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5662e7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | #!/usr/bin/env python3
# SPDX-License-Identifier: Apache-2.0
"""Demo media of ptv3-p150: run the p150 on point clouds and save its outputs, then render bird's-eye views
(``tt_ptv3.viz``, numpy + Pillow) next to the fp32 CPU reference.
# 1. on the device (one devrun window): the served model (serve profile seg_det, default runtime params)
bin/devrun -t 1800 -- python code/scripts/make_demo.py run --out OUT \
--input code/tt_ptv3/samples/test_pcd.npz --input research/ptv3/public_data/inputs/ps019_f40.pcd5.npy \
[--min-voxels-zero research/ptv3/public_data/inputs/ns0103_k20.pcd5.npy] [--glob 'DIR/*.pcd5.npy']
# 2. on the host: BEV renders (p150 | CPU reference | differences) and a GIF of a sequence
python code/scripts/make_demo.py compare OUT/test_pcd.tt.npz --reference code/tt_ptv3/samples/test_pcd.reference.json \
--points code/tt_ptv3/samples/test_pcd.npz --out media/ptv3_test_pcd_tt_vs_cpu.png --title "..."
python code/scripts/make_demo.py gif 'OUT/ps019_f*.tt.npz' --points-dir DIR --out media/x.gif --title "..."
``run`` writes per input ``<stem>.tt.npz`` (``label_ids``, ``scores``, ``classification``, ``entropy``,
``filter_keep``: the per-point outputs in input order) and ``<stem>.tt.json`` (detections as ``/predict`` dicts,
``timing_ms``, ``meta``, the runtime params). ``.npy`` inputs with 5 columns (the research ``pcd5`` layout: x, y, z,
intensity 0-255, ring) are cut to the first 4. A reference is either a stored ``/predict`` body (``*.json``) or a
research golden (``*.golden.npz``: ``point_label15``, ``boxes`` [M, 11]).
"""
from __future__ import annotations
import argparse
import base64
import glob
import io
import json
import sys
import time
from pathlib import Path
import numpy as np
HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(HERE.parent))
DET = ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN", "TRAFFIC_CONE", "BARRIER"]
def _stem(path: Path) -> str:
name = path.name
for suf in (".pcd5.npy", ".npz", ".npy", ".pcd"):
if name.endswith(suf):
return name[: -len(suf)]
return path.stem
def _points(path: Path) -> np.ndarray:
from tt_ptv3 import io as tio
if path.suffix == ".npy":
a = np.load(path, allow_pickle=False)
return np.ascontiguousarray(a[:, :4], np.float32)
return np.asarray(tio.load_points(str(path)).points, np.float32)
def cmd_run(a) -> int:
from tt_ptv3 import PTv3
jobs = [(Path(p), {}) for p in a.input] + [(Path(p), {"min_voxels": 0}) for p in a.min_voxels_zero]
for pattern in a.glob:
jobs += [(Path(p), {}) for p in sorted(glob.glob(pattern))]
out = Path(a.out)
out.mkdir(parents=True, exist_ok=True)
kw = {"weights_dir": a.weights_dir} if a.weights_dir else {}
t0 = time.time()
with PTv3.from_pretrained(device_id=a.device_id, variant="seg_det", **kw) as model:
print(f"from_pretrained {time.time() - t0:.1f} s; {model.info.get('device')}", flush=True)
for path, params in jobs:
o = model(_points(path), **params)
stem = _stem(path)
np.savez_compressed(out / f"{stem}.tt.npz", label_ids=np.asarray(o.label_ids), scores=np.asarray(o.scores),
classification=np.asarray(o.classification), entropy=np.asarray(o.entropy),
filter_keep=np.asarray(o.filter_keep))
dets = o.to_dict().get("detections") or []
(out / f"{stem}.tt.json").write_text(json.dumps({"input": str(path), "params": params, "detections": dets,
"timing_ms": o.timing_ms, "meta": o.meta},
default=lambda v: v.tolist() if hasattr(v, "tolist") else str(v)))
print(f"{stem}: {len(o.label_ids)} points, {len(dets)} boxes, total {o.timing_ms.get('total', 0):.1f} ms",
flush=True)
return 0
def _decode(spec):
return np.load(io.BytesIO(base64.b64decode(spec["data"])))[spec["key"]]
def load_tt(path: Path):
with np.load(path) as z:
labels = z["label_ids"]
side = json.loads(Path(str(path)[: -len(".tt.npz")] + ".tt.json").read_text())
return labels, side["detections"]
def load_reference(path: Path):
"""``(labels per point, detection dicts)`` of a stored /predict body or a research golden."""
if path.suffix == ".json":
b = json.loads(path.read_text())
return _decode(b["labels"]), b.get("detections") or []
with np.load(path) as z:
labels, boxes = z["point_label15"], z["boxes"]
dets = [{"label": DET[int(r[0])], "score": float(r[1]), "center": [float(r[2]), float(r[3]), float(r[4])],
"size": [float(r[5]), float(r[6]), float(r[7])], "yaw": float(r[8])} for r in boxes]
return labels, dets
def cmd_compare(a) -> int:
from tt_ptv3.viz import render_compare, save
labels, dets = load_tt(Path(a.tt))
ref_labels, refs = load_reference(Path(a.reference))
xyz = _points(Path(a.points))[:, :3]
img = render_compare(xyz, labels, ref_labels, dets, refs, title=a.title, lines=a.line, half_range=a.range,
px_per_m=a.px_per_m) # None: the panels fit 960 px (2 per row)
print(save(img, a.out), img.size, f"labels agree {float((labels == ref_labels).mean()):.4%}")
return 0
def cmd_bev(a) -> int:
from tt_ptv3.viz import render_bev, save
labels, dets = load_tt(Path(a.tt))
img = render_bev(_points(Path(a.points))[:, :3], labels, dets, title=a.title, lines=a.line, half_range=a.range,
px_per_m=a.px_per_m, caption="p150: per-point class + 3D boxes")
print(save(img, a.out), img.size)
return 0
def cmd_gif(a) -> int:
from tt_ptv3.viz import render_bev
frames = []
for p in sorted(glob.glob(a.tt)):
stem = Path(p).name[: -len(".tt.npz")]
labels, dets = load_tt(Path(p))
xyz = _points(Path(a.points_dir) / f"{stem}.pcd5.npy")[:, :3]
img = render_bev(xyz, labels, dets, title=f"{a.title} ({stem})", lines=a.line, half_range=a.range,
px_per_m=a.px_per_m, caption="p150: per-point class + 3D boxes")
frames.append(img.convert("P", palette=1, colors=64)) # adaptive palette: small GIF
frames[0].save(a.out, save_all=True, append_images=frames[1:], duration=int(1000 / a.fps), loop=0, optimize=True)
print(a.out, len(frames), "frames", Path(a.out).stat().st_size, "bytes")
return 0
def main(argv=None) -> int:
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
sub = ap.add_subparsers(dest="cmd", required=True)
r = sub.add_parser("run")
r.add_argument("--out", required=True)
r.add_argument("--input", action="append", default=[])
r.add_argument("--min-voxels-zero", action="append", default=[], help="inputs below the node's 16k-voxel floor")
r.add_argument("--glob", action="append", default=[])
r.add_argument("--device-id", type=int, default=0)
r.add_argument("--weights-dir")
for name, extra in (("compare", ("--reference",)), ("bev", ())):
c = sub.add_parser(name)
c.add_argument("tt")
c.add_argument("--points", required=True)
for e in extra:
c.add_argument(e, required=True)
c.add_argument("--out", required=True)
c.add_argument("--title", default="")
c.add_argument("--line", action="append", default=[])
c.add_argument("--range", type=float, default=40.0)
c.add_argument("--px-per-m", type=float, default=None if name == "compare" else 8.0)
g = sub.add_parser("gif")
g.add_argument("tt", help="glob of <stem>.tt.npz")
g.add_argument("--points-dir", required=True)
g.add_argument("--out", required=True)
g.add_argument("--title", default="")
g.add_argument("--line", action="append", default=[])
g.add_argument("--range", type=float, default=40.0)
g.add_argument("--px-per-m", type=float, default=6.0)
g.add_argument("--fps", type=float, default=5.0)
a = ap.parse_args(argv)
return {"run": cmd_run, "compare": cmd_compare, "bev": cmd_bev, "gif": cmd_gif}[a.cmd](a)
if __name__ == "__main__":
sys.exit(main())
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