amend deeplabv3 export.py

model_zoo/official/cv/deeplabv3/README.md

@@ -496,7 +496,7 @@ Note: There OS is output stride, and MS is multiscale.
 | Loss Function              | Softmax Cross Entropy                                  |
 | Outputs                    | probability                                       |
 | Loss                       | 0.0065883575                                       |
-| Speed                      | 60 ms/step（1pc, s16）<br> 480 ms/step（8pcs, s16） <br> 244 ms/step (8pcs, s8)      |  
+| Speed                      | 60 fps（1pc, s16）<br> 480 fps（8pcs, s16） <br> 244 fps (8pcs, s8)      |  
 | Total time                 | 8pcs: 706 mins                     |
 | Parameters (M)             | 58.2                                       |
 | Checkpoint for Fine tuning | 443M (.ckpt file)                       |

model_zoo/official/cv/deeplabv3/README_CN.md

@@ -510,7 +510,7 @@ python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA  \
 | 损失函数 | Softmax交叉熵 |
 | 输出 | 概率 |
 | 损失 | 0.0065883575 |
-| 速度 | 31毫秒/步（单卡，s8）<br> 234毫秒/步（8卡，s8） |  
+| 速度 | 31 帧数/秒（单卡，s8）<br> 234 帧数/秒（8卡，s8） |  
 | 微调检查点 | 443M （.ckpt文件） |
 | 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3) |
 

model_zoo/official/cv/deeplabv3/export.py

@@ -17,7 +17,7 @@ import argparse
 import numpy as np
 
 from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
-
+from eval import BuildEvalNetwork
 from src.nets import net_factory
 
 parser = argparse.ArgumentParser(description='checkpoint export')
@@ -43,6 +43,7 @@ if __name__ == '__main__':
         network = net_factory.nets_map['deeplab_v3_s16']('eval', args.num_classes, 16, True)
     else:
         network = net_factory.nets_map['deeplab_v3_s8']('eval', args.num_classes, 8, True)
+    network = BuildEvalNetwork(network)
     param_dict = load_checkpoint(args.ckpt_file)
 
     # load the parameter into net