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62 lines
2.7 KiB
Markdown
62 lines
2.7 KiB
Markdown
English | [简体中文](README_CN.md)
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# PP-MSVSR Python Deployment Example
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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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
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```bash
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# Download the deployment example code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/sr/ppmsvsr/python
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# Download VSR model files and test videos
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-MSVSR_reds_x4.tar
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tar -xvf PP-MSVSR_reds_x4.tar
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wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4
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# CPU inference
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python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device cpu
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# GPU inference
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python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device gpu
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# 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.）
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python infer.py --model PP-MSVSR_reds_x4 --video vsr_src.mp4 --frame_num 2 --device gpu --use_trt True
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```
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## VSR Python Interface
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```python
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fd.vision.sr.PPMSVSR(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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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](https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/zh_CN/tutorials/video_super_resolution.md) for more information
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. Paddle format by default
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### predict function
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> ```python
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> PPMSVSR.predict(frames)
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> ```
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>
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> Model prediction interface. Input images and output detection results.
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>
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> **Parameter**
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>
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> > * **frames**(list[np.ndarray]): Input data in HWC or BGR format. Frames are the video frame sequences
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> **Return** list[np.ndarray] is the video frame sequence after SR
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## Other Documents
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- [PP-MSVSR Model Description](..)
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- [PP-MSVSR C++ Deployment](../cpp)
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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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