Python 运行 - GPU 推理 - windows

• 环境准备
• python 代码

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。