code style fix

docs/en/faq/develop_a_new_model.md

@@ -107,15 +107,15 @@ bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
 ### Pybind
 
 * Create Pybind file
-  
+
   * Create path
-    
+
     * FastDeploy/fastdeploy/vision/classification/contrib/resnet_pybind.cc (FastDeploy/C++ code/vision model/taks name/external model/model name_pybind.cc)
-  
+
   * Create content
-    
+
     * Use Pybind to bind function variables from C++ to Python, please refer to [resnet_pybind.cc](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-270af0d65720310e2cfbd5373c391b2110d65c0f4efa547f7b7eeffcb958bdec) for more details.
-      
+
       ```C++
       void BindResNet(pybind11::module& m) {
       pybind11::class_<vision::classification::ResNet, FastDeployModel>(
@@ -129,13 +129,13 @@ bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
       ```
 
 * Call Pybind function
-  
+
   * modify path
-    
+
     * FastDeploy/fastdeploy/vision/classification/classification_pybind.cc (FastDeploy/C++ code/vision model/task name/task name}_pybind.cc)
-  
+
   * modify content
-    
+
     ```C++
     void BindResNet(pybind11::module& m);
     void BindClassification(pybind11::module& m) {
@@ -151,7 +151,7 @@ bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
   * Create path
     * FastDeploy/python/fastdeploy/vision/classification/contrib/resnet.py (FastDeploy/Python code/fastdeploy/vision model/task name/external model/model name.py)
   * Create content
-    * Create ResNet class inherited from FastDeployModel, and implement `\_\_init\_\_`, Pybind bonded functions (such as `predict()`), and `functions to assign and get global variables bound to Pybind`. Please refer to [resnet.py](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-a4dc5ec2d450e91f1c03819bf314c238b37ac678df56d7dea3aab7feac10a157) for details 
+    * Create ResNet class inherited from FastDeployModel, and implement `\_\_init\_\_`, Pybind bonded functions (such as `predict()`), and `functions to assign and get global variables bound to Pybind`. Please refer to [resnet.py](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-a4dc5ec2d450e91f1c03819bf314c238b37ac678df56d7dea3aab7feac10a157) for details
 
 ```python
 class ResNet(FastDeployModel):
@@ -245,7 +245,7 @@ make
 
 
 
-To make the code clear for understanding,  developers can annotate the newly-added code. 
+To make the code clear for understanding,  developers can annotate the newly-added code.
 
 - C++ code
   Developers need to add annotations for functions and variables in the resnet.h file, there are three annotating methods as follows, please refer to [resnet.h](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff- 69128489e918f305c208476ba793d8167e77de2aa7cadf5dcbac30da448bd28e) for more details.
@@ -265,8 +265,8 @@ std::vector<int> size;
 bool Initialize();
 ```
 
-- Python 
-  The following example is to demonstrate how to annotate functions and variables in resnet.py file. For more details, please refer to [resnet.py](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-a4dc5ec2d450e91f1c03819bf314c238b37ac678df56d7dea3aab7feac10a157). 
+- Python
+  The following example is to demonstrate how to annotate functions and variables in resnet.py file. For more details, please refer to [resnet.py](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-a4dc5ec2d450e91f1c03819bf314c238b37ac678df56d7dea3aab7feac10a157).
 
 ```python
   def predict(self, input_image, topk=1):

serving/README_EN.md

@@ -38,6 +38,6 @@ Users can also compile the image by themselves according to their own needs, ref
 ## Other Tutorials
 
 - [How to Prepare Serving Model Repository](docs/zh_CN/model_repository.md)
-- [Serving Deployment Configuration for Runtime](docs/zh_CN/model_configuration.md) 
+- [Serving Deployment Configuration for Runtime](docs/zh_CN/model_configuration.md)
 - [Serving Deployment Demo](docs/zh_CN/demo.md)
   - [YOLOV5 - Detection Task](../examples/vision/detection/yolov5/serving/README.md)

serving/docs/EN/model_repository-en.md

@@ -1,6 +1,6 @@
 # Model Repository
 
-FastDeploy starts the serving by specifying one or more models in the model repository to deploy the service. When the serving is running, the models in the service can be modified following [Model Management](https://github.com/triton-inference-server/server/blob/main/docs/model_management.md), and obtain serving from one or more model repositories specified at the serving initiation. 
+FastDeploy starts the serving by specifying one or more models in the model repository to deploy the service. When the serving is running, the models in the service can be modified following [Model Management](https://github.com/triton-inference-server/server/blob/main/docs/model_management.md), and obtain serving from one or more model repositories specified at the serving initiation.
 
 ## Repository Architecture
 
@@ -64,7 +64,7 @@ Example: A minimal model repository directory for deploying Paddle models
       └── config.pbtxt
 ```
 
-To deploy an ONNX model, model with the name `model.onnx` must be included in the version directory 
+To deploy an ONNX model, model with the name `model.onnx` must be included in the version directory
 
 Example: A minimal model repository directory for deploying ONNX models
 

tools/auto_compression/README_EN.md

@@ -41,7 +41,7 @@ Fastdeploy Auto Compression can include multiple strategies, At present, offline
 ##### 1. Prepare models and Calibration data set
 
 Developers need to prepare the model to be quantized and the Calibration dataset on their own.
-In this demo, developers can execute the following command to download the yolov5s.onnx model to be quantized and calibration data set. 
+In this demo, developers can execute the following command to download the yolov5s.onnx model to be quantized and calibration data set.
 
 ```shell
 # Download yolov5.onnx
@@ -109,7 +109,7 @@ To complete the quantization, developers only need to provide a customized model
 | --method      | Quantization method selection, PTQ for post-training quantization, QAT for quantization distillation training |
 | --save_dir    | Output of quantized model paths, which can be deployed directly in FastDeploy                                 |
 
-## 3. FastDeploy One-Click Model Auto Compression Config file examples 
+## 3. FastDeploy One-Click Model Auto Compression Config file examples
 FastDeploy currently provides users with compression [config](./configs/) files of multiple models, and the corresponding FP32 model, Users can directly download and experience it.
 
 | Config文件                | 待压缩的FP32模型 | 备注                                                       |

tools/auto_compression/configs/README_EN.md

@@ -35,7 +35,7 @@ PTQ:
   calibration_method: 'avg'                   #Activate calibration algorithm of post-training quantization , Options: avg, abs_max, hist, KL, mse, emd
   skip_tensor_list: None                      #Developers can skip some conv layers‘ quantization
 
-#Traning 
+#Traning
 TrainConfig:
   train_iter: 3000
   learning_rate: 0.00001
@@ -49,5 +49,3 @@ TrainConfig:
 ## More details
 
 FastDeploy one-click quantization tool is powered by PaddeSlim, please refer to [Automated Compression of Hyperparameter Tutorial](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/hyperparameter_tutorial.md) for more details.
-
-