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.")
