--- a
+++ b/evaluate.py
@@ -0,0 +1,102 @@
+# -*- 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
+from get_logger import get_logger
+from res_network import Resnet18, Resnet34
+
+
+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('--batch_size', type=int, default=48)
+    parse.add_argument('--test_batch_size', type=int, default=256)
+    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='/home/tiger/projects/rscup2019_classifier/data')
+    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='/home/tiger/projects/rscup2019_classifier/model_out/logistic_out_6.pth')
+    return parse.parse_args()
+
+
+def evalute(args):
+    val_set = RSDataset(rootpth=args.data_dir, mode='val')
+    val_loader = DataLoader(val_set,
+                            batch_size=args.test_batch_size,
+                            drop_last=True,
+                            shuffle=True,
+                            pin_memory=True,
+                            num_workers=args.num_workers)
+    net = Resnet34()
+    net.eval()
+    net.load_state_dict(torch.load(args.eval_model_path))
+    net.cuda()
+
+    total = [0 for i in range(45)]
+    correct = [0 for i in range(45)]
+    with torch.no_grad():
+        for img, lb in val_loader:
+            img, lb = img.cuda(), lb.cuda()
+            outputs = net(img)
+            outputs = torch.sigmoid(outputs)
+            predicted = torch.max(outputs, dim=1)[1]
+            res = predicted == lb
+
+            for label_idx in range(args.test_batch_size):
+                label_single = lb[label_idx]
+                correct[label_single] += res[label_idx].item()
+                total[label_single] += 1
+            # print(correct, total)
+
+        acc_str = 'Accuracy: {}\n'.format(sum(correct)/sum(total))
+        for acc_idx in range(45):
+            try:
+                acc = correct[acc_idx] / total[acc_idx]
+            except:
+                acc = 0
+            finally:
+                acc_str += 'classID: {},\taccuracy: {}\n'.format(acc_idx+1, acc)
+    print(acc_str)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed(args.seed)
+
+    evalute(args)