U dt@sddlZddlZddlZddlZddlmZddlmZddl m Z ddl m Z ddl m Z ddl mZddlmZdd lmZdd lmZdd lmZdd lmZd dlmZd dlmZmZeeZ ddZ!dDddZ"dEddZ#dFddZ$ddZ%dGddZ&dHdd Z'dId"d#Z(dJd%d&Z)dKd'd(Z*dLd)d*Z+dMd,d-Z,d.d/Z-dNd1d2Z.d3d4Z/d5d6Z0dOd8d9Z1dPd:d;Z2dQdd?Z4d@dAZ5dRdBdCZ6dS)SN)Optional)range) struct_pb2)Summary)HistogramProto)SummaryMetadata) TensorProto)TensorShapeProto)TextPluginData)PrCurvePluginData) layout_pb2)make_np)_prepare_videoconvert_to_HWCcCs,t|tjs|n|}|jtjkr(dSdS)Nr ) isinstancenpZndarraynumpydtypeuint8)tensorZ convertedrC/tmp/pip-unpacked-wheel-ua33x9lu/torch/utils/tensorboard/summary.py_calc_scale_factorsrblackc Csddlm} m} | } | |} ||||f\} }}}| j| |f| |f||f||f| |fg||d|r|}| |\}}td|}| j | ||d|f| ||fg|d| j | ||||f||| d|S)Nr) ImageDraw ImageFont)widthfillg?r)r )r font) PILrrZ load_defaultZDrawlinegetsizerceilZ rectangletext)imageZxminZyminZxmaxZymax display_strcolorZ color_textZ thicknessrrr!ZdrawleftrighttopZbottomZ text_bottom text_widthZ text_heightmarginrrr_draw_single_boxs4    r/csddl}ddlm}ddlm}m}mmm}m }ddl m } m } m } m} m} ddlm}m}m}ttstdtdt|tstdtd|pi}t|tstd |D]B\}kst|trtfd d |Dstd qg}|}D]\}|dkr,qt|tsDt|tr||j_ |krvt!j"d d|Dd}nd}|#||$d|dqt||r||j_%|krt!j"dd|Dd}nd}|#||$d|dqt|t&rX||j_'|kr6t!j"dd|Dd}nd}|#||$d|dqt||j(rt)|d}||j_ |#||$ddqt*dq||| d}t+t+j,| |-dd}t.t.j$| |dgd}fdd|/D}|||d}||| d}t+t+j,| |-dd}t.t.j$| |dgd}||$d d!}||| d"}t+t+j,| |-dd}t.t.j$| |dgd}|||fS)#a2Outputs three `Summary` protocol buffers needed by hparams plugin. `Experiment` keeps the metadata of an experiment, such as the name of the hyperparameters and the name of the metrics. `SessionStartInfo` keeps key-value pairs of the hyperparameters `SessionEndInfo` describes status of the experiment e.g. STATUS_SUCCESS Args: hparam_dict: A dictionary that contains names of the hyperparameters and their values. metric_dict: A dictionary that contains names of the metrics and their values. hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that contains names of the hyperparameters and all discrete values they can hold Returns: The `Summary` protobufs for Experiment, SessionStartInfo and SessionEndInfo rN) string_types) Experiment HParamInfo MetricInfo MetricNameStatusDataType) PLUGIN_NAMEPLUGIN_DATA_VERSIONEXPERIMENT_TAGSESSION_START_INFO_TAGSESSION_END_INFO_TAG)HParamsPluginDataSessionEndInfoSessionStartInfoz>parameter: hparam_dict should be a dictionary, nothing logged.z>parameter: metric_dict should be a dictionary, nothing logged.zIparameter: hparam_domain_discrete should be a dictionary, nothing logged.c3s |]}t|tVqdS)N)rtype.0d) hparam_dictkrr szhparams..zWparameter: hparam_domain_discrete[{}] should be a list of same type as hparam_dict[{}].cSsg|]}tj|dqS)) number_valuerValuer@rrr szhparams..)valuesZDATA_TYPE_FLOAT64)namer?domain_discretecSsg|]}tj|dqS)) string_valuerGr@rrrrIsZDATA_TYPE_STRINGcSsg|]}tj|dqS)) bool_valuerGr@rrrrIsZDATA_TYPE_BOOL)rKr?z=value should be one of int, float, str, bool, or torch.Tensor)Zsession_start_infoversion plugin_namecontent plugin_data)tagmetadatavaluecsg|]}|ddqS)rU)rKr)rArD)r3r4rrrIs)Z hparam_infosZ metric_infos)Z experimentrOZSTATUS_SUCCESS)status)Zsession_end_inforO)0torchsixr0Z#tensorboard.plugins.hparams.api_pb2r1r2r3r4r5r6Z$tensorboard.plugins.hparams.metadatar7r8r9r:r;Z+tensorboard.plugins.hparams.plugin_data_pb2r<r=r>rdictloggerwarning TypeErroritemslistallformatintfloathparamsrFrZ ListValueappendrHrMboolrNZTensorr ValueErrorr PluginDataSerializeToStringrkeys)rCZ metric_dictZhparam_domain_discreter[r0r1r2r5r6r7r8r9r:r;r<r=r>vZhpsZssirLrRsmdZmtsexpZseir)r3r4rCrDrrgIs                      rgFcCst|}|jdkstdt|}|rxt|gdd}|rJt|gdd}tjdd}t|d }ttj |||d gd Sttj ||d gd Sd S)aOutputs 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. rzscalar should be 0DDT_FLOAT) float_valrZ DT_DOUBLE)Z double_valrZscalarsrQrSrUrrVrW)rUZ simple_valueN) rsqueezendimAssertionErrorrfrrrkrrH)rKscalar collectionsZ new_styleZdouble_precisionrrTrorrrrxs$   rxc Cs,t|||||||d}ttj||dgdS)aOutputs 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_squaresZ bucket_limitZbucketrUZhistorW)rrrH) rKr{r|r}r~rZ bucket_limitsZ bucket_countshistrrr histogram_raw/s rcCs0t|}t|t||}ttj||dgdS)adOutputs 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. rrW)rmake_histogramastyperfrrH)rKrJbinsmax_binsrrrr histogramOsrc Cs|jdkrtd|d}tj||d\}}t|}|dk r||kr||}||}|dkrztj|d||ggddd}|d|jdd}t|jd f|j }|dd||dd<|d|d<|}t tj |dtj d } tj | d| dd gd d \} } t| } t| d } ~ | dkr2|| d | ntdg|d| g}|| | d }|jdksr|jdkrztd ||} t||t||| ||dS)zDConvert values into a histogram proto using logic from histogram.cc.rzThe input has no element.)rNZconstant)Z pad_widthmodeZconstant_values)Zaxisr )rr+)Zsidez1The histogram is empty, please file a bug report.rz)sizerjreshaperrlenpadr~emptyrcumsumZgreaterint32Z searchsortedreZ concatenatedotrr{r|tolist) rJrrcountsZlimitsZnum_binsZ subsamplingZsubsampling_remainderZ new_limitsZ cum_countsstartendZsum_sqrrrrdsP      0 rNCHWcCsXt|}t||}t|}|tj}||tj}t||d}ttj ||dgdS)aOutputs 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). Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. )rescalerUr'rW) rrrrrfloat32r make_imagerrH)rUrr dataformats scale_factorr'rrrr's   r'CHWcCs\t|}t||}t|}|tjt|}t|tj|||d}ttj ||dgdS)z0Outputs a `Summary` protocol buffer with images.)rroislabelsrrW) rrrrrrrrrrH)rUZ tensor_imageZ tensor_boxesrrrr'rrr image_boxess  rc Csf|jd}t|}|D]J}t|||df||df||df||df|dkrRdn||dd}q|S)Nrr rZRed)r(r))shaperr/)Z disp_imageZboxesrZ num_boxesZlist_gtirrr draw_boxess      rcCsddlm}|j\}}}t||}t||} ||} |dk rPt| ||d} | | |f|j} ddl} | } | j | dd| } | t j|||| dS)zg?r r)rrrr) rrfloorrZfloat64rrmaximumr)rrrrZ_MINIMUM_COUNTZbucket_indicesZ float_labelsZhistogram_rangeZ tp_buckets_Z fp_bucketsrrrrrrrrrrs2      rc Csddl}ddlm}||}|j|||||j||d} td|dt t j |jddt j |jddt j |jd dgd d }t j | |||| d } | S) aCreates 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. 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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. r)MeshPluginDatarcSsg|]}|ddk r|qS)rNr)rArrrrrI9s zmesh..cSsg|] \}}|qSrr)rArrrrrrI;srW) Z(tensorboard.plugins.mesh.plugin_data_pb2rrrVrZVERTEXZFACEZCOLORZget_components_bitmaskrhrr)rUZverticescolorsZfacesrrrrrVrZ summariesZtensorsrrrrrrmeshs2     r )rrr)NNN)NFF)N)N)r r)r rN)N)r NN)r)r)rN)rN)NN)NN)7rloggingrrrtypingrZ six.movesrZgoogle.protobufrZ$tensorboard.compat.proto.summary_pb2rrrZ#tensorboard.compat.proto.tensor_pb2rZ)tensorboard.compat.proto.tensor_shape_pb2r Z(tensorboard.plugins.text.plugin_data_pb2r Z,tensorboard.plugins.pr_curve.plugin_data_pb2r Z!tensorboard.plugins.custom_scalarr Z _convert_npr_utilsrr getLogger__name__r^rr/rgrxrrrr'rrrrrrrr&rrrrrr rrrrsd               * ? (  6 $    + %   !;