o
    iZ                     @  sH  d dl mZ d dlZd dlZd dlZd dlmZmZmZ d dl	m
Z
 d dlmZmZ d dlZd dlmZ d dlmZ d dlmZmZmZmZmZmZ ed	ejed
\ZZeddd\ZZeddd\e_Zedddd\ZZeddd\Z Zedded\Z!Zerd dl"m#Z# ned	ejeddd\Z#ZdZ$ee%dZ&G dd dZ'dS )    )annotationsN)CallableMappingSequence)Path)TYPE_CHECKINGAny)Dataset)
get_logger)
CommonKeys
IgniteInfoensure_tupleflatten_dictmin_versionoptional_importzignite.engineEventsmlflowz1Please install mlflow before using MLFlowHandler.)
descriptorzmlflow.entitiesz:Please install mlflow.entities before using MLFlowHandler.zmlflow.exceptionsMlflowException)namer   pandasz0Please install pandas for recording the dataset.tqdmz4.47.0)Enginer   	decorator)as_typeLoss)module_namec                   @  s   e Zd ZdZddgZdddddddejdd dd ded	dddd
dfdZd,d-Zd[d0d1Z	d\d4d5Z
d\d6d7Zd8d9 Zed:d; Zd]d^d@dAZd_dCdDZd`dadHdIZdJdK ZdbdLdMZdbdNdOZd\dPdQZd\dRdSZd\dTdUZd\dVdWZdcdXdYZdS )dMLFlowHandlera  
    MLFlowHandler defines a set of Ignite Event-handlers for the MLFlow tracking logics.
    It can be used for any Ignite Engine(trainer, validator and evaluator).
    And it can track both epoch level and iteration level logging, then MLFlow can store
    the data and visualize.
    The expected data source is Ignite ``engine.state.output`` and ``engine.state.metrics``.

    Default behaviors:
        - When EPOCH_COMPLETED, track each dictionary item in
          ``engine.state.metrics`` in MLFlow.
        - When ITERATION_COMPLETED, track expected item in
          ``self.output_transform(engine.state.output)`` in MLFlow, default to `Loss`.

    Usage example is available in the tutorial:
    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.

    Args:
        tracking_uri: connects to a tracking URI. can also set the `MLFLOW_TRACKING_URI` environment
            variable to have MLflow find a URI from there. in both cases, the URI can either be
            an HTTP/HTTPS URI for a remote server, a database connection string, or a local path
            to log data to a directory. The URI defaults to path `mlruns`.
            for more details: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tracking_uri.
        iteration_log: whether to log data to MLFlow when iteration completed, default to `True`.
            ``iteration_log`` can be also a function and it will be interpreted as an event filter
            (see https://pytorch.org/ignite/generated/ignite.engine.events.Events.html for details).
            Event filter function accepts as input engine and event value (iteration) and should return True/False.
        epoch_log: whether to log data to MLFlow when epoch completed, default to `True`.
            ``epoch_log`` can be also a function and it will be interpreted as an event filter.
            See ``iteration_log`` argument for more details.
        epoch_logger: customized callable logger for epoch level logging with MLFlow.
            Must accept parameter "engine", use default logger if None.
        iteration_logger: customized callable logger for iteration level logging with MLFlow.
            Must accept parameter "engine", use default logger if None.
        dataset_logger: customized callable logger to log the dataset information with MLFlow.
            Must accept parameter "dataset_dict", use default logger if None.
        dataset_dict: a dictionary in which the key is the name of the dataset and the value is a PyTorch
            dataset, that needs to be recorded. This arg is only useful when MLFlow version >= 2.4.0.
            For more details about how to log data with MLFlow, please go to the website:
            https://mlflow.org/docs/latest/python_api/mlflow.data.html.
        dataset_keys: a key or a collection of keys to indicate contents in the dataset that
            need to be stored by MLFlow.
        output_transform: a callable that is used to transform the
            ``ignite.engine.state.output`` into a scalar to track, or a dictionary of {key: scalar}.
            By default this value logging happens when every iteration completed.
            The default behavior is to track loss from output[0] as output is a decollated list
            and we replicated loss value for every item of the decollated list.
            `engine.state` and `output_transform` inherit from the ignite concept:
            https://pytorch-ignite.ai/concepts/03-state/, explanation and usage example are in the tutorial:
            https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
        global_epoch_transform: a callable that is used to customize global epoch number.
            For example, in evaluation, the evaluator engine might want to track synced epoch number
            with the trainer engine.
        state_attributes: expected attributes from `engine.state`, if provided, will extract them
            when epoch completed.
        tag_name: when iteration output is a scalar, `tag_name` is used to track, defaults to `'Loss'`.
        experiment_name: the experiment name of MLflow, default to `'monai_experiment'`. An experiment can be
            used to record several runs.
        run_name: the run name in an experiment. A run can be used to record information about a workflow,
            like the loss, metrics and so on.
        experiment_param: a dict recording parameters which will not change through the whole workflow,
            like torch version, cuda version and so on.
        artifacts: paths to images that need to be recorded after running the workflow.
        optimizer_param_names: parameter names in the optimizer that need to be recorded during running the
            workflow, default to `'lr'`.
        close_on_complete: whether to close the mlflow run in `complete` phase in workflow, default to False.

    For more details of MLFlow usage, please refer to: https://mlflow.org/docs/latest/index.html.

    
max_epochsepoch_lengthNTc                 C  s   | d S )Nr    xr    r    _/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/handlers/mlflow_handler.py<lambda>   s    zMLFlowHandler.<lambda>c                 C  s   | S Nr    r!   r    r    r#   r$      s    monai_experimentlrFtracking_uri
str | Noneiteration_log$bool | Callable[[Engine, int], bool]	epoch_logepoch_loggerCallable[[Engine], Any] | Noneiteration_loggerdataset_logger-Callable[[Mapping[str, Dataset]], Any] | Nonedataset_dictMapping[str, Dataset] | Nonedataset_keysstroutput_transformr   global_epoch_transformstate_attributesSequence[str] | Nonetag_nameexperiment_namerun_nameexperiment_paramdict | None	artifactsstr | Sequence[Path] | Noneoptimizer_param_namesstr | Sequence[str]close_on_completeboolreturnNonec                 C  s   || _ || _|| _|| _|| _|	| _|
| _|| _|| _|| _	|| _
|| _t|| _t|| _tj|r4|nd d| _tjjtjjj| _|| _d | _d | _|| _t|| _d S )N)r(   )r*   r,   r-   r/   r0   r6   r7   r8   r:   r;   r<   r=   r   r?   rA   r   ZMlflowClientcliententitiesZ	RunStatus	to_stringFINISHEDrun_finish_statusrC   
experimentcur_runr2   r4   )selfr(   r*   r,   r-   r/   r0   r2   r4   r6   r7   r8   r:   r;   r<   r=   r?   rA   rC   r    r    r#   __init__|   s*   

zMLFlowHandler.__init__
param_dictdictc                 C  sP   | j du rdS t| }| j| j jjj}|j}|D ]	}||v r%||= qdS )z
        Delete parameters in given dict, if they are already logged by current mlflow run.

        Args:
            param_dict: parameter dict to be logged to mlflow.
        N)	rM   listkeysrG   get_runinforun_iddataparams)rN   rP   key_listlog_dataZlog_param_dictkeyr    r    r#   _delete_exist_param_in_dict   s   
z)MLFlowHandler._delete_exist_param_in_dictenginer   c                 C  s   | | jtjs|tj| j | jr0| | jtjs0tj}t| jr)|| jd}||| j | j	rP| | j
tjsPtj}t| j	rI|| j	d}||| j
 | | jtjs`|tj| j | jru| | jtjsw|tj| j dS dS dS )z
        Register a set of Ignite Event-Handlers to a specified Ignite engine.

        Args:
            engine: Ignite Engine, it can be a trainer, validator or evaluator.

        )event_filterN)has_event_handlerstartr   STARTEDadd_event_handlerr*   iteration_completedITERATION_COMPLETEDcallabler,   epoch_completedEPOCH_COMPLETEDcomplete	COMPLETEDrC   close)rN   r]   eventr    r    r#   attach   s"   

zMLFlowHandler.attachc                   s     jstdj djsWjdu r dtd njj	jj
}fdd|D }fdd|D }|rLj|d	 jj_njjjj
d
_jr`j  fddjD }| | jrj dS j dS )z?
        Check MLFlow status and start if not active.

        zFailed to set experiment '' as the active experimentNrun_z%Y%m%d_%H%M%Sc                   s"   g | ]}|j j ksjs|qS r    )rU   r<   .0r)r<   rN   r    r#   
<listcomp>      " z'MLFlowHandler.start.<locals>.<listcomp>c                   s   g | ]}|j j jkr|qS r    )rU   statusrK   ro   rN   r    r#   rr          )experiment_idr<   c                      i | ]
}|t  j|d qS r%   getattrstaterp   attrr]   r    r#   
<dictcomp>       z'MLFlowHandler.start.<locals>.<dictcomp>)_set_experimentrL   
ValueErrorr;   rM   r<   timestrftimerG   Zsearch_runsrx   rT   rU   rV   Z
create_runr=   _log_paramsdefault_tracking_paramsr\   r0   r2   _default_dataset_log)rN   r]   runsattrsr    )r]   r<   rN   r#   r`      s&    

zMLFlowHandler.startc                 C  s   | j }|sPtdD ]F}z| j| j}|s"| j| j}| j|}W  n+ tyO } zdt|v rCt	
d td |dkrB|n|W Y d }~q	d }~ww |jtjjjkratd| j d|| _ d S )N   ZRESOURCE_ALREADY_EXISTSz4Experiment already exists; delaying before retrying.      z!Cannot set a deleted experiment 'rm   )rL   rangerG   Zget_experiment_by_namer;   Zcreate_experimentZget_experimentr   r5   loggerwarningr   sleepZlifecycle_stager   rH   ZLifecycleStageZACTIVEr   )rN   rL   Z_retry_timerx   er    r    r#   r      s.   

	
zMLFlowHandler._set_experimentc                 C  s&   | j }| d| j| d| jd iS )NZ_digest_samplesnum_rows)r   digestprofile)Zpandas_datasetdataset_namer    r    r#   _get_pandas_dataset_info  s   z&MLFlowHandler._get_pandas_dataset_infotrainsample_dictdict[str, Any]contextc           
        s   | j std| j| j jj| _  fdd| j jjD }tt	|}  d| }t
|}tjj||dttfdd| j jj}t	|sitj g}| jj| j jj|d t}	| |	 d S d S )	Nz,Current Run is not Active to log the datasetc                   s   g | ]}|j j r|qS r    )datasetr   
startswith)rp   r"   )r   r    r#   rr     rv   z.MLFlowHandler._log_dataset.<locals>.<listcomp>Z	_dataset_)r   c                   s   | j j jkS r%   )r   r   r!   )r   r    r#   r$     s    z,MLFlowHandler._log_dataset.<locals>.<lambda>)rV   datasets)rM   r   rG   rT   rU   rV   inputsZdataset_inputsr5   lenr   	DataFramer   rW   from_pandasrR   filterrH   ZDatasetInputZ_to_mlflow_entityZ
log_inputsr   r   r   )
rN   r   r   Z
logged_setZdataset_countr   Z	sample_dfZexist_dataset_listr   Zdataset_infor    )r   r   r#   _log_dataset  s"   

zMLFlowHandler._log_datasetrX   c                 C  s>   | j stddd | D }| jj| j jjg |g d d S )Nz'Current Run is not Active to log paramsc                 S  s"   g | ]\}}t j|t|qS r    )r   rH   Paramr5   rp   r[   valuer    r    r#   rr   )  rs   z-MLFlowHandler._log_params.<locals>.<listcomp>rV   metricsrX   tags)rM   r   itemsrG   	log_batchrU   rV   )rN   rX   Z
params_arrr    r    r#   r   &  s   zMLFlowHandler._log_paramsr   step
int | Nonec                   s\   | j std| j jj}tt d  fddt| D }| jj	||g g d d S )Nz(Current Run is not Active to log metricsi  c                   s&   g | ]\}}t j|| pd qS )r   )r   rH   Metricr   r   	timestampr    r#   rr   2  s    z.MLFlowHandler._log_metrics.<locals>.<listcomp>r   )
rM   r   rU   rV   intr   r   r   rG   r   )rN   r   r   rV   Zmetrics_arrr    r   r#   _log_metrics,  s   

zMLFlowHandler._log_metricsc                 C  sj   g }| j D ]-}|s
qtj|r|| qt|D ]\}}}|D ]}tj||}|| q"qq|S )z
        Log artifacts to mlflow. Given a path, all files in the path will be logged recursively.
        Given a file, it will be logged to mlflow.
        )r?   ospathisfileappendwalkjoin)rN   artifact_list	path_nameroot_	filenamesfilename	file_pathr    r    r#   _parse_artifacts7  s   
zMLFlowHandler._parse_artifactsc                 C  s>   | j r| jr|  }|D ]}| j| jjj| qdS dS dS )zM
        Handler for train or validation/evaluation completed Event.
        N)r?   rM   r   rG   Zlog_artifactrU   rV   )rN   r   artifactr    r    r#   rh   J  s   zMLFlowHandler.completec                 C  s*   | j r| j| j jj| j d| _ dS dS )z9
        Stop current running logger of MLFlow.

        N)rM   rG   Zset_terminatedrU   rV   rK   ru   r    r    r#   rj   S  s   
zMLFlowHandler.closec                 C  &   | j dur|  | dS | | dS )a  
        Handler for train or validation/evaluation epoch completed Event.
        Track epoch level log, default values are from Ignite `engine.state.metrics` dict.

        Args:
            engine: Ignite Engine, it can be a trainer, validator or evaluator.

        N)r-   _default_epoch_logrN   r]   r    r    r#   rf   \     
	zMLFlowHandler.epoch_completedc                 C  r   )z
        Handler for train or validation/evaluation iteration completed Event.
        Track iteration level log.

        Args:
            engine: Ignite Engine, it can be a trainer, validator or evaluator.

        N)r/   _default_iteration_logr   r    r    r#   rc   j  r   z!MLFlowHandler.iteration_completedc                   s`    j j}|sdS |  j j}| j||d | jdur. fdd| jD }| j||d dS dS )a(  
        Execute epoch level log operation.
        Default to track the values from Ignite `engine.state.metrics` dict and
        track the values of specified attributes of `engine.state`.

        Args:
            engine: Ignite Engine, it can be a trainer, validator or evaluator.

        Nr   c                   ry   r%   rz   r}   r   r    r#   r     r   z4MLFlowHandler._default_epoch_log.<locals>.<dictcomp>)r|   r   r7   epochr   r8   )rN   r]   Zlog_dictcurrent_epochr   r    r   r#   r   x  s   

z MLFlowHandler._default_epoch_logc                   s   |  |jj}|du rdS t|ts!| jt|tjr| n|i}| j	||jj
d t|drM|j}| jD ]  fddt|jD }| j	||jj
d q5dS dS )a  
        Execute iteration log operation based on Ignite `engine.state.output` data.
        Log the values from `self.output_transform(engine.state.output)`.
        Since `engine.state.output` is a decollated list and we replicated the loss value for every item
        of the decollated list, the default behavior is to track the loss from `output[0]`.

        Args:
            engine: Ignite Engine, it can be a trainer, validator or evaluator.

        Nr   	optimizerc                   s(   i | ]\}}  d | t |  qS )Z_group_)float)rp   iparam_group
param_namer    r#   r     s    z8MLFlowHandler._default_iteration_log.<locals>.<dictcomp>)r6   r|   output
isinstancerQ   r:   torchTensoritemr   	iterationhasattrr   rA   	enumerateparam_groups)rN   r]   lossZcur_optimizerrX   r    r   r#   r     s   



z$MLFlowHandler._default_iteration_logc           	   
   C  s   |du rdS t |dkrtd | D ]d\}}|du r%td| di }t|dg }t|d| dD ]<}| jD ]6}||vrEg ||< ||v rN|| }ntd	| d
t	|t
sjtdt| d| d q;|| | q;q6| || qdS )a  
        Execute dataset log operation based on the input dataset_dict. The dataset_dict should have a format
        like:
            {
                "dataset_name0": dataset0,
                "dataset_name1": dataset1,
                ......
            }
        The keys stand for names of datasets, which will be logged as prefixes of dataset names in MLFlow.
        The values are PyTorch datasets from which sample names are abstracted to build a Pandas DataFrame.
        If the input dataset_dict is None, this function will directly return and do nothing.

        To use this function, every sample in the input datasets must contain keys specified by the `dataset_keys`
        parameter.
        This function will log a PandasDataset to MLFlow inputs, generated from the Pandas DataFrame.
        For more details about PandasDataset, please refer to this link:
        https://mlflow.org/docs/latest/python_api/mlflow.data.html#mlflow.data.pandas_dataset.PandasDataset

        Please note that it may take a while to record the dataset if it has too many samples.

        Args:
            dataset_dict: a dictionary in which the key is the name of the dataset and the value is a PyTorch
                dataset, that needs to be recorded.

        Nr   zThere is no dataset to log!zThe z0 dataset of is None. Cannot record it by MLFlow.rW   zRecording the z datasetzUnexpect key 'z' in the sample.zExpected type string, got type z of the z( name.May log an empty dataset in MLFlow)r   warningswarnr   AttributeErrorr{   r   r4   KeyErrorr   r5   typer   r   )	rN   r2   Zdataset_typer   r   Zdataset_samplessampler[   Zvalue_to_logr    r    r#   r     s0   



z"MLFlowHandler._default_dataset_log)&r(   r)   r*   r+   r,   r+   r-   r.   r/   r.   r0   r1   r2   r3   r4   r5   r6   r   r7   r   r8   r9   r:   r5   r;   r5   r<   r)   r=   r>   r?   r@   rA   rB   rC   rD   rE   rF   )rP   rQ   rE   rF   )r]   r   rE   rF   )r   )r   r   r   r5   rE   rF   )rX   r   rE   rF   r%   )r   r   r   r   rE   rF   )rE   rF   )r2   r3   rE   rF   )__name__
__module____qualname____doc__r   r   IMAGEDEFAULT_TAGrO   r\   rl   r`   r   staticmethodr   r   r   r   r   rh   rj   rf   rc   r   r   r   r    r    r    r#   r   2   sN    G
+

 



	
	


r   )(
__future__r   r   r   r   collections.abcr   r   r   pathlibr   typingr   r   r   torch.utils.datar	   monai.apps.utilsr
   monai.utilsr   r   r   r   r   r   OPT_IMPORT_VERSIONr   r   r   rH   r   r   r   ignite.enginer   r   r   r   r   r    r    r    r#   <module>   s:    


