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⚡️ Speed up method ObjectDetectionEvalProcessor._change_bbox_bounds_for_image_size by 312% #11

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⚡️ Speed up method ObjectDetectionEvalProcessor._change_bbox_bounds_for_image_size by 312% #11

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41 changes: 39 additions & 2 deletions unstructured/metrics/object_detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

import numpy as np
import torch
from numba import njit

IOU_THRESHOLDS = torch.tensor(
[0.5000, 0.5500, 0.6000, 0.6500, 0.7000, 0.7500, 0.8000, 0.8500, 0.9000, 0.9500]
Expand Down Expand Up @@ -303,8 +304,8 @@ def _change_bbox_bounds_for_image_size(
Returns:
clipped_boxes: Clipped bboxes in XYXY format of [..., 4] shape
"""
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(min=0, max=img_shape[1])
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(min=0, max=img_shape[0])
# Use Numba-accelerated function for inplace fast clipping
_numba_clip_boxes(boxes, img_shape[0], img_shape[1])
return boxes

@staticmethod
Expand Down Expand Up @@ -697,6 +698,42 @@ def _compute_detection_metrics_per_cls(
return ap, precision, recall


@njit(cache=True, fastmath=True)
def _numba_clip_boxes(boxes: np.ndarray, img_height: int, img_width: int) -> np.ndarray:
# This helper efficiently clips _inplace_ for (..., 4)-shaped XYXY boxes:
# x1 = min(max(x1, 0), img_width)
# y1 = min(max(y1, 0), img_height)
# x2 = min(max(x2, 0), img_width)
# y2 = min(max(y2, 0), img_height)
#
# Works with both 2D (N, 4) and higher dimensional (..., 4) arrays.
boxes.shape
boxes_reshaped = boxes.reshape(-1, 4)
for i in range(boxes_reshaped.shape[0]):
# x1
if boxes_reshaped[i, 0] < 0:
boxes_reshaped[i, 0] = 0
if boxes_reshaped[i, 0] > img_width:
boxes_reshaped[i, 0] = img_width
# y1
if boxes_reshaped[i, 1] < 0:
boxes_reshaped[i, 1] = 0
if boxes_reshaped[i, 1] > img_height:
boxes_reshaped[i, 1] = img_height
# x2
if boxes_reshaped[i, 2] < 0:
boxes_reshaped[i, 2] = 0
if boxes_reshaped[i, 2] > img_width:
boxes_reshaped[i, 2] = img_width
# y2
if boxes_reshaped[i, 3] < 0:
boxes_reshaped[i, 3] = 0
if boxes_reshaped[i, 3] > img_height:
boxes_reshaped[i, 3] = img_height
# No need to reshape as it is a view of original
return boxes


if __name__ == "__main__":
from dataclasses import asdict

Expand Down