README_en.md

attgan_celeba

Module Name	attgan_celeba
Category	image generation
Network	AttGAN
Dataset	Celeba
Fine-tuning supported or not	No
Module Size	167MB
Latest update date	2021-02-26
Data indicators	-

I. Basic Information

• Application Effect Display

  • Sample results:

    
    The image attributes are: original image, Bald, Bangs, Black_Hair, Blond_Hair, Brown_Hair, Bushy_Eyebrows, Eyeglasses, Gender, Mouth_Slightly_Open, Mustache, No_Beard, Pale_Skin, Aged
    

• Module Introduction

  • AttGAN is a Generative Adversarial Network, which uses classification loss and reconstruction loss to train the network. The PaddleHub Module is trained one Celeba dataset and currently supports attributes of "Bald", "Bangs", "Black_Hair", "Blond_Hair", "Brown_Hair", "Bushy_Eyebrows", "Eyeglasses", "Gender", "Mouth_Slightly_Open", "Mustache", "No_Beard", "Pale_Skin", "Aged".

II. Installation

• 1、Environmental Dependence

  • paddlepaddle >= 1.5.2

  • paddlehub >= 1.0.0 | How to install PaddleHub

• 2、Installation

  • $ hub install attgan_celeba==1.0.0

  • In case of any problems during installation, please refer to:Windows_Quickstart | Linux_Quickstart | Mac_Quickstart.

III. Module API Prediction

• 1、Command line Prediction

  • $ hub run attgan_celeba --image "/PATH/TO/IMAGE" --style "target_attribute" 

  • Parameters

    • image: Input image path.

    • style: Specify the attributes to be converted. The options are "Bald", "Bangs", "Black_Hair", "Blond_Hair", "Brown_Hair", "Bushy_Eyebrows", "Eyeglasses", "Gender", "Mouth_Slightly_Open", "Mustache", "No_Beard", "Pale_Skin", "Aged". You can choose one of the options.

  • If you want to call the Hub module through the command line, please refer to: PaddleHub Command Line Instruction

• 2、Prediction Code Example

  • import paddlehub as hub
    
    attgan = hub.Module(name="attgan_celeba")
    
    test_img_path = ["/PATH/TO/IMAGE"]
    trans_attr = ["Bangs"]
    
    # set input dict
    input_dict = {"image": test_img_path, "style": trans_attr}
    
    # execute predict and print the result
    results = attgan.generate(data=input_dict)
    print(results)

• 3、API

  • def generate(data)

    • Style transfer API.

    • Parameter

      • data(list[dict]): Each element in the list is dict and each field is:
        • image (list[str])： Each element in the list is the path of the image to be converted.
        • style (list[str])： Each element in the list is a string, fill in the face attributes to be converted.

    • Return

      • res (list[str]): Save path of the result.

IV. Release Note

• 1.0.0

  First release