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perf: Numba JIT kernel per score_by_shift (2.1x speedup)

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- Nuovo modulo pm2d/_jit_kernels.py con _jit_score_by_shift Numba njit
  parallel + fastmath + boundscheck=False
- Parallelizzazione per riga output (no race condition su acc)
- Fallback automatico numpy se numba non installato
- Warmup automatico al module import (evita JIT lag al 1 match)

Benchmark clip.png (13 istanze):
  prima (numpy + threads): 1.55s
  dopo (numba + threads):  0.72s
  speedup: 2.1x

Pipeline totale full (refine+subpix): 0.80s

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
AdrianoDev
2026-04-24 01:30:31 +02:00
parent 51ed53cedd
commit b20b11c029
5 changed files with 169 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files
pm2d/__init__.py
+8 -1
View File
@@ -1,7 +1,14 @@
from pm2d.matcher import EdgeShapeMatcher, Match, Template
from pm2d.line_matcher import LineShapeMatcher, Match as LineMatch
from pm2d._jit_kernels import HAS_NUMBA, _warmup as _warmup_jit
# Precompila kernel JIT in background al primo import (evita lag al 1° match)
try:
_warmup_jit()
except Exception:
pass
__all__ = [
"EdgeShapeMatcher", "Match", "Template",
"LineShapeMatcher", "LineMatch",
"LineShapeMatcher", "LineMatch", "HAS_NUMBA",
]
pm2d/_jit_kernels.py
+110
View File
@@ -0,0 +1,110 @@
"""Numba JIT kernels per hot-path del matching.
Fallback automatico a numpy se numba non disponibile o kernel fallisce.
"""
from __future__ import annotations
import numpy as np
try:
import numba as nb
HAS_NUMBA = True
except ImportError: # pragma: no cover
HAS_NUMBA = False
def _numpy_score_by_shift(
resp: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
bin_has_data: np.ndarray | None = None,
) -> np.ndarray:
"""Fallback numpy (stessa logica di LineShapeMatcher._score_by_shift)."""
_, H, W = resp.shape
acc = np.zeros((H, W), dtype=np.float32)
n = len(dx)
for i in range(n):
b = int(bins[i])
if bin_has_data is not None and not bin_has_data[b]:
continue
ddx = int(dx[i]); ddy = int(dy[i])
y0s = max(0, -ddy); y1s = min(H, H - ddy)
x0s = max(0, -ddx); x1s = min(W, W - ddx)
if y0s >= y1s or x0s >= x1s:
continue
y0r = y0s + ddy; y1r = y1s + ddy
x0r = x0s + ddx; x1r = x1s + ddx
acc[y0s:y1s, x0s:x1s] += resp[b, y0r:y1r, x0r:x1r]
if n > 0:
acc /= n
return acc
if HAS_NUMBA:
@nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
def _jit_score_by_shift(
resp: np.ndarray, # float32 (N_BINS, H, W)
dx: np.ndarray, # int32 (N,)
dy: np.ndarray, # int32 (N,)
bins: np.ndarray, # int8 or int32 (N,)
bin_active: np.ndarray, # bool_ (N_BINS,)
) -> np.ndarray:
n_bins, H, W = resp.shape
N = dx.shape[0]
acc = np.zeros((H, W), dtype=np.float32)
# Parallelizza per riga: niente race (ogni y scrive solo acc[y, :])
for y in nb.prange(H):
for i in range(N):
b = bins[i]
if not bin_active[b]:
continue
ddy = dy[i]
yy = y + ddy
if yy < 0 or yy >= H:
continue
ddx = dx[i]
x_lo = 0 if ddx >= 0 else -ddx
x_hi = W if ddx <= 0 else W - ddx
for x in range(x_lo, x_hi):
acc[y, x] += resp[b, yy, x + ddx]
if N > 0:
inv = 1.0 / N
for y in nb.prange(H):
for x in range(W):
acc[y, x] *= inv
return acc
# Warmup: precompila con dummy data
def _warmup():
resp = np.zeros((8, 32, 32), dtype=np.float32)
dx = np.zeros(1, dtype=np.int32)
dy = np.zeros(1, dtype=np.int32)
b = np.zeros(1, dtype=np.int8)
ba = np.ones(8, dtype=np.bool_)
_jit_score_by_shift(resp, dx, dy, b, ba)
else: # pragma: no cover
def _jit_score_by_shift(resp, dx, dy, bins, bin_active):
raise RuntimeError("numba non disponibile")
def _warmup():
pass
def score_by_shift(
resp: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
bin_has_data: np.ndarray | None = None,
) -> np.ndarray:
"""Dispatch: JIT se possibile, fallback numpy."""
if not HAS_NUMBA or len(dx) == 0:
return _numpy_score_by_shift(resp, dx, dy, bins, bin_has_data)
# Normalizza tipi per Numba
resp_f = np.ascontiguousarray(resp, dtype=np.float32)
dx_i = np.ascontiguousarray(dx, dtype=np.int32)
dy_i = np.ascontiguousarray(dy, dtype=np.int32)
bins_i = np.ascontiguousarray(bins, dtype=np.int8)
if bin_has_data is None:
bin_active = np.ones(resp.shape[0], dtype=np.bool_)
else:
bin_active = np.ascontiguousarray(bin_has_data, dtype=np.bool_)
return _jit_score_by_shift(resp_f, dx_i, dy_i, bins_i, bin_active)
pm2d/line_matcher.py
+4 -20
View File
@@ -33,6 +33,8 @@ from dataclasses import dataclass
import cv2
import numpy as np
from pm2d._jit_kernels import score_by_shift as _jit_score_by_shift, HAS_NUMBA
N_BINS = 8 # orientamenti quantizzati modulo π
@@ -303,27 +305,9 @@ class LineShapeMatcher:
) -> np.ndarray:
"""score[y,x] = Σ_i resp[bin_i][y+dy_i, x+dx_i] / len(dx).
Ottimizzazione: se `bin_has_data` è fornito, skippa feature il cui
bin non ha pixel attivi nella scena (contribuzione = 0).
Dispatch a kernel Numba JIT se disponibile, altrimenti fallback numpy.
"""
_, H, W = resp.shape
acc = np.zeros((H, W), dtype=np.float32)
n = len(dx)
for i in range(n):
b = int(bins[i])
if bin_has_data is not None and not bin_has_data[b]:
continue
ddx = int(dx[i]); ddy = int(dy[i])
y0s = max(0, -ddy); y1s = min(H, H - ddy)
x0s = max(0, -ddx); x1s = min(W, W - ddx)
if y0s >= y1s or x0s >= x1s:
continue
y0r = y0s + ddy; y1r = y1s + ddy
x0r = x0s + ddx; x1r = x1s + ddx
acc[y0s:y1s, x0s:x1s] += resp[b, y0r:y1r, x0r:x1r]
if n > 0:
acc /= n
return acc
return _jit_score_by_shift(resp, dx, dy, bins, bin_has_data)
@staticmethod
def _subpixel_peak(acc: np.ndarray, x: int, y: int) -> tuple[float, float]:
pyproject.toml
+1
View File
@@ -3,6 +3,7 @@ name = "shape-model-2d"
version = "0.1.0"
requires-python = ">=3.13"
dependencies = [
"numba>=0.65.0",
"numpy>=1.24",
"opencv-python>=4.8",
]
uv.lock
Generated
+46
View File
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revision = 3
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name = "numpy"
version = "2.4.4"
@@ -75,12 +119,14 @@ name = "shape-model-2d"
version = "0.1.0"
source = { virtual = "." }
dependencies = [
{ name = "numba" },
{ name = "numpy" },
{ name = "opencv-python" },
]
[package.metadata]
requires-dist = [
{ name = "numba", specifier = ">=0.65.0" },
{ name = "numpy", specifier = ">=1.24" },
{ name = "opencv-python", specifier = ">=4.8" },
]