o wi3@sfddlZddlZddlZddlZddlZddlmZddlm Z ddl m Z ddZ Gdd d Z dS) N) compute_curve)figure_to_image)make_npcsfdd}|S)Ncs4|js td|dS|g|Ri|dS)Nz+ERROR: No Visdom server currently connected)server_connectedprint _try_connect)selfargskwargsfnW/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/tensorboardX/visdom_writer.pywrappers z"_check_connection..wrapperr)r rrr r_check_connections rc@seZdZddZddZed+ddZed,d d Zed d Zed-ddZ ed.ddZ ed/ddZ ed0ddZ ed1ddZ ed,ddZeddZed2d d!Zed3d"d#Zed4d%d&Ze d4d'd(Zd)d*ZdS)5 VisdomWritercOsVzddlm}Wn tytdwi|_d|_||i||_i|_|dS)Nr)Visdomz4Visdom visualization requires installation of VisdomF)visdomr ImportError scalar_dictrviswindowsr)r r r rrrr__init__s  zVisdomWriter.__init__cCs\d}|j|_|js%|dkr%td|d8}|j|_|js%|dks|js,JddS)Nrrg?z%No connection could be formed quickly)rcheck_connectionrtimesleep)r startup_secrrrr's   zVisdomWriter._try_connectNdefaultcCs|j|dur i|j|<|j||du}|r#|j|||gn|g|j||<|d|}|r6|nt|j||}|rU|jjt|t||d|j|ddS|jjt|t|||d|dd|j|<dS)aNAdd 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_valrrr add_scalar0s8    zVisdomWriter.add_scalarcCs"|D] }||||||qdS)a?Adds 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)r7)r r3tag_scalar_dictr2keyrrr add_scalarsYszVisdomWriter.add_scalarscCsBt|d}t|j|Wdn1swYi|_dS)aExports 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 pathfrrrexport_scalars_to_jsonss  z#VisdomWriter.export_scalars_to_json tensorflowcCs$t|}|jjt|d|iddS)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 r(r,N)rr histogram)r r0valuesr2binsrrr add_histograms zVisdomWriter.add_histogramcCs"t|}|jj|||dddS)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) )r(captionrCN)rrimage)r r0 img_tensorr2rHrrr add_imageszVisdomWriter.add_imageTcCs||t|||dS)aRender 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)rKr)r r0figurer2closerrr add_figures zVisdomWriter.add_figurec Cs|j}t|dkrst|dD]a}t|tjr6ddl}|||ddddddddfdddd}n||ddddddddfdddd}d} | }||  tj }|jddvsfJd |j j |d |id qdS|j j |d |id dS) a^Add 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 rOrNr)rrQrOz_Visdom requires the last dimension to be color, which can be 1 (grayscale), 3 (RGB) or 4 (RGBA)fpstensorr,)shaper.range isinstancenpndarraytorch from_numpypermutenumpyastypeuint8rvideo) r r0 vid_tensorr2rSrVir[ind_vid scale_factorrrr add_videos(    .zVisdomWriter.add_videoDcCs t|}|jj|d|iddS)azAdd audio data to summary. Args: tag (string): Data identifier snd_tensor (torch.Tensor, numpy.array, or string/blobname): Sound data global_step (int): Global step value to record sample_rate (int): sample rate in Hz Shape: snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1]. sample_frequencyrTN)rraudio)r r0 snd_tensorr2 sample_raterrr add_audios  zVisdomWriter.add_audiocCs|dur|}|j|dS)aMAdd text data to summary. Args: tag (string): Data identifier text_string (string): String to save global_step (int): Global step value to record Examples:: writer.add_text('lstm', 'This is an lstm', 0) writer.add_text('rnn', 'This is an rnn', 10) N)rtext)r r0 text_stringr2rrradd_texts zVisdomWriter.add_textcCdSNr)r prototxtrrradd_onnx_graphzVisdomWriter.add_onnx_graphFcKrprqr)r modelinput_to_modelverboser rrr add_graphrtzVisdomWriter.add_graphcCrprqr)r matmetadata label_imgr2r0metadata_headerrrr add_embeddingrtzVisdomWriter.add_embeddingc Csht|t|}}t||||}|dddf|dddf}} |jj| ||d|dddddS) aAdds precision recall curve. Args: tag (string): Data identifier labels (torch.Tensor, numpy.array, or string/blobname): Ground truth data. Binary label for each element. predictions (torch.Tensor, numpy.array, or string/blobname): The probability that an element be classified as true. Value should in [0, 1] global_step (int): Global step value to record num_thresholds (int): Number of thresholds used to draw the curve. rON PR Curve for recall precisionr'r+)rrrr/) r r0labels predictionsr2num_thresholdsweightsraw_datarrrrr add_pr_curves " zVisdomWriter.add_pr_curvec Cs8t|t|}}|jj|||d|dddddS)aAdds precision recall curve with raw data. Args: tag (string): Data identifier true_positive_counts (torch.Tensor, numpy.array, or string/blobname): true positive counts false_positive_counts (torch.Tensor, numpy.array, or string/blobname): false positive counts true_negative_counts (torch.Tensor, numpy.array, or string/blobname): true negative counts false_negative_counts (torch.Tensor, numpy.array, or string/blobname): false negative counts precision (torch.Tensor, numpy.array, or string/blobname): precision recall (torch.Tensor, numpy.array, or string/blobname): recall global_step (int): Global step value to record num_thresholds (int): Number of thresholds used to draw the curve. see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md rrrr'r+N)rrr/) r r0true_positive_countsfalse_positive_countstrue_negative_countsfalse_negative_countsrrr2rrrrradd_pr_curve_raw#s zVisdomWriter.add_pr_curve_rawcCs|`|`tdSrq)rrgccollect)r rrrrMDs zVisdomWriter.close)Nrrq)NrB)NN)NT)NrO)Nrg)NF)NNNrN)Nr~N)__name__ __module__ __qualname__rrrr7r:rArGrKrNrfrlrorsrxr}rrrMrrrrrsB (      &       r)rr=mathrr^rYsummaryrutilsrx2numrrrrrrrs