FiQfddlmZddlZddlZddlZddlZddlZddlZddl Z ddl m cm Z ddlmZGddej Zd!d"dZ d# d$dZd%dZd Zd Zd Zd Zd&d'd Zd(d)dZdZdZdZdZdZdZ dZ!d*d+dZ"d,dZ#d-d.dZ$dZ% d/ d0dZ&d1d2dZ'dZ(d3d4dZ)d5dZ*dZ+d Z,y)6) annotationsN) NOT_MACOS14c2eZdZdZdd dZdZdZdZdZy) Profilea7Ultralytics Profile class for timing code execution. Use as a decorator with @Profile() or as a context manager with 'with Profile():'. Provides accurate timing measurements with CUDA synchronization support for GPU operations. Attributes: t (float): Accumulated time in seconds. device (torch.device): Device used for model inference. cuda (bool): Whether CUDA is being used for timing synchronization. Examples: Use as a context manager to time code execution >>> with Profile(device=device) as dt: ... pass # slow operation here >>> print(dt) # prints "Elapsed time is 9.5367431640625e-07 s" Use as a decorator to time function execution >>> @Profile() ... def slow_function(): ... time.sleep(0.1) Ncx||_||_t|xrt|j d|_y)zInitialize the Profile class. Args: t (float): Initial accumulated time in seconds. device (torch.device, optional): Device used for model inference to enable CUDA synchronization. cudaN)tdeviceboolstr startswithr)selfr r s W/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/utils/ops.py__init__zProfile.__init__)s2 BCK$:$:6$BC c0|j|_|S)z Start timing.)timestartrs r __enter__zProfile.__enter__4sYY[  rc|j|jz |_|xj|jz c_y)z Stop timing.N)rrdtr )rtypevalue tracebacks r__exit__zProfile.__exit__9s*))+ * $''rc"d|jdS)zIReturn a human-readable string representing the accumulated elapsed time.zElapsed time is z s)r rs r__str__zProfile.__str__>s!$&&,,rc|jr)tjj|jt j S)z9Get current time with CUDA synchronization if applicable.)rtorch synchronizer r perf_counterrs rrz Profile.timeBs. 99 JJ " "4;; /  ""r)N)r floatr ztorch.device | None) __name__ __module__ __qualname____doc__rrrrrrrrrs!, D  -#rrc|j\}}tj|jdk|jdk|j |kD|j |kDgj dk\r$|j d|}|j d|}|dkD|dkDz||kz||kz}||}||}t|r]tj|j|j|j |j g|jStjd|jS)a$Convert segment coordinates to bounding box coordinates. Converts a single segment label to a box label by finding the minimum and maximum x and y coordinates. Applies inside-image constraint and clips coordinates when necessary. Args: segment (np.ndarray): Segment coordinates in format (N, 2) where N is number of points. width (int): Width of the image in pixels. height (int): Height of the image in pixels. Returns: (np.ndarray): Bounding box coordinates in xyxy format [x1, y1, x2, y2]. rdtype) Tnparrayminmaxsumclipanyr-zeros)segmentwidthheightxyinsides r segment2boxr>Is 99DAq xx1aeegk1557U?AEEGftjtjtj tj fn=tjtjtjtj f\}}}}dddf}fdtddD\}}}||||} } |dz | z|dz | zg} | dz | z|dz | zg} || d} || d} || z| z} || z| z }|| z | z }|| z | z}|| |||gdS)aConvert batched Oriented Bounding Boxes (OBB) from [xywh, rotation] to [xy1, xy2, xy3, xy4] format. Args: x (np.ndarray | torch.Tensor): Boxes in [cx, cy, w, h, rotation] format with shape (N, 5) or (B, N, 5). Rotation values should be in radians from [-pi/4, 3pi/4). Returns: (np.ndarray | torch.Tensor): Converted corner points with shape (N, 4, 2) or (B, N, 4, 2). .NrAc36K|]}d||dzfyw).r@Nr)).0ir;s r z!xywhr2xyxyxyxy..s":1S!a!e)^$:srf) rTr rUcossincatstackr0 concatenaterange)r;rrrrctrrar`r cos_value sin_valuevec1vec2pt1pt2pt3pt4s` rxywhr2xyxyxyxyrxs2 a & EIIuyy%++6ffbffbnnbhh 7Cc5 C!G*C:eAqk:KAq%u:s5zyI EI q1uy0 1D BFY A 1 2D tR=D tR=D *t C *t C *t C *t C #sC%r **rct|tjr|jnt j |}|d|dz|d<|d|dz|d<|S)aConvert bounding box from [x1, y1, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right. Args: x (np.ndarray | torch.Tensor): Input bounding box coordinates. Returns: (np.ndarray | torch.Tensor): Bounding box coordinates in xyxy format. rErCrFrDrrs r ltwh2xyxyrrrcg}|D]^}|j\}}|j|j|j|j|jg`t t j |S)aConvert segment coordinates to bounding box labels in xywh format. Args: segments (list): List of segments where each segment is a list of points, each point is [x, y] coordinates. Returns: (np.ndarray): Bounding box coordinates in xywh format. )r/rr2r3rnr0r1)segmentsrJsr;r<s rsegments2boxesrsg E ;ss1 aeegquuw9:; RXXe_ %%rc t|D]D\}}t||k(rtj||ddddffd}tjdt|dz t||kr|t|z n|}tj t|}t||kr+tj |tj|||n|}tjtdDcgc] }tj|||dd|f"c}tjjddj|<G|Scc}w)a2Resample segments to n points each using linear interpolation. Args: segments (list): List of (N, 2) arrays where N is the number of points in each segment. n (int): Number of points to resample each segment to. Returns: (list): Resampled segments with n points each. rr@N)axisrAr,rf) enumeraterr0rlinspacearangeinsert searchsortedrinterpfloat32rr/)rnrrr;xps rresample_segmentsrs(#  1 q6Q;  NNAq1ay> 2 KK3q6A:SVaZq3q6zQ G YYs1v 8;A BIIaB/ 4 NNuQxH!BIIaQq!tW5HPRPZPZ [ c cdegi j l l     OIs8%E c|j|jk7r|j|j}|j\}}}|dkr||jspt |j dj jD]4\}\}}}} d||d|f<d||| df<d||ddd|f<d||dd|df<6|Stj|dddddfdd\}}}} tj||j|jddddf} tj||j|jddddf} || |k\| |kz| |k\z| | kzzS)aCrop masks to bounding box regions. Args: masks (torch.Tensor): Masks with shape (N, H, W). boxes (torch.Tensor): Bounding box coordinates with shape (N, 4) in xyxy pixel format. Returns: (torch.Tensor): Cropped masks. 2r)r2Nr.r@r) r tor_is_cudarr^rGrVr chunkrr-) masksrJrr`rarrjrkrlrmrcs r crop_maskrsg ||u||#&kkGAq!2vemm#,U[[Q[-?-E-E-G-K-K-M#N ! ABBE!SbS&ME!RS&M E!Q)  E!Q)   !  U1a:%61=BB LL5<    rc|j\}}}||jj|dzjd||}t|d|d}t ||}|j dj S)aApply masks to bounding boxes using mask head output with native upsampling. Args: protos (torch.Tensor): Mask prototypes with shape (mask_dim, mask_h, mask_w). masks_in (torch.Tensor): Mask coefficients with shape (N, mask_dim) where N is number of masks after NMS. bboxes (torch.Tensor): Bounding boxes with shape (N, 4) where N is number of masks after NMS. shape (tuple): Input image size as (height, width). Returns: (torch.Tensor): Binary mask tensor with shape (N, H, W). rfNrr#)r_r$r scale_masksrrr)rrrr_rrrrs rprocess_mask_nativersw IAr2  ++Ar2 2 8 8R DE d U +A .E eV $E 99S>    rc|jdd\}}|dd\}}||k(r||k(r|S|At||z ||z }|t||zz |t||zz } } |r| dz} | dz} n|d\} } |rt| dz t| dz fnd\} } |t| dzz } |t| dzz }tj|d| | | |fj |dS) aRescale segment masks to target shape. Args: masks (torch.Tensor): Masks with shape (N, C, H, W). shape (tuple[int, int]): Target height and width as (height, width). ratio_pad (tuple, optional): Ratio and padding values as ((ratio_h, ratio_w), (pad_w, pad_h)). padding (bool): Whether masks are based on YOLO-style augmented images with padding. Returns: (torch.Tensor): Rescaled masks. rANr@rB)rr.rr)r_r2rGrrr$)rr_rLrMim1_him1_wim0_him0_wrOpad_wpad_htopleftbottomrights rrrs";;qr?LE5!9LE5 ~%5. 55=%%-0edl 33uuUT\?R7Ru  QJE QJE | u<+]_)patternreplstring)resub)rs r clean_strr!s 66=CPQ RRrct|tjr!tj||jSt j||jS)zKCreate empty torch.Tensor or np.ndarray with same shape and dtype as input.r,)rTr rUrir-r0)r;s rririsA1;Au||1L5  AQWW -qRTR_R_`aijipipRqqr)r#)r8 np.ndarrayr9rVr:rVreturnr$)NTF)rItuple[int, int]rJtorch.Tensor | np.ndarrayrKr&rLz tuple | NonerMr rNr r%r')r;rV)r#r#rr)rarVr`rVrtrVrurV)r#r#Fr#)rarVr`rVr5r r~r$)i)rrV)r torch.TensorrJr(r%r()F)rr )NT) rr(r_r&rLz.tuple[tuple[int, int], tuple[int, int]] | NonerMr r%r()NFT)rr rMr )r)rznp.ndarray | torch.Tensorrr r%zlist[np.ndarray])rr(r%r$)- __future__r contextlibrWrrrrr0r torch.nn.functionalnn functionalrultralytics.utilsrContextDecoratorrr>rRrZrHrdrnrrr|rrrrrrrrrrrrrrrrrr!rir)rrr0sV#  )4#j))4#nB# '<'< $'< '< '<  '<  '<'+<   & .E6!6!,AE !] !] !]>!] !]  !]H F2$> ] Srr