighted_mean_over_completed_episodes(): + logs = [ + {"n": 10.0, "episode_return": 1.0, "total_distance_travelled_sum": 100.0, "total_infraction_count": 1.0}, + {"n": 990.0, "episode_return": 3.0, "total_distance_travelled_sum": 9900.0, "total_infraction_count": 9.0}, + ] + metrics = finalize_environment_metrics(_accumulate(logs)) + assert metrics["n"] == 1000.0 + assert np.isclose(metrics["episode_return"], (10 * 1.0 + 990 * 3.0) / 1000) + assert metrics["total_distance_travelled"] == 10000.0 + assert metrics["total_infractions"] == 10.0 + assert np.isclose(metrics["distance_per_agent"], 10.0) + + +def test_unweighted_batch_stats_keep_plain_mean(): + sums = {} + for value in (0.5, 1.5): + accumulate_environment_metric(sums, "obs/mean", value, 1.0) + assert finalize_environment_metrics(sums)["obs/mean"] == 1.0 + + +def test_array_values_count_each_element(): + sums = {} + accumulate_environment_metric(sums, "score", np.array([1.0, 3.0]), 2.0) + assert finalize_environment_metrics(sums)["score"] == 2.0 diff --git a/tests/unit_tests/test_nuplan_bridge_goals.py b/tests/unit_tests/test_nuplan_bridge_goals.py new file mode 100644 index 0000000000..0eb64e4117 --- /dev/null +++ b/tests/unit_tests/test_nuplan_bridge_goals.py @@ -0,0 +1,145 @@ +"""roadblock_centroid_goals: route roadblock centroids become goals from the ego's block on, off-polygon +centroids snap to a lane baseline, goals behind the ego or too close together are dropped.""" + +from types import SimpleNamespace + +import numpy as np +import pytest + + +nuplan = pytest.importorskip("nuplan") + +from pufferlib.ocean.cosim import nuplan_bridge as nb + + +class _BlockMap: + """Route blocks along x: A [0,40] (ego inside), B [40,60], C [60,70], D an L-bend whose centroid is off-polygon.""" + + def __init__(self): + from shapely.geometry import Polygon + + def block(polygon, lane_xy): + path = [SimpleNamespace(x=x, y=y) for x, y in lane_xy] + return SimpleNamespace( + polygon=polygon, interior_edges=[SimpleNamespace(baseline_path=SimpleNamespace(discrete_path=path))] + ) + + self.blocks = { + "A": block(Polygon([(0, 0), (40, 0), (40, 4), (0, 4)]), [(0, 2), (40, 2)]), + "B": block(Polygon([(40, 0), (60, 0), (60, 4), (40, 4)]), [(40, 2), (60, 2)]), + "C": block(Polygon([(60, 0), (70, 0), (70, 4), (60, 4)]), [(60, 2), (70, 2)]), + "D": block( + Polygon([(70, 0), (110, 0), (110, 4), (74, 4), (74, 40), (70, 40)]), + [(72, 2), (108, 2), (72, 20), (72, 38)], + ), + } + + def get_map_object(self, block_id, layer): + block = self.blocks.get(block_id) + if block is not None: + block.id = block_id + return block + + +def test_roadblock_centroid_goals_skip_behind_thin_and_snap_off_polygon_centroids(): + map_api = _BlockMap() + goals = nb.roadblock_centroid_goals( + map_api, ["A", "B", "C", "D"], 30.0, 2.0, 0.0, min_spacing=20.0, min_ahead_m=15.0 + ) + centroid_d = map_api.blocks["D"].polygon.centroid + assert not map_api.blocks["D"].polygon.contains(centroid_d) + lane_d = np.array([(72, 2), (108, 2), (72, 20), (72, 38)], dtype=np.float64) + snapped_d = lane_d[np.argmin(np.hypot(lane_d[:, 0] - centroid_d.x, lane_d[:, 1] - centroid_d.y))] + # A's centroid (20, 2) is behind the ego, C's (65, 2) is 15 m past B's (50, 2): both dropped + np.testing.assert_allclose(goals, [[50.0, 2.0], snapped_d]) + + +def test_roadblock_centroid_goals_follow_the_route_through_turns(): + # ego at the end of A facing east; a route that turns back west after B keeps its goals (only the ego + # block's own centroid is heading-gated), and the repeated block id of a loop is kept as a revisit + map_api = _BlockMap() + goals = nb.roadblock_centroid_goals( + map_api, ["A", "B", "A", "B"], 30.0, 2.0, 0.0, min_spacing=10.0, min_ahead_m=15.0 + ) + np.testing.assert_allclose(goals, [[50.0, 2.0], [20.0, 2.0], [50.0, 2.0]]) + + +def test_roadblock_centroid_goals_start_at_the_nearest_block_and_empty_without_blocks(): + goals = nb.roadblock_centroid_goals(_BlockMap(), ["A", "B", "C"], 45.0, 9.0, 0.0, min_spacing=10.0, min_ahead_m=2.0) + np.testing.assert_allclose(goals, [[50.0, 2.0], [65.0, 2.0]]) # ego beside B: A is never visited + assert nb.roadblock_centroid_goals( + _BlockMap(), ["nope"], 0.0, 0.0, 0.0, min_spacing=20.0, min_ahead_m=15.0 + ).shape == (0, 2) + + +def test_extend_route_past_loop_cut_appends_the_logged_remainder(): + assert nb.extend_route_past_loop_cut(["1", "2", "3"], ["1", "2", "3", "4", "2", "5"]) == [ + "1", + "2", + "3", + "4", + "2", + "5", + ] + assert nb.extend_route_past_loop_cut(["9", "1", "2"], ["1", "2", "3"]) == [ + "9", + "1", + "2", + "3", + ] # prepended start fix + assert nb.extend_route_past_loop_cut(["1", "2"], ["1", "2"]) == ["1", "2"] + assert nb.extend_route_past_loop_cut([], ["1"]) == [] + + +class _LaneMap: + """Two-lane road A -> connector B -> road C -> connector D -> road E; only lane 1 (y=6) flows into D.""" + + def __init__(self): + from shapely.geometry import Polygon + + self.blocks, self.lanes = {}, {} + + def add(block_id, x0, x1, lane_ys): + lanes = [] + for k, y in enumerate(lane_ys): + path = [SimpleNamespace(x=float(x), y=float(y)) for x in np.arange(x0, x1 + 1e-9, 2.0)] + lane = SimpleNamespace( + id=f"{block_id}{k}", + baseline_path=SimpleNamespace(discrete_path=path), + outgoing_edges=[], + get_roadblock_id=lambda block_id=block_id: block_id, + ) + lanes.append(lane) + self.lanes[lane.id] = lane + self.blocks[block_id] = SimpleNamespace( + id=block_id, polygon=Polygon([(x0, 0), (x1, 0), (x1, 8), (x0, 8)]), interior_edges=lanes + ) + + add("A", 0, 40, [2, 6]) + add("B", 40, 60, [2, 6]) + add("C", 60, 100, [2, 6]) + add("D", 100, 120, [6]) + add("E", 120, 160, [6]) + for src, dst in [("A0", "B0"), ("A1", "B1"), ("B0", "C0"), ("B1", "C1"), ("C1", "D0"), ("D0", "E0")]: + self.lanes[src].outgoing_edges.append(self.lanes[dst]) + + def get_map_object(self, block_id, layer): + return self.blocks.get(block_id) + + +def test_roadblock_lane_goals_keep_the_ego_lane_until_the_graph_forces_a_change(): + goals = nb.roadblock_lane_goals(_LaneMap(), list("ABCDE"), 10.0, 2.0, 0.0, min_spacing=20.0, min_ahead_m=5.0) + # lane 0 through A and B; C's goal moves to lane 1 because only lane 1 flows into D + np.testing.assert_allclose(goals, [[20, 2], [50, 2], [80, 6], [110, 6], [140, 6]]) + + +def test_roadblock_lane_goals_seed_from_the_co_directional_lane_nearest_the_ego(): + goals = nb.roadblock_lane_goals(_LaneMap(), list("ABCDE"), 10.0, 5.0, 0.0, min_spacing=20.0, min_ahead_m=5.0) + np.testing.assert_allclose(goals, [[20, 6], [50, 6], [80, 6], [110, 6], [140, 6]]) + # facing west no lane is co-directional: nearest lane, own-block goal behind the ego dropped + goals = nb.roadblock_lane_goals(_LaneMap(), list("ABCDE"), 10.0, 5.0, np.pi, min_spacing=20.0, min_ahead_m=5.0) + np.testing.assert_allclose(goals, [[50, 6], [80, 6], [110, 6], [140, 6]]) + assert nb.roadblock_lane_goals(_LaneMap(), ["nope"], 0.0, 0.0, 0.0, min_spacing=20.0, min_ahead_m=5.0).shape == ( + 0, + 2, + ) diff --git a/tests/unit_tests/test_nuplan_bridge_light_matching.py b/tests/unit_tests/test_nuplan_bridge_light_matching.py new file mode 100644 index 0000000000..176506dce0 --- /dev/null +++ b/tests/unit_tests/test_nuplan_bridge_light_matching.py @@ -0,0 +1,56 @@ +"""match_connectors_to_stop_lines: nearest co-directional traffic light by stop-line segment distance.""" + +import os +import sys + +import numpy as np +import pytest + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + +nuplan = pytest.importorskip("nuplan") + +from pufferlib.ocean.cosim import nuplan_bridge as nb + +SOUTH = -np.pi / 2 +IDENTITY = nb.NuPlanTransform(0.0, 0.0) +# element 0: 25 m stop line across a seven-lane southbound approach (y = -1), midpoint at x = 12.5 +# element 1: 6 m oncoming stop line 8 m away, element 2: cross-street light 5 m away, element 3: stop sign on top of the entry +STOP_LINES = np.array([[0.0, -1.0, 25.0, -1.0], [-5.0, 8.0, 1.0, 8.0], [-5.0, -3.0, -5.0, 3.0], [-1.0, 0.0, 3.0, 0.0]]) +HEADINGS = np.array([SOUTH, np.pi / 2, 0.0, SOUTH]) +TYPES = np.array([nb.TRAFFIC_TYPE_LIGHT, nb.TRAFFIC_TYPE_LIGHT, nb.TRAFFIC_TYPE_LIGHT, 2], np.int32) + + +def match(entries, **kwargs): + return nb.match_connectors_to_stop_lines(entries, IDENTITY, STOP_LINES, HEADINGS, TYPES, **kwargs) + + +def test_outer_lane_matches_the_wide_stop_line_by_segment_distance(): + assert match({"outer": (1.5, 0.0, SOUTH), "middle": (12.5, 0.0, SOUTH)}) == {"outer": 0, "middle": 0} + + +def test_oncoming_cross_street_and_stop_sign_elements_are_never_candidates(): + assert match({"c": (-2.0, 4.0, SOUTH)}) == {"c": 0} # element 1 (oncoming) and 2 (cross) are nearer + assert match({"far": (-2.0, 40.0, SOUTH)}) == {} + + +def test_skewed_stop_line_is_kept_within_the_heading_tolerance(): + assert match({"c": (1.5, 0.0, SOUTH - np.radians(60.0))}) == {"c": 0} + assert match({"c": (1.5, 0.0, SOUTH - np.radians(80.0))}) == {} + + +def test_max_distance_and_empty_map(): + assert match({"c": (1.5, 14.0, SOUTH)}) == {"c": 0} + assert match({"c": (1.5, 17.0, SOUTH)}) == {} + assert match({"c": (1.5, 17.0, SOUTH)}, max_dist_m=19.0) == {"c": 0} + assert ( + nb.match_connectors_to_stop_lines({"c": (0.0, 0.0, 0.0)}, IDENTITY, STOP_LINES[:0], HEADINGS[:0], TYPES[:0]) + == {} + ) + + +def test_point_to_segment_distance_clamps_to_endpoints(): + seg = np.array([[0.0, 0.0, 10.0, 0.0]]) + np.testing.assert_allclose(nb.point_to_segment_distance(5.0, 3.0, seg), [3.0]) + np.testing.assert_allclose(nb.point_to_segment_distance(-4.0, 3.0, seg), [5.0]) + np.testing.assert_allclose(nb.point_to_segment_distance(14.0, 3.0, seg), [5.0]) diff --git a/tests/unit_tests/test_nuplan_bridge_partner_sizes.py b/tests/unit_tests/test_nuplan_bridge_partner_sizes.py new file mode 100644 index 0000000000..704edc5bea --- /dev/null +++ b/tests/unit_tests/test_nuplan_bridge_partner_sizes.py @@ -0,0 +1,31 @@ +"""floor_vru_partner_sizes: pedestrian/bicycle boxes grow to the floor on each axis, vehicles keep their size.""" + +import os +import sys + +import numpy as np +import pytest + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + +nuplan = pytest.importorskip("nuplan") + +from pufferlib.ocean.cosim import nuplan_bridge as nb + + +def test_floor_grows_only_vru_boxes_below_the_floor(): + types = np.array([1, 2, 3, 2], np.int32) # vehicle, pedestrian, bicycle, pedestrian + lengths = np.array([4.5, 0.5, 1.7, 0.9], np.float32) + widths = np.array([1.9, 0.6, 0.5, 0.7], np.float32) + ln, wd = nb.floor_vru_partner_sizes(types, lengths, widths, 0.8) + np.testing.assert_allclose(ln, [4.5, 0.8, 1.7, 0.9]) + np.testing.assert_allclose(wd, [1.9, 0.8, 0.8, 0.8]) + assert ln.dtype == np.float32 and wd.dtype == np.float32 + + +def test_floor_leaves_inputs_untouched(): + types = np.array([2], np.int32) + lengths = np.array([0.4], np.float32) + widths = np.array([0.4], np.float32) + nb.floor_vru_partner_sizes(types, lengths, widths, 0.8) + assert lengths[0] == pytest.approx(0.4) and widths[0] == pytest.approx(0.4) diff --git a/tests/unit_tests/test_nuplan_partner_filter.py b/tests/unit_tests/test_nuplan_partner_filter.py new file mode 100644 index 0000000000..4539a5e0ee --- /dev/null +++ b/tests/unit_tests/test_nuplan_partner_filter.py @@ -0,0 +1,72 @@ +"""Static nuPlan clutter (cones, poles, barriers) reaches the shadow env only when it stands inside a lane +or lane-connector polygon; intersection corners and crosswalks (drivable area) do not count.""" + +from enum import Enum +from types import SimpleNamespace + +import pytest + +nuplan = pytest.importorskip("nuplan") # noqa: F841 (partner_tracked_objects imports nuPlan datatypes) + +from pufferlib.ocean.cosim import nuplan_bridge as nb # noqa: E402 + + +class FakeType(Enum): + VEHICLE = 0 + PEDESTRIAN = 1 + BICYCLE = 2 + TRAFFIC_CONE = 3 + GENERIC_OBJECT = 6 + EGO = 7 + + +def _obj(type_name, token, x=0.0): + return SimpleNamespace( + tracked_object_type=FakeType[type_name], track_token=token, center=SimpleNamespace(x=x, y=0.0) + ) + + +class FakeMap: + def __init__(self, lane_x, connector_x=()): + self.lane_x = lane_x + self.connector_x = set(connector_x) + self.queries = 0 + + def is_in_layer(self, point, layer): + self.queries += 1 + assert layer.name == "LANE", f"unexpected layer {layer}" + return point.x in self.lane_x + + def get_all_map_objects(self, point, layer): + self.queries += 1 + assert layer.name == "LANE_CONNECTOR", f"unexpected layer {layer}" + return ["connector"] if point.x in self.connector_x else [] + + +def test_moving_agents_kept_static_off_road_dropped(): + objs = [ + _obj("VEHICLE", "v1", 50.0), + _obj("PEDESTRIAN", "p1", 60.0), + _obj("BICYCLE", "b1", 70.0), + _obj("TRAFFIC_CONE", "c_on_lane", 1.0), + _obj("TRAFFIC_CONE", "c_off", 2.0), + _obj("GENERIC_OBJECT", "g_off", 3.0), + _obj("GENERIC_OBJECT", "g_on_connector", 4.0), + _obj("EGO", "ego", 0.0), + ] + map_api = FakeMap(lane_x={1.0}, connector_x={4.0}) + cache = {} + kept = nb.partner_tracked_objects(objs, map_api, cache) + assert [o.track_token for o in kept] == ["v1", "p1", "b1", "c_on_lane", "g_on_connector"] + assert cache == {"c_on_lane": True, "c_off": False, "g_off": False, "g_on_connector": True} + assert map_api.queries == 7 # lane hit: 1 query; connector hit or miss: 2 + + +def test_static_verdict_is_cached_per_track(): + objs = [_obj("TRAFFIC_CONE", "c1", 1.0), _obj("GENERIC_OBJECT", "g1", 9.0)] + map_api = FakeMap(lane_x={1.0}) + cache = {} + nb.partner_tracked_objects(objs, map_api, cache) + nb.partner_tracked_objects(objs, map_api, cache) + assert map_api.queries == 3 + assert [o.track_token for o in nb.partner_tracked_objects(objs, map_api, cache)] == ["c1"] diff --git a/tests/unit_tests/test_nuplan_planner_startup_jerk_cap.py b/tests/unit_tests/test_nuplan_planner_startup_jerk_cap.py new file mode 100644 index 0000000000..dcfe1474af --- /dev/null +++ b/tests/unit_tests/test_nuplan_planner_startup_jerk_cap.py @@ -0,0 +1,31 @@ +"""Start-up jerk caps map onto the continuous jerk action's asymmetric scaling (brake JERK_LONG[0], accel JERK_LONG[-1]).""" + +import os +import sys + +import pytest + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + +nuplan = pytest.importorskip("nuplan") + +from pufferlib.ocean.cosim.nuplan.planner import startup_jerk_action_bounds +from pufferlib.ocean.drive import binding + + +def test_caps_scale_by_the_env_jerk_range(): + brake_range = abs(float(binding.JERK_LONG[0])) + accel_range = float(binding.JERK_LONG[-1]) + lo, hi = startup_jerk_action_bounds(2.0, 3.0) + assert lo == pytest.approx(-3.0 / brake_range) + assert hi == pytest.approx(2.0 / accel_range) + + +def test_zero_cap_leaves_that_side_uncapped(): + assert startup_jerk_action_bounds(0.0, 0.0) == (-1.0, 1.0) + assert startup_jerk_action_bounds(2.0, 0.0)[0] == -1.0 + assert startup_jerk_action_bounds(0.0, 2.0)[1] == 1.0 + + +def test_caps_beyond_the_env_range_saturate_at_the_action_limits(): + assert startup_jerk_action_bounds(100.0, 100.0) == (-1.0, 1.0) diff --git a/tests/unit_tests/test_obs_replay_capture.py b/tests/unit_tests/test_obs_replay_capture.py new file mode 100644 index 0000000000..c0c70ed343 --- /dev/null +++ b/tests/unit_tests/test_obs_replay_capture.py @@ -0,0 +1,60 @@ +"""Co-sim ObsReplayCapture: its frame call must track Drive.get_obs_html_frame and the replay must render.""" + +import os + +import numpy as np + +from pufferlib import viz +from pufferlib.ocean.cosim.obs_replay import ObsReplayCapture +from pufferlib.ocean.drive import binding +from pufferlib.ocean.drive.drive import Drive + +REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +MAP_DIR = os.path.join(REPO_ROOT, "pufferlib", "resources", "drive", "binaries", "sdc_replay_test") +NUM_GOALS = 3 +STEP_COUNT = 5 + + +def _fixture_env(): + return Drive( + num_agents=1, + min_agents_per_env=1, + max_agents_per_env=1, + num_maps=1, + map_dir=MAP_DIR, + simulation_mode="replay", + goal_source="gt", + control_mode="control_sdc_only", + sdc_controller="replay", + non_sdc_controller="replay", + scenario_length=400, + resample_frequency=1_000_000, + termination_mode=False, + terminate_on_goal=False, + report_interval=1, + num_goals=NUM_GOALS, + goal_radius=2.0, + ) + + +def test_capture_writes_goal_reward_coef_chunks(tmp_path): + env = _fixture_env() + obs, _ = env.reset() + capture = ObsReplayCapture(env, None, tmp_path / "route", max_steps=STEP_COUNT) + for _ in range(STEP_COUNT): + actions = env.action_space.sample() + capture.capture(obs, actions, {}, None) + obs, *_ = env.step(actions) + sdc_idx = env.get_state()[0]["active_agent_indices"][0] + html_path = capture.write(render_html=True) + env.close() + + assert len(capture) == STEP_COUNT + assert os.path.getsize(html_path) > 0 + _, chunks = viz.read_replay_zlib(str(tmp_path / "route.replay.zlib")) + agent_cap = capture.agent_cap + assert chunks["goals_f32"].shape == (STEP_COUNT, agent_cap, NUM_GOALS * binding.GOAL_XY_FIELDS) + assert chunks["rewards_f32"].shape == (STEP_COUNT, agent_cap, binding.REWARD_F32_FIELDS) + assert chunks["coefs_f32"].shape == (STEP_COUNT, agent_cap, binding.NUM_REWARD_COEFS) + assert np.any(chunks["goals_f32"][0, sdc_idx] != 0.0) + assert np.any(chunks["coefs_f32"][0, sdc_idx] != 0.0) diff --git a/tests/unit_tests/test_ppo_accumulation.py b/tests/unit_tests/test_ppo_accumulation.py new file mode 100644 index 0000000000..4ae077110b --- /dev/null +++ b/tests/unit_tests/test_ppo_accumulation.py @@ -0,0 +1,101 @@ +"""Filtered PPO batches keep epoch boundaries and sample weights when accumulated.""" + +import contextlib +import math +from collections import defaultdict +from types import SimpleNamespace +from unittest.mock import Mock + +import pytest +import torch + +from pufferlib.pufferl import PuffeRL + + +class ValuePolicy(torch.nn.Module): + def __init__(self): + super().__init__() + self.value = torch.nn.Linear(1, 1, bias=False) + torch.nn.init.constant_(self.value.weight, 0.25) + + def forward(self, observations, state=None): + return observations.new_zeros((observations.shape[0], 2)), self.value(observations) + + +@pytest.mark.parametrize( + "retained_count, microbatch_size, accumulation_count", + [(6, 8, 4), (6, 4, 2), (10, 4, 2), (8, 4, 2), (10, 4, 1), (0, 4, 2)], +) +def test_filtered_ppo_accumulation_matches_optimizer_batches(retained_count, microbatch_size, accumulation_count): + trainer = PuffeRL.__new__(PuffeRL) + trainer.policy = ValuePolicy() + trainer.config = { + "device": "cpu", + "cpu_offload": False, + "update_epochs": 3, + "adv_filter_enabled": True, + "adv_filter_ewma_beta": 0.25, + "adv_filter_threshold_scale": 0.01, + "adv_filter_leak_fraction": 0.0, + "min_batch_size": None, + "clip_coef": 0.2, + "vf_clip_coef": None, + "vf_coef": 1.0, + "ent_coef": 0.0, + "ent_coef_anneal": False, + "max_grad_norm": 1e6, + } + trainer.amp_context = contextlib.nullcontext() + trainer.compress_observations = False + trainer.separate_grad_clip = False + trainer.minibatch_size = microbatch_size + trainer.accumulate_minibatches = accumulation_count + trainer.ema_max = 1.0 + trainer.vecenv = SimpleNamespace(single_observation_space=SimpleNamespace(shape=(1,))) + + # Four transitions fall below the filter and two are masked out of training. + transition_count = retained_count + 6 + observations = torch.linspace(0.5, 1.5, transition_count).reshape(1, transition_count, 1) + returns = 2.0 * observations.squeeze(-1) + advantages = torch.zeros_like(returns) + advantages[:, :retained_count] = 1.0 + advantages[:, -2:] = 1.0 + masks = torch.ones_like(returns, dtype=torch.bool) + masks[:, -2:] = False + trainer.observations = observations + trainer.actions = torch.zeros_like(returns, dtype=torch.int32) + trainer.logprobs = torch.full_like(returns, -math.log(2.0)) + trainer.values = torch.zeros_like(returns) + trainer._compute_advantages = Mock(return_value=(advantages, returns, masks)) + trainer.optimizer = torch.optim.SGD(trainer.policy.parameters(), lr=0.1) + gradients = [] + trainer.optimizer.register_step_pre_hook( + lambda *_: gradients.append(trainer.policy.value.weight.grad.detach().clone()) + ) + + # Compare against ordinary SGD on each full optimizer batch, including its partial tail. + optimizer_batch_size = microbatch_size * accumulation_count + reference_policy = ValuePolicy() + reference_optimizer = torch.optim.SGD(reference_policy.parameters(), lr=0.1) + reference_gradients = [] + random_seed = 7 + torch.manual_seed(random_seed) + for _ in range(trainer.config["update_epochs"]): + permutation = torch.randperm(retained_count) + for start in range(0, retained_count, optimizer_batch_size): + indices = permutation[start : start + optimizer_batch_size] + predictions = reference_policy.value(observations[0, indices]).squeeze(-1) + loss = 0.5 * (predictions - returns[0, indices]).square().mean() + loss.backward() + reference_gradients.append(reference_policy.value.weight.grad.detach().clone()) + reference_optimizer.step() + reference_optimizer.zero_grad() + + torch.manual_seed(random_seed) + trainer._train_ppo_transition(defaultdict(float), Mock(), epoch=1) + + expected_updates = trainer.config["update_epochs"] * math.ceil(retained_count / optimizer_batch_size) + assert len(gradients) == expected_updates + for gradient, reference_gradient in zip(gradients, reference_gradients, strict=True): + torch.testing.assert_close(gradient, reference_gradient) + torch.testing.assert_close(trainer.policy.value.weight, reference_policy.value.weight) diff --git a/tests/unit_tests/test_replay_ghost.py b/tests/unit_tests/test_replay_ghost.py new file mode 100644 index 0000000000..c7fc571960 --- /dev/null +++ b/tests/unit_tests/test_replay_ghost.py @@ -0,0 +1,119 @@ +"""Explicit ghost (logged/human trajectory) in interactive replays: encoder override + in-place rewrite.""" + +from types import SimpleNamespace + +import numpy as np +import pytest + +from pufferlib import viz +from pufferlib.ocean.cosim import nuplan_bridge as nb + +FRAMES, AGENT_CAP, TRAFFIC_CAP = 6, 3, 2 + + +def _replay(ghost=None): + replay = { + "env": { + "num_goals": 2, + "goal_radius": 2.0, + "reward_conditioning": False, + "obs_slots_partners_n": 4, + "obs_slots_lane_n": 4, + "obs_slots_boundary_n": 4, + "obs_slots_traffic_controls_n": 1, + }, + "agent_f32": np.zeros((FRAMES, AGENT_CAP, 12), np.float32), + "agent_i32": np.zeros((FRAMES, AGENT_CAP, 10), np.int32), + "metrics_f32": np.zeros((FRAMES, AGENT_CAP, 4), np.float32), + "puffer_f32": np.zeros((FRAMES, AGENT_CAP, 4), np.float32), + "traffic_i16": np.zeros((FRAMES, TRAFFIC_CAP, 3), np.int16), + "raw_action": np.ones((FRAMES, 1, 2), np.float32), + "clipped_action": np.ones((FRAMES, 1, 2), np.float32), + "value": np.zeros((FRAMES, 1), np.float32), + "entropy": np.zeros((FRAMES, 1), np.float32), + "obs": None, + } + if ghost is not None: + replay["ghost_f32"] = ghost + return replay + + +def _scenario_with_logged_agent(): + return { + "num_total_agents": AGENT_CAP, + "active_agent_indices": [0], + "agents": [ + { + "log_trajectory_x": list(range(FRAMES)), + "log_trajectory_y": [0.0] * FRAMES, + "log_heading": [0.0] * FRAMES, + "log_valid": [1] * FRAMES, + "sim_length": 4.0, + "sim_width": 2.0, + } + ], + } + + +def _ghost_values(): + ghost = np.zeros((FRAMES, 1, 5), np.float32) + ghost[:, 0, 0] = np.arange(FRAMES) * 10.0 + ghost[:, 0, 1] = 7.0 + ghost[:, 0, 2] = 0.5 + ghost[:, 0, 3] = 5.0 + ghost[:, 0, 4] = 2.3 + return ghost + + +def test_explicit_ghost_overrides_scenario_log(tmp_path): + path = tmp_path / "a.replay.zlib" + ghost = _ghost_values() + viz.save_interactive_replay_zlib(_scenario_with_logged_agent(), _replay(ghost), str(path)) + _, chunks = viz.read_replay_zlib(path) + assert np.array_equal(chunks["ghost_f32"], ghost) + + +def test_scenario_log_still_used_without_explicit_ghost(tmp_path): + path = tmp_path / "b.replay.zlib" + viz.save_interactive_replay_zlib(_scenario_with_logged_agent(), _replay(), str(path)) + _, chunks = viz.read_replay_zlib(path) + assert np.array_equal(chunks["ghost_f32"][:, 0, 0], np.arange(FRAMES, dtype=np.float32)) + assert np.all(chunks["ghost_f32"][:, 0, 4] == 2.0) + + +def test_explicit_ghost_shape_mismatch_is_rejected(tmp_path): + with pytest.raises(ValueError, match="ghost_f32 shape"): + viz.save_interactive_replay_zlib({}, _replay(_ghost_values()[:2]), str(tmp_path / "c.replay.zlib")) + + +def test_set_replay_ghost_rewrites_only_the_ghost_chunk(tmp_path): + path = tmp_path / "d.replay.zlib" + viz.save_interactive_replay_zlib({}, _replay(), str(path)) + header_before, chunks_before = viz.read_replay_zlib(path) + chunks_before = {name: arr.copy() for name, arr in chunks_before.items()} + assert not chunks_before["ghost_f32"].any() + + ghost = _ghost_values() + viz.set_replay_ghost(path, ghost) + header_after, chunks_after = viz.read_replay_zlib(path) + assert header_after == header_before + for name, arr in chunks_before.items(): + expected = ghost if name == "ghost_f32" else arr + assert np.array_equal(chunks_after[name], expected), name + with pytest.raises(ValueError, match="ghost chunk shape"): + viz.set_replay_ghost(path, ghost[:1]) + + +def test_logged_ego_boxes_in_bin_frame(): + def ego_state(i): + return SimpleNamespace( + center=SimpleNamespace(x=100.0 + i, y=200.0, heading=0.25), + car_footprint=SimpleNamespace(length=5.1, width=2.3), + ) + + scenario = SimpleNamespace(get_number_of_iterations=lambda: 4, get_ego_state_at_iteration=ego_state) + boxes = nb.logged_ego_boxes(scenario, nb.NuPlanTransform(100.0, 200.0)) + assert boxes.shape == (4, 5) and boxes.dtype == np.float32 + assert np.allclose(boxes[:, 0], [0.0, 1.0, 2.0, 3.0]) + assert np.allclose(boxes[:, 1], 0.0) + assert np.allclose(boxes[:, 2:], [0.25, 5.1, 2.3]) diff --git a/tests/unit_tests/test_route_goal_window_sliding.py b/tests/unit_tests/test_route_goal_window_sliding.py new file mode 100644 index 0000000000..df95a6b0da --- /dev/null +++ b/tests/unit_tests/test_route_goal_window_sliding.py @@ -0,0 +1,102 @@ +"""RouteGoalWindow: batch windows replace once exhausted; sliding windows refill after every consumed goal.""" + +import os +import sys + +import numpy as np + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + +from pufferlib.ocean.cosim.goals import RouteGoalWindow, route_goals_from_target_points, route_goals_from_xy + +NUM_GOALS = 3 +GOAL_RADIUS_M = 10.0 +ROUTE_XY = np.array([[20.0, 0.0], [40.0, 0.0], [60.0, 0.0], [80.0, 0.0], [100.0, 0.0]]) + + +class _FakeEnv: + """Records pushed windows; goal consumption is driven by the test through `consume`.""" + + def __init__(self): + self.num_goals = NUM_GOALS + self.goal_radius = GOAL_RADIUS_M + self.pushed_windows = [] + self.current_goal_idx = 0 + self.goal_count = 0 + + def set_agent_goals(self, agent_idx, gx, gy, gz, gdir_x=None, gdir_y=None): + self.pushed_windows.append(np.column_stack([gx, gy]).tolist()) + self.current_goal_idx = 0 + self.goal_count = len(gx) + + def get_agent_goal_progress(self, agent_idx): + return self.current_goal_idx, self.goal_count + + def consume(self): + self.current_goal_idx += 1 + + +def test_route_goals_first_direction_comes_from_origin(): + goals = np.array([[10.0, 40.0], [10.0, 80.0]]) # left turn: goals up the cross street + default = route_goals_from_xy(goals) + np.testing.assert_allclose(default[0, 3:5], [0.0, 0.0]) + with_origin = route_goals_from_xy(goals, origin_xy=(0.0, 0.0)) + np.testing.assert_allclose(with_origin[0, 3:5], [10.0, 40.0]) + np.testing.assert_allclose(with_origin[1, 3:5], [0.0, 40.0]) + np.testing.assert_allclose(with_origin[:, :2], goals) + + +def test_target_points_become_route_goals_with_dense_route_direction(): + # CARLA frame: straight east 0..100, then a left turn north (CARLA y down -> north is -y) + dense = [(x, 0.0, 0.0) for x in range(101)] + [(100.0, -y, 0.0) for y in range(1, 41)] + targets = [(0.0, 0.0, 0.0), (100.0, 0.0, 0.0), (100.0, -40.0, 0.0)] # start, junction entry, route end + to_bin = lambda x, y: (x + 5.0, -y) # the CARLA->bin transform flips y + goals = route_goals_from_target_points(targets, dense, to_bin, to_bin(0.0, 0.0), skip_radius=10.0) + assert goals.shape == (2, 5) # the start point under the ego is dropped + np.testing.assert_allclose(goals[:, :2], [[105.0, 0.0], [105.0, 40.0]]) + np.testing.assert_allclose(goals[0, 3:5], [1.0, 0.0]) # arriving at the entry: heading east + np.testing.assert_allclose(goals[1, 3:5], [0.0, 1.0]) # at the end: heading north in the bin frame + kept = route_goals_from_target_points(targets, dense, to_bin, to_bin(50.0, 0.0), skip_radius=10.0) + assert kept.shape == (3, 5) # nothing near the ego: every target point kept + + +def test_batch_window_replaces_only_when_exhausted(): + env = _FakeEnv() + window = RouteGoalWindow(env, route_goals_from_xy(ROUTE_XY)) + window.sync(0.0, 0.0, 0.0) + assert env.pushed_windows == [[[20.0, 0.0], [40.0, 0.0], [60.0, 0.0]]] + env.consume() + window.sync(22.0, 0.0, 0.0) + assert len(env.pushed_windows) == 1 + env.consume() + env.consume() + window.sync(62.0, 0.0, 0.0) + assert env.pushed_windows[-1] == [[80.0, 0.0], [100.0, 0.0]] + + +def test_sliding_window_refills_after_each_consumed_goal(): + env = _FakeEnv() + window = RouteGoalWindow(env, route_goals_from_xy(ROUTE_XY), sliding=True) + window.sync(0.0, 0.0, 0.0) + env.consume() + window.sync(22.0, 0.0, 0.0) + assert env.pushed_windows[-1] == [[40.0, 0.0], [60.0, 0.0], [80.0, 0.0]] + assert window.window_start == 1 and window.current_index == 1 + env.consume() + window.sync(42.0, 0.0, 0.0) + assert env.pushed_windows[-1] == [[60.0, 0.0], [80.0, 0.0], [100.0, 0.0]] + env.consume() + window.sync(62.0, 0.0, 0.0) + assert env.pushed_windows[-1] == [[80.0, 0.0], [100.0, 0.0]] # route end: the last window stays partial + env.consume() + env.consume() + window.sync(102.0, 0.0, 0.0) + assert len(env.pushed_windows) == 4 # exhausted at the route end: nothing left to push + + +def test_sliding_window_keeps_skip_ahead_when_goal_passed_without_consuming(): + env = _FakeEnv() + window = RouteGoalWindow(env, route_goals_from_xy(ROUTE_XY), sliding=True) + window.sync(0.0, 0.0, 0.0) + window.sync(35.0, 0.0, 0.0) # 15 m past the first goal, never consumed + assert env.pushed_windows[-1] == [[40.0, 0.0], [60.0, 0.0], [80.0, 0.0]] diff --git a/tests/unit_tests/test_stagger_first_episode.py b/tests/unit_tests/test_stagger_first_episode.py new file mode 100644 index 0000000000..1426f672ea --- /dev/null +++ b/tests/unit_tests/test_stagger_first_episode.py @@ -0,0 +1,92 @@ +"""Tests for `stagger_first_episode`: the first episode after an env is created ends at a +random step in [init_step_min_horizon, scenario_length], so the sub-envs of a worker stop +running their episodes in lockstep; every later episode keeps the full scenario_length.""" + +import numpy as np +import pytest + +from pufferlib.ocean.drive.drive import Drive + + +MAP_DIR = "pufferlib/resources/drive/binaries/carla" +SCENARIO_LENGTH = 120 +MIN_HORIZON = 20 +# Single-agent sub-envs: one independent first-episode draw per agent. +NUM_ENVS = 16 + + +def _make_env(seed, **overrides): + kwargs = { + "num_agents": NUM_ENVS, + "min_agents_per_env": 1, + "max_agents_per_env": 1, + "num_maps": 1, + "allow_map_subset": True, + "map_dir": MAP_DIR, + "simulation_mode": "gigaflow", + "action_type": "continuous", + "dynamics_model": "jerk", + "scenario_length": SCENARIO_LENGTH, + "init_step_min_horizon": MIN_HORIZON, + "resample_frequency": 1_000_000, + "termination_mode": 0, + "stagger_first_episode": True, + "seed": seed, + } + kwargs.update(overrides) + return Drive(**kwargs) + + +def _truncation_ticks(env, num_ticks): + """Per agent, the ticks at which the env reported a truncation (zero actions, no early resets).""" + ticks = [[] for _ in range(env.num_agents)] + actions = np.zeros_like(env.actions) + for tick in range(1, num_ticks + 1): + _, _, _, truncations, _ = env.step(actions) + for agent_idx in np.flatnonzero(truncations): + ticks[agent_idx].append(tick) + return ticks + + +def _first_episode_ends(seed, **overrides): + env = _make_env(seed, **overrides) + try: + env.reset(seed=seed) + return [agent_ticks[0] for agent_ticks in _truncation_ticks(env, SCENARIO_LENGTH + 1)] + finally: + env.close() + + +def test_first_episode_is_staggered_and_later_episodes_are_full_length(): + env = _make_env(seed=0) + try: + env.reset(seed=0) + ticks = _truncation_ticks(env, 2 * SCENARIO_LENGTH + 2) + finally: + env.close() + + first_ends = [agent_ticks[0] for agent_ticks in ticks] + assert all(MIN_HORIZON <= end <= SCENARIO_LENGTH for end in first_ends), first_ends + assert len(set(first_ends)) >= 2, first_ends + # one deferred-reset tick, then a full-length second episode + for agent_ticks in ticks: + assert agent_ticks[1] == agent_ticks[0] + SCENARIO_LENGTH + 1, agent_ticks + + +def test_flag_off_keeps_full_length_first_episode(): + first_ends = _first_episode_ends(seed=0, stagger_first_episode=False) + assert first_ends == [SCENARIO_LENGTH] * NUM_ENVS + + +def test_first_episode_ends_are_deterministic_per_seed(): + same_seed_a = _first_episode_ends(seed=7) + same_seed_b = _first_episode_ends(seed=7) + other_seed = _first_episode_ends(seed=8) + assert same_seed_a == same_seed_b + assert same_seed_a != other_seed + + +@pytest.mark.parametrize("init_step_min_horizon", [0, SCENARIO_LENGTH + 1]) +def test_rejects_horizon_outside_scenario(init_step_min_horizon): + with pytest.raises(ValueError, match="stagger_first_episode"): + _make_env(seed=0, init_step_min_horizon=init_step_min_horizon) diff --git a/tests/unit_tests/test_training_transfers.py b/tests/unit_tests/test_training_transfers.py new file mode 100644 index 0000000000..4169bcd3f0 --- /dev/null +++ b/tests/unit_tests/test_training_transfers.py @@ -0,0 +1,133 @@ +"""Pinned rollout ownership and gradient logging must preserve training values.""" + +import contextlib +from unittest.mock import Mock + +import numpy as np +import pytest +import torch +from torch.utils._python_dispatch import TorchDispatchMode + +from pufferlib.pufferl import PuffeRL +from pufferlib.vector import Multiprocessing + + +@pytest.fixture +def shared_vector(): + # Isolate registration lifecycle from process startup and simulator configuration. + vector = Multiprocessing.__new__(Multiprocessing) + vector.processes = [Mock()] + vector.buf = {"observations": np.arange(32, dtype=np.float32).reshape(2, 4, 4)} + vector.zero_copy = True + vector._driver_env_open = False + vector._observation_registration = None + return vector + + +def test_observation_registration_is_once_and_released_on_own_device(shared_vector, monkeypatch): + runtime = Mock() + runtime.cudaHostRegister.return_value = 0 + runtime.cudaHostUnregister.return_value = 0 + device_context = Mock(side_effect=lambda device: contextlib.nullcontext()) + synchronize = Mock() + monkeypatch.setattr(torch.cuda, "cudart", lambda: runtime) + monkeypatch.setattr(torch.cuda, "current_device", lambda: 3) + monkeypatch.setattr(torch.cuda, "device", device_context) + monkeypatch.setattr(torch.cuda, "synchronize", synchronize) + observations = shared_vector.buf["observations"] + + shared_vector.pin_observations() + shared_vector.pin_observations() + runtime.cudaHostRegister.assert_called_once_with(observations.ctypes.data, observations.nbytes, 0) + synchronize.assert_not_called() + + shared_vector.close() + shared_vector.close() + device_context.assert_called_once_with(3) + synchronize.assert_called_once() + runtime.cudaHostUnregister.assert_called_once_with(observations.ctypes.data) + assert shared_vector._observation_registration is None + + +def test_registration_failure_does_not_unregister_unowned_memory(shared_vector, monkeypatch): + runtime = Mock() + runtime.cudaHostRegister.return_value = 2 + monkeypatch.setattr(torch.cuda, "cudart", lambda: runtime) + monkeypatch.setattr(torch.cuda, "current_device", lambda: 0) + with pytest.raises(RuntimeError, match="cudaHostRegister failed"): + shared_vector.pin_observations() + shared_vector.close() + runtime.cudaHostUnregister.assert_not_called() + + +def test_copying_vector_does_not_register_unused_shared_buffer(shared_vector, monkeypatch): + shared_vector.zero_copy = False + runtime = Mock() + monkeypatch.setattr(torch.cuda, "cudart", runtime) + shared_vector.pin_observations() + shared_vector.close() + runtime.assert_not_called() + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required for host registration") +def test_registered_observation_views_transfer_exactly_and_unpin(shared_vector): + device = torch.cuda.current_device() + shared_vector.pin_observations() + view = torch.as_tensor(shared_vector.buf["observations"][1]) + try: + assert view.is_pinned() + copied = view.to(device=device, non_blocking=True) + torch.testing.assert_close(copied.cpu(), view, rtol=0, atol=0) + finally: + shared_vector.close() + assert not view.is_pinned() + + +class RejectScalarReadback(TorchDispatchMode): + def __torch_dispatch__(self, function, types, args=(), kwargs=None): + if function is torch.ops.aten._local_scalar_dense.default: + raise AssertionError("Gradient clipping read a scalar back to the CPU") + return function(*args, **(kwargs or {})) + + +@pytest.mark.parametrize("separate", [False, True]) +@pytest.mark.parametrize( + "device", + ["cpu", pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA unavailable"))], +) +def test_gradient_norms_stay_on_device_without_changing_updates(separate, device): + trainer = PuffeRL.__new__(PuffeRL) + trainer.config = {"max_grad_norm": 0.5} + trainer.policy = torch.nn.ParameterList( + [ + torch.nn.Parameter(torch.tensor([1.0, 2.0], device=device)), + torch.nn.Parameter(torch.tensor([3.0, 4.0], device=device)), + ] + ) + trainer.actor_params = [trainer.policy[0]] + trainer.critic_params = [trainer.policy[1]] + trainer.separate_grad_clip = separate + reference = [torch.nn.Parameter(parameter.detach().clone()) for parameter in trainer.policy] + optimizer = torch.optim.AdamW(trainer.policy.parameters(), lr=0.01) + reference_optimizer = torch.optim.AdamW(reference, lr=0.01) + groups = [[reference[0]], [reference[1]]] if separate else [reference] + keys = ["actor_grad_norm", "critic_grad_norm"] if separate else ["grad_norm"] + + for magnitude in (1.0, 0.01, 10.0): + for parameter, other in zip(trainer.policy, reference): + gradient = torch.tensor([3.0, 4.0], device=device) * magnitude + parameter.grad = gradient.clone() + other.grad = gradient.clone() + expected_norms = [torch.nn.utils.clip_grad_norm_(group, 0.5).item() for group in groups] + losses = {} + with RejectScalarReadback(): + trainer._clip_gradients(losses) + optimizer.step() + reference_optimizer.step() + for key, expected_norm in zip(keys, expected_norms): + assert isinstance(losses[key], torch.Tensor) + assert losses[key].device.type == device + assert not losses[key].requires_grad + assert losses[key].item() == expected_norm + for parameter, other in zip(trainer.policy, reference): + torch.testing.assert_close(parameter, other, rtol=0, atol=0) diff --git a/weights/k_scaled_0036_1000/config.yaml b/weights/k_scaled_0036_1000/config.yaml new file mode 100644 index 0000000000..316cd71133 --- /dev/null +++ b/weights/k_scaled_0036_1000/config.yaml @@ -0,0 +1,268 @@ +agent_index: null +controlled_exp: + train: + ent_coef: + values: + - 0.01 + - 0.005 + learning_rate: + values: + - 0.001 + - 0.003 + - 0.01 +env: + action_type: continuous + allow_map_subset: false + base_max_speed_mps: 20.0 + collision_behavior: stop + compute_eval_metrics: false + control_mode: control_vehicles + disable_red_light_infractions: 0 + dt: 0.3 + dynamics_model: jerk + eval_perceived_size_margin_m: 0.1 + eval_standstill_jerk_deadband_mps3: 0.0 + eval_training_render: false + goal_heading_max_deg: 60.0 + goal_radius: 2.0 + goal_reach_requires_speed: true + goal_regen_mode: finite + goal_source: map + goal_speed: 3.0 + goal_speed_randomization: false + inactive_agent_threshold: 0.4 + init_mode: create_all_valid + init_step: 0 + init_step_min_horizon: 20 + init_step_spread: false + map_dir: /home/bjaeger/PufferDrive/pufferlib/resources/drive/binaries/carla_128_affine + max_agents_per_env: 150 + max_goal_spacing: 200.0 + max_speed_mps: null + min_agents_per_env: 1 + min_goal_spacing: 20.0 + non_sdc_controller: policy + non_vehicle_controller: auto + num_agents: 1024 + num_goals: 3 + num_maps: 128 + obs_boundary_stride: 1 + obs_dropout_boundary: 0.4 + obs_dropout_lane: 0.5 + obs_goal_lane_distance: true + obs_lane_stride: 2 + obs_norm_goal_offset_m: 600.0 + obs_norm_road_seg_length_m: 10.0 + obs_norm_road_seg_width_m: 5.0 + obs_norm_speed_mps: 60.0 + obs_norm_veh_length_m: 10.0 + obs_norm_veh_width_m: 5.0 + obs_norm_xy_offset_m: 200.0 + obs_norm_z_m: 10.0 + obs_partner_relative_velocity: true + obs_range_partner_m: 200.0 + obs_range_road_behind_m: 60.0 + obs_range_road_front_m: 200.0 + obs_range_road_side_m: 50.0 + obs_range_traffic_control_m: 200.0 + obs_slots_boundary_n: 50 + obs_slots_lane_n: 70 + obs_slots_partners_n: 20 + obs_slots_traffic_controls_n: 4 + offroad_behavior: stop + partner_blindness_duration_seconds: 3.0 + partner_blindness_prob: 0.05 + partner_blindness_trigger_prob: 0.03 + phantom_braking_duration_seconds: 3.0 + phantom_braking_freeze_steering: false + phantom_braking_prob: 0.1 + phantom_braking_trigger_prob: 0.03 + pose_noise_xy_m: 0.025 + pose_noise_yaw_deg: 0.25 + replay_expert_agents: 1 + resample_frequency: 256000 + reset_accel_on_stop: true + reward_ade: 0.0 + reward_center_bias: 0.0 + reward_collision: 1.5 + reward_comfort: 0.05 + reward_conditioning: true + reward_goal: 1.0 + reward_lane_align: 0.025 + reward_lane_center: 0.001 + reward_log_sampling: false + reward_offroad: 1.5 + reward_overspeed: 0.05 + reward_randomization: true + reward_reverse: 0.005 + reward_stop_line: 1.0 + reward_timestep: 2.5e-05 + reward_vel_align: 1.0 + reward_velocity: 0.0025 + scenario_length: 2500 + sdc_controller: policy + simulation_mode: gigaflow + spawn_heading_max_deg: 30.0 + spawn_initial_speed: 0.0 + spawn_lateral_offset_max_frac: 1.0 + terminate_on_goal: 0 + termination_mode: 1 + traffic_light_behavior: stop + traffic_light_junction_phases: 0 + use_map_cache: 1 + use_neighbor_cache: 1 +env_name: puffer_drive +eval: + action_selection: mean + base_max_speed_mps: null + benchmark_config: pufferlib/config/evaluation/benchmark.yaml + benchmarks: null + capture_observations: false + disable_red_light_infractions: null + dt: null + failure_replay_csv: null + goal_radius: null + goal_regen_mode: null + goal_source: null + goal_speed: null + map_dir: null + max_goal_spacing: null + max_rendered_failures: null + max_sdc_replay_workers: 4 + min_goal_spacing: null + num_agents: 256 + num_scenarios: null + obs_slots_partners_n: null + observation_replay_wave_size: 16 + observation_replay_writer_count: 4 + output_dir_name: eval + output_name: null + output_subdir: null + render_filter: null + render_scenarios: false + reward_comfort: null + reward_lane_center: null + scenario_offset: 0 +eval_simulation: null +fps: 15 +gif_path: eval.gif +git: + commit_hash: bfb49c1eca1cf1a69e59981d09b4246a71897b2b +load_id: null +load_model_path: null +local_rank: 0 +max_runs: 200 +max_suggestion_cost: 3600 +neptune: false +neptune_name: pufferai +neptune_project: ablations +no_model_upload: {} +num_scenarios: 3 +package: ocean +policy: + action_type: discrete + actor_head_layer_norm: false + actor_hidden_size: 256 + actor_num_layers: 1 + backbone_activation: gelu + backbone_hidden_size: 1024 + backbone_layer_norm: false + backbone_num_layers: 3 + boundary_input_size: 256 + context_input_size: 128 + critic_head_layer_norm: false + critic_hidden_size: 256 + critic_num_layers: 1 + ego_input_size: 128 + encoder_activation: relu + encoder_layer_norm: true + fp32_heads: true + lane_input_size: 256 + mask_padded_features: true + partner_input_size: 256 + shared_network: false + traffic_control_input_size: 128 +policy_name: Drive +render: 0 +render_mode: auto +rnn: + hidden_size: 512 + input_size: 512 +rnn_name: null +run_name: k_scaled_0036_1000 +save_frames: 0 +sweep: + downsample: 10 + goal: maximize + method: Protein + metric: score +tag: null +tb: true +train: + adam_beta1: 0.9 + adam_beta2: 0.999 + adam_eps: 1.0e-08 + adam_weight_decay: 0.01 + adv_filter_enabled: true + adv_filter_ewma_beta: 0.25 + adv_filter_leak_fraction: 0.0 + adv_filter_threshold_scale: 0.01 + adv_sampling_prio_alpha: 0.8499999999999999 + adv_sampling_prio_beta0: 0.8499999999999999 + anneal_lr: true + batch_size: auto + bptt_horizon: 128 + checkpoint_interval: 50 + clip_coef: 0.2 + compile: true + compile_fullgraph: false + compile_mode: default + cpu_offload: false + data_dir: /home/bjaeger/PufferDrive/experiments/k_scaled_0036_1000 + device: cuda + ent_coef: 0.01 + ent_coef_anneal: false + ent_coef_anneal_start_frac: 0.75 + ent_coef_final: 0.001 + evaluation_benchmarks: carla_fast + evaluation_interval_epochs: null + final_model_name: final_model.pt + gae_lambda: 0.95 + gamma: 0.999 + learning_rate: 0.0005 + max_grad_norm: 0.5 + max_minibatch_size: 131072 + min_batch_size: null + minibatch_size: 131072 + name: k_scaled_0036_1000 + normalize_rewards: false + optimizer: adamw + precision: bfloat16 + project: ablations + render: false + render_interval: 1000 + resume_state_path: null + rollout_dtype: float32 + seed: 1000 + separate_grad_clip: true + tf32: false + torch_deterministic: false + total_timesteps: 15625000000 + update_epochs: 3 + use_rnn: false + use_value_bootstrapping: true + vf_clip_coef: null + vf_coef: 0.5 + vtrace_c_clip: 1 + vtrace_rho_clip: 1 +vec: + backend: Multiprocessing + batch_size: auto + num_envs: 16 + num_workers: auto + seed: 42 + zero_copy: true +video_path: videos +wandb: true +wandb_group: emerge_ +wandb_project: nightly-multi-long diff --git a/weights/k_scaled_0036_1000/eval/carla_self_play/episode_metrics.csv b/weights/k_scaled_0036_1000/eval/carla_self_play/episode_metrics.csv new file mode 100644 index 0000000000..723743dd14 --- /dev/null +++ b/weights/k_scaled_0036_1000/eval/carla_self_play/episode_metrics.csv @@ -0,0 +1,40001 @@ 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b/weights/k_scaled_0036_1000/final_model.pt new file mode 100644 index 0000000000..13a8fadb9c Binary files /dev/null and b/weights/k_scaled_0036_1000/final_model.pt differ diff --git a/weights/mimolette/eval/carla_fast/20260831-220427/resolved_benchmark.yaml b/weights/mimolette/eval/carla_fast/20260831-220427/resolved_benchmark.yaml new file mode 100644 index 0000000000..82eeee6822 --- /dev/null +++ b/weights/mimolette/eval/carla_fast/20260831-220427/resolved_benchmark.yaml @@ -0,0 +1,232 @@ +benchmark_config: /home/jaeger/ordnung/kesai/PufferDrive/pufferlib/config/evaluation/benchmark.yaml +checkpoint_config: /home/jaeger/ordnung/kesai/PufferDrive/weights/mimolette/config.yaml +benchmark: + name: carla_fast + simulation_mode: gigaflow + seed: 42 + num_scenarios: 250 + num_maps: 8 + max_agents_per_env: 50 + scenario_length: 500 + control_mode: control_vehicles + map_dir: /home/jaeger/ordnung/kesai/PufferDrive/pufferlib/resources/drive/binaries/carla +args: + load_model_path: weights/mimolette/models/model_puffer_drive_003815.pt + load_id: null + render_mode: auto + video_path: videos + num_scenarios: 250 + render: 0 + agent_index: null + save_frames: 0 + gif_path: eval.gif + fps: 15 + max_runs: 200 + wandb: false + wandb_project: pufferlib + wandb_group: debug + run_name: null + neptune: false + neptune_name: pufferai + neptune_project: ablations + tb: false + local_rank: 0 + tag: null + eval_simulation: null + package: ocean + env_name: puffer_drive + policy_name: Drive + rnn_name: null + max_suggestion_cost: 3600 + vec: + backend: Multiprocessing + num_envs: 2 + num_workers: auto + batch_size: auto + zero_copy: true + seed: 42 + env: + simulation_mode: gigaflow + num_agents: 50 + min_agents_per_env: 1 + max_agents_per_env: 50 + action_type: discrete + dynamics_model: jerk + dt: 0.0667 + spawn_initial_speed: 0.0 + collision_behavior: stop + offroad_behavior: stop + traffic_light_behavior: stop + use_map_cache: 1 + scenario_length: 500 + resample_frequency: 500 + termination_mode: 0 + inactive_agent_threshold: 0.4 + terminate_on_goal: 0 + init_step: 0 + init_step_spread: false + init_step_min_horizon: 20 + control_mode: control_vehicles + sdc_controller: policy + non_sdc_controller: policy + non_vehicle_controller: auto + init_mode: create_all_valid + compute_eval_metrics: true + goal_regen_mode: finite + goal_source: route + obs_goal_lane_distance: true + goal_radius: 10.0 + goal_speed: 3.0 + num_goals: 3 + min_goal_spacing: 30.0 + max_goal_spacing: 30.0 + reward_conditioning: true + reward_randomization: false + reward_goal: 1.0 + reward_collision: 3.0 + reward_offroad: 3.0 + reward_stop_line: 1.0 + reward_comfort: 0.05 + reward_lane_align: 0.025 + reward_vel_align: 1.0 + reward_lane_center: 0.0038 + reward_center_bias: 0.0 + reward_velocity: 0.0025 + reward_reverse: 0.005 + reward_timestep: 2.5e-05 + reward_overspeed: 0.05 + reward_ade: 0.0 + map_dir: /home/jaeger/ordnung/kesai/PufferDrive/pufferlib/resources/drive/binaries/carla + num_maps: 8 + obs_slots_lane_n: 70 + obs_slots_boundary_n: 50 + obs_slots_partners_n: 16 + obs_slots_traffic_controls_n: 4 + obs_dropout_lane: 0.0 + obs_dropout_boundary: 0.0 + obs_lane_stride: 2 + obs_boundary_stride: 1 + obs_norm_goal_offset_m: 200.0 + obs_norm_xy_offset_m: 200.0 + obs_norm_veh_length_m: 10.0 + obs_norm_veh_width_m: 5.0 + obs_norm_road_seg_length_m: 10.0 + obs_norm_road_seg_width_m: 5.0 + obs_range_road_front_m: 200.0 + obs_range_road_behind_m: 60.0 + obs_range_road_side_m: 50.0 + obs_range_partner_m: 200.0 + obs_range_traffic_control_m: 200.0 + partner_blindness_prob: 0.0 + partner_blindness_trigger_prob: 0.0 + partner_blindness_duration_seconds: 3.0 + phantom_braking_prob: 0.0 + phantom_braking_trigger_prob: 0.0 + phantom_braking_duration_seconds: 3.0 + traffic_control_scope: 0 + eval_mode: true + policy: + ego_input_size: 128 + partner_input_size: 256 + lane_input_size: 256 + boundary_input_size: 256 + traffic_control_input_size: 128 + context_input_size: 128 + encoder_activation: relu + encoder_layer_norm: true + mask_padded_features: false + backbone_hidden_size: 1024 + backbone_num_layers: 3 + backbone_activation: gelu + backbone_layer_norm: false + actor_hidden_size: 256 + actor_num_layers: 1 + critic_hidden_size: 256 + critic_num_layers: 1 + shared_network: false + action_type: discrete + rnn: + input_size: 512 + hidden_size: 512 + train: + name: pufferai + project: ablations + seed: 42 + evaluation_interval_epochs: null + evaluation_benchmarks: carla_fast + cosim_debug_at_start: true + torch_deterministic: false + cpu_offload: false + device: cpu + optimizer: adamw + anneal_lr: true + amp: true + precision: bfloat16 + total_timesteps: 500000000000 + learning_rate: 0.0005 + gamma: 0.999 + gae_lambda: 0.95 + update_epochs: 3 + clip_coef: 0.2 + vf_coef: 0.5 + vf_clip_coef: null + max_grad_norm: 0.5 + normalize_rewards: false + ent_coef: 0.01 + adam_beta1: 0.9 + adam_beta2: 0.999 + adam_eps: 1.0e-08 + data_dir: experiments + resume_state_path: null + checkpoint_interval: 50 + batch_size: auto + minibatch_size: 65536 + max_minibatch_size: 65536 + bptt_horizon: 128 + compile: false + compile_mode: default + compile_fullgraph: false + vtrace_rho_clip: 1 + vtrace_c_clip: 1 + adv_sampling_prio_alpha: 0.8499999999999999 + adv_sampling_prio_beta0: 0.8499999999999999 + adv_filter_ewma_beta: 0.25 + adv_filter_threshold_scale: 0.01 + render: false + render_interval: 1000 + obs_only: true + show_grid: false + show_lasers: false + show_human_logs: true + render_map: none + use_rnn: false + sweep: + method: Protein + metric: score + goal: maximize + downsample: 10 + eval: + num_agents: 50 + max_sdc_replay_workers: 4 + benchmark_config: pufferlib/config/evaluation/benchmark.yaml + benchmarks: carla_fast + output_name: null + render_scenarios: false + render_filter: null + max_rendered_failures: null + failure_replay_csv: null + capture_observations: false + observation_replay_wave_size: 16 + observation_replay_writer_count: 4 + action_selection: mode + controlled_exp: + train: + learning_rate: + values: + - 0.001 + - 0.003 + - 0.01 + ent_coef: + values: + - 0.01 + - 0.005