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* add paddle_trt in benchmark * update benchmark in device * update benchmark * update result doc * fixed for CI * update python api_docs * update index.rst * add runtime cpp examples * deal with comments * Update infer_paddle_tensorrt.py * Add runtime quick start * deal with comments Co-authored-by: Jason <928090362@qq.com>
53 lines
1.5 KiB
Markdown
53 lines
1.5 KiB
Markdown
# Python推理
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确认开发环境已安装FastDeploy，参考[FastDeploy安装](../../build_and_install/)安装预编译的FastDeploy，或根据自己需求进行编译安装。
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本文档以 PaddleClas 分类模型 MobileNetV2 为例展示CPU上的推理示例
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## 1. 获取模型
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``` python
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import fastdeploy as fd
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model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
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fd.download_and_decompress(model_url, path=".")
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```
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## 2. 配置后端
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- 更多后端的示例可参考[examples/runtime](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/runtime)
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``` python
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option = fd.RuntimeOption()
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option.set_model_path("mobilenetv2/inference.pdmodel",
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"mobilenetv2/inference.pdiparams")
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# **** CPU 配置 ****
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option.use_cpu()
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option.use_ort_backend()
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option.set_cpu_thread_num(12)
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# 初始化构造runtime
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runtime = fd.Runtime(option)
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# 获取模型输入名
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input_name = runtime.get_input_info(0).name
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# 构造随机数据进行推理
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results = runtime.infer({
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input_name: np.random.rand(1, 3, 224, 224).astype("float32")
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})
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print(results[0].shape)
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```
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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
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```
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[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init Runtime initialized with Backend::OrtBackend in device Device::CPU.
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```
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## 其它文档
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- [不同后端Runtime demo示例](../../../../examples/runtime/README.md)
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- [切换模型推理的硬件和后端](../../faq/how_to_change_backend.md)
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