add readme_en for 100 models

modules/image/Image_editing/colorization/user_guided_colorization/README_en.md

@@ -77,7 +77,7 @@
                        T.RGB2LAB()], to_rgb=True)
               ```
 
-                - `transforms` The data enhancement module defines lots of data preprocessing methods. Users can replace the data preprocessing methods according to their needs.
+              - `transforms` The data enhancement module defines lots of data preprocessing methods. Users can replace the data preprocessing methods according to their needs.
 
         - Step2: Download the dataset
             - ```python

modules/image/Image_gan/style_transfer/msgnet/README.md

@@ -50,13 +50,14 @@ $ hub run msgnet --input_path "/PATH/TO/ORIGIN/IMAGE" --style_path "/PATH/TO/STY
 
 - ### 2.预测代码示例
 
+
 ```python
 import paddle
 import paddlehub as hub
 
 if __name__ == '__main__':
     model = hub.Module(name='msgnet')
-    result = model.predict(origin=["venice-boat.jpg"], style="candy.jpg", visualization=True, save_path ='style_tranfer')
+    result = model.predict(origin=["/PATH/TO/ORIGIN/IMAGE"], style="/PATH/TO/STYLE/IMAGE", visualization=True, save_path ="/PATH/TO/SAVE/IMAGE")
 ```
 
 
@@ -86,7 +87,7 @@ if __name__ == '__main__':
                 - `transforms`: 数据预处理方式。
                 - `mode`: 选择数据模式，可选项有 `train`, `test`， 默认为`train`。
 
-                - 数据集的准备代码可以参考 [minicoco.py](../../paddlehub/datasets/flowers.py)。`hub.datasets.MiniCOCO()`会自动从网络下载数据集并解压到用户目录下`$HOME/.paddlehub/dataset`目录。
+                - 数据集的准备代码可以参考 [minicoco.py](../../paddlehub/datasets/minicoco.py)。`hub.datasets.MiniCOCO()`会自动从网络下载数据集并解压到用户目录下`$HOME/.paddlehub/dataset`目录。
 
         - Step3: 加载预训练模型
 
@@ -117,7 +118,7 @@ if __name__ == '__main__':
 
             if __name__ == '__main__':
                 model = hub.Module(name='msgnet', load_checkpoint="/PATH/TO/CHECKPOINT")
-                result = model.predict(origin=["venice-boat.jpg"], style="candy.jpg", visualization=True, save_path ='style_tranfer')
+                result = model.predict(origin=["/PATH/TO/ORIGIN/IMAGE"], style="/PATH/TO/STYLE/IMAGE", visualization=True, save_path ="/PATH/TO/SAVE/IMAGE")
             ```
 
             - 参数配置正确后，请执行脚本`python predict.py`， 加载模型具体可参见[加载](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-rc/api/paddle/framework/io/load_cn.html#load)。

modules/image/Image_gan/style_transfer/msgnet/README_en.md

@@ -0,0 +1,185 @@
+# msgnet
+
+|Module Name|msgnet|
+| :--- | :---: | 
+|Category|image editing|
+|Network|msgnet|
+|Dataset|COCO2014|
+|Fine-tuning supported or not|Yes|
+|Module Size|68MB|
+|Data indicators|-|
+|Latest update date|2021-07-29|
+
+
+## I. Basic Information 
+
+- ### Application Effect Display
+    - Sample results:
+    <p align="center">
+    <img src="https://user-images.githubusercontent.com/35907364/130910325-d72f34b2-d567-4e77-bb60-35148864301e.jpg" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130910195-9433e4a7-3596-4677-85d2-2ffc16939597.png" width = "450" height = "300" hspace='10'/>
+    </p>
+
+- ### Module Introduction
+
+    - Msgnet is a style transfer model. We will show how to use PaddleHub to finetune the pre-trained model and complete the prediction.
+    - For more information, please refer to [msgnet](https://github.com/zhanghang1989/PyTorch-Multi-Style-Transfer)
+
+## II. Installation
+
+- ### 1、Environmental Dependence
+
+    - paddlepaddle >= 2.0.0
+
+    - paddlehub >= 2.0.0
+
+- ### 2、Installation
+
+    - ```shell
+      $ hub install msgnet
+      ```
+
+    - In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_ch/get_start/windows_quickstart.md)
+    | [Linux_Quickstart](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_ch/get_start/mac_quickstart.md)  
+
+
+## III. Module API Prediction
+
+- ### 1、Command line Prediction
+
+```
+$ hub run msgnet --input_path "/PATH/TO/ORIGIN/IMAGE" --style_path "/PATH/TO/STYLE/IMAGE"
+```
+
+- ### 2、Prediction Code Example
+
+```python
+import paddle
+import paddlehub as hub
+
+if __name__ == '__main__':
+    model = hub.Module(name='msgnet')
+    result = model.predict(origin=["/PATH/TO/ORIGIN/IMAGE"], style="/PATH/TO/STYLE/IMAGE", visualization=True, save_path ="/PATH/TO/SAVE/IMAGE")
+```
+
+
+
+- ### 3.Fine-tune and Encapsulation
+
+    - After completing the installation of PaddlePaddle and PaddleHub, you can start using the msgnet model to fine-tune datasets such as [MiniCOCO](../../docs/reference/datasets.md#class-hubdatasetsMiniCOCO) by executing `python train.py`.
+
+    - Steps:
+
+        - Step1: Define the data preprocessing method
+
+            - ```python
+              import paddlehub.vision.transforms as T
+
+              transform = T.Compose([T.Resize((256, 256), interpolation='LINEAR')])
+              ```
+
+            - `transforms` The data enhancement module defines lots of data preprocessing methods. Users can replace the data preprocessing methods according to their needs.
+
+        - Step2: Download the dataset
+            - ```python
+              from paddlehub.datasets.minicoco import MiniCOCO
+
+              styledata = MiniCOCO(transform=transform, mode='train')
+
+              ```
+                * `transforms`: data preprocessing methods.
+                * `mode`: Select the data mode, the options are `train`, `test`, `val`. Default is `train`.
+
+                - Dataset preparation can be referred to [minicoco.py](../../paddlehub/datasets/minicoco.py). `hub.datasets.MiniCOCO()` will be automatically downloaded from the network and decompressed to the `$HOME/.paddlehub/dataset` directory under the user directory.
+
+        - Step3: Load the pre-trained model
+
+            - ```python
+              model = hub.Module(name='msgnet', load_checkpoint=None)
+              ```
+                * `name`: model name.
+                * `load_checkpoint`: Whether to load the self-trained model, if it is None, load the provided parameters.
+
+        - Step4: Optimization strategy
+
+            - ```python
+              optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())
+              trainer = Trainer(model, optimizer, checkpoint_dir='test_style_ckpt')
+              trainer.train(styledata, epochs=101, batch_size=4, eval_dataset=styledata, log_interval=10, save_interval=10)
+              ```
+
+
+    - Model prediction
+
+        -   When Fine-tune is completed, the model with the best performance on the verification set will be saved in the `${CHECKPOINT_DIR}/best_model` directory. We use this model to make predictions. The `predict.py` script is as follows:
+            ```python
+            import paddle
+            import paddlehub as hub
+
+            if __name__ == '__main__':
+                model = hub.Module(name='msgnet', load_checkpoint="/PATH/TO/CHECKPOINT")
+                result = model.predict(origin=["/PATH/TO/ORIGIN/IMAGE"], style="/PATH/TO/STYLE/IMAGE", visualization=True, save_path ="/PATH/TO/SAVE/IMAGE")
+            ```
+
+            - **Args**
+                * `origin`: Image path or ndarray data with format [H, W, C], BGR;
+                * `style`: Style image path;
+                * `visualization`: Whether to save the recognition results as picture files;
+                * `save_path`: Save path of the result, default is 'style_tranfer'.
+
+
+## IV. Server Deployment
+
+- PaddleHub Serving can deploy an online service of style transfer.
+
+- ### Step 1: Start PaddleHub Serving
+
+    - Run the startup command:
+
+    - ```shell
+      $ hub serving start -m msgnet
+      ```
+
+    - The servitization API is now deployed and the default port number is 8866.
+
+    - **NOTE:**  If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
+
+
+- ### Step 2: Send a predictive request
+
+    - With a configured server, use the following lines of code to send the prediction request and obtain the result:
+
+        ```python
+        import requests
+        import json
+        import cv2
+        import base64
+
+        import numpy as np
+
+
+        def cv2_to_base64(image):
+            data = cv2.imencode('.jpg', image)[1]
+            return base64.b64encode(data.tostring()).decode('utf8')
+
+        def base64_to_cv2(b64str):
+            data = base64.b64decode(b64str.encode('utf8'))
+            data = np.fromstring(data, np.uint8)
+            data = cv2.imdecode(data, cv2.IMREAD_COLOR)
+            return data
+
+        # Send an HTTP request
+        org_im = cv2.imread('/PATH/TO/ORIGIN/IMAGE')
+        style_im = cv2.imread('/PATH/TO/STYLE/IMAGE')
+        data = {'images':[[cv2_to_base64(org_im)], cv2_to_base64(style_im)]}
+        headers = {"Content-type": "application/json"}
+        url = "http://127.0.0.1:8866/predict/msgnet"
+        r = requests.post(url=url, headers=headers, data=json.dumps(data))
+        data = base64_to_cv2(r.json()["results"]['data'][0])
+        cv2.imwrite('style.png', data)
+        ```
+
+## V. Release Note
+
+- 1.0.0
+
+  First release