Sample inputs shipped with diffusion-planner-p150
Only REDISTRIBUTABLE data goes here (Apache-2.0 / MIT / CC-BY with attribution). Data whose license is unstated or
non-commercial (the Autoware demo rosbag, nuScenes, Argoverse 2, ...) is NOT shipped; the nuScenes-derived planner
instants used for local agreement tests live in the porting workspace only (research/diffusion-planner/public_data).
Never use the .bin suffix here (or .pt, .pth, .ckpt, .safetensors): tt-model's staging silently drops
those suffixes from code/ (CODE_IGNORE, tt-model-manager src/tt_kernel/build.py:328-332).
Each sample is one .npz holding exactly the 15 raw (pre-normalization, ego-frame, batch-1, float32) tensors of the
node's DiffusionPlannerCore::create_input_data() (reference/config.py INPUT_SCHEMA): pass it as
model(inputs="<file>.npz") or as the /predict field inputs (server/client.py --inputs <file>.npz).
Next to each sample, <stem>.reference.json is the /predict body of the fp32 CPU reference
(tt_diffusion_planner.reference.ReferencePlanner, Output.to_dict(), timing removed) on it;
server/smoke_test.py compares the served trajectory with it (ADE / FDE gates). Regenerate both the reference bodies
and tests/goldens/ with code/scripts/ref_golden.py whenever the reference, the weights or the post-processing
changes.
| file | content | source | license |
|---|---|---|---|
kashiwanoha_dense.npz (default sample) |
one planning instant on the kashiwanoha test map: ego at 6 m/s on a 17-lanelet route, 88 neighbours (constant-speed vehicles), 123 lanes (24 lanelets with traffic lights in the map), 60 line strings (stop lines and road borders), no intersection polygon; 113,893 B, sha256 d8c2aaef...99f7 |
Lanelet2 map AutowareFoundation/map-carla-kashiwanoha 0.2.0 (lanelet2_map.osm, sha256 4fe358f2...4e6d, byte-identical to autoware_universe planning/autoware_diffusion_planner/test_map); scene scripted by research/diffusion-planner/scripts/dp_scene.py (dp_reference.py --scene osm --osm .../lanelet2_map.osm --n-agents 120 --seed 7), a Python port of the node's tensor construction (simplifications: SPEC 7) |
Apache-2.0 (map: Apache-2.0 per its dataset card) |
straight_road.npz |
procedural 3-lane straight road: ego at 8 m/s, 12 neighbours (vehicles and pedestrians), 33 lanes with a green traffic light ahead, 6 route lanes, 1 intersection polygon, 9 line strings (borders + stop line); the precision-sensitive scene of SPEC 0.5; 10,832 B, sha256 b9979b90...d381 |
generated by research/diffusion-planner/scripts/dp_scene.py (dp_reference.py --scene straight), no external data |
Apache-2.0 (this repo) |