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https://github.com/firestar5683/StarPilot.git
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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
# Copied from https://github.com/mlcommons/training/blob/637c82f9e699cd6caf108f92efb2c1d446b630e0/single_stage_detector/ssd/model/boxes.py
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import torch
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from torch import Tensor
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from typing import Tuple
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def _upcast(t: Tensor) -> Tensor:
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# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
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if t.is_floating_point():
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return t if t.dtype in (torch.float32, torch.float64) else t.float()
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else:
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return t if t.dtype in (torch.int32, torch.int64) else t.int()
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def box_area(boxes: Tensor) -> Tensor:
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"""
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Computes the area of a set of bounding boxes, which are specified by their
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(x1, y1, x2, y2) coordinates.
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Args:
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boxes (Tensor[N, 4]): boxes for which the area will be computed. They
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are expected to be in (x1, y1, x2, y2) format with
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``0 <= x1 < x2`` and ``0 <= y1 < y2``.
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Returns:
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Tensor[N]: the area for each box
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"""
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boxes = _upcast(boxes)
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return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
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# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
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# with slight modifications
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def _box_inter_union(boxes1: Tensor, boxes2: Tensor) -> Tuple[Tensor, Tensor]:
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area1 = box_area(boxes1)
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area2 = box_area(boxes2)
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lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
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rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
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wh = _upcast(rb - lt).clamp(min=0) # [N,M,2]
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inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
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union = area1[:, None] + area2 - inter
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return inter, union
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def box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
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"""
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Return intersection-over-union (Jaccard index) between two sets of boxes.
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Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
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``0 <= x1 < x2`` and ``0 <= y1 < y2``.
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Args:
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boxes1 (Tensor[N, 4]): first set of boxes
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boxes2 (Tensor[M, 4]): second set of boxes
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Returns:
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Tensor[N, M]: the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2
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"""
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inter, union = _box_inter_union(boxes1, boxes2)
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iou = inter / union
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return iou |