JThGJSrSSKrSSKrSSKJrJrJr SSKr\(aSSKJ r SSK J r SSK J r SSKJrJr SSKJr SS KJr S S KJr S S KJrJrJrJrJr S S KJr S SKJ r S SK!J"r" S SK#J$r$J%r%J&r&J'r'J(r(J)r)J*r*J+r+J,r,J-r-J.r.J/r/J0r0J1r1 SS/r2"SS5r3"SS5r4g)zkProvide an API for writing protocol buffers to event files to be consumed by TensorBoard for visualization.N)Optional TYPE_CHECKINGUnion)Figure)tf) event_pb2)Event SessionLog)ProjectorConfig)EventFileWriter)make_np)get_embedding_infomake_mat make_spritemake_tsv write_pbtxt)load_onnx_graph)graph)figure_to_image)audiocustom_scalars histogram histogram_rawhparamsimage image_boxesmeshpr_curve pr_curve_rawscalar tensor_prototextvideo FileWriter SummaryWritercb\rSrSrSrSSjrSrSSjrSSjrSSjr SS jr S r S r S r S rg)r%+aWrites protocol buffers to event files to be consumed by TensorBoard. The `FileWriter` class provides a mechanism to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training. c<[U5n[XX45Ulg)aCreate a `FileWriter` and an event file. On construction the writer creates a new event file in `log_dir`. The other arguments to the constructor control the asynchronous writes to the event file. Args: log_dir: A string. Directory where event file will be written. max_queue: Integer. Size of the queue for pending events and summaries before one of the 'add' calls forces a flush to disk. Default is ten items. flush_secs: Number. How often, in seconds, to flush the pending events and summaries to disk. Default is every two minutes. filename_suffix: A string. Suffix added to all event filenames in the log_dir directory. More details on filename construction in tensorboard.summary.writer.event_file_writer.EventFileWriter. N)strr event_writer)selflog_dir max_queue flush_secsfilename_suffixs V/var/www/auris/envauris/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py__init__FileWriter.__init__5s,g,+   c6URR5$)z6Return the directory where event file will be written.)r+ get_logdirr,s r1r6FileWriter.get_logdirPs  ++--r4NcUc[R"5OUUlUb[U5UlURR U5 g)a%Add an event to the event file. Args: event: An `Event` protocol buffer. step: Number. Optional global step value for training process to record with the event. walltime: float. Optional walltime to override the default (current) walltime (from time.time()) seconds after epoch N)time wall_timeintstepr+ add_event)r,eventr=walltimes r1r>FileWriter.add_eventTs?*2)9$))+x  TEJ ##E*r4cP[R"US9nURXBU5 g)aAdd a `Summary` protocol buffer to the event file. This method wraps the provided summary in an `Event` protocol buffer and adds it to the event file. Args: summary: A `Summary` protocol buffer. global_step: Number. Optional global step value for training process to record with the summary. walltime: float. Optional walltime to override the default (current) walltime (from time.time()) seconds after epoch )summaryN)rr r>)r,rC global_stepr@r?s r1 add_summaryFileWriter.add_summaryes 0 u84r4cUSnUSn[R"UR"5S9nURUSU5 [R"SUR5S9n[R"US9nURUSU5 g)zAdd a `Graph` and step stats protocol buffer to the event file. Args: graph_profile: A `Graph` and step stats protocol buffer. walltime: float. Optional walltime to override the default (current) walltime (from time.time()) seconds after epoch rr  graph_defNstep1)tag run_metadata)tagged_run_metadata)rr SerializeToStringr>TaggedRunMetadata)r, graph_profiler@r stepstatsr?trms r1 add_graphFileWriter.add_graphus}a !!$ %*A*A*CD udH-))i&A&A&C C8 udH-r4cp[R"UR"5S9nURUSU5 g)zAdd a `Graph` protocol buffer to the event file. Args: graph: A `Graph` protocol buffer. walltime: float. Optional walltime to override the default (current) _get_file_writerfrom time.time()) rHN)rr rNr>)r,rr@r?s r1add_onnx_graphFileWriter.add_onnx_graphs+%*A*A*CD udH-r4c8URR5 gzrFlushes the event file to disk. Call this method to make sure that all pending events have been written to disk. N)r+flushr7s r1rZFileWriter.flushs !r4c8URR5 g)zvFlushes the event file to disk and close the file. Call this method when you do not need the summary writer anymore. N)r+closer7s r1r]FileWriter.closes !r4c8URR5 g)zReopens the EventFileWriter. Can be called after `close()` to add more events in the same directory. The events will go into a new events file. Does nothing if the EventFileWriter was not closed. N)r+reopenr7s r1r`FileWriter.reopens   "r4)r+) xNNN)__name__ __module__ __qualname____firstlineno____doc__r2r6r>rErSrVrZr]r`__static_attributes__r4r1r%r%+s4 6.+"5 .& .""#r4c \rSrSrSrS*SjrSrSrS+SjrS,Sjr S-S jr S-S jr S.S jr S-S jr S/S jrS0SjrS1SjrS2S\S\S\S4S\\S\S\\SS4 SjjrS3SjrS4SjrS-SjrSrS5Sjr\S5rS6Sjr S7Sjr!S7S jr"S8S!jr#S8S"jr$S#r%S9S$jr&S%r'S&r(S'r)S(r*S)r+g):r&aWrites entries directly to event files in the log_dir to be consumed by TensorBoard. The `SummaryWriter` class provides a high-level API to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training. Nc([RRS5 U(d`SSKnSSKJn UR 5R S5n [RRSU S-UR5-U-5nXl X0l X@l XPlX`lS=UlUlUR%5 Sn /n /n U S :a0U R'U 5 U R'U *5 U S -n U S :aM0U SSS 2S/-U -Ulg) ahCreate a `SummaryWriter` that will write out events and summaries to the event file. Args: log_dir (str): Save directory location. Default is runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run. Use hierarchical folder structure to compare between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc. for each new experiment to compare across them. comment (str): Comment log_dir suffix appended to the default ``log_dir``. If ``log_dir`` is assigned, this argument has no effect. purge_step (int): When logging crashes at step :math:`T+X` and restarts at step :math:`T`, any events whose global_step larger or equal to :math:`T` will be purged and hidden from TensorBoard. Note that crashed and resumed experiments should have the same ``log_dir``. max_queue (int): Size of the queue for pending events and summaries before one of the 'add' calls forces a flush to disk. Default is ten items. flush_secs (int): How often, in seconds, to flush the pending events and summaries to disk. Default is every two minutes. filename_suffix (str): Suffix added to all event filenames in the log_dir directory. More details on filename construction in tensorboard.summary.writer.event_file_writer.EventFileWriter. Examples:: from torch.utils.tensorboard import SummaryWriter # create a summary writer with automatically generated folder name. writer = SummaryWriter() # folder location: runs/May04_22-14-54_s-MacBook-Pro.local/ # create a summary writer using the specified folder name. writer = SummaryWriter("my_experiment") # folder location: my_experiment # create a summary writer with comment appended. writer = SummaryWriter(comment="LR_0.1_BATCH_16") # folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/ z tensorboard.create.summarywriterrN)datetimez %b%d_%H-%M-%Sruns_g-q=g@xDg?)torch_C_log_api_usage_oncesocketrqnowstrftimeospathjoin gethostnamer- purge_stepr.r/r0 file_writer all_writers_get_file_writerappend default_bins) r,r-commentrr.r/r0rxrq current_timevbuckets neg_bucketss r1r2SummaryWriter.__init__sd $$%GH  )#<<>22?CLggll s*V-?-?-AAGKG $"$./324+   $h NN1    r " HA$h("-3g=r4c URb URc[URURUR UR 5UlURR5UR0UlURbnURnURR[USS95 URR[U[[RS9S95 SUlUR$)z?Return the default FileWriter instance. Recreates it if closed.Nz brain.Event:2)r= file_version)status)r= session_log) rrr%r-r.r/r0r6rr>r r START)r,most_recent_steps r1rSummaryWriter._get_file_writers    #t'7'7'?) dnndoot?S?S D !% 0 0 ; ; =t?O?OPD *#'??   **/oN  **-$.j6F6F$G #'r4cUR$)z7Return the directory where event files will be written.r-r7s r1r6SummaryWriter.get_logdirs ||r4c[RRS5 [U5[Ld[U5[La [ S5e[ XU5upgnU(d[[R"55n[RRUR5R5U5n [U S9n U RR!Xe5 U RR!Xu5 U RR!X5 UR#5HupU R%XU5 M SSS5 g!,(df  g=f)aAdd a set of hyperparameters to be compared in TensorBoard. Args: hparam_dict (dict): Each key-value pair in the dictionary is the name of the hyper parameter and it's corresponding value. The type of the value can be one of `bool`, `string`, `float`, `int`, or `None`. metric_dict (dict): Each key-value pair in the dictionary is the name of the metric and it's corresponding value. Note that the key used here should be unique in the tensorboard record. Otherwise the value you added by ``add_scalar`` will be displayed in hparam plugin. In most cases, this is unwanted. hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that contains names of the hyperparameters and all discrete values they can hold run_name (str): Name of the run, to be included as part of the logdir. If unspecified, will use current timestamp. global_step (int): Global step value to record Examples:: from torch.utils.tensorboard import SummaryWriter with SummaryWriter() as w: for i in range(5): w.add_hparams({'lr': 0.1*i, 'bsize': i}, {'hparam/accuracy': 10*i, 'hparam/loss': 10*i}) Expected result: .. image:: _static/img/tensorboard/add_hparam.png :scale: 50 % ztensorboard.logging.add_hparamsz1hparam_dict and metric_dict should be dictionary.rN)rurvrwtypedict TypeErrorrr*r:r{r|r}rr6r&rrEitems add_scalar) r, hparam_dict metric_dicthparam_domain_discreterun_namerDexpssiseilogdirw_hpkrs r1 add_hparamsSummaryWriter.add_hparamssP $$%FG  D (D,=T,IOP P :PQ #499;'Hd335@@BHM 6 *d    ( ( :    ( ( :    ( ( :#))+k2, + * *s A=E Ec[RRS5 [XXVS9nUR 5R XsU5 g)aAdd scalar data to summary. Args: tag (str): Data identifier scalar_value (float or string/blobname): Value to save global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) with seconds after epoch of event new_style (boolean): Whether to use new style (tensor field) or old style (simple_value field). New style could lead to faster data loading. Examples:: from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() x = range(100) for i in x: writer.add_scalar('y=2x', i * 2, i) writer.close() Expected result: .. image:: _static/img/tensorboard/add_scalar.png :scale: 50 % ztensorboard.logging.add_scalar) new_styledouble_precisionN)rurvrwr!rrE)r,rK scalar_valuerDr@rrrCs r1rSummaryWriter.add_scalarVsBD $$%EF   ++G(Kr4c@[RRS5 Uc[R"5OUnUR 5R 5nUR 5HupgUS-URSS5-S-U-nURceXRR5;aURUn O9[XRURUR5n XRU'U R[X5X45 M g)amAdd many scalar data to summary. Args: main_tag (str): The parent name for the tags tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event Examples:: from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() r = 5 for i in range(100): writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r), 'xcosx':i*np.cos(i/r), 'tanx': np.tan(i/r)}, i) writer.close() # This call adds three values to the same scalar plot with the tag # 'run_14h' in TensorBoard's scalar section. Expected result: .. image:: _static/img/tensorboard/add_scalars.png :scale: 50 % ztensorboard.logging.add_scalarsN/rs)rurvrwr:rr6rreplacerkeysr%r.r/r0rEr!) r,main_tagtag_scalar_dictrDr@ fw_logdirrKrfw_tagfws r1 add_scalarsSummaryWriter.add_scalarss: $$%FG"*"2499;))+668 !0!6!6!8 C_x'7'7S'AACG#MF##/ //))..00%%f-NNDOOT=Q=Q,.  ( NN6(9; Q"9r4c[RRS5 [X5nUR 5R XSU5 g)aAdd tensor data to summary. Args: tag (str): Data identifier tensor (torch.Tensor): tensor to save global_step (int): Global step value to record Examples:: from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() x = torch.tensor([1,2,3]) writer.add_scalar('x', x) writer.close() Expected result: Summary::tensor::float_val [1,2,3] ::tensor::shape [3] ::tag 'x' ztensorboard.logging.add_tensorN)rurvrwr"rrE)r,rKtensorrDr@rCs r1 add_tensorSummaryWriter.add_tensors:6 $$%EFs+ ++G(Kr4c [RRS5 [U[5(aUS:Xa UR nUR 5R[XXFS9X55 g)aaAdd histogram to summary. Args: tag (str): Data identifier values (torch.Tensor, numpy.ndarray, or string/blobname): Values to build histogram global_step (int): Global step value to record bins (str): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html walltime (float): Optional override default walltime (time.time()) seconds after epoch of event Examples:: from torch.utils.tensorboard import SummaryWriter import numpy as np writer = SummaryWriter() for i in range(10): x = np.random.random(1000) writer.add_histogram('distribution centers', x + i, i) writer.close() Expected result: .. image:: _static/img/tensorboard/add_histogram.png :scale: 50 % z!tensorboard.logging.add_histogram tensorflow)max_binsN) rurvrw isinstancer*rrrEr)r,rKvaluesrDbinsr@rs r1 add_histogramSummaryWriter.add_histogramsZH $$%HI dC T\%9$$D ++ c4 ;[ r4c [RRS5 [U5[U5:wa [ S5eUR 5R [XX4XVXx5U U 5 g)a@Add histogram with raw data. Args: tag (str): Data identifier min (float or int): Min value max (float or int): Max value num (int): Number of values sum (float or int): Sum of all values sum_squares (float or int): Sum of squares for all values bucket_limits (torch.Tensor, numpy.ndarray): Upper value per bucket. The number of elements of it should be the same as `bucket_counts`. bucket_counts (torch.Tensor, numpy.ndarray): Number of values per bucket global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/README.md Examples:: from torch.utils.tensorboard import SummaryWriter import numpy as np writer = SummaryWriter() dummy_data = [] for idx, value in enumerate(range(50)): dummy_data += [idx + 0.001] * value bins = list(range(50+2)) bins = np.array(bins) values = np.array(dummy_data).astype(float).reshape(-1) counts, limits = np.histogram(values, bins=bins) sum_sq = values.dot(values) writer.add_histogram_raw( tag='histogram_with_raw_data', min=values.min(), max=values.max(), num=len(values), sum=values.sum(), sum_squares=sum_sq, bucket_limits=limits[1:].tolist(), bucket_counts=counts.tolist(), global_step=0) writer.close() Expected result: .. image:: _static/img/tensorboard/add_histogram_raw.png :scale: 50 % z%tensorboard.logging.add_histogram_rawz;len(bucket_limits) != len(bucket_counts), see the document.N)rurvrwlen ValueErrorrrEr) r,rKminmaxnumsum sum_squares bucket_limits bucket_countsrDr@s r1add_histogram_rawSummaryWriter.add_histogram_rawsl| $$%LM } ]!3 3M  ++ #Cm     r4c[RRS5 UR5R [ XUS9X45 g)a$Add image data to summary. Note that this requires the ``pillow`` package. Args: tag (str): Data identifier img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event dataformats (str): Image data format specification of the form CHW, HWC, HW, WH, etc. Shape: img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job. Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``. Examples:: from torch.utils.tensorboard import SummaryWriter import numpy as np img = np.zeros((3, 100, 100)) img[0] = np.arange(0, 10000).reshape(100, 100) / 10000 img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000 img_HWC = np.zeros((100, 100, 3)) img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000 writer = SummaryWriter() writer.add_image('my_image', img, 0) # If you have non-default dimension setting, set the dataformats argument. writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC') writer.close() Expected result: .. image:: _static/img/tensorboard/add_image.png :scale: 50 % ztensorboard.logging.add_image dataformatsNrurvrwrrErr,rK img_tensorrDr@rs r1 add_imageSummaryWriter.add_imageAs;\ $$%DE ++ #{ ;[ r4c[RRS5 UR5R [ XUS9X45 g)abAdd batched image data to summary. Note that this requires the ``pillow`` package. Args: tag (str): Data identifier img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event dataformats (str): Image data format specification of the form NCHW, NHWC, CHW, HWC, HW, WH, etc. Shape: img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be accepted. e.g. NCHW or NHWC. Examples:: from torch.utils.tensorboard import SummaryWriter import numpy as np img_batch = np.zeros((16, 3, 100, 100)) for i in range(16): img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i writer = SummaryWriter() writer.add_images('my_image_batch', img_batch, 0) writer.close() Expected result: .. image:: _static/img/tensorboard/add_images.png :scale: 30 % ztensorboard.logging.add_imagesrNrrs r1 add_imagesSummaryWriter.add_imagests;N $$%EF ++ #{ ;[ r4c [RRS5 Ub6[U[5(aU/n[ U5UR S:waSnUR5R[UUUUUUS9UU5 g)aAdd image and draw bounding boxes on the image. Args: tag (str): Data identifier img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data box_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Box data (for detected objects) box should be represented as [x1, y1, x2, y2]. global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event rescale (float): Optional scale override dataformats (str): Image data format specification of the form NCHW, NHWC, CHW, HWC, HW, WH, etc. labels (list of string): The label to be shown for each bounding box. Shape: img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformats`` argument. e.g. CHW or HWC box_tensor: (torch.Tensor, numpy.ndarray, or string/blobname): NX4, where N is the number of boxes and each 4 elements in a row represents (xmin, ymin, xmax, ymax). z(tensorboard.logging.add_image_with_boxesNr)rescalerlabels) rurvrwrr*rshaperrEr) r,rKr box_tensorrDr@rrrs r1add_image_with_boxes"SummaryWriter.add_image_with_boxess@ $$%OP  &#&& 6{j..q11 ++ '     r4rKfigurerrDr]r@returnc[RRS5 [U[5(aUR U[ X$5UUSS9 gUR U[ X$5UUSS9 g)a|Render matplotlib figure into an image and add it to summary. Note that this requires the ``matplotlib`` package. Args: tag: Data identifier figure: Figure or a list of figures global_step: Global step value to record close: Flag to automatically close the figure walltime: Optional override default walltime (time.time()) seconds after epoch of event ztensorboard.logging.add_figureNCHWrCHWN)rurvrwrlistrr)r,rKrrDr]r@s r1 add_figureSummaryWriter.add_figuresp( $$%EF fd # # NN."   NN.!  r4c[RRS5 UR5R [ XU5X55 g)aAdd video data to summary. Note that this requires the ``moviepy`` package. Args: tag (str): Data identifier vid_tensor (torch.Tensor): Video data global_step (int): Global step value to record fps (float or int): Frames per second walltime (float): Optional override default walltime (time.time()) seconds after epoch of event Shape: vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`. ztensorboard.logging.add_videoN)rurvrwrrEr$)r,rK vid_tensorrDfpsr@s r1 add_videoSummaryWriter.add_videos: $$%DE ++ #3 ' r4c[RRS5 UR5R [ XUS9X55 g)aAdd audio data to summary. Args: tag (str): Data identifier snd_tensor (torch.Tensor): Sound data global_step (int): Global step value to record sample_rate (int): sample rate in Hz walltime (float): Optional override default walltime (time.time()) seconds after epoch of event Shape: snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1]. ztensorboard.logging.add_audio) sample_rateN)rurvrwrrEr)r,rK snd_tensorrDrr@s r1 add_audioSummaryWriter.add_audio s: $$%DE ++ #{ ;[ r4c[RRS5 UR5R [ X5X45 g)arAdd text data to summary. Args: tag (str): Data identifier text_string (str): String to save global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event Examples:: writer.add_text('lstm', 'This is an lstm', 0) writer.add_text('rnn', 'This is an rnn', 10) ztensorboard.logging.add_textN)rurvrwrrEr#)r,rK text_stringrDr@s r1add_textSummaryWriter.add_text!s8 $$%CD ++  "K r4c[RRS5 UR5R [ U55 g)Nz"tensorboard.logging.add_onnx_graph)rurvrwrrVr)r,prototxts r1rVSummaryWriter.add_onnx_graph4s1 $$%IJ ..x/HIr4c[RRS5 UR5R [ XX455 g)aAdd graph data to summary. Args: model (torch.nn.Module): Model to draw. input_to_model (torch.Tensor or list of torch.Tensor): A variable or a tuple of variables to be fed. verbose (bool): Whether to print graph structure in console. use_strict_trace (bool): Whether to pass keyword argument `strict` to `torch.jit.trace`. Pass False when you want the tracer to record your mutable container types (list, dict) ztensorboard.logging.add_graphN)rurvrwrrSr)r,modelinput_to_modelverboseuse_strict_traces r1rSSummaryWriter.add_graph8s8 $$%DE )) % C r4cUnURSS[S5S35nURSS[S5S35nURSS[S5-5nU$)N%02xr\z%%%02x)rord)rawstrretvals r1_encodeSummaryWriter._encodeLsaqS#%78qS#%78h#d)&<= r4c`[RRS5 [U5nUcSn[ U5R S5SUR U53n[RRUR5R5U5n[RRn U RU5(a1U R!U5(a [#S5 O [%SUS35eU R'U5 Ub-UR(S[+U5:XdS 5e[-X(US 9 Ub2UR(SUR(S:XdS 5e[/X85 UR0S :XdS 5e[3X5 [5US5(d[75Ul[;X#XtU5n UR8R<R?U /5 SSK J!n U REUR85n [GUR5R5U 5 g)aWAdd embedding projector data to summary. Args: mat (torch.Tensor or numpy.ndarray): A matrix which each row is the feature vector of the data point metadata (list): A list of labels, each element will be converted to string label_img (torch.Tensor): Images correspond to each data point global_step (int): Global step value to record tag (str): Name for the embedding metadata_header (list): A list of headers for multi-column metadata. If given, each metadata must be a list with values corresponding to headers. Shape: mat: :math:`(N, D)`, where N is number of data and D is feature dimension label_img: :math:`(N, C, H, W)` Examples:: import keyword import torch meta = [] while len(meta)<100: meta = meta+keyword.kwlist # get some strings meta = meta[:100] for i, v in enumerate(meta): meta[i] = v+str(i) label_img = torch.rand(100, 3, 10, 32) for i in range(100): label_img[i]*=i/100.0 writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img) writer.add_embedding(torch.randn(100, 5), label_img=label_img) writer.add_embedding(torch.randn(100, 5), metadata=meta) .. note:: Categorical (i.e. non-numeric) metadata cannot have more than 50 unique values if they are to be used for coloring in the embedding projector. z!tensorboard.logging.add_embeddingNrrzKwarning: Embedding dir exists, did you set global_step for add_embedding()?zPath: `z(` exists, but is a file. Cannot proceed.z&#labels should equal with #data points)metadata_headerz&#images should equal with #data pointsz@mat should be 2D, where mat.size(0) is the number of data points_projector_config) text_format)$rurvrwrr*zfillrr{r|r}rr6riogfileexistsisdirprintNotADirectoryErrormakedirsrrrrndimrhasattrr r r embeddingsextendgoogle.protobufrMessageToStringr) r,matmetadata label_imgrDrKr subdir save_pathfsembedding_infor config_pbtxts r1 add_embeddingSummaryWriter.add_embeddingUsb $$%HIcl  K  $**1-.a S0A/BCGGLL!6!6!8!C!C!EvN UU[[ 99Y  xx ""a)i[(PQ KK "  99Q<3$ 87 8 X/ J   !  22 87 82  - HHM N M N  t011%4%6D "+ c  ))00.1AB/"2243I3IJ D))+668,Gr4c [RRS5 [U5[U5p2UR 5R [ XX5U5UU5 g)aAdd precision recall curve. Plotting a precision-recall curve lets you understand your model's performance under different threshold settings. With this function, you provide the ground truth labeling (T/F) and prediction confidence (usually the output of your model) for each target. The TensorBoard UI will let you choose the threshold interactively. Args: tag (str): Data identifier labels (torch.Tensor, numpy.ndarray, or string/blobname): Ground truth data. Binary label for each element. predictions (torch.Tensor, numpy.ndarray, or string/blobname): The probability that an element be classified as true. Value should be in [0, 1] global_step (int): Global step value to record num_thresholds (int): Number of thresholds used to draw the curve. walltime (float): Optional override default walltime (time.time()) seconds after epoch of event Examples:: from torch.utils.tensorboard import SummaryWriter import numpy as np labels = np.random.randint(2, size=100) # binary label predictions = np.random.rand(100) writer = SummaryWriter() writer.add_pr_curve('pr_curve', labels, predictions, 0) writer.close() z tensorboard.logging.add_pr_curveN)rurvrwrrrEr)r,rKr predictionsrDnum_thresholdsweightsr@s r1 add_pr_curveSummaryWriter.add_pr_curvesQR $$%GH%fow{/C  ++ S+w G   r4c [RRS5 UR5R [ UUUUUUUU U 5 UU 5 g)aAdd precision recall curve with raw data. Args: tag (str): Data identifier true_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): true positive counts false_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): false positive counts true_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): true negative counts false_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): false negative counts precision (torch.Tensor, numpy.ndarray, or string/blobname): precision recall (torch.Tensor, numpy.ndarray, or string/blobname): recall global_step (int): Global step value to record num_thresholds (int): Number of thresholds used to draw the curve. walltime (float): Optional override default walltime (time.time()) seconds after epoch of event see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md z$tensorboard.logging.add_pr_curve_rawN)rurvrwrrEr ) r,rKtrue_positive_countsfalse_positive_countstrue_negative_countsfalse_negative_counts precisionrecallrDr)r*r@s r1add_pr_curve_rawSummaryWriter.add_pr_curve_rawsY< $$%KL ++ $%$%    r4c[RRS5 X#SU/00nUR5R [ U55 g)aShorthand for creating multilinechart. Similar to ``add_custom_scalars()``, but the only necessary argument is *tags*. Args: tags (list): list of tags that have been used in ``add_scalar()`` Examples:: writer.add_custom_scalars_multilinechart(['twse/0050', 'twse/2330']) z5tensorboard.logging.add_custom_scalars_multilinechart MultilineNrurvrwrrErr,tagscategorytitlelayouts r1!add_custom_scalars_multilinechart/SummaryWriter.add_custom_scalars_multilinechartsI $$ C [$$789 ++N6,BCr4c[RRS5 [U5S:XdeX#SU/00nUR 5R [ U55 g)aHShorthand for creating marginchart. Similar to ``add_custom_scalars()``, but the only necessary argument is *tags*, which should have exactly 3 elements. Args: tags (list): list of tags that have been used in ``add_scalar()`` Examples:: writer.add_custom_scalars_marginchart(['twse/0050', 'twse/2330', 'twse/2006']) z2tensorboard.logging.add_custom_scalars_marginchartMarginN)rurvrwrrrErr9s r1add_custom_scalars_marginchart,SummaryWriter.add_custom_scalars_marginchart0sY $$ @ 4yA~~Xt$456 ++N6,BCr4c[RRS5 UR5R [ U55 g)aCreate special chart by collecting charts tags in 'scalars'. NOTE: This function can only be called once for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called before or after the training loop. Args: layout (dict): {categoryName: *charts*}, where *charts* is also a dictionary {chartName: *ListOfProperties*}. The first element in *ListOfProperties* is the chart's type (one of **Multiline** or **Margin**) and the second element should be a list containing the tags you have used in add_scalar function, which will be collected into the new chart. Examples:: layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]}, 'USA':{ 'dow':['Margin', ['dow/aaa', 'dow/bbb', 'dow/ccc']], 'nasdaq':['Margin', ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}} writer.add_custom_scalars(layout) z&tensorboard.logging.add_custom_scalarsNr8)r,r=s r1add_custom_scalars SummaryWriter.add_custom_scalarsFs3* $$%MN ++N6,BCr4c [RRS5 UR5R [ XX4U5Xg5 g)aAdd meshes or 3D point clouds to TensorBoard. The visualization is based on Three.js, so it allows users to interact with the rendered object. Besides the basic definitions such as vertices, faces, users can further provide camera parameter, lighting condition, etc. Please check https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene for advanced usage. Args: tag (str): Data identifier vertices (torch.Tensor): List of the 3D coordinates of vertices. colors (torch.Tensor): Colors for each vertex faces (torch.Tensor): Indices of vertices within each triangle. (Optional) config_dict: Dictionary with ThreeJS classes names and configuration. global_step (int): Global step value to record walltime (float): Optional override default walltime (time.time()) seconds after epoch of event Shape: vertices: :math:`(B, N, 3)`. (batch, number_of_vertices, channels) colors: :math:`(B, N, 3)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`. faces: :math:`(B, N, 3)`. The values should lie in [0, number_of_vertices] for type `uint8`. Examples:: from torch.utils.tensorboard import SummaryWriter vertices_tensor = torch.as_tensor([ [1, 1, 1], [-1, -1, 1], [1, -1, -1], [-1, 1, -1], ], dtype=torch.float).unsqueeze(0) colors_tensor = torch.as_tensor([ [255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255], ], dtype=torch.int).unsqueeze(0) faces_tensor = torch.as_tensor([ [0, 2, 3], [0, 3, 1], [0, 1, 2], [1, 3, 2], ], dtype=torch.int).unsqueeze(0) writer = SummaryWriter() writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor) writer.close() ztensorboard.logging.add_meshN)rurvrwrrEr)r,rKverticescolorsfaces config_dictrDr@s r1add_meshSummaryWriter.add_mesh^s=| $$%CD ++ { ;[ r4cURcgURR5HnUR5 M grY)rrrZr,writers r1rZSummaryWriter.flushs5    # &&--/F LLN0r4cURcgURR5H#nUR5 UR5 M% S=UlUlgrf)rrrZr]rrPs r1r]SummaryWriter.closesN    # &&--/F LLN LLN0/324+r4cU$rfrmr7s r1 __enter__SummaryWriter.__enter__s r4c$UR5 grf)r])r,exc_typeexc_valexc_tbs r1__exit__SummaryWriter.__exit__s  r4) r rrrr0r/r-r.r)NrdNrbrcrd)NNN)NNFFre)NrNN)NNr)NNr)NNr rN)NTN)NN)NiDN)NFT)NNNdefaultN)NNN)r_untitled)NNNNN),rgrhrirjrkr2rr6rrrrrrrrrr*rrrr<boolfloatrrrrrVrS staticmethodrr%r+r4r>rCrFrMrZr]rVr\rlrmr4r1r&r&sN>` * $ 53v'LR*R` LH ) jI XMR1 hMS* b1 n&*$( $ $hX./$c] $  $ 5/ $ $L *NR ( &J KO ( gH\/ t- `/9D&/9D,D8A F 3r4)5rkr{r:typingrrrrumatplotlib.figurertensorboard.compatrtensorboard.compat.protor"tensorboard.compat.proto.event_pb2r r 2tensorboard.plugins.projector.projector_config_pb2r ,tensorboard.summary.writer.event_file_writerr _convert_npr _embeddingrrrrr _onnx_graphr_pytorch_graphr_utilsrrCrrrrrrrrrr r!r"r#r$__all__r%r&rmr4r1rrswq 11 (!.@NH XX(!#"  )~#~#BLLr4