import argparse
from utilbox.regex_util import regex_key_val
from euterpe.common.loader import DataLoader
def parse():
parser = argparse.ArgumentParser()
parser.add_argument('--kv', type=str)
parser.add_argument('--set', type=str)
return parser.parse_args()
pass
if __name__ == '__main__':
args = parse()
list_kv = DataLoader._read_key_val(args.kv)
list_set = DataLoader._read_key(args.set)
list_subset = DataLoader._subset_data(list_kv, list_set)
for k, v in list_subset:
print('{} {}'.format(k, v))
pass
parser.add_argument('--dump', action='store_true', default=False)
return parser.parse_args()
if __name__ == '__main__':
args = parse()
args.config = os.path.splitext(args.model)[0] + '.cfg'
data_cfg = yaml.load(open(args.data_cfg))
# construct all subset feat & feat_len
if os.path.exists(args.key):
_key = DataLoader._read_key(args.key)
else:
_key = args.key.split()
_feat_all = DataLoader._read_key_val(data_cfg['feat']['all'])
_feat_len_all_path = '{}_len{}'.format(
*os.path.splitext(data_cfg['feat']['all']))
_feat_len_all = DataLoader._read_key_val(_feat_len_all_path)
_feat_kv = DataLoader._subset_data(_feat_all, _key)
_feat_len_kv = DataLoader._subset_data(_feat_len_all, _key)
model = ModelSerializer.load_config(args.config)
model.load_state_dict(torch.load(args.model))
model.eval() # set as eval mode
if args.gpu >= 0:
torch.cuda.set_device(args.gpu)
torch.cuda.manual_seed(123)
model.cuda(args.gpu)