def export_model(_, *args, **kwargs): """ Export DLC models for the model zoo or for live inference.\n Saves the pose configuration, snapshot files, and frozen graph of the model to a directory named exported-models within the project directory Parameters ----------- cfg_path : string\n \tpath to the DLC Project config.yaml file iteration : int, optional\n \tthe model iteration you wish to export.\n \tIf None, uses the iteration listed in the config file shuffle : int, optional\n \tthe shuffle of the model to export. default = 1 trainingsetindex : int, optional\n \tthe index of the training fraction for the model you wish to export. default = 1 snapshotindex : int, optional\n \tthe snapshot index for the weights you wish to export.\n \tIf None, uses the snapshotindex as defined in 'config.yaml'. Default = None TFGPUinference : bool, optional\n \tuse the tensorflow inference model? Default = True\n \tFor inference using DeepLabCut-live, it is recommended to set TFGPIinference=False overwrite : bool, optional\n \tif the model you wish to export has already been exported, whether to overwrite. default = False make_tar : bool, optional\n \tDo you want to compress the exported directory to a tar file? Default = True\n \tThis is necessary to export to the model zoo, but not for live inference. """ from deeplabcut import export_model export_model(*args, **kwargs)
print("CREATING TRAININGSET 2") dlc.create_training_dataset(path_config_file, Shuffles=[2],net_type=net_type,augmenter_type=augmenter_type2) cfg=dlc.auxiliaryfunctions.read_config(path_config_file) posefile=os.path.join(cfg['project_path'],'dlc-models/iteration-'+str(cfg['iteration'])+'/'+ cfg['Task'] + cfg['date'] + '-trainset' + str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(2),'train/pose_cfg.yaml') DLC_config=dlc.auxiliaryfunctions.read_plainconfig(posefile) DLC_config['save_iters']=numiter DLC_config['display_iters']=1 DLC_config['multi_step']=[[0.001,numiter]] print("CHANGING training parameters to end quickly!") dlc.auxiliaryfunctions.write_config(posefile,DLC_config) print("TRAIN") dlc.train_network(path_config_file, shuffle=2,allow_growth=True) print("EVALUATE") dlc.evaluate_network(path_config_file,Shuffles=[2],plotting=False) print("ANALYZING some individual frames") dlc.analyze_time_lapse_frames(path_config_file,os.path.join(cfg['project_path'],'labeled-data/reachingvideo1/')) ''' print("Export model...") dlc.export_model(path_config_file, shuffle=1, make_tar=False) print( "ALL DONE!!! - default/imgaug cases of DLCcore training and evaluation are functional (no extract outlier or refinement tested)." )
print("Network evaluated....") print("Analyzing video with auto_track....") deeplabcut.analyze_videos( config_path, [new_video_path], save_as_csv=True, destfolder=DESTFOLDER, cropping=[0, 50, 0, 50], allow_growth=True, use_shelve=USE_SHELVE, auto_track=False, ) print("Export model...") deeplabcut.export_model(config_path, shuffle=1, make_tar=False) print("Merging datasets...") trainIndices, testIndices = deeplabcut.mergeandsplit( config_path, trainindex=0, uniform=True ) print("Creating two identical splits...") deeplabcut.create_multianimaltraining_dataset( config_path, Shuffles=[4, 5], trainIndices=[trainIndices, trainIndices], testIndices=[testIndices, testIndices], ) print("ALL DONE!!! - default multianimal cases are functional.")
DLC_config["display_iters"] = 2 DLC_config["multi_step"] = [[0.001, 10]] print("CHANGING training parameters to end quickly!") deeplabcut.auxiliaryfunctions.write_plainconfig(posefile, DLC_config) print("TRAINING shuffle 2, with smaller allocated memory") deeplabcut.train_network(path_config_file, shuffle=2, allow_growth=True) print("ANALYZING some individual frames") deeplabcut.analyze_time_lapse_frames( path_config_file, os.path.join(cfg["project_path"], "labeled-data/reachingvideo1/") ) print("Export model...") deeplabcut.export_model(path_config_file, shuffle=2, make_tar=False) trainIndices, testIndices = deeplabcut.mergeandsplit( path_config_file, trainindex=0, uniform=True ) deeplabcut.create_training_dataset( path_config_file, Shuffles=[4, 5], trainIndices=[trainIndices, trainIndices], testIndices=[testIndices, testIndices], ) print("ALL DONE!!! - default cases are functional.") print("Re-import DLC with env. variable set to test DLC light mode.")