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README.md

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PP-MSVSR Python Deployment Example

Before deployment, two steps require confirmation

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

# Download the deployment example code 
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/sr/ppmsvsr/python

# Download VSR model files and test videos
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-MSVSR_reds_x4.tar
tar -xvf PP-MSVSR_reds_x4.tar
wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4
# CPU inference
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device cpu
# GPU inference
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device gpu
# TensorRT inference on GPU （Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device gpu --use_trt True

VSR Python Interface

fd.vision.sr.PPMSVSR(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)

PP-MSVSR model loading and initialization, among which model_file and params_file are the Paddle inference files exported from the training model. Refer to Model Export for more information

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

predict function

PPMSVSR.predict(frames)

Model prediction interface. Input images and output detection results.

Parameter

  • frames(list[np.ndarray]): Input data in HWC or BGR format. Frames are the video frame sequences

Return list[np.ndarray] is the video frame sequence after SR

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