o &ÕjŸjã@sddlZddlZddlZddlmZddlmZddlmZddlmZddlm Z ddlm Z ddlm Z dd lm Z dd lm Z dd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZGdd„deƒZGdd„deƒZ dS)éN)Úaudio)Ú embedding)Ú histogram)Úhparam)Úimage)Ú meta_data)Úpr_curve)Ú roc_curve)Úscalar)Úscalars)Útext)Útranslate_graph)Úbfile)Úlogger)Úfigure_to_image)Ú merge_images)Úmd5)ÚRecordFileWriterc@s`eZdZdZdd„Zdd„Zddd„Zdd d „Zdd d „Zdd d„Z dd„Z dd„Z dd„Z dS)ÚDummyFileWriterz8A fake file writer that writes nothing to the disk. cCs ||_dS©N©Ú_logdir)ÚselfÚlogdir©rúW/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/visualdl/writer/writer.pyÚ__init__,s zDummyFileWriter.__init__cCó|jS)z7Returns the directory where event file will be written.r©rrrrÚ get_logdir/ózDummyFileWriter.get_logdirNcCódSrr)rÚeventÚstepÚwalltimerrrÚ add_event3ózDummyFileWriter.add_eventcCr!rr)rÚsummaryZ global_stepr$rrrÚ add_summary6r&zDummyFileWriter.add_summarycCr!rr)rZ graph_profiler$rrrÚ add_graph9r&zDummyFileWriter.add_graphcCr!rr)rÚgraphr$rrrÚadd_onnx_graph<r&zDummyFileWriter.add_onnx_graphcCr!rrrrrrÚflush?r&zDummyFileWriter.flushcCr!rrrrrrÚcloseBr&zDummyFileWriter.closecCr!rrrrrrÚreopenEr&zDummyFileWriter.reopen©NNr) Ú__name__Ú __module__Ú __qualname__Ú__doc__rrr%r(r)r+r,r-r.rrrrr(s     rc@s$eZdZdZ        d:dd„Zed d „ƒZd d „Zed d„ƒZ    d;dd„Z ddd„Z    d?d d!„Z       d@d"d#„Z $ dAd%d&„ZdBd'd(„Zdœsz,LogWriter.add_embeddings..rX)rYÚlabelsÚ labels_metaÚ hot_vectorsr#r$)rZrÚwarningr`ÚnpZndarrayÚtolistÚlistÚrangeÚlenr[r\rIr]r) rrYZmatÚmetadataZmetadata_headerr$rurwrvr#rrrÚadd_embeddingsIsB<   ÿ ÿ  úÿzLogWriter.add_embeddingsé@c Cs`d|vrtdƒ‚|durtt ¡dƒn|}t|tƒr t |¡}| ¡ t |||||d¡dS)apAdd an audio to vdl record file. Args: tag (string): Data identifier audio (np.ndarray or list): audio represented by a numpy.array step (int): Step of audio sample_rate (int): Sample rate of audio walltime (int): Wall time of audio Example: import wave CHUNK = 4096 f = wave.open(audio_path, "rb") wavdata = [] chunk = f.readframes(CHUNK) while chunk: data = np.frombuffer(chunk, dtype='uint8') wavdata.extend(data) chunk = f.readframes(CHUNK) audio_data = np.array(wavdata) writer.add_audio(tag="audio_test", audio_array=audio_data, step=0, sample_rate=8000) rVrWNrX)rYÚ audio_arrayÚ sample_rater#r$) rZr[r\r`r{ryÚarrayrIr]r)rrYrr#r‚r$rrrÚ add_audio©s!  ûÿzLogWriter.add_audioc Cs^d|vrtdƒ‚tj||d\}}|durtt ¡dƒn|}| ¡ t|||||d¡dS)a¥Add an histogram to vdl record file. Args: tag (string): Data identifier value (np.ndarray or list): value represented by a numpy.array or list step (int): Step of histogram walltime (int): Wall time of audio buckets (int): Number of buckets, default is 10 Example: values = np.arange(0, 1000) with LogWriter(logdir="./log/histogram_test/train") as writer: for index in range(5): writer.add_histogram(tag='default', values=values+index, step=index) rVrW)ZbinsNrX)rYÚhistÚ bin_edgesr#r$)rZryrr[r\rIr])rrYÚvaluesr#r$Zbucketsr…r†rrrÚ add_histogram×sûÿzLogWriter.add_histogramcCsht|ƒtur tdƒ‚t|ƒturtdƒ‚|dur tt ¡dƒn|}| ¡ tt |j ƒ|||d¡dS)aÂAdd an histogram to vdl record file. Args: hparams_dict (dictionary): 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 `string`, `float` or `int`. metrics_list (list): Name of all metrics. walltime (int): Wall time of hparams. Examples:: from visualdl import LogWriter # Remember use add_scalar to log your metrics data! with LogWriter('./log/hparams_test/train/run1') as writer: writer.add_hparams({'lr': 0.1, 'bsize': 1, 'opt': 'sgd'}, ['hparam/accuracy', 'hparam/loss']) for i in range(10): writer.add_scalar('hparam/accuracy', i, i) writer.add_scalar('hparam/loss', 2*i, i) with LogWriter('./log/hparams_test/train/run2') as writer: writer.add_hparams({'lr': 0.2, 'bsize': 2, 'opt': 'relu'}, ['hparam/accuracy', 'hparam/loss']) for i in range(10): writer.add_scalar('hparam/accuracy', 1.0/(i+1), i) writer.add_scalar('hparam/loss', 5*i, i) z!hparam_dict should be dictionary!zmetric_list should be list!NrX)ÚnameZ hparam_dictZ metric_listr$) ÚtyperaÚ TypeErrorr{r[r\rIr]rrrO)rZ hparams_dictZ metrics_listr$rrrÚ add_hparamsõs  üÿzLogWriter.add_hparamsc CóPd|vrtdƒ‚|durtt ¡dƒn|}| ¡ t|||||||d¡dS)aüAdd an precision-recall curve to vdl record file. Args: tag (string): Data identifier labels (np.ndarray or list): Binary labels for each element. predictions (np.ndarray or list): The probability that an element be classified as true. step (int): Step of pr curve. weights (float): Multiple of data to display on the curve. num_thresholds (int): Number of thresholds used to draw the curve. walltime (int): Wall time of pr curve. Example: with LogWriter(logdir="./log/pr_curve_test/train") as writer: for index in range(3): labels = np.random.randint(2, size=100) predictions = np.random.rand(100) writer.add_pr_curve(tag='default', labels=labels, predictions=predictions, step=index) rVrWNrX©rYruÚ predictionsr#r$Únum_thresholdsÚweights)rZr[r\rIr]r©rrYrurr#rr‘r$rrrÚ add_pr_curvesùÿzLogWriter.add_pr_curvec Cr)aõAdd an ROC curve to vdl record file. Args: tag (string): Data identifier labels (numpy.ndarray or list): Binary labels for each element. predictions (numpy.ndarray or list): The probability that an element be classified as true. step (int): Step of pr curve. weights (float): Multiple of data to display on the curve. num_thresholds (int): Number of thresholds used to draw the curve. walltime (int): Wall time of pr curve. Example: with LogWriter(logdir="./log/roc_curve_test/train") as writer: for index in range(3): labels = np.random.randint(2, size=100) predictions = np.random.rand(100) writer.add_roc_curve(tag='default', labels=labels, predictions=predictions, step=index) rVrWNrXrŽ)rZr[r\rIr]r r’rrrÚ add_roc_curveGsùÿzLogWriter.add_roc_curveFc Cszzt|||ƒ}Wnty}z td |¡ƒ|‚d}~wwt |jdt ¡|jf¡}t  |d¡}|  |¡|  ¡dS)a/ Add a model graph to vdl graph file. Args: model (paddle.nn.Layer): Model to draw. input_spec (list[paddle.static.InputSpec|Tensor]): Describes the input of the saved model's forward arguments. verbose (bool): Whether to print some graph statistic information in console. Note: Paddlepaddle is required to use add_graph interface. Example: import paddle import paddle.nn as nn import paddle.nn.functional as F from visualdl import LogWriter class MyNet(nn.Layer): def __init__(self): super(MyNet, self).__init__() self.conv1 = nn.Conv2D(in_channels=1, out_channels=20, kernel_size=5, stride=1, padding=2) self.max_pool1 = nn.MaxPool2D(kernel_size=2, stride=2) self.conv2 = nn.Conv2D(in_channels=20, out_channels=20, kernel_size=5, stride=1, padding=2) self.max_pool2 = nn.MaxPool2D(kernel_size=2, stride=2) self.fc = nn.Linear(in_features=980, out_features=10) def forward(self, inputs): x = self.conv1(inputs) x = F.relu(x) x = self.max_pool1(x) x = self.conv2(x) x = F.relu(x) x = self.max_pool2(x) x = paddle.reshape(x, [x.shape[0], -1]) x = self.fc(x) return x net = MyNet() with LogWriter(logdir="./log/graph_test/") as writer: writer.add_graph( model=net, input_spec=[paddle.static.InputSpec([-1, 1, 28, 28], 'float32')], verbose=True) z%Failed to save model graph, error: {}Nzvdlgraph.%010d.log%sÚw) r Ú ExceptionÚprintÚformatrr@rr\rCZBFileÚwriter-)rÚmodelZ input_specÚverboseÚresultÚeZgraph_file_nameÚwriterrrrr)ps(€þþ   zLogWriter.add_graphcCs*|jiurdS|j ¡D]}| ¡q dS)z)Flush all data in cache to disk. N)rHr‡r,©rržrrrr,¤s  ÿzLogWriter.flushcCs>|jiurdS|j ¡D] }| ¡| ¡q d|_i|_dS)z4Close all writers after flush data to disk. N)rHr‡r,r-rGrŸrrrr-¬s   zLogWriter.closecCs|SrrrrrrÚ __enter__·r&zLogWriter.__enter__cCs | ¡dSr)r-)rÚexc_typeÚexc_valÚexc_tbrrrÚ__exit__ºs zLogWriter.__exit__)Nr5r6r7r5Tr5r5)rTr5rNr)Nrdr/)rlrmNrd)NNNNNNN)r€N)Nr6)r6NN)F)r0r1r2r3rÚpropertyrrIrOrKr_rcrhrirkrqrr„rˆrŒr“r”r)r,r-r r¤rrrrr4Isr ø,   ü      ù) ød û . , ù0 ù )4 r4)!r>r\ÚnumpyryZ!visualdl.component.base_componentrrrrrrrr r r r Zvisualdl.component.graphr Z visualdl.iorZvisualdl.server.logrZvisualdl.utils.figure_utilrZvisualdl.utils.img_utilrZvisualdl.utils.md5_utilrZvisualdl.writer.record_writerrÚobjectrr4rrrrÚs.                  !