def main(_): os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu_index solver = Solver(FLAGS) if FLAGS.is_train: solver.train() else: solver.test()
def main(): # make a string that describes the current running setup num = 0 run_setup_str = f"{args.source}2{args.target}_k_{args.num_k}_kq_{args.num_kq}_lamb_{args.lamb_marg_loss}" while os.path.exists(f"record/{run_setup_str}_run_{num}.txt"): num += 1 run_setup_str = f"{run_setup_str}_run_{num}" # eg, svhn2mnist_k_4_kq_4_lamb_10.0_run_5 # set file names for records (storing training stats) record_train = f"record/{run_setup_str}.txt" record_test = f"record/{run_setup_str}_test.txt" if not os.path.exists('record'): os.mkdir('record') # create a folder for records if not exist # set the checkpoint dir name (storing model params) checkpoint_dir = f'checkpoint/{run_setup_str}' if not os.path.exists('checkpoint'): os.mkdir('checkpoint') # create a folder if not exist if not os.path.exists(checkpoint_dir): os.mkdir(checkpoint_dir) # create a folder if not exist #### # create a solver: load data, create models (or load existing models), # and create optimizers solver = Solver(args, source=args.source, target=args.target, nsamps_q=args.nsamps_q, lamb_marg_loss=args.lamb_marg_loss, learning_rate=args.lr, batch_size=args.batch_size, optimizer=args.optimizer, num_k=args.num_k, num_kq=args.num_kq, all_use=args.all_use, checkpoint_dir=checkpoint_dir, save_epoch=args.save_epoch) # run it (test or training) if args.eval_only: solver.test(0) else: # training count = 0 for t in range(args.max_epoch): num = solver.train(t, record_file=record_train) count += num if t % 1 == 0: # run it on test data every epoch (and save models) solver.test(t, record_file=record_test, save_model=args.save_model) if count >= 20000 * 10: break
def main(config): os.environ["CUDA_VISIBLE_DEVICES"] = str(config["gpu_no"]) save_path = Path(config["training"]["save_path"]) save_path.mkdir(parents=True, exist_ok=True) mode = config["mode"] solver = Solver(config) if mode == "train": solver.train() if mode == "test": solver.test()
def main(config): os.environ["CUDA_VISIBLE_DEVICES"] = str(config["gpu_no"]) mode = config["mode"] save_path = Path(config["training"]["save_path"]) / config["version"] save_path.mkdir(parents=True, exist_ok=True) config["save_path"] = save_path datasets = DataLoader(mode, **config["dataset"]) solver = Solver(config, datasets) if mode == "train": solver.train() if mode == "test": solver.test()