a OSicV@stddlZddlZddlZddlmZddlmZmZmZm Z m Z m Z ddl Z ddlZddlmZmZmZmZgdZed+e ejeejfeeee e eefeeejd d d Zed,e ejeejfe eejefe edd ddZed-ejeje eee e ee ee eeeffee eeeffe eee ee eejd ddZed.ejejee e ee ee eeeffee eeeffejdddZed/ejeje ee eefe e ee eeeffeeejdddZ eejejdddZ!eejejdd d!Z"ejd"d#d$Z#ed%d&d'Z$edd(d)d*Z%dS)0N) FunctionType)AnyBinaryIOListOptionalTupleUnion)Image ImageColor ImageDraw ImageFont) make_grid save_imagedraw_bounding_boxesdraw_segmentation_masksdraw_keypoints flow_to_imageF)tensornrowpadding normalize value_range scale_each pad_valuereturnc stjstjsttt|sjt|trX|D] }t|s4t dt |q4nt dt |d| vrt d|d}t|trtj|dd}|dkr|d}|dkr|dd krt|||fd}|d}|d kr|d d krt|||fd }|d ur|}|d urLt|tsLt d ddfdd} |d ur|D]}| ||qnn | ||t|tjst d|dd kr|dS|d} t|| } ttt| | } t|d|t|d|} }|d }||| | ||| |f|}d}t| D]h}t| D]X}|| krdqD|d || || |d||||||||d }qPqD|S)a) Make a grid of images. Args: tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W) or a list of images all of the same size. nrow (int, optional): Number of images displayed in each row of the grid. The final grid size is ``(B / nrow, nrow)``. Default: ``8``. padding (int, optional): amount of padding. Default: ``2``. normalize (bool, optional): If True, shift the image to the range (0, 1), by the min and max values specified by ``value_range``. Default: ``False``. value_range (tuple, optional): tuple (min, max) where min and max are numbers, then these numbers are used to normalize the image. By default, min and max are computed from the tensor. range (tuple. optional): .. warning:: This parameter was deprecated in ``0.12`` and will be removed in ``0.14``. Please use ``value_range`` instead. scale_each (bool, optional): If ``True``, scale each image in the batch of images separately rather than the (min, max) over all images. Default: ``False``. pad_value (float, optional): Value for the padded pixels. Default: ``0``. Returns: grid (Tensor): the tensor containing grid of images. z:tensor or list of tensors expected, got a list containing z(tensor or list of tensors expected, got rangezmThe parameter 'range' is deprecated since 0.12 and will be removed in 0.14. Please use 'value_range' instead.rdimrTNzNvalue_range has to be a tuple (min, max) if specified. min and max are numberscSs,|j||d||t||ddS)N)minmaxgh㈵>)clamp_sub_div_r%)imglowhighr,M/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/utils.pynorm_ip]szmake_grid..norm_ipcs>|dur||d|dn|t|t|dS)Nrr")floatr$r%)trr.r,r- norm_rangeaszmake_grid..norm_rangez%tensor should be of type torch.Tensor) torchjit is_scripting is_tracing_log_api_usage_oncer is_tensor isinstancelist TypeErrortypekeyswarningswarnstackr unsqueezesizecatclonetupleTensorsqueezer$intmathceilr/new_fullrnarrowcopy_)rrrrrrrkwargsr0r2ZnmapsZxmapsZymapsheightwidth num_channelsgridkyxr,r1r-r sn$                & "   r )rfpformatrcKsxtjstjsttt|fi|}|dd dd ddd dtj  }t|}|j||ddS) a# Save a given Tensor into an image file. Args: tensor (Tensor or list): Image to be saved. If given a mini-batch tensor, saves the tensor as a grid of images by calling ``make_grid``. fp (string or file object): A filename or a file object format(Optional): If omitted, the format to use is determined from the filename extension. If a file object was used instead of a filename, this parameter should always be used. **kwargs: Other arguments are documented in ``make_grid``. g?rr"rcpu)rWN)r3r4r5r6r7rr muladd_r&permutetouint8numpyr fromarraysave)rrVrWrNrRndarrimr,r,r-rs 0 rr") imageboxeslabelscolorsfillrPfont font_sizercCstjstjsttt|tjsr"r!z+Only grayscale and RGB images are supportedz/boxes doesn't contain any box. No box was drawnNzNumber of boxes (z) and labels (z/) mismatch. Please specify labels for each box.zNumber of colors (z ) is less than number of boxes (z). cSs$g|]}t|trt|n|qSr,)r9strr getrgb).0colorr,r,r- z'draw_bounding_boxes..z=Argument 'font_size' will be ignored since 'font' is not set. )rirBr")r!r"r"rRGBA)d)rPoutlinerh)rPru)rhridtype))r3r4r5r6r7rr9rFr;r<rwr^ ValueErrorr rBshaper>r?len_generate_color_paletter:r Z load_defaulttruetypetiler\rYr_r r`r]int64tolistr Drawzip rectangletext from_numpynparray)rdrerfrgrhrPrirj num_boxesZtxt_fontrb img_to_drawZ img_boxesdrawbboxrolabelZ fill_colormarginr,r,r-rsb&                   *r皙?)rdmasksalphargrc CsVtjstjsttt|tjsr?r{r:rErlr rmappendrdetachrDrr]) rdrrrgZ num_masks out_dtypeZcolors_rormaskoutr,r,r-r sV            "    rr!)rd keypoints connectivityrgradiusrPrcCstjstjsttt|tjs.)r3rrrr,rr-r{sr{)objrcCsD|jdsdS|jj}t|tr(|j}tj|jd|dS)a Logs API usage(module and name) within an organization. In a large ecosystem, it's often useful to track the PyTorch and TorchVision APIs usage. This API provides the similar functionality to the logging module in the Python stdlib. It can be used for debugging purpose to log which methods are used and by default it is inactive, unless the user manually subscribes a logger via the `SetAPIUsageLogger method `_. Please note it is triggered only once for the same API call within a process. It does not collect any data from open-source users since it is no-op by default. For more information, please refer to * PyTorch note: https://pytorch.org/docs/stable/notes/large_scale_deployments.html#api-usage-logging; * Logging policy: https://github.com/pytorch/vision/issues/5052; Args: obj (class instance or method): an object to extract info from. torchvisionNr) __module__ startswith __class____name__r9rr3_Cr7)rnamer,r,r-r7s   r7)rrFNFr)N)NNFr"NN)rN)NNrr!)&rIpathlibr>typesrtypingrrrrrrr_rr3PILr r r r __all__no_gradrFrHrr/r rlPathrrrrrrrr{r7r,r,r,r-s   q 0i0OH!"/