!12184 Fix some spelling mistakes in model_zoo training script

From: @zlq2020 Reviewed-by: @guoqi1024 Signed-off-by: @guoqi1024
4 years ago · adfe6e1bc2
--- a/model_zoo/official/cv/mobilenetv2_quant/Readme.md
+++ b/model_zoo/official/cv/mobilenetv2_quant/Readme.md
@@ -20,7 +20,6 @@

 # [MobileNetV2 Description](#contents)


 MobileNetV2 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.

 [Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
@@ -38,11 +37,10 @@ The overall network architecture of MobileNetV2 is show below:
 Dataset used: [imagenet](http://www.image-net.org/)

 - Dataset size: ~125G, 1.2W colorful images in 1000 classes
 	- Train: 120G, 1.2W images
 	- Test: 5G, 50000 images
    - Train: 120G, 1.2W images
    - Test: 5G, 50000 images
 - Data format: RGB images.
 	- Note: Data will be processed in src/dataset.py

  - Note: Data will be processed in src/dataset.py

 # [Features](#contents)

@@ -54,14 +52,13 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)

 - Hardware:Ascend
  - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
    - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below
  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)


 # [Script description](#contents)

 ## [Script and sample code](#contents)
@@ -84,7 +81,6 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
  ├── export.py     # export checkpoint files into air/onnx
 ```


 ## [Script Parameters](#contents)

 Parameters for both training and evaluation can be set in config.py
@@ -113,13 +109,11 @@ Parameters for both training and evaluation can be set in config.py

 ### Usage


 You can start training using python or shell scripts. The usage of shell scripts as follows:

 - bash run_train.sh [Ascend] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)
 - bash run_train.sh [GPU] [DEVICE_ID_LIST] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)


 ### Launch

 ``` bash
@@ -133,7 +127,7 @@ You can start training using python or shell scripts. The usage of shell scripts
 Training result will be stored in the example path. Checkpoints trained by `Ascend` will be stored at `./train/device$i/checkpoint` by default, and training log  will be redirected to `./train/device$i/train.log`. Checkpoints trained by `GPU` will be stored in `./train/checkpointckpt_$i` by default, and training log will be redirected to `./train/train.log`.  
 `train.log` is as follows:

 ```
 ``` bash
 epoch: [  0/200], step:[  624/  625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
 epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
 epoch: [  1/200], step:[  624/  625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
@@ -150,7 +144,7 @@ You can start training using python or shell scripts. The usage of shell scripts

 ### Launch

 ```
 ``` bash
 # infer example
  shell:
      Ascend: sh run_infer_quant.sh Ascend ~/imagenet/val/ ~/train/mobilenet-60_1601.ckpt
@@ -160,9 +154,9 @@ You can start training using python or shell scripts. The usage of shell scripts

 ### Result

 Inference result will be stored in the example path, you can find result like the followings in `./val/infer.log`.
 Inference result will be stored in the example path, you can find result like the following in `./val/infer.log`.

 ```
 ``` bash
 result: {'acc': 0.71976314102564111}
 ```

--- a/model_zoo/official/cv/mobilenetv2_quant/src/launch.py
+++ b/model_zoo/official/cv/mobilenetv2_quant/src/launch.py
@@ -35,7 +35,7 @@ def parse_args():
        >>> parse_args()
    """
    parser = ArgumentParser(description="mindspore distributed training launch "
                                        "helper utilty that will spawn up "
                                        "helper utility that will spawn up "
                                        "multiple distributed processes")
    parser.add_argument("--nproc_per_node", type=int, default=1,
                        help="The number of processes to launch on each node, "
--- a/model_zoo/official/cv/resnet50_quant/README.md
+++ b/model_zoo/official/cv/resnet50_quant/README.md
@@ -37,19 +37,18 @@ The overall network architecture of Resnet50 is show below:
 Dataset used: [ImageNet2012](http://www.image-net.org/)

 - Dataset size 224*224 colorful images in 1000 classes
  - Train：1,281,167 images  
  - Test： 50,000 images   
    - Train：1,281,167 images
    - Test： 50,000 images
 - Data format：jpeg
  - Note：Data will be processed in dataset.py
   - Note：Data will be processed in dataset.py
 - Download the dataset, the directory structure is as follows:

 ```
 ```python
 └─dataset
    ├─ilsvrc                # train dataset
    └─validation_preprocess # evaluate dataset
 ```


 # [Features](#contents)

 ## [Mixed Precision](#contents)
@@ -60,14 +59,13 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)

 - Hardware:Ascend
  - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
    - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)


 # [Script description](#contents)

 ## [Script and sample code](#contents)
@@ -124,18 +122,19 @@ Parameters for both training and evaluation can be set in config.py
 ### Usage

 - Ascend: sh run_train.sh Ascend [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)

 ### Launch

 ```
 ```bash
  # training example
  Ascend: bash run_train.sh Ascend ~/hccl.json ~/imagenet/train/ ~/pretrained_ckeckpoint
 ```

 ### Result

 Training result will be stored in the example path. Checkpoints will be stored at `./train/device$i/` by default, and training log  will be redirected to `./train/device$i/train.log` like followings. 
 Training result will be stored in the example path. Checkpoints will be stored at `./train/device$i/` by default, and training log  will be redirected to `./train/device$i/train.log` like following.

 ```
 ```bash
 epoch: 1 step: 5004, loss is 4.8995576
 epoch: 2 step: 5004, loss is 3.9235563
 epoch: 3 step: 5004, loss is 3.833077
@@ -153,7 +152,7 @@ You can start training using python or shell scripts. The usage of shell scripts

 ### Launch

 ```
 ```bash
 # infer example
  shell:
      Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/Resnet50-30_5004.ckpt
@@ -163,9 +162,9 @@ You can start training using python or shell scripts. The usage of shell scripts

 ### Result

 Inference result will be stored in the example path, you can find result like the followings in `./eval/infer.log`.
 Inference result will be stored in the example path, you can find result like the following in `./eval/infer.log`.

 ```
 ```bash
 result: {'acc': 0.76576314102564111}
 ```

@@ -191,7 +190,7 @@ result: {'acc': 0.76576314102564111}
 | Total time                 | 8pcs: 17 hours(30 epochs with pretrained)                   |
 | Parameters (M)             | 25.5                                                        |
 | Checkpoint for Fine tuning | 197M (.ckpt file)                                           |
 | Scripts                    | [resnet50-quant script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet50_quant) | 
 | Scripts                    | [resnet50-quant script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet50_quant) |

 ### Inference Performance

--- a/model_zoo/official/cv/resnet50_quant/src/launch.py
+++ b/model_zoo/official/cv/resnet50_quant/src/launch.py
@@ -34,7 +34,7 @@ def parse_args():
        >>> parse_args()
    """
    parser = ArgumentParser(description="mindspore distributed training launch "
                                        "helper utilty that will spawn up "
                                        "helper utility that will spawn up "
                                        "multiple distributed processes")
    parser.add_argument("--nproc_per_node", type=int, default=1,
                        help="The number of processes to launch on each node, "
--- a/model_zoo/official/cv/yolov3_darknet53_quant/eval.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/eval.py
@@ -93,7 +93,7 @@ class DetectionEngine:

    def _nms(self, dets, thresh):
        """Calculate NMS."""
        # conver xywh -> xmin ymin xmax ymax
        # convert xywh -> xmin ymin xmax ymax
        x1 = dets[:, 0]
        y1 = dets[:, 1]
        x2 = x1 + dets[:, 2]
@@ -185,7 +185,7 @@ class DetectionEngine:

                x_top_left = x - w / 2.
                y_top_left = y - h / 2.
                # creat all False
                # create all False
                flag = np.random.random(cls_emb.shape) > sys.maxsize
                for i in range(flag.shape[0]):
                    c = cls_argmax[i]
--- a/model_zoo/official/cv/yolov3_darknet53_quant/scripts/run_eval.sh
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/scripts/run_eval.sh
@@ -59,7 +59,7 @@ cp ../*.py ./eval$3
 cp -r ../src ./eval$3
 cd ./eval$3 || exit
 env > env.log
 echo "start infering for device $DEVICE_ID"
 echo "start inferring for device $DEVICE_ID"
 python eval.py \
    --data_dir=$DATASET_PATH \
    --pretrained=$CHECKPOINT_PATH \
--- a/model_zoo/official/cv/yolov3_darknet53_quant/src/transforms.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/src/transforms.py
@@ -94,7 +94,7 @@ def get_interp_method(interp, sizes=()):
        Neighbors method. (used by default).
        4: Lanczos interpolation over 8x8 pixel neighborhood.
        9: Cubic for enlarge, area for shrink, bilinear for others
        10: Random select from interpolation method metioned above.
        10: Random select from interpolation method mentioned above.
        Note:
        When shrinking an image, it will generally look best with AREA-based
        interpolation, whereas, when enlarging an image, it will generally look best
--- a/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo.py
@@ -58,7 +58,7 @@ class YoloBlock(nn.Cell):

    Args:
        in_channels: Integer. Input channel.
        out_chls: Interger. Middle channel.
        out_chls: Integer. Middle channel.
        out_channels: Integer. Output channel.

    Returns:
@@ -108,7 +108,7 @@ class YOLOv3(nn.Cell):
     Args:
         backbone_shape: List. Darknet output channels shape.
         backbone: Cell. Backbone Network.
         out_channel: Interger. Output channel.
         out_channel: Integer. Output channel.

     Returns:
         Tensor, output tensor.
--- a/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo_dataset.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/src/yolo_dataset.py
@@ -44,7 +44,7 @@ def has_valid_annotation(anno):
    # if all boxes have close to zero area, there is no annotation
    if _has_only_empty_bbox(anno):
        return False
    # keypoints task have a slight different critera for considering
    # keypoints task have a slight different criteria for considering
    # if an annotation is valid
    if "keypoints" not in anno[0]:
        return True
--- a/model_zoo/official/cv/yolov3_darknet53_quant/train.py
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/train.py
@@ -121,7 +121,7 @@ def parse_args():
        args.rank = get_rank()
        args.group_size = get_group_size()

    # select for master rank save ckpt or all rank save, compatiable for model parallel
    # select for master rank save ckpt or all rank save, compatible for model parallel
    args.rank_save_ckpt_flag = 0
    if args.is_save_on_master:
        if args.rank == 0:
@@ -140,6 +140,14 @@ def conver_training_shape(args):
    training_shape = [int(args.training_shape), int(args.training_shape)]
    return training_shape

 def build_quant_network(network):
    quantizer = QuantizationAwareTraining(bn_fold=True,
                                          per_channel=[True, False],
                                          symmetric=[True, False],
                                          one_conv_fold=False)
    network = quantizer.quantize(network)
    return network


 def train():
    """Train function."""
@@ -168,11 +176,7 @@ def train():
    config = ConfigYOLOV3DarkNet53()
    # convert fusion network to quantization aware network
    if config.quantization_aware:
        quantizer = QuantizationAwareTraining(bn_fold=True,
                                              per_channel=[True, False],
                                              symmetric=[True, False],
                                              one_conv_fold=False)
        network = quantizer.quantize(network)
        network = build_quant_network(network)

    network = YoloWithLossCell(network)
    args.logger.info('finish get network')
@@ -198,11 +202,8 @@ def train():

    lr = get_lr(args)

    opt = Momentum(params=get_param_groups(network),
                   learning_rate=Tensor(lr),
                   momentum=args.momentum,
                   weight_decay=args.weight_decay,
                   loss_scale=args.loss_scale)
    opt = Momentum(params=get_param_groups(network), learning_rate=Tensor(lr), momentum=args.momentum,
                   weight_decay=args.weight_decay, loss_scale=args.loss_scale)

    network = TrainingWrapper(network, opt)
    network.set_train()
@@ -213,9 +214,7 @@ def train():
        ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval,
                                       keep_checkpoint_max=ckpt_max_num)
        save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/')
        ckpt_cb = ModelCheckpoint(config=ckpt_config,
                                  directory=save_ckpt_path,
                                  prefix='{}'.format(args.rank))
        ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=save_ckpt_path, prefix='{}'.format(args.rank))
        cb_params = _InternalCallbackParam()
        cb_params.train_network = network
        cb_params.epoch_num = ckpt_max_num
--- a/model_zoo/official/cv/yolov3_resnet18/README.md
+++ b/model_zoo/official/cv/yolov3_resnet18/README.md
@@ -4,7 +4,7 @@
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
 - [Environment Requirements](#environment-requirements)
 - [Quick Start](#quick-start)    
 - [Quick Start](#quick-start)
 - [Script Description](#script-description)
    - [Script and Sample Code](#script-and-sample-code)
    - [Script Parameters](#script-parameters)
@@ -19,7 +19,6 @@
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)


 # [YOLOv3_ResNet18 Description](#contents)

 YOLOv3 network based on ResNet-18, with support for training and evaluation.
@@ -30,24 +29,25 @@ YOLOv3 network based on ResNet-18, with support for training and evaluation.

 The overall network architecture of YOLOv3 is show below:

 And we use ResNet18 as the backbone of YOLOv3_ResNet18. The architecture of ResNet18 has 4 stages. The ResNet architecture performs the initial convolution and max-pooling using 7×7 and 3×3 kernel sizes respectively. Afterward,  every stage of the network has different Residual blocks (2, 2, 2, 2) containing two 3×3 conv layers. Finally, the network has an Average Pooling layer followed by a fully connected layer. 

 And we use ResNet18 as the backbone of YOLOv3_ResNet18. The architecture of ResNet18 has 4 stages. The ResNet architecture performs the initial convolution and max-pooling using 7×7 and 3×3 kernel sizes respectively. Afterward,  every stage of the network has different Residual blocks (2, 2, 2, 2) containing two 3×3 conv layers. Finally, the network has an Average Pooling layer followed by a fully connected layer.

 # [Dataset](#contents)

 Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

 Dataset used: [COCO2017](<http://images.cocodataset.org/>) 
 Dataset used: [COCO2017](<http://images.cocodataset.org/>)

 - Dataset size：19G
  - Train：18G，118000 images  
  - Val：1G，5000 images 
  - Annotations：241M，instances，captions，person_keypoints etc
    - Train：18G，118000 images  
    - Val：1G，5000 images
    - Annotations：241M，instances，captions，person_keypoints etc
 - Data format：image and json files
  - Note：Data will be processed in dataset.py

 - Dataset

    1. The directory structure is as follows:

        ```
        .
        ├── annotations  # annotation jsons
@@ -55,7 +55,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
        └── val2017      # infer dataset
        ```

    2. Organize the dataset infomation into a TXT file, each row in the file is as follows:
    2. Organize the dataset information into a TXT file, each row in the file is as follows:

        ```
        train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
@@ -63,63 +63,61 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)

        Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. `dataset.py` is the parsing script, we read image from an image path joined by the `image_dir`(dataset directory) and the relative path in `anno_path`(the TXT file path), `image_dir` and `anno_path` are external inputs.


 # [Environment Requirements](#contents)

 - Hardware（Ascend）
  - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. 
    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
  - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)



 # [Quick Start](#contents)

 After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows: 
 After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows:

 - runing on Ascend
 - running on Ascend

    ```shell script
    #run standalone training example
    sh run_standalone_train.sh [DEVICE_ID] [EPOCH_SIZE] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH]
    

    #run distributed training example
    sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH] [RANK_TABLE_FILE]
   

    #run evaluation example
    sh run_eval.sh [DEVICE_ID] [CKPT_PATH] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH]
    ```

 # [Script Description](#contents)

 ## [Script and Sample Code](#contents)

 ```
 ```python
 └── cv
    ├── README.md                           // descriptions about all the models
    ├── mindspore_hub_conf.md               // config for mindspore hub
    └── yolov3_resnet18        
    └── yolov3_resnet18
        ├── README.md                       // descriptions about yolov3_resnet18
        ├── scripts 
        ├── scripts
            ├── run_distribute_train.sh     // shell script for distributed on Ascend
            ├── run_standalone_train.sh     // shell script for distributed on Ascend
            └── run_eval.sh                 // shell script for evaluation on Ascend
        ├── src 
        ├── src
            ├── dataset.py                  // creating dataset
            ├── yolov3.py                   // yolov3 architecture
            ├── config.py                   // parameter configuration 
            ├── config.py                   // parameter configuration
            └── utils.py                    // util function
        ├── train.py                        // training script 
        ├── train.py                        // training script
        └── eval.py                         // evaluation script  
 ```

 ## [Script Parameters](#contents)

  ```
  Major parameters in train.py and config.py as follows:

  ```python
    device_num: Use device nums, default is 1.
    lr: Learning rate, default is 0.001.
    epoch_size: Epoch size, default is 50.
@@ -133,24 +131,23 @@ After installing MindSpore via the official website, you can start training and
    img_shape: Image height and width used as input to the model.
  ```


 ## [Training Process](#contents)

 ### Training on Ascend

 To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.**

 - Stand alone mode

    ```
    ```bash
    sh run_standalone_train.sh 0 50 ./Mindrecord_train ./dataset ./dataset/train.txt
    ```

    The input variables are device id, epoch size, mindrecord directory path, dataset directory path and train TXT file path.


 - Distributed mode

    ```
    ```bash
    sh run_distribute_train.sh 8 150 /data/Mindrecord_train /data /data/train.txt /data/hccl.json
    ```

@@ -158,7 +155,7 @@ To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and

 You will get the loss value and time of each step as following:

  ```
  ```bash
  epoch: 145 step: 156, loss is 12.202981
  epoch time: 25599.22742843628, per step time: 164.0976117207454
  epoch: 146 step: 156, loss is 16.91706
@@ -168,19 +165,20 @@ You will get the loss value and time of each step as following:
  epoch: 148 step: 156, loss is 10.431475
  epoch time: 23634.241580963135, per step time: 151.50154859591754
  epoch: 149 step: 156, loss is 14.665991
  epoch time: 24118.8325881958, per step time: 154.60790120638333 
  epoch time: 24118.8325881958, per step time: 154.60790120638333
  epoch: 150 step: 156, loss is 10.779521
  epoch time: 25319.57221031189, per step time: 162.30495006610187
  ```

 Note the results is two-classification(person and face) used our own annotations with coco2017, you can change `num_classes` in `config.py` to train your dataset. And we will suport 80 classifications in coco2017 the near future.
 Note the results is two-classification(person and face) used our own annotations with coco2017, you can change `num_classes` in `config.py` to train your dataset. And we will support 80 classifications in coco2017 the near future.

 ## [Evaluation Process](#contents)

 ### Evaluation on Ascend

 To eval, run `eval.py` with the dataset `image_dir`, `anno_path`(eval txt), `mindrecord_dir` and `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorial/training/en/master/use/save_model.html) file.

  ```
  ```bash
  sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt
  ```

@@ -188,18 +186,18 @@ The input variables are device id, checkpoint path, mindrecord directory path, d

 You will get the precision and recall value of each class:

  ```
  ```bash
  class 0 precision is 88.18%, recall is 66.00%
  class 1 precision is 85.34%, recall is 79.13%
  ```

 Note the precision and recall values are results of two-classification(person and face) used our own annotations with coco2017.


 # [Model Description](#contents)

 ## [Performance](#contents)

 ### Evaluation Performance 
 ### Evaluation Performance

 | Parameters                 | Ascend                                                      |
 | -------------------------- | ----------------------------------------------------------- |
@@ -217,7 +215,6 @@ Note the precision and recall values are results of two-classification(person an
 | Parameters (M)             | 189                                                         |
 | Scripts                    | [yolov3_resnet18 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_resnet18) | [yolov3_resnet18 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_resnet18) |


 ### Inference Performance

 | Parameters          | Ascend                                          |
@@ -233,9 +230,9 @@ Note the precision and recall values are results of two-classification(person an

 # [Description of Random Situation](#contents)

 In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py. 
 In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.

 # [ModelZoo Homepage](#contents)

 # [ModelZoo Homepage](#contents)  
 Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  

--- a/model_zoo/official/cv/yolov3_resnet18/scripts/run_distribute_train.sh
+++ b/model_zoo/official/cv/yolov3_resnet18/scripts/run_distribute_train.sh
@@ -15,7 +15,7 @@
 # ============================================================================

 echo "======================================================================================================================================================="
 echo "Please run the scipt as: "
 echo "Please run the script as: "
 echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH RANK_TABLE_FILE PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
 echo "For example: sh run_distribute_train.sh 8 150 /data/Mindrecord_train /data /data/train.txt /data/hccl.json /opt/yolov3-150.ckpt(optional) 100(optional)"
 echo "It is better to use absolute path."
@@ -47,7 +47,7 @@ then
    exit 1
 fi

 echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
 echo "After running the script, the network runs in the background. The log will be generated in LOGx/log.txt"

 export RANK_TABLE_FILE=$6
 export RANK_SIZE=$1
--- a/model_zoo/official/cv/yolov3_resnet18/scripts/run_eval.sh
+++ b/model_zoo/official/cv/yolov3_resnet18/scripts/run_eval.sh
@@ -15,7 +15,7 @@
 # ============================================================================

 echo "=============================================================================================================="
 echo "Please run the scipt as: "
 echo "Please run the script as: "
 echo "sh run_eval.sh DEVICE_ID CKPT_PATH MINDRECORD_DIR IMAGE_DIR ANNO_PATH"
 echo "for example: sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt"
 echo "=============================================================================================================="
--- a/model_zoo/official/cv/yolov3_resnet18/scripts/run_standalone_train.sh
+++ b/model_zoo/official/cv/yolov3_resnet18/scripts/run_standalone_train.sh
@@ -15,7 +15,7 @@
 # ============================================================================

 echo "========================================================================================================================================="
 echo "Please run the scipt as: "
 echo "Please run the script as: "
 echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE MINDRECORD_DIR IMAGE_DIR ANNO_PATH PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
 echo "for example: sh run_standalone_train.sh 0 50 ./Mindrecord_train ./dataset ./dataset/train.txt /opt/yolov3-50.ckpt(optional) 30(optional)"
 echo "========================================================================================================================================="