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bc4c433 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | """Classify occupancy NPZs from ``models/<run_id>/best.pt``.
Rebuilds ``OccupancyMLP`` or ``OccupancyEncoder`` from the checkpoint
``kind``. Query XYZ (and envelope, when conditioned) use the stored
per-mesh AABB, not a fresh map. Face-token checkpoints are rejected.
"""
from __future__ import annotations
from pathlib import Path
from typing import Any
import numpy as np
import torch
from scatteringnet.config import OccupancyConfig, as_data_relative, load_config, repo_root
from scatteringnet.data_npz import load_points_labels, load_points_labels_mesh
from scatteringnet.geometry.mesh_io import load_obj_triangles
from scatteringnet.geometry.surface import apply_envelope_aabb, project_envelope_dim, sample_surface_points
from scatteringnet.metrics import occupancy_metrics
from scatteringnet.normalize import apply_normalization
from scatteringnet.occupancy_encoder import (
OccupancyEncoder,
envelope_dim_from_ckpt,
envelope_seed_from_ckpt,
)
from scatteringnet.occupancy_encoder import CHECKPOINT_KIND as ENCODER_KIND
from scatteringnet.occupancy_mlp import OccupancyMLP
from scatteringnet.occupancy_mlp import CHECKPOINT_KIND as MLP_KIND
def resolve_best_pt(
*,
checkpoint: Path | str | None = None,
run_id: str | None = None,
root: Path | None = None,
) -> Path:
"""Resolve ``best.pt`` from an explicit path or ``runs/<id>/checkpoint_dir.txt``."""
base = (root or repo_root()).resolve()
if checkpoint is not None:
path = Path(checkpoint)
if not path.is_absolute():
path = base / path
if not path.is_file():
raise FileNotFoundError(f"checkpoint not found: {path}")
return path
if not run_id:
raise ValueError("pass checkpoint= or run_id=")
pointer = base / "runs" / str(run_id) / "checkpoint_dir.txt"
if not pointer.is_file():
raise FileNotFoundError(f"run pointer not found: {pointer}")
rel = pointer.read_text(encoding="utf-8").strip()
path = Path(rel)
if not path.is_absolute():
path = base / path
best = path / "best.pt" if path.is_dir() else path
if not best.is_file():
raise FileNotFoundError(f"best.pt not found: {best}")
return best
def load_occupancy_model(
ckpt: dict[str, Any],
device: torch.device,
) -> OccupancyMLP | OccupancyEncoder:
"""Rebuild the head recorded in ``ckpt['kind']`` and load weights."""
hidden = int(ckpt["hidden"])
depth = int(ckpt["depth"])
kind = str(ckpt["kind"])
if kind == MLP_KIND:
model: OccupancyMLP | OccupancyEncoder = OccupancyMLP(hidden=hidden, depth=depth)
elif kind == ENCODER_KIND:
latent = int(ckpt["latent_dim"]) if ckpt.get("latent_dim") is not None else hidden
enc = str(ckpt.get("shape_encoder") or "surface").strip().lower()
if enc == "mesh":
raise ValueError(
"face-token occupancy checkpoints (shape_encoder='mesh') "
"are no longer supported"
)
knn_k = int(ckpt["knn_k"]) if ckpt.get("knn_k") is not None else 0
knn_local = None
if knn_k > 0 and ckpt.get("knn_local_dim") is not None:
knn_local = int(ckpt["knn_local_dim"])
model = OccupancyEncoder(
hidden=hidden,
depth=depth,
latent_dim=latent,
shape_encoder=enc,
knn_k=knn_k,
knn_local_dim=knn_local,
envelope_dim=envelope_dim_from_ckpt(ckpt),
)
else:
raise ValueError(f"unsupported checkpoint kind {kind!r}")
model.load_state_dict(ckpt["state_dict"])
return model.to(device).eval()
def _part_for_npz(ckpt: dict[str, Any], npz_path: Path, data_dir: Path) -> dict[str, Any]:
rel = as_data_relative(npz_path, data_dir)
name = npz_path.name
for part in ckpt.get("parts") or []:
stored = str(part.get("npz", ""))
if stored == rel or Path(stored).name == name:
return part
raise KeyError(f"no AABB part for {npz_path.name} in checkpoint")
def infer_npz(
cfg: OccupancyConfig,
*,
checkpoint: Path | str | None = None,
run_id: str | None = None,
npz_path: Path | str | None = None,
root: Path | None = None,
) -> dict[str, float]:
"""
Score one NPZ with ``best.pt``. Returns accuracy / IoU / F1.
"""
ckpt_path = resolve_best_pt(checkpoint=checkpoint, run_id=run_id, root=root)
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)
model = load_occupancy_model(ckpt, cfg.device)
if npz_path is None:
rels = ckpt.get("npz_paths") or []
if not rels:
raise ValueError("checkpoint has no npz_paths; pass npz_path=")
npz = cfg.data_dir / str(rels[0])
else:
npz = Path(npz_path)
if not npz.is_absolute():
npz = cfg.data_dir / npz
part = _part_for_npz(ckpt, npz, cfg.data_dir)
center = np.asarray(part["center"], dtype=np.float32)
scale = float(part["scale"])
geom = None
shape_id = None
enc = str(ckpt.get("shape_encoder", "none")).strip().lower()
if enc == "mesh":
raise ValueError(
"face-token occupancy checkpoints (shape_encoder='mesh') "
"are no longer supported"
)
if enc == "surface":
points, labels, mesh_path = load_points_labels_mesh(npz, cfg.data_dir)
vertices, faces = load_obj_triangles(mesh_path)
cache_key = str(mesh_path.resolve())
n_surface = int(ckpt.get("n_surface") or cfg.n_surface)
world = sample_surface_points(
vertices,
faces,
n_surface,
seed=envelope_seed_from_ckpt(ckpt),
cache_key=cache_key,
)
arr = apply_envelope_aabb(world, center, scale)
arr = project_envelope_dim(arr, envelope_dim_from_ckpt(ckpt))
geom = torch.from_numpy(arr).unsqueeze(0)
shape_id = torch.zeros((), dtype=torch.long)
else:
points, labels = load_points_labels(npz)
xyz = torch.from_numpy(apply_normalization(points, center, scale))
y = torch.from_numpy(np.asarray(labels, dtype=np.float32)).unsqueeze(1)
logits_rows: list[torch.Tensor] = []
with torch.no_grad():
for start in range(0, int(xyz.shape[0]), int(cfg.batch_size)):
sl = slice(start, start + int(cfg.batch_size))
batch_xyz = xyz[sl].to(cfg.device)
if geom is None:
logits_rows.append(model(batch_xyz).cpu())
else:
b = int(batch_xyz.shape[0])
geom_b = geom.expand(b, -1, -1).to(cfg.device)
sid = shape_id.expand(b).to(cfg.device)
logits_rows.append(model(batch_xyz, geom_b, sid).cpu())
logits = torch.cat(logits_rows, dim=0)
scores = occupancy_metrics(logits, y)
print(
f"checkpoint={ckpt_path} npz={npz.name} "
f"acc={scores.accuracy:.4f} iou={scores.inside_iou:.4f} "
f"f1={scores.inside_f1:.4f}"
)
return {
"accuracy": scores.accuracy,
"inside_iou": scores.inside_iou,
"inside_f1": scores.inside_f1,
}
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Infer occupancy from models/<id>/best.pt")
parser.add_argument("--checkpoint", default=None, help="Path to best.pt")
parser.add_argument("--run-id", default=None, help="runs/<id> folder name")
parser.add_argument("--npz", default=None, help="NPZ path (data_dir-relative or absolute)")
args = parser.parse_args()
infer_npz(
load_config(),
checkpoint=args.checkpoint,
run_id=args.run_id,
npz_path=args.npz,
)
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