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feat(01-04): move GPR impl to components/gpr/faiss_gpr.py, shim core/gpr.py

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- Create components/gpr/faiss_gpr.py with 269 LOC (verbatim copy + module docstring)
- Inline numpy fallback kept as specified (Phase 4 VPR-03 owns the split)
- Update components/gpr/__init__.py: barrel-export GlobalPlaceRecognition (impl),
  IGlobalPlaceRecognition (protocol), _faiss, _FAISS_AVAILABLE
- Replace core/gpr.py with re-export shim preserving all public names
This commit is contained in:
Yuzviak
2026-05-11 08:48:11 +03:00
parent bae8587c51
commit 55ef732b96
3 changed files with 306 additions and 270 deletions
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src/gps_denied/components/gpr/__init__.py
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"""GPR component barrel exports.
``GlobalPlaceRecognition`` resolves to the Faiss-backed implementation
(faiss_gpr.py). The structural Protocol lives in protocol.py and is
re-exported as ``IGlobalPlaceRecognition``.
"""
from gps_denied.components.gpr.faiss_gpr import ( # noqa: F401
GlobalPlaceRecognition,
_faiss,
_FAISS_AVAILABLE,
)
from gps_denied.components.gpr.protocol import ( # noqa: F401
IGlobalPlaceRecognition,
)
__all__ = [
"GlobalPlaceRecognition",
"IGlobalPlaceRecognition",
"_faiss",
"_FAISS_AVAILABLE",
]
src/gps_denied/components/gpr/faiss_gpr.py
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"""Faiss-backed GlobalPlaceRecognition with inline numpy fallback.
Phase 4 (VPR-03) may split numpy fallback into a sibling module.
"""
import json
import logging
import os
from abc import ABC, abstractmethod
from typing import Dict, List
import numpy as np
from gps_denied.core.models import IModelManager
from gps_denied.schemas import GPSPoint
from gps_denied.schemas.gpr import DatabaseMatch, TileCandidate
from gps_denied.schemas.satellite import TileBounds
logger = logging.getLogger(__name__)
# Attempt to import Faiss (optional — only available on Jetson or with faiss-cpu installed)
try:
import faiss as _faiss # type: ignore
_FAISS_AVAILABLE = True
logger.info("Faiss available — real index search enabled")
except ImportError:
_faiss = None # type: ignore
_FAISS_AVAILABLE = False
logger.info("Faiss not available — using numpy L2 fallback for GPR")
class IGlobalPlaceRecognition(ABC):
@abstractmethod
def retrieve_candidate_tiles(self, image: np.ndarray, top_k: int) -> List[TileCandidate]:
pass
@abstractmethod
def compute_location_descriptor(self, image: np.ndarray) -> np.ndarray:
pass
@abstractmethod
def query_database(self, descriptor: np.ndarray, top_k: int) -> List[DatabaseMatch]:
pass
@abstractmethod
def rank_candidates(self, candidates: List[TileCandidate]) -> List[TileCandidate]:
pass
@abstractmethod
def load_index(self, flight_id: str, index_path: str) -> bool:
pass
@abstractmethod
def retrieve_candidate_tiles_for_chunk(self, chunk_images: List[np.ndarray], top_k: int) -> List[TileCandidate]:
pass
@abstractmethod
def compute_chunk_descriptor(self, chunk_images: List[np.ndarray]) -> np.ndarray:
pass
class GlobalPlaceRecognition(IGlobalPlaceRecognition):
"""AnyLoc-VLAD-DINOv2 coarse localisation component — sprint 1 GPR baseline.
GPR-01: load_index() tries to open a real Faiss .index file; falls back to
a NumPy L2 mock when the file is missing or Faiss is not installed.
GPR-02: Descriptor computed via DINOv2 engine (TRT FP16 on Jetson, Mock on
dev/CI). INT8 quantization is disabled — broken for ViT on Jetson
(NVIDIA/TRT#4348, facebookresearch/dinov2#489).
GPR-03: Candidates ranked by descriptor similarity (L2 → converted to [0,1]).
Selected over NetVLAD (deprecated, −2.4% R@1 on MSLS 2024) and SuperPoint+
LightGlue (unvalidated for cross-view UAV↔satellite gap at sprint 1).
Stage 2 evaluation: SP+LG+FAISS per _docs/03_backlog/stage2_ideas/.
Long-term target: EigenPlaces (ICCV 2023) — cleaner ONNX export.
Ref: docs/superpowers/specs/2026-04-18-oss-stack-tech-audit-design.md §2.3
"""
_DIM = 4096 # DINOv2 VLAD descriptor dimension
def __init__(self, model_manager: IModelManager):
self.model_manager = model_manager
# Index storage — one of: Faiss index OR numpy matrix
self._faiss_index = None # faiss.IndexFlatIP or similar
self._np_descriptors: np.ndarray | None = None # (N, DIM) fallback
self._metadata: Dict[int, dict] = {}
self._is_loaded = False
# ------------------------------------------------------------------
# GPR-02: Descriptor extraction via DINOv2
# ------------------------------------------------------------------
def compute_location_descriptor(self, image: np.ndarray) -> np.ndarray:
"""Run DINOv2 inference and return an L2-normalised descriptor."""
engine = self.model_manager.get_inference_engine("DINOv2")
desc = engine.infer(image)
norm = np.linalg.norm(desc)
return desc / max(norm, 1e-12)
def compute_chunk_descriptor(self, chunk_images: List[np.ndarray]) -> np.ndarray:
"""Mean-aggregate per-frame DINOv2 descriptors for a chunk."""
if not chunk_images:
return np.zeros(self._DIM, dtype=np.float32)
descs = [self.compute_location_descriptor(img) for img in chunk_images]
agg = np.mean(descs, axis=0)
return agg / max(np.linalg.norm(agg), 1e-12)
# ------------------------------------------------------------------
# GPR-01: Index loading
# ------------------------------------------------------------------
def load_index(self, flight_id: str, index_path: str) -> bool:
"""Load a Faiss descriptor index from disk (GPR-01).
Falls back to a NumPy random-vector mock when:
- `index_path` does not exist, OR
- Faiss is not installed (dev/CI without faiss-cpu).
"""
logger.info("Loading GPR index for flight=%s path=%s", flight_id, index_path)
# Try real Faiss load ------------------------------------------------
if _FAISS_AVAILABLE and os.path.isfile(index_path):
try:
self._faiss_index = _faiss.read_index(index_path)
# Load companion metadata JSON if present
meta_path = os.path.splitext(index_path)[0] + "_meta.json"
if os.path.isfile(meta_path):
with open(meta_path) as f:
raw = json.load(f)
self._metadata = {int(k): v for k, v in raw.items()}
# Deserialise GPSPoint / TileBounds from dicts
for idx, m in self._metadata.items():
if isinstance(m.get("gps_center"), dict):
m["gps_center"] = GPSPoint(**m["gps_center"])
if isinstance(m.get("bounds"), dict):
bounds_d = m["bounds"]
for corner in ("nw", "ne", "sw", "se", "center"):
if isinstance(bounds_d.get(corner), dict):
bounds_d[corner] = GPSPoint(**bounds_d[corner])
m["bounds"] = TileBounds(**bounds_d)
else:
self._metadata = self._generate_stub_metadata(self._faiss_index.ntotal)
self._is_loaded = True
logger.info("Faiss index loaded: %d vectors", self._faiss_index.ntotal)
return True
except Exception as exc:
logger.warning("Faiss load failed (%s) — falling back to numpy mock", exc)
# NumPy mock fallback ------------------------------------------------
logger.info("GPR: using numpy mock index (dev/CI mode)")
db_size = 1000
vecs = np.random.rand(db_size, self._DIM).astype(np.float32)
norms = np.linalg.norm(vecs, axis=1, keepdims=True)
self._np_descriptors = vecs / np.maximum(norms, 1e-12)
self._metadata = self._generate_stub_metadata(db_size)
self._is_loaded = True
return True
@staticmethod
def _generate_stub_metadata(n: int) -> Dict[int, dict]:
"""Generate placeholder tile metadata for dev/CI mock index."""
meta: Dict[int, dict] = {}
for i in range(n):
meta[i] = {
"tile_id": f"tile_{i:06d}",
"gps_center": GPSPoint(lat=49.0 + np.random.rand(), lon=32.0 + np.random.rand()),
"bounds": TileBounds(
nw=GPSPoint(lat=49.1, lon=32.0),
ne=GPSPoint(lat=49.1, lon=32.1),
sw=GPSPoint(lat=49.0, lon=32.0),
se=GPSPoint(lat=49.0, lon=32.1),
center=GPSPoint(lat=49.05, lon=32.05),
gsd=0.6,
),
}
return meta
# ------------------------------------------------------------------
# GPR-03: Similarity search ranked by descriptor distance
# ------------------------------------------------------------------
def query_database(self, descriptor: np.ndarray, top_k: int) -> List[DatabaseMatch]:
"""Search the index for the top-k most similar tiles.
Uses Faiss when loaded, numpy L2 otherwise.
Results are sorted by ascending L2 distance (= descending similarity).
"""
if not self._is_loaded:
logger.error("GPR index not loaded — call load_index() first.")
return []
q = descriptor.astype(np.float32).reshape(1, -1)
# Faiss path
if self._faiss_index is not None:
try:
distances, indices = self._faiss_index.search(q, top_k)
results = []
for dist, idx in zip(distances[0], indices[0]):
if idx < 0:
continue
sim = 1.0 / (1.0 + float(dist))
meta = self._metadata.get(int(idx), {"tile_id": f"tile_{idx}"})
results.append(DatabaseMatch(
index=int(idx),
tile_id=meta.get("tile_id", str(idx)),
distance=float(dist),
similarity_score=sim,
))
return results
except Exception as exc:
logger.warning("Faiss search failed: %s", exc)
# NumPy path
if self._np_descriptors is None:
return []
diff = self._np_descriptors - q # (N, DIM)
distances = np.sum(diff ** 2, axis=1)
top_indices = np.argsort(distances)[:top_k]
results = []
for idx in top_indices:
dist = float(distances[idx])
sim = 1.0 / (1.0 + dist)
meta = self._metadata.get(int(idx), {"tile_id": f"tile_{idx}"})
results.append(DatabaseMatch(
index=int(idx),
tile_id=meta.get("tile_id", str(idx)),
distance=dist,
similarity_score=sim,
))
return results
def rank_candidates(self, candidates: List[TileCandidate]) -> List[TileCandidate]:
"""Sort candidates by descriptor similarity (descending) — GPR-03."""
return sorted(candidates, key=lambda c: c.similarity_score, reverse=True)
def _matches_to_candidates(self, matches: List[DatabaseMatch]) -> List[TileCandidate]:
candidates = []
for rank, match in enumerate(matches, 1):
meta = self._metadata.get(match.index, {})
gps = meta.get("gps_center", GPSPoint(lat=49.0, lon=32.0))
bounds = meta.get("bounds", TileBounds(
nw=GPSPoint(lat=49.1, lon=32.0), ne=GPSPoint(lat=49.1, lon=32.1),
sw=GPSPoint(lat=49.0, lon=32.0), se=GPSPoint(lat=49.0, lon=32.1),
center=GPSPoint(lat=49.05, lon=32.05), gsd=0.6,
))
candidates.append(TileCandidate(
tile_id=match.tile_id,
gps_center=gps,
bounds=bounds,
similarity_score=match.similarity_score,
rank=rank,
))
return self.rank_candidates(candidates)
def retrieve_candidate_tiles(self, image: np.ndarray, top_k: int = 5) -> List[TileCandidate]:
desc = self.compute_location_descriptor(image)
matches = self.query_database(desc, top_k)
return self._matches_to_candidates(matches)
def retrieve_candidate_tiles_for_chunk(
self, chunk_images: List[np.ndarray], top_k: int = 5
) -> List[TileCandidate]:
desc = self.compute_chunk_descriptor(chunk_images)
matches = self.query_database(desc, top_k)
return self._matches_to_candidates(matches)
src/gps_denied/core/gpr.py
+15 -270
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@@ -1,271 +1,16 @@
"""Global Place Recognition (Component F08).
"""Legacy import path for GPR. Phase 1 shim — code lives in components/gpr/."""
from gps_denied.components.gpr.protocol import (
IGlobalPlaceRecognition, # noqa: F401
)
from gps_denied.components.gpr.faiss_gpr import (
GlobalPlaceRecognition,
_faiss,
_FAISS_AVAILABLE,
)
GPR-01: Loads a real Faiss index from disk when available; numpy-L2 fallback for dev/CI.
GPR-02: DINOv2/AnyLoc TRT FP16 on Jetson; MockInferenceEngine on dev/CI (via ModelManager).
GPR-03: Candidates ranked by DINOv2 descriptor similarity (dot-product / L2 distance).
"""
import json
import logging
import os
from abc import ABC, abstractmethod
from typing import Dict, List
import numpy as np
from gps_denied.core.models import IModelManager
from gps_denied.schemas import GPSPoint
from gps_denied.schemas.gpr import DatabaseMatch, TileCandidate
from gps_denied.schemas.satellite import TileBounds
logger = logging.getLogger(__name__)
# Attempt to import Faiss (optional — only available on Jetson or with faiss-cpu installed)
try:
import faiss as _faiss # type: ignore
_FAISS_AVAILABLE = True
logger.info("Faiss available — real index search enabled")
except ImportError:
_faiss = None # type: ignore
_FAISS_AVAILABLE = False
logger.info("Faiss not available — using numpy L2 fallback for GPR")
class IGlobalPlaceRecognition(ABC):
@abstractmethod
def retrieve_candidate_tiles(self, image: np.ndarray, top_k: int) -> List[TileCandidate]:
pass
@abstractmethod
def compute_location_descriptor(self, image: np.ndarray) -> np.ndarray:
pass
@abstractmethod
def query_database(self, descriptor: np.ndarray, top_k: int) -> List[DatabaseMatch]:
pass
@abstractmethod
def rank_candidates(self, candidates: List[TileCandidate]) -> List[TileCandidate]:
pass
@abstractmethod
def load_index(self, flight_id: str, index_path: str) -> bool:
pass
@abstractmethod
def retrieve_candidate_tiles_for_chunk(self, chunk_images: List[np.ndarray], top_k: int) -> List[TileCandidate]:
pass
@abstractmethod
def compute_chunk_descriptor(self, chunk_images: List[np.ndarray]) -> np.ndarray:
pass
class GlobalPlaceRecognition(IGlobalPlaceRecognition):
"""AnyLoc-VLAD-DINOv2 coarse localisation component — sprint 1 GPR baseline.
GPR-01: load_index() tries to open a real Faiss .index file; falls back to
a NumPy L2 mock when the file is missing or Faiss is not installed.
GPR-02: Descriptor computed via DINOv2 engine (TRT FP16 on Jetson, Mock on
dev/CI). INT8 quantization is disabled — broken for ViT on Jetson
(NVIDIA/TRT#4348, facebookresearch/dinov2#489).
GPR-03: Candidates ranked by descriptor similarity (L2 → converted to [0,1]).
Selected over NetVLAD (deprecated, −2.4% R@1 on MSLS 2024) and SuperPoint+
LightGlue (unvalidated for cross-view UAV↔satellite gap at sprint 1).
Stage 2 evaluation: SP+LG+FAISS per _docs/03_backlog/stage2_ideas/.
Long-term target: EigenPlaces (ICCV 2023) — cleaner ONNX export.
Ref: docs/superpowers/specs/2026-04-18-oss-stack-tech-audit-design.md §2.3
"""
_DIM = 4096 # DINOv2 VLAD descriptor dimension
def __init__(self, model_manager: IModelManager):
self.model_manager = model_manager
# Index storage — one of: Faiss index OR numpy matrix
self._faiss_index = None # faiss.IndexFlatIP or similar
self._np_descriptors: np.ndarray | None = None # (N, DIM) fallback
self._metadata: Dict[int, dict] = {}
self._is_loaded = False
# ------------------------------------------------------------------
# GPR-02: Descriptor extraction via DINOv2
# ------------------------------------------------------------------
def compute_location_descriptor(self, image: np.ndarray) -> np.ndarray:
"""Run DINOv2 inference and return an L2-normalised descriptor."""
engine = self.model_manager.get_inference_engine("DINOv2")
desc = engine.infer(image)
norm = np.linalg.norm(desc)
return desc / max(norm, 1e-12)
def compute_chunk_descriptor(self, chunk_images: List[np.ndarray]) -> np.ndarray:
"""Mean-aggregate per-frame DINOv2 descriptors for a chunk."""
if not chunk_images:
return np.zeros(self._DIM, dtype=np.float32)
descs = [self.compute_location_descriptor(img) for img in chunk_images]
agg = np.mean(descs, axis=0)
return agg / max(np.linalg.norm(agg), 1e-12)
# ------------------------------------------------------------------
# GPR-01: Index loading
# ------------------------------------------------------------------
def load_index(self, flight_id: str, index_path: str) -> bool:
"""Load a Faiss descriptor index from disk (GPR-01).
Falls back to a NumPy random-vector mock when:
- `index_path` does not exist, OR
- Faiss is not installed (dev/CI without faiss-cpu).
"""
logger.info("Loading GPR index for flight=%s path=%s", flight_id, index_path)
# Try real Faiss load ------------------------------------------------
if _FAISS_AVAILABLE and os.path.isfile(index_path):
try:
self._faiss_index = _faiss.read_index(index_path)
# Load companion metadata JSON if present
meta_path = os.path.splitext(index_path)[0] + "_meta.json"
if os.path.isfile(meta_path):
with open(meta_path) as f:
raw = json.load(f)
self._metadata = {int(k): v for k, v in raw.items()}
# Deserialise GPSPoint / TileBounds from dicts
for idx, m in self._metadata.items():
if isinstance(m.get("gps_center"), dict):
m["gps_center"] = GPSPoint(**m["gps_center"])
if isinstance(m.get("bounds"), dict):
bounds_d = m["bounds"]
for corner in ("nw", "ne", "sw", "se", "center"):
if isinstance(bounds_d.get(corner), dict):
bounds_d[corner] = GPSPoint(**bounds_d[corner])
m["bounds"] = TileBounds(**bounds_d)
else:
self._metadata = self._generate_stub_metadata(self._faiss_index.ntotal)
self._is_loaded = True
logger.info("Faiss index loaded: %d vectors", self._faiss_index.ntotal)
return True
except Exception as exc:
logger.warning("Faiss load failed (%s) — falling back to numpy mock", exc)
# NumPy mock fallback ------------------------------------------------
logger.info("GPR: using numpy mock index (dev/CI mode)")
db_size = 1000
vecs = np.random.rand(db_size, self._DIM).astype(np.float32)
norms = np.linalg.norm(vecs, axis=1, keepdims=True)
self._np_descriptors = vecs / np.maximum(norms, 1e-12)
self._metadata = self._generate_stub_metadata(db_size)
self._is_loaded = True
return True
@staticmethod
def _generate_stub_metadata(n: int) -> Dict[int, dict]:
"""Generate placeholder tile metadata for dev/CI mock index."""
meta: Dict[int, dict] = {}
for i in range(n):
meta[i] = {
"tile_id": f"tile_{i:06d}",
"gps_center": GPSPoint(lat=49.0 + np.random.rand(), lon=32.0 + np.random.rand()),
"bounds": TileBounds(
nw=GPSPoint(lat=49.1, lon=32.0),
ne=GPSPoint(lat=49.1, lon=32.1),
sw=GPSPoint(lat=49.0, lon=32.0),
se=GPSPoint(lat=49.0, lon=32.1),
center=GPSPoint(lat=49.05, lon=32.05),
gsd=0.6,
),
}
return meta
# ------------------------------------------------------------------
# GPR-03: Similarity search ranked by descriptor distance
# ------------------------------------------------------------------
def query_database(self, descriptor: np.ndarray, top_k: int) -> List[DatabaseMatch]:
"""Search the index for the top-k most similar tiles.
Uses Faiss when loaded, numpy L2 otherwise.
Results are sorted by ascending L2 distance (= descending similarity).
"""
if not self._is_loaded:
logger.error("GPR index not loaded — call load_index() first.")
return []
q = descriptor.astype(np.float32).reshape(1, -1)
# Faiss path
if self._faiss_index is not None:
try:
distances, indices = self._faiss_index.search(q, top_k)
results = []
for dist, idx in zip(distances[0], indices[0]):
if idx < 0:
continue
sim = 1.0 / (1.0 + float(dist))
meta = self._metadata.get(int(idx), {"tile_id": f"tile_{idx}"})
results.append(DatabaseMatch(
index=int(idx),
tile_id=meta.get("tile_id", str(idx)),
distance=float(dist),
similarity_score=sim,
))
return results
except Exception as exc:
logger.warning("Faiss search failed: %s", exc)
# NumPy path
if self._np_descriptors is None:
return []
diff = self._np_descriptors - q # (N, DIM)
distances = np.sum(diff ** 2, axis=1)
top_indices = np.argsort(distances)[:top_k]
results = []
for idx in top_indices:
dist = float(distances[idx])
sim = 1.0 / (1.0 + dist)
meta = self._metadata.get(int(idx), {"tile_id": f"tile_{idx}"})
results.append(DatabaseMatch(
index=int(idx),
tile_id=meta.get("tile_id", str(idx)),
distance=dist,
similarity_score=sim,
))
return results
def rank_candidates(self, candidates: List[TileCandidate]) -> List[TileCandidate]:
"""Sort candidates by descriptor similarity (descending) — GPR-03."""
return sorted(candidates, key=lambda c: c.similarity_score, reverse=True)
def _matches_to_candidates(self, matches: List[DatabaseMatch]) -> List[TileCandidate]:
candidates = []
for rank, match in enumerate(matches, 1):
meta = self._metadata.get(match.index, {})
gps = meta.get("gps_center", GPSPoint(lat=49.0, lon=32.0))
bounds = meta.get("bounds", TileBounds(
nw=GPSPoint(lat=49.1, lon=32.0), ne=GPSPoint(lat=49.1, lon=32.1),
sw=GPSPoint(lat=49.0, lon=32.0), se=GPSPoint(lat=49.0, lon=32.1),
center=GPSPoint(lat=49.05, lon=32.05), gsd=0.6,
))
candidates.append(TileCandidate(
tile_id=match.tile_id,
gps_center=gps,
bounds=bounds,
similarity_score=match.similarity_score,
rank=rank,
))
return self.rank_candidates(candidates)
def retrieve_candidate_tiles(self, image: np.ndarray, top_k: int = 5) -> List[TileCandidate]:
desc = self.compute_location_descriptor(image)
matches = self.query_database(desc, top_k)
return self._matches_to_candidates(matches)
def retrieve_candidate_tiles_for_chunk(
self, chunk_images: List[np.ndarray], top_k: int = 5
) -> List[TileCandidate]:
desc = self.compute_chunk_descriptor(chunk_images)
matches = self.query_database(desc, top_k)
return self._matches_to_candidates(matches)
__all__ = [
"GlobalPlaceRecognition",
"IGlobalPlaceRecognition",
"_faiss",
"_FAISS_AVAILABLE",
]