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| 1 | +"""Shared validation helpers for FastVisionOps operations.""" |
| 2 | + |
| 3 | +from __future__ import annotations |
| 4 | + |
| 5 | +from collections.abc import Sequence |
| 6 | + |
| 7 | +import numpy as np |
| 8 | +from numpy.typing import ArrayLike, NDArray |
| 9 | + |
| 10 | + |
| 11 | +def validate_threshold(name: str, value: float) -> float: |
| 12 | + value = float(value) |
| 13 | + if not np.isfinite(value) or not 0.0 <= value <= 1.0: |
| 14 | + raise ValueError(f"{name} must be finite and in [0, 1], got {value!r}") |
| 15 | + return value |
| 16 | + |
| 17 | + |
| 18 | +def validate_offset(offset: float) -> float: |
| 19 | + offset = float(offset) |
| 20 | + if offset not in (0.0, 1.0): |
| 21 | + raise ValueError(f"offset must be 0 or 1, got {offset!r}") |
| 22 | + return offset |
| 23 | + |
| 24 | + |
| 25 | +def validate_max_detections(value: int | None) -> int | None: |
| 26 | + if value is None: |
| 27 | + return None |
| 28 | + if ( |
| 29 | + isinstance(value, (bool, np.bool_)) |
| 30 | + or not isinstance(value, (int, np.integer)) |
| 31 | + or value < 0 |
| 32 | + ): |
| 33 | + raise ValueError("max_detections must be a non-negative integer or None") |
| 34 | + return int(value) |
| 35 | + |
| 36 | + |
| 37 | +def validate_boxes(boxes: ArrayLike) -> NDArray[np.float64]: |
| 38 | + result = np.ascontiguousarray(boxes, dtype=np.float64) |
| 39 | + if result.ndim != 2 or result.shape[1:] != (4,): |
| 40 | + raise ValueError(f"boxes must have shape (N, 4), got {result.shape}") |
| 41 | + if not np.isfinite(result).all(): |
| 42 | + raise ValueError("boxes must contain only finite values") |
| 43 | + if result.size and ( |
| 44 | + np.any(result[:, 2] < result[:, 0]) |
| 45 | + or np.any(result[:, 3] < result[:, 1]) |
| 46 | + ): |
| 47 | + raise ValueError("each box must satisfy x2 >= x1 and y2 >= y1") |
| 48 | + return result |
| 49 | + |
| 50 | + |
| 51 | +def validate_scores( |
| 52 | + scores: ArrayLike, |
| 53 | + num_items: int, |
| 54 | + *, |
| 55 | + ndim: int, |
| 56 | +) -> NDArray[np.float64]: |
| 57 | + result = np.ascontiguousarray(scores, dtype=np.float64) |
| 58 | + if result.ndim != ndim: |
| 59 | + shape = "(N,)" if ndim == 1 else "(N, C)" |
| 60 | + raise ValueError(f"scores must have shape {shape}, got {result.shape}") |
| 61 | + if result.shape[0] != num_items: |
| 62 | + raise ValueError( |
| 63 | + "boxes/masks and scores must contain the same number of items, " |
| 64 | + f"got {num_items} and {result.shape[0]}" |
| 65 | + ) |
| 66 | + if ndim == 2 and result.shape[1] == 0: |
| 67 | + raise ValueError("scores must contain at least one class") |
| 68 | + if not np.isfinite(result).all(): |
| 69 | + raise ValueError("scores must contain only finite values") |
| 70 | + return result |
| 71 | + |
| 72 | + |
| 73 | +def validate_masks(masks: ArrayLike) -> NDArray[np.bool_]: |
| 74 | + result = np.asarray(masks) |
| 75 | + if result.ndim < 2: |
| 76 | + raise ValueError(f"masks must have shape (N, ...), got {result.shape}") |
| 77 | + if result.dtype != np.bool_: |
| 78 | + raise TypeError(f"masks must have boolean dtype, got {result.dtype}") |
| 79 | + return np.ascontiguousarray(result) |
| 80 | + |
| 81 | + |
| 82 | +def validate_batch( |
| 83 | + boxes: Sequence[ArrayLike], |
| 84 | + scores: Sequence[ArrayLike], |
| 85 | +) -> None: |
| 86 | + if len(boxes) != len(scores): |
| 87 | + raise ValueError( |
| 88 | + "boxes and scores batches must have equal length, " |
| 89 | + f"got {len(boxes)} and {len(scores)}" |
| 90 | + ) |
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