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dc71d7e | 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 | # file: 03_infer_halfedge.py
# -*- coding: utf-8 -*-
import argparse
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import HeteroConv, SAGEConv, GlobalAttention, JumpingKnowledge, BatchNorm
from torch_geometric.data import HeteroData
from brep_extractor_utils import load_coedge_arrays, make_heterodata
class HalfEdgeGNN(nn.Module):
def __init__(
self,
coedge_in: int,
face_in: int,
edge_in: int,
global_in: int,
hidden=256,
layers=6,
dropout=0.2,
num_classes=3,
jk_mode="cat",
gating_dim=None,
):
super().__init__()
self.convs = nn.ModuleList(); self.bns = nn.ModuleList()
self.encoders = nn.ModuleDict({
"coedge": nn.Sequential(nn.Linear(coedge_in, hidden), nn.ReLU(), nn.Dropout(dropout)),
"face": nn.Sequential(nn.Linear(face_in, hidden), nn.ReLU(), nn.Dropout(dropout)),
"edge": nn.Sequential(nn.Linear(edge_in, hidden), nn.ReLU(), nn.Dropout(dropout)),
})
for _ in range(layers):
conv = HeteroConv({
('coedge','next','coedge'): SAGEConv((hidden,hidden), hidden),
('coedge','prev','coedge'): SAGEConv((hidden,hidden), hidden),
('coedge','mate','coedge'): SAGEConv((hidden,hidden), hidden),
('coedge','to_face','face'): SAGEConv((hidden, hidden), hidden),
('face','to_coedge','coedge'): SAGEConv((hidden, hidden), hidden),
('coedge','to_edge','edge'): SAGEConv((hidden, hidden), hidden),
('edge','to_coedge','coedge'): SAGEConv((hidden, hidden), hidden),
('face','to_edge','edge'): SAGEConv((hidden, hidden), hidden),
('edge','to_face','face'): SAGEConv((hidden, hidden), hidden),
}, aggr='sum')
self.convs.append(conv)
self.bns.append(nn.ModuleDict({
"coedge": BatchNorm(hidden),
"face": BatchNorm(hidden),
"edge": BatchNorm(hidden),
}))
self.jk = JumpingKnowledge(mode=jk_mode)
self.jk_out = hidden * layers if jk_mode == "cat" else hidden
if gating_dim is None:
gating_dim = hidden
self.gating_dim = gating_dim
self.gate = nn.Sequential(
nn.Linear(self.jk_out, self.jk_out//2),
nn.ReLU(),
nn.Linear(self.jk_out//2, 1),
)
self.pool = GlobalAttention(self.gate)
self.proj = nn.Identity() if self.jk_out == gating_dim else nn.Linear(self.jk_out, gating_dim)
self.global_mlp = nn.Sequential(
nn.Linear(global_in, gating_dim),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(gating_dim, 2 * gating_dim),
)
self.head = nn.Sequential(
nn.Linear(gating_dim, hidden),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden, num_classes),
)
def forward(self, data: HeteroData):
x = {
"coedge": self.encoders["coedge"](data["coedge"].x),
"face": self.encoders["face"](data["face"].x),
"edge": self.encoders["edge"](data["edge"].x),
}
outs = []
for conv, bn in zip(self.convs, self.bns):
x_new = conv(x, data.edge_index_dict)
x = {k: F.relu(bn[k](x_new[k]) + x[k]) for k in x}
outs.append(x["coedge"])
xj = self.jk(outs)
g = self.pool(xj, data['coedge'].batch)
g0 = self.proj(g)
global_x = data["global"].x
if global_x.dim() == 1:
global_x = global_x.view(1, -1)
if global_x.size(0) != g0.size(0):
raise RuntimeError(
f"Global feature batch mismatch: {global_x.size(0)} vs {g0.size(0)}"
)
gb = self.global_mlp(global_x)
gamma, beta = gb.chunk(2, dim=-1)
gamma = torch.sigmoid(gamma)
g_mod = g0 * gamma + beta
return self.head(g_mod)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--model", required=True)
ap.add_argument("--npz", required=True, help="Path to a processed BRep extractor npz file")
ap.add_argument("--tau", type=float, default=0.0, help="Reject threshold; below this outputs random")
ap.add_argument("--min_conf", type=float, default=0.85, help="Hard minimum confidence for known classes")
ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
args = ap.parse_args()
try:
ckpt = torch.load(args.model, map_location="cpu", weights_only=False)
except TypeError:
ckpt = torch.load(args.model, map_location="cpu")
if "global_in" not in ckpt or "gating_dim" not in ckpt:
raise RuntimeError(
"Checkpoint missing gating metadata. Please retrain with global gating enabled."
)
labels = ckpt["labels"]; inv_labels = {v:k for k,v in labels.items()}
random_id = labels.get("random")
if (args.tau > 0 or args.min_conf > 0) and random_id is None:
raise RuntimeError("Model labels do not include 'random'; retrain a 4-class model.")
stats = ckpt["stats"]
if not all(k in stats for k in ("coedge", "face", "edge")):
raise RuntimeError("Checkpoint missing heterograph stats; retrain required.")
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("Checkpoint missing heterograph input dims; retrain required.")
graph_data = load_coedge_arrays(Path(args.npz))
if int(graph_data["coedge_x"].shape[1]) != int(coedge_in):
raise RuntimeError(
f"Coedge feature dim mismatch: npz={int(graph_data['coedge_x'].shape[1])} "
f"ckpt={int(coedge_in)}"
)
if int(graph_data["face_x"].shape[1]) != int(face_in):
raise RuntimeError(
f"Face feature dim mismatch: npz={int(graph_data['face_x'].shape[1])} "
f"ckpt={int(face_in)}"
)
if int(graph_data["edge_x"].shape[1]) != int(edge_in):
raise RuntimeError(
f"Edge feature dim mismatch: npz={int(graph_data['edge_x'].shape[1])} "
f"ckpt={int(edge_in)}"
)
if int(graph_data["global_x"].shape[0]) != int(ckpt["global_in"]):
raise RuntimeError(
f"Global feature dim mismatch: npz={int(graph_data['global_x'].shape[0])} "
f"ckpt={int(ckpt['global_in'])}"
)
data = make_heterodata(
graph_data["coedge_x"],
graph_data["face_x"],
graph_data["edge_x"],
graph_data["next"],
graph_data["mate"],
graph_data["coedge_face"],
graph_data["coedge_edge"],
graph_data["global_x"],
label=None,
norm_stats=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)
global_in = ckpt["global_in"]
gating_dim = ckpt["gating_dim"]
model = HalfEdgeGNN(coedge_in=coedge_in, face_in=face_in, edge_in=edge_in, global_in=global_in,
hidden=ckpt["hp"]["hidden"],
layers=ckpt["hp"]["layers"], dropout=ckpt["hp"]["dropout"],
num_classes=len(labels), gating_dim=gating_dim).to(args.device)
model.load_state_dict(ckpt["state_dict"]); model.eval()
with torch.no_grad():
logits = model(data.to(args.device))
probs = F.softmax(logits, dim=-1).cpu().numpy()[0]
pred = int(probs.argmax())
conf = float(probs[pred])
arg_label = inv_labels[pred]
effective_tau = max(args.tau, args.min_conf)
if conf < effective_tau and random_id is not None:
final_label = "random"
else:
final_label = arg_label
print(f"Argmax: {arg_label} (conf={conf:.4f})")
print(f"Predicted: {final_label} (tau={effective_tau:.2f})")
for i, p in enumerate(probs):
print(f"{inv_labels[i]:>6s}: {p:.4f}")
if __name__ == "__main__":
main()
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