|hQddlZddlmZddlmZddlmZddlmZm Z ddl m Z eejdZ Gdd ejZGd d eZdd Zdd ZdZddZy)N)LOGGER) check_version)bbox_iouprobiou)xywhr2xyxyxyxyz1.10.0c eZdZdZddededededef fd ZejdZ d Z d Z d Z d Z dd ZdZeddZedZxZS)TaskAlignedAssignerao 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. topk num_classesalphabetaepscht|||_||_||_||_||_y)a4 Initialize a TaskAlignedAssigner object with customizable hyperparameters. Args: topk (int, optional): The number of top candidates to consider. num_classes (int, optional): The number of object classes. alpha (float, optional): The alpha parameter for the classification component of the task-aligned metric. beta (float, optional): The beta parameter for the localization component of the task-aligned metric. eps (float, optional): A small value to prevent division by zero. N)super__init__r r r rr)selfr r r rr __class__s T/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/utils/tal.pyrzTaskAlignedAssigner.__init__s4  &  c |jd|_|jd|_|j |jdk(rzt j |d|j t j|t j|t j|dt j|dfS |j||||||S#tjj$rdtjd||||||fDcgc]}|jncc}w}}|j|} t fd| DcYSwxYw)a Compute the task-aligned assignment. Args: pd_scores (torch.Tensor): Predicted classification scores with shape (bs, num_total_anchors, num_classes). pd_bboxes (torch.Tensor): Predicted bounding boxes with shape (bs, num_total_anchors, 4). anc_points (torch.Tensor): Anchor points with shape (num_total_anchors, 2). gt_labels (torch.Tensor): Ground truth labels with shape (bs, n_max_boxes, 1). gt_bboxes (torch.Tensor): Ground truth boxes with shape (bs, n_max_boxes, 4). mask_gt (torch.Tensor): Mask for valid ground truth boxes with shape (bs, n_max_boxes, 1). Returns: target_labels (torch.Tensor): Target labels with shape (bs, num_total_anchors). target_bboxes (torch.Tensor): Target bounding boxes with shape (bs, num_total_anchors, 4). target_scores (torch.Tensor): Target scores with shape (bs, num_total_anchors, num_classes). fg_mask (torch.Tensor): Foreground mask with shape (bs, num_total_anchors). target_gt_idx (torch.Tensor): Target ground truth indices with shape (bs, num_total_anchors). References: https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py rr).rz7CUDA OutOfMemoryError in TaskAlignedAssigner, using CPUc3@K|]}|jywN)to).0tdevices r z.TaskAlignedAssigner.forward..Zs6!f6s)shapebs n_max_boxesrtorch full_liker zeros_like_forwardcudaOutOfMemoryErrorrwarningcputuple) r pd_scores pd_bboxes anc_points gt_labels gt_bboxesmask_gtr cpu_tensorsresultrs @rforwardzTaskAlignedAssigner.forward/s5.//!$$??1-!!   q  & 143C3CD  +  +  6!23  6!23   7==Iz9iY`a azz** 7 NNT U-6 :yZcel,mnq1557nnKn"T]]K0F6v66 6  7sC) rr+r,r.r/r-r0 mask_in_gtsr@rA mask_topkr?s rr9z TaskAlignedAssigner.get_pos_masks"33J J !%!5!5iIW`bmpwbw!x h// WY[]_c_h_hHiHnHnHp/q {*W4x//rc|jd}|j}tj|j|j |g|j |j}tj|j|j |g|j |j}tjd|j|j gtj} tj|jjddjd|j | d<|jd| d<|| dd d | df|||<|jdjd|j dd|} |jdjdd|d|} |j| | ||<|j|j |j|j"z} | |fS) a8 Compute alignment metric given predicted and ground truth bounding boxes. Args: pd_scores (torch.Tensor): Predicted classification scores with shape (bs, num_total_anchors, num_classes). pd_bboxes (torch.Tensor): Predicted bounding boxes with shape (bs, num_total_anchors, 4). gt_labels (torch.Tensor): Ground truth labels with shape (bs, n_max_boxes, 1). gt_bboxes (torch.Tensor): Ground truth boxes with shape (bs, n_max_boxes, 4). mask_gt (torch.Tensor): Mask for valid ground truth boxes with shape (bs, n_max_boxes, h*w). Returns: align_metric (torch.Tensor): Alignment metric combining classification and localization. overlaps (torch.Tensor): IoU overlaps between predicted and ground truth boxes. r8dtyper)rT)endr5rrN)rr>r"zerosr r!rTrlongarangeviewrOsqueezer=iou_calculationpowr r) rr+r,r.r/r0narA bbox_scoresindpd_boxesgt_boxesr@s rrMz#TaskAlignedAssigner.get_box_metricss__R ,,.;;)9)92>ioo^g^n^nokk477D,<,ITTU^_I z1-{{7== 9K[K[\ yArr2%**YM]M]^tyy! LA  % %b)Aq!a!e)O*Dd K L !!,"2A6w}}--rctj|jtj|jd}|||j zz}|j j|}|jd|jd|}|jdtj|jd|jd|jftj|j}|jd|jdd|dddddfjdd|j} tj | dkD|d}|||fS) a Compute target labels, target bounding boxes, and target scores for the positive anchor points. Args: gt_labels (torch.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 (torch.Tensor): Ground truth bounding boxes of shape (b, max_num_obj, 4). target_gt_idx (torch.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 (torch.Tensor): A boolean tensor of shape (b, h*w) indicating the positive (foreground) anchor points. Returns: target_labels (torch.Tensor): Target labels for positive anchor points with shape (b, h*w). target_bboxes (torch.Tensor): Target bounding boxes for positive anchor points with shape (b, h*w, 4). target_scores (torch.Tensor): Target scores for positive anchor points with shape (b, h*w, num_classes). )rVrTr).Nr5rrrSrUN)r"rYr int64rr!rXflattenrZrrfrWr scatter_r=repeatwhere) rr.r/rBrC batch_indrDrErFfg_scores_masks rr;zTaskAlignedAssigner.get_targetss7(LLTWWEKK HXHXYZcd % D4D4D(DD !(002=A "r9??2+>? N  Q   #]%8%8%;T=M=M N++ '' q-"9"9""=qA At,33Aq$:J:JK NQ$6 qI m]::rc:|jd}|j\}}}|jdddjdd\}}tj|d|z ||dz fdj|||d} | j dj |S) ae 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. 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]. rr5rrUNr6)rrZchunkr"catamingt_) xy_centersr/r n_anchorsr n_boxes_ltrb bbox_deltass rrLz,TaskAlignedAssigner.select_candidates_in_gtss"$$Q' "GQAq)//15BiiD!1B!6Z=M8M NTUV[[\^`girtvw "&&s++rc|jd}|jdkDr|jddkDjd|d}|j d}t j |j|j|j}|jd|jddt j|||j}|jd}|j d}|||fS)aa 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). r8rr5rS) sumrjr=rOargmaxr"rWrrTrrzr|float)r?rAr!rCmask_multi_gtsmax_overlaps_idxis_max_overlapsrBs rr:z+TaskAlignedAssigner.select_highest_overlaps-s ,,r" ;;=1 %//2Q6>>r;PRSN'q1 #kk(..W_WfWfgO  $ $Q(8(B(B1(Eq I{{>?HMSSUHll2&G + gx//r) Pg?g@& .>r)r)__name__ __module__ __qualname____doc__intrrr"no_gradr3r%r9rMr\rNr; staticmethodrLr: __classcell__)rs@rr r s SCUX]jo$U]]_*7*7X#ZJ06 &D [.@);V,,,00rr c&eZdZdZdZedZy)RotatedTaskAlignedAssignerzSAssigns ground-truth objects to rotated bounding boxes using a task-aligned metric.cVt||jdjdS)z)Calculate IoU for rotated bounding boxes.r5r)rr[rfrgs rr\z*RotatedTaskAlignedAssigner.iou_calculationOs%y),44R8??BBrc<t|}|jdd\}}}}||z }||z }||z } ||zjd} ||zjd} | |zjd} | |zjd} | dk\| | kz| dk\z| | kzS)a Select the positive anchor center in gt for rotated bounding boxes. Args: xy_centers (torch.Tensor): Anchor center coordinates with shape (h*w, 2). gt_bboxes (torch.Tensor): Ground truth bounding boxes with shape (b, n_boxes, 5). Returns: (torch.Tensor): Boolean mask of positive anchors with shape (b, n_boxes, h*w). rr8rr5r)rsplitr)rr/cornersabrdabadapnorm_abnorm_ad ap_dot_ab ap_dot_ads rrLz3RotatedTaskAlignedAssigner.select_candidates_in_gtsSs!+]]1"]- 1a U U!^7--B-'7--B-'"WMMbM) "WMMbM) Q9#78INKy\cOcddrN)rrrrr\rrLrrrrLs!]Ceerrc gg}}|J|dj|dj}}t|D]\}}t|tr||j ddn!t ||dt ||df\} } tj| |||z} tj| |||z} trtj| | dntj| | \} } |jtj| | fdjdd|jtj| | zdf||| !tj|tj|fS) zGenerate anchors from features.NrrUr)rVrrTij)indexingr5rS)rTr enumerate isinstancelistrrr"rY TORCH_1_10meshgridappendstackrZfullr) featsstridesgrid_cell_offset anchor_points stride_tensorrTristridehwsxsys r make_anchorsrosY#%r=M   !HNNE!HOO6Ew'Y 6%/t%rsc !& 5,,h 7 {0")){0| e!4 eF > (a -r