h3jSSKrSSKrSSKrSSKrSSKrSSKJr SSKJ r SSK J r Sr "SS5r g) N) compute_curve)figure_to_image)make_npc^U4SjnU$)Ncx>UR(d[S5 UR5 gT"U/UQ70UD6 g)Nz+ERROR: No Visdom server currently connected)server_connectedprint _try_connect)selfargskwargsfns T/var/www/fran/franai/venv/lib/python3.13/site-packages/tensorboardX/visdom_writer.pywrapper"_check_connection..wrappers6$$ ? @      4!$!&!)rrs` r_check_connectionrs" Nrc8\rSrSrSrSr\SSj5r\SSj5r\S5r \SSj5r \SS j5r \SS j5r \SS j5r \SS j5r\SS j5r\S5r\SSj5r\SSj5r\SSj5r\SSj5rSrSrg) VisdomWritercSSKJn 0UlSUlU"U0UD6Ul0UlUR5 g![a [S5ef=f)Nr)Visdomz4Visdom visualization requires installation of VisdomF)visdomr ImportError scalar_dictr viswindowsr )r r rrs r__init__VisdomWriter.__init__sf H %  %4*6*   HFH H Hs ;AcJSnURR5UlUR(dYUS:aS[R"S5 US-nURR5UlUR(dUS:aMSUR(dS5eg)Nrrg?z%No connection could be formed quickly)rcheck_connectionr timesleep)r startup_secs rr VisdomWriter._try_connect's} $ 9 9 ;''K!O JJsO 3 K$(HH$=$=$?D !''K!O$$M&MM$rNc VURRU5c0URU'URURU5SLnU(aURUUU/-OU/URUU'USU3nU(aUO[URUU5nU(a=URR [ U5[ U5USUR US9 gURR [ U5[ U5UUSUS.S9UR U'g)aAdd scalar data to Visdom. Plots the values in a plot titled {main_tag}-{tag}. Args: tag (string): Data identifier scalar_value (float or string/blobname): Value to save global_step (int): Global step value to record main_tag (string): Data group identifier N-append)XYnameupdatewintimesteptitlexlabelylabelr+r,r-opts)rgetlenrlinerr)r tag scalar_value global_stepmain_tagexists plot_namex_vals r add_scalarVisdomWriter.add_scalar0s0      ) 1)+D  X &!!(+//4D@$+/*:*:8*DS*I N++7. "3'j#' * D4D4DX4Ns4S0T  HHMM%.,'LL+  '+hhmm%.,'&(! '4 'DLL #rc@UHnURXBUX15 M g)aAdds many scalar data to summary. Note that this function also keeps logged scalars in memory. In extreme case it explodes your RAM. Args: tag (string): Data identifier main_tag (string): Data group identifier tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values global_step (int): Global step value to record Examples:: writer.add_scalars('run_14h',{'xsinx':i*np.sin(i/r), 'xcosx':i*np.cos(i/r), 'arctanx': numsteps*np.arctan(i/r)}, i) This function adds three plots: 'run_14h-xsinx', 'run_14h-xcosx', 'run_14h-arctanx' with the corresponding values. N)rA)r r=tag_scalar_dictr<keys r add_scalarsVisdomWriter.add_scalarsYs!.#C OOC!5{ M#rc[US5n[R"URU5 SSS5 0Ulg!,(df  N=f)zExports to the given 'path' an ASCII file containing all the scalars written so far by this instance, with the following format: {writer_id : [[timestamp, step, value], ...], ...} The scalars saved by ``add_scalars()`` will be flushed after export. wN)openjsondumpr)r pathfs rexport_scalars_to_json#VisdomWriter.export_scalars_to_jsonss8$_ IId&& *_s "? A cd[U5nURR[U5SU0S9 g)aAdd histogram to summary. Args: tag (string): Data identifier values (torch.Tensor, numpy.array, or string/blobname): Values to build histogram global_step (int): Global step value to record bins (string): one of {'tensorflow', 'auto', 'fd', ...}, this determines how the bins are made. You can find other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html r2r6N)rr histogram)r r:valuesr<binss r add_histogramVisdomWriter.add_histograms, 76?'3@rcR[U5nURRX!US.S9 g)aAdd image data to summary. Note that this requires the ``pillow`` package. Args: tag (string): Data identifier img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data global_step (int): Global step value to record Shape: img_tensor: :math:`(C, H, W)`. Use ``torchvision.utils.make_grid()`` to prepare it is a good idea. C = colors (can be 1 - grayscale, 3 - RGB, 4 - RGBA) )r2captionrRN)rrimage)r r: img_tensorr<rYs r add_imageVisdomWriter.add_images%Z(  z'(JKrc<URU[X$5U5 g)a^Render matplotlib figure into an image and add it to summary. Note that this requires the ``matplotlib`` package. Args: tag (string): Data identifier figure (matplotlib.pyplot.figure) or list of figures: figure or a list of figures global_step (int): Global step value to record close (bool): Flag to automatically close the figure N)r\r)r r:figurer<closes r add_figureVisdomWriter.add_figures sOF:KHrc bURn[U5S:a[US5Hn[U[R 5(a7SSKnURX&SS2SS2SS2SS245RSSSS5nO#X&SS2SS2SS2SS24RSSSS5nSn UR5nX-R[R5nURSS;dS 5eURRUS U0S 9 M gURRUS U0S 9 g) aAdd video data to summary. Note that this requires the ``moviepy`` package. Args: tag (string): Data identifier vid_tensor (torch.Tensor): Video data, the pixel value should in [0, 1] global_step (int): Global step value to record fps (float or int): Frames per second Shape: vid_tensor: :math:`(B, C, T, H, W)`. (if following tensorboardX format) vid_tensor: :math:`(T, H, W, C)`. (if following visdom format) B = batches, C = colors (1, 3, or 4), T = time frames, H = height, W = width rNr)rrfrdz_Visdom requires the last dimension to be color, which can be 1 (grayscale), 3 (RGB) or 4 (RGBA)fpstensorr6)shaper8range isinstancenpndarraytorch from_numpypermutenumpyastypeuint8rvideo) r r: vid_tensorr<rhrkirpind_vid scale_factors r add_videoVisdomWriter.add_videos    u:>58_j"**55 #.."aAq=133:71aA3F)Aq!Q7??1aKG" "--/"199"((C}}Q'94vuv4gUCLA!%$ HHNN*E3 N&&PNN2   A ALL  I I$A$AL99 ##   < PT   @rr)rrKmathr$rsrnsummaryrutilsrx2numrrrrrrrs. ""oor