#!/usr/bin/env python import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': machine = System.MultNet(root=os.path.abspath( sys.argv[0])[:-len(sys.argv[0])], trainer_file='trainer_depth.yaml', dataset_file='../../datasets/aug_full.yaml', cnn_type='cnn.yaml') action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final() machine.plot(print_loss=False, print_val=False) elif action == 'p': machine.plot(print_loss=False, print_val=False) elif action == 'P': machine.plot()
import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': scene = 'indoor/apt1-kitchen' machine = System.MultNet( root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])], #trainer_file= '../relative_pnp.yaml', #trainer_file='../self_multiscale_depth_trainer.yaml', trainer_file='../nn_index.yaml', #trainer_file='../pnp-5-images.yaml', #trainer_file='../pnp-1-image.yaml', dataset_file='../../../../datasets/' + scene + '.yaml', cnn_type='../multiscale_cnn.yaml') action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final() machine.plot(print_loss=False, print_val=False)
#!/usr/bin/env python import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': machine = System.MultNet( root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])], trainer_file='trainer.yaml', #trainer_file='feat_trainer.yaml', dataset_file='../../../../Exp_ICIP/datasets/heads.yaml') action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final() machine.plot(print_loss=False, print_val=False) elif action == 'p': machine.plot(print_loss=False, print_val=False) elif action == 'P': machine.plot()
#!/usr/bin/env python import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': scene = 'chess' machine = System.MultNet( root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])], #trainer_file='../../feat_trainer.yaml', trainer_file='trainer.yaml', dataset_file='../../../datasets/' + scene + '.yaml', #cnn_type='../cnn.yaml' cnn_type='../vladcnn.yaml') action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final() machine.plot(print_loss=False, print_val=False) elif action == 'p':
#!/usr/bin/env python import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': machine = System.MultNet( root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])], #trainer_file='trainer.yaml', trainer_file='icp_trainer.yaml', dataset_file='../datasets/heads224.yaml') action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final() machine.plot(print_loss=False, print_val=False) elif action == 'p': machine.plot(print_loss=False, print_val=False) elif action == 'P': machine.plot()
import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': machine = System.MultNet(root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])], #trainer_file='posenet_trainer.yaml', trainer_file='trainer.yaml', dataset_file='../datasets/seq_heads.yaml' #dataset_file = '../datasets/minimal_heads.yaml' ) action = input('Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n') if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final() machine.plot(print_loss=False, print_val=False) elif action == 'p': machine.plot(print_loss=False, print_val=False) elif action == 'P': machine.plot()
#!/usr/bin/env python import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': machine = System.MultNet( root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])]) action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() elif action == 'p': machine.plot(print_loss=False, print_val=False) elif action == 'P': machine.plot() elif action == 'm': machine.map_print() elif action == 'mf': machine.map_print(final=True) elif action == '': machine.train() machine.test()
#!/usr/bin/env python import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': machine = System.MultNet( root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])], #trainer_file='posenet_trainer.yaml', trainer_file='trainer.yaml') action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final() machine.plot(print_loss=False, print_val=False) elif action == 'p': machine.plot(print_loss=False, print_val=False) elif action == 'P': machine.plot() elif action == 'm':
#!/usr/bin/env python import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': scene = 'self-sup/KingsCollege' machine = System.MultNet( root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])], # trainer_file='../../feat_trainer.yaml', # trainer_file= 'trainer.yaml', trainer_file='self_multiscale_depth_trainer.yaml', dataset_file='../../../datasets/' + scene + '.yaml', cnn_type='../../multiscale_cnn.yaml') action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final() machine.plot(print_loss=False, print_val=False) elif action == 'p':