Adriano
ba54b42fdc
Due ottimizzazioni chiave:
1. Spread bitmap uint8 invece di response map (N_BINS, H, W) float32
- 32x meno memoria, cache-friendly
- Nuovi kernel Numba: _jit_score_bitmap, _jit_popcount_density
- Formato: spread[y,x] bit b = bin b attivo nel raggio di spread
- _refine_angle usa slicing su bitmap con mask & bit
2. Pre-NMS prima di refine_angle/verify_ncc
- Problema: loop 'for raw in candidati' applicava refine+verify A OGNI
candidato prima del check NMS → 2000+ refine chiamati per ~25 match
- Fix: pre-NMS su (cx, cy) subpixel, limita a max_matches*3 candidati,
poi refine + verify solo su quelli
- Esempio worst case: lama_full_fast 55.9s → 1.13s (49x)
Benchmark suite 16 scenari (4 immagini x full/part x fast/preciso):
prima: totale find 94.6s
dopo: totale find 27.3s (3.5x globale)
casi peggiori <5s (prima erano >50s)
ROI parziali (solo metà oggetto) funzionano in tutti i casi.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
203 lines
7.0 KiB
Python
203 lines
7.0 KiB
Python
"""Numba JIT kernels per hot-path del matching.
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Fallback automatico a numpy se numba non disponibile o kernel fallisce.
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"""
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from __future__ import annotations
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import numpy as np
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try:
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import numba as nb
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HAS_NUMBA = True
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except ImportError: # pragma: no cover
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HAS_NUMBA = False
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def _numpy_score_by_shift(
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resp: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
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bin_has_data: np.ndarray | None = None,
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) -> np.ndarray:
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"""Fallback numpy (stessa logica di LineShapeMatcher._score_by_shift)."""
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_, H, W = resp.shape
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acc = np.zeros((H, W), dtype=np.float32)
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n = len(dx)
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for i in range(n):
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b = int(bins[i])
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if bin_has_data is not None and not bin_has_data[b]:
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continue
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ddx = int(dx[i]); ddy = int(dy[i])
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y0s = max(0, -ddy); y1s = min(H, H - ddy)
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x0s = max(0, -ddx); x1s = min(W, W - ddx)
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if y0s >= y1s or x0s >= x1s:
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continue
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y0r = y0s + ddy; y1r = y1s + ddy
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x0r = x0s + ddx; x1r = x1s + ddx
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acc[y0s:y1s, x0s:x1s] += resp[b, y0r:y1r, x0r:x1r]
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if n > 0:
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acc /= n
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return acc
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if HAS_NUMBA:
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@nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
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def _jit_score_by_shift(
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resp: np.ndarray, # float32 (N_BINS, H, W)
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dx: np.ndarray, # int32 (N,)
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dy: np.ndarray, # int32 (N,)
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bins: np.ndarray, # int8 (N,)
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bin_active: np.ndarray, # bool_ (N_BINS,)
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) -> np.ndarray:
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_, H, W = resp.shape
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N = dx.shape[0]
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acc = np.zeros((H, W), dtype=np.float32)
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for y in nb.prange(H):
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for i in range(N):
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b = bins[i]
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if not bin_active[b]:
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continue
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ddy = dy[i]
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yy = y + ddy
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if yy < 0 or yy >= H:
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continue
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ddx = dx[i]
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x_lo = 0 if ddx >= 0 else -ddx
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x_hi = W if ddx <= 0 else W - ddx
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for x in range(x_lo, x_hi):
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acc[y, x] += resp[b, yy, x + ddx]
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if N > 0:
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inv = 1.0 / N
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for y in nb.prange(H):
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for x in range(W):
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acc[y, x] *= inv
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return acc
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@nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
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def _jit_score_bitmap(
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spread: np.ndarray, # uint8 (H, W), bit b = bin b attivo
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dx: np.ndarray, # int32 (N,)
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dy: np.ndarray, # int32 (N,)
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bins: np.ndarray, # int8 (N,) bin per ogni feature
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bit_active: np.uint8, # bitmask bin attivi in scena
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) -> np.ndarray:
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"""score[y,x] = (Σ_i [bit bins[i] acceso in spread[y+dy_i, x+dx_i]]) / N.
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32× meno memoria di response map float32 → cache-friendly.
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"""
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H, W = spread.shape
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N = dx.shape[0]
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acc = np.zeros((H, W), dtype=np.float32)
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for y in nb.prange(H):
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for i in range(N):
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b = bins[i]
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mask = np.uint8(1) << b
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if (bit_active & mask) == 0:
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continue
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ddy = dy[i]
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yy = y + ddy
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if yy < 0 or yy >= H:
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continue
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ddx = dx[i]
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x_lo = 0 if ddx >= 0 else -ddx
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x_hi = W if ddx <= 0 else W - ddx
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for x in range(x_lo, x_hi):
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if spread[yy, x + ddx] & mask:
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acc[y, x] += 1.0
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if N > 0:
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inv = 1.0 / N
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for y in nb.prange(H):
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for x in range(W):
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acc[y, x] *= inv
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return acc
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@nb.njit(cache=True, parallel=True, fastmath=True, boundscheck=False)
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def _jit_popcount_density(spread: np.ndarray) -> np.ndarray:
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"""Conta bit set per pixel: ritorna (H, W) float32 in [0..8]."""
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H, W = spread.shape
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out = np.zeros((H, W), dtype=np.float32)
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for y in nb.prange(H):
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for x in range(W):
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v = spread[y, x]
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# popcount manuale
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v = (v & 0x55) + ((v >> 1) & 0x55)
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v = (v & 0x33) + ((v >> 2) & 0x33)
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v = (v & 0x0F) + ((v >> 4) & 0x0F)
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out[y, x] = float(v)
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return out
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def _warmup():
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resp = np.zeros((8, 32, 32), dtype=np.float32)
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dx = np.zeros(1, dtype=np.int32)
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dy = np.zeros(1, dtype=np.int32)
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b = np.zeros(1, dtype=np.int8)
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ba = np.ones(8, dtype=np.bool_)
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_jit_score_by_shift(resp, dx, dy, b, ba)
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spread = np.zeros((32, 32), dtype=np.uint8)
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_jit_score_bitmap(spread, dx, dy, b, np.uint8(0xFF))
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_jit_popcount_density(spread)
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else: # pragma: no cover
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def _jit_score_by_shift(resp, dx, dy, bins, bin_active):
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raise RuntimeError("numba non disponibile")
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def _jit_score_bitmap(spread, dx, dy, bins, bit_active):
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raise RuntimeError("numba non disponibile")
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def _jit_popcount_density(spread):
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raise RuntimeError("numba non disponibile")
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def _warmup():
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pass
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def score_bitmap(
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spread: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
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bit_active: int,
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) -> np.ndarray:
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"""Dispatch bitmap: JIT se numba, fallback numpy."""
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if HAS_NUMBA and len(dx) > 0:
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return _jit_score_bitmap(
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np.ascontiguousarray(spread, dtype=np.uint8),
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np.ascontiguousarray(dx, dtype=np.int32),
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np.ascontiguousarray(dy, dtype=np.int32),
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np.ascontiguousarray(bins, dtype=np.int8),
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np.uint8(bit_active),
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)
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# Fallback numpy (lento): converte bitmap a response 3D
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H, W = spread.shape
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resp = np.zeros((8, H, W), dtype=np.float32)
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for b in range(8):
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resp[b] = ((spread >> b) & 1).astype(np.float32)
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return _numpy_score_by_shift(resp, dx, dy, bins, None)
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def popcount_density(spread: np.ndarray) -> np.ndarray:
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if HAS_NUMBA:
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return _jit_popcount_density(np.ascontiguousarray(spread, dtype=np.uint8))
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# Fallback
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H, W = spread.shape
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out = np.zeros((H, W), dtype=np.float32)
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for b in range(8):
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out += ((spread >> b) & 1).astype(np.float32)
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return out
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def score_by_shift(
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resp: np.ndarray, dx: np.ndarray, dy: np.ndarray, bins: np.ndarray,
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bin_has_data: np.ndarray | None = None,
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) -> np.ndarray:
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"""Dispatch: JIT se possibile, fallback numpy."""
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if not HAS_NUMBA or len(dx) == 0:
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return _numpy_score_by_shift(resp, dx, dy, bins, bin_has_data)
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# Normalizza tipi per Numba
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resp_f = np.ascontiguousarray(resp, dtype=np.float32)
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dx_i = np.ascontiguousarray(dx, dtype=np.int32)
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dy_i = np.ascontiguousarray(dy, dtype=np.int32)
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bins_i = np.ascontiguousarray(bins, dtype=np.int8)
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if bin_has_data is None:
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bin_active = np.ones(resp.shape[0], dtype=np.bool_)
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else:
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bin_active = np.ascontiguousarray(bin_has_data, dtype=np.bool_)
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return _jit_score_by_shift(resp_f, dx_i, dy_i, bins_i, bin_active)
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