def main(): # torch.multiprocessing.freeze_support() use_cuda = torch.cuda.is_available() parser = argparse.ArgumentParser() parser.add_argument('--seed', type=int, default=1) args = parser.parse_args() # load configuration from yaml file config = HParams.load( os.path.join(os.path.dirname(os.path.abspath(__file__)), "hparams.yaml")) data_config = config.data_io model_config = config.model exp_config = config.experiment # check asset dir and get logger root_dir = "/" if use_cuda else get_project_root("Deep-Generative-Model") asset_path = os.path.join(root_dir, "assets", "test") # change subdirectory check_asset_dir(asset_path, config) logger.logging_verbosity(1) logger.add_filehandler(os.path.join(asset_path, "log.txt")) tf_logger = get_tflogger(asset_path) # data_config['root_path'] = os.path.join(root_dir, data_config['root_path']) # seed if args.seed > 0: torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) np.random.seed(args.seed) random.seed(args.seed) logger.info("configuration complete") # get loader train_loader = get_loader(train=True, **data_config) test_loader = get_loader(train=False, **data_config) for batch in train_loader: print(batch['img'].size()) break
device = torch.device("cuda" if use_cuda else "cpu") parser = argparse.ArgumentParser() parser.add_argument('--index', type=int, help='Experiment Number', default='e') parser.add_argument('--kfold', type=int, help='5 fold (0,1,2,3,4)',default='e') parser.add_argument('--voca', type=bool, help='large voca is True', default=False) parser.add_argument('--model', type=str, default='crf') parser.add_argument('--pre_model', type=str, help='btc, cnn, crnn', default='e') parser.add_argument('--dataset1', type=str, help='Dataset', default='isophonic_221') parser.add_argument('--dataset2', type=str, help='Dataset', default='uspop_185') parser.add_argument('--dataset3', type=str, help='Dataset', default='robbiewilliams') parser.add_argument('--restore_epoch', type=int, default=1000) parser.add_argument('--early_stop', type=bool, help='no improvement during 10 epoch -> stop', default=True) args = parser.parse_args() config = HParams.load("run_config.yaml") if args.voca == True: config.feature['large_voca'] = True config.model['num_chords'] = 170 config.model['probs_out'] = True # Result save path asset_path = config.path['asset_path'] ckpt_path = config.path['ckpt_path'] result_path = config.path['result_path'] restore_epoch = args.restore_epoch experiment_num = str(args.index) ckpt_file_name = 'idx_'+experiment_num+'_%03d.pth.tar' tf_logger = TF_Logger(os.path.join(asset_path, 'tensorboard', 'idx_'+experiment_num)) logger.info("==== Experiment Number : %d " % args.index)
parser.add_argument('--voca', type=bool, help='large voca is True', default=True) parser.add_argument('--model', type=str, help='btc, cnn, crnn', default='btc') #----- parser.add_argument('--dataset1', type=str, help='Dataset', default='ce200') parser.add_argument('--dataset2', type=str, help='Dataset', default='NA') parser.add_argument('--dataset3', type=str, help='Dataset', default='NA') #----- #parser.add_argument('--dataset1', type=str, help='Dataset', default='isophonic') #parser.add_argument('--dataset2', type=str, help='Dataset', default='uspop') #parser.add_argument('--dataset3', type=str, help='Dataset', default='robbiewilliams') parser.add_argument('--restore_epoch', type=int, default=1000) parser.add_argument('--early_stop', type=bool, help='no improvement during 10 epoch -> stop', default=True) args = parser.parse_args() experiment_num = str(args.index) config = HParams.load("config/run_config_idx"+experiment_num+".yaml") if args.voca == True: config.feature['large_voca'] = True config.model['num_chords'] = 170 # Result save path asset_path = config.path['asset_path'] ckpt_path = config.path['ckpt_path'] result_path = config.path['result_path'] restore_epoch = args.restore_epoch #experiment_num = str(args.index) ckpt_file_name = 'idx_'+experiment_num+'_%03d.pt' tf_logger = TF_Logger(os.path.join(asset_path, 'tensorboard', 'idx_'+experiment_num)) logger.info("==== Experiment Number : %d " % args.index)
default=True) parser.add_argument('--model', type=str, help='btc, cnn, crnn', default='btc') #----- parser.add_argument('--dataset1', type=str, help='Dataset', default='ce200') #----- #parser.add_argument('--dataset1', type=str, help='Dataset', default='isophonic') #parser.add_argument('--dataset2', type=str, help='Dataset', default='uspop') #parser.add_argument('--dataset3', type=str, help='Dataset', default='robbiewilliams') parser.add_argument('--restore_epoch', type=int, default=1) parser.add_argument('--early_stop', type=bool, help='no improvement during 10 epoch -> stop', default=True) args = parser.parse_args() config = HParams.load("config/run_config_idx0.yaml") if args.voca == True: config.feature['large_voca'] = True config.model['num_chords'] = 170 # Result save path asset_path = config.path['asset_path'] ckpt_path = config.path['ckpt_path'] result_path = config.path['result_path'] restore_epoch = args.restore_epoch experiment_num = str(args.index) ckpt_file_name = 'idx_' + experiment_num + '_%03d.pt' tf_logger = TF_Logger( os.path.join(asset_path, 'tensorboard', 'idx_' + experiment_num)) logger.info("==== Experiment Number : %d " % args.index)
type=int, default="0", help='GPU index') parser.add_argument('--ngpu', type=int, default=4, help='0 = CPU.') parser.add_argument('--optim_name', type=str, default='adam') parser.add_argument('--restore_epoch', type=int, default=-1) parser.add_argument('--load_rhythm', dest='load_rhythm', action='store_true') parser.add_argument('--seed', type=int, default=1) args = parser.parse_args() use_cuda = torch.cuda.is_available() device = torch.device("cuda:%d" % args.gpu_index if use_cuda else "cpu") hparam_file = os.path.join(os.getcwd(), "hparams.yaml") config = HParams.load(hparam_file) data_config = config.data_io model_config = config.model exp_config = config.experiment # configuration asset_root = config.asset_root asset_path = os.path.join(asset_root, 'idx%03d' % args.idx) make_save_dir(asset_path, config) logger.logging_verbosity(1) logger.add_filehandler(os.path.join(asset_path, "log.txt")) # seed if args.seed > 0: torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed)
import torch import numpy as np import random import os if __name__ == '__main__': torch.multiprocessing.freeze_support() use_cuda = torch.cuda.is_available() parser = argparse.ArgumentParser() parser.add_argument('--seed', type=int, default=1) args = parser.parse_args() # load configuration from yaml file config = HParams.load( os.path.join(os.path.dirname(os.path.abspath(__file__)), "hparams.yaml")) data_config = config.data_io model_config = config.model exp_config = config.experiment # check asset dir and get logger root_dir = "/" if use_cuda else get_project_root("Deep-Generative-Model") asset_path = os.path.join(root_dir, "assets", "test") # change subdirectory check_asset_dir(asset_path, config) logger.logging_verbosity(1) logger.add_filehandler(os.path.join(asset_path, "log.txt")) tf_logger = get_tflogger(asset_path) data_config['root_path'] = os.path.join(root_dir, data_config['root_path'])