optimized readme and add per_step_time

model_zoo/official/cv/yolov3_darknet53/README.md

@@ -117,13 +117,13 @@ python train.py \
     --lr_scheduler=cosine_annealing > log.txt 2>&1 &
 
 # standalone training example(1p) by shell script
-sh run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
+bash run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
 
 # For Ascend device, distributed training example(8p) by shell script
-sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
+bash run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
 
 # For GPU device, distributed training example(8p) by shell script
-sh run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
+bash run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
 
 # run evaluation by python command
 python eval.py \
@@ -132,7 +132,7 @@ python eval.py \
     --testing_shape=416 > log.txt 2>&1 &
 
 # run evaluation by shell script
-sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
+bash run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
 ```
 
 ## [Script Description](#contents)
@@ -269,13 +269,13 @@ The model checkpoint will be saved in outputs directory.
 For Ascend device, distributed training example(8p) by shell script
 
 ```command
-sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
+bash run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
 ```
 
 For GPU device, distributed training example(8p) by shell script
 
 ```command
-sh run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
+bash run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
 ```
 
 The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log.txt`. The loss value will be achieved as follows:
@@ -307,7 +307,7 @@ python eval.py \
     --pretrained=yolov3.ckpt \
     --testing_shape=416 > log.txt 2>&1 &
 OR
-sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
+bash run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
 ```
 
 The above python command will run in the background. You can view the results through the file "log.txt". The mAP of the test dataset will be as follows:

model_zoo/official/cv/yolov3_darknet53/README_CN.md

@@ -90,7 +90,7 @@ YOLOv3使用DarkNet53执行特征提取，这是YOLOv2中的Darknet-19和残差
       可以从网站[下载](https://pjreddie.com/media/files/darknet53.conv.74) darknet53.conv.74文件。
       也可以在linux系统中使用指令下载该文件。
 
-      ```command
+   ```command
       wget https://pjreddie.com/media/files/darknet53.conv.74
       ```
 
@@ -118,17 +118,17 @@ python train.py \
 
 ```shell script
 # shell脚本单机训练示例(1卡)
-sh run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
+bash run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
 ```
 
 ```shell script
 # 对于Ascend设备，使用shell脚本分布式训练示例(8卡)
-sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
+bash run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
 ```
 
 ```shell script
 # 对于GPU设备，使用shell脚本分布式训练示例(8卡)
-sh run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
+bash run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
 ```
 
 ```python
@@ -141,7 +141,7 @@ python eval.py \
 
 ```shell script
 # 通过shell脚本运行评估
-sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
+bash run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
 ```
 
 # 脚本说明
@@ -270,13 +270,13 @@ python train.py \
 对于Ascend设备，使用shell脚本分布式训练示例(8卡)
 
 ```shell script
-sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
+bash run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
 ```
 
 对于GPU设备，使用shell脚本分布式训练示例(8卡)
 
 ```shell script
-sh run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
+bash run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
 ```
 
 上述shell脚本将在后台运行分布训练。您可以通过`train_parallel[X]/log.txt`文件查看结果。损失值的实现如下：
@@ -312,7 +312,7 @@ python eval.py \
 或者
 
 ```shell script
-sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
+bash run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
 ```
 
 上述python命令将在后台运行，您可以通过log.txt文件查看结果。测试数据集的mAP如下：

model_zoo/official/cv/yolov3_darknet53/train.py

@@ -274,10 +274,12 @@ def train():
         if i % args.log_interval == 0:
             time_used = time.time() - t_end
             epoch = int(i / args.steps_per_epoch)
+            per_step_time = time_used/args.log_interval
             fps = args.per_batch_size * (i - old_progress) * args.group_size / time_used
             if args.rank == 0:
                 args.logger.info(
-                    'epoch[{}], iter[{}], {}, {:.2f} imgs/sec, lr:{}'.format(epoch, i, loss_meter, fps, lr[i]))
+                    'epoch[{}], iter[{}], {}, {:.2f} imgs/sec, lr:{},'
+                    ' per_step_time:{}'.format(epoch, i, loss_meter, fps, lr[i], per_step_time))
             t_end = time.time()
             loss_meter.reset()
             old_progress = i