English | 简体中文
Before deployment, two steps require confirmation
-
- Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements.
-
- Install FastDeploy Python whl package. Refer to FastDeploy Python Installation.
This directory provides examples that infer.py
fast finishes the deployment of ResNet50_vd 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/classification/resnet/python
# Download the ResNet50_vd model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50.onnx
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
# CPU inference
python infer.py --model resnet50.onnx --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
# GPU inference
python infer.py --model resnet50.onnx --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
# Use 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 resnet50.onnx --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
The result returned after running is as follows
ClassifyResult(
label_ids: 332,
scores: 0.825349,
)
fd.vision.classification.ResNet(model_file, params_file, runtime_option=None, model_format=ModelFormat.ONNX)
Parameter
- model_file(str): Model file path
- params_file(str): Parameter file path
- runtime_option(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
- model_format(ModelFormat): Model format. ONNX format by default
ResNet.predict(input_image, topk=1)Model prediction interface. Input images and output results directly.
parameter
- input_image(np.ndarray): Input data in HWC or BGR format
- topk(int): Return the topk classification results with the highest prediction probability. Default 1
Return
Return
fastdeploy.vision.ClassifyResult
structure. Refer to Vision Model Prediction Results for the description of the structure.