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31f43c9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 | """Inference adapter for the winner-2025 pipeline.
Loads:
- DGCNN vertex classifier (3 heads: cls/offset/conf)
- DGCNN edge classifier (1 head)
And exposes:
- refine_winner_candidates(candidates, sample, model, device, threshold)
For each candidate, build the 4×4×4 m cubic patch with 11D point
features (winner spec), run the model, return only candidates that
pass the classification threshold and were shifted to the model's
offset.
- score_edges(vertices, sample, model, device, threshold)
For each pair of vertices within MAX_PAIR_DIST, build the 6D
cylindrical patch and ask the model whether the edge exists.
Both functions degrade gracefully if torch is missing or the checkpoint
is not found — they return None and the caller falls back to the
heuristic pipeline.
"""
from __future__ import annotations
import os
import numpy as np
from pathlib import Path
# Lazy torch import — only required at training/inference time, not at
# submission package import time.
_torch = None
_DGCNNVertexClassifier = None
_DGCNNEdgeClassifier = None
def _ensure_torch():
global _torch, _DGCNNVertexClassifier, _DGCNNEdgeClassifier
if _torch is not None:
return True
try:
import torch as _t
_torch = _t
except Exception:
return False
# Try multiple import paths for DGCNN classes:
# 1. Full package (local development)
# 2. Submission-directory copy (HF container)
for _module_path in [
"s23dr.models.dgcnn",
"dgcnn",
"submission.dgcnn",
]:
try:
_mod = __import__(_module_path, fromlist=["DGCNNVertexClassifier", "DGCNNEdgeClassifier"])
_DGCNNVertexClassifier = _mod.DGCNNVertexClassifier
_DGCNNEdgeClassifier = _mod.DGCNNEdgeClassifier
break
except Exception:
continue
if _DGCNNVertexClassifier is None:
return False
return True
def _resolve_model_path(path: str) -> str | None:
"""Try multiple locations for a model checkpoint."""
candidates = [
path,
os.path.join(os.path.dirname(__file__), os.path.basename(path)),
os.path.join(os.path.dirname(__file__), path),
os.path.basename(path),
]
for c in candidates:
if os.path.exists(c):
return c
return None
def load_vertex_model(path="checkpoints/vertex_model_dgcnn.pt", device="cuda"):
if not _ensure_torch():
return None
path = _resolve_model_path(path)
if path is None:
return None
try:
ckpt = _torch.load(path, map_location=device, weights_only=False)
state = ckpt['model'] if isinstance(ckpt, dict) and 'model' in ckpt else ckpt
model = _DGCNNVertexClassifier(in_channels=11).to(device)
model.load_state_dict(state)
model.eval()
return model
except Exception:
return None
def load_edge_model(path="checkpoints/edge_model_dgcnn.pt", device="cuda"):
if not _ensure_torch():
return None
path = _resolve_model_path(path)
if path is None:
return None
try:
ckpt = _torch.load(path, map_location=device, weights_only=False)
state = ckpt['model'] if isinstance(ckpt, dict) and 'model' in ckpt else ckpt
model = _DGCNNEdgeClassifier(in_channels=6).to(device)
model.load_state_dict(state)
model.eval()
return model
except Exception:
return None
def refine_winner_candidates(
candidates,
sample,
model,
device="cuda",
cls_threshold: float = 0.5,
apply_offset: bool = True,
batch_size: int = 64,
max_points: int = 1024,
patch_size: float = 4.0,
):
"""Run DGCNN vertex refinement on Stage 1 winner candidates.
Args:
candidates: list of dicts from generate_vertex_candidates
(each must have 'xyz' and 'point_ids').
sample: raw HF dataset entry.
model: loaded DGCNNVertexClassifier (or compatible).
device: torch device.
cls_threshold: keep candidate if sigmoid(cls_logit) ≥ threshold.
apply_offset: shift accepted candidates by predicted offset.
Returns:
list of (xyz, score) for accepted candidates, OR None on failure.
"""
if model is None or not candidates:
return None
if not _ensure_torch():
return None
try:
from hoho2025.example_solutions import convert_entry_to_human_readable
from s23dr.data_prep.patch_extraction import (
_get_all_points_with_features, _project_and_get_gestalt_labels,
extract_vertex_patch,
)
except Exception:
return None
good = convert_entry_to_human_readable(sample)
colmap_rec = good.get('colmap') or good.get('colmap_binary')
if colmap_rec is None:
return None
all_xyz, all_rgb, all_pids = _get_all_points_with_features(colmap_rec)
if len(all_xyz) == 0:
return None
depth_shapes = [(np.array(d).shape[0], np.array(d).shape[1]) for d in good['depth']]
all_gestalt = _project_and_get_gestalt_labels(
all_xyz, colmap_rec, good['gestalt'], good['image_ids'], depth_shapes,
)
patches = []
cand_idx = []
for i, cand in enumerate(candidates):
patch = extract_vertex_patch(
cand['xyz'], all_xyz, all_rgb, all_gestalt,
cand.get('point_ids', set()), all_pids,
patch_size=patch_size, max_points=max_points,
)
if patch is None:
continue
patches.append(patch)
cand_idx.append(i)
if not patches:
return []
accepted = []
with _torch.no_grad():
for start in range(0, len(patches), batch_size):
end = min(start + batch_size, len(patches))
batch = np.stack(patches[start:end], axis=0) # (B, 11, N)
x = _torch.from_numpy(batch).to(device)
cls_logits, pred_offset, pred_conf = model(x)
cls_logits = cls_logits.squeeze(-1).cpu().numpy()
pred_offset = pred_offset.cpu().numpy()
pred_conf = pred_conf.squeeze(-1).cpu().numpy()
probs = 1.0 / (1.0 + np.exp(-cls_logits))
for k in range(end - start):
if probs[k] < cls_threshold:
continue
ci = cand_idx[start + k]
xyz = candidates[ci]['xyz'].copy()
if apply_offset:
xyz = xyz + pred_offset[k]
accepted.append((xyz.astype(np.float64), float(probs[k])))
return accepted
def score_edges(
vertices: np.ndarray,
sample,
model,
device: str = "cuda",
threshold: float = 0.5,
max_pair_dist: float = 8.0,
batch_size: int = 64,
max_points: int = 1024,
):
"""Run DGCNN edge classifier over all vertex pairs within max_pair_dist.
Returns list of (i, j, prob) for pairs where the model says "edge".
"""
if model is None or vertices is None or len(vertices) < 2:
return None
if not _ensure_torch():
return None
try:
from hoho2025.example_solutions import convert_entry_to_human_readable
from s23dr.data_prep.patch_extraction import (
_get_all_points_with_features, extract_edge_patch,
)
except Exception:
return None
good = convert_entry_to_human_readable(sample)
colmap_rec = good.get('colmap') or good.get('colmap_binary')
if colmap_rec is None:
return None
all_xyz, all_rgb, _ = _get_all_points_with_features(colmap_rec)
if len(all_xyz) == 0:
return None
n = len(vertices)
pairs = []
patches = []
for i in range(n):
for j in range(i + 1, n):
dist = float(np.linalg.norm(vertices[i] - vertices[j]))
if dist > max_pair_dist:
continue
patch = extract_edge_patch(
vertices[i], vertices[j], all_xyz, all_rgb, max_points=max_points,
)
if patch is None:
continue
pairs.append((i, j))
patches.append(patch)
if not patches:
return []
out = []
with _torch.no_grad():
for start in range(0, len(patches), batch_size):
end = min(start + batch_size, len(patches))
batch = np.stack(patches[start:end], axis=0)
x = _torch.from_numpy(batch).to(device)
logits = model(x).squeeze(-1).cpu().numpy()
probs = 1.0 / (1.0 + np.exp(-logits))
for k in range(end - start):
if probs[k] >= threshold:
i, j = pairs[start + k]
out.append((int(i), int(j), float(probs[k])))
return out
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