a PSicÁ ã@sNddlZddlmZddlmZddlmZd ejejee ejd œd d „Z dS) éNé)Ú_log_api_usage_onceé)Ú_upcast_non_float)Ú_diou_iou_lossÚnoneçH¯¼šò×z>)Úboxes1Úboxes2Ú reductionÚepsÚreturncCs@tj ¡stj ¡sttƒt|ƒ}t|ƒ}t||ƒ\}}|jdd\}}}} |jdd\} } } } ||}| |}| | }| | }dtj dt  t  ||¡t  ||¡d¡}t  ¡$|d|||}Wdƒn1sæ0Y|||}|dkr*|  ¡dkr| ¡n d | ¡}n|d kr<| ¡}|S) aã Gradient-friendly IoU loss with an additional penalty that is non-zero when the boxes do not overlap. This loss function considers important geometrical factors such as overlap area, normalized central point distance and aspect ratio. This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable. Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and ``0 <= y1 < y2``, and The two boxes should have the same dimensions. Args: boxes1 : (Tensor[N, 4] or Tensor[4]) first set of boxes boxes2 : (Tensor[N, 4] or Tensor[4]) second set of boxes reduction : (string, optional) Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be applied to the output. ``'mean'``: The output will be averaged. ``'sum'``: The output will be summed. Default: ``'none'`` eps : (float): small number to prevent division by zero. Default: 1e-7 Returns: Tensor: Loss tensor with the reduction option applied. Reference: Zhaohui Zheng et al.: Complete Intersection over Union Loss: https://arxiv.org/abs/1911.08287 éÿÿÿÿ)ÚdimérrNÚmeanrgÚsum)ÚtorchÚjitÚ is_scriptingÚ is_tracingrÚcomplete_box_iou_lossrrÚunbindÚpiÚpowÚatanÚno_gradÚnumelrr)r r r r Ú diou_lossÚiouÚx1Úy1Úx2Úy2Úx1gÚy1gÚx2gÚy2gÚw_predÚh_predÚw_gtÚh_gtÚvÚalphaÚloss©r/úU/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/ops/ciou_loss.pyrs(%2 2  $ r)rr) rÚutilsrÚ_utilsrrrÚTensorÚstrÚfloatrr/r/r/r0Ús   üû