o 0 h@s~ddlZddlZddlZddlZddlmZmZddlZddlZ ddl m Z ddl m Z mZmZddlmZddlmZddlmZddlmZdd lmZd d lmZd d lmZmZgd Ze e!Z"de#de$fddZ%de$de#fddZ&dej'de(e$fddZ)dej'de(e#fddZ*dej'de(efddZ+iej,dde)fej-dde)fej.dde)fej/dd e+fej#dd e+fej0d!d"e+fej1d!d"e+fej2d#d$e+fej3d%d$e+fej4d%d$e+fej5d&d$e+fej6d&d$e+fej$d'd$e+fej7d'd$e+fej8d'd$e+fej9d(d)e+fej:d*d+e*fej;d*d+e*fejd-d.e+fej?d/d0e*fej@d/d0e*fej3d%d1e+fejAd%d1e+fejBd%d1e+fi ZCd2d3ZD 4 4 5dhd6d7ZEdid8d9ZFdjd;d<ZGd=d>ZHd?d@ZIdkdAdBZJdkdCdDZKdldFdGZL dmdIdJZMdkdKdLZNdndMdNZOdodPdQZPdRdSZQdpdUdVZRdWdXZSdYdZZT dqd\d]ZUdqd^d_ZVdrd`daZWdbdcZXdddeZY drdfdgZZdS)sN)AnyOptional) 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_curvemeshfreturncCtd|}td|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&bufr/S/var/www/vscode/kcb/lib/python3.10/site-packages/torch/utils/tensorboard/summary.pyr6s rr)cCr()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)r.r/r/r0rCs rtcCsdd|DS)NcSsg|]}t|qSr/)r.0xr/r/r0 Nz'_tensor_to_half_val..flattentolistr1r/r/r0_tensor_to_half_valMsr;cCst|SN)torch view_as_realr8r9r:r/r/r0_tensor_to_complex_valPsr?cCs |Sr<r7r:r/r/r0_tensor_to_listSs r@DT_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_valcCs,t|tjs |n|}|jtjkrdSdS)Nr ) isinstancenpndarraynumpydtypeuint8)tensor convertedr/r/r0_calc_scale_factorusr`blackc Csddlm} m} | } | |} ||||f\} }}}| j| |f| |f||f||f| |fg||d|ru|}| |\}}}}||||}}td|}| j | ||d|f| ||fg|d| j | ||||f||| d|S)Nr) ImageDraw ImageFont)widthfillg?rb)rf)rffont) PILrcrd load_defaultDrawlinegetbboxrYceil rectangler )rxminyminxmaxymax display_strcolor color_text thicknessrcrdrgdrawleftrighttopbottom text_bottom_left_top_right_bottom text_width text_heightmarginr/r/r0_draw_single_boxzs6    rcsjddl}ddlm}m}m}mmm}ddlm }m } m } m } m } ddlm} m}m}tts:tdtdt|tsHtdtd|pKi}t|tsUtd|D]$\}vsrt|trrtfd d |Ds}td d d qYg}|}D]\}|durqt|ttfr||j_|vrtjdd|Dd}nd}| ||!d|dqt|t"r||j_#|vrtjdd|Dd}nd}| ||!d|dqt|t$r||j_%|vr tjdd|Dd}nd}| ||!d|dqt||j&r;t'|d}||j_| ||!ddqt(d| || d}t)t)j*| |+dd}t,t,j!| |dgd}fdd|-D}|||d}| || d }t)t)j*| |+dd}t,t,j!||dgd}||!d!d"}| || d#}t)t)j*| |+dd}t,t,j!| |dgd}|||fS)$a2Output three `Summary` protocol buffers needed by hparams plugin. `Experiment` keeps the metadata of an experiment, such as the name of the hyperparameters and the name of the metrics. `SessionStartInfo` keeps key-value pairs of the hyperparameters `SessionEndInfo` describes status of the experiment e.g. STATUS_SUCCESS Args: hparam_dict: A dictionary that contains names of the hyperparameters and their values. metric_dict: A dictionary that contains names of the metrics and their values. hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that contains names of the hyperparameters and all discrete values they can hold Returns: The `Summary` protobufs for Experiment, SessionStartInfo and SessionEndInfo rN)DataType Experiment HParamInfo MetricInfo MetricNameStatus)EXPERIMENT_TAGPLUGIN_DATA_VERSION PLUGIN_NAMESESSION_END_INFO_TAGSESSION_START_INFO_TAG)HParamsPluginDataSessionEndInfoSessionStartInfoz>parameter: hparam_dict should be a dictionary, nothing logged.z>parameter: metric_dict should be a dictionary, nothing logged.zIparameter: hparam_domain_discrete should be a dictionary, nothing logged.c3s"|] }t|tVqdSr<)rXtyper3d) hparam_dictkr/r0 s zhparams..z"parameter: hparam_domain_discrete[z/] should be a list of same type as hparam_dict[z].cSg|]}tj|dqS)) number_valuerValuerr/r/r0r5 zhparams..)valuesDATA_TYPE_FLOAT64)namerdomain_discretecSr)) string_valuerrr/r/r0r5rDATA_TYPE_STRINGcSr)) bool_valuerrr/r/r0r5(rDATA_TYPE_BOOL)rrz=value should be one of int, float, str, bool, or torch.Tensor)session_start_infoversion plugin_namecontent plugin_data)tagmetadatavaluecsg|] }|ddqS)r)rr/)r3r)rrr/r0r5J) hparam_infos metric_infos) experimentrSTATUS_SUCCESS)status)session_end_infor).r=#tensorboard.plugins.hparams.api_pb2rrrrrr$tensorboard.plugins.hparams.metadatarrrrr+tensorboard.plugins.hparams.plugin_data_pb2rrrrXdictloggerwarning TypeErroritemslistallintfloatrrr ListValueappendrstrrboolrTensorr ValueErrorr PluginDataSerializeToStringrkeys)r metric_dicthparam_domain_discreter=rrrrrrrrrrrrvhpsssirrsmdmtsexpseir/)rrrrr0rs                   rFc Cst|}|jdksJd|jd|jdt|}|rEt|gdd}|r.t|gdd}tjd d }t|d }ttj |||d gd Sttj ||dgd 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.rD)rEr\rF)rGr\scalarsrrrr^rr)r simple_value) rsqueezendimsizerrrrrr) rr^ collections new_styledouble_precisionrr!rrr/r/r0rbs(     rc Cs||jdkr td|jtvr3t|j\}}}tdid|dtdd|jDd|||i}n t|d|jtj d d }t|d }t t j |||d gd S)aOutputs a `Summary` protocol buffer containing the full tensor. The generated Summary has a Tensor.proto containing the input Tensor. Args: tag: 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_shapecSr)r)r Dimr2r/r/r0r5sz tensor_proto..dimz has unsupported tensor dtype r^rrrrr^rNr/) numelitemsizerr\_TENSOR_TYPE_MAPrr shaperrrr)rr^r\ field_name conversion_fnr!rrr/r/r0r!s$    r!c Cs,t|||||||d}ttj||dgdS)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_countshistr/r/r0rs rcCs0t|}t|t||}ttj||dgdS)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_binsrr/r/r0rsrc Cs|jdkr td|d}tj||d\}}t|}|dure||kre||}||}|dkr=tj|d||ggddd}|d|jdd}t|jd f|j }|dd||dd<|d|d<|}t t |d} tj | d| dd gd d \} } t | } t | d } ~ | dkr|| d | n tdg|d| g}|| | d }|jdks|jdkrtd ||} t||t||| ||d S)zDConvert values into a histogram proto using logic from histogram.cc.rzThe input has no element.)rNconstant) pad_widthmodeconstant_values)axisr ry)sidez1The histogram is empty, please file a bug report.r)rrreshaperYrlenpadremptyr\cumsumgreater searchsortedr concatenatedotrrrr9) rrrcountslimitsnum_bins subsamplingsubsampling_remainder new_limits cum_countsstartendsum_sqr/r/r0rsP      . rNCHWcCs`t|}t||}t|}|tj}||ddtj}t||d}t t j ||dgdS)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. rrW)rescalerrr) rrr`rrYfloat32clipr]rrr)rr^r dataformats scale_factorrr/r/r0r s   rCHWcCsdt|}t||}t|}|tjt|}t|ddtj|||d}t t j ||dgdS)z/Output a `Summary` protocol buffer with images.rrW)rroislabelsrr) rrrrYrr`rrr]rr)r tensor_image tensor_boxesrrr rr/r/r0rEs rc Csf|jd}t|}|D]%}t|||df||df||df||df|dur)dn||dd}q |S)Nrr rbRed)rsrt)rranger) disp_imageboxesr  num_boxeslist_gtr)r/r/r0rVs      rcCsddlm}|j\}}}t||}t||} ||} |dur(t| ||d} |jj} | | |f| } ddl } | } | j | dd| }| tj||||dS)z=Convert a numpy representation of an image to Image protobuf.r)ImageN)r PNG)formatheightre colorspaceencoded_image_string)rhr*rr fromarrayr ResamplingLANCZOSresizeioBytesIOsavegetvaluecloser)r^rrr r*r.rechannel scaled_height scaled_widthr ANTIALIASr5output image_stringr/r/r0rgs(     rcCs\t|}t|}t|}|tj}||ddtj}t||}t t j ||dgdS)NrrWrr) rrr`rrYrrr]rrr)rr^fpsrrr/r/r0rs  rc Cs`zddl}WntytdYdSwzddlm}Wnty,tddYdSwddl}|j\}}}}|jt||d} |jddd j } z | j | ddd Wn$t ywz | j | ddd Wnt yt| j | dd YnwYnwt | d  } | } Wdn1swYzt| WntytdYnwtj|||| dS)Nrzadd_video needs package moviepy)editorz6moviepy is installed, but can't import moviepy.editor.z2Some packages could be missing [imageio, requests])rAz.gifF)suffixdelete)verboser)rE progress_bar)rErbz5The temporary file used by moviepy cannot be deleted.r-)moviepy ImportErrorprintrBtempfilerImageSequenceCliprNamedTemporaryFiler write_gifropenreadosremoveOSErrorrrrr*) r^rArHmpyrK_thwcrfilenamer& tensor_stringr/r/r0rsP        rDc Cs t|}|}t|dkrtd|dd}|jdks#Jd|ttj j d}ddl }ddl }| }||d}|d|d||||jWdn1sawY|}|tj|d|jd|d d } ttj|| d gd S) Nr z4warning: audio amplitude out of range, auto clipped.rz%input tensor should be 1 dimensional.zg?r r)rr%rr) rYint32floorrfloat64rrmaximumr)r rrr_MINIMUM_COUNTbucket_indices float_labelshistogram_range tp_buckets_ fp_bucketsrrrrrrr/r/r0r$>s2       r$c Csddl}ddlm}||}|j|||||j||d} td|dt t j |jddt j |jddt j |jd dgd d }t j | |||| d } | 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_configrDrrr rbrrr)r=tensorboard.plugins.meshr as_tensorcreate_summary_metadatarrrr9r rrrget_instance_name) r display_name descriptionr^ra componentsrr=rtensor_metadatatensor_summaryr/r/r0_get_tensor_summary_s8     rcCsd}|dur tj|dd}|S)z5Parse and returns JSON string from python dictionary.z{}NT) sort_keys)jsondumps) config_dictrr/r/r0_get_json_configsrc Csddlm}ddlm}t|} g} ||jf||jf||jfg} dd| D} |dd| D} | D]\} }| t |||| || | q3t | dS)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)MeshPluginDatacSsg|] }|ddur|qS)rNr/)r3r^r/r/r0r5rzmesh..cSsg|]\}}|qSr/r/)r3r^rar/r/r0r5r6r) rr(tensorboard.plugins.mesh.plugin_data_pb2rrVERTEXFACECOLORget_components_bitmaskrrr)rverticescolorsfacesrrrrrr summariestensorsrr^rar/r/r0r%s2     r%)rararb)NNN)NFFr<)r r)r rN)r NN)r@)r[)rN)NN)[rloggingrQr+typingrrr=r[rYgoogle.protobufr$tensorboard.compat.proto.summary_pb2rrr#tensorboard.compat.proto.tensor_pb2r)tensorboard.compat.proto.tensor_shape_pb2r !tensorboard.plugins.custom_scalarr ,tensorboard.plugins.pr_curve.plugin_data_pb2r (tensorboard.plugins.text.plugin_data_pb2r _convert_npr_utilsrr__all__ getLogger__name__rrrrrrrr;r?r@halffloat16bfloat16rrdoubleint8r]qint8rdshortrqint32int64 complex32chalf complex64cfloatr complex128cdoublequint8quint4x2rr`rrrr!rrrrrrrrrrrr r"r#r$rrr%r/r/r/r0s                                      + >+% !  7&    +%  !;