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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:

  • 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

II. Installation

III. Module API Prediction

  • 1、Command line Prediction

    • $ hub run msgnet --input_path "/PATH/TO/ORIGIN/IMAGE" --style_path "/PATH/TO/STYLE/IMAGE"
    • If you want to call the Hub module through the command line, please refer to: PaddleHub Command Line Instruction

  • 2、Prediction Code Example

    •  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 by executing python train.py.

    • Steps:

      • Step1: Define the data preprocessing method

        • 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

        • 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. 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

        • 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

        • 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: 
        • 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")
          • Parameters
            • 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:

      • 

        • 

      

      $ 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:
      

        

      • 

        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)
        

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V. Release Note


    

  • 

    1.0.0
    

    First release
    

    •