File size: 8,957 Bytes
97aa5af | 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 | import copy
import sys
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Tuple
import numpy as np
import open3d as o3d
import torch
from tools import metrics
from r3pm_net.config_loader import get_method_paths
try:
from easydict import EasyDict as edict # type: ignore
except Exception: # pragma: no cover
class edict(dict):
"""Minimal EasyDict fallback (dot access)."""
def __getattr__(self, k):
try:
return self[k]
except KeyError as e:
raise AttributeError(k) from e
def __setattr__(self, k, v):
self[k] = v
@dataclass
class _RegTRRunner:
regtr_root: Path
regtr_src: Path
ckpt_path: Path
config_path: Path
device: torch.device
cfg: edict
model: torch.nn.Module
num_points: int
_RUNNER: Optional[_RegTRRunner] = None
_METHOD_CFG = get_method_paths().get("regtr", {})
class _RegTRImportContext:
"""Temporarily make RegTR's `src/` importable without polluting global imports.
RegTR uses top-level packages like `models` and `utils`, which can collide with
other third-party repos loaded into the same Python process (e.g. OverlapPredator).
We therefore:
- temporarily add RegTR `src/` to sys.path
- import the needed symbols
- then restore sys.path and restore common conflicting sys.modules entries
"""
_CONFLICT_PREFIXES = (
"models",
"utils",
"cvhelpers",
"data_loaders",
"datasets",
"kernels",
)
def __init__(self, regtr_src: Path):
self.regtr_src = regtr_src
self._inserted = False
self._prev_modules: dict[str, object] = {}
self._cleared_keys: set[str] = set()
def _iter_conflicting_module_keys(self) -> list[str]:
keys: list[str] = []
for prefix in self._CONFLICT_PREFIXES:
if prefix in sys.modules:
keys.append(prefix)
dot = prefix + "."
for k in list(sys.modules.keys()):
if k.startswith(dot):
keys.append(k)
# de-dup while preserving order
seen = set()
out = []
for k in keys:
if k not in seen:
seen.add(k)
out.append(k)
return out
def __enter__(self):
if str(self.regtr_src) not in sys.path:
sys.path.insert(0, str(self.regtr_src))
self._inserted = True
# Save & clear potentially-colliding modules so `import models...` resolves
# to RegTR's `src/models`, not some other repo's `models` package.
for k in self._iter_conflicting_module_keys():
if k in sys.modules:
self._prev_modules[k] = sys.modules[k]
sys.modules.pop(k, None)
self._cleared_keys.add(k)
return self
def __exit__(self, exc_type, exc, tb):
# First remove any RegTR-introduced modules under the same prefixes, then restore.
for prefix in self._CONFLICT_PREFIXES:
sys.modules.pop(prefix, None)
dot = prefix + "."
for k in list(sys.modules.keys()):
if k.startswith(dot):
sys.modules.pop(k, None)
for k, mod in self._prev_modules.items():
sys.modules[k] = mod
# Remove RegTR src path if we inserted it.
if self._inserted:
try:
sys.path.remove(str(self.regtr_src))
except ValueError:
pass
return False
def _maybe_downsample_xyz(xyz: np.ndarray, max_points: int) -> np.ndarray:
if max_points <= 0 or xyz.shape[0] <= max_points:
return xyz
idx = np.random.permutation(xyz.shape[0])[:max_points]
return xyz[idx]
def _init_runner(
regtr_root: Path,
ckpt_path: Path,
config_path: Path,
*,
device: Optional[str | torch.device] = None,
) -> _RegTRRunner:
regtr_src = (regtr_root / "src").resolve()
if not regtr_src.exists():
raise FileNotFoundError(f"RegTR src directory not found: {regtr_src}")
if device is None:
device_t = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
device_t = device if isinstance(device, torch.device) else torch.device(device)
with _RegTRImportContext(regtr_src):
from utils.misc import load_config # type: ignore
from models.regtr import RegTR # type: ignore
cfg = edict(load_config(str(config_path)))
model = RegTR(cfg).to(device_t)
state = torch.load(str(ckpt_path), map_location=device_t)
state_dict = state["state_dict"] if isinstance(state, dict) and "state_dict" in state else state
model.load_state_dict(state_dict, strict=False)
model.eval()
num_points = int(getattr(cfg, "num_points", 1024) or 1024)
return _RegTRRunner(
regtr_root=regtr_root,
regtr_src=regtr_src,
ckpt_path=ckpt_path,
config_path=config_path,
device=device_t,
cfg=cfg,
model=model,
num_points=num_points,
)
def regtr_reg_and_eval(
source: "o3d.geometry.PointCloud",
target: "o3d.geometry.PointCloud",
*,
gt_transformation: Optional[np.ndarray] = None,
regtr_root: str | Path = _METHOD_CFG.get("root", "/home/ykashefbahrami/RegTR"),
ckpt_path: str | Path = _METHOD_CFG.get("ckpt_path", "/home/ykashefbahrami/RegTR/trained_models/modelnet/ckpt/model-best.pth"),
config_path: str | Path = _METHOD_CFG.get("config_path", "/home/ykashefbahrami/RegTR/trained_models/modelnet/config.yaml"),
device: Optional[str | torch.device] = None,
) -> Tuple["o3d.geometry.PointCloud", tuple]:
"""Run RegTR (ModelNet checkpoint) on a (source, target) pair and evaluate.
Returns:
pc_result: transformed copy of `source` (using estimated pose src->tgt)
eval_results: tuple shaped like `metrics.all_evaluations(...)` with GT provided
"""
global _RUNNER
regtr_root_p = Path(regtr_root).resolve()
ckpt_path_p = Path(ckpt_path).resolve()
config_path_p = Path(config_path).resolve()
if not ckpt_path_p.exists():
raise FileNotFoundError(
f"RegTR checkpoint not found: {ckpt_path_p}\n"
f"Expected ModelNet weights at: {regtr_root_p}/trained_models/modelnet/ckpt/model-best.pth"
)
if not config_path_p.exists():
raise FileNotFoundError(
f"RegTR config not found: {config_path_p}\n"
f"Expected ModelNet config at: {regtr_root_p}/trained_models/modelnet/config.yaml"
)
if device is None:
requested_device = None
else:
requested_device = device if isinstance(device, torch.device) else torch.device(device)
if (
_RUNNER is None
or _RUNNER.regtr_root != regtr_root_p
or _RUNNER.ckpt_path != ckpt_path_p
or _RUNNER.config_path != config_path_p
or (requested_device is not None and _RUNNER.device != requested_device)
):
_RUNNER = _init_runner(regtr_root_p, ckpt_path_p, config_path_p, device=device)
src_xyz = np.asarray(source.points, dtype=np.float32)
tgt_xyz = np.asarray(target.points, dtype=np.float32)
src_xyz = _maybe_downsample_xyz(src_xyz, _RUNNER.num_points)
tgt_xyz = _maybe_downsample_xyz(tgt_xyz, _RUNNER.num_points)
# Build batch the way RegTR expects it: list-of-tensors per batch element.
data_batch = {
"src_xyz": [torch.from_numpy(src_xyz).float().to(_RUNNER.device)],
"tgt_xyz": [torch.from_numpy(tgt_xyz).float().to(_RUNNER.device)],
}
# Ensure RegTR's internal imports won't be confused by other repos.
# The forward path itself does not re-import, but its modules reference top-level
# packages (`models`, `utils`) which we keep isolated during the call.
with _RegTRImportContext(_RUNNER.regtr_src):
with torch.no_grad():
# Warm-up to avoid first-run overhead in timings.
_RUNNER.model(data_batch)
start = time.time()
with torch.no_grad():
outputs = _RUNNER.model(data_batch)
end = time.time()
pose = outputs["pose"][-1, 0].detach().cpu().numpy()
if pose.shape != (4, 4):
# pad a row of [0, 0, 0, 1] to the pose because the pose is a 3x4 matrix in the original code
pose = np.vstack([pose, [0, 0, 0, 1]])
if pose.shape != (4, 4): # sanity check, should not happen
raise ValueError(f"Unexpected RegTR pose shape: {pose.shape}")
pose = pose.astype(np.float64)
pc_result = copy.deepcopy(source).transform(pose)
eval_results = metrics.all_evaluations(
source,
target,
pc_result,
end - start,
gt_transformation=gt_transformation,
est_transformation=pose,
corres=None,
)
return pc_result, eval_results
|