# trainnewGAN_task.execute("6", "eccv", "exp_pretrained_F_IL") # train_da.execute("8", "eccv", "all_da_v2") # train_da_shared.execute("1", "eccv", "all_da_shared_E1E12_l1_10") # train_da_shared.execute("9", "eccv", "all_da_shared_E1E12_l1_25_aug") # train_da_shared.execute("3", "eccv", "all_da_shared_E1E12_l1_25_new_nopretrained") # train_wdgrl_withoutgen.execute("0", "eccv", "exp_wdgrl_better") # train_da_no5.execute("8", "eccv", "all_da_aug_no5_E1E12") # testGAN.execute("7", "eccv", "all_da_orig") # trainnewGAN.execute("4", "eccv", "exp_reconstruct") # trainnewGAN.execute("4", "eccv", "experiment_1") if args.single_process == 'validation': execute_validation("0", "eccv", "experiment_1", "SeqVal") if args.single_process == 'drive': execute_drive("0", "eccv", "experiment_1", 'Town02') else: # TODO: of course this change from gpu to gpu , but for now we just assume at least a K40 # Maybe the latest voltas will be underused # OBS: This usage is also based on my tensorflow experiences, maybe pytorch allows more. allocation_parameters = { 'gpu_value': 3.5, 'train_cost': 2, 'validation_cost': 1.5, 'drive_cost': 1.5 } params = {
elif args.single_process == 'validation': if len(args.validation_datasets) == 1: execute_validation("0", args.folder, args.exp, args.validation_datasets[0], args.model, False) else: execute_validation("0", args.folder, args.exp, args.validation_datasets, args.model, False) elif args.single_process == 'drive': driving_environments = fix_driving_environments( list(args.driving_environments)) execute_drive("0", args.folder, args.exp, driving_environments[0], False, no_screen=args.no_screen) else: raise ( " Invalid name for single process, chose from (train, validation, test)" ) else: # TODO: of course this change from gpu to gpu , but for now we just assume at least a K40 # Maybe the latest voltas will be underused # OBS: This usage is also based on my tensorflow experiences, maybe pytorch allows more. allocation_parameters = {
execute_train(gpu="0", exp_batch=args.folder, exp_alias=args.exp, suppress_output=False, number_of_workers=args.number_of_workers) elif args.single_process == 'validation': execute_validation(gpu="0", exp_batch=args.folder, exp_alias=args.exp, dataset=args.validation_datasets[0], suppress_output=False) elif args.single_process == 'drive': drive_params['suppress_output'] = False execute_drive("0", args.folder, args.exp, list(args.driving_environments)[0], drive_params) else: raise Exception( "Invalid name for single process, chose from (train, validation, test)" ) else: #### # MODE 2: Folder execution. Execute train/validation/drive for all experiments on # a certain training folder #### # We set by default that each gpu has a value of 3.5, allowing a training and # a driving/validation allocation_parameters = { 'gpu_value': args.gpu_value,