import fire
from utils import remove_all_files
from tensorpack.callbacks.saver import ModelSaver
def train(case='default', ckpt=None, gpu=None, r=False):
'''
:param case: experiment case name
:param ckpt: checkpoint to load model
:param gpu: comma separated list of GPU(s) to use
:param r: start from the beginning.
'''
hp.set_hparam_yaml(case)
if r:
remove_all_files(hp.logdir)
# model
model = IAFVocoder(batch_size=hp.train.batch_size, length=hp.signal.length)
# dataset
dataset = Dataset(hp.data_path,
hp.train.batch_size,
length=hp.signal.length)
print('dataset size is {}'.format(len(dataset.wav_files)))
# set logger for event and model saver
logger.set_logger_dir(hp.logdir)
train_conf = TrainConfig(
model=model,
help='experiment case name of train1')
parser.add_argument('case2',
type=str,
help='experiment case name of train2')
parser.add_argument('-ckpt', help='checkpoint to load model.')
parser.add_argument('-gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('-r',
action='store_true',
help='start training from the beginning.')
arguments = parser.parse_args()
return arguments
if __name__ == '__main__':
args = get_arguments()
print(args.case2)
hp.set_hparam_yaml(args.case2,
default_file='hparams/{}.yaml'.format(args.case2))
logdir_train1 = '{}/{}/train1'.format(hp.logdir_path, args.case1)
logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2)
if args.r:
remove_all_files(logdir_train2)
print('case1: {}, case2: {}, logdir1: {}, logdir2: {}'.format(
args.case1, args.case2, logdir_train1, logdir_train2))
train(args, logdir1=logdir_train1, logdir2=logdir_train2)
print("Done")
work_sheets = utils.get_all_worksheets(xlsx_path)
for sheet_name, work_sheet in work_sheets.iteritems():
sheet_item = sheet(work_sheet)
sheet_item.debug_print()
all_sheet[sheet_name] = sheet_item
generate_code = 1
xlsx_file = ''
if len(sys.argv) >= 2:
generate_code = int(sys.argv[1])
if len(sys.argv) >= 3:
xlsx_file = os.path.basename(sys.argv[2])
if xlsx_file == '':
utils.remove_all_files(JSON_DIR)
utils.remove_all_files(CSHARP_DIR)
for sheet_name, sheet_item in all_sheet.iteritems():
if generate_code == 1:
export_csharp(sheet_item)
export_json(sheet_item)
else:
if all_xlsx.has_key(xlsx_file):
work_sheets = utils.get_all_worksheets(all_xlsx[xlsx_file])
for sheet_name, work_sheet in work_sheets.iteritems():
sheet_item = all_sheet[sheet_name]
if generate_code == 1:
export_csharp(sheet_item)
export_json(sheet_item)
export_hash_helper()