a yµÿfíAã@s”ddlZddlmZddlmZddlmZmZddlm Z eej dƒZ Gdd„dej ƒZ Gd d „d e ƒZdd d „Zddd„Zdd„Zddd„ZdS)éNé)Ú check_version)Úbbox_iouÚprobiou)Úxywhr2xyxyxyxyz1.10.0csveZdZdZd‡fdd„ Ze ¡d d „ƒZd d „Zd d„Z dd„Z ddd„Z dd„Z e ddd„ƒZe dd„ƒZ‡ZS)ÚTaskAlignedAssignerao A task-aligned assigner for object detection. This class assigns ground-truth (gt) objects to anchors based on the task-aligned metric, which combines both classification and localization information. Attributes: topk (int): The number of top candidates to consider. num_classes (int): The number of object classes. alpha (float): The alpha parameter for the classification component of the task-aligned metric. beta (float): The beta parameter for the localization component of the task-aligned metric. eps (float): A small value to prevent division by zero. é éPçð?ç@ç•Ö&è .>cs2tƒ ¡||_||_||_||_||_||_dS)zJInitialize a TaskAlignedAssigner object with customizable hyperparameters.N)ÚsuperÚ__init__ÚtopkÚ num_classesÚbg_idxÚalphaÚbetaÚeps)Úselfrrrrr©Ú __class__©úQ/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/utils/tal.pyrs zTaskAlignedAssigner.__init__cCs(|jd|_|jd|_|jdkr‚|j}t |d|j¡ |¡t |¡ |¡t |¡ |¡t |d¡ |¡t |d¡ |¡fS|  ||||||¡\}} } |  || |j¡\} } }|  ||| | ¡\} }}| |9} | j ddd}| |j ddd}| |||j   d¡ d¡}||}| |||  ¡| fS)a™ Compute the task-aligned assignment. Reference code is available at https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py. Args: pd_scores (Tensor): shape(bs, num_total_anchors, num_classes) pd_bboxes (Tensor): shape(bs, num_total_anchors, 4) anc_points (Tensor): shape(num_total_anchors, 2) gt_labels (Tensor): shape(bs, n_max_boxes, 1) gt_bboxes (Tensor): shape(bs, n_max_boxes, 4) mask_gt (Tensor): shape(bs, n_max_boxes, 1) Returns: target_labels (Tensor): shape(bs, num_total_anchors) target_bboxes (Tensor): shape(bs, num_total_anchors, 4) target_scores (Tensor): shape(bs, num_total_anchors, num_classes) fg_mask (Tensor): shape(bs, num_total_anchors) target_gt_idx (Tensor): shape(bs, num_total_anchors) rr).réÿÿÿÿT)ÚdimÚkeepdiméþÿÿÿ)ÚshapeÚbsÚ n_max_boxesÚdeviceÚtorchZ full_likerÚtoZ zeros_likeÚ get_pos_maskÚselect_highest_overlapsÚ get_targetsZamaxrÚ unsqueezeÚbool)rÚ pd_scoresÚ pd_bboxesÚ anc_pointsÚ gt_labelsÚ gt_bboxesÚmask_gtr!Úmask_posÚ align_metricÚoverlapsÚ target_gt_idxÚfg_maskÚ target_labelsÚ target_bboxesÚ target_scoresZpos_align_metricsZ pos_overlapsZnorm_align_metricrrrÚforward&s*   û ÿ zTaskAlignedAssigner.forwardc CsZ| ||¡}| ||||||¡\}} |j|| dd|j¡ ¡d} | ||} | || fS)z'Get in_gts mask, (b, max_num_obj, h*w).r)Ú topk_mask)Úselect_candidates_in_gtsÚget_box_metricsÚselect_topk_candidatesÚexpandrr() rr)r*r,r-r+r.Z mask_in_gtsr0r1Z mask_topkr/rrrr$Zs   z TaskAlignedAssigner.get_pos_maskc Cs"|jd}| ¡}tj|j|j|g|j|jd}tj|j|j|g|j|jd}tjd|j|jgtjd} tj |jd  dd¡  d|j¡| d<|  d¡| d<|| dd d …| df|||<|  d¡  d|jdd¡|} |  d¡  dd|d¡|} | | | ¡||<| |j¡| |j¡} | |fS) zICompute alignment metric given predicted and ground truth bounding boxes.r©Údtyper!é)r>)ÚendrrrN)rr(r"Úzerosrr r>r!ÚlongÚarangeÚviewr<Úsqueezer'Úiou_calculationÚpowrr) rr)r*r,r-r.Únar1Z bbox_scoresÚindZpd_boxesZgt_boxesr0rrrr:fs $"z#TaskAlignedAssigner.get_box_metricscCst||ddd d¡ d¡S)z.IoU calculation for horizontal bounding boxes.FT)ÚxywhZCIoUrr)rrEÚclamp_©rr-r*rrrrF{sz#TaskAlignedAssigner.iou_calculationTNc Csàtj||jd|d\}}|dur>|jdddd|jk |¡}| |d¡tj|jtj|j d}tj |dd…dd…dd…ftj|j d}t |jƒD],}|  d|dd…dd…||d…f|¡q–| |dkd¡|  |j¡S) ah Select the top-k candidates based on the given metrics. Args: metrics (Tensor): A tensor of shape (b, max_num_obj, h*w), where b is the batch size, max_num_obj is the maximum number of objects, and h*w represents the total number of anchor points. largest (bool): If True, select the largest values; otherwise, select the smallest values. topk_mask (Tensor): An optional boolean tensor of shape (b, max_num_obj, topk), where topk is the number of top candidates to consider. If not provided, the top-k values are automatically computed based on the given metrics. Returns: (Tensor): A tensor of shape (b, max_num_obj, h*w) containing the selected top-k candidates. r)rÚlargestNT)rrr=r)r"rÚmaxrZ expand_asZ masked_fill_rArZint8r!Z ones_likeÚrangeZ scatter_add_r#r>) rÚmetricsrMr8Z topk_metricsZ topk_idxsZ count_tensorZonesÚkrrrr;s**z*TaskAlignedAssigner.select_topk_candidatesc CsÒtj|jtj|jdd}|||j}| ¡ ¡|}| d|j d¡|}|  d¡tj |j d|j d|j ftj|jd}|  d| d¡d¡|dd…dd…df dd|j ¡} t | dk|d¡}|||fS) aÚ Compute target labels, target bounding boxes, and target scores for the positive anchor points. Args: gt_labels (Tensor): Ground truth labels of shape (b, max_num_obj, 1), where b is the batch size and max_num_obj is the maximum number of objects. gt_bboxes (Tensor): Ground truth bounding boxes of shape (b, max_num_obj, 4). target_gt_idx (Tensor): Indices of the assigned ground truth objects for positive anchor points, with shape (b, h*w), where h*w is the total number of anchor points. fg_mask (Tensor): A boolean tensor of shape (b, h*w) indicating the positive (foreground) anchor points. Returns: (Tuple[Tensor, Tensor, Tensor]): A tuple containing the following tensors: - target_labels (Tensor): Shape (b, h*w), containing the target labels for positive anchor points. - target_bboxes (Tensor): Shape (b, h*w, 4), containing the target bounding boxes for positive anchor points. - target_scores (Tensor): Shape (b, h*w, num_classes), containing the target scores for positive anchor points, where num_classes is the number of object classes. )r@r>r!).Nrrrr=r?N)r"rCrZint64r!r rBÚflattenrDrrKrArÚscatter_r'ÚrepeatÚwhere) rr,r-r2r3Z batch_indr4r5r6Zfg_scores_maskrrrr&¢s ý"zTaskAlignedAssigner.get_targetsc Csn|jd}|j\}}}| ddd¡ dd¡\}}tj|d|||dfdd |||d¡} |  d¡ |¡S) aw Select positive anchor centers within ground truth bounding boxes. Args: xy_centers (torch.Tensor): Anchor center coordinates, shape (h*w, 2). gt_bboxes (torch.Tensor): Ground truth bounding boxes, shape (b, n_boxes, 4). eps (float, optional): Small value for numerical stability. Defaults to 1e-9. Returns: (torch.Tensor): Boolean mask of positive anchors, shape (b, n_boxes, h*w). Note: b: batch size, n_boxes: number of ground truth boxes, h: height, w: width. Bounding box format: [x_min, y_min, x_max, y_max]. rrrér?N©ré)rrDÚchunkr"ÚcatZaminZgt_) Ú xy_centersr-rZ n_anchorsrZn_boxesÚ_ÚltÚrbZ bbox_deltasrrrr9Òs   .z,TaskAlignedAssigner.select_candidates_in_gtscCs’| d¡}| ¡dkr~| d¡dk d|d¡}| d¡}tj|j|j|j d}|  d| d¡d¡t  |||¡  ¡}| d¡}| d¡}|||fS)a  Select anchor boxes with highest IoU when assigned to multiple ground truths. Args: mask_pos (torch.Tensor): Positive mask, shape (b, n_max_boxes, h*w). overlaps (torch.Tensor): IoU overlaps, shape (b, n_max_boxes, h*w). n_max_boxes (int): Maximum number of ground truth boxes. Returns: target_gt_idx (torch.Tensor): Indices of assigned ground truths, shape (b, h*w). fg_mask (torch.Tensor): Foreground mask, shape (b, h*w). mask_pos (torch.Tensor): Updated positive mask, shape (b, n_max_boxes, h*w). Note: b: batch size, h: height, w: width. rrrr=) ÚsumrNr'r<Zargmaxr"rArr>r!rSrUÚfloat)r/r1r r3Zmask_multi_gtsZmax_overlaps_idxZis_max_overlapsr2rrrr%ês     z+TaskAlignedAssigner.select_highest_overlaps)rr r r r )TN)r )Ú__name__Ú __module__Ú __qualname__Ú__doc__rr"Zno_gradr7r$r:rFr;r&Ú staticmethodr9r%Ú __classcell__rrrrr s  3  #0 rc@s$eZdZdZdd„Zedd„ƒZdS)ÚRotatedTaskAlignedAssignerzSAssigns ground-truth objects to rotated bounding boxes using a task-aligned metric.cCst||ƒ d¡ d¡S)z+IoU calculation for rotated bounding boxes.rr)rrErKrLrrrrFsz*RotatedTaskAlignedAssigner.iou_calculationcCs–t|ƒ}|jddd\}}}}||}||}||} ||jdd} ||jdd} | |jdd} | |jdd} | dk| | k@| dk@| | k@S)a Select the positive anchor center in gt for rotated bounding boxes. Args: xy_centers (Tensor): shape(h*w, 2) gt_bboxes (Tensor): shape(b, n_boxes, 5) Returns: (Tensor): shape(b, n_boxes, h*w) rrrWrr)rÚsplitr_)r[r-ZcornersÚaÚbr\ÚdÚabÚadZapZnorm_abZnorm_adZ ap_dot_abZ ap_dot_adrrrr9s z3RotatedTaskAlignedAssigner.select_candidates_in_gtsN)rarbrcrdrFrer9rrrrrg srgçà?c Csîgg}}|dusJ‚|dj|dj}}t|ƒD]¤\}}||j\} } } } tj| ||d|} tj| ||d|} trŠtj| | ddn t | | ¡\} } | t  | | fd¡  dd¡¡| tj | | df|||d ¡q4t  |¡t  |¡fS) zGenerate anchors from features.Nr)r@r!r>Zij)Zindexingrr?rr=) r>r!Ú enumeraterr"rCÚ TORCH_1_10ZmeshgridÚappendÚstackrDÚfullrZ)ZfeatsÚstridesZgrid_cell_offsetÚ anchor_pointsZ stride_tensorr>r!ÚiZstrider\ÚhÚwÚsxZsyrrrÚ make_anchors/s  $"rzTrc CsX| d|¡\}}||}||}|rH||d}||} t || f|¡St ||f|¡S)z.Transform distance(ltrb) to box(xywh or xyxy).r?)rYr"rZ) ZdistancerurJrr]r^Úx1y1Úx2y2Zc_xyZwhrrrÚ dist2bbox>s r}cCs4| dd¡\}}t ||||fd¡ d|d¡S)z#Transform bbox(xyxy) to dist(ltrb).r?rrg{®Gáz„?)rYr"rZrK)ruZbboxZreg_maxr{r|rrrÚ bbox2distJsr~c Cs|jd|d\}}t |¡t |¡}}||djd|d\}} ||| |||| |} } tj| | g|d|} tj| ||g|dS)aó Decode predicted rotated bounding box coordinates from anchor points and distribution. Args: pred_dist (torch.Tensor): Predicted rotated distance, shape (bs, h*w, 4). pred_angle (torch.Tensor): Predicted angle, shape (bs, h*w, 1). anchor_points (torch.Tensor): Anchor points, shape (h*w, 2). dim (int, optional): Dimension along which to split. Defaults to -1. Returns: (torch.Tensor): Predicted rotated bounding boxes, shape (bs, h*w, 4). r?rWr)rhr"ÚcosÚsinrZ) Z pred_distZ pred_anglerurr]r^rr€ZxfZyfÚxÚyZxyrrrÚ dist2rboxPs "rƒ)rn)Tr)r)r"Ztorch.nnÚnnZchecksrrPrrÚopsrÚ __version__rpÚModulerrgrzr}r~rƒrrrrÚs    #