Revert "约束预测框互不重叠"（eba533a、9439cbb）
去掉强制非重叠：detect.py 不再给 DetectionValidator.postprocess 打补丁， 配置里移除 train.max_overlap 与 agnostic_nms，可视化脚本恢复为直接使用模型原始输出， engine/detection 里的 suppress_overlaps / filter_overlapping_outputs 一并移除。 约束会把 mAP50 从 0.555 压到 0.483（precision 0.536->0.612），代价是按置信度抑制时 高分错框会压掉本可命中的低分框。需要时用 git revert 这次提交即可取回。 按类别分目录的可视化产出结构（36fff98）保留。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
Shaun Zhou
co-authored by
Claude Opus 5
Claude Opus 5
parent
36fff984b7
commit
154677032f
@@ -10,12 +10,10 @@ from engine.detection import ( # noqa: E402
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COLOR_UNMATCHED,
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Box,
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draw_boxes,
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filter_overlapping_outputs,
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from_yolo,
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iou,
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match_boxes,
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parse_label,
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suppress_overlaps,
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)
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@@ -131,117 +129,3 @@ def test_draw_handles_boxes_at_the_border():
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prediction = Box(0, 0.0, 0.0, 0.5, 0.05, 0.9)
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draw_boxes(image, [], [prediction], ["blackdot"], match_boxes([prediction], []))
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assert COLOR_UNMATCHED in _colors(image)
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# ---------------- 不重叠约束 ----------------
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def test_suppress_keeps_the_highest_score_of_an_overlapping_pair():
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"""同一处缺陷被框两次时，留置信度高的那个。"""
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strong = Box(0, 0.3, 0.3, 0.7, 0.7, 0.9)
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weak = Box(1, 0.35, 0.35, 0.75, 0.75, 0.4)
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assert suppress_overlaps([weak, strong]) == [1] # 下标 1 = strong
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def test_suppress_is_cross_class():
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"""NMS 只在同类内抑制；跨类重复要靠这一步。"""
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a = Box(0, 0.3, 0.3, 0.7, 0.7, 0.9)
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b = Box(5, 0.31, 0.31, 0.69, 0.69, 0.8)
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assert suppress_overlaps([a, b]) == [0]
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def test_suppress_keeps_disjoint_boxes():
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"""真正分开的多个缺陷不能被误伤。"""
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boxes = [
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Box(0, 0.0, 0.0, 0.2, 0.2, 0.9),
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Box(0, 0.5, 0.5, 0.7, 0.7, 0.8),
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Box(1, 0.8, 0.1, 0.95, 0.3, 0.7),
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]
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assert suppress_overlaps(boxes) == [0, 1, 2]
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def test_touching_edges_are_not_overlap():
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"""边缘相接、面积不相交，IoU=0，应当都保留。"""
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left = Box(0, 0.0, 0.0, 0.5, 1.0, 0.9)
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right = Box(0, 0.5, 0.0, 1.0, 1.0, 0.8)
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assert suppress_overlaps([left, right]) == [0, 1]
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def test_contained_box_is_dropped_even_when_iou_is_below_the_threshold():
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"""整图大框套一个小框：面积悬殊导致 IoU 很小，但显然是同一处目标的重复表达。
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这正是 `contained` 的用武之地——只在允许轻微重叠（max_iou>0）时才有区别，
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因为 max_iou=0 时任何包含都已经因 IoU>0 被抑制了。
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"""
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big = Box(0, 0.0, 0.0, 1.0, 1.0, 0.9)
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small = Box(1, 0.45, 0.45, 0.55, 0.55, 0.8)
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assert iou(big, small) < 0.02
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assert suppress_overlaps([big, small], max_iou=0.1) == [0]
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assert suppress_overlaps([big, small], max_iou=0.1, contained=False) == [0, 1]
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def test_max_iou_allows_slight_overlap():
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a = Box(0, 0.0, 0.0, 0.5, 0.5, 0.9)
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b = Box(0, 0.4, 0.4, 0.9, 0.9, 0.8) # IoU 约 0.02
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assert suppress_overlaps([a, b]) == [0]
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assert suppress_overlaps([a, b], max_iou=0.1) == [0, 1]
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def test_suppress_handles_empty_and_single():
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assert suppress_overlaps([]) == []
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assert suppress_overlaps([Box(0, 0.1, 0.1, 0.2, 0.2, 0.5)]) == [0]
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# ---------------- 验证器输出的过滤 ----------------
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def test_filter_overlapping_outputs_drops_the_lower_score():
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"""ultralytics 验证器的输出是张量字典，筛选要对每个"每框一行"的张量同步生效。"""
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torch = pytest.importorskip("torch")
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output = {
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"bboxes": torch.tensor([[0.0, 0.0, 10.0, 10.0], [1.0, 1.0, 11.0, 11.0],
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[50.0, 50.0, 60.0, 60.0]]),
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"conf": torch.tensor([0.9, 0.5, 0.8]),
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"cls": torch.tensor([0.0, 1.0, 2.0]),
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"extra": torch.zeros((3, 0)),
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}
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filtered = filter_overlapping_outputs([output])[0]
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assert filtered["conf"].tolist() == pytest.approx([0.9, 0.8]) # 中间那个与第一个重叠
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assert filtered["cls"].tolist() == [0.0, 2.0]
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assert filtered["bboxes"].shape == (2, 4)
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assert filtered["extra"].shape == (2, 0)
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def test_filter_keeps_the_object_untouched_when_nothing_overlaps():
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torch = pytest.importorskip("torch")
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output = {
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"bboxes": torch.tensor([[0.0, 0.0, 1.0, 1.0], [5.0, 5.0, 6.0, 6.0]]),
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"conf": torch.tensor([0.9, 0.8]),
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"cls": torch.tensor([0.0, 0.0]),
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}
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filtered = filter_overlapping_outputs([output])
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assert filtered[0] is output # 没有变化时原样返回，不做无谓的拷贝
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def test_filter_handles_images_with_no_detection():
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torch = pytest.importorskip("torch")
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empty = {"bboxes": torch.zeros((0, 4)), "conf": torch.zeros(0), "cls": torch.zeros(0)}
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assert filter_overlapping_outputs([empty])[0]["conf"].numel() == 0
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def test_filter_can_empty_an_image_entirely():
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"""全部互相重叠时只留一个；这里构造两个几乎重合的框。"""
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torch = pytest.importorskip("torch")
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output = {
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"bboxes": torch.tensor([[0.0, 0.0, 10.0, 10.0], [0.1, 0.1, 10.1, 10.1]]),
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"conf": torch.tensor([0.3, 0.9]),
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"cls": torch.tensor([1.0, 1.0]),
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}
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filtered = filter_overlapping_outputs([output])[0]
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assert filtered["conf"].tolist() == pytest.approx([0.9])
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