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@ -339,7 +339,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
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# GIoU
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# GIoU
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pxy = torch.sigmoid(ps[:, 0:2]) # pxy = pxy * s - (s - 1) / 2, s = 1.5 (scale_xy)
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pxy = torch.sigmoid(ps[:, 0:2]) # pxy = pxy * s - (s - 1) / 2, s = 1.5 (scale_xy)
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pbox = torch.cat((pxy, torch.exp(ps[:, 2:4]) * anchor_vec[i]), 1) # predicted box
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pbox = torch.cat((pxy, torch.exp(ps[:, 2:4]).clamp(max=1E2) * anchor_vec[i]), 1) # predicted box
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giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
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giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
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lbox += (1.0 - giou).mean() # giou loss
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lbox += (1.0 - giou).mean() # giou loss
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