batch_input = batch[:-1]
# forward
cls_predict = model.forward(*batch_input)
batch_acc, batch_eq_num = evaluate_acc(cls_predict, cls_truth)
batch_num = cls_truth.shape[0]
eq_num += batch_eq_num
all_num += batch_num
acc = eq_num / all_num
return acc
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--in', dest='in_infix', type=str, default='default', help='input path infix')
parser.add_argument('--out', type=str, default='default', help='output path infix')
parser.add_argument('--slot', type=str, default='directed_by', help='output path infix')
parser.add_argument('--train', action='store_true', default=False, help='enable train step')
parser.add_argument('--test', action='store_true', default=False, help='enable test step')
parser.add_argument('--gpuid', type=int, default=None, help='gpuid')
args = parser.parse_args()
in_infix = args.in_infix + '/' + args.slot
out_infix = args.out + '/' + args.slot
init_logging(out_infix=out_infix)
main('config/game_config.yaml', in_infix=in_infix, out_infix=out_infix, slot=args.slot,
is_train=args.train, is_test=args.test, gpuid=args.gpuid)
type=str,
default='config.yaml',
help='config path')
parser.add_argument('-in',
dest='in_infix',
type=str,
default='default',
help='input data_path infix')
parser.add_argument('-out',
type=str,
default='default',
help='output data_path infix')
parser.add_argument('-train',
action='store_true',
default=False,
help='enable train step')
parser.add_argument('-test',
action='store_true',
default=False,
help='enable test step')
parser.add_argument('-gpuid', type=int, default=None, help='gpuid')
args = parser.parse_args()
init_logging(out_infix=args.out)
main(args.config,
args.in_infix,
args.out,
is_train=args.train,
is_test=args.test,
gpuid=args.gpuid)
__author__ = "Han"
__email__ = "*****@*****.**"
import os
import sys
sys.path.append(os.getcwd())
import argparse
import torch
import logging
from collections import OrderedDict
from models import *
from utils.config import init_logging, read_config
init_logging()
logger = logging.getLogger(__name__)
def transform(pre_model_path, tar_model_path, cur_model):
pre_weight = torch.load(pre_model_path,
map_location=lambda storage, loc: storage)
pre_keys = pre_weight.keys()
pre_value = pre_weight.values()
cur_weight = cur_model.state_dict()
del cur_weight['model.embedding_layer.weight']
cur_keys = cur_weight.keys()
assert len(pre_keys) == len(cur_keys)
logging.info('pre-keys: ' + str(pre_keys))