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Two steps before deployment

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Install FastDeploy Python whl package. Refer to FastDeploy Python Installation

This directory provides examples that infer.py fast finishes the deployment of AnimeGAN on CPU/GPU and GPU accelerated by TensorRT. The script is as follows

# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/generation/anemigan/python
# Download prepared test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/style_transfer_testimg.jpg

# CPU inference
python infer.py --model animegan_v1_hayao_60  --image style_transfer_testimg.jpg  --device cpu
# GPU inference
python infer.py --model animegan_v1_hayao_60 --image style_transfer_testimg.jpg  --device gpu

fd.vision.generation.AnimeGAN(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)

AnimeGAN model loading and initialization, among which model_file and params_file are the model file and parameter file for Paddle inference.

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. PADDLE format by default

AnimeGAN.predict(input_image)

Model prediction interface. Input images and output style transfer results.

Parameter

• input_image(np.ndarray): Input data in HWC or BGR format

Return np.ndarray, the image after style transfer in BGR format

AnimeGAN.batch_predict function (input_images)

Model prediction interface. Input a set of images and output style transfer results

Parameter

• input_images(list(np.ndarray)): Input data in HWC or BGR format

Return list(np.ndarray), a set of images after style transfer in BGR format

• Style Transfer Model Description
• C++ Deployment
• How to switch the model inference backend engine