Fix Mlflow logging | Add possibility of optional calling of "after run" hooks.

[ihoholko]

Mlflow Logging

Mlflow doesn't support such characters as @. Pull request fixes this by replacing @ with _at_.

MlflowLogger: Invalid metric name: 'val/AR@100'. Names may only contain alphanumerics, underscores (_), dashes (-), periods (.), spaces ( ), and slashes (/) 

after run hooks.

Current pipeline doesn't allow to log artifacts and perform test after train with active hooks. This PR fixes this by adding optinal call_hook_after_run to train and test methods. :

    # start training
    runner.train(call_hook_after_run = False)
    
    runner.load_checkpoint(runner.message_hub.get_info('best_ckpt'))
    
    # start testing
    results = runner.test(call_hook_after_run = False)
    
    
    
    if mlflow.active_run():
        results = {k.replace('@', '_at_'): v for k, v in results.items()}
        mlflow.log_metrics(results)
        
        mlflow.log_artifact(runner.message_hub.get_info('best_ckpt'))
        mlflow.pytorch.log_model(runner.model, 'model')
        
        
    runner.call_hook('after_run')

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[HAOCHENYE]

Hi! Please sign the CLA

[HAOCHENYE]

Hi! Please follow the guide in contributing.md to fix the lint error

[OpenMMLab-Assistant-004]

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[HAOCHENYE]

Thanks for your contribution, the modification of MLFLow makes sense to me, however, we need to reconsider the modifications for the runner. I believe the core issue lies in when to close the visualizer. It appears that closing it in the LoggerHook.after_run is not a very good approach.

[mmeendez8]

@HAOCHENYE should we create a new PR for fixing the MLFlow logging issue? It is currently a problem in mmdet:
open-mmlab/mmdetection#10258