o ej@sddlZddlZddlZddlmZddlmZddlmZddlmZddlm Z ddlm Z ddlm Z dd lm Z dd lm Z dd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZGdddeZGdddeZ dS)N)audio) embedding) histogram)hparam)image) meta_data)pr_curve) roc_curve)scalar)scalars)text)translate_graph)bfile)logger)figure_to_image) merge_images)md5)RecordFileWriterc@s`eZdZdZddZddZdddZdd d Zdd d Zdd dZ ddZ ddZ ddZ dS)DummyFileWriterz8A fake file writer that writes nothing to the disk. cCs ||_dSN_logdir)selflogdirrFD:\Projects\ConvertPro\env\Lib\site-packages\visualdl/writer/writer.py__init__,s zDummyFileWriter.__init__cC|jS)z7Returns the directory where event file will be written.rrrrr get_logdir/zDummyFileWriter.get_logdirNcCdSrr)reventstepwalltimerrr add_event3zDummyFileWriter.add_eventcCr!rr)rsummaryZ global_stepr$rrr add_summary6r&zDummyFileWriter.add_summarycCr!rr)rZ graph_profiler$rrr add_graph9r&zDummyFileWriter.add_graphcCr!rr)rgraphr$rrradd_onnx_graph<r&zDummyFileWriter.add_onnx_graphcCr!rrrrrrflush?r&zDummyFileWriter.flushcCr!rrrrrrcloseBr&zDummyFileWriter.closecCr!rrrrrrreopenEr&zDummyFileWriter.reopenNNr) __name__ __module__ __qualname____doc__rrr%r(r)r+r,r-r.rrrrr(s     rc@s$eZdZdZ        d:ddZed d Zd d Zed dZ    d;ddZ dddZ    d?d d!Z       d@d"d#Z $ dAd%d&ZdBd'd(Zdsz,LogWriter.add_embeddings..rX)rYlabels labels_meta hot_vectorsr#r$)rZrwarningr`npZndarraytolistlistrangelenr[r\rIr]r) rrYmatmetadataZmetadata_headerr$rurwrvr#rrradd_embeddingsIsB<      zLogWriter.add_embeddings@c Cs`d|vrtd|durttdn|}t|tr t|}|t |||||ddS)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)rY audio_array sample_rater#r$) rZr[r\r`r{ryarrayrIr]r)rrYrr#rr$rrr add_audios!  zLogWriter.add_audioc Cs^d|vrtdtj||d\}}|durttdn|}|t|||||ddS)aAdd 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)rYhist bin_edgesr#r$)rZryrr[r\rIr])rrYvaluesr#r$Zbucketsrrrrr add_histogramszLogWriter.add_histogramcCsht|tur tdt|turtd|dur ttdn|}|tt |j |||ddS)aAdd 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)nameZ hparam_dictZ metric_listr$) typera TypeErrorr{r[r\rIr]rrrO)rZ hparams_dictZ metrics_listr$rrr add_hparamss  zLogWriter.add_hparamsc CPd|vrtd|durttdn|}|t|||||||ddS)aAdd 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) rVrWNrXrYru predictionsr#r$num_thresholdsweights)rZr[r\rIr]rrrYrurr#rrr$rrr add_pr_curveszLogWriter.add_pr_curvec Cr)aAdd 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) rVrWNrXr)rZr[r\rIr]r rrrr add_roc_curveGszLogWriter.add_roc_curveFc Cszzt|||}Wnty}z td||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%sw) r Exceptionprintformatrr@rr\rCZBFilewriter-)rmodelZ input_specverboseresulteZgraph_file_namewriterrrrr)ps(   zLogWriter.add_graphcCs*|jiurdS|jD]}|q dS)z)Flush all data in cache to disk. 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