imsize=config['exp_params']['img_size'], **config['model_params']) experiment = VAEXperiment(model, config['exp_params']) model_save_path = '{}/{}/version_{}'.format( config['logging_params']['save_dir'], config['logging_params']['name'], tt_logger.version) if config['logging_params']['resume'] == None: weights = [ os.path.join(model_save_path, x) for x in os.listdir(model_save_path) if '.ckpt' in x ] weights.sort(key=lambda x: os.path.getmtime(x)) model_path = weights[-1] print('loading: ', model_path) experiment = VAEXperiment.load_from_checkpoint(model_path, vae_model=model, params=config['exp_params']) else: model_path = '{}/{}'.format(model_save_path, config['logging_params']['resume']) experiment = VAEXperiment.load_from_checkpoint(model_path, vae_model=model, params=config['exp_params']) experiment.eval() experiment.freeze() experiment.sample_interpolate( save_dir=config['logging_params']['save_dir'], name=config['logging_params']['name'], version=config['logging_params']['version'], save_svg=True, other_interpolations=config['logging_params']['other_interpolations'])
runner = Trainer(default_root_dir=f"{tt_logger.save_dir}", logger=tt_logger, val_check_interval=1., num_sanity_val_steps=5, **config['trainer_params']) print(f"======= Training {config['model_params']['name']} =======") if not args.reg_only: runner.fit(experiment) # NN_Reg part starts here... dir_path = f"{config['logging_params']['save_dir']}/{config['logging_params']['name']}/version_{config['logging_params']['version']}" ckpt_path = glob.glob(dir_path + '/checkpoints/*')[0] experiment = VAEXperiment.load_from_checkpoint(ckpt_path, vae_model=model, params=config['exp_params'], map_location='cuda:0') nn_reg_tt_logger = TestTubeLogger( save_dir=config['logging_params']['save_dir'], name=config['logging_params']['name'], debug=False, create_git_tag=False, version=f"version_{config['logging_params']['version']}/nn_reg", prefix='nn_reg_', ) nn_reg_model = vae_models[config['reg_params']['name']](**config['reg_params']) nn_reg_experiment = RegExperiment(experiment.model, nn_reg_model, config['reg_exp_params']) nn_reg_runner = Trainer(default_root_dir=f"{nn_reg_tt_logger.save_dir}",