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Before deployment, two steps require confirmation
-
- Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
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- Install FastDeploy Python whl package. Refer to FastDeploy Python Installation
This directory provides examples that infer.py fast finishes the deployment of EDVR on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
# Download deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/sr/edvr/python
# Download VSR model files and test videos
wget https://bj.bcebos.com/paddlehub/fastdeploy/EDVR_M_wo_tsa_SRx4.tar
tar -xvf EDVR_M_wo_tsa_SRx4.tar
wget https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4
# CPU inference
python infer.py --model EDVR_M_wo_tsa_SRx4 --video vsr_src.mp4 --frame_num 5 --device cpu
# GPU inference
python infer.py --model EDVR_M_wo_tsa_SRx4 --video vsr_src.mp4 --frame_num 5 --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 EDVR_M_wo_tsa_SRx4 --video vsr_src.mp4 --frame_num 5 --device gpu --use_trt Truefd.vision.sr.EDVR(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)EDVR 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
EDVR.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 video frame sequences.
Return list[np.ndarray] is the video frame sequence after SR