--- a
+++ b/predict.py
@@ -0,0 +1,87 @@
+# -*- coding: utf-8 -*-
+"""
+@File    : trian_res34.py
+@Time    : 2019/6/23 15:40
+@Author  : Parker
+@Email   : now_cherish@163.com
+@Software: PyCharm
+@Des     : 
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+import torch.optim as optim
+from tensorboardX import SummaryWriter
+
+import numpy as np
+import time
+import datetime
+import argparse
+import os
+import os.path as osp
+
+from rs_dataset import RSDataset_test
+from get_logger import get_logger
+from res_network import Resnet18, Resnet34, Resnet101
+from tqdm import tqdm
+
+
+def parse_args():
+    parse = argparse.ArgumentParser()
+    parse.add_argument('--epoch', type=int, default=15)
+    parse.add_argument('--schedule_step', type=int, default=2)
+
+    parse.add_argument('--test_batch_size', type=int, default=128)
+    parse.add_argument('--num_workers', type=int, default=16)
+
+    parse.add_argument('--eval_fre', type=int, default=1)
+    parse.add_argument('--msg_fre', type=int, default=10)
+    parse.add_argument('--save_fre', type=int, default=2)
+
+    parse.add_argument('--name', type=str, default='res34_baseline',
+                       help='unique out file name of this task include log/model_out/tensorboard log')
+    parse.add_argument('--data_dir', type=str, default='/media/tiger/zzr/rsna')
+    parse.add_argument('--log_dir', type=str, default='./logs')
+    parse.add_argument('--tensorboard_dir', type=str, default='./tensorboard')
+    parse.add_argument('--model_out_dir', type=str, default='./model_out')
+    parse.add_argument('--model_out_name', type=str, default='final_model.pth')
+    parse.add_argument('--seed', type=int, default=5, help='random seed')
+    parse.add_argument('--eval_model_path', type=str, default='/media/tiger/zzr/rsna_script/model_out/191005-002929_temp/out_9.pth')
+    return parse.parse_args()
+
+
+def predict(args):
+    test_set = RSDataset_test(rootpth=args.data_dir, mode='test')
+    test_loader = DataLoader(test_set,
+                            batch_size=args.test_batch_size,
+                            drop_last=False,
+                            shuffle=False,
+                            pin_memory=True,
+                            num_workers=args.num_workers)
+    net = Resnet18()
+    net.eval()
+    net.load_state_dict(torch.load(args.eval_model_path))
+    net.cuda()
+
+    labels = ["epidural", "intraparenchymal", "intraventricular",
+              "subarachnoid", "subdural", "any"]
+    with open("result.csv", 'w') as fp:
+        fp.write('ID,Label\n')
+        with torch.no_grad():
+            for img, name in tqdm(test_loader):
+                img = img.cuda()
+                outputs = net(img).cpu().numpy()
+                for idx, i in enumerate(name):
+                    for idxj, j in enumerate(labels):
+                        fp.write(i + '_' + j + ',' + str(outputs[idx][idxj]) + '\n')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed(args.seed)
+
+    predict(args)