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da6986a | 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 | """Loaded-model wrapper + classify helpers. All in-process; no subprocess."""
from __future__ import annotations
from pathlib import Path
from typing import Dict
import numpy as np
import torch
import torch.nn.functional as F
from heg_brep.model import HalfEdgeGNN, resolve_reject_label
from heg_brep.graph_data import load_coedge_arrays, make_heterodata
def _align_2d(x: np.ndarray, target_dim: int) -> np.ndarray:
cur = int(x.shape[1])
if cur == target_dim:
return x
if cur > target_dim:
return x[:, :target_dim]
pad = np.zeros((x.shape[0], target_dim - cur), dtype=x.dtype)
return np.concatenate([x, pad], axis=1)
def _align_1d(x: np.ndarray, target_dim: int) -> np.ndarray:
cur = int(x.shape[0])
if cur == target_dim:
return x
if cur > target_dim:
return x[:target_dim]
pad = np.zeros((target_dim - cur,), dtype=x.dtype)
return np.concatenate([x, pad], axis=0)
class LoadedModel:
"""A pass1 / elbow / tee checkpoint loaded into memory once."""
def __init__(self, ckpt_path: Path, device: str = "cpu"):
try:
ckpt = torch.load(str(ckpt_path), map_location="cpu", weights_only=False)
except TypeError:
ckpt = torch.load(str(ckpt_path), map_location="cpu")
if "global_in" not in ckpt or "gating_dim" not in ckpt:
raise RuntimeError(f"Checkpoint {ckpt_path} missing gating metadata.")
stats = ckpt["stats"]
if not all(k in stats for k in ("coedge", "face", "edge")):
raise RuntimeError(f"Checkpoint {ckpt_path} missing heterograph stats.")
coedge_in = ckpt.get("coedge_in", ckpt.get("node_in"))
face_in = ckpt.get("face_in")
edge_in = ckpt.get("edge_in")
if coedge_in is None or face_in is None or edge_in is None:
raise RuntimeError(f"Checkpoint {ckpt_path} missing input dims.")
labels = ckpt["labels"]
model = HalfEdgeGNN(
coedge_in=coedge_in, face_in=face_in, edge_in=edge_in,
global_in=ckpt["global_in"], hidden=ckpt["hp"]["hidden"],
layers=ckpt["hp"]["layers"], dropout=ckpt["hp"]["dropout"],
num_classes=len(labels), gating_dim=ckpt["gating_dim"],
).to(device)
model.load_state_dict(ckpt["state_dict"])
model.eval()
self.model = model
self.device = device
self.coedge_in = int(coedge_in)
self.face_in = int(face_in)
self.edge_in = int(edge_in)
self.global_in = int(ckpt["global_in"])
self.stats = stats
self.labels = labels
self.inv_labels = {v: k for k, v in labels.items()}
self.reject_label = resolve_reject_label(labels, None)
@torch.no_grad()
def predict(self, npz_path: Path, min_conf: float = 0.0, tau: float = 0.0) -> Dict[str, object]:
g = load_coedge_arrays(npz_path)
g["coedge_x"] = _align_2d(g["coedge_x"], self.coedge_in)
g["face_x"] = _align_2d(g["face_x"], self.face_in)
g["edge_x"] = _align_2d(g["edge_x"], self.edge_in)
g["global_x"] = _align_1d(g["global_x"], self.global_in)
data = make_heterodata(
g["coedge_x"], g["face_x"], g["edge_x"],
g["next"], g["mate"], g["coedge_face"], g["coedge_edge"],
g["global_x"], label=None, norm_stats=self.stats,
)
data["coedge"].batch = torch.zeros(data["coedge"].x.size(0), dtype=torch.long)
data["global"].batch = torch.zeros(1, dtype=torch.long)
data["face"].batch = torch.zeros(data["face"].x.size(0), dtype=torch.long)
data["edge"].batch = torch.zeros(data["edge"].x.size(0), dtype=torch.long)
logits = self.model(data.to(self.device))
probs = F.softmax(logits, dim=-1).cpu().numpy()[0]
pred = int(probs.argmax())
conf = float(probs[pred])
argmax_label = self.inv_labels[pred]
effective_tau = max(tau, min_conf)
if conf < effective_tau and self.reject_label is not None:
predicted_label = self.reject_label
else:
predicted_label = argmax_label
return {
"argmax_label": argmax_label,
"argmax_conf": conf,
"predicted_label": predicted_label,
}
ELBOW_ROUTES = {"elbow"}
TEE_ROUTES = {"tee"}
class TwoPassClassifier:
"""Convenience wrapper that owns all three models and routes pass1 → pass2."""
def __init__(self, pass1: LoadedModel,
elbow: "LoadedModel | None",
tee: "LoadedModel | None",
pass2_min_conf: float = 0.85,
pass2_tau: float = 0.0):
self.pass1 = pass1
self.elbow = elbow
self.tee = tee
self.pass2_min_conf = pass2_min_conf
self.pass2_tau = pass2_tau
def classify_npz(self, npz_path: Path) -> Dict[str, object]:
p1 = self.pass1.predict(npz_path, min_conf=0.0, tau=0.0)
route = str(p1["argmax_label"]).strip().lower()
out = {
"pass1_argmax": p1["argmax_label"],
"pass1_conf": float(p1["argmax_conf"]),
"route": route,
"pass2_argmax": "",
"pass2_predicted": "",
"pass2_conf": None,
"final_label": "",
"final_conf": None,
}
if route in ELBOW_ROUTES and self.elbow is not None:
specialist = self.elbow
elif route in TEE_ROUTES and self.tee is not None:
specialist = self.tee
else:
# pipe / miscellaneous: no specialist, passthrough as random
out["final_label"] = "random"
out["final_conf"] = out["pass1_conf"]
return out
p2 = specialist.predict(npz_path, min_conf=self.pass2_min_conf, tau=self.pass2_tau)
out["pass2_argmax"] = p2["argmax_label"]
out["pass2_predicted"] = p2["predicted_label"]
out["pass2_conf"] = float(p2["argmax_conf"])
out["final_label"] = p2["predicted_label"]
out["final_conf"] = out["pass2_conf"]
return out
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