update loading pre-trained models saved remotely using hub API

tutorials/source_en/use/multi_platform_inference.md

@@ -53,14 +53,54 @@ MindSpore supports the following inference scenarios based on the hardware platf
 
 ### Inference Using a Checkpoint File
 
-1. Input a validation dataset to validate a model using the `model.eval` API. The processing method of the validation dataset is the same as that of the training dataset.
+1. Input a validation dataset to validate a model using the `model.eval` API. 
+
+   1.1 Local Storage
+
+     When the pre-trained models are saved locally, the steps of performing inference on validation dataset are as follows: firstly creating a model, then loading model and parameters using `load_checkpoint` and `load_param_into_net` in `mindspore.train.serialization` module, and finally performing inference on validation dataset once created. The processing method of the validation dataset is the same as that of the training dataset.
+
     ```python
-    res = model.eval(dataset)
+    network = LeNet5(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Testing ==============")
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+    dataset = create_dataset(os.path.join(args.data_path, "test"),
+                             cfg.batch_size,
+                             1)
+    acc = model.eval(dataset, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== {} ==============".format(acc))
     ```
     In the preceding information:  
     `model.eval` is an API for model validation. For details about the API, see <https://www.mindspore.cn/api/en/r0.6/api/python/mindspore/mindspore.html#mindspore.Model.eval>.
     > Inference sample code: <https://gitee.com/mindspore/mindspore/blob/r0.6/model_zoo/official/cv/lenet/eval.py>.
 
+    1.2 Remote Storage
+    
+    When the pre-trained models are saved remotely, the steps of performing inference on validation dataset are as follows: firstly creating a model, then loading model and parameters using `hub.load_weights`, and finally performing inference on validation dataset once created. The processing method of the validation dataset is the same as that of the training dataset.
+
+    ```python
+    network = LeNet5(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Testing ==============")
+    hub.load_weights(network, network_name="lenet", **{"device_target":
+                     "ascend", "dataset":"mnist", "version": "0.5.0"})
+    dataset = create_dataset(os.path.join(args.data_path, "test"),
+                             cfg.batch_size,
+                             1)
+    acc = model.eval(dataset, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== {} ==============".format(acc))
+    ``` 
+    In the preceding information:
+        
+    `hub.load_weights` is an API for loading model parameters. PLease check the details in <https://www.mindspore.cn/api/en/r0.6/api/python/mindspore/mindspore.hub.html#mindspore.hub.load_weights>.
+
 2. Use the `model.predict` API to perform inference.
    ```python
    model.predict(input_data)
@@ -106,4 +146,5 @@ Similar to the inference on a GPU, the following steps are required:
 
 ## On-Device Inference
 
-MindSpore Predict is an inference engine for on-device inference. For details, see [On-Device Inference](https://www.mindspore.cn/tutorial/en/r0.6/advanced_use/on_device_inference.html).
\ No newline at end of file
+MindSpore Predict is an inference engine for on-device inference. For details, see [On-Device Inference](https://www.mindspore.cn/tutorial/en/r0.6/advanced_use/on_device_inference.html).
+

tutorials/source_zh_cn/use/multi_platform_inference.md

@@ -53,14 +53,52 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。
 
 ### 使用checkpoint格式文件推理
 
-1. 使用`model.eval`接口来进行模型验证，你只需传入验证数据集即可，验证数据集的处理方式与训练数据集相同。
+1. 使用`model.eval`接口来进行模型验证。                                  
+
+   1.1 模型已保存在本地  
+
+   首先构建模型，然后使用mindspore.train.serialization模块的`load_checkpoint`和`load_param_into_net`从本地加载模型与参数，传入验证数据集后即可进行模型推理，验证数据集的处理方式与训练数据集相同。
+
     ```python
-    res = model.eval(dataset)
+    network = LeNet5(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Testing ==============")
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+    dataset = create_dataset(os.path.join(args.data_path, "test"),
+                             cfg.batch_size,
+                             1)
+    acc = model.eval(dataset, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== {} ==============".format(acc))
     ```
     其中，  
     `model.eval`为模型验证接口，对应接口说明：<https://www.mindspore.cn/api/zh-CN/r0.6/api/python/mindspore/mindspore.html#mindspore.Model.eval>。
     > 推理样例代码：<https://gitee.com/mindspore/mindspore/blob/r0.6/model_zoo/official/cv/lenet/eval.py>。
 
+   1.2 模型保存在华为云
+    
+   首先构建模型，然后使用`hub.load_weights`从云端加载模型参数，传入验证数据集后即可进行推理，验证数据集的处理方式与训练数据集相同。
+    ```python
+    network = LeNet5(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Testing ==============")
+    hub.load_weights(network, network_name="lenet", **{"device_target":
+                     "ascend", "dataset":"mnist", "version": "0.5.0"})
+    dataset = create_dataset(os.path.join(args.data_path, "test"),
+                             cfg.batch_size,
+                             1)
+    acc = model.eval(dataset, dataset_sink_mode=args.dataset_sink_mode)
+    print("============== {} ==============".format(acc))
+    ``` 
+    其中，  
+    `hub.load_weights`为加载模型参数接口，对应接口说明：<https://www.mindspore.cn/api/zh-CN/r0.6/api/python/mindspore/mindspore.hub.html#mindspore.hub.load_weights>。
+
 2. 使用`model.predict`接口来进行推理操作。
    ```python
    model.predict(input_data)
@@ -106,4 +144,4 @@ Ascend 310 AI处理器上搭载了ACL框架，他支持OM格式，而OM格式需
 
 ## 端侧推理
 
-端侧推理需使用MindSpore Predict推理引擎，详细操作请参考[端侧推理教程](https://www.mindspore.cn/tutorial/zh-CN/r0.6/advanced_use/on_device_inference.html)。
\ No newline at end of file
+端侧推理需使用MindSpore Predict推理引擎，详细操作请参考[端侧推理教程](https://www.mindspore.cn/tutorial/zh-CN/r0.6/advanced_use/on_device_inference.html)。