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@ -284,21 +284,46 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
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return iou
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def box_iou(boxes1, boxes2):
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# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
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"""
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Return intersection-over-union (Jaccard index) of boxes.
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Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
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Arguments:
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boxes1 (Tensor[N, 4])
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boxes2 (Tensor[M, 4])
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Returns:
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iou (Tensor[N, M]): the NxM matrix containing the pairwise
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IoU values for every element in boxes1 and boxes2
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"""
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def box_area(boxes):
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return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
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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 = (rb - lt).clamp(min=0) # [N,M,2]
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inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
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iou = inter / (area1[:, None] + area2 - inter)
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return iou
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def wh_iou(box1, box2):
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# Returns the IoU of wh1 to wh2. wh1 is 2, wh2 is nx2
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box2 = box2.t()
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# Returns the IoU of wh1 to wh2. wh1 is 2, wh2 is 2xn
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# w, h = box1
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w1, h1 = box1[0], box1[1]
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w2, h2 = box2[0], box2[1]
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# Intersection area
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inter_area = torch.min(w1, w2) * torch.min(h1, h2)
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inter = torch.min(w1, w2) * torch.min(h1, h2)
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# Union Area
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union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
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return inter_area / union_area # iou
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return inter / (w1 * h1 + w2 * h2 - inter) # iou = inter / (area1 + area2 - inter)
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class FocalLoss(nn.Module):
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@ -422,8 +447,9 @@ def build_targets(model, targets):
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# iou of targets-anchors
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t, a = targets, []
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gwh = t[:, 4:6] * ng
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gwht = gwh.t()
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if nt:
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iou = torch.stack([wh_iou(x, gwh) for x in anchor_vec], 0)
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iou = torch.stack([wh_iou(x, gwht) for x in anchor_vec], 0)
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if use_all_anchors:
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na = len(anchor_vec) # number of anchors
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