a Dfb @sdZddlZddlZddlmZddlZddlZddlm Z m Z z*ddl Z ddl m Z m Z ee dshJWneefydZ Yn0ddZGd d d ZdS) z2Main Logger class for ClearML experiment tracking.N)Path) Annotatorcolors)DatasetTask __version__cCs\|dd}tj|d}t|}ttt|dtt|d}t|dkrbt dnt|dkrvt d t |d}t |}Wd n1s0Yt |hd sJd t}|d rt||d nd |d <|drt||dnd |d<|dr8t||dnd |d<|d|d<|d|d<|S)zQLoad in a clearml dataset and fill the internal data_dict with its contents. clearml://) dataset_idz*.yamlz*.ymlz{More than one yaml file was found in the dataset root, cannot determine which one contains the dataset definition this way.rztNo yaml definition found in dataset root path, check that there is a correct yaml file inside the dataset root path.N>nctestvaltrainnameszThe right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')rr rr r)replacergetrZget_local_copylistglobstrlen ValueErroropenyaml safe_loadsetkeys issupersetdictresolve)Zclearml_info_stringr datasetZdataset_root_pathZyaml_filenamesfZdataset_definition data_dictr#Y/data/NAS7/SABIOD/METHODE/ermites/yolo-dyni/yolov5/utils/loggers/clearml/clearml_utils.pyconstruct_datasetsJ   (   (   r%c@s,eZdZdZddZd ddZd dd Zd S) ClearmlLoggeraLog training runs, datasets, models, and predictions to ClearML. This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics and analyses. By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. cCsd|_t|_d|_|j|_t|_d|_d|_|jrtj |j dkrH|j nd|j dkrZ|j nddgd|j d d id |_|jj |d d |jj |dd |jjdddd|jdrt|j|_|j|_dS)a) - Initialize ClearML Task, this object will capture the experiment - Upload dataset version to ClearML Data if opt.upload_dataset is True arguments: opt (namespace) -- Commandline arguments for this run hyp (dict) -- Hyperparameters for this run rNz runs/trainYOLOv5expTrainingTpytorchF) project_nameZ task_nametagsZ output_uriZreuse_last_task_idZauto_connect_frameworksZHyperparameters)nameArgszultralytics/yolov5:latestz7--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"zpip install clearml)Zdocker_argumentsZdocker_setup_bash_scriptr) current_epochrcurrent_epoch_logged_imagesmax_imgs_to_log_per_epoch bbox_intervalclearmltaskr"rinitprojectr.exist_okconnectZset_base_dockerdata startswithr%)selfopthypr#r#r$__init__Bs2    zClearmlLogger.__init__ Debug SamplescCsf|D]\}|rtd|j}|r2t|dnd}|jj||j | dt ||dqdS)z Log files (images) as debug samples in the ClearML task. arguments: files (List(PosixPath)) a list of file paths in PosixPath format title (str) A title that groups together images with the same values z _batch(\d+)rr )titleseriesZ local_path iterationN) existsresearchr.intgroupsr5 get_logger report_imagergroupr)r<filesrAr!itrCr#r#r$log_debug_samplests zClearmlLogger.log_debug_samples?cCs^t|j|jkrZ|jdkrZ|j|jdkrZ||jvrZtt|d dd dd}t |dd}tt|dddf|dddf|ddddfD]z\}\} } } t|} |t| } tt| d d}| d |d }| |kr|j| | d |j| || d q|}|jjd|j|j|d|j|dS)a Draw the bounding boxes on a single image and report the result as a ClearML debug sample. arguments: image_path (PosixPath) the path the original image file boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] class_names (dict): dict containing mapping of class int to class name image (Tensor): A torch tensor containing the actual image data rT)impilNdz: %)outline)labelcolorzBounding Boxes)rArBrCimage)rr1r2r0r3npascontiguousarraymoveaxismulclampbytecpunumpyr enumerateziprrGroundfloat rectangle box_labelresultr5rIrJr.add)r< image_pathboxes class_namesr[Zconf_thresholdrR annotatoriconfZclass_nrboxrZ class_nameZconfidence_percentagerYZannotated_imager#r#r$log_image_with_boxess& . F  z"ClearmlLogger.log_image_with_boxesN)r@)rO)__name__ __module__ __qualname____doc__r?rNrtr#r#r#r$r&7s 2 r&)rxrrEpathlibrrcr\r utils.plotsrrr4rrhasattr ImportErrorAssertionErrorr%r&r#r#r#r$s  #