sgtddlZddlZddlZddlZddlmZmZmZddlZddl Z ddl m Z ddl mZmZmZddlmZddlmZddlmZddlmZdd lmZd d lmZd d lmZmZgd Z ejBe"Z#de$de%fdZ&de%de$fdZ'dejPdee%fdZ)dejPdee$fdZ*dejPdeefdZ+iejXdde)fejZdde)fej\dde)fej^dde+fejHdde+fej`dde+fejbdde+fejddde+fejfd de+fejhd de+fejjd!de+fejld!de+fejJd"de+fejnd"de+fejpd"de+fejrd#d$e+fejtd%d&e*fejvd%d&e*fejxd'd&e*fejzd'd&e*fej|d(d)e+fej~d*d+e*fejd*d+e*fejfd d,e+fejd d,e+fejd d,e+fi ZCd-ZD dDd.ZEdEd/ZFdFd0ZGd1ZHd2ZIdGd3ZJdGd4ZKdHd5ZL dId6ZMdGd7ZNdJd8ZOdKd9ZPd:ZQdLd;ZRd<ZSd=ZT dMd>ZUdMd?ZVdNd@ZWdAZXdBZY dNdCZZy)ON)AnyListOptional) struct_pb2)HistogramProtoSummarySummaryMetadata) TensorProto)TensorShapeProto) layout_pb2)PrCurvePluginData)TextPluginData)make_np)_prepare_videoconvert_to_HWC) half_to_int int_to_halfhparamsscalar histogram_raw histogrammake_histogramimage image_boxes draw_boxes make_imagevideo make_videoaudiocustom_scalarstext tensor_proto pr_curve_rawpr_curve compute_curvemeshfreturnc`tjd|}tjd|dS)aACasts a half-precision float value into an integer. Converts a half precision floating point value, such as `torch.half` or `torch.bfloat16`, into an integer value which can be written into the half_val field of a TensorProto for storage. To undo the effects of this conversion, use int_to_half(). r(irstructpackunpack)r(bufs R/var/www/html/venv/lib/python3.12/site-packages/torch/utils/tensorboard/summary.pyrr6s* ++c1 C ==c "1 %%r+c`tjd|}tjd|dS)zCasts an integer value to a half-precision float. Converts an integer value obtained from half_to_int back into a floating point value. r+r(rr,)r+r0s r1rrCs* ++c1 C ==c "1 %%r2tcv|jjDcgc] }t|c}Scc}wN)flattentolistr)r4xs r1_tensor_to_half_valr:Ms)$%IIK$6$6$8 9qKN 99 9s6cdtj|jjSr6)torch view_as_realr7r8r4s r1_tensor_to_complex_valr?Ps%   a ( ( * 1 1 33r2c>|jjSr6)r7r8r>s r1_tensor_to_listrASs 99;   r2DT_HALFhalf_val DT_BFLOAT16DT_FLOAT float_val DT_DOUBLE double_valDT_INT8int_valDT_UINT8DT_INT16DT_INT32DT_INT64 int64_val DT_COMPLEX32 scomplex_val DT_COMPLEX64DT_BOOLbool_val DT_COMPLEX128 dcomplex_val uint32_valct|tjs|jn|}|jtj k(rdSdS)Nr) isinstancenpndarraynumpydtypeuint8)tensor converteds r1_calc_scale_factorrbus6&0&D &I288+144r2c ddlm} m} | j} | j |} ||||f\} }}}| j | |f| |f||f||f| |fg|||r|}| j |\}}}}||z ||z }}tjd|z}| j| ||z d|zz f| |z|fg|| j| |z||z |z f||| |S)Nr) ImageDraw ImageFont)widthfillg?)rg)rgfont) PILrdre load_defaultDrawlinegetbboxr[ceil rectangler")rxminyminxmaxymax display_strcolor color_text thicknessrdreridrawleftrighttopbottom text_bottom_left_top_right_bottom text_width text_heightmargins r1_draw_single_boxrzs/)  ! ! #D >>% D"&dD!9T5#vII tVnufos|dC[Q   '+||K'@$tVW"(5.'D.K  +, {[01v:=> "K0    F]K+5> ?   Lr2c  ddl}ddlm}m}m}m}m}m} ddlm } m } m } m } m }ddlm}m}m}t%t&s t(j+dt-dt%|t&s t(j+dt-d|xsi}t%|t&s t-d|j/D]@\}vs&t%|t0rt3fd |Dr0t-d d d g}|}j/D],\}| t%|t4t6fr||j8_|vr|Dcgc]}t=j@| c}}nd}|jC||jAd|t%|tDr||j8_#|vr|Dcgc]}t=j@|c}}nd}|jC||jAd|1t%|tHr||j8_%|vr|Dcgc]}t=j@|c}}nd}|jC||jAd|t%||jLrLtO|d}||j8_|jC||jAd$tQd||| }tStSjT| |jW}tYtYj@||g}|j[Dcgc]}|||}}|||}||| }tStSjT| |jW}tYtYj@| |g}|| jAd !}||| "}tStSjT| |jW}tYtYj@| |g}|||fScc}wcc}wcc}wcc}w)#a2Output three `Summary` protocol buffers needed by hparams plugin. `Experiment` keeps the metadata of an experiment, such as the name of the hyperparameters and the name of the metrics. `SessionStartInfo` keeps key-value pairs of the hyperparameters `SessionEndInfo` describes status of the experiment e.g. STATUS_SUCCESS Args: hparam_dict: A dictionary that contains names of the hyperparameters and their values. metric_dict: A dictionary that contains names of the metrics and their values. hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that contains names of the hyperparameters and all discrete values they can hold Returns: The `Summary` protobufs for Experiment, SessionStartInfo and SessionEndInfo rN)DataType Experiment HParamInfo MetricInfo MetricNameStatus)EXPERIMENT_TAGPLUGIN_DATA_VERSION PLUGIN_NAMESESSION_END_INFO_TAGSESSION_START_INFO_TAG)HParamsPluginDataSessionEndInfoSessionStartInfoz>parameter: hparam_dict should be a dictionary, nothing logged.z>parameter: metric_dict should be a dictionary, nothing logged.zIparameter: hparam_domain_discrete should be a dictionary, nothing logged.c3NK|]}t|tywr6)rZtype).0d hparam_dictks r1 zhparams..s!Fqz!T+a.%9:Fs"%z"parameter: hparam_domain_discrete[z/] should be a list of same type as hparam_dict[z].) number_value)valuesDATA_TYPE_FLOAT64)namerdomain_discrete) string_valueDATA_TYPE_STRING) bool_valueDATA_TYPE_BOOL)rrz=value should be one of int, float, str, bool, or torch.Tensor)session_start_infoversion plugin_namecontent plugin_data)tagmetadatavaluer)r) hparam_infos metric_infos) experimentrSTATUS_SUCCESS)status)session_end_infor).r<#tensorboard.plugins.hparams.api_pb2rrrrrr$tensorboard.plugins.hparams.metadatarrrrr+tensorboard.plugins.hparams.plugin_data_pb2rrrrZdictloggerwarning TypeErroritemslistallintfloatrrr ListValueValueappendstrrboolrTensorr ValueErrorr PluginDataSerializeToStringrkeys)r metric_dicthparam_domain_discreter<rrrrrrrrrrrrrrvhpsssirrrsmdrmtsexpseis` ` r1rrs( k4 (WX L   k4 (WX L  49r ,d 3 W  ',,.1 [ a&FAFF4QC7fghfiikl   C  C!!#L 1 9  a#u &*+CKKN '**BLBVBV"8!:#((a8C#' JJ!(;<$3   a *+CKKN '**","6"6"8!:#((a8##' JJ!(:;$3   a ()CKKN %**","6"6"8!:#((A6##' JJ!(89$3   a & 1 A*+CKKN ' JJzqx~~>Q/RS T  K  UL \ 3@STG #..#W-F-F-H  C +ACPQ RC7B7G7G7I J!::!, - JC J #C 8C38KLG #..#W-F-F-H  C >CHI JC  -= > ?C>QRG #..#W-F-F-H  C +?#NO PC S=G,,D Ks*R/ :R4 R9 9R>ct|j}|jdk(s"Jd|jd|jdt |}|rct |gd}|rt |gd}t jd }t | }ttj||| g Sttj||g S)aOutput a `Summary` protocol buffer containing a single scalar value. The generated Summary has a Tensor.proto containing the input Tensor. Args: name: A name for the generated node. Will also serve as the series name in TensorBoard. tensor: A real numeric Tensor containing a single value. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[GraphKeys.SUMMARIES]`. new_style: Whether to use new style (tensor field) or old style (simple_value field). New style could lead to faster data loading. Returns: A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf. Raises: ValueError: If tensor has the wrong shape or type. rzBTensor should contain one element (0 dimensions). Was given size: z and z dimensions.rE)rFr^rG)rHr^scalarsrrrr`rr)r simple_value) rsqueezendimsizerr r rrr) rr` collections new_styledouble_precisionrr#rrs r1rrbs"V_ $ $ &F qx KFKK=X]^d^i^i]jjvwx6]F"fXZH &6(+NL%00YG +6 '   gmm6JKLLr2c|j|jzdk\r td|jtvret|j\}}}t d id|dt |jDcgc]}t j|c}|||i}nt|d|jtjd }t| }ttj||| g Scc}w)aOutputs a `Summary` protocol buffer containing the full tensor. The generated Summary has a Tensor.proto containing the input Tensor. Args: name: A name for the generated node. Will also serve as the series name in TensorBoard. tensor: Tensor to be converted to protobuf Returns: A tensor protobuf in a `Summary` protobuf. Raises: ValueError: If tensor is too big to be converted to protobuf, or tensor data type is not supported lzAtensor is bigger than protocol buffer's hard limit of 2GB in sizer^ tensor_shaperdimz has unsupported tensor dtype r`rrrrr`r) numelitemsizerr^_TENSOR_TYPE_MAPr r shapeDimr rrr) rr`r^ field_name conversion_fnr9r#rrs r1r#r#s||~'G4 O  ||''+;FLL+I(z="  0?E||L!)--15L!M&1  C5 >v||nMNN!,,BK k 2C '--C#lST UUMs2Dc jt|||||||}ttj||gS)aOutput a `Summary` protocol buffer with a histogram. The generated [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) has one summary value containing a histogram for `values`. Args: name: A name for the generated node. Will also serve as a series name in TensorBoard. min: A float or int min value max: A 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: A numeric `Tensor` with upper value per bucket bucket_counts: A numeric `Tensor` with number of values per bucket Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. minmaxnumsum sum_squares bucket_limitbucketrhistor)rrr) rrrrrr bucket_limits bucket_countshists r1rrs>*     " D '--D=> ??r2ct|}t|jt||}t t j ||gS)adOutput a `Summary` protocol buffer with a histogram. The generated [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) has one summary value containing a histogram for `values`. This op reports an `InvalidArgument` error if any value is not finite. Args: name: A name for the generated node. Will also serve as a series name in TensorBoard. values: A real numeric `Tensor`. Any shape. Values to use to build the histogram. Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. rr)rrastyperrr)rrbinsmax_binsrs r1rrs="V_F &--.h ?D '--D=> ??r2c 8|jdk(r td|jd}tj||\}}t |}|||kDr||z}||z}|dk7rtj |d||z ggdd}|jd|jd}tj|jd zf|j}|dd||dd|d|d<|}tjtj|d} tj| d| dd z gd \} } t| } t| d z} ~ | dkDr|| d z | ntjdg|d| g}|| | d z}|jdk(s|jdk(r td |j|} t!|j#|j%t ||j| |j'|j' S)zDConvert values into a histogram proto using logic from histogram.cc.rzThe input has no element.)rNconstant) pad_widthmodeconstant_values)axisrr{)sidez1The histogram is empty, please file a bug report.r)rrreshaper[rlenpadremptyr^cumsumgreater searchsortedr concatenatedotrrrr8) rrrcountslimitsnum_bins subsamplingsubsampling_remainder new_limits cum_countsstartendsum_sqs r1rrs {{a455 ^^B F\\&t4NFF6{H8 3(* (; 6 A %VV{-BBCD ! F K044"4=XXv{{Q0&,,?  "[1 3B 22::fa01JaB!1C-D7SJE3 JE c(Q,C$)19uqy3"..1#vds|AT2U EC!G $F {{a6;;!+LMM ZZ F  JJL JJL K JJL]]_}} r2cFt|}t||}t|}|jtj }||zj ddjtj}t||}ttj||gS)aJOutput a `Summary` protocol buffer with images. The summary has up to `max_images` summary values containing images. The images are built from `tensor` which must be 3-D with shape `[height, width, channels]` and where `channels` can be: * 1: `tensor` is interpreted as Grayscale. * 3: `tensor` is interpreted as RGB. * 4: `tensor` is interpreted as RGBA. The `name` in the outputted Summary.Value protobufs is generated based on the name, with a suffix depending on the max_outputs setting: * If `max_outputs` is 1, the summary value tag is '*name*/image'. * If `max_outputs` is greater than 1, the summary value tags are generated sequentially as '*name*/image/0', '*name*/image/1', etc. Args: tag: A name for the generated node. Will also serve as a series name in TensorBoard. tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width, channels]` where `channels` is 1, 3, or 4. 'tensor' can either have values in [0, 1] (float32) or [0, 255] (uint8). The image() function will scale the image values to [0, 255] by applying a scale factor of either 1 (uint8) or 255 (float32). Out-of-range values will be clipped. Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. rrY)rescalerrr) rrrbrr[float32clipr_rrr)rr`r dataformats scale_factorrs r1rr s6V_F FK 0F%f-L ]]2:: &F|# ) )!S 1 8 8 BF vw /E '--Cu=> ??r2cXt|}t||}t|}|jtjt |z}t |jddjtj|||}ttj||gS)z/Output a `Summary` protocol buffer with images.rrY)rroislabelsrr) rrrr[r rbrr!r_rr)r tensor_image tensor_boxesrr"r&rs r1rrEs<(L!,  ??r2c |jd}t|}|D]/}t|||df||df||df||df|dn||d}1|S)NrrrhRed)rurv)rranger) disp_imageboxesr& num_boxeslist_gtr+s r1rrVsy AIIG   %  !Q$K !Q$K !Q$K !Q$K &F1I    r2cddlm}|j\}}}t||z}t||z} |j |} |t | ||} |j j} | j| |f| } ddl } | j} | j| d| j}| jtj||||S)z=Convert a numpy representation of an image to Image protobuf.r)ImageN)r&PNG)formatheightrf colorspaceencoded_image_string)rjr2rr fromarrayr ResamplingLANCZOSresizeioBytesIOsavegetvaluecloser)r`rr%r&r2r6rfchannel scaled_height scaled_widthr ANTIALIASr=output image_strings r1rrgs#\\FE7()Muw'L OOF #E 5$v6  ((I LL, 6 BE ZZ\F JJveJ$??$L LLN ==)  r2cBt|}t|}t|}|jtj }||zj ddjtj}t||}ttj||gS)NrrYrr) rrrbrr[r r!r_rrr)rr`fpsr#rs r1rrs| V_F F #F%f-L ]]2:: &F|# ) )!S 1 8 8 BF vs #E '--Cu=> ??r2c ddl} ddlm}ddl}|j \}}}}|j t||} |jdd j} | j| dd t| d 5} | j} ddd tj| t'j(||| S#t$rtdYywxYw#t$rtddYywxYw#t$r; | j| dd n"#t$r| j| d YnwxYwYwxYw#1swYxYw#t $rt"j%dYwxYw)Nrzadd_video needs package moviepy)editorz6moviepy is installed, but can't import moviepy.editor.z2Some packages could be missing [imageio, requests])rIz.gifF)suffixdelete)verboser)rN progress_bar)rNrbz5The temporary file used by moviepy cannot be deleted.r5)moviepy ImportErrorprintrKtempfilerImageSequenceCliprNamedTemporaryFiler write_gifropenreadosremoveOSErrorrrrr2) r`rIrQmpyrTr4hwcr!filenamer( tensor_strings r1rrsw)JAq!Q  f3 7D**&*GLLH4 xt< h ! !P ( ==am G  /0  D @    4 4 NN8UN G 4 NN8UN 3 44 !! PNOPsvCCC9<EE CCC65C69 D=DD=D74D=6D77D=<D=E  E-,E-c@t|}|j}t|jdkDrt d|j dd}|j dk(sJd|tjtjjzjd}ddl }ddl }|j}|j|d5}|jd|j!d|j#||j%|j&ddd|j)}|j+t-j.|d|j0d|d } t-t-j2|| g S#1swYrxYw) Nrz4warning: audio amplitude out of range, auto clipped.rz%input tensor should be 1 dimensional.zrX setnchannels setsampwidth setframerate writeframesdatar@rArAudiorr) rr`rearrayr=rmfio wave_write audio_stringr s r1r r sH FOE MMOE 5z~~! DE 2q! ::?CCC? RXXbhh'++ + 3 3E :E  **,C 3 +"" ,uzz* + <<>LIIK MMkk"o)  E '--Cu=> ??++s AFFc g}|jD]\}}g}|jD]\}}|d}|ddk(rht|dk(sJtjtjj |d|d|dg}tj ||} n-tj | } tj || } |j| |jtj|| tj| }tjd } t| } td|jgt} ttj d| | gS)NrrMarginr*rh)rlowerupper)series)titlerr)r} multiline)r}chart)categoryr!rr DT_STRINGr^ string_valrcustom_scalars__config__rr)rr r MarginChartContentSeriesChartMultilineChartContentrCategoryLayoutr rr rr rr)layout categoriesrrcharts chart_namechart_meatadatatagsmgccrmlccrrr`s r1r!r!s}J F1+,779 ! 'J"1%Dq!X-4yA~%~!44"55<<"&q'aQ=#((z$G!77DA"((zTJ MM%  ! *--AVDE%F(   3F!,,9IJK k 2C ,,./%'F  MM8RU V  r2c @tjdtdj}t|}t d|j dgt t jd g  }ttj|d z||gS)Nr"r)rrrrutf_8)encodingrrrrz /text_summaryrr) r rrrr encoder rrr)rr"rrr`s r1r"r"s!,,N1$=$O$O$QK k 2C KKK12%+;+?+?Q+G*HIF }}!6VTU r2c |dkDrd}tj||||||f} td|j} t j d| } t | } t d| jdjttj| jd tj| jd  g  } ttj|| | gS)Nrrnum_thresholds pr_curvesrrrErrrrr^rFrrr)r[stackr rr rr r r8r rrrr)rtpfptnfn precisionrecallrweightsrrpr_curve_plugin_datarrr`s r1r$r$ s 88RRY7 8D,.",,)=K k 2C ,,r"))+% $$$**Q-8 $$$**Q-8  F '--C#fMN OOr2c t|d}t||||}td|j}t j d|}t |}t d|jd jttj|jd tj|jd  g  } ttj||| gS)Nr)rrrrrrrrErrrrrrr)rr&r rr rr r r8r rrrr) rr& predictionsrrrrrrrr`s r1r%r%#s-N  NG D-.",,)=K k 2C ,,r"))+% $$$**Q-8 $$$**Q-8  F '--C#fMN OOr2cd}|d}tjtj||dz z}|jtj}d|dz f}tj |||||z\}} tj |||d|z |z\} } tj |dddddd} tj | dddddd} | d| z } | d| z }| tj|| | zz }| tj|| |zz }tj| | | |||fS)NgHz>g?rr)rr,rr) r[int32floorrfloat64rrmaximumr)r&rrr_MINIMUM_COUNTbucket_indices float_labelshistogram_range tp_buckets_ fp_bucketsrrrrrrs r1r&r&>sLNXXbhh{nq6H'IJKN==,L.1,-OLL w& MJ LL |#w. MJ :dd# $TrT *B :dd# $TrT *B AB ABRZZR88I "**^R"W5 5F 88RRY7 88r2c ddl}ddlm}|j|}|j |||||j ||} t d|jdjttj|j dtj|j dtj|j d g  }tj|j|||| } | S) aCreate a tensor summary with summary metadata. Args: name: Uniquely identifiable name of the summary op. Could be replaced by combination of name and type to make it unique even outside of this summary. display_name: Will be used as the display name in TensorBoard. Defaults to `name`. description: A longform readable description of the summary data. Markdown is supported. tensor: Tensor to display in summary. content_type: Type of content inside the Tensor. components: Bitmask representing present parts (vertices, colors, etc.) that belong to the summary. json_config: A string, JSON-serialized dictionary of ThreeJS classes configuration. Returns: Tensor summary with metadata. rNr) json_configrErrrrhrrr)r<tensorboard.plugins.meshr as_tensorcreate_summary_metadatarr r r8r rrrget_instance_name) r display_name descriptionr`ri componentsrr<rtensor_metadatatensor_summarys r1_get_tensor_summaryr_s.1 U__V $F66  7O..$++-% $$&,,q/: $$&,,q/: $$&,,q/:  F]]  & &t\ : N r2c<d}|tj|d}|S)z5Parse and returns JSON string from python dictionary.z{}T) sort_keys)jsondumps) config_dictrs r1_get_json_configrs#Kjj= r2c zddlm}ddlm}t |} g} ||j f||j f||jfg} | D cgc] } | d |  } } |j| D cgc]\} } |  c} } }| D]%\} } | jt|||| | || 't| Scc} wcc} } w)a'Output a merged `Summary` protocol buffer with a mesh/point cloud. Args: tag: A name for this summary operation. vertices: Tensor of shape `[dim_1, ..., dim_n, 3]` representing the 3D coordinates of vertices. faces: Tensor of shape `[dim_1, ..., dim_n, 3]` containing indices of vertices within each triangle. colors: Tensor of shape `[dim_1, ..., dim_n, 3]` containing colors for each vertex. display_name: If set, will be used as the display name in TensorBoard. Defaults to `name`. description: A longform readable description of the summary data. Markdown is supported. config_dict: Dictionary with ThreeJS classes names and configuration. Returns: Merged summary for mesh/point cloud representation. rr)MeshPluginDatar) rr(tensorboard.plugins.mesh.plugin_data_pb2rrVERTEXFACECOLORget_components_bitmaskrrr)rverticescolorsfacesrrrrrr summariestensorsr`rirs r1r'r's,2G";/KI >(() ##$ %%&G %,E&vay/DvEGE004;<06<<J!(          ##%Frs  && & <F8JC 2 0   8 $ &5 &S &&3&5&:5<<:DI:4ell4tE{4 u|| S   JJJ(;< MMIz+>? NN]J0CD MMJ _=   KK*k?;   MMK?  LL; o> JJI7 KK*i9 KK*i9 KK*i9 KK*i9 II I7 KK*i9 LL:y/:  KK*k?;!" OOnn6LM#$ KK..2HI OOnn6LM LL>>3IJ JJJ8 8NO MMO^5KL KK*lO< LL:|_= NNZ?5<5 (Vzz(MV"VJ@B@,4n"@LKO@""4@(V@<"J  IMP4P69B8vOS3$r2