modify the resnet model path to the official/cv/resnet

docs/source_en/network_list.md

@@ -9,8 +9,8 @@
 |Computer Version (CV) | Image Classification  | [AlexNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/alexnet/src/alexnet.py)          |  Supported |  Supported | Doing
 | Computer Version (CV)  | Image Classification  | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/googlenet/src/googlenet.py)                                               |  Supported     | Doing | Doing
 | Computer Version (CV)  | Image Classification  | [LeNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/src/lenet.py)              |  Supported |  Supported | Supported
-| Computer Version (CV)  | Image Classification  | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py)          |  Supported |  Supported | Doing
-|Computer Version (CV)  | Image Classification  | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py)                                              |  Supported |Doing | Doing
+| Computer Version (CV)  | Image Classification  | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)          |  Supported |  Supported | Doing
+|Computer Version (CV)  | Image Classification  | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)                                              |  Supported |Doing | Doing
 | Computer Version (CV)  | Image Classification  | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/vgg16/src/vgg.py)                |  Supported |  Doing | Doing
 | Computer Version (CV)  | Mobile Image Classification<br>Image Classification<br>Semantic Tegmentation  | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv2/src/mobilenetV2.py)                                            |  Supported |  Supported | Doing
 | Computer Version (CV)  | Mobile Image Classification<br>Image Classification<br>Semantic Tegmentation  | [MobileNetV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv3/src/mobilenetV3.py)                                            |  Doing |  Supported | Doing

docs/source_zh_cn/network_list.md

@@ -9,8 +9,8 @@
 |计算机视觉（CV） | 图像分类（Image Classification）  | [AlexNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/alexnet/src/alexnet.py)          |  Supported |  Supported | Doing
 | 计算机视觉（CV）  | 图像分类（Image Classification）  | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/googlenet/src/googlenet.py)                                               |  Supported     | Doing | Doing
 | 计算机视觉（CV）  | 图像分类（Image Classification）  | [LeNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/src/lenet.py)              |  Supported |  Supported | Supported
-| 计算机视觉（CV）  | 图像分类（Image Classification）  | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py)          |  Supported |  Supported | Doing
-|计算机视觉（CV）  | 图像分类（Image Classification）  | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py)                                              |  Supported |Doing | Doing
+| 计算机视觉（CV）  | 图像分类（Image Classification）  | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)          |  Supported |  Supported | Doing
+|计算机视觉（CV）  | 图像分类（Image Classification）  | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)                                              |  Supported |Doing | Doing
 | 计算机视觉（CV）  | 图像分类（Image Classification）  | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/vgg16/src/vgg.py)                |  Supported |  Doing | Doing
 | 计算机视觉（CV）  | 移动端图像分类（Mobile Image Classification）<br>目标检测（Image Classification）<br>语义分割（Semantic Tegmentation）  | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv2/src/mobilenetV2.py)                                            |  Supported |  Supported | Doing
 | 计算机视觉（CV）  | 移动端图像分类（Mobile Image Classification）<br>目标检测（Image Classification）<br>语义分割（Semantic Tegmentation）  | [MobileNetV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/mobilenetv3/src/mobilenetV3.py)                                            |  Doing |  Supported | Doing

tutorials/source_en/advanced_use/computer_vision_application.md

@@ -143,7 +143,7 @@ CNN is a standard algorithm for image classification tasks. CNN uses a layered s
 
 ResNet is recommended. First, it is deep enough with 34 layers, 50 layers, or 101 layers. The deeper the hierarchy, the stronger the representation capability, and the higher the classification accuracy. Second, it is learnable. The residual structure is used. The lower layer is directly connected to the upper layer through the shortcut connection, which solves the problem of gradient disappearance caused by the network depth during the reverse propagation. In addition, the ResNet network has good performance, including the recognition accuracy, model size, and parameter quantity.
 
-MindSpore Model Zoo has a ResNet [model](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py). The calling method is as follows:
+MindSpore Model Zoo has a ResNet [model](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py). The calling method is as follows:
 
 ```python
 network = resnet50(class_num=10)

tutorials/source_en/advanced_use/network_migration.md

@@ -79,7 +79,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
                               num_shards=device_num, shard_id=rank_id)
    ```
 
-   Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/dataset.py>.
+   Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/dataset.py>.
 
 3. Build a network.
 
@@ -214,7 +214,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
 
 6. Build the entire network.
 
-   The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
+   The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
 
 7. Define a loss function and an optimizer.
 

tutorials/source_zh_cn/advanced_use/computer_vision_application.md

@@ -145,7 +145,7 @@ tar -zvxf cifar-10-binary.tar.gz
 
 ResNet通常是较好的选择。首先，它足够深，常见的有34层，50层，101层。通常层次越深，表征能力越强，分类准确率越高。其次，可学习，采用了残差结构，通过shortcut连接把低层直接跟高层相连，解决了反向传播过程中因为网络太深造成的梯度消失问题。此外，ResNet网络的性能很好，既表现为识别的准确率，也包括它本身模型的大小和参数量。
 
-MindSpore Model Zoo中已经实现了ResNet模型，可以采用[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py)。调用方法如下：
+MindSpore Model Zoo中已经实现了ResNet模型，可以采用[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)。调用方法如下：
 
 ```python
 network = resnet50(class_num=10)

tutorials/source_zh_cn/advanced_use/network_migration.md

@@ -77,7 +77,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
                                num_shards=device_num, shard_id=rank_id)
     ```
     
-    然后对数据进行了数据增强、数据清洗和批处理等操作。代码详见<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/dataset.py>。
+    然后对数据进行了数据增强、数据清洗和批处理等操作。代码详见<https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/dataset.py>。
 
 3. 构建网络。
 
@@ -210,7 +210,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
 
 6. 构造整网。
 
-    将定义好的多个子网连接起来就是整个[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py)网络的结构了。同样遵循先定义后使用的原则，在`__init__`中定义所有用到的子网，在`construct`中连接子网。
+    将定义好的多个子网连接起来就是整个[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)网络的结构了。同样遵循先定义后使用的原则，在`__init__`中定义所有用到的子网，在`construct`中连接子网。
 
 7. 定义损失函数和优化器。
 
@@ -269,4 +269,4 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
 
 1. [常用数据集读取样例](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/loading_the_datasets.html)
 
-2. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)
+2. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)
\ No newline at end of file