How to use the deeplabcut.utils.auxiliaryfunctions.GetModelFolder function in deeplabcut

if gputouse is not None: #gpu selection
            os.environ['CUDA_VISIBLE_DEVICES'] = str(gputouse)

    tf.reset_default_graph()
    start_path=os.getcwd() #record cwd to return to this directory in the end

    cfg = auxiliaryfunctions.read_config(config)
    trainFraction = cfg['TrainingFraction'][trainingsetindex]

    if cropping is not None:
        cfg['cropping']=True
        cfg['x1'],cfg['x2'],cfg['y1'],cfg['y2']=cropping
        print("Overwriting cropping parameters:", cropping)
        print("These are used for all videos, but won't be save to the cfg file.")

    modelfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetModelFolder(trainFraction,shuffle,cfg)))
    path_test_config = Path(modelfolder) / 'test' / 'pose_cfg.yaml'
    try:
        dlc_cfg = load_config(str(path_test_config))
    except FileNotFoundError:
        raise FileNotFoundError("It seems the model for shuffle %s and trainFraction %s does not exist."%(shuffle,trainFraction))

    # Check which snapshots are available and sort them by # iterations
    try:
      Snapshots = np.array([fn.split('.')[0]for fn in os.listdir(os.path.join(modelfolder , 'train'))if "index" in fn])
    except FileNotFoundError:
      raise FileNotFoundError("Snapshots not found! It seems the dataset for shuffle %s has not been trained/does not exist.\n Please train it before using it to analyze videos.\n Use the function 'train_network' to train the network for shuffle %s."%(shuffle,shuffle))

    if cfg['snapshotindex'] == 'all':
        print("Snapshotindex is set to 'all' in the config.yaml file. Running video analysis with all snapshots is very costly! Use the function 'evaluate_network' to choose the best the snapshot. For now, changing snapshot index to -1!")
        snapshotindex = -1
    else:

if gputouse is not None:  # gpu selection
        os.environ['CUDA_VISIBLE_DEVICES'] = str(gputouse)

    vers = (tf.__version__).split('.')
    if int(vers[0]) == 1 and int(vers[1]) > 12:
        TF = tf.compat.v1
    else:
        TF = tf

    TF.reset_default_graph()
    start_path=os.getcwd() #record cwd to return to this directory in the end

    cfg = auxiliaryfunctions.read_config(config)
    trainFraction = cfg['TrainingFraction'][trainingsetindex]
    modelfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetModelFolder(trainFraction,shuffle,cfg)))
    path_test_config = Path(modelfolder) / 'test' / 'pose_cfg.yaml'
    try:
        dlc_cfg = load_config(str(path_test_config))
    except FileNotFoundError:
        raise FileNotFoundError("It seems the model for shuffle %s and trainFraction %s does not exist."%(shuffle,trainFraction))
    # Check which snapshots are available and sort them by # iterations
    try:
      Snapshots = np.array([fn.split('.')[0]for fn in os.listdir(os.path.join(modelfolder , 'train'))if "index" in fn])
    except FileNotFoundError:
      raise FileNotFoundError("Snapshots not found! It seems the dataset for shuffle %s has not been trained/does not exist.\n Please train it before using it to analyze videos.\n Use the function 'train_network' to train the network for shuffle %s."%(shuffle,shuffle))

    if cfg['snapshotindex'] == 'all':
        print("Snapshotindex is set to 'all' in the config.yaml file. Running video analysis with all snapshots is very costly! Use the function 'evaluate_network' to choose the best the snapshot. For now, changing snapshot index to -1!")
        snapshotindex = -1
    else:
        snapshotindex=cfg['snapshotindex']

#reload logger.
    import importlib
    import logging
    importlib.reload(logging)
    logging.shutdown()

    from deeplabcut.pose_estimation_tensorflow.train import train
    from deeplabcut.utils import auxiliaryfunctions

    TF.reset_default_graph()
    start_path=os.getcwd()

    # Read file path for pose_config file. >> pass it on
    cfg = auxiliaryfunctions.read_config(config)
    modelfoldername=auxiliaryfunctions.GetModelFolder(cfg["TrainingFraction"][trainingsetindex],shuffle,cfg)
    poseconfigfile=Path(os.path.join(cfg['project_path'],str(modelfoldername),"train","pose_cfg.yaml"))
    if not poseconfigfile.is_file():
      print("The training datafile ", poseconfigfile, " is not present.")
      print("Probably, the training dataset for this specific shuffle index was not created.")
      print("Try with a different shuffle/trainingsetfraction or use function 'create_training_dataset' to create a new trainingdataset with this shuffle index." )
    else:
      # Set environment variables
      if autotune is not False: #see: https://github.com/tensorflow/tensorflow/issues/13317
          os.environ['TF_CUDNN_USE_AUTOTUNE'] = '0'
      if gputouse is not None:
        os.environ['CUDA_VISIBLE_DEVICES'] = str(gputouse)

      try:
          train(str(poseconfigfile),displayiters,saveiters,maxiters,max_to_keep=max_snapshots_to_keep,keepdeconvweights=keepdeconvweights,allow_growth=allow_growth) #pass on path and file name for pose_cfg.yaml!
      except BaseException as e:
          raise e

start_path=os.getcwd()
    # Read file path for pose_config file. >> pass it on
    cfg = auxiliaryfunctions.read_config(config)

    # Loading human annotatated data
    trainingsetfolder=auxiliaryfunctions.GetTrainingSetFolder(cfg)
    #Data=pd.read_hdf(os.path.join(cfg["project_path"],str(trainingsetfolder),'CollectedData_' + cfg["scorer"] + '.h5'),'df_with_missing')

    # Get list of body parts to evaluate network for
    comparisonbodyparts=auxiliaryfunctions.IntersectionofBodyPartsandOnesGivenbyUser(cfg,comparisonbodyparts)
    ##################################################
    # Load data...
    ##################################################
    trainFraction=cfg["TrainingFraction"][trainingsetindex]
    datafn,metadatafn=auxiliaryfunctions.GetDataandMetaDataFilenames(trainingsetfolder,trainFraction,shuffle,cfg)
    modelfolder=os.path.join(cfg["project_path"],str(auxiliaryfunctions.GetModelFolder(trainFraction,shuffle,cfg)))
    path_test_config = Path(modelfolder) / 'test' / 'pose_cfg.yaml'
    # Load meta data
    data, trainIndices, testIndices, trainFraction=auxiliaryfunctions.LoadMetadata(os.path.join(cfg["project_path"],metadatafn))

    try:
        dlc_cfg = load_config(str(path_test_config))
    except FileNotFoundError:
        raise FileNotFoundError("It seems the model for shuffle %s and trainFraction %s does not exist."%(shuffle,trainFraction))

    ########################### RESCALING (to global scale)
    if rescale==True:
        scale=dlc_cfg['global_scale']
        print("Rescaling Data to ", scale)
        Data=pd.read_hdf(os.path.join(cfg["project_path"],str(trainingsetfolder),'CollectedData_' + cfg["scorer"] + '.h5'),'df_with_missing')*scale
    else:
        scale=1

config = auxiliaryfunctions.read_config(cfg)
    config['bodyparts'] = ['ankle1','knee1','hip1','hip2','knee2','ankle2','wrist1','elbow1','shoulder1','shoulder2','elbow2','wrist2','chin','forehead']
    config['skeleton'] = [['ankle1', 'knee1'],['ankle2', 'knee2'],['knee1', 'hip1'],['knee2', 'hip2'],['hip1', 'hip2'], ['shoulder1', 'shoulder2'], ['shoulder1', 'hip1'], ['shoulder2', 'hip2'], ['shoulder1', 'elbow1'], ['shoulder2', 'elbow2'], ['chin', 'forehead'], ['elbow1', 'wrist1'], ['elbow2', 'wrist2']]
    config['default_net_type']='resnet_101'
    auxiliaryfunctions.write_config(cfg,config)
    config = auxiliaryfunctions.read_config(cfg)

    train_dir = Path(os.path.join(config['project_path'],str(auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)),'train'))
    test_dir = Path(os.path.join(config['project_path'],str(auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)),'test'))

    # Create the model directory
    train_dir.mkdir(parents=True,exist_ok=True)
    test_dir.mkdir(parents=True,exist_ok=True)

    modelfoldername=auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)

    path_train_config = str(os.path.join(config['project_path'],Path(modelfoldername),'train','pose_cfg.yaml'))
    path_test_config = str(os.path.join(config['project_path'],Path(modelfoldername),'test','pose_cfg.yaml'))

    # Download the weights and put then in appropriate directory
    cwd = os.getcwd()
    os.chdir(train_dir)
    print("Checking if the weights are already available, otherwise I will download them!")
    weightfilename=auxfun_models.download_mpii_weigths(train_dir)
    os.chdir(cwd)

    # Create the pose_config.yaml files
    parent_path = Path(os.path.dirname(deeplabcut.__file__))
    defaultconfigfile = str(parent_path / 'pose_cfg.yaml')
    trainingsetfolder = auxiliaryfunctions.GetTrainingSetFolder(config)
    datafilename,metadatafilename=auxiliaryfunctions.GetDataandMetaDataFilenames(trainingsetfolder,trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)

DTYPE = [('image', 'O'), ('size', 'O'), ('joints', 'O')]
                MatlabData = np.array(
                    [(np.array([data[item]['image']], dtype='U'),
                      np.array([data[item]['size']]),
                      boxitintoacell(data[item]['joints']))
                     for item in range(len(data))],
                    dtype=DTYPE)

                sio.savemat(os.path.join(project_path,datafilename), {'dataset': MatlabData})

                ################################################################################
                # Creating file structure for training &
                # Test files as well as pose_yaml files (containing training and testing information)
                #################################################################################
                modelfoldername=auxiliaryfunctions.GetModelFolder(trainFraction,shuffle,cfg)
                auxiliaryfunctions.attempttomakefolder(Path(config).parents[0] / modelfoldername,recursive=True)
                auxiliaryfunctions.attempttomakefolder(str(Path(config).parents[0] / modelfoldername)+ '/'+ '/train')
                auxiliaryfunctions.attempttomakefolder(str(Path(config).parents[0] / modelfoldername)+ '/'+ '/test')

                path_train_config = str(os.path.join(cfg['project_path'],Path(modelfoldername),'train','pose_cfg.yaml'))
                path_test_config = str(os.path.join(cfg['project_path'],Path(modelfoldername),'test','pose_cfg.yaml'))
                #str(cfg['proj_path']+'/'+Path(modelfoldername) / 'test'  /  'pose_cfg.yaml')

                items2change = {
                    "dataset": datafilename,
                    "metadataset": metadatafilename,
                    "num_joints": len(bodyparts),
                    "all_joints": [[i] for i in range(len(bodyparts))],
                    "all_joints_names": [str(bpt) for bpt in bodyparts],
                    "init_weights": model_path,
                    "project_path": str(cfg['project_path']),

Full path of the config.yaml file as a string.

    shuffle: int, optional
        Integer value specifying the shuffle index to select for training. Default is set to 1

    trainingsetindex: int, optional
        Integer specifying which TrainingsetFraction to use. By default the first (note that TrainingFraction is a list in config.yaml).

    Returns the triple: trainposeconfigfile, testposeconfigfile, snapshotfolder

    '''
    from deeplabcut.utils import auxiliaryfunctions
    # Read file path for pose_config file. >> pass it on
    cfg = auxiliaryfunctions.read_config(config)

    modelfoldername=auxiliaryfunctions.GetModelFolder(cfg["TrainingFraction"][trainingsetindex],shuffle,cfg)
    trainposeconfigfile=Path(os.path.join(cfg['project_path'],str(modelfoldername),"train","pose_cfg.yaml"))
    testposeconfigfile=Path(os.path.join(cfg['project_path'],str(modelfoldername),"test","pose_cfg.yaml"))
    snapshotfolder=Path(os.path.join(cfg['project_path'],str(modelfoldername),'train'))

    return trainposeconfigfile,testposeconfigfile,snapshotfolder

Windows:
    >>> deeplabcut.create_pretrained_human_project('human','Bill',[r'C:\yourusername\rig-95\Videos\reachingvideo1.avi'],r'C:\yourusername\analysis\project' copy_videos=False)
    Users must format paths with either:  r'C:\ OR 'C:\\ <- i.e. a double backslash \ \ )
    --------
    """

    cfg=deeplabcut.create_new_project(project,experimenter,videos,working_directory,copy_videos,videotype)

    config = auxiliaryfunctions.read_config(cfg)
    config['bodyparts'] = ['ankle1','knee1','hip1','hip2','knee2','ankle2','wrist1','elbow1','shoulder1','shoulder2','elbow2','wrist2','chin','forehead']
    config['skeleton'] = [['ankle1', 'knee1'],['ankle2', 'knee2'],['knee1', 'hip1'],['knee2', 'hip2'],['hip1', 'hip2'], ['shoulder1', 'shoulder2'], ['shoulder1', 'hip1'], ['shoulder2', 'hip2'], ['shoulder1', 'elbow1'], ['shoulder2', 'elbow2'], ['chin', 'forehead'], ['elbow1', 'wrist1'], ['elbow2', 'wrist2']]
    config['default_net_type']='resnet_101'
    auxiliaryfunctions.write_config(cfg,config)
    config = auxiliaryfunctions.read_config(cfg)

    train_dir = Path(os.path.join(config['project_path'],str(auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)),'train'))
    test_dir = Path(os.path.join(config['project_path'],str(auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)),'test'))

    # Create the model directory
    train_dir.mkdir(parents=True,exist_ok=True)
    test_dir.mkdir(parents=True,exist_ok=True)

    modelfoldername=auxiliaryfunctions.GetModelFolder(trainFraction=config['TrainingFraction'][0],shuffle=1,cfg=config)

    path_train_config = str(os.path.join(config['project_path'],Path(modelfoldername),'train','pose_cfg.yaml'))
    path_test_config = str(os.path.join(config['project_path'],Path(modelfoldername),'test','pose_cfg.yaml'))

    # Download the weights and put then in appropriate directory
    cwd = os.getcwd()
    os.chdir(train_dir)
    print("Checking if the weights are already available, otherwise I will download them!")
    weightfilename=auxfun_models.download_mpii_weigths(train_dir)