环境准备

FastDeploy预编译库下载

conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
pip install fastdeploy_gpu_python-0.0.0-cp38-cp38-win_amd64.whl

python 代码

import fastdeploy as fd
import cv2
import os


model_path = "D:\\file\\ai\\models\\paddle\\ppyoloe\\infer_model"
image_path = "D:\\code\\fastdeploy\\pythonProject1\\image\\OIP1.jpg"
topk = 1
device = "gpu"
device_id = 1  # 仅当使用 GPU 时需要设置
backend = "paddle"

# 配置runtime，加载模型
# option = build_option(model_path, device, device_id, backend)
option = fd.RuntimeOption()

model_file = os.path.join(model_path, "inference.pdmodel")
params_file = os.path.join(model_path, "inference.pdiparams")
config_file = os.path.join(model_path, "inference.yml")
# 加载模型
model = fd.vision.detection.PaddleDetectionModel(
    model_file, params_file, config_file, runtime_option=option)
dump_result = dict()
im = cv2.imread(image_path)
# 推理
result = model.predict(im)
print(result)
# 预测结果可视化
vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("visualized_result.jpg", vis_im)
print("Visualized result save in ./visualized_result.jpg")

model_path 包含以下内容，模型在文章关联资源处。