o oi2(@sdZddlZddlmZddlmZmZmZmZddl m Z ddl m Z ddl mZddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddl m!Z!ddl"m#Z#m$Z$GdddeeZdS)z' TensorBoard Logger ------------------ N) Namespace)AnyDictOptionalUnion)Tensor)override)_TENSORBOARD_AVAILABLE)TensorBoardLogger)_is_dir)_convert_params)_PATH)ModelCheckpoint)save_hparams_to_yaml)Logger)_OMEGACONF_AVAILABLE)rank_zero_onlyrank_zero_warncsreZdZdZdZ      d,ded eed eee efd e d e d edeede ffdd Z e edeffdd Ze edefddZe edefddZee d-deeee fefdeeee fddffdd Zee d-dddeeddfd d!Zeed.fd"d# Zeed$eddffd%d& Zed'eddfd(d)Zede fd*d+ZZS)/r aR Log to local or remote file system in `TensorBoard `_ format. Implemented using :class:`~tensorboardX.SummaryWriter`. Logs are saved to ``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes preinstalled. This logger supports logging to remote filesystems via ``fsspec``. Make sure you have it installed and you don't have tensorflow (otherwise it will use tf.io.gfile instead of fsspec). Example: .. testcode:: :skipif: not _TENSORBOARD_AVAILABLE or not _TENSORBOARDX_AVAILABLE from lightning.pytorch import Trainer from lightning.pytorch.loggers import TensorBoardLogger logger = TensorBoardLogger("tb_logs", name="my_model") trainer = Trainer(logger=logger) Args: save_dir: Save directory name: Experiment name. Defaults to ``'default'``. If it is the empty string then no per-experiment subdirectory is used. version: Experiment version. If version is not specified the logger inspects the save directory for existing versions, then automatically assigns the next available version. If it is a string then it is used as the run-specific subdirectory name, otherwise ``'version_${version}'`` is used. log_graph: Adds the computational graph to tensorboard. This requires that the user has defined the `self.example_input_array` attribute in their model. default_hp_metric: Enables a placeholder metric with key `hp_metric` when `log_hyperparams` is called without a metric (otherwise calls to log_hyperparams without a metric are ignored). prefix: A string to put at the beginning of metric keys. sub_dir: Sub-directory to group TensorBoard logs. If a sub_dir argument is passed then logs are saved in ``/save_dir/name/version/sub_dir/``. Defaults to ``None`` in which logs are saved in ``/save_dir/name/version/``. \**kwargs: Additional arguments used by :class:`tensorboardX.SummaryWriter` can be passed as keyword arguments in this logger. To automatically flush to disk, `max_queue` sets the size of the queue for pending logs before flushing. `flush_secs` determines how many seconds elapses before flushing. z hparams.yamllightning_logsNFTsave_dirnameversion log_graphdefault_hp_metricprefixsub_dirkwargsc sNtjd||||||d||rtstdtt|o t|_i|_dS)N)root_dirrrrrrzPYou set `TensorBoardLogger(log_graph=True)` but `tensorboard` is not available. )super__init__r rstr _log_graphhparams) selfrrrrrrrr __class__rY/home/ubuntu/.local/lib/python3.10/site-packages/lightning/pytorch/loggers/tensorboard.pyr!Us$    zTensorBoardLogger.__init__returncstjtj|jS)zParent directory for all tensorboard checkpoint subdirectories. If the experiment name parameter is an empty string, no experiment subdirectory is used and the checkpoint will be saved in "save_dir/version" )ospathjoinr rrr%r&rr(rqs zTensorBoardLogger.root_dircCsft|jtr |jnd|j}tj|j|}t|jtr%tj||j}tj|}tj |}|S)zThe directory for this run's tensorboard checkpoint. By default, it is named ``'version_${self.version}'`` but it can be overridden by passing a string value for the constructor's version parameter instead of ``None`` or an int. version_) isinstancerr"r*r+r,rr expandvars expanduser)r%rlog_dirrrr(r2|s    zTensorBoardLogger.log_dircCs|jS)zGets the save directory where the TensorBoard experiments are saved. Returns: The local path to the save directory where the TensorBoard experiments are saved. ) _root_dirr-rrr(rs zTensorBoardLogger.save_dirparamsmetricscsXtr ddlm}m}t|}trt||r||j||_n|j|t j ||dS)aRecord hyperparameters. TensorBoard logs with and without saved hyperparameters are incompatible, the hyperparameters are then not displayed in the TensorBoard. Please delete or move the previously saved logs to display the new ones with hyperparameters. Args: params: a dictionary-like container with the hyperparameters metrics: Dictionary with metric names as keys and measured quantities as values r) Container OmegaConf)r4r5) r omegaconfr6r7r r/merger$updater log_hyperparams)r%r4r5r6r7r&rr(r;s z!TensorBoardLogger.log_hyperparamsmodelzpl.LightningModule input_arraycCs|jsdS|dur |jn|}|durtddSt|ttfs+tdt|ddS||}||}t j j |j ||WddS1sNwYdS)NzCould not log computational graph to TensorBoard: The `model.example_input_array` attribute is not set or `input_array` was not given.zlCould not log computational graph to TensorBoard: The `input_array` or `model.example_input_array` has type z which can't be traced by TensorBoard. Make the input array a tuple representing the positional arguments to the model's `forward()` implementation.)r#example_input_arrayrr/rtupletype_on_before_batch_transfer_apply_batch_transfer_handlerplcoremodule_jit_is_scripting experiment add_graph)r%r<r=rrr(rs$  "zTensorBoardLogger.log_graphcsPt|j}tj||j}t|j|r$|j |s&t ||j dSdSdSN) r saver2r*r+r,NAME_HPARAMS_FILEr _fsisfilerr$)r%dir_path hparams_filer&rr(rJs zTensorBoardLogger.savestatuscs$t||dkr|dSdS)Nsuccess)r finalizerJ)r%rPr&rr(rRs  zTensorBoardLogger.finalizecheckpoint_callbackcCsdS)zCalled after model checkpoint callback saves a new checkpoint. 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