o i:!@s6ddlZddlmZddlmZdaGdddZdS)N)Value) SummaryWriterc@s^eZdZdZ   dddZdd d Zdd d ZdddZeddZ e ddZ ddZ dS)GlobalSummaryWriteraA class that implements an event writer that supports concurrent logging and global logging across different modules. The GlobalSummaryWriter class provides a set of API to write TensorBoard events from different processes. The writer instance can be accessed from different processes or modules. Also, the instance maintains the ``global_step`` value itself so that the interleaved requests to write an event will not conflict each other. This ensures that the resulting event file is TensorBoard compatible. With GlobalSummaryWriter, you can easily log the metrics of your parallel-trained model. The GlobalSummaryWriter and also be used like the ``logging`` module of Python. See how ``getSummaryWriter`` is used below. N xTc Ksnt||||||||d|_t|_t|_t|_t|_ t|_ t|_ dS)a Initialize a GlobalSummaryWriter. The resulting instance will maintain a monotonically increasing ``global_step`` for the the event to be written. So there is no need to pass the global_step when calling its member functions such as ``add_scalar()``. All arguments for the constructor will be passed to the ordinary ``SummaryWriter.__init__()`` directly. Examples:: import multiprocessing as mp import numpy as np import time from tensorboardX import GlobalSummaryWriter w = GlobalSummaryWriter() def train(x): w.add_scalar('poolmap/1', x*np.random.randn()) time.sleep(0.05*np.random.randint(0, 10)) w.add_scalar('poolmap/2', x*np.random.randn()) with mp.Pool() as pool: pool.map(train, range(100)) Expected result: .. image:: _static/img/tensorboard/add_scalar_global.png :scale: 50 % )logdircomment purge_step max_queue flush_secsfilename_suffix write_to_disklog_dirN) rsmwmpLocklockManagerdictscalar_tag_to_stepimage_tag_to_stephistogram_tag_to_steptext_tag_to_stepaudio_tag_to_step) selfr r r r r rrrcoalesce_processkwargsrN/home/ubuntu/.local/lib/python3.10/site-packages/tensorboardX/global_writer.py__init__s  zGlobalSummaryWriter.__init__cCsl|j)||jvr|j|d7<nd|j|<|j|||j||WddS1s/wYdS)zAdd scalar data to summary. Args: tag (string): Data identifier scalar_value (float): Value to save walltime (float): Optional override default walltime (time.time()) of event rrN)rrr add_scalar)rtag scalar_valuewalltimerrr r"@s   "zGlobalSummaryWriter.add_scalarCHWcCsp|j+||jvr|j|d7<nd|j|<|jj|||j|||dWddS1s1wYdS)aUAdd image data to summary. Note that this requires the ``pillow`` package. Args: tag (string): Data identifier img_tensor (torch.Tensor, numpy.array): An `uint8` or `float` Tensor of shape `[channel, height, width]` where `channel` is 1, 3, or 4. The elements in img_tensor can either have values in [0, 1] (float32) or [0, 255] (uint8). Users are responsible to scale the data in the correct range/type. walltime (float): Optional override default walltime (time.time()) of event. dataformats (string): This parameter specifies the meaning of each dimension of the input tensor. Shape: img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to convert a batch of tensor into 3xHxW format or use ``add_images()`` and let us do the job. Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitible as long as corresponding ``dataformats`` argument is passed. e.g. CHW, HWC, HW. rr)r% dataformatsN)rrr add_image)rr# img_tensorr%r'rrr r(is   "zGlobalSummaryWriter.add_imagecCsn|j*||jvr|j|d7<nd|j|<|jj|||j||dWddS1s0wYdS)zAdd text data to summary. Args: tag (string): Data identifier text_string (string): String to save walltime (float): Optional override default walltime (time.time()) of event rr) global_stepr%N)rrradd_text)rr# text_stringr%rrr r+s   "zGlobalSummaryWriter.add_textcCs0ttdr tjdur tatdtjjtS)aqGet the writer from global namespace. Examples:: # main.py import global_1 import global_2 # global1.py from tensorboardX import GlobalSummaryWriter writer = GlobalSummaryWriter.getSummaryWriter() # This creates a new instance. writer.add_text('my_log', 'greeting from global1') # global2.py from tensorboardX import GlobalSummaryWriter writer = GlobalSummaryWriter.getSummaryWriter() # Get the instance in global1.py. writer.add_text('my_log', 'greeting from global2') rNzUsing the global logger in:)hasattr_writerrrprint file_writer get_logdirrrrr getSummaryWritersz$GlobalSummaryWriter.getSummaryWritercCs |jSN)r_get_file_writerrrrr r0s zGlobalSummaryWriter.file_writercCs|j|jdSr3)rflushcloser5rrr r7s zGlobalSummaryWriter.close) NrNrrrTNTr3)Nr&) __name__ __module__ __qualname____doc__r!r"r(r+ staticmethodr2propertyr0r7rrrr r s  * ) /   r)multiprocessingrrwriterrr.rrrrr s