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1.5 KiB
简体中文 ｜ English
Python推理
在运行demo前，需确认以下两个步骤
-
- 软硬件环境满足要求，参考FastDeploy环境要求
-
- FastDeploy Python whl包安装，参考FastDeploy Python安装
本文档以 PaddleClas 分类模型 MobileNetV2 为例展示 CPU 上的推理示例
1. 获取模型
import fastdeploy as fd
model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
fd.download_and_decompress(model_url, path=".")
2. 配置后端
option = fd.RuntimeOption()
option.set_model_path("mobilenetv2/inference.pdmodel",
"mobilenetv2/inference.pdiparams")
# **** CPU 配置 ****
option.use_cpu()
option.use_ort_backend()
option.set_cpu_thread_num(12)
# 初始化构造runtime
runtime = fd.Runtime(option)
# 获取模型输入名
input_name = runtime.get_input_info(0).name
# 构造随机数据进行推理
results = runtime.infer({
input_name: np.random.rand(1, 3, 224, 224).astype("float32")
})
print(results[0].shape)
加载完成，会输出提示如下，说明初始化的后端，以及运行的硬件设备
[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OrtBackend in device Device::CPU.